Zombie Outbreak

task 1

load packages

task 2

Getting census API key

## 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] "aeb63576bd50b4e3e7ce9beabbac2d8ccc2bf6ce"

task 3

downloading population data, getting 2020 ACS data for population density

nyc_pop <- get_acs(geography = "tract", variables = "B01003_001", state = "NY", county = c("Bronx", "Kings", "New York", "Queens", "Richmond"), geometry = TRUE, year = 2020)
## Getting data from the 2016-2020 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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task 4

loading my download NYC building footprint data

build_footprint <- st_read("/Users/elvagao/Desktop/Rstudio_GTECH/Project_GTECH38520/Building_Footprints/geo_export_1aaeed34-8dab-47fe-a8f7-60738ad1c437.shp") 
## Reading layer `geo_export_1aaeed34-8dab-47fe-a8f7-60738ad1c437' from data source `/Users/elvagao/Desktop/Rstudio_GTECH/Project_GTECH38520/Building_Footprints/geo_export_1aaeed34-8dab-47fe-a8f7-60738ad1c437.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 1082678 features and 16 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: -74.2555 ymin: 40.49843 xmax: -73.70006 ymax: 40.91505
## Geodetic CRS:  WGS 84

task 5

tract geometry

nyc_tracts <- tracts(state = "NY", county = c("New York", "Kings", "Queens", "Bronx", "Richmond"), cb = TRUE, year = 2020) %>% st_transform(2263)

task 6

simplify geometry and crop to NYC tracts

build_footprint <- st_transform(build_footprint, st_crs(nyc_pop))

task 7

fixing the geometries before intersecting (fixed after knowing the error)

build_footprint <- st_make_valid(build_footprint)

task 8

try again intersecting again

build_footprint <- st_intersection(build_footprint, st_union(nyc_pop))
## Warning: attribute variables are assumed to be spatially constant throughout
## all geometries

*warning meaning that I am intersecting many building polygons with a single, merged geometry. If a single building is split into many pieces during intersection, the attribute values would stay across those pieces.

task 9

count buildings per tract

building_count <- st_join(build_footprint, nyc_pop) %>% group_by(GEOID) %>% summarise(num_buildings = n())

task 10

joining back to population data

nyc_merged <- nyc_pop %>% left_join(st_drop_geometry(building_count), by = "GEOID") %>% mutate(num_buildings = replace_na(num_buildings, 0))

task 11

suitable score

nyc_merged <- nyc_merged %>% mutate(pop_density_scaled = scale(estimate), building_density_scaled = scale(num_buildings), suitability = -1 * (pop_density_scaled + building_density_scaled))

task 12

Define a suitability score

nyc_merged <- nyc_merged %>% mutate(suitability = scale(num_buildings) - scale(estimate))
nyc_merged$suitability[is.infinite(nyc_merged$suitability)] <- 0

task 13

NA

nyc_merged <- nyc_merged %>% mutate(suitability = replace_na(suitability, 0))

task 14

summary(nyc_merged$suitability)
##        V1         
##  Min.   :-7.4073  
##  1st Qu.:-0.7976  
##  Median : 0.1802  
##  Mean   : 0.0000  
##  3rd Qu.: 0.8308  
##  Max.   : 5.7789
sum(is.na(nyc_merged$suitability))
## [1] 0
sum(is.nan(nyc_merged$suitability))
## [1] 0
nyc_merged$suitability[is.na(nyc_merged$suitability) | is.nan(nyc_merged$suitability)] <- 0

task 15

suitability map, they update he ‘RdYlGn’ to ‘brewer.rd_yl_gn’ version and putting 0 for midpoint for clarity

tmap_mode("view") 
## ℹ tmap mode set to "view".
tm_shape(nyc_merged) + tm_polygons(fill = "suitability", fill.scale = tm_scale_intervals(style = "quantile", values = "brewer.rd_yl_gn", midpoint = 0), fill.legend = tm_legend(title = "Suitability")) + tm_borders() + tm_tiles("OpenStreetMap") + tm_title("NYC Safety Zones") 
## Registered S3 method overwritten by 'jsonify':
##   method     from    
##   print.json jsonlite

task 16