Project 2
Gabby Rodriguez
2023-11-21
# 1. Redo the analysis for mapping Hispanic population and add histogram in the map (2').
library(tidycensus)
census_api_key("bd2af251ff3d1ee389e731f6d917f605cb300fdb", overwrite = "TRUE") ## To install your API key for use in future sessions, run this function with `install = TRUE`.var=c('B03002_012E','B03002_004E','B05003_002E', 'B05003_013E','B01001_001E', 'B01002I_001E', 'B19013I_001E')
Miami_segregation <- get_acs(geography = "tract", variables = var, county = "Miami-Dade",
state = "FL",output="wide", geometry = TRUE) ## 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)`.##
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|======================================================================| 100%names(Miami_segregation)[3] <- 'Hispanic'
names(Miami_segregation)[5] <- 'Black or African American'
names(Miami_segregation)[7] <- 'Male'
names(Miami_segregation)[9] <- 'Female'
names(Miami_segregation)[11] <- 'TotalPop'
names(Miami_segregation)[13] <- 'MedianAge'
names(Miami_segregation)[15] <- 'MedianHouseholdIncome'
Miami_segregation$B03002_012M <- NULL
Miami_segregation$B03002_004M <- NULL
Miami_segregation$B05003_002M <- NULL
Miami_segregation$B05003_013M <- NULL
Miami_segregation$B01001_001M <- NULL
Miami_segregation$B01002I_001M <- NULL
Miami_segregation$B19013I_001M <- NULL
library(tmap)## The legacy packages maptools, rgdal, and rgeos, underpinning the sp package,
## which was just loaded, will retire in October 2023.
## Please refer to R-spatial evolution reports for details, especially
## https://r-spatial.org/r/2023/05/15/evolution4.html.
## It may be desirable to make the sf package available;
## package maintainers should consider adding sf to Suggests:.
## The sp package is now running under evolution status 2
## (status 2 uses the sf package in place of rgdal)## Breaking News: tmap 3.x is retiring. Please test v4, e.g. with
## remotes::install_github('r-tmap/tmap')library(sf)## Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUEMiami_segregation$pct_Hisp <- 100*Miami_segregation$Hispanic/Miami_segregation$TotalPop
Miami_segregation <- Miami_segregation[-707,]
tm_shape(Miami_segregation) +tm_fill(col = "pct_Hisp")+ tm_layout(title = "Hispanic Percent")
tm_shape(Miami_segregation) +
tm_fill(col = "Hispanic",legend.hist = TRUE)+
tm_layout(title = "Hispanic Pop")+
tm_layout(legend.outside = TRUE,frame = FALSE)
# 2. Following Q1, add border for census tracts (1').
tm_shape(Miami_segregation) +
tm_fill(col = "Hispanic",legend.hist = TRUE)+
tm_layout(title = "Hispanic Pop")+
tm_borders(alpha=.4) +
tm_layout(legend.outside = TRUE,frame = FALSE)
# 3. Following Q2, add compass and scale bar for the map (1').
tmap_mode("plot")## tmap mode set to plottingtm_shape(Miami_segregation) +
tm_fill(col = "Hispanic", legend.hist = TRUE)+
tm_layout(title = "Hispanic Pop")+
tm_borders(alpha=.4) +
tm_compass(type = "8star",
position = c("RIGHT", "BOTTOM"),
show.labels = 2,
text.size = 0.6) +
tm_layout(legend.outside = TRUE,frame = TRUE)+
tm_scale_bar(position = c("RIGHT", "BOTTOM"))
# 4. Following Q3, set color intervals as "red" for the map (1').
tm_shape(Miami_segregation) +
tm_fill(col = "Hispanic", palette = "Reds", legend.hist = TRUE)+
tm_layout(title = "Hispanic Pop")+
tm_borders(alpha=.4) +
tm_compass(type = "8star",
position = c("RIGHT", "BOTTOM"),
show.labels = 2,
text.size = 0.6) +
tm_layout(legend.outside = TRUE,frame = TRUE)+
tm_scale_bar(position = c("RIGHT", "BOTTOM"))
# 5. Make an interactive map to show the spatial distribution of percentage of Hispanic population (2').
tmap_mode("view")## tmap mode set to interactive viewingtm_shape(Miami_segregation) +
tm_fill(col = "Hispanic", palette = "Reds", legend.hist = TRUE)+
tm_layout(title = "Hispanic Pop")+
tm_borders(alpha=.4) +
tm_layout(legend.outside = TRUE,frame = TRUE)+
tm_scale_bar(position = c("RIGHT", "BOTTOM"))# 6. Retrieve the census tract shapefile data of the county (1').
census_tract <-st_read(dsn = "/Users/gabbyrodriguez/Documents/UTSA MSURP/Urban Planning Methods/Project 2/Census Tract Data/Tract.shp")## Reading layer `Tract' from data source
## `/Users/gabbyrodriguez/Documents/UTSA MSURP/Urban Planning Methods/Project 2/Census Tract Data/Tract.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 707 features and 74 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -80.8736 ymin: 25.13742 xmax: -80.06279 ymax: 25.97944
## Geodetic CRS: WGS 84# 7. Find any point shapefile data within the county and make a map with census tract as the background (2').
library(tigris)## To enable caching of data, set `options(tigris_use_cache = TRUE)`
## in your R script or .Rprofile.MIA_Library <-st_read(dsn = "/Users/gabbyrodriguez/Documents/UTSA MSURP/Urban Planning Methods/Project 2/County_Library/Library.shp")## Reading layer `Library' from data source
## `/Users/gabbyrodriguez/Documents/UTSA MSURP/Urban Planning Methods/Project 2/County_Library/Library.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 50 features and 25 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -80.44164 ymin: 25.50632 xmax: -80.12072 ymax: 25.9604
## Geodetic CRS: WGS 84MIA_Library <- st_transform(MIA_Library, crs=4326)
Miami_tracts<- tracts(state = "FL", county = "Miami-Dade",cb=T)## Retrieving data for the year 2021tmap_mode("view")## tmap mode set to interactive viewingtm_shape(Miami_tracts) +
tm_borders() + # Add borders for polygons (customize as needed)
tm_shape(MIA_Library) +
tm_dots(size = 0.02, col = "red", alpha = 0.7) + # Customize point appearance
tm_layout(title = "Miami-Dade Libraries", frame = FALSE)# 8. Find any line shapefile data within the county and make a map with census tract as the background (2').
bike_lanes <-st_read(dsn = "/Users/gabbyrodriguez/Documents/UTSA MSURP/Urban Planning Methods/Project 2/Bike_lane/BikeLane.shp")## Reading layer `BikeLane' from data source
## `/Users/gabbyrodriguez/Documents/UTSA MSURP/Urban Planning Methods/Project 2/Bike_Lane/BikeLane.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 178 features and 5 fields
## Geometry type: LINESTRING
## Dimension: XY
## Bounding box: xmin: -80.48534 ymin: 25.43657 xmax: -80.12091 ymax: 25.97454
## Geodetic CRS: WGS 84bike_lanes <- st_transform(bike_lanes, crs=4326)
Miami_tracts<- tracts(state = "FL", county = "Miami-Dade",cb=T)## Retrieving data for the year 2021buffer_distance <- 0.5 # adjust as needed
lines_polygon <- st_buffer(bike_lanes, dist = buffer_distance)
tm_shape(Miami_tracts) +
tm_borders() +
tm_shape(lines_polygon) +
tm_borders(lwd = 2, col = "green") + # Customize line appearance
tm_layout(frame = FALSE)