R Packages

library( geojsonio )   # read shapefiles
library( sp )          # work with shapefiles
library( sf )          # work with shapefiles - simple features format
library( dplyr )       # data wrangling 
library( pander )      # formatting RMD tables
library( tidycensus )  # download census data + shapefiles
library( cartogram )   # spatial maps w/ tract size bias reduction
library( maptools )    # spatial object manipulation

Create Dorling Shapefile for Metro Area

build_dorling <- function( cbsa.name, include.plot=TRUE )
{

    these.counties  <- crosswalk$cbsaname == cbsa.name
    these.fips <- crosswalk$fipscounty[ these.counties ]
    these.fips <- na.omit( these.fips )
  
    state.fips <- substr( these.fips, 1, 2 )
    county.fips <- substr( these.fips, 3, 5 )

    # combine all counties
    d.sf <- NULL

    for( i in 1:length(these.fips) )
    {
       d.temp <- NULL
       
       try(  

          d.temp <- 
            get_acs( geography = "tract", 
               variables = "B01003_001",  # population
               state = state.fips[i], 
               county = county.fips[i], 
               geometry = TRUE ) %>%
            select( GEOID, estimate ) %>%
            rename( POP = estimate )
           
           ) # end of try

       d.sf <- rbind( d.sf, d.temp )
    }


    # merge shapefile data with census data in new dataframe:
    # fix leading zeros problem
    # census.dat$tractid <- as.numeric( as.character( census.dat$tractid ) )
    d.sf$GEOID <- as.numeric( as.character( d.sf$GEOID ) )
    d.sf <- merge( d.sf, census.dat, by.x="GEOID", by.y="tractid", all.x=T )

    # convert sf to sp class
    d.sf <- d.sf[ ! st_is_empty( d.sf ) , ]
    d.sp <- as_Spatial( d.sf )

    # project map and remove empty tracts
    d.sp <- spTransform( d.sp, CRS("+proj=longlat +datum=WGS84") )
    d.sp  <- d.sp[ d.sp$POP != 0 & (! is.na( d.sp$POP )) , ]

    # save simple features shapefile as geojson
    metro.name <- tolower( cbsa.name )
    metro.name <- gsub( ",", "", metro.name )
    metro.name <- gsub( " ", "-", metro.name )
    file.name <- paste0( "../maps/metros-shapefile/", metro.name, "-sf.geojson" )
    geojson_write( d.sp, file=file.name, geometry="polygon" )

    # class( d.sp )
    # plot( d.sp)

    # project map and remove empty tracts
    # d.sp <- spTransform( d.sp, CRS("+init=epsg:3395") )
    # d.sp  <- d.sp[ d.sp$POP != 0 & (! is.na( d.sp$POP )) , ]

    # standardizes pop numbers for scaling
    d.sp$pop.w <- d.sp$POP / ( 2 * median(d.sp$POP) ) 
    total.pop <- sum( d.sp$POP, na.rm=T )
  # k.scale <-  0.7 * ( 1 / ( log( total.pop ) - 10 ) ) 
    k.scale <-   ( 0.34 / ( log10( total.pop ) - 4.4 ) ) - ( 0.6 / log10( total.pop ) ) 
        
    # convert census tract polygons to dorling cartogram
  d.sp <- spTransform( d.sp, CRS("+init=epsg:3395") )
    dorling.map <- cartogram_dorling( x=d.sp, weight="pop.w", k=k.scale )  # k=0.05

    # convert to WGS84 for GitHub capatability 
    metro.j <- spTransform( dorling.map, CRS("+proj=longlat +datum=WGS84") )

    # save dorling shapefile as geojson
    metro.name <- tolower( cbsa.name )
    metro.name <- gsub( ",", "", metro.name )
    metro.name <- gsub( " ", "-", metro.name )
    file.name <- paste0( "../maps/metros-dorling/", metro.name, "-dorling-v1.geojson" )

    geojson_write( metro.j, file=file.name, geometry="polygon" )


    if( include.plot )
    {
      
      par( mar=c(0,0,4,0), mfrow=c(1,2) )
      plot( d.sp, main=paste0("Census Tracts of \n", cbsa.name ) )
    plot( d.sp, border="gray80", main=paste0("Dorling Cartogram of \n", cbsa.name ) )
      plot( dorling.map, col=gray(0.5,0.5), add=TRUE  )

    }

    
    return( dorling.map )

    
} # END OF build_dorling() FUNCTION 
# unit tests: 


# load data
census_api_key( "b431c35dad89e2863681311677d12581e8f24c24" )
crosswalk <- readRDS( "../data/data-raw/cbsa-crosswalk.rds" ) 
census.dat <- read.csv( "../data/data-raw/LTDB_Std_2010_fullcount.csv" )

# fix leading zeros issue:
# convert both IDs to numeric so they both 
# drop the leading zeros
census.dat$tractid <- as.numeric( as.character( census.dat$tractid ) )


fort.worth <- "Fort Worth-Arlington, TX"
fw <- build_dorling( cbsa.name = fort.worth )


seattle <- "Seattle-Bellevue-Everett, WA"
sea <- build_dorling( cbsa.name = seattle )

phoenix <- "Phoenix-Mesa-Scottsdale, AZ"
phx <- build_dorling( cbsa.name = phoenix )

lynchburg <- "Lynchburg, VA"
lburg <- build_dorling( cbsa.name = lynchburg )

Loop Over Metros

Load data:

# URL <- "https://raw.githubusercontent.com/DS4PS/cpp-529-master/master/data/cbsatocountycrosswalk.csv"
# crosswalk <- read.csv( URL, stringsAsFactors=F, colClasses="character" )

# CENSUS DATA
# fix leading zeros issue:
# convert both IDs to numeric so they both 
# drop the leading zeros
census.dat <- read.csv( "../data/data-raw/LTDB_Std_2010_fullcount.csv" )
census.dat$tractid <- as.numeric( as.character( census.dat$tractid ) )


# Metro to County Crosswalk
crosswalk <- readRDS( "../data/data-raw/cbsa-crosswalk.rds" ) 
metro.areas <- unique( crosswalk$cbsaname )
metro.areas[ metro.areas == "" ] <- NA
metro.areas <- na.omit( metro.areas )
metro.areas <- metro.areas[ 1:379 ]  # drop Puerto Rico metros 
metro.areas <- sort(metro.areas)

Apply the function to each metro area:

for( j in metro.areas )
{
  metro.j <- build_dorling( cbsa.name = j )
  cat( "<br><hr><br>" )
}


































































































































































































































































































































































































































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