Midterm Exam
Md Rizwanul Hasan, UID: U00696227
10/1/2020
Cleaning the Global environment and the console
rm(list = ls()) # Clears the global environment for a fresh start
cat('\f') # Cleans the consoleSet working directory
setwd("C:/Rizwan/Education/UofM PhD Work/Seminar in Earth Sciences/Exams/Midterm")
getwd()## [1] "C:/Rizwan/Education/UofM PhD Work/Seminar in Earth Sciences/Exams/Midterm"Loading libraries
This code allows to load multiple library at once.
my_library <- c("sf", "dplyr", "RColorBrewer", "ggplot2", "gridExtra", "classInt")
lapply(my_library, library, character.only = TRUE)## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combineUnzipping the Data
unzip(zipfile = "midterm_data.zip", exdir = getwd())
ls()## [1] "my_library"Loading the data
dsn1 <- "Memphis_Demographics_GCS.shp"
dsn2 <- "Shelby.gdb"
MemDemog <- st_read(dsn1)## Reading layer `Memphis_Demographics_GCS' from data source `C:\Rizwan\Education\UofM PhD Work\Seminar in Earth Sciences\Exams\Midterm\Memphis_Demographics_GCS.shp' using driver `ESRI Shapefile'
## Simple feature collection with 503 features and 4 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -90.31029 ymin: 34.99417 xmax: -89.73822 ymax: 35.26718
## geographic CRS: NAD83Shelby <- st_read(dsn2)## Multiple layers are present in data source C:\Rizwan\Education\UofM PhD Work\Seminar in Earth Sciences\Exams\Midterm\Shelby.gdb, reading layer `Community_Garden_shelby'.
## Use `st_layers' to list all layer names and their type in a data source.
## Set the `layer' argument in `st_read' to read a particular layer.## Warning in evalq((function (..., call. = TRUE, immediate. = FALSE, noBreaks. =
## FALSE, : automatically selected the first layer in a data source containing more
## than one.## Reading layer `Community_Garden_shelby' from data source `C:\Rizwan\Education\UofM PhD Work\Seminar in Earth Sciences\Exams\Midterm\Shelby.gdb' using driver `OpenFileGDB'
## Simple feature collection with 33 features and 17 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 756844.4 ymin: 298726.4 xmax: 794158.8 ymax: 341723.2
## projected CRS: NAD83 / Tennessee (ftUS)Data <- as_tibble(read.table("fips_demographics.txt", header = TRUE, sep = ","))
head(Data)## # A tibble: 6 x 18
## OBJECTID STATE_FIPS BLKGRP FIPS POP2010 POP2013 WHITE BLACK MALES FEMALES
## <int> <int> <int> <dbl> <int> <int> <int> <int> <int> <int>
## 1 1 47 1 4.72e11 999 1007 75 871 456 543
## 2 2 47 4 4.72e11 698 683 68 318 357 341
## 3 3 47 1 4.72e11 1203 1063 116 1035 531 672
## 4 4 47 1 4.72e11 293 293 80 213 40 253
## 5 5 47 2 4.72e11 862 859 520 186 414 448
## 6 6 47 5 4.72e11 1101 1135 674 210 570 531
## # ... with 8 more variables: MED_AGE <dbl>, AVE_FAM_SZ <dbl>, HSE_UNITS <int>,
## # OWNER_OCC <int>, RENTER_OCC <int>, GlobalID <chr>, Shape__Area <dbl>,
## # Shape__Length <dbl>Read all layers from the geodatabase
LayerList <- st_layers(dsn2)
LayerList## Driver: OpenFileGDB
## Available layers:
## layer_name geometry_type features fields
## 1 Community_Garden_shelby Point 33 17
## 2 School_shelby Point 391 17
## 3 Rail_shelby Multi Line String 339 8
## 4 Parks_shelby Multi Polygon 226 6
## 5 Library_shelby Point 22 16
## 6 Law_Enforcement_shelby Point 37 17
## 7 Hospital_shelby Point 21 17
## 8 Health_Centers_shelby Point 31 16
## 9 Grocer_shelby Point 60 16
## 10 Fire_shelby Point 89 17
## 11 Farmer_Market_shelby Point 14 17
## 12 County_shelby Multi Polygon 1 23
## 13 roads_shelby Multi Line String 2763 8
## 14 Memphis_City_Demographics Multi Polygon 503 19class(LayerList)## [1] "sf_layers"LayerList[1]## $name
## [1] "Community_Garden_shelby" "School_shelby"
## [3] "Rail_shelby" "Parks_shelby"
## [5] "Library_shelby" "Law_Enforcement_shelby"
## [7] "Hospital_shelby" "Health_Centers_shelby"
## [9] "Grocer_shelby" "Fire_shelby"
## [11] "Farmer_Market_shelby" "County_shelby"
## [13] "roads_shelby" "Memphis_City_Demographics"Question 1
Name = LayerList$name
Layers <- lapply(c(1:length(Name)), function(i) {
st_read(dsn2, layer = Name[i])
})## Reading layer `Community_Garden_shelby' from data source `C:\Rizwan\Education\UofM PhD Work\Seminar in Earth Sciences\Exams\Midterm\Shelby.gdb' using driver `OpenFileGDB'
## Simple feature collection with 33 features and 17 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 756844.4 ymin: 298726.4 xmax: 794158.8 ymax: 341723.2
## projected CRS: NAD83 / Tennessee (ftUS)
## Reading layer `School_shelby' from data source `C:\Rizwan\Education\UofM PhD Work\Seminar in Earth Sciences\Exams\Midterm\Shelby.gdb' using driver `OpenFileGDB'
## Simple feature collection with 391 features and 17 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 734398 ymin: 263306.1 xmax: 876914 ymax: 397840.4
## projected CRS: NAD83 / Tennessee (ftUS)
## Reading layer `Rail_shelby' from data source `C:\Rizwan\Education\UofM PhD Work\Seminar in Earth Sciences\Exams\Midterm\Shelby.gdb' using driver `OpenFileGDB'
## Simple feature collection with 339 features and 8 fields
## geometry type: MULTILINESTRING
## dimension: XY
## bbox: xmin: 727063.6 ymin: 263436.6 xmax: 883773.4 ymax: 407636.1
## projected CRS: NAD83 / Tennessee (ftUS)
## Reading layer `Parks_shelby' from data source `C:\Rizwan\Education\UofM PhD Work\Seminar in Earth Sciences\Exams\Midterm\Shelby.gdb' using driver `OpenFileGDB'
## Simple feature collection with 226 features and 6 fields
## geometry type: GEOMETRY
## dimension: XY
## bbox: xmin: 732044.5 ymin: 265123.9 xmax: 878833.9 ymax: 410431.8
## projected CRS: NAD83 / Tennessee (ftUS)
## Reading layer `Library_shelby' from data source `C:\Rizwan\Education\UofM PhD Work\Seminar in Earth Sciences\Exams\Midterm\Shelby.gdb' using driver `OpenFileGDB'
## Simple feature collection with 22 features and 16 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 752812.9 ymin: 272549.6 xmax: 875503.7 ymax: 390129.8
## projected CRS: NAD83 / Tennessee (ftUS)
## Reading layer `Law_Enforcement_shelby' from data source `C:\Rizwan\Education\UofM PhD Work\Seminar in Earth Sciences\Exams\Midterm\Shelby.gdb' using driver `OpenFileGDB'
## Simple feature collection with 37 features and 17 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 752117.2 ymin: 278903.1 xmax: 872274.7 ymax: 390481
## projected CRS: NAD83 / Tennessee (ftUS)
## Reading layer `Hospital_shelby' from data source `C:\Rizwan\Education\UofM PhD Work\Seminar in Earth Sciences\Exams\Midterm\Shelby.gdb' using driver `OpenFileGDB'
## Simple feature collection with 21 features and 17 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 760308.5 ymin: 279451.1 xmax: 856890.3 ymax: 351008.6
## projected CRS: NAD83 / Tennessee (ftUS)
## Reading layer `Health_Centers_shelby' from data source `C:\Rizwan\Education\UofM PhD Work\Seminar in Earth Sciences\Exams\Midterm\Shelby.gdb' using driver `OpenFileGDB'
## Simple feature collection with 31 features and 16 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 757687.9 ymin: 277327.6 xmax: 872210.3 ymax: 391961
## projected CRS: NAD83 / Tennessee (ftUS)
## Reading layer `Grocer_shelby' from data source `C:\Rizwan\Education\UofM PhD Work\Seminar in Earth Sciences\Exams\Midterm\Shelby.gdb' using driver `OpenFileGDB'
## Simple feature collection with 60 features and 16 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 755399 ymin: 271441.9 xmax: 875874.3 ymax: 401002.8
## projected CRS: NAD83 / Tennessee (ftUS)
## Reading layer `Fire_shelby' from data source `C:\Rizwan\Education\UofM PhD Work\Seminar in Earth Sciences\Exams\Midterm\Shelby.gdb' using driver `OpenFileGDB'
## Simple feature collection with 89 features and 17 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 739899.8 ymin: 269570.5 xmax: 873174.7 ymax: 392902.6
## projected CRS: NAD83 / Tennessee (ftUS)
## Reading layer `Farmer_Market_shelby' from data source `C:\Rizwan\Education\UofM PhD Work\Seminar in Earth Sciences\Exams\Midterm\Shelby.gdb' using driver `OpenFileGDB'
## Simple feature collection with 14 features and 17 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 754547.3 ymin: 279243.4 xmax: 875874.3 ymax: 390219.8
## projected CRS: NAD83 / Tennessee (ftUS)
## Reading layer `County_shelby' from data source `C:\Rizwan\Education\UofM PhD Work\Seminar in Earth Sciences\Exams\Midterm\Shelby.gdb' using driver `OpenFileGDB'
## Simple feature collection with 1 feature and 23 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 677966.9 ymin: 261279.3 xmax: 885736.8 ymax: 412664.8
## projected CRS: NAD83 / Tennessee (ftUS)
## Reading layer `roads_shelby' from data source `C:\Rizwan\Education\UofM PhD Work\Seminar in Earth Sciences\Exams\Midterm\Shelby.gdb' using driver `OpenFileGDB'
## Simple feature collection with 2763 features and 8 fields
## geometry type: MULTILINESTRING
## dimension: XY
## bbox: xmin: -90.12757 ymin: 34.99427 xmax: -89.6358 ymax: 35.40568
## geographic CRS: WGS 84
## Reading layer `Memphis_City_Demographics' from data source `C:\Rizwan\Education\UofM PhD Work\Seminar in Earth Sciences\Exams\Midterm\Shelby.gdb' using driver `OpenFileGDB'
## Simple feature collection with 503 features and 19 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 677979 ymin: 263221.8 xmax: 850942.3 ymax: 364390.3
## projected CRS: NAD83 / Tennessee (ftUS)names(Layers) <- Name
Schools <- Layers$School_shelby
Libraries <- Layers$Library_shelby
libBuff_2km <- st_buffer(Libraries, 6561.68) # Multiplied by 6561.68 to convert from feet to kilometers
libBuff_intersec <- st_intersection(libBuff_2km, Schools)## Warning: attribute variables are assumed to be spatially constant throughout all
## geometriesnSchools <- libBuff_intersec %>% group_by(Library_Na) %>% summarise(n_Schools=n()) %>% filter(n_Schools>=10)## `summarise()` ungrouping output (override with `.groups` argument)plot(st_geometry(Libraries), axes = T, main = "Schools within 2KM of the Libraries", xlab = "Easting (ft)", ylab = "Northing (ft)", cex.main = 1.5)
plot(st_geometry(libBuff_2km), add = T, border = "red")
plot(st_geometry(libBuff_intersec), add = T, col = "blue")
cat("Number of schools exists within 2 km of bufferzones is: ", sum(nSchools$n_Schools), sep = '')## Number of schools exists within 2 km of bufferzones is: 56The list of the Libraries having most schools within 2 km bufferzone.
head(nSchools)## Simple feature collection with 5 features and 2 fields
## geometry type: MULTIPOINT
## dimension: XY
## bbox: xmin: 750390.5 ymin: 300350.1 xmax: 807441.6 ymax: 329737.9
## projected CRS: NAD83 / Tennessee (ftUS)
## # A tibble: 5 x 3
## Library_Na n_Schools Shape
## <chr> <int> <MULTIPOINT [US_survey_foot]>
## 1 Cornelia Crenshaw Br~ 12 ((757511.5 321188.3), (757738.8 317564), (758~
## 2 Gaston Park Branch L~ 12 ((750390.5 309804.1), (752022.2 306007.4), (7~
## 3 North Branch Library 11 ((762900.2 326318), (763058.3 325328.8), (763~
## 4 Poplar White Station~ 10 ((798720.7 309226.4), (800709.8 308824), (801~
## 5 Randolph Branch Libr~ 11 ((787640 326204.7), (788289.8 321280.7), (789~Question 2
Checking the projection systems
Before joining to layers, it is necessary to check their projection systems. We used st_crs to check the projection systems.
st_crs(MemDemog)## Coordinate Reference System:
## User input: NAD83
## wkt:
## GEOGCRS["NAD83",
## DATUM["North American Datum 1983",
## ELLIPSOID["GRS 1980",6378137,298.257222101,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## CS[ellipsoidal,2],
## AXIS["latitude",north,
## ORDER[1],
## ANGLEUNIT["degree",0.0174532925199433]],
## AXIS["longitude",east,
## ORDER[2],
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4269]]st_crs(Schools)## Coordinate Reference System:
## User input: NAD83 / Tennessee (ftUS)
## wkt:
## PROJCRS["NAD83 / Tennessee (ftUS)",
## BASEGEOGCRS["NAD83",
## DATUM["North American Datum 1983",
## ELLIPSOID["GRS 1980",6378137,298.257222101,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4269]],
## CONVERSION["SPCS83 Tennessee zone (US Survey feet)",
## METHOD["Lambert Conic Conformal (2SP)",
## ID["EPSG",9802]],
## PARAMETER["Latitude of false origin",34.3333333333333,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8821]],
## PARAMETER["Longitude of false origin",-86,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8822]],
## PARAMETER["Latitude of 1st standard parallel",36.4166666666667,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8823]],
## PARAMETER["Latitude of 2nd standard parallel",35.25,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8824]],
## PARAMETER["Easting at false origin",1968500,
## LENGTHUNIT["US survey foot",0.304800609601219],
## ID["EPSG",8826]],
## PARAMETER["Northing at false origin",0,
## LENGTHUNIT["US survey foot",0.304800609601219],
## ID["EPSG",8827]]],
## CS[Cartesian,2],
## AXIS["easting (X)",east,
## ORDER[1],
## LENGTHUNIT["US survey foot",0.304800609601219]],
## AXIS["northing (Y)",north,
## ORDER[2],
## LENGTHUNIT["US survey foot",0.304800609601219]],
## USAGE[
## SCOPE["unknown"],
## AREA["USA - Tennessee"],
## BBOX[34.98,-90.31,36.68,-81.65]],
## ID["EPSG",2274]]st_crs(Libraries)## Coordinate Reference System:
## User input: NAD83 / Tennessee (ftUS)
## wkt:
## PROJCRS["NAD83 / Tennessee (ftUS)",
## BASEGEOGCRS["NAD83",
## DATUM["North American Datum 1983",
## ELLIPSOID["GRS 1980",6378137,298.257222101,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4269]],
## CONVERSION["SPCS83 Tennessee zone (US Survey feet)",
## METHOD["Lambert Conic Conformal (2SP)",
## ID["EPSG",9802]],
## PARAMETER["Latitude of false origin",34.3333333333333,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8821]],
## PARAMETER["Longitude of false origin",-86,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8822]],
## PARAMETER["Latitude of 1st standard parallel",36.4166666666667,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8823]],
## PARAMETER["Latitude of 2nd standard parallel",35.25,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8824]],
## PARAMETER["Easting at false origin",1968500,
## LENGTHUNIT["US survey foot",0.304800609601219],
## ID["EPSG",8826]],
## PARAMETER["Northing at false origin",0,
## LENGTHUNIT["US survey foot",0.304800609601219],
## ID["EPSG",8827]]],
## CS[Cartesian,2],
## AXIS["easting (X)",east,
## ORDER[1],
## LENGTHUNIT["US survey foot",0.304800609601219]],
## AXIS["northing (Y)",north,
## ORDER[2],
## LENGTHUNIT["US survey foot",0.304800609601219]],
## USAGE[
## SCOPE["unknown"],
## AREA["USA - Tennessee"],
## BBOX[34.98,-90.31,36.68,-81.65]],
## ID["EPSG",2274]]Reprojecting the shape file
MemDemog_ftUS <- st_transform(MemDemog, st_crs(Schools))
st_crs(MemDemog_ftUS)## Coordinate Reference System:
## User input: NAD83 / Tennessee (ftUS)
## wkt:
## PROJCRS["NAD83 / Tennessee (ftUS)",
## BASEGEOGCRS["NAD83",
## DATUM["North American Datum 1983",
## ELLIPSOID["GRS 1980",6378137,298.257222101,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4269]],
## CONVERSION["SPCS83 Tennessee zone (US Survey feet)",
## METHOD["Lambert Conic Conformal (2SP)",
## ID["EPSG",9802]],
## PARAMETER["Latitude of false origin",34.3333333333333,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8821]],
## PARAMETER["Longitude of false origin",-86,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8822]],
## PARAMETER["Latitude of 1st standard parallel",36.4166666666667,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8823]],
## PARAMETER["Latitude of 2nd standard parallel",35.25,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8824]],
## PARAMETER["Easting at false origin",1968500,
## LENGTHUNIT["US survey foot",0.304800609601219],
## ID["EPSG",8826]],
## PARAMETER["Northing at false origin",0,
## LENGTHUNIT["US survey foot",0.304800609601219],
## ID["EPSG",8827]]],
## CS[Cartesian,2],
## AXIS["easting (X)",east,
## ORDER[1],
## LENGTHUNIT["US survey foot",0.304800609601219]],
## AXIS["northing (Y)",north,
## ORDER[2],
## LENGTHUNIT["US survey foot",0.304800609601219]],
## USAGE[
## SCOPE["unknown"],
## AREA["USA - Tennessee"],
## BBOX[34.98,-90.31,36.68,-81.65]],
## ID["EPSG",2274]]Calculating population growth, defining breaks, and ploting the data
MemDemog_ftUS_merged <- merge(MemDemog_ftUS,Data, by.x = "FIPS", by.y = "FIPS")
Pop_Growth <- MemDemog_ftUS_merged %>%
group_by(FIPS) %>%
summarise(Pop2010 = POP2010,
Pop2013 = POP2013,
rate = (Pop2013 - Pop2010)/Pop2010)## `summarise()` ungrouping output (override with `.groups` argument)breaks_qt <- classIntervals(c(min(Pop_Growth$rate)-0.00001,
Pop_Growth$rate), n = 7, style = "quantile")## Warning in classIntervals(c(min(Pop_Growth$rate) - 1e-05, Pop_Growth$rate), :
## var has missing values, omitted in finding classesbreaks_qt## style: quantile
## [-0.1869436,-0.04643758) [-0.04643758,-0.02499779)
## 72 71
## [-0.02499779,-0.008012499) [-0.008012499,0.001669955)
## 72 71
## [0.001669955,0.01529958) [0.01529958,0.03988599)
## 72 71
## [0.03988599,0.2933151]
## 72Pop_Growth <- mutate(Pop_Growth, growth_rate_cat = cut(rate, breaks_qt$brks))
ggplot(Pop_Growth)+
geom_sf(aes(fill=growth_rate_cat))+
scale_fill_brewer(palette = "YlOrRd")