library(tmap) # Load tmap for creating interactive and static maps
## Breaking News: tmap 3.x is retiring. Please test v4, e.g. with
## remotes::install_github('r-tmap/tmap')
library(tidycensus) # Load tidycensus for accessing U.S. Census Bureau data directly
library(tidycensus)
library(sf)
## Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE
library(tigris)
## To enable caching of data, set `options(tigris_use_cache = TRUE)`
## in your R script or .Rprofile.
library(tmap)
library(raster)
## Loading required package: sp
#Hispanic population= B03002_012E
#total population=B01003_001E
var=c('B03002_012E','B01003_001E')
Travis_segregation <- get_acs(geography = "tract", variables = var, county = "Travis",
state = "TX",output="wide", geometry = TRUE)
## Getting data from the 2018-2022 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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names(Travis_segregation)[3] <- 'Hispanic'
names(Travis_segregation)[5] <- 'TotalPop'
Travis_segregation$B01003_001M<- NULL
Travis_segregation$B03002_012M<- NULL
Travis_segregation$pct_Hispanic <- 100*Travis_segregation$Hispanic/Travis_segregation$TotalPop
tm_shape(Travis_segregation) +tm_fill(col = "pct_Hispanic")+ tm_layout(title = "Hispanic Percent")+
#add compass
tm_compass(type = "8star",
position = c("RIGHT", "BOTTOM"),
show.labels = 2,
text.size = 0.6) +
tm_layout(legend.outside = TRUE,frame = FALSE)+
tm_scale_bar(position = c("RIGHT", "BOTTOM"))
texas_counties<-counties(state = "Texas",cb=T)
## Retrieving data for the year 2022
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Travis<-texas_counties[texas_counties$NAME=="Travis",]
Travis<-st_transform(Travis,crs = 4326) #define the coordinate reference system
#get texas landmarks
tx_landmarks <- landmarks(state = "TX",type = 'point')
## Retrieving data for the year 2022
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tx_landmarks<-st_transform(tx_landmarks,crs = 4326) #define the coordinate reference system
# use clip analysis to have the tx_landmarks in Travis county
clipped_landmark_Travis <- st_intersection(tx_landmarks, Travis)
## Warning: attribute variables are assumed to be spatially constant throughout
## all geometries
#plot the landmark for Travis
tm_shape(Travis) +
tm_borders() + # Add borders for polygons (customize as needed)
tm_shape(Travis_segregation) +
tm_dots(size = 0.02, col = "red", alpha = 0.7) + # Customize point appearance
tm_basemap() +
tm_layout(title = "Travis Landmarks", frame = FALSE)
bike_paths<-st_read(dsn = "C:/Users/dagob/OneDrive/Desktop/Data/Project 2/Park_Trails_and_Roads/Park_Trails_and_Roads.shp")
## Reading layer `Park_Trails_and_Roads' from data source
## `C:\Users\dagob\OneDrive\Desktop\Data\Project 2\Park_Trails_and_Roads\Park_Trails_and_Roads.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 364 features and 14 fields
## Geometry type: MULTILINESTRING
## Dimension: XY
## Bounding box: xmin: 2989166 ymin: 10035310 xmax: 3194239 ymax: 10143610
## Projected CRS: NAD83 / Texas Central (ftUS)
tmap_options(check.and.fix = TRUE)
buffer_distance <- 0.01 # adjust as needed
lines_polygon <- st_buffer(bike_paths, dist = buffer_distance)
tm_shape(Travis) +
tm_borders(alpha=.4,lwd = 2, col = "red") +
tm_shape(lines_polygon) +
tm_borders(lwd = 2, col = "red") + # Customize line appearance
tm_layout(frame = FALSE)
Park<-st_read(dsn = "C:/Users/dagob/OneDrive/Desktop/Data/Project 2/parks_public/Parks_Public.shp")
## Reading layer `Parks_Public' from data source
## `C:\Users\dagob\OneDrive\Desktop\Data\Project 2\Parks_Public\Parks_Public.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 31 features and 20 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 2988791 ymin: 10023720 xmax: 3194489 ymax: 10162930
## Projected CRS: NAD83 / Texas Central (ftUS)
tm_shape(Travis) +
tm_borders(alpha=.4,lwd = 2, col = "red") +
tm_shape(Park) +
tm_polygons(col="green")
## Warning: The shape Park is invalid (after reprojection). See sf::st_is_valid
tm_layout(frame = FALSE)
## $tm_layout
## $tm_layout$frame
## [1] FALSE
##
## $tm_layout$style
## [1] NA
##
##
## attr(,"class")
## [1] "tm"
tm_shape(Travis) +
tm_borders(alpha=.4,lwd = 2, col = "red") +
tm_shape(lines_polygon) +
tm_borders(lwd = 2, col = "red") + # Customize line appearance
tm_layout(frame = FALSE)+
tm_shape(Park) +
tm_polygons(col="green")
## Warning: The shape Park is invalid (after reprojection). See sf::st_is_valid
tm_layout(frame = FALSE)
## $tm_layout
## $tm_layout$frame
## [1] FALSE
##
## $tm_layout$style
## [1] NA
##
##
## attr(,"class")
## [1] "tm"
wd <- ("C:/Users/dagob/OneDrive/Desktop/Data/Project 2/")
setwd(wd)
DEM <- raster(paste0(wd, "USGS_13_n31w098_20211103.tif"))
DEM <- setMinMax(DEM)
min<- minValue(DEM)
max<- maxValue(DEM)
plot(DEM < 350, main = "Elevation criteria",
col = c("#ffffff", "#0000ff"))
plot(terrain(DEM, opt = "slope", unit = "degrees") < 3,
main = "Slope criteria",
col = c("#ffffff", "#ff9900"))
plot(terrain(DEM, opt = "aspect", unit = "degrees") > 22.5 &
terrain(DEM, opt = "aspect", unit = "degrees") < 157.5,
main = "Aspect criteria")
#This script analyzes and visualizes geospatial data for Travis County, Texas, using R packages like tidycensus,
#tmap, sf, and raster. It retrieves census data to calculate and map the percentage of Hispanic residents. Travis
#County boundaries and landmarks are processed and clipped for focused analysis, while bike paths and parks are
#visualized using shapefiles. Digital Elevation Model (DEM) data is used for terrain analysis, highlighting areas
#by elevation, slope, and aspect. The workflow integrates demographic, infrastructure, and terrain data, producing
#thematic maps and insights into the county's spatial characteristics.