Home Electrification Equity Project Mapping Application

Description

This repository contains functions for generating vulnerability data using R and visualizing it on a Leaflet interactive web map using JavaScript. It is a work in progress (in other words, there are likely some bugs) and will be updated regularly.

The following maps were produced using resources available in this repository:

Bay Area Home Electrification Equity Project (HEEP) Map

NYC Area New York State Energy Research and Development Authority (NYSERDA) Solar Projects Map

R component

R functions

At this point in time, the following R functions are available:

• download_USCB_TIGER_files.R: Function to download necessary files from USCB TIGER website.

• generate_map_variables.R: Function to generate data at the USCB tract level from data sources available via API. This function also generates JS files utilized by Leaflet's legend and layer controls.

• generate_USCB_spatial_file.R: Function to generate tract spatial files in compact topojson format with option to merge to tabular data.

required R packages

Required packages that must be installed to run this code:

• data.table: for handling large data.frames more efficiently

• geos: for processing large scale geometry collections

• sf: for reading in shapefiles and performing spatial processes

• terra: for reading in and processing raster data

• httr: for making http requests to the USCB geocoding API and MRLC Geoserver

• jsonlite: for processing JSON from the USCB geocoding API

• igraph: for collapsing directional multipart polylines in edges files into single part polylines

• geojsonio: for converting shapefiles into space saving topojson format

Usage

Here is an R code sample for generating spatial data and supporting files for a Leaflet map of the five boroughs of New York City with custom settings:

gh_path <- 'https://raw.githubusercontent.com/gmculp/JS_equity_mapping/refs/heads/main'
source(file.path(gh_path,"R/download_USCB_TIGER_files.R"))
source(file.path(gh_path,"R/generate_map_variables.R"))
source(file.path(gh_path,"R/generate_USCB_spatial_file.R"))

###specify place to store USCB TIGER files###
USCB_TIGER.path <- "C:/map_resources/census_files"

###specify data table containing state and county FIPS codes###
FIPS.dt <- data.table(state=rep("36",5),county=c("061","005","047","081","085"))

###specify vintage of tract geographies (e.g., 2010, 2020)###
geo.year <- "2020"

###automatically download all necessary files from USCB TIGER website###
###you will only have to do this once for each decennial census year###
###for 2020###
download_USCB_TIGER_files(FIPS.dt,USCB_TIGER.path,geo.year)

###load JSON file containing data and map specs###
specs <- fromJSON(file.path(gh_path,'R/API_variables.json'), simplifyVector = FALSE)

###you can change the default specs to suit your region either mannually in a text editor...###
###...or programmatically in R###
###remove old energy cost variable###
n1 <- which(sapply(specs$data_sources$CDC, FUN=function(X) X$survey == "BRFSS"))
n2 <- which(sapply(specs$data_sources$CDC[[n1]]$members, FUN=function(X) X$variable_name == "SHUTUTILITY"))
specs$data_sources$CDC[[n1]]$members[[n2]] <- NULL
	
###add new energy cost variable###
n1 <- which(sapply(specs$data_sources$USCB, FUN=function(X) X$survey == "ACS5"))
n2 <- length(specs$data_sources$USCB[[n1]]$members)+1
	
specs$data_sources$USCB[[n1]]$members[[n2]] <- 
list(variable_name="energy_cost", 
	variable_label="Energy Cost",
	title="Percentage of income spent on energy costs",
	group="house_energy",
	variables=lapply(c("B19025_001E", "B25032_001E", "B25032_003E", paste0("B25132_00",4:9,"E"), paste0("B25133_00",4:9,"E"), paste0("B25135_00",4:6,"E")),function(j) j),
	formula="round(((12*((49 * B25132_004E)+(99 * B25132_005E)+(149 * B25132_006E)+(199 * B25132_007E)+(249 * B25132_008E)+(300 * B25132_009E)+(24 * B25133_004E)+(49 * B25133_005E)+(74 * B25133_006E)+(99 * B25133_007E)+(149 * B25133_008E)+(200 * B25133_009E)+(249 * B25135_004E)+(749 * B25135_005E)+(1000 * B25135_006E)))/B19025_001E)*100,2)",
	order="asc"
) 

###switch to viridis palette###
specs$color_palette <- lapply(viridis::viridis_pal(option = "B")(9)[3:7],function(j) j)

###specify place to store files for Leaflet###
output.path <- "C:/map_resources/Leaflet_files/NYC"

###generate table containing variables by tract GEOID and JS files for legend and layer controls###
generate_map_variables(specs, FIPS.dt, USCB_TIGER.path, geo.year, output.path, in_clus = 20)

###generate topojson enriched with variable data###
generate_USCB_spatial_file(FIPS.dt, USCB_TIGER.path, geo.year, geo.type="tract", omit.unpopulated=TRUE, omit.artifacts=TRUE, omit.coast=TRUE, output.path, input.file_name=file.path(output.path,"map_variables.txt"), in_clus=20, na_color = specs$NA_color)

###this line of code will generate a topojson file (in this case, at the PUMA level) without variable data###
generate_USCB_spatial_file(FIPS.dt, USCB_TIGER.path, geo.year, geo.type="puma", omit.unpopulated=TRUE, omit.artifacts=TRUE, omit.coast=TRUE, output.path, in_clus=20)