Project 2
Omar
2024-12-10
library(tidycensus)
library(tmap)## Breaking News: tmap 3.x is retiring. Please test v4, e.g. with
## remotes::install_github('r-tmap/tmap')library(sf)## Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUElibrary(tigris)## To enable caching of data, set `options(tigris_use_cache = TRUE)`
## in your R script or .Rprofile.library(raster)## Loading required package: spcensus_api_key("0d539976d5203a96fa55bbf4421110d4b3db3648", overwrite = TRUE)## To install your API key for use in future sessions, run this function with `install = TRUE`.variables <- load_variables(2020, "acs5", cache = TRUE)
#Q1
var <- c(total_pop = "B03002_001",
hsipanic = "B03002_012")
st <-"TX"
ct <-"Harris County"
harriss <- get_acs(geography = "tract", variables = var, count=ct,
state = st,output="wide", year = 2021, 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)`.## | | | 0% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |== | 4% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============= | 19% | |============== | 20% | |=============== | 21% | |=============== | 22% | |================ | 23% | |================= | 25% | |================== | 26% | |==================== | 28% | |===================== | 30% | |====================== | 32% | |======================= | 33% | |========================= | 35% | |========================== | 37% | |=========================== | 39% | |============================ | 41% | |============================== | 43% | |=============================== | 45% | |================================= | 47% | |================================== | 48% | |=================================== | 50% | |===================================== | 53% | |====================================== | 55% | |======================================= | 56% | |======================================== | 57% | |========================================== | 59% | |========================================== | 60% | |=========================================== | 61% | |============================================ | 63% | |============================================== | 66% | |=============================================== | 68% | |================================================= | 70% | |================================================== | 71% | |=================================================== | 73% | |===================================================== | 75% | |====================================================== | 77% | |======================================================= | 79% | |========================================================= | 81% | |========================================================== | 83% | |============================================================ | 85% | |============================================================= | 87% | |============================================================== | 89% | |================================================================ | 91% | |================================================================= | 93% | |================================================================== | 95% | |==================================================================== | 97% | |===================================================================== | 99% | |======================================================================| 100%harriss$hispanic_pct <- harriss$hsipanicE/harriss$total_popE
tm_shape(harriss) +
tm_polygons(col = "hispanic_pct", palette = "YlOrRd", title = "Hispanic %") +
tm_compass(type = "8star", position = c("right", "top"), size = 1) +
tm_scale_bar(position = c("left", "bottom")) +
tm_layout(legend.position = c("center", "bottom"),
legend.outside = TRUE,
legend.outside.position = "right")
#Q2
Harris_tracts<- tracts(state = "TX", county = "Harris County", cb=T)## Retrieving data for the year 2022## | | | 0% | |= | 1% | |== | 3% | |=== | 5% | |===== | 7% | |====== | 9% | |======= | 11% | |========= | 13% | |========== | 15% | |============ | 16% | |============= | 18% | |============== | 20% | |================ | 23% | |================= | 25% | |=================== | 27% | |==================== | 29% | |===================== | 31% | |======================= | 33% | |======================== | 34% | |========================= | 36% | |=========================== | 38% | |============================ | 41% | |============================== | 43% | |=============================== | 44% | |================================= | 46% | |================================== | 48% | |=================================== | 50% | |===================================== | 52% | |====================================== | 54% | |======================================== | 57% | |========================================= | 59% | |========================================== | 60% | |============================================ | 62% | |============================================= | 64% | |============================================== | 66% | |================================================ | 68% | |================================================= | 70% | |=================================================== | 72% | |==================================================== | 74% | |===================================================== | 76% | |======================================================= | 78% | |======================================================== | 80% | |========================================================== | 82% | |=========================================================== | 84% | |============================================================ | 86% | |============================================================== | 88% | |=============================================================== | 90% | |================================================================ | 92% | |================================================================== | 94% | |=================================================================== | 96% | |===================================================================== | 98% | |======================================================================| 100%Community_Centers<-st_read(dsn = "C:/Users/عمر/Desktop/Fall 2024/Rstudio/project 2/Harris_County_Community_Centers/Harris_County_Community_Centers.shp")## Reading layer `Harris_County_Community_Centers' from data source
## `C:\Users\عمر\Desktop\Fall 2024\Rstudio\project 2\Harris_County_Community_Centers\Harris_County_Community_Centers.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 94 features and 17 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 2960593 ymin: 13752700 xmax: 3235328 ymax: 13969850
## Projected CRS: NAD83 / Texas South Central (ftUS)tm_shape(Harris_tracts) +
tm_borders() +
tm_shape(Community_Centers) +
tm_dots(col = "blue", size = 0.1, alpha = 0.5) +
tm_layout(frame = FALSE)
#Q3
water_q<-st_read(dsn = "C:/Users/عمر/Desktop/Fall 2024/Rstudio/project 2/tl_rd22_48201_linearwater/tl_rd22_48201_linearwater.shp")## Reading layer `tl_rd22_48201_linearwater' from data source
## `C:\Users\عمر\Desktop\Fall 2024\Rstudio\project 2\tl_rd22_48201_linearwater\tl_rd22_48201_linearwater.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 4166 features and 5 fields
## Geometry type: LINESTRING
## Dimension: XY
## Bounding box: xmin: -95.96073 ymin: 29.49734 xmax: -94.90849 ymax: 30.17061
## Geodetic CRS: NAD83tm_shape(Harris_tracts) +
tm_borders() +
tm_shape(water_q) +
tm_lines(col = "blue", lwd = 2) +
tm_layout(frame = FALSE)
#Q4
zipcode <-st_read(dsn = "C:/Users/عمر/Desktop/Fall 2024/Rstudio/project 2/Harris_County_Zipcodes/Harris_County_Zipcodes.shp")## Reading layer `Harris_County_Zipcodes' from data source
## `C:\Users\عمر\Desktop\Fall 2024\Rstudio\project 2\Harris_County_Zipcodes\Harris_County_Zipcodes.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 155 features and 10 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 2928863 ymin: 13748310 xmax: 3265257 ymax: 13992550
## Projected CRS: NAD83 / Texas South Central (ftUS)tmap_options(check.and.fix = TRUE)
tm_shape(Harris_tracts) +
tm_borders() +
tm_shape(zipcode) +
tm_polygons(col = "green") +
tm_layout(frame = FALSE)
#Q5
water_q <- st_make_valid(water_q)
zipcode <- st_make_valid(zipcode)
water_q <- st_transform(water_q, crs = 4326)
zipcode <- st_transform(zipcode, crs = 4326)
overlay_result <- st_intersection(water_q, zipcode)## Warning: attribute variables are assumed to be spatially constant throughout
## all geometries#Q6
DEM <- raster('C:/Users/عمر/Desktop/Fall 2024/Rstudio/project 2/USGS_13_n30w096_20240229.tif')
DEM## class : RasterLayer
## dimensions : 10812, 10812, 116899344 (nrow, ncol, ncell)
## resolution : 9.259259e-05, 9.259259e-05 (x, y)
## extent : -96.00056, -94.99944, 28.99944, 30.00056 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=NAD83 +no_defs
## source : USGS_13_n30w096_20240229.tif
## names : Layer_1#Q7
DEM <- setMinMax(DEM)
cellStats(DEM, min)## [1] -12.24206cellStats(DEM, max)## [1] 80.40253#Q8
plot(DEM < 350, main = "Elevation criteria",
col = c("#ffffff", "#0000ff"))
#Q9
plot(terrain(DEM, opt = "slope", unit = "degrees") < 3,
main = "Slope criteria",
col = c("#ffffff", "#ff9900"))
#Q10
plot(terrain(DEM, opt = "aspect", unit = "degrees") > 22.5 &
terrain(DEM, opt = "aspect", unit = "degrees") < 157.5,
main = "Aspect criteria")
#Q11
#In this project, I analyzed different data layers to understand the characteristics of Harris County. First, I created a map showing the percentage of the Hispanic population. Next, I mapped community centers such as libraries and other facilities. I also visualized linear water and ZIP code boundaries. I also did an overlay analysis for linerwater and ZIP code regions. I used a Digital Elevation Model (DEM) to study the county's topography. I identified areas with elevations below 350 meters and slopes less than 3 degrees, I also created a map showing the direction of the terrain, focusing on areas facing between 22.5° and 157.5°. ##Q11 In this project, I analyzed different data layers to understand the characteristics of Harris County. First, I created a map showing the percentage of the Hispanic population. Next, I mapped community centers such as libraries and other facilities. I also visualized linear water and ZIP code boundaries. I also did an overlay analysis for linerwater and ZIP code regions. I used a Digital Elevation Model (DEM) to study the county’s topography. I identified areas with elevations below 350 meters and slopes less than 3 degrees, I also created a map showing the direction of the terrain, focusing on areas facing between 22.5° and 157.5°.