Assignment 5
Joslynn Carson
November 2024
# Please download the storm channels data ("STORM CHANNELS") and Corridor Plans ("CORRIDOR PLANS") from: https://www.sanantonio.gov/GIS/GISDataLinks to an external site.
# Transform the coordinate reference systems of both datasets to crs = 4326
library(sf)## Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUElibrary(ggplot2)
storm_channels <- st_read("C:/Users/cruzs/Downloads/StormChannels")## Reading layer `StormChannels' from data source
## `C:\Users\cruzs\Downloads\StormChannels' using driver `ESRI Shapefile'
## Simple feature collection with 12042 features and 17 fields
## Geometry type: MULTILINESTRING
## Dimension: XY
## Bounding box: xmin: 2024999 ymin: 13600410 xmax: 2249487 ymax: 13824430
## Projected CRS: NAD83 / Texas South Central (ftUS)corridor_plans <- st_read("C:/Users/cruzs/Downloads/CorridorPlans")## Reading layer `CorridorPlans' from data source
## `C:\Users\cruzs\Downloads\CorridorPlans' using driver `ESRI Shapefile'
## Simple feature collection with 12 features and 4 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: 2058688 ymin: 13665110 xmax: 2178301 ymax: 13793820
## Projected CRS: NAD83 / Texas South Central (ftUS)storm_channels <- st_transform(storm_channels, crs = 4326)
corridor_plans <- st_transform(corridor_plans, crs = 4326)
# To find what parts of storm channels are within the corridor plan area, please intersect corridor plans with storm channels
intersected_storm_channels <- st_intersection(storm_channels, corridor_plans)## Warning: attribute variables are assumed to be spatially constant throughout
## all geometries# Visualize the intersected storm channels with a background of Bexar County census tracts
library(tigris)## To enable caching of data, set `options(tigris_use_cache = TRUE)`
## in your R script or .Rprofile.bexar_tracts <- tracts(state = "TX", county = "Bexar", cb = TRUE)## Retrieving data for the year 2022## | | | 0% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 10% | |============= | 18% | |============== | 20% | |======================== | 35% | |========================== | 38% | |=========================== | 39% | |============================= | 41% | |============================== | 43% | |================================ | 45% | |================================ | 46% | |=================================== | 51% | |==================================== | 52% | |===================================== | 52% | |===================================== | 54% | |====================================== | 55% | |======================================= | 56% | |========================================= | 58% | |========================================= | 59% | |========================================== | 59% | |=========================================== | 61% | |=========================================== | 62% | |============================================ | 63% | |============================================= | 64% | |============================================== | 65% | |============================================== | 66% | |=============================================== | 67% | |=============================================== | 68% | |================================================= | 69% | |================================================= | 70% | |================================================== | 71% | |================================================== | 72% | |===================================================== | 75% | |===================================================== | 76% | |====================================================== | 77% | |======================================================== | 80% | |========================================================= | 81% | |========================================================= | 82% | |========================================================== | 83% | |=========================================================== | 84% | |=============================================================== | 91% | |================================================================ | 92% | |================================================================= | 92% | |================================================================= | 93% | |================================================================== | 95% | |==================================================================== | 97% | |==================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100%bexar_tracts <- st_transform(st_as_sf(bexar_tracts), crs = 4326)
ggplot() +
geom_sf(data = bexar_tracts, fill = "lightblue", color = "navyblue") +
geom_sf(data = intersected_storm_channels, color = "orange") +
labs(title = "Intersected Storm Channels within Corridor Plans in Bexar County",
subtitle = "Background: Bexar County Census Tracts",
caption = "Data source: San Antonio GIS") +
theme_minimal()
# Read the raster file (LC09_028040_20230826.tif) Download LC09_028040_20230826.tif). Open this document with ReadSpeaker docReader, which is the land surface temperature of Dallas.
library(raster)## Loading required package: splibrary(ggplot2)
library(rasterVis)## Warning: package 'rasterVis' was built under R version 4.4.2## Loading required package: latticelst_raster <- raster("C:/Users/cruzs/Downloads/LC09_028040_20230826.tif")
# Convert the Kelvin into Celsius (C = K - 273.15) and then plot the raster data.
lst_celsius <- lst_raster - 273.15
gplot(lst_celsius) +
geom_tile(aes(fill = value)) +
scale_fill_viridis_c(option = "C", name = "Temperature (°C)") +
labs(
title = "Land Surface Temperature in Dallas in Celsius (°C)",
x = "Longitude",
y = "Latitude",
) +
theme_minimal()
# Only visualize the areas with temperatures higher than 40 Degree Celsius
lst_above_40 <- calc(lst_celsius, fun = function(x) { ifelse(x > 40, x, NA) })
gplot(lst_above_40) +
geom_tile(aes(fill = value)) +
scale_fill_viridis_c(option = "C", name = "Temperature (°C)", na.value = "transparent") +
labs(
title = "Land Surface Temperature > 40°C in Dallas",
x = "Longitude",
y = "Latitude",
) +
theme_minimal()