Project 2
Salomon Mendoza
2023-11-28
#1. Redo the analysis for mapping Hispanic population and add histogram in the map (2').
library(tidycensus)## Warning: package 'tidycensus' was built under R version 4.2.3library(sf)## Linking to GEOS 3.9.3, GDAL 3.5.2, PROJ 8.2.1; sf_use_s2() is TRUElibrary(tmap)## Warning: package 'tmap' was built under R version 4.2.3## Breaking News: tmap 3.x is retiring. Please test v4, e.g. with
## remotes::install_github('r-tmap/tmap')library(tigris)## To enable caching of data, set `options(tigris_use_cache = TRUE)`
## in your R script or .Rprofile.##
## Attaching package: 'tigris'## The following object is masked from 'package:tidycensus':
##
## fips_codescensus_api_key("edd28860119f5a254a2915190d528839b25a475e",install="TRUE", overwrite = TRUE)## Your original .Renviron will be backed up and stored in your R HOME directory if needed.## Your API key has been stored in your .Renviron and can be accessed by Sys.getenv("CENSUS_API_KEY").
## To use now, restart R or run `readRenviron("~/.Renviron")`## [1] "edd28860119f5a254a2915190d528839b25a475e"var <- c('B19013_001E','B03002_001E','B03002_004E','B01001_002E','B01001_026E','B01003_001E','B01002_001E')
Travis_County <- get_acs(geography = "tract",
variables = var,
county = "Travis",
state = "TX",
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(Travis_County)[3] <-"HHIncome"
names(Travis_County)[5] <-"Hispanic"
names(Travis_County)[7] <-"Black"
names(Travis_County)[9] <-"Male"
names(Travis_County)[11] <-"Female"
names(Travis_County)[13] <-"Totalpop"
names(Travis_County)[15] <- "MDage"
Travis_County$B19013_001M <- NULL
Travis_County$B03002_001M <- NULL
Travis_County$B03002_004M <- NULL
Travis_County$B01001_002M <- NULL
Travis_County$B01001_026M <- NULL
Travis_County$B01003_001M <- NULL
Travis_County$B01002_001M <- NULL
#Q2-4 Histogram + Boarders + Compass + Color interval + tmap
tmap_mode("plot")## tmap mode set to plottingtm_shape(Travis_County) +
tm_fill(col = "Hispanic",palette = "Reds", legend.hist = TRUE)+
tm_layout(title = "Hispanic Pop")+
tm_borders(alpha=.7) +
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
tmap_mode("view")## tmap mode set to interactive viewingtm_shape(Travis_County) +
tm_fill(col = "Hispanic",palette = "Reds", legend.hist = TRUE)+
tm_layout(title = "Hispanic Pop")+
tm_borders(alpha=.7) +
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"))## Compass not supported in view mode.#6. Retrieve the census tract shapefile data of the county (1').
Travis_tracts<- tracts(state = "TX", county = "Travis",cb=T)## Retrieving data for the year 2021#st_write(Travis_tracts, "C:/Users/salom/OneDrive/School_Stuff/UTSA/Project 2/Travis.shp")
#7. Find any point shapefile data within the county and make a map with census tract as the background (2').
Travis_parks <- st_read(dsn = "C:/Users/salom/Downloads/Park_Facilities/Park_Facilities.shp")## Reading layer `Park_Facilities' from data source
## `C:\Users\salom\Downloads\Park_Facilities\Park_Facilities.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 707 features and 8 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 2991024 ymin: 10034790 xmax: 3194189 ymax: 10144570
## Projected CRS: NAD83 / Texas Central (ftUS)tmap_mode("plot")## tmap mode set to plottingtm_shape(Travis_tracts) +
tm_borders() + # Add borders for polygons (customize as needed)
tm_shape(Travis_parks) +
tm_dots(size = 0.05, col = "red", alpha = 0.7) + # Customize point appearance
tm_basemap() +
tm_layout(main.title = "Travis Park Facilities", main.title.position = "center", frame = TRUE)
Travis_parks <-st_transform(Travis_parks,crs = 4326)
#8. Find any line shapefile data within the county and make a map with census tract as the background (2').
Bike_paths <- st_read(dsn = "C:/Users/salom/OneDrive/School_Stuff/UTSA/Project 2/Bike_paths.shp")## Reading layer `Bike_paths' from data source
## `C:\Users\salom\OneDrive\School_Stuff\UTSA\Project 2\Bike_paths.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 17753 features and 13 fields
## Geometry type: MULTILINESTRING
## Dimension: XY
## Bounding box: xmin: -98.17051 ymin: 29.99595 xmax: -97.3698 ymax: 30.65473
## Geodetic CRS: WGS 84buffer_distance <- 0.01 # adjust as needed
lines_polygon <- st_buffer(Bike_paths, dist = buffer_distance)
tmap_mode("plot")## tmap mode set to plottingtm_shape(lines_polygon) +
tm_borders() +
tm_layout(frame = FALSE)
tm_shape(Travis_tracts) +
tm_borders() +
tm_shape(lines_polygon) +
tm_borders(lwd = 2, col = "green") + # Customize line appearance
tm_layout(main.title = "Travis County Bike Facilities", main.title.position = "center", frame = TRUE)
#9. Find any polygon shapefile data within the county and make a map with census tract as the background (2').
Travis_prec <- st_read(dsn = "C:/Users/salom/Downloads/Commissioner_Precincts/Commissioner_Precincts.shp")## Reading layer `Commissioner_Precincts' from data source
## `C:\Users\salom\Downloads\Commissioner_Precincts\Commissioner_Precincts.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 4 features and 10 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: 2977919 ymin: 9982435 xmax: 3230644 ymax: 10200210
## Projected CRS: NAD83 / Texas Central (ftUS)tmap_mode("plot")## tmap mode set to plottingtm_shape(Travis_prec) +
tm_borders() +
tm_fill(col = "PRECINCT", palette = "Blues") +
tm_layout(main.title = "Commissioner Precincts", main.title.position = "center")+
tm_shape(Travis_tracts) +
tm_borders(col = "black", alpha = 1)
#10. Have overlay analysis between the point shapefile and the county's census tract shapefile so as to find the census tract ID of each point (2').
Travis_parks <-st_transform(Travis_parks,crs = 4326)
Travis_tracts <-st_transform(Travis_tracts,crs = 4326)
Park_travis_ct <- st_intersection(Travis_parks,Travis_tracts)## Warning: attribute variables are assumed to be spatially constant throughout all
## geometriesplot(Park_travis_ct)## Warning: plotting the first 9 out of 21 attributes; use max.plot = 21 to plot
## all
#12. Read the DEM data and get the min and max of elevations (2').
library(raster)## Loading required package: spDEM_ATX <- raster('C:/Users/salom/Downloads/USGS_13_n31w098_20211103.tif')
DEM_ATX <- setMinMax(DEM_ATX)
cellStats(DEM_ATX, max)## [1] 398.0261cellStats(DEM_ATX, min)## [1] 79.33839#13. Make a map that shows areas with elevation lower than 350m (1').
plot(DEM_ATX < 350, main = "Elevation criteria",
col = c("#ffffff", "#0000ff"))
#14. Make a map that shows the slop degree of topography map is less than 3 (1').
plot(terrain(DEM_ATX, opt = "slope", unit = "degrees") < 3,
main = "Slope criteria",
col = c("#ffffff", "#ff9900"))
#15. Make a map that shows the aspect criteria of topography map is less than between 22.5 and 157.5 (2').
plot(terrain(DEM_ATX, opt = "aspect", unit = "degrees") > 22.5 &
terrain(DEM_ATX, opt = "aspect", unit = "degrees") < 157.5,
main = "Aspect criteria")
#16. Submit your code and result to Rpubs using R markdown (1').