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`SUNGEO` / Sub-National Geospatial Data Archive: Geoprocessing Toolkit

R package for integrating spatially-misaligned GIS datasets.

Version 1.3.0 (May 14, 2024)

Jason Byers, Marty Davidson, Yuri M. Zhukov

Center for Political Studies, Institute for Social Research

University of Michigan

Feedback, bug reports welcome: zhukov-at-umich-dot-edu or ymz2-at-georgetown-dot-edu

df2sf / Convert data.frame object into simple features object
fix_geom / Fix polygon geometries
geocode_osm / Geocode addresses with OpenStreetMap
geocode_osm_batch / Batch geocode addresses with OpenStreetMap
get_data / Download data from SUNGEO server
get_info / Information on available SUNGEO data files
hot_spot / Automatically calculate Local G hot spot intensity
line2poly / Line-in-polygon analysis
make_ticker / Make date ticker
merge_list / Merge list of tables on common variable(s)
nesting / Relative scale and nesting coefficients
point2poly_krige / Point-to-polygon interpolation, ordinary and universal Kriging method
point2poly_simp / Point-to-polygon interpolation, simple overlay method
point2poly_tess / Point-to-polygon interpolation, tessellation method
poly2poly_ap / Area and population weighted polygon-to-polygon interpolation
sf2raster / Convert simple features object into regularly spaced raster
smart_round / Smart numerical rounding function
utm_select / Automatically convert geographic (degree) to planar coordinates (meters)
update_bbox / Update bounding box of sf object

To install in R:

library(devtools)
devtools::install_github("zhukovyuri/SUNGEO", dependencies = TRUE)

Load package:

library(SUNGEO)

Read help files:

?poly2poly_ap
?utm_select
?get_data

Example: geocode an address with OpenStreetMap

# Get geographic coordinates for the Big House (top match only)
geocode_osm("Michigan Stadium")
geocode_osm("Big House")

# Return detailed results for top match
geocode_osm("Michigan Stadium", details=TRUE)

# Return detailed results for all matches
geocode_osm("Michigan Stadium", details=TRUE, return_all = TRUE)

Example: geocode multiple addresses

# Geocode multiple addresses (top matches only)
geocode_osm_batch(c("Ann Arbor","East Lansing","Columbus"))

# ... with progress reports
geocode_osm_batch(c("Ann Arbor","East Lansing","Columbus"), 
                  verbose = TRUE)

# Return detailed results for all matches
geocode_osm_batch(c("Ann Arbor","East Lansing","Columbus"),
                  details = TRUE, return_all = TRUE)

Example: scale and nesting metrics for two polygons

# Load source polygons (legislative districts)
data(clea_deu2009)

# Load destination polygons (grid cells)
data(hex_05_deu)

# Preview
plot(clea_deu2009["geometry"])
plot(hex_05_deu["geometry"],add=TRUE,border="grey")

# Calculate all scale and nesting metrics at once
nest_1 <- nesting(
              poly_from = clea_deu2009,
              poly_to = hex_05_deu
              )
nest_1

# Calculate just Relative Nesting, in the opposite direction
nest_2 <- nesting(
              poly_from = hex_05_deu,
              poly_to = clea_deu2009,
              metrix = "rn"
              )
nest_2

Example: area-weighted polygon-to-polygon interpolation

# Load legislative election results (from CLEA)
data(clea_deu2009)

# Visualize voter turnout at constituency level
plot(clea_deu2009["to1"])

# Load 0.5 degree hexagonal grid
data(hex_05_deu)

# Interpolate
out_1 <- poly2poly_ap(poly_from = clea_deu2009,
                      poly_to = hex_05_deu,
                      poly_to_id = "HEX_ID",
                      varz = "to1"
                      )

# Visualize voter turnout at grid cell level
plot(out_1["to1_aw"])

Example: population-weighted polygon-to-polygon interpolation

# Load population raster (from GPW v4)
data(gpw4_deu2010)

# Interpolate
out_2 <- poly2poly_ap(poly_from = clea_deu2009,
                      poly_to = hex_05_deu,
                      poly_to_id = "HEX_ID",
                      varz = "to1",
                      methodz = "pw",
                      pop_raster = gpw4_deu2010)

# Visualize voter turnout at grid cell level
plot(out_2["to1_pw"])

Example: point-to-polygon interpolation using tessellation method and area weights

# Load point-level election results
data(clea_deu2009_pt)

# Interpolate
out_4 <- point2poly_tess(pointz = clea_deu2009_pt,
                         polyz = hex_05_deu,
                         poly_id = "HEX_ID",
                         varz = "to1")

# Visualize voter turnout at grid cell level 
plot(out_4["to1_aw"])

Example: point-to-polygon interpolation using ordinary Kriging

# Ordinary Kriging with one outcome variable
out_5 <- point2poly_krige(pointz = clea_deu2009_pt,
                          polyz = clea_deu2009,
                          yvarz = "to1")

# Compare observed values to predictions
par(mfrow=c(1,2))
plot(clea_deu2009["to1"], key.pos = NULL, reset = FALSE)
plot(out_5["to1.pred"], key.pos = NULL, reset = FALSE)

# Ordinary Kriging with multiple outcome variables
out_6 <- point2poly_krige(pointz = clea_deu2009_pt,
                          polyz = clea_deu2009,
                          yvarz = c("to1","pvs1_margin"))

# Compare observed values to predictions
par(mfrow=c(1,2))
plot(clea_deu2009["pvs1_margin"], key.pos = NULL, reset = FALSE)
plot(out_6["pvs1_margin.pred"], key.pos = NULL, reset = FALSE)

Example: point-to-polygon interpolation using universal Kriging

# Universal Kriging with one outcome variable and one covariate
out_7 <- point2poly_krige(pointz = clea_deu2009_pt,
                        polyz = clea_deu2009,
                        yvarz = "to1",
                        rasterz = gpw4_deu2010)

# Compare observed values to predictions
par(mfrow=c(1,2))
plot(clea_deu2009["to1"], key.pos = NULL, reset = FALSE)
plot(out_7["to1.pred"], key.pos = NULL, reset = FALSE)

Example: line-in-polygon analysis

# Load road data (from Digital Chart of the World) and extract highways
data(highways_deu1992)

# Basic map overlay
plot(hex_05_deu["geometry"])
plot(highways_deu1992$geometry, add=TRUE, col = "blue", lwd=2)

# Calculate road lengths, densities and distances from each polygon to nearest highway
out_8 <- line2poly(linez = highways_deu1992,
                   polyz = hex_05_deu,
                   poly_id = "HEX_ID")
                   
# Visualize results
plot(out_8["line_length"])
plot(out_8["line_density"])
plot(out_8["line_distance"])

# Replace missing road lengths and densities with 0's, rename variables
out_9 <- line2poly(linez = highways_deu1992,
                   polyz = hex_05_deu,
                   poly_id = "HEX_ID",
                   outvar_name = "road",
                   na_val = 0)

# Visualize results
plot(out_9["road_length"])
plot(out_9["road_density"])
plot(out_9["road_distance"])
dev.off()

Example: Automatically find a planar CRS for a GIS dataset

# Visualize original geometries (WGS1984, degrees)
plot(clea_deu2009["geometry"], axes=TRUE)

# Find a suitable CRS and re-project
out_10 <- utm_select(clea_deu2009)

# Visualize transformed geometries (UTM 32N, meters)
plot(out_10["geometry"], axes=TRUE)

# proj4string of transformed data
utm_select(clea_deu2009, return_list=TRUE)$proj_out

Example: Rasterization of polygons

# Transform sf polygon layer into 1km-by-1km RasterLayer (requires planar CRS)
out_11 <- sf2raster(polyz_from = utm_select(clea_deu2009),
                   input_variable = "to1")
# Visualize raster
terra::plot(out_11)

# 25km-by-25km RasterLayer (requires planar CRS)
out_12 <- sf2raster(polyz_from = utm_select(clea_deu2009),
                   input_variable = "to1",
                   grid_dim = c(100, 100))
# Visualize raster
terra::plot(out_12)

Example: Create cartogram

# Cartogram of turnout scaled by number of valid votes
out_13 <- sf2raster(polyz_from = utm_select(clea_deu2009),
                  input_variable = "to1",
                  cartogram = TRUE,
                  carto_var = "vv1")
terra::plot(out_13)

Example: reverse rasterization

# Polygonization of cartogram raster
out_14a <- sf2raster(polyz_from = utm_select(clea_deu2009),
                    input_variable = "to1",
                    cartogram = TRUE,
                    carto_var = "vv1",
                    return_list = TRUE)
out_14 <- sf2raster(reverse = TRUE,
                   poly_to = out_14a$poly_to,
                   return_output = out_14a$return_output,
                   return_field = out_14a$return_field)
plot(out_14["to1"])

Example: download data through SUNGEO API

# Single country, single topic
out_15 <- get_data(country_name="Afghanistan",topics="Demographics:Population:GHS")
out_15

# Multiple countries, multiple topics
out_16 <- get_data(
    country_name=c("Albania","Moldova"),
    topics=c("Demographics:Ethnicity:EPR","Demographics:Population:GHS"))
out_16

# Other boundary sets, spatial and time units
out_17 <- get_data(
    country_name="Albania",
    topics="Weather:AirTemperatureAndPrecipitation:NOAA",
    geoset="GAUL",geoset_yr=1990,space_unit="adm2",time_unit="month",
    year_min=1990,year_max=1991)
out_17

Example: get list of available data through SUNGEO API

# Get list of all available data
out_18 <- get_info()
out_18["summary"]
out_18["topics"]
out_18["geosets"]

# Get list of available data for a single country
out_19 <- get_info(country_names="Afghanistan")
out_19["summary"]
out_19["topics"]
out_19["geosets"]

# Get list of available data for a single topic
out_20 <- get_info(topics="Elections:LowerHouse:CLEA")
out_20["summary"]
out_20["topics"]

# Get list of available data for a multiple countries and topics
out_21 <- get_info(
           country_names=c("Afghanistan","Zambia"),
           topics=c("Elections:LowerHouse:CLEA","Events:PoliticalViolence:GED"))
out_21["summary"]

Additional examples in help files of individual functions.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.

SUNGEO / Sub-National Geospatial Data Archive: Geoprocessing Toolkit

`SUNGEO` / Sub-National Geospatial Data Archive: Geoprocessing Toolkit