VISUALIZING URBAN DECAY: Detroit

This term project aims to define and visualize the shifting nature of urban decay in Detroit in the 2010s to the present. In doing so, this notebook is dedicated to visualizing four separate components that the author has selected, which will act as the defining quantifiers for “urban decay.” Therefore, the notebook is split into four primary parts:

  1. Population Changes
  2. Vacancy Changes in Residential Properties
  3. Property Sales
  4. Demolitions

These four topics are regarded as primary indicators of urban decay, and this project takes a temporal approach. 1. It assumes that ‘urban decay’ is a result of the following process: the exodus of people, the increase in vacancy, the increase of property sales (typically foreclosed properties at a low price), and then, in an attempt to grapple with the physical consequences of urban decay; demolitions.

Detroit, Michigan


# Loading and Installing all Necessary Packages

library(flexdashboard)
library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.2 ──✔ ggplot2 3.4.2     ✔ purrr   1.0.1
✔ tibble  3.2.1     ✔ dplyr   1.1.1
✔ tidyr   1.3.0     ✔ stringr 1.5.0
✔ readr   2.1.3     ✔ forcats 0.5.2── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(sf)
Linking to GEOS 3.10.2, GDAL 3.4.2, PROJ 8.2.1; sf_use_s2() is TRUE
library(lubridate)

Attaching package: ‘lubridate’

The following objects are masked from ‘package:base’:

    date, intersect, setdiff, union
library(jsonlite)

Attaching package: ‘jsonlite’

The following object is masked from ‘package:purrr’:

    flatten
library(plotly)

Attaching package: ‘plotly’

The following object is masked from ‘package:ggplot2’:

    last_plot

The following object is masked from ‘package:stats’:

    filter

The following object is masked from ‘package:graphics’:

    layout
library(hablar)

Attaching package: ‘hablar’

The following object is masked from ‘package:forcats’:

    fct

The following object is masked from ‘package:dplyr’:

    na_if

The following object is masked from ‘package:tibble’:

    num
library(RColorBrewer)
# devtools::install_github("hadley/emo")
library(emo)
library(tmap)
library(rsconnect)
library(ggplot2)
#install.packages("tidycensus")
library(tidycensus)
#install.packages('gganimate')
library(gganimate)
#install.packages("transformr")
library(transformr)

Attaching package: ‘transformr’

The following object is masked from ‘package:sf’:

    st_normalize

POPULATION

In the following section, I manipulate and map population data provided by the American Community Survey. It is important to note that the data selected is for Wayne County, where the city of Detroit resides. The reasoning for this is twofold: 1. It was easier to download tract level data for the entire county. 2. I believe that mapping that illustrates changes in population of one specific region (in this case the city of Detroit) should include the surrounding area. Perhaps this mapping will find that as people move in or out of Detroit, the rest of Wayne County may have the opposite reaction (i.e., people moving from the city center to the suburbs, etc.)


# Downloading population data from TidyCensus for mapping. Population is one of they key factors looked into for this research project, as it marks the influx/exodus of people in Detroit. I limited my time period to 2010-2021, as I wanted to see changes in the Detroit area in the most recent decade.


pop21 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2021)
Getting data from the 2017-2021 5-year ACS
Warning: • You have not set a Census API key. Users without a key are limited to 500
queries per day and may experience performance limitations.
ℹ For best results, get a Census API key at
http://api.census.gov/data/key_signup.html and then supply the key to the
`census_api_key()` function to use it throughout your tidycensus session.Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '26' for state 'MI'
Using FIPS code '163' for 'Wayne County'

  |                                                                                                                                          
  |                                                                                                                                    |   0%
  |                                                                                                                                          
  |=                                                                                                                                   |   1%
  |                                                                                                                                          
  |==                                                                                                                                  |   1%
  |                                                                                                                                          
  |==                                                                                                                                  |   2%
  |                                                                                                                                          
  |===                                                                                                                                 |   2%
  |                                                                                                                                          
  |====                                                                                                                                |   3%
  |                                                                                                                                          
  |=====                                                                                                                               |   4%
  |                                                                                                                                          
  |======                                                                                                                              |   4%
  |                                                                                                                                          
  |======                                                                                                                              |   5%
  |                                                                                                                                          
  |=======                                                                                                                             |   5%
  |                                                                                                                                          
  |=======                                                                                                                             |   6%
  |                                                                                                                                          
  |========                                                                                                                            |   6%
  |                                                                                                                                          
  |=========                                                                                                                           |   7%
  |                                                                                                                                          
  |==========                                                                                                                          |   7%
  |                                                                                                                                          
  |==========                                                                                                                          |   8%
  |                                                                                                                                          
  |===========                                                                                                                         |   8%
  |                                                                                                                                          
  |============                                                                                                                        |   9%
  |                                                                                                                                          
  |=============                                                                                                                       |  10%
  |                                                                                                                                          
  |===============                                                                                                                     |  11%
  |                                                                                                                                          
  |=================                                                                                                                   |  13%
  |                                                                                                                                          
  |==================                                                                                                                  |  13%
  |                                                                                                                                          
  |===================                                                                                                                 |  14%
  |                                                                                                                                          
  |====================                                                                                                                |  15%
  |                                                                                                                                          
  |======================                                                                                                              |  17%
  |                                                                                                                                          
  |=======================                                                                                                             |  17%
  |                                                                                                                                          
  |=========================                                                                                                           |  19%
  |                                                                                                                                          
  |==========================                                                                                                          |  20%
  |                                                                                                                                          
  |============================                                                                                                        |  21%
  |                                                                                                                                          
  |=============================                                                                                                       |  22%
  |                                                                                                                                          
  |=================================                                                                                                   |  25%
  |                                                                                                                                          
  |====================================                                                                                                |  27%
  |                                                                                                                                          
  |======================================                                                                                              |  29%
  |                                                                                                                                          
  |========================================                                                                                            |  30%
  |                                                                                                                                          
  |==========================================                                                                                          |  32%
  |                                                                                                                                          
  |===============================================                                                                                     |  36%
  |                                                                                                                                          
  |=================================================                                                                                   |  37%
  |                                                                                                                                          
  |====================================================                                                                                |  39%
  |                                                                                                                                          
  |=========================================================                                                                           |  43%
  |                                                                                                                                          
  |==========================================================                                                                          |  44%
  |                                                                                                                                          
  |==============================================================                                                                      |  47%
  |                                                                                                                                          
  |=================================================================                                                                   |  49%
  |                                                                                                                                          
  |=======================================================================                                                             |  54%
  |                                                                                                                                          
  |========================================================================                                                            |  55%
  |                                                                                                                                          
  |==============================================================================                                                      |  59%
  |                                                                                                                                          
  |===================================================================================                                                 |  63%
  |                                                                                                                                          
  |======================================================================================                                              |  66%
  |                                                                                                                                          
  |=============================================================================================                                       |  70%
  |                                                                                                                                          
  |=======================================================================================================                             |  78%
  |                                                                                                                                          
  |===========================================================================================================                         |  81%
  |                                                                                                                                          
  |=====================================================================================================================               |  89%
  |                                                                                                                                          
  |===========================================================================================================================         |  93%
  |                                                                                                                                          
  |==================================================================================================================================  |  98%
  |                                                                                                                                          
  |====================================================================================================================================| 100%
pop20 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2020)
Getting data from the 2016-2020 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '26' for state 'MI'
Using FIPS code '163' for 'Wayne County'

  |                                                                                                                                          
  |                                                                                                                                    |   0%
  |                                                                                                                                          
  |====                                                                                                                                |   3%
  |                                                                                                                                          
  |========                                                                                                                            |   6%
  |                                                                                                                                          
  |=============                                                                                                                       |  10%
  |                                                                                                                                          
  |========================                                                                                                            |  18%
  |                                                                                                                                          
  |====================================                                                                                                |  27%
  |                                                                                                                                          
  |======================================                                                                                              |  28%
  |                                                                                                                                          
  |=================================================================                                                                   |  49%
  |                                                                                                                                          
  |==================================================================================                                                  |  62%
  |                                                                                                                                          
  |======================================================================================                                              |  65%
  |                                                                                                                                          
  |========================================================================================                                            |  66%
  |                                                                                                                                          
  |============================================================================================                                        |  70%
  |                                                                                                                                          
  |================================================================================================                                    |  73%
  |                                                                                                                                          
  |=================================================================================================                                   |  73%
  |                                                                                                                                          
  |===================================================================================================                                 |  75%
  |                                                                                                                                          
  |=======================================================================================================                             |  78%
  |                                                                                                                                          
  |==========================================================================================================                          |  80%
  |                                                                                                                                          
  |==================================================================================================================                  |  87%
  |                                                                                                                                          
  |============================================================================================================================        |  94%
  |                                                                                                                                          
  |====================================================================================================================================| 100%
pop19 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2019)
Getting data from the 2015-2019 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '26' for state 'MI'
Using FIPS code '163' for 'Wayne County'

  |                                                                                                                                          
  |                                                                                                                                    |   0%
  |                                                                                                                                          
  |===                                                                                                                                 |   3%
  |                                                                                                                                          
  |=========                                                                                                                           |   7%
  |                                                                                                                                          
  |=============                                                                                                                       |  10%
  |                                                                                                                                          
  |========================                                                                                                            |  18%
  |                                                                                                                                          
  |==============================                                                                                                      |  23%
  |                                                                                                                                          
  |=======================================                                                                                             |  29%
  |                                                                                                                                          
  |===============================================================================                                                     |  60%
  |                                                                                                                                          
  |===============================================================================================                                     |  72%
  |                                                                                                                                          
  |==================================================================================================                                  |  75%
  |                                                                                                                                          
  |====================================================================================================================================| 100%
pop18 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2018)
Getting data from the 2014-2018 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '26' for state 'MI'
Using FIPS code '163' for 'Wayne County'

  |                                                                                                                                          
  |                                                                                                                                    |   0%
  |                                                                                                                                          
  |=============                                                                                                                       |  10%
  |                                                                                                                                          
  |=====================                                                                                                               |  16%
  |                                                                                                                                          
  |==================================================                                                                                  |  38%
  |                                                                                                                                          
  |=============================================================================                                                       |  58%
  |                                                                                                                                          
  |=======================================================================================                                             |  66%
  |                                                                                                                                          
  |=============================================================================================                                       |  71%
  |                                                                                                                                          
  |=======================================================================================================================             |  90%
  |                                                                                                                                          
  |====================================================================================================================================| 100%
pop17 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2017)
Getting data from the 2013-2017 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '26' for state 'MI'
Using FIPS code '163' for 'Wayne County'

  |                                                                                                                                          
  |                                                                                                                                    |   0%
  |                                                                                                                                          
  |===                                                                                                                                 |   2%
  |                                                                                                                                          
  |===========                                                                                                                         |   8%
  |                                                                                                                                          
  |============                                                                                                                        |   9%
  |                                                                                                                                          
  |===============                                                                                                                     |  12%
  |                                                                                                                                          
  |======================                                                                                                              |  17%
  |                                                                                                                                          
  |================================                                                                                                    |  24%
  |                                                                                                                                          
  |=============================================================                                                                       |  46%
  |                                                                                                                                          
  |=======================================================================================================                             |  78%
  |                                                                                                                                          
  |========================================================================================================================            |  91%
  |                                                                                                                                          
  |====================================================================================================================================| 100%
pop16 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2016)
Getting data from the 2012-2016 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '26' for state 'MI'
Using FIPS code '163' for 'Wayne County'

  |                                                                                                                                          
  |                                                                                                                                    |   0%
  |                                                                                                                                          
  |========                                                                                                                            |   6%
  |                                                                                                                                          
  |=========                                                                                                                           |   7%
  |                                                                                                                                          
  |=============                                                                                                                       |  10%
  |                                                                                                                                          
  |=====================                                                                                                               |  16%
  |                                                                                                                                          
  |===========================================                                                                                         |  33%
  |                                                                                                                                          
  |===================================================================                                                                 |  51%
  |                                                                                                                                          
  |============================================================================                                                        |  57%
  |                                                                                                                                          
  |=====================================================================================================================               |  88%
  |                                                                                                                                          
  |====================================================================================================================================| 100%
pop15 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2015)
Getting data from the 2011-2015 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '26' for state 'MI'
Using FIPS code '163' for 'Wayne County'

  |                                                                                                                                          
  |                                                                                                                                    |   0%
  |                                                                                                                                          
  |=                                                                                                                                   |   1%
  |                                                                                                                                          
  |==                                                                                                                                  |   2%
  |                                                                                                                                          
  |=======                                                                                                                             |   6%
  |                                                                                                                                          
  |==============                                                                                                                      |  10%
  |                                                                                                                                          
  |================================                                                                                                    |  24%
  |                                                                                                                                          
  |==================================                                                                                                  |  26%
  |                                                                                                                                          
  |=====================================                                                                                               |  28%
  |                                                                                                                                          
  |========================================                                                                                            |  31%
  |                                                                                                                                          
  |===========================================                                                                                         |  33%
  |                                                                                                                                          
  |===================================================                                                                                 |  39%
  |                                                                                                                                          
  |========================================================                                                                            |  43%
  |                                                                                                                                          
  |================================================================================                                                    |  61%
  |                                                                                                                                          
  |=====================================================================================                                               |  65%
  |                                                                                                                                          
  |=======================================================================================================                             |  78%
  |                                                                                                                                          
  |========================================================================================================                            |  79%
  |                                                                                                                                          
  |=============================================================================================================                       |  82%
  |                                                                                                                                          
  |======================================================================================================================              |  90%
  |                                                                                                                                          
  |==========================================================================================================================          |  93%
  |                                                                                                                                          
  |=============================================================================================================================       |  95%
  |                                                                                                                                          
  |====================================================================================================================================| 100%
pop14 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2014)
Getting data from the 2010-2014 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '26' for state 'MI'
Using FIPS code '163' for 'Wayne County'

  |                                                                                                                                          
  |                                                                                                                                    |   0%
  |                                                                                                                                          
  |======================================                                                                                              |  29%
  |                                                                                                                                          
  |=======================================================================                                                             |  54%
  |                                                                                                                                          
  |==============================================================================                                                      |  59%
  |                                                                                                                                          
  |===============================================================================================                                     |  72%
  |                                                                                                                                          
  |=============================================================================================================                       |  82%
  |                                                                                                                                          
  |=========================================================================================================================           |  91%
  |                                                                                                                                          
  |===============================================================================================================================     |  96%
  |                                                                                                                                          
  |====================================================================================================================================| 100%
pop13 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2013)
Getting data from the 2009-2013 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '26' for state 'MI'
Using FIPS code '163' for 'Wayne County'

  |                                                                                                                                          
  |                                                                                                                                    |   0%
  |                                                                                                                                          
  |===========                                                                                                                         |   8%
  |                                                                                                                                          
  |==================================                                                                                                  |  26%
  |                                                                                                                                          
  |===============================================================================                                                     |  60%
  |                                                                                                                                          
  |============================================================================================================================        |  94%
  |                                                                                                                                          
  |====================================================================================================================================| 100%
pop12 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2012)
Getting data from the 2008-2012 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '26' for state 'MI'
Using FIPS code '163' for 'Wayne County'

  |                                                                                                                                          
  |                                                                                                                                    |   0%
  |                                                                                                                                          
  |=                                                                                                                                   |   1%
  |                                                                                                                                          
  |==                                                                                                                                  |   2%
  |                                                                                                                                          
  |===                                                                                                                                 |   2%
  |                                                                                                                                          
  |====                                                                                                                                |   3%
  |                                                                                                                                          
  |========                                                                                                                            |   6%
  |                                                                                                                                          
  |=============                                                                                                                       |  10%
  |                                                                                                                                          
  |===================                                                                                                                 |  15%
  |                                                                                                                                          
  |=============================                                                                                                       |  22%
  |                                                                                                                                          
  |======================================                                                                                              |  29%
  |                                                                                                                                          
  |========================================                                                                                            |  30%
  |                                                                                                                                          
  |========================================                                                                                            |  31%
  |                                                                                                                                          
  |=========================================                                                                                           |  31%
  |                                                                                                                                          
  |==========================================                                                                                          |  32%
  |                                                                                                                                          
  |=============================================                                                                                       |  34%
  |                                                                                                                                          
  |===============================================                                                                                     |  36%
  |                                                                                                                                          
  |=====================================================                                                                               |  40%
  |                                                                                                                                          
  |========================================================                                                                            |  43%
  |                                                                                                                                          
  |==================================================================                                                                  |  50%
  |                                                                                                                                          
  |===========================================================================                                                         |  57%
  |                                                                                                                                          
  |===================================================================================                                                 |  63%
  |                                                                                                                                          
  |=============================================================================================                                       |  70%
  |                                                                                                                                          
  |=============================================================================================                                       |  71%
  |                                                                                                                                          
  |=================================================================================================                                   |  74%
  |                                                                                                                                          
  |===================================================================================================                                 |  75%
  |                                                                                                                                          
  |========================================================================================================                            |  79%
  |                                                                                                                                          
  |==========================================================================================================                          |  81%
  |                                                                                                                                          
  |===========================================================================================================                         |  81%
  |                                                                                                                                          
  |==============================================================================================================                      |  84%
  |                                                                                                                                          
  |===================================================================================================================                 |  87%
  |                                                                                                                                          
  |======================================================================================================================              |  90%
  |                                                                                                                                          
  |========================================================================================================================            |  91%
  |                                                                                                                                          
  |==========================================================================================================================          |  92%
  |                                                                                                                                          
  |================================================================================================================================    |  97%
  |                                                                                                                                          
  |====================================================================================================================================| 100%
pop11 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2011)
Getting data from the 2007-2011 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '26' for state 'MI'
Using FIPS code '163' for 'Wayne County'

  |                                                                                                                                          
  |                                                                                                                                    |   0%
  |                                                                                                                                          
  |==                                                                                                                                  |   2%
  |                                                                                                                                          
  |============                                                                                                                        |   9%
  |                                                                                                                                          
  |======================                                                                                                              |  17%
  |                                                                                                                                          
  |==========================                                                                                                          |  20%
  |                                                                                                                                          
  |============================                                                                                                        |  21%
  |                                                                                                                                          
  |===============================                                                                                                     |  23%
  |                                                                                                                                          
  |=================================                                                                                                   |  25%
  |                                                                                                                                          
  |====================================                                                                                                |  27%
  |                                                                                                                                          
  |======================================                                                                                              |  29%
  |                                                                                                                                          
  |=========================================                                                                                           |  31%
  |                                                                                                                                          
  |==========================================                                                                                          |  32%
  |                                                                                                                                          
  |==============================================                                                                                      |  35%
  |                                                                                                                                          
  |===============================================                                                                                     |  35%
  |                                                                                                                                          
  |================================================                                                                                    |  36%
  |                                                                                                                                          
  |===================================================                                                                                 |  39%
  |                                                                                                                                          
  |=====================================================                                                                               |  40%
  |                                                                                                                                          
  |============================================================                                                                        |  45%
  |                                                                                                                                          
  |===============================================================                                                                     |  48%
  |                                                                                                                                          
  |===================================================================                                                                 |  51%
  |                                                                                                                                          
  |======================================================================                                                              |  53%
  |                                                                                                                                          
  |=========================================================================                                                           |  55%
  |                                                                                                                                          
  |===========================================================================                                                         |  57%
  |                                                                                                                                          
  |============================================================================                                                        |  58%
  |                                                                                                                                          
  |================================================================================                                                    |  60%
  |                                                                                                                                          
  |==================================================================================                                                  |  62%
  |                                                                                                                                          
  |=====================================================================================                                               |  64%
  |                                                                                                                                          
  |=========================================================================================                                           |  68%
  |                                                                                                                                          
  |============================================================================================                                        |  70%
  |                                                                                                                                          
  |==================================================================================================                                  |  75%
  |                                                                                                                                          
  |=========================================================================================================                           |  80%
  |                                                                                                                                          
  |==========================================================================================================                          |  80%
  |                                                                                                                                          
  |===============================================================================================================                     |  84%
  |                                                                                                                                          
  |==================================================================================================================                  |  87%
  |                                                                                                                                          
  |========================================================================================================================            |  91%
  |                                                                                                                                          
  |==========================================================================================================================          |  93%
  |                                                                                                                                          
  |==============================================================================================================================      |  95%
  |                                                                                                                                          
  |===============================================================================================================================     |  96%
  |                                                                                                                                          
  |====================================================================================================================================| 100%
pop10 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2010)
Getting data from the 2006-2010 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '26' for state 'MI'
Using FIPS code '163' for 'Wayne County'

  |                                                                                                                                          
  |                                                                                                                                    |   0%
  |                                                                                                                                          
  |===========                                                                                                                         |   8%
  |                                                                                                                                          
  |==============================                                                                                                      |  23%
  |                                                                                                                                          
  |=========================================================================                                                           |  55%
  |                                                                                                                                          
  |====================================================================================                                                |  64%
  |                                                                                                                                          
  |====================================================================================================================================| 100%
# This data is pulled from the American Community Survey (ACS) and uses the 1-year estimate. This means that this data has a margin of error.


# Tidying my data
stastibble <- function(pop){as_tibble(pop)}
pop21_tibble <- stastibble(pop21)
pop20_tibble <- stastibble(pop20)
pop19_tibble <- stastibble(pop19)
pop18_tibble <- stastibble(pop18)
pop17_tibble <- stastibble(pop17)
pop16_tibble <- stastibble(pop16)
pop15_tibble <- stastibble(pop15)
pop14_tibble <- stastibble(pop14)
pop13_tibble <- stastibble(pop13)
pop12_tibble <- stastibble(pop12)
pop11_tibble <- stastibble(pop11)
pop10_tibble <- stastibble(pop10)

# Joining the data, making one dataframe with all of the years collected (2010 - 2021)

pop_joined <- left_join(pop21_tibble, pop20_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop19_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop18_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop17_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop16_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop15_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop14_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop13_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop12_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop11_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop10_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

# Transforming my data into an object with geographical/spatial information (an SF object)
pop_joined <- drop_na(pop_joined)
pop_joined_sf <- st_as_sf(pop_joined, sf_column_name = 'geometry', crs = 4326)
Warning: st_crs<- : replacing crs does not reproject data; use st_transform for that
# Renaming columns

colnames(pop_joined_sf)[4] = "estimate21"
colnames(pop_joined_sf)[7] = "estimate20"
colnames(pop_joined_sf)[8] = "estimate19"
colnames(pop_joined_sf)[9] = "estimate18"
colnames(pop_joined_sf)[10] = "estimate17"
colnames(pop_joined_sf)[11] = "estimate16"
colnames(pop_joined_sf)[12] = "estimate15"
colnames(pop_joined_sf)[13] = "estimate14"
colnames(pop_joined_sf)[14] = "estimate13"
colnames(pop_joined_sf)[15] = "estimate12"
colnames(pop_joined_sf)[16] = "estimate11"
colnames(pop_joined_sf)[17] = "estimate10"

str(pop_joined_sf)
sf [520 × 17] (S3: sf/tbl_df/tbl/data.frame)
 $ GEOID     : chr [1:520] "26163563900" "26163542400" "26163542300" "26163517000" ...
 $ NAME      : chr [1:520] "Census Tract 5639, Wayne County, Michigan" "Census Tract 5424, Wayne County, Michigan" "Census Tract 5423, Wayne County, Michigan" "Census Tract 5170, Wayne County, Michigan" ...
 $ variable  : chr [1:520] "B01003_001" "B01003_001" "B01003_001" "B01003_001" ...
 $ estimate21: num [1:520] 2605 2587 2671 2905 2078 ...
 $ moe       : num [1:520] 428 297 671 403 252 755 464 488 860 554 ...
 $ geometry  :sfc_MULTIPOLYGON of length 520; first list element: List of 1
  ..$ :List of 1
  .. ..$ : num [1:9, 1:2] -83.5 -83.5 -83.5 -83.4 -83.4 ...
  ..- attr(*, "class")= chr [1:3] "XY" "MULTIPOLYGON" "sfg"
 $ estimate20: num [1:520] 2528 2340 2473 3261 2016 ...
 $ estimate19: num [1:520] 2315 2763 1989 3111 1963 ...
 $ estimate18: num [1:520] 2343 2709 1887 3359 2062 ...
 $ estimate17: num [1:520] 2339 2511 1869 3450 1994 ...
 $ estimate16: num [1:520] 2312 2320 1814 3268 1650 ...
 $ estimate15: num [1:520] 2368 2235 1838 2995 1649 ...
 $ estimate14: num [1:520] 2392 2393 2175 2788 1701 ...
 $ estimate13: num [1:520] 2412 2550 2130 2507 1468 ...
 $ estimate12: num [1:520] 2478 2515 2177 2354 1570 ...
 $ estimate11: num [1:520] 2585 2592 2343 2218 1545 ...
 $ estimate10: num [1:520] 2479 2828 2459 2341 1739 ...
 - attr(*, "sf_column")= chr "geometry"
 - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA ...
  ..- attr(*, "names")= chr [1:16] "GEOID" "NAME" "variable" "estimate.x" ...
# Making a static map first to test out aesthetics

detroit_map_population <- ggplot() + geom_sf(data = pop_joined_sf, color = "black", aes(fill = estimate21)) + theme_light(base_size = 10) + scale_fill_gradient(low = "lightblue1", high = "indianred4")

detroit_map_population

# to utilize the gganimate package, you have to turn you data from wide format to long format
 pop_joined_Sf_long <- pivot_longer(data = pop_joined_sf, cols =c(estimate21, estimate20:estimate10))
# remapping my data that is now in long format
pop_joined_Sf_long %>% group_by(name)
Simple feature collection with 6240 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -83.6 ymin: 42 xmax: -82.9 ymax: 42.5
Geodetic CRS:  WGS 84
detroit_map_pop_forgif <- ggplot() + geom_sf(data = pop_joined_Sf_long, color = "black", aes(fill = value)) + theme_light(base_size = 10) + scale_fill_gradient(low = "lightblue1", high = "indianred4")
detroit_map_pop_forgif


# Renaming columns

pop_joined_Sf_long$name[pop_joined_Sf_long$name == 'estimate21'] <- '2021'
pop_joined_Sf_long$name[pop_joined_Sf_long$name == 'estimate20'] <- '2020'
pop_joined_Sf_long$name[pop_joined_Sf_long$name == 'estimate19'] <- '2019'
pop_joined_Sf_long$name[pop_joined_Sf_long$name == 'estimate18'] <- '2018'
pop_joined_Sf_long$name[pop_joined_Sf_long$name == 'estimate17'] <- '2017'
pop_joined_Sf_long$name[pop_joined_Sf_long$name == 'estimate16'] <- '2016'
pop_joined_Sf_long$name[pop_joined_Sf_long$name == 'estimate15'] <- '2015'
pop_joined_Sf_long$name[pop_joined_Sf_long$name == 'estimate14'] <- '2014'
pop_joined_Sf_long$name[pop_joined_Sf_long$name == 'estimate13'] <- '2013'
pop_joined_Sf_long$name[pop_joined_Sf_long$name == 'estimate12'] <- '2012'
pop_joined_Sf_long$name[pop_joined_Sf_long$name == 'estimate11'] <- '2011'
pop_joined_Sf_long$name[pop_joined_Sf_long$name == 'estimate10'] <- '2010'

# Transforming the year column from character vectors to integers

colnames(pop_joined_Sf_long)[6] <- 'year'
pop_joined_Sf_long$year <- as.integer(pop_joined_Sf_long$year)

# Creating the gif of the population map using gganimate package


detroit_map_pop_gif <- ggplot() + geom_sf(data = pop_joined_Sf_long, color = "black", aes(fill = value)) + theme_light(base_size = 10) + scale_fill_gradient(low = "lightblue1", high = "indianred4") + transition_time(year) + labs(title = "Population by Census Tract by Year: {frame_time}")

detroit_map_pop_gif

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!
knitr::include_graphics("detroit_map_pop_gif.gif")

# Making a graph that shows population fluctuation over time. While th emap gif is correct and functional, the change in population is too limited to accurately assess change. It is supplemented with this gif to provide a better visualization of population change. 

sum_pop_joined_Sf_long <- aggregate(x = pop_joined_Sf_long$value,               
          by = list(pop_joined_Sf_long$year),            
          FUN = sum) 

colnames(sum_pop_joined_Sf_long)[1] = 'year'
colnames(sum_pop_joined_Sf_long)[2] = 'value'

detroit_line_graph_population <- ggplot()+ geom_point(data = sum_pop_joined_Sf_long, aes(x = year, y= value)) + transition_time(year) + labs(title = "Population Total in Detroit by Year: {frame_time}")

detroit_line_graph_population

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!

VACANCIES

In the following section, I will map changes in vacancies in Detroit in the 2010s and early 2020s. This data is all provided by the American Community Survey 1-Year Estimates

Detroit, Michigan

# Downloading vacancy data, this data is also taken from the American Community Survey, but has been edited/tidied outside of R studio. 

# Vacancy Data - 1 Year Estimates from the American Community Survey

detroit_vacancies <- read_csv("detroitvac.characteristics.compiled - Sheet1 (2).csv")
Rows: 11 Columns: 9── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
dbl (2): Year, For migrant workers
num (7): Total, For rent, Rented, not occupied, For sale only, Sold, not occupied, For seasonal, recreational, or occasional use, Other va...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Making the GIF!

detroit_vacancies$Year <- as.integer(detroit_vacancies$Year)

detroit_line_graph_vacancies <- ggplot()+ geom_point(data = detroit_vacancies, aes(x = Year, y= Total)) + theme_bw() + transition_time(Year) + labs(title = "Total Vacancies in Detroit by Year: {frame_time}")

detroit_line_graph_vacancies

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!
knitr::include_graphics("detroit_line_graph_vacancies.gif")

# Filtering to only 'rented not occupied' vacancies in the Detroit area

detroit_vacancies$`Rented, not occupied`<- as.integer(detroit_vacancies$`Rented, not occupied`)

detroit_line_graph_vacancies_1 <- ggplot() +  geom_point(data = detroit_vacancies, aes(x = Year, y= `Rented, not occupied`)) + transition_time(Year) + theme_bw() + labs(title = "Rented, not Occupied Vacancies in Detroit by Year: {frame_time}")

detroit_line_graph_vacancies_1

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!
knitr::include_graphics("detroit_line_graph_vacancies_1.gif")
# Filtering to only 'sold, not occupied' vacancies in the Detroit area

detroit_line_graph_vacancies_2 <- ggplot() + geom_point(data = detroit_vacancies, aes(x = Year, y= `Sold, not occupied`)) + transition_time(Year) + theme_bw() + labs(title = "Sold, not Occupied Vacancies in Detroit by Year: {frame_time}")

detroit_line_graph_vacancies_2

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!
knitr::include_graphics("detroit_line_graph_vacancies_2.gif")

# Downloading vacancy data for Wayne County from the ACS at the Tract Level

vacancy_tract_21 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2021)
Getting data from the 2017-2021 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '26' for state 'MI'
Using FIPS code '163' for 'Wayne County'
vacancy_tract_20 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2020)
Getting data from the 2016-2020 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '26' for state 'MI'
Using FIPS code '163' for 'Wayne County'
vacancy_tract_19 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2019)
Getting data from the 2015-2019 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '26' for state 'MI'
Using FIPS code '163' for 'Wayne County'
vacancy_tract_18 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2018)
Getting data from the 2014-2018 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '26' for state 'MI'
Using FIPS code '163' for 'Wayne County'
vacancy_tract_17 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2017)
Getting data from the 2013-2017 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '26' for state 'MI'
Using FIPS code '163' for 'Wayne County'
vacancy_tract_16 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2016)
Getting data from the 2012-2016 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '26' for state 'MI'
Using FIPS code '163' for 'Wayne County'
vacancy_tract_15 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2015)
Getting data from the 2011-2015 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '26' for state 'MI'
Using FIPS code '163' for 'Wayne County'
vacancy_tract_14 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2014)
Getting data from the 2010-2014 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '26' for state 'MI'
Using FIPS code '163' for 'Wayne County'
vacancy_tract_13 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2013)
Getting data from the 2009-2013 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '26' for state 'MI'
Using FIPS code '163' for 'Wayne County'
vacancy_tract_12 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2012)
Getting data from the 2008-2012 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '26' for state 'MI'
Using FIPS code '163' for 'Wayne County'
vacancy_tract_11 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2011)
Getting data from the 2007-2011 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '26' for state 'MI'
Using FIPS code '163' for 'Wayne County'
vacancy_tract_10 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2010)
Getting data from the 2006-2010 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '26' for state 'MI'
Using FIPS code '163' for 'Wayne County'
# Tidying the vacancy data

stastibble <- function(vacancy){as_tibble(vacancy)}
vacancy_tract_21_tibble <- stastibble(vacancy_tract_21)
vacancy_tract_20_tibble <- stastibble(vacancy_tract_20)
vacancy_tract_19_tibble <- stastibble(vacancy_tract_19)
vacancy_tract_18_tibble <- stastibble(vacancy_tract_18)
vacancy_tract_17_tibble <- stastibble(vacancy_tract_17)
vacancy_tract_16_tibble <- stastibble(vacancy_tract_16)
vacancy_tract_15_tibble <- stastibble(vacancy_tract_15)
vacancy_tract_14_tibble <- stastibble(vacancy_tract_14)
vacancy_tract_13_tibble <- stastibble(vacancy_tract_13)
vacancy_tract_12_tibble <- stastibble(vacancy_tract_12)
vacancy_tract_11_tibble <- stastibble(vacancy_tract_11)
vacancy_tract_10_tibble <- stastibble(vacancy_tract_10)

# Joining vacancy data to make a compiled data frame that has all years.
vacancy_joined <- left_join(vacancy_tract_21_tibble, vacancy_tract_20_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_19_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_18_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_17_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_16_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_15_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_14_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_13_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_12_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined<- left_join(vacancy_joined, vacancy_tract_11_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_10_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')
colnames(vacancy_joined)[4] = "est2021"
colnames(vacancy_joined)[7] = "est2020"
colnames(vacancy_joined)[8] = "est2019"
colnames(vacancy_joined)[9] = "est2018"
colnames(vacancy_joined)[10] = "est2017"
colnames(vacancy_joined)[11] = "est2016"
colnames(vacancy_joined)[12] = "est2015"
colnames(vacancy_joined)[13] = "est2014"
colnames(vacancy_joined)[14] = "est2013"
colnames(vacancy_joined)[15] = "est2012"
colnames(vacancy_joined)[16] = "est2011"
colnames(vacancy_joined)[17] = "est2010"
vacancy_joined_sf <- st_as_sf(vacancy_joined, sf_column_name = 'geometry', crs = 4326)
Warning: st_crs<- : replacing crs does not reproject data; use st_transform for that
vacancy_joined_sf <- drop_na(vacancy_joined_sf)

vacancy_joined_Sf_long <- pivot_longer(data = vacancy_joined_sf, cols =c(est2021, est2020:est2010))

vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2021'] <- '2021'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2020'] <- '2020'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2019'] <- '2019'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2018'] <- '2018'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2017'] <- '2017'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2016'] <- '2016'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2015'] <- '2015'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2014'] <- '2014'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2013'] <- '2013'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2012'] <- '2012'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2011'] <- '2011'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2010'] <- '2010'

# Making the vacancy data gif

colnames(vacancy_joined_Sf_long)[6] <- 'year' 
vacancy_joined_Sf_long$year <- as.integer(vacancy_joined_Sf_long$year)

detroit_map_vacancy_gif <- ggplot() + geom_sf(data = vacancy_joined_Sf_long, color = "black", aes(fill = value)) + theme_light(base_size = 10) + scale_fill_gradient(low = "lightyellow1", high = "darkorchid4") + transition_time(year) + labs(title = "Vacancy by Census Tract by Year: {frame_time}")

detroit_map_vacancy_gif

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!
knitr::include_graphics("detroit_map_vacancy_gif.gif")

PROPERTY SALES

This section will focus on the frequency and price of property sales in the Detroit area from 2011 - 2022. This data is sourced from Detroit’s Open Data Portal (link here: https://data.detroitmi.gov/datasets/property-sales-1/explore) , and importantly, has been adjusted for inflation. There are charts created for total real estate prices, as well as average prices, in the given time period.

# Downloading property sales from Detroit's open data portal 

Detroit_propertysales <- read_csv("property_sales.csv")
Rows: 377839 Columns: 16── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr  (10): parcel_number, address, grantor, grantee, liberpage, sale_terms, verified_by, sale_instrument, property_class, ecf
dbl   (4): object_id, sale_number, sale_price, sale_trans
lgl   (1): geom
date  (1): sale_date
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
detroit_property_sales_year <- format(as.Date(Detroit_propertysales$sale_date, format="%Y/%m/%d"),"%Y")
data.frame(detroit_property_sales_year)
detroit_propsales_year_1 <- cbind(detroit_property_sales_year, Detroit_propertysales)
detroit_propsales_year_1

Detroit_propertysales_2011 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2011)
Detroit_propertysales_2012 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2012)
Detroit_propertysales_2013 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2013)
Detroit_propertysales_2014 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2014)
Detroit_propertysales_2015 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2015)
Detroit_propertysales_2016 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2016)
Detroit_propertysales_2017 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2017)
Detroit_propertysales_2018 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2018)
Detroit_propertysales_2019 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2019)
Detroit_propertysales_2020 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2020)
Detroit_propertysales_2021 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2021)
Detroit_propertysales_2022 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2022)
# adding all detroit property sales and creating a gif that illustrates the change in total sale price by year. 

sum_detroit_propsales_year_1 <- aggregate(x = detroit_propsales_year_1$sale_price,               
          by = list(detroit_propsales_year_1$detroit_property_sales_year),            
          FUN = sum) 
colnames(sum_detroit_propsales_year_1)[1] = 'year'
colnames(sum_detroit_propsales_year_1)[2] = 'price'

sum_detroit_propsales_year_1$year <- as.integer(sum_detroit_propsales_year_1$year)

detroit_line_graph_salesprices <- ggplot()+ geom_point(data = sum_detroit_propsales_year_1, aes(x = year, y= price)) + theme_bw() + transition_time(year) + labs(title = "Real Estate Prices in Detroit by Year: {frame_time}")

detroit_line_graph_salesprices

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!
knitr::include_graphics("detroit_line_graph_salesprices.gif")

# Graphing the average sale prices in Detroit

avg_detroit_propsales_year_1 <- aggregate(x = detroit_propsales_year_1$sale_price,               
          by = list(detroit_propsales_year_1$detroit_property_sales_year),            
          FUN = mean) 

colnames(avg_detroit_propsales_year_1)[1] = 'year'
colnames(avg_detroit_propsales_year_1)[2] = 'price'

avg_detroit_propsales_year_1$year <- as.integer(avg_detroit_propsales_year_1$year)

detroit_line_graph_salesprices_avg <- ggplot()+ geom_point(data = avg_detroit_propsales_year_1, aes(x = year, y= price)) + theme_bw() + transition_time(year) + labs(title = " Average Real Estate Prices in Detroit by Year: {frame_time}")

detroit_line_graph_salesprices_avg

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!
knitr::include_graphics("detroit_line_graph_salesprices_avg.gif")
# Filtering to property class 401, which refers to residential in nature and recently improved.
detroitsales_401 <- detroit_propsales_year_1 %>% dplyr::filter(., property_class == 401)

# Filtering to property class 402, which refers to residential and nature and vacant.
detroitsales_402 <- detroit_propsales_year_1 %>% dplyr::filter(., property_class == 402)

# Graphing the total sale prices in Detroit - by property class 401

sum_detroit_propsales_year_401 <- aggregate(x = detroitsales_401$sale_price,               
          by = list(detroitsales_401$detroit_property_sales_year),            
          FUN = sum) 
colnames(sum_detroit_propsales_year_401)[1] = 'year'
colnames(sum_detroit_propsales_year_401)[2] = 'price'

sum_detroit_propsales_year_401$year <- as.integer(sum_detroit_propsales_year_401$year)

detroit_line_graph_salesprices_401 <- ggplot()+ geom_point(data = sum_detroit_propsales_year_401, aes(x = year, y= price)) + theme_bw() + transition_time(year) + labs(title = "Real Estate Prices - Residential in Nature and Improved in Detroit by Year: {frame_time}")

detroit_line_graph_salesprices_401

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!
knitr::include_graphics("detroit_line_graph_salesprices_401.gif")
# Graphing the average sale prices in Detroit - by property class 401

avg_detroit_propsales_year_401 <- aggregate(x = detroitsales_401$sale_price,               
          by = list(detroitsales_401$detroit_property_sales_year),            
          FUN = mean) 
colnames(avg_detroit_propsales_year_401)[1] = 'year'
colnames(avg_detroit_propsales_year_401)[2] = 'price'

avg_detroit_propsales_year_401$year <- as.integer(avg_detroit_propsales_year_401$year)

detroit_line_graph_salesprices_401_avg <- ggplot()+ geom_point(data = avg_detroit_propsales_year_401, aes(x = year, y= price)) + theme_bw() + transition_time(year) + labs(title = " Average Real Estate Prices - Residential in Nature and Improved in Detroit by Year: {frame_time}")

detroit_line_graph_salesprices_401_avg

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!
knitr::include_graphics("detroit_line_graph_salesprices_401_avg.gif")
# Graphing the total sale prices in Detroit - by property class 402

sum_detroit_propsales_year_402 <- aggregate(x = detroitsales_402$sale_price,               
          by = list(detroitsales_402$detroit_property_sales_year),            
          FUN = sum) 
colnames(sum_detroit_propsales_year_402)[1] = 'year'
colnames(sum_detroit_propsales_year_402)[2] = 'price'

sum_detroit_propsales_year_402$year <- as.integer(sum_detroit_propsales_year_402$year)

detroit_line_graph_salesprices_402 <- ggplot()+ geom_point(data = sum_detroit_propsales_year_402, aes(x = year, y= price), color = "royalblue", size = 4) + theme_bw() + transition_time(year) + labs(title = "Real Estate Prices - Residential and Vacant in Detroit by Year: {frame_time}")

detroit_line_graph_salesprices_402

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!
knitr::include_graphics("detroit_line_graph_salesprices_402.gif")
# Graphing the average sale prices in Detroit - by property class 402

avg_detroit_propsales_year_402 <- aggregate(x = detroitsales_402$sale_price,               
          by = list(detroitsales_402$detroit_property_sales_year),            
          FUN = mean) 
colnames(avg_detroit_propsales_year_402)[1] = 'year'
colnames(avg_detroit_propsales_year_402)[2] = 'price'

avg_detroit_propsales_year_402$year <- as.integer(avg_detroit_propsales_year_402$year)

detroit_line_graph_salesprices_402_avg <- ggplot()+ geom_point(data = avg_detroit_propsales_year_402, aes(x = year, y= price)) + theme_bw() + transition_time(year) + labs(title = " Average Real Estate Prices - Residential and Vacant in Detroit by Year: {frame_time}")

detroit_line_graph_salesprices_402_avg

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!
knitr::include_graphics("detroit_line_graph_salesprices_402_avg.gif")

DEMOLITION

Detroit, Michigan

This section focuses on demolitions, and draws directly from Detroit’s Open Data Portal. The link for this data can be found here: https://data.detroitmi.gov/datasets/detroitmi::completed-residential-demolitions/explore

# Downloading residential demolition data - from Detroit's Open Data Portal

residential_demolitions <- read_csv("Completed_Residential_Demolitions.csv")
Rows: 25604 Columns: 21── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (13): address, parcel_id, contractor_name, funding_source, demolition_date, demo_rfp_group, commercial, neighborhood, emergency_demo, ...
dbl  (8): X, Y, price, council_district, street_number, property_longitude, property_latitude, ObjectId
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Tidying demolition data...

year_demo<- format(as.Date(residential_demolitions$demolition_date, format="%Y/%m/%d"),"%Y")
data.frame(residential_demolitions)
residential_demo_year <- cbind(year_demo, residential_demolitions)
residential_demo_year
# Transforming it into a spatial object

residential_demo_year <- st_as_sf(residential_demo_year, coords = c("X", "Y"), crs = 4326)
# Mapping residential demolitions from 2014 - 2022 (2014 is the earliest date available)
residential_demo_year$year_demo <- as.integer(residential_demo_year$year_demo)

detroit_map_demo <- ggplot() + geom_sf(data = residential_demo_year, color = "darkgreen", aes(fill = year_demo), size = .05, alpha = .5) + theme_light(base_size = 10) + scale_fill_gradient(low = "lightyellow1", high = "darkorchid4") 

detroit_map_demo


# Making it into a gif!

residential_demo_year$year_demo <- as.integer(residential_demo_year$year_demo)

detroit_map_demo_gif <- ggplot() + geom_sf(data = residential_demo_year, color = "darkgreen", aes(fill = year_demo), size = .05, alpha = .5) + theme_light(base_size = 10) + scale_fill_gradient(low = "lightyellow1", high = "darkorchid4") + transition_time(year_demo) + labs(title = "Demolitions by Year: {frame_time}")

detroit_map_demo_gif

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!
knitr::include_graphics("detroit_map_demo_gif.gif")
Below, I am saving all of the animations to be reinserted into this notebook.
anim_save("detroit_map_demo_gif.gif", detroit_map_demo_gif)

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!
anim_save("detroit_map_pop_gif.gif", detroit_map_pop_gif)

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!
anim_save("detroit_line_graph_population.gif", detroit_line_graph_population)

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!
anim_save("detroit_line_graph_vacancies_1.gif", detroit_line_graph_vacancies_1)

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!
anim_save("detroit_line_graph_vacancies_2.gif", detroit_line_graph_vacancies_2)

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!
anim_save("detroit_map_vacancy_gif.gif", detroit_map_vacancy_gif)

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!
anim_save("detroit_line_graph_salesprices.gif", detroit_line_graph_salesprices)

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!
anim_save("detroit_line_graph_salesprices_401.gif", detroit_line_graph_salesprices_401) 

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!
anim_save("detroit_line_graph_salesprices_402.gif", detroit_line_graph_salesprices_402) 

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!
anim_save("detroit_line_graph_salesprices_401_avg.gif", detroit_line_graph_salesprices_401_avg) 

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!
anim_save("detroit_line_graph_salesprices_402_avg.gif", detroit_line_graph_salesprices_402_avg)

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!
anim_save("detroit_line_graph_salesprices_avg.gif", detroit_line_graph_salesprices_avg)

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!
anim_save("detroit_line_graph_vacancies.gif", detroit_line_graph_vacancies)

Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!
#install.packages("gifski")
library(gifski)
library(rsconnect)
library(shiny)

Attaching package: ‘shiny’

The following object is masked from ‘package:rsconnect’:

    serverInfo

The following object is masked from ‘package:jsonlite’:

    validate

Citations

Data

United State Census Bureau. (2010-2020). American Community Survey. Explore census data. Wayne County, Michigan. https://data.census.gov/table?q=vacancy&g=040XX00US26&tid=ACSDT1Y2021.B25002

United State Census Bureau. (2010-2020). American Community Survey. Explore census data. Wayne County, Michigan. https://data.census.gov/table?q=population&g=040XX00US26&tid=ACSDP1Y2021.DP05

Housing Data. Zillow. (2023, April 25). https://www.zillow.com/research/data/

Property sales. Detroit’s Open Data Portal. (n.d.-b). https://data.detroitmi.gov/datasets/property-sales-1/explore

Completed residential demolitions. Detroit’s Open Data Portal. (n.d.). https://data.detroitmi.gov/datasets/detroitmi::completed-residential-demolitions/explore?location=42.352748%2C-83.100004%2C10.98

Images

Detroit. Pure Michigan | Official Travel & Tourism Website for Michigan. (2023, May 11). https://www.michigan.org/city/detroit

Klinefelter, Q. (2013, March 1). Michigan officials take control of Detroit’s empty wallet. NPR. https://www.npr.org/2013/03/01/173241269/michigan-officials-take-control-of-detroits-empty-wallet

Owens, C. (2015, June 12). How Detroit taught itself to demolish vacant houses safely. Next City. https://nextcity.org/urbanist-news/detroit-demolition-blight-teardowns-safety

---
title: "Visualizing Urban Decay: Detroit"
subtitle: "Plan 6122 Term Project"
author: Theodora Catrina
date: May 11, 2023
output: html_notebook
---

### VISUALIZING URBAN DECAY: Detroit

This term project aims to define and visualize the shifting nature of urban decay in Detroit in the 2010s to the present. In doing so, this notebook is dedicated to visualizing four separate components that the author has selected, which will act as the defining quantifiers for "urban decay." Therefore, the notebook is split into four primary parts:

1.  Population Changes
2.  Vacancy Changes in Residential Properties
3.  Property Sales
4.  Demolitions

These four topics are regarded as primary indicators of urban decay, and this project takes a temporal approach. 1. It assumes that 'urban decay' is a result of the following process: the exodus of people, the increase in vacancy, the increase of property sales (typically foreclosed properties at a low price), and then, in an attempt to grapple with the physical consequences of urban decay; demolitions.

![Detroit, Michigan](Detroit,%20Michigan.png)

```{r}

# Loading and Installing all Necessary Packages

library(flexdashboard)
library(tidyverse)
library(sf)
library(lubridate)
library(jsonlite)
library(plotly)
library(hablar)
library(RColorBrewer)
# devtools::install_github("hadley/emo")
library(emo)
library(tmap)
library(rsconnect)
library(ggplot2)
```

```{r}
#install.packages("tidycensus")
```

```{r}
library(tidycensus)
#install.packages('gganimate')
library(gganimate)
```

```{r}
#install.packages("transformr")
library(transformr)
```

# POPULATION

In the following section, I manipulate and map population data provided by the American Community Survey. It is important to note that the data selected is for Wayne County, where the city of Detroit resides. The reasoning for this is twofold: 1. It was easier to download tract level data for the entire county. 2. I believe that mapping that illustrates changes in population of one specific region (in this case the city of Detroit) should include the surrounding area. Perhaps this mapping will find that as people move in or out of Detroit, the rest of Wayne County may have the opposite reaction (i.e., people moving from the city center to the suburbs, etc.)

```{r}

# Downloading population data from TidyCensus for mapping. Population is one of they key factors looked into for this research project, as it marks the influx/exodus of people in Detroit. I limited my time period to 2010-2021, as I wanted to see changes in the Detroit area in the most recent decade.


pop21 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2021)
pop20 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2020)
pop19 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2019)
pop18 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2018)
pop17 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2017)
pop16 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2016)
pop15 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2015)
pop14 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2014)
pop13 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2013)
pop12 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2012)
pop11 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2011)
pop10 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2010)

# This data is pulled from the American Community Survey (ACS) and uses the 1-year estimate. This means that this data has a margin of error.

```

```{r}

# Tidying my data
stastibble <- function(pop){as_tibble(pop)}
pop21_tibble <- stastibble(pop21)
pop20_tibble <- stastibble(pop20)
pop19_tibble <- stastibble(pop19)
pop18_tibble <- stastibble(pop18)
pop17_tibble <- stastibble(pop17)
pop16_tibble <- stastibble(pop16)
pop15_tibble <- stastibble(pop15)
pop14_tibble <- stastibble(pop14)
pop13_tibble <- stastibble(pop13)
pop12_tibble <- stastibble(pop12)
pop11_tibble <- stastibble(pop11)
pop10_tibble <- stastibble(pop10)
```

```{r}

# Joining the data, making one dataframe with all of the years collected (2010 - 2021)

pop_joined <- left_join(pop21_tibble, pop20_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop19_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop18_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop17_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop16_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop15_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop14_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop13_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop12_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop11_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop10_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

```

```{r}
# Transforming my data into an object with geographical/spatial information (an SF object)
pop_joined <- drop_na(pop_joined)
pop_joined_sf <- st_as_sf(pop_joined, sf_column_name = 'geometry', crs = 4326)

```

```{r}
# Renaming columns

colnames(pop_joined_sf)[4] = "estimate21"
colnames(pop_joined_sf)[7] = "estimate20"
colnames(pop_joined_sf)[8] = "estimate19"
colnames(pop_joined_sf)[9] = "estimate18"
colnames(pop_joined_sf)[10] = "estimate17"
colnames(pop_joined_sf)[11] = "estimate16"
colnames(pop_joined_sf)[12] = "estimate15"
colnames(pop_joined_sf)[13] = "estimate14"
colnames(pop_joined_sf)[14] = "estimate13"
colnames(pop_joined_sf)[15] = "estimate12"
colnames(pop_joined_sf)[16] = "estimate11"
colnames(pop_joined_sf)[17] = "estimate10"


```

```{r}
str(pop_joined_sf)
```

```{r}
# Making a static map first to test out aesthetics

detroit_map_population <- ggplot() + geom_sf(data = pop_joined_sf, color = "black", aes(fill = estimate21)) + theme_light(base_size = 10) + scale_fill_gradient(low = "lightblue1", high = "indianred4")

detroit_map_population
```

```{r}
# to utilize the gganimate package, you have to turn you data from wide format to long format
 pop_joined_Sf_long <- pivot_longer(data = pop_joined_sf, cols =c(estimate21, estimate20:estimate10))
```

```{r}
# remapping my data that is now in long format
pop_joined_Sf_long %>% group_by(name)
detroit_map_pop_forgif <- ggplot() + geom_sf(data = pop_joined_Sf_long, color = "black", aes(fill = value)) + theme_light(base_size = 10) + scale_fill_gradient(low = "lightblue1", high = "indianred4")
detroit_map_pop_forgif
```

```{r}

# Renaming columns

pop_joined_Sf_long$name[pop_joined_Sf_long$name == 'estimate21'] <- '2021'
pop_joined_Sf_long$name[pop_joined_Sf_long$name == 'estimate20'] <- '2020'
pop_joined_Sf_long$name[pop_joined_Sf_long$name == 'estimate19'] <- '2019'
pop_joined_Sf_long$name[pop_joined_Sf_long$name == 'estimate18'] <- '2018'
pop_joined_Sf_long$name[pop_joined_Sf_long$name == 'estimate17'] <- '2017'
pop_joined_Sf_long$name[pop_joined_Sf_long$name == 'estimate16'] <- '2016'
pop_joined_Sf_long$name[pop_joined_Sf_long$name == 'estimate15'] <- '2015'
pop_joined_Sf_long$name[pop_joined_Sf_long$name == 'estimate14'] <- '2014'
pop_joined_Sf_long$name[pop_joined_Sf_long$name == 'estimate13'] <- '2013'
pop_joined_Sf_long$name[pop_joined_Sf_long$name == 'estimate12'] <- '2012'
pop_joined_Sf_long$name[pop_joined_Sf_long$name == 'estimate11'] <- '2011'
pop_joined_Sf_long$name[pop_joined_Sf_long$name == 'estimate10'] <- '2010'

# Transforming the year column from character vectors to integers

colnames(pop_joined_Sf_long)[6] <- 'year'
pop_joined_Sf_long$year <- as.integer(pop_joined_Sf_long$year)
```

```{r}

# Creating the gif of the population map using gganimate package


detroit_map_pop_gif <- ggplot() + geom_sf(data = pop_joined_Sf_long, color = "black", aes(fill = value)) + theme_light(base_size = 10) + scale_fill_gradient(low = "lightblue1", high = "indianred4") + transition_time(year) + labs(title = "Population by Census Tract by Year: {frame_time}")

detroit_map_pop_gif
```

```{r, fig.cap="Detroit Changing Population Gif"}
knitr::include_graphics("detroit_map_pop_gif.gif")
```

```{r}

# Making a graph that shows population fluctuation over time. While th emap gif is correct and functional, the change in population is too limited to accurately assess change. It is supplemented with this gif to provide a better visualization of population change. 

sum_pop_joined_Sf_long <- aggregate(x = pop_joined_Sf_long$value,               
          by = list(pop_joined_Sf_long$year),            
          FUN = sum) 

colnames(sum_pop_joined_Sf_long)[1] = 'year'
colnames(sum_pop_joined_Sf_long)[2] = 'value'

detroit_line_graph_population <- ggplot()+ geom_point(data = sum_pop_joined_Sf_long, aes(x = year, y= value)) + transition_time(year) + labs(title = "Population Total in Detroit by Year: {frame_time}")

detroit_line_graph_population

```

# VACANCIES

In the following section, I will map changes in vacancies in Detroit in the 2010s and early 2020s. This data is all provided by the American Community Survey 1-Year Estimates

![Detroit, Michigan](detroit_downtown_wide-8014894df3515227478a8e2ba470047a8b3cdf0b-s1400-c100.jpeg)

```{r}
# Downloading vacancy data, this data is also taken from the American Community Survey, but has been edited/tidied outside of R studio. 

# Vacancy Data - 1 Year Estimates from the American Community Survey

detroit_vacancies <- read_csv("detroitvac.characteristics.compiled - Sheet1 (2).csv")
```

```{r}

# Making the GIF!

detroit_vacancies$Year <- as.integer(detroit_vacancies$Year)

detroit_line_graph_vacancies <- ggplot()+ geom_point(data = detroit_vacancies, aes(x = Year, y= Total)) + theme_bw() + transition_time(Year) + labs(title = "Total Vacancies in Detroit by Year: {frame_time}")

detroit_line_graph_vacancies
```

```{r, fig.cap="Detroit Changing Vacancies Gif"}
knitr::include_graphics("detroit_line_graph_vacancies.gif")
```

```{r}

# Filtering to only 'rented not occupied' vacancies in the Detroit area

detroit_vacancies$`Rented, not occupied`<- as.integer(detroit_vacancies$`Rented, not occupied`)

detroit_line_graph_vacancies_1 <- ggplot() +  geom_point(data = detroit_vacancies, aes(x = Year, y= `Rented, not occupied`)) + transition_time(Year) + theme_bw() + labs(title = "Rented, not Occupied Vacancies in Detroit by Year: {frame_time}")

detroit_line_graph_vacancies_1
```

```{r, fig.cap="Detroit Changing Vacancies - Rented, not Occupied Vacancies Gif"}
knitr::include_graphics("detroit_line_graph_vacancies_1.gif")
```


```{r}
# Filtering to only 'sold, not occupied' vacancies in the Detroit area

detroit_line_graph_vacancies_2 <- ggplot() + geom_point(data = detroit_vacancies, aes(x = Year, y= `Sold, not occupied`)) + transition_time(Year) + theme_bw() + labs(title = "Sold, not Occupied Vacancies in Detroit by Year: {frame_time}")

detroit_line_graph_vacancies_2
```

```{r, fig.cap="Sold, Not Occupied Vacancies in Detroit by Year"}
knitr::include_graphics("detroit_line_graph_vacancies_2.gif")
```

```{r}

# Downloading vacancy data for Wayne County from the ACS at the Tract Level

vacancy_tract_21 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2021)
vacancy_tract_20 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2020)
vacancy_tract_19 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2019)
vacancy_tract_18 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2018)
vacancy_tract_17 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2017)
vacancy_tract_16 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2016)
vacancy_tract_15 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2015)
vacancy_tract_14 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2014)
vacancy_tract_13 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2013)
vacancy_tract_12 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2012)
vacancy_tract_11 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2011)
vacancy_tract_10 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2010)
```

```{r}
# Tidying the vacancy data

stastibble <- function(vacancy){as_tibble(vacancy)}
vacancy_tract_21_tibble <- stastibble(vacancy_tract_21)
vacancy_tract_20_tibble <- stastibble(vacancy_tract_20)
vacancy_tract_19_tibble <- stastibble(vacancy_tract_19)
vacancy_tract_18_tibble <- stastibble(vacancy_tract_18)
vacancy_tract_17_tibble <- stastibble(vacancy_tract_17)
vacancy_tract_16_tibble <- stastibble(vacancy_tract_16)
vacancy_tract_15_tibble <- stastibble(vacancy_tract_15)
vacancy_tract_14_tibble <- stastibble(vacancy_tract_14)
vacancy_tract_13_tibble <- stastibble(vacancy_tract_13)
vacancy_tract_12_tibble <- stastibble(vacancy_tract_12)
vacancy_tract_11_tibble <- stastibble(vacancy_tract_11)
vacancy_tract_10_tibble <- stastibble(vacancy_tract_10)
```

```{r}

# Joining vacancy data to make a compiled data frame that has all years.
vacancy_joined <- left_join(vacancy_tract_21_tibble, vacancy_tract_20_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_19_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_18_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_17_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_16_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_15_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_14_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_13_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_12_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined<- left_join(vacancy_joined, vacancy_tract_11_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_10_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')
```

```{r}
colnames(vacancy_joined)[4] = "est2021"
colnames(vacancy_joined)[7] = "est2020"
colnames(vacancy_joined)[8] = "est2019"
colnames(vacancy_joined)[9] = "est2018"
colnames(vacancy_joined)[10] = "est2017"
colnames(vacancy_joined)[11] = "est2016"
colnames(vacancy_joined)[12] = "est2015"
colnames(vacancy_joined)[13] = "est2014"
colnames(vacancy_joined)[14] = "est2013"
colnames(vacancy_joined)[15] = "est2012"
colnames(vacancy_joined)[16] = "est2011"
colnames(vacancy_joined)[17] = "est2010"
```

```{r}
vacancy_joined_sf <- st_as_sf(vacancy_joined, sf_column_name = 'geometry', crs = 4326)

vacancy_joined_sf <- drop_na(vacancy_joined_sf)

vacancy_joined_Sf_long <- pivot_longer(data = vacancy_joined_sf, cols =c(est2021, est2020:est2010))

vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2021'] <- '2021'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2020'] <- '2020'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2019'] <- '2019'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2018'] <- '2018'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2017'] <- '2017'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2016'] <- '2016'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2015'] <- '2015'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2014'] <- '2014'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2013'] <- '2013'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2012'] <- '2012'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2011'] <- '2011'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2010'] <- '2010'




```

```{r}
# Making the vacancy data gif

colnames(vacancy_joined_Sf_long)[6] <- 'year' 
vacancy_joined_Sf_long$year <- as.integer(vacancy_joined_Sf_long$year)

detroit_map_vacancy_gif <- ggplot() + geom_sf(data = vacancy_joined_Sf_long, color = "black", aes(fill = value)) + theme_light(base_size = 10) + scale_fill_gradient(low = "lightyellow1", high = "darkorchid4") + transition_time(year) + labs(title = "Vacancy by Census Tract by Year: {frame_time}")

detroit_map_vacancy_gif
```

```{r, fig.cap="Sold, Not Occupied Vacancies in Detroit by Year"}
knitr::include_graphics("detroit_map_vacancy_gif.gif")
```

# PROPERTY SALES

This section will focus on the frequency and price of property sales in the Detroit area from 2011 - 2022. This data is sourced from Detroit's Open Data Portal (link here: <https://data.detroitmi.gov/datasets/property-sales-1/explore>) , and importantly, has been adjusted for inflation. There are charts created for total real estate prices, as well as average prices, in the given time period.

```{r}
# Downloading property sales from Detroit's open data portal 

Detroit_propertysales <- read_csv("property_sales.csv")
```

```{r}
detroit_property_sales_year <- format(as.Date(Detroit_propertysales$sale_date, format="%Y/%m/%d"),"%Y")
data.frame(detroit_property_sales_year)
detroit_propsales_year_1 <- cbind(detroit_property_sales_year, Detroit_propertysales)
detroit_propsales_year_1

Detroit_propertysales_2011 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2011)
Detroit_propertysales_2012 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2012)
Detroit_propertysales_2013 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2013)
Detroit_propertysales_2014 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2014)
Detroit_propertysales_2015 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2015)
Detroit_propertysales_2016 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2016)
Detroit_propertysales_2017 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2017)
Detroit_propertysales_2018 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2018)
Detroit_propertysales_2019 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2019)
Detroit_propertysales_2020 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2020)
Detroit_propertysales_2021 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2021)
Detroit_propertysales_2022 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2022)
```

```{r}
# adding all detroit property sales and creating a gif that illustrates the change in total sale price by year. 

sum_detroit_propsales_year_1 <- aggregate(x = detroit_propsales_year_1$sale_price,               
          by = list(detroit_propsales_year_1$detroit_property_sales_year),            
          FUN = sum) 
colnames(sum_detroit_propsales_year_1)[1] = 'year'
colnames(sum_detroit_propsales_year_1)[2] = 'price'

sum_detroit_propsales_year_1$year <- as.integer(sum_detroit_propsales_year_1$year)

detroit_line_graph_salesprices <- ggplot()+ geom_point(data = sum_detroit_propsales_year_1, aes(x = year, y= price)) + theme_bw() + transition_time(year) + labs(title = "Real Estate Prices in Detroit by Year: {frame_time}")

detroit_line_graph_salesprices

```

```{r, fig.cap="Total Real Estate Prices in Detroit by Year"}
knitr::include_graphics("detroit_line_graph_salesprices.gif")
```

```{r}

# Graphing the average sale prices in Detroit

avg_detroit_propsales_year_1 <- aggregate(x = detroit_propsales_year_1$sale_price,               
          by = list(detroit_propsales_year_1$detroit_property_sales_year),            
          FUN = mean) 

colnames(avg_detroit_propsales_year_1)[1] = 'year'
colnames(avg_detroit_propsales_year_1)[2] = 'price'

avg_detroit_propsales_year_1$year <- as.integer(avg_detroit_propsales_year_1$year)

detroit_line_graph_salesprices_avg <- ggplot()+ geom_point(data = avg_detroit_propsales_year_1, aes(x = year, y= price)) + theme_bw() + transition_time(year) + labs(title = " Average Real Estate Prices in Detroit by Year: {frame_time}")

detroit_line_graph_salesprices_avg

```

```{r, fig.cap="Average Real Estate Prices in Detroit by Year"}
knitr::include_graphics("detroit_line_graph_salesprices_avg.gif")
```

```{r}
# Filtering to property class 401, which refers to residential in nature and recently improved.
detroitsales_401 <- detroit_propsales_year_1 %>% dplyr::filter(., property_class == 401)

# Filtering to property class 402, which refers to residential and nature and vacant.
detroitsales_402 <- detroit_propsales_year_1 %>% dplyr::filter(., property_class == 402)

```

```{r}
# Graphing the total sale prices in Detroit - by property class 401

sum_detroit_propsales_year_401 <- aggregate(x = detroitsales_401$sale_price,               
          by = list(detroitsales_401$detroit_property_sales_year),            
          FUN = sum) 
colnames(sum_detroit_propsales_year_401)[1] = 'year'
colnames(sum_detroit_propsales_year_401)[2] = 'price'

sum_detroit_propsales_year_401$year <- as.integer(sum_detroit_propsales_year_401$year)

detroit_line_graph_salesprices_401 <- ggplot()+ geom_point(data = sum_detroit_propsales_year_401, aes(x = year, y= price)) + theme_bw() + transition_time(year) + labs(title = "Real Estate Prices - Residential in Nature and Improved in Detroit by Year: {frame_time}")

detroit_line_graph_salesprices_401
```

```{r, fig.cap="Average Real Estate Prices of Residential Properties That Have Recently Been Improved in Detroit by Year"}
knitr::include_graphics("detroit_line_graph_salesprices_401.gif")
```

```{r}
# Graphing the average sale prices in Detroit - by property class 401

avg_detroit_propsales_year_401 <- aggregate(x = detroitsales_401$sale_price,               
          by = list(detroitsales_401$detroit_property_sales_year),            
          FUN = mean) 
colnames(avg_detroit_propsales_year_401)[1] = 'year'
colnames(avg_detroit_propsales_year_401)[2] = 'price'

avg_detroit_propsales_year_401$year <- as.integer(avg_detroit_propsales_year_401$year)

detroit_line_graph_salesprices_401_avg <- ggplot()+ geom_point(data = avg_detroit_propsales_year_401, aes(x = year, y= price)) + theme_bw() + transition_time(year) + labs(title = " Average Real Estate Prices - Residential in Nature and Improved in Detroit by Year: {frame_time}")

detroit_line_graph_salesprices_401_avg
```

```{r, fig.cap="Average Real Estate Prices of Residential Properties That Have Recently Been Improved in Detroit by Year"}
knitr::include_graphics("detroit_line_graph_salesprices_401_avg.gif")
```

```{r}
# Graphing the total sale prices in Detroit - by property class 402

sum_detroit_propsales_year_402 <- aggregate(x = detroitsales_402$sale_price,               
          by = list(detroitsales_402$detroit_property_sales_year),            
          FUN = sum) 
colnames(sum_detroit_propsales_year_402)[1] = 'year'
colnames(sum_detroit_propsales_year_402)[2] = 'price'

sum_detroit_propsales_year_402$year <- as.integer(sum_detroit_propsales_year_402$year)

detroit_line_graph_salesprices_402 <- ggplot()+ geom_point(data = sum_detroit_propsales_year_402, aes(x = year, y= price), color = "royalblue", size = 4) + theme_bw() + transition_time(year) + labs(title = "Real Estate Prices - Residential and Vacant in Detroit by Year: {frame_time}")

detroit_line_graph_salesprices_402
```

```{r, fig.cap="Total Real Estate Prices of Residential Properties That are Vacant in Detroit by Year"}
knitr::include_graphics("detroit_line_graph_salesprices_402.gif")
```

```{r}
# Graphing the average sale prices in Detroit - by property class 402

avg_detroit_propsales_year_402 <- aggregate(x = detroitsales_402$sale_price,               
          by = list(detroitsales_402$detroit_property_sales_year),            
          FUN = mean) 
colnames(avg_detroit_propsales_year_402)[1] = 'year'
colnames(avg_detroit_propsales_year_402)[2] = 'price'

avg_detroit_propsales_year_402$year <- as.integer(avg_detroit_propsales_year_402$year)

detroit_line_graph_salesprices_402_avg <- ggplot()+ geom_point(data = avg_detroit_propsales_year_402, aes(x = year, y= price)) + theme_bw() + transition_time(year) + labs(title = " Average Real Estate Prices - Residential and Vacant in Detroit by Year: {frame_time}")

detroit_line_graph_salesprices_402_avg
```

```{r, fig.cap="Average Real Estate Prices of Residential Properties That are Vacant in Detroit by Year"}
knitr::include_graphics("detroit_line_graph_salesprices_402_avg.gif")
```

# DEMOLITION

![Detroit, Michigan](Detroit_Demolitions.jpeg)

This section focuses on demolitions, and draws directly from Detroit's Open Data Portal. The link for this data can be found here: <https://data.detroitmi.gov/datasets/detroitmi::completed-residential-demolitions/explore>

```{r}
# Downloading residential demolition data - from Detroit's Open Data Portal

residential_demolitions <- read_csv("Completed_Residential_Demolitions.csv")
```

```{r}
# Tidying demolition data...

year_demo<- format(as.Date(residential_demolitions$demolition_date, format="%Y/%m/%d"),"%Y")
data.frame(residential_demolitions)
residential_demo_year <- cbind(year_demo, residential_demolitions)
residential_demo_year
```

```{r}
# Transforming it into a spatial object

residential_demo_year <- st_as_sf(residential_demo_year, coords = c("X", "Y"), crs = 4326)
```

```{r}
# Mapping residential demolitions from 2014 - 2022 (2014 is the earliest date available)
residential_demo_year$year_demo <- as.integer(residential_demo_year$year_demo)

detroit_map_demo <- ggplot() + geom_sf(data = residential_demo_year, color = "darkgreen", aes(fill = year_demo), size = .05, alpha = .5) + theme_light(base_size = 10) + scale_fill_gradient(low = "lightyellow1", high = "darkorchid4") 

detroit_map_demo
```

```{r}

# Making it into a gif!

residential_demo_year$year_demo <- as.integer(residential_demo_year$year_demo)

detroit_map_demo_gif <- ggplot() + geom_sf(data = residential_demo_year, color = "darkgreen", aes(fill = year_demo), size = .05, alpha = .5) + theme_light(base_size = 10) + scale_fill_gradient(low = "lightyellow1", high = "darkorchid4") + transition_time(year_demo) + labs(title = "Demolitions by Year: {frame_time}")

detroit_map_demo_gif


```

```{r, fig.cap="Number of Demolished Properties by Yeara"}
knitr::include_graphics("detroit_map_demo_gif.gif")
```

###### Below, I am saving all of the animations to be reinserted into this notebook.

```{r}
anim_save("detroit_map_demo_gif.gif", detroit_map_demo_gif)
```

```{r}
anim_save("detroit_map_pop_gif.gif", detroit_map_pop_gif)
```

```{r}
anim_save("detroit_line_graph_population.gif", detroit_line_graph_population)
```

```{r}
anim_save("detroit_line_graph_vacancies_1.gif", detroit_line_graph_vacancies_1)
```

```{r}
anim_save("detroit_line_graph_vacancies_2.gif", detroit_line_graph_vacancies_2)
```

```{r}
anim_save("detroit_map_vacancy_gif.gif", detroit_map_vacancy_gif)
```

```{r}
anim_save("detroit_line_graph_salesprices.gif", detroit_line_graph_salesprices)
```

```{r}
anim_save("detroit_line_graph_salesprices_401.gif", detroit_line_graph_salesprices_401) 
```

```{r}
anim_save("detroit_line_graph_salesprices_402.gif", detroit_line_graph_salesprices_402) 
```

```{r}
anim_save("detroit_line_graph_salesprices_401_avg.gif", detroit_line_graph_salesprices_401_avg) 
```

```{r}
anim_save("detroit_line_graph_salesprices_402_avg.gif", detroit_line_graph_salesprices_402_avg)
```

```{r}
anim_save("detroit_line_graph_salesprices_avg.gif", detroit_line_graph_salesprices_avg)
```

```{r}
anim_save("detroit_line_graph_vacancies.gif", detroit_line_graph_vacancies)
```

```{r}
#install.packages("gifski")
```

```{r}
library(gifski)
library(rsconnect)
library(shiny)

```

## Citations

## Data

United State Census Bureau. (2010-2020). American Community Survey. Explore census data. Wayne County, Michigan. <https://data.census.gov/table?q=vacancy&g=040XX00US26&tid=ACSDT1Y2021.B25002>

United State Census Bureau. (2010-2020). American Community Survey. Explore census data. Wayne County, Michigan. <https://data.census.gov/table?q=population&g=040XX00US26&tid=ACSDP1Y2021.DP05>

*Housing Data*. Zillow. (2023, April 25). <https://www.zillow.com/research/data/>

*Property sales*. Detroit's Open Data Portal. (n.d.-b). <https://data.detroitmi.gov/datasets/property-sales-1/explore>

*Completed residential demolitions*. Detroit's Open Data Portal. (n.d.). <https://data.detroitmi.gov/datasets/detroitmi::completed-residential-demolitions/explore?location=42.352748%2C-83.100004%2C10.98>

## Images

*Detroit*. Pure Michigan \| Official Travel & Tourism Website for Michigan. (2023, May 11). <https://www.michigan.org/city/detroit>

Klinefelter, Q. (2013, March 1). *Michigan officials take control of Detroit's empty wallet*. NPR. <https://www.npr.org/2013/03/01/173241269/michigan-officials-take-control-of-detroits-empty-wallet>

Owens, C. (2015, June 12). *How Detroit taught itself to demolish vacant houses safely*. Next City. <https://nextcity.org/urbanist-news/detroit-demolition-blight-teardowns-safety>
