| Title: | Sub-National Geospatial Data Archive: Geoprocessing Toolkit |
|---|---|
| Description: | Integrates spatially-misaligned GIS datasets across incompatible geographic units. Part of the Sub-National Geospatial Data Archive System. For the underlying methods, see Zhukov, Byers, Davidson, and Kollman (2024) "Integrating Data Across Misaligned Spatial Units," Political Analysis, Volume 32, Number 1, pp. 17-33 <doi:10.1017/pan.2023.5>. |
| Authors: | Yuri M. Zhukov [aut, cre], Jason Byers [aut], Marty Davidson [aut] |
| Maintainer: | Yuri M. Zhukov <[email protected]> |
| License: | GPL-2 |
| Version: | 1.4.0 |
| Built: | 2026-06-02 12:46:55 UTC |
| Source: | https://github.com/zhukovyuri/sungeo |
Census of geospatial and processed data files available to download using SUNGEO::get_data().
data(available_data)data(available_data)
List of 42 data.table objects Geoset:GADM :Classes ‘data.table’ and 'data.frame': 249 obs. of 4 variables Geoset:GAUL :Classes ‘data.table’ and 'data.frame': 242 obs. of 4 variables Geoset:geoBoundaries :Classes ‘data.table’ and 'data.frame': 197 obs. of 4 variables Geoset:GRED :Classes ‘data.table’ and 'data.frame': 74 obs. of 4 variables Geoset:HEXGRID :Classes ‘data.table’ and 'data.frame': 199 obs. of 4 variables Geoset:MPIDR :Classes ‘data.table’ and 'data.frame': 52 obs. of 4 variables Geoset:NHGIS :Classes ‘data.table’ and 'data.frame': 1 obs. of 4 variables Geoset:PRIOGRID :Classes ‘data.table’ and 'data.frame': 199 obs. of 4 variables Geoset:SHGIS :Classes ‘data.table’ and 'data.frame': 68 obs. of 4 variables
Codes for available countries (ISO 3166-1 alpha-3). Character string.
Names of available countries. Character string.
Years of available historical boundary files. Character string.
Available spatial units of analysis. Character string.
Elections:LowerHouse:CLEA :Classes ‘data.table’ and 'data.frame': 168 obs. of 6 variables Demographics:Ethnicity:EPR :Classes ‘data.table’ and 'data.frame': 180 obs. of 6 variables Demographics:Ethnicity:GREG :Classes ‘data.table’ and 'data.frame': 234 obs. of 6 variables Demographics:Population:GHS :Classes ‘data.table’ and 'data.frame': 257 obs. of 6 variables Events:PoliticalViolence:ABADarfur :Classes ‘data.table’ and 'data.frame': 1 obs. of 6 variables Events:PoliticalViolence:ACLED :Classes ‘data.table’ and 'data.frame': 100 obs. of 6 variables Events:PoliticalViolence:BeissingerProtest :Classes ‘data.table’ and 'data.frame': 15 obs. of 6 variables Events:PoliticalViolence:BeissingerRiot :Classes ‘data.table’ and 'data.frame': 15 obs. of 6 variables Events:PoliticalViolence:BeissingerUkraine :Classes ‘data.table’ and 'data.frame': 1 obs. of 6 variables Events:PoliticalViolence:COCACW :Classes ‘data.table’ and 'data.frame': 1 obs. of 6 variables Events:PoliticalViolence:ESOCAfghanistanWITS :Classes ‘data.table’ and 'data.frame': 1 obs. of 6 variables Events:PoliticalViolence:ESOCIraqSIGACT :Classes ‘data.table’ and 'data.frame': 1 obs. of 6 variables Events:PoliticalViolence:ESOCIraqWITS :Classes ‘data.table’ and 'data.frame': 1 obs. of 6 variables Events:PoliticalViolence:ESOCMexicoDrugRelatedMurders :Classes ‘data.table’ and 'data.frame': 1 obs. of 6 variables Events:PoliticalViolence:ESOCMexicoHomicide :Classes ‘data.table’ and 'data.frame': 1 obs. of 6 variables Events:PoliticalViolence:ESOCPakistanBFRS :Classes ‘data.table’ and 'data.frame': 1 obs. of 6 variables Events:PoliticalViolence:ESOCPakistanWITS :Classes ‘data.table’ and 'data.frame': 1 obs. of 6 variables Events:PoliticalViolence:GED :Classes ‘data.table’ and 'data.frame': 121 obs. of 6 variables Events:PoliticalViolence:Lankina :Classes ‘data.table’ and 'data.frame': 1 obs. of 6 variables Events:PoliticalViolence:NIRI :Classes ‘data.table’ and 'data.frame': 12 obs. of 6 variables Events:PoliticalViolence:NVMS :Classes ‘data.table’ and 'data.frame': 1 obs. of 6 variables Events:PoliticalViolence:PITF :Classes ‘data.table’ and 'data.frame': 133 obs. of 6 variables Events:PoliticalViolence:SCAD :Classes ‘data.table’ and 'data.frame': 60 obs. of 6 variables Events:PoliticalViolence:yzCaucasus2000 :Classes ‘data.table’ and 'data.frame': 1 obs. of 6 variables Events:PoliticalViolence:yzChechnya :Classes ‘data.table’ and 'data.frame': 1 obs. of 6 variables Events:PoliticalViolence:yzLibya :Classes ‘data.table’ and 'data.frame': 1 obs. of 6 variables Events:PoliticalViolence:yzUkraine2014 :Classes ‘data.table’ and 'data.frame': 1 obs. of 6 variables Infrastructure:Roads:gRoads :Classes ‘data.table’ and 'data.frame': 240 obs. of 6 variables Infrastructure:NightLights:DMSP :Classes ‘data.table’ and 'data.frame': 257 obs. of 6 variables PublicHealth:Covid19:JHUCSSEC19 :Classes ‘data.table’ and 'data.frame': 207 obs. of 6 variables Terrain:Elevation:ETOPO1 :Classes ‘data.table’ and 'data.frame': 256 obs. of 6 variables Terrain:LandCover:GLCC :Classes ‘data.table’ and 'data.frame': 257 obs. of 6 variables Weather:AirTemperatureAndPrecipitation:NOAA :Classes ‘data.table’ and 'data.frame': 209 obs. of 6 variables
Codes for available countries (ISO 3166-1 alpha-3). Character string.
Names of available countries. Character string.
Range of available years for data topic. Character string.
Available time units. Character string.
Available spatial units. Character string.
Names of available geographic boundary data sources. Character string.
Sub-National Geospatial Data Archive System: Geoprocessing Toolkit (updated March 17, 2023).
Reference table of country names and ISO-3166 codes, adapted from countrycode package.
data(cc_dict)data(cc_dict)
data.table object, with 8626 obs. of 3 variables:
Country names. Character string.
Alternative spellings of country names, ASCII characters only. Character string.
Country codes (ISO 3166-1 alpha-3). Character string.
Vincent Arel-Bundock. Package countrycode: Convert Country Names and Country Code, version 1.40. CRAN (October 12, 2022).
A simple feature collection containing the spatial geometries of electoral constituency borders, and data on turnout levels, votes shares and other attributes of lower chamber legislative elections.
data(clea_deu2009)data(clea_deu2009)
Simple feature collection with 16 features and 10 fields. geometry type: MULTIPOLYGON. dimension: XY. bbox: xmin: 5.867281 ymin: 47.27096 xmax: 15.04388 ymax: 55.05902. epsg (SRID): 4326. proj4string: +proj=longlat +datum=WGS84 +no_defs.
Constituency number. Numeric.
Constituency name. Character.
Country number. Numeric.
Country name. Character.
Year and month of election (YYYYMM). Character.
Turnout in first round. Numeric.
Number of valid votes in first round. Numeric.
Popular vote share margin in first round. Numeric.
Incumbent party name.
Party name of popular vote share winner in first round. Character.
Constituency-Level Elections Archive (CLEA) https://electiondataarchive.org/
A data.frame object containing the geographic centroids of electoral contituencies, and data on turnout levels, votes shares and other attributes of lower chamber legislative elections.
data(clea_deu2009_df)data(clea_deu2009_df)
data.frame with 16 observations and 12 variables.
Constituency number. Numeric.
Constituency name. Character.
Country number. Numeric.
Country name. Character.
Year and month of election (YYYYMM). Character.
Turnout in first round. Numeric.
Number of valid votes in first round. Numeric.
Popular vote share margin in first round. Numeric.
Incumbent party name.
Party name of popular vote share winner in first round. Character.
Longitude of constituency centroid. Numeric.
Latitude of constituency centroid. Numeric.
Constituency-Level Elections Archive (CLEA) https://electiondataarchive.org/
A simple feature collection containing the geographic centroids of electoral contituencies, and data on turnout levels, votes shares and other attributes of lower chamber legislative elections.
data(clea_deu2009_pt)data(clea_deu2009_pt)
Simple feature collection with 16 features and 10 fields. geometry type: POINT. dimension: XY. bbox: xmin: 6.953882 ymin: 48.54535 xmax: 13.40315 ymax: 54.18635. epsg (SRID): 4326. proj4string: +proj=longlat +datum=WGS84 +no_defs.
Constituency number. Numeric.
Constituency name. Character.
Country number. Numeric.
Country name. Character.
Year and month of election (YYYYMM). Character.
Turnout in first round. Numeric.
Number of valid votes in first round. Numeric.
Popular vote share margin in first round. Numeric.
Incumbent party name.
Party name of popular vote share winner in first round. Character.
Constituency-Level Elections Archive (CLEA) https://electiondataarchive.org/
Function takes in x-, y-coordinates, and a data.frame of variables (optional) and returns an SFC object
df2sf( x_coord, y_coord, input_data = NULL, file = NULL, n_max = Inf, start = 0, projection_input = "EPSG:4326", zero.policy = FALSE, show_removed = FALSE )df2sf( x_coord, y_coord, input_data = NULL, file = NULL, n_max = Inf, start = 0, projection_input = "EPSG:4326", zero.policy = FALSE, show_removed = FALSE )
x_coord |
Numeric vector with longitude or easting projected coordinates. When |
y_coord |
Numeric vector with latitude or northing projected coordinates. Must be equal to the vector length of |
input_data |
Optional data frame object, containing |
file |
Optional path to csv file. Overrides |
n_max |
Maximum number of rows to read in |
start |
Number of rows to skip in |
projection_input |
Projection string associated with |
zero.policy |
If |
show_removed |
If |
If show_removed==FALSE, returns an sf object, with rows corresponding to non-usable coordinates removed. If show_removed==TRUE, returns a list, with an sf object (Spatial_Coordinates), and a vector of indices corresponding to non-usable coordinates removed (Removed_Rows).
# Coordinates supplied as vectors data(clea_deu2009_df) out_1 <- df2sf(x_coord=clea_deu2009_df$longitude,y_coord = clea_deu2009_df$latitude) class(out_1) plot(out_1$geometry) # Coordinates supplied as column mames out_2 <- df2sf(x_coord="longitude",y_coord ="latitude", input_data = clea_deu2009_df) plot(out_2["geometry"]) # Load from external file tmp <- tempfile() write.csv(clea_deu2009_df,file=tmp) out_3 <- df2sf(x_coord="longitude",y_coord ="latitude", file=tmp) plot(out_3["geometry"])# Coordinates supplied as vectors data(clea_deu2009_df) out_1 <- df2sf(x_coord=clea_deu2009_df$longitude,y_coord = clea_deu2009_df$latitude) class(out_1) plot(out_1$geometry) # Coordinates supplied as column mames out_2 <- df2sf(x_coord="longitude",y_coord ="latitude", input_data = clea_deu2009_df) plot(out_2["geometry"]) # Load from external file tmp <- tempfile() write.csv(clea_deu2009_df,file=tmp) out_3 <- df2sf(x_coord="longitude",y_coord ="latitude", file=tmp) plot(out_3["geometry"])
Function to check validity and fix broken geometries in simple features polygon objects
fix_geom(x, n_it = 10)fix_geom(x, n_it = 10)
x |
Polygon layer to be checked and fixed. |
n_it |
Number of iterations. Default is 10. Numeric.. |
Returns a sf polygon object, with self-intersections and other geometry problems fixed.
# Fix geometries for a single dataset data(clea_deu2009) out_1 <- fix_geom(clea_deu2009) all(sf::st_is_valid(out_1))# Fix geometries for a single dataset data(clea_deu2009) out_1 <- fix_geom(clea_deu2009) all(sf::st_is_valid(out_1))
Finds geographic coordinates of addresses and place names (forward geocoding), or converts longitude/latitude coordinates to place names and administrative units (reverse geocoding), using OpenStreetMap's Nominatim API.
geocode_osm( query, match_num = 1, return_all = FALSE, details = FALSE, reverse = FALSE, lon = NULL, lat = NULL, zoom = 10, user_agent = NULL )geocode_osm( query, match_num = 1, return_all = FALSE, details = FALSE, reverse = FALSE, lon = NULL, lat = NULL, zoom = 10, user_agent = NULL )
query |
Address or place name to be geocoded. Character string. Ignored
if |
match_num |
If query matches multiple locations, which match to return? Default is 1 (highest-ranking match, by relevance). Numeric. |
return_all |
Should all matches be returned? Overrides |
details |
Should detailed results be returned? Default is |
reverse |
Should reverse geocoding be performed (coordinates to address)?
Default is |
lon |
Longitude of the point to reverse geocode. Required if
|
lat |
Latitude of the point to reverse geocode. Required if
|
zoom |
Zoom level for reverse geocoding, controlling the level of detail returned (0 = country, 10 = city, 18 = building). Default is 10. Numeric. |
user_agent |
Valid User-Agent identifying the application for OSM Nominatim. If none supplied, function will attempt to auto-detect. Character string. |
Note that the Nominatim Usage Policy stipulates an absolute maximum
of 1 request per second
(https://operations.osmfoundation.org/policies/nominatim/).
For batch geocoding of multiple addresses, please use
geocode_osm_batch.
A data.frame object.
For forward geocoding (reverse=FALSE):
query. User-supplied address query. Character string.
osm_id. OpenStreetMap ID. Character string.
address. OpenStreetMap address. Character string.
longitude. Horizontal coordinate. Numeric.
latitude. Vertical coordinate. Numeric.
If details=TRUE, also contains:
osm_type. OpenStreetMap feature type. Character string.
importance. Relevance of Nominatim match to query, from 0
(worst) to 1 (best). Numeric.
bbox_ymin. Minimum vertical coordinate of bounding box. Numeric.
bbox_ymax. Maximum vertical coordinate of bounding box. Numeric.
bbox_xmin. Minimum horizontal coordinate of bounding box. Numeric.
bbox_xmax. Maximum horizontal coordinate of bounding box. Numeric.
For reverse geocoding (reverse=TRUE):
osm_name. Full display name from OpenStreetMap. Character string.
osm_adm0_code. ISO country code. Character string.
osm_adm0. Country name. Character string.
osm_adm1. State or first-level administrative division. Character string.
osm_adm2. District or county. Character string.
osm_adm3. Municipality. Character string.
osm_adm4. Borough, village or street. Character string.
# Geocode an address (top match only) geocode_osm("Michigan Stadium") # 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) # Reverse geocode a coordinate pair geocode_osm(reverse = TRUE, lon = -83.74868, lat = 42.26587, zoom = 18)# Geocode an address (top match only) geocode_osm("Michigan Stadium") # 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) # Reverse geocode a coordinate pair geocode_osm(reverse = TRUE, lon = -83.74868, lat = 42.26587, zoom = 18)
Finds geographic coordinates of multiple addresses and place names (forward geocoding), or converts multiple longitude/latitude coordinate pairs to place names and administrative units (reverse geocoding), using OpenStreetMap's Nominatim API.
geocode_osm_batch( query = NULL, delay = 1, return_all = FALSE, match_num = 1, details = FALSE, reverse = FALSE, lon = NULL, lat = NULL, zoom = 10, user_agent = NULL, verbose = FALSE )geocode_osm_batch( query = NULL, delay = 1, return_all = FALSE, match_num = 1, details = FALSE, reverse = FALSE, lon = NULL, lat = NULL, zoom = 10, user_agent = NULL, verbose = FALSE )
query |
Addresses or place names to be geocoded. Character string.
Ignored if |
delay |
Delay between requests, in seconds. Default is 1. Numeric. |
return_all |
Should all matches be returned? Overrides |
match_num |
If query matches multiple locations, which match to return? Default is 1 (highest-ranking match, by relevance). Numeric. |
details |
Should detailed results be returned? Default is |
reverse |
Should reverse geocoding be performed (coordinates to
address)? Default is |
lon |
Longitudes of points to reverse geocode. Required if
|
lat |
Latitudes of points to reverse geocode. Required if
|
zoom |
Zoom level for reverse geocoding, controlling the level of detail returned (0 = country, 10 = city, 18 = building). Default is 10. Numeric. |
user_agent |
Valid User-Agent identifying the application for OSM Nominatim. If none supplied, function will attempt to auto-detect. Character string. |
verbose |
Print status messages and progress? Default is |
Wrapper function for geocode_osm. Because the
Nominatim Usage Policy stipulates an absolute maximum of 1 request per
second, this function facilitates batch geocoding by adding a small delay
between queries
(https://operations.osmfoundation.org/policies/nominatim/).
A data.frame object.
For forward geocoding (reverse=FALSE):
query. User-supplied address query. Character string.
osm_id. OpenStreetMap ID. Character string.
address. OpenStreetMap address. Character string.
longitude. Horizontal coordinate. Numeric.
latitude. Vertical coordinate. Numeric.
If details=TRUE, also contains:
osm_type. OpenStreetMap feature type. Character string.
importance. Relevance of Nominatim match to query, from 0
(worst) to 1 (best). Numeric.
bbox_ymin. Minimum vertical coordinate of bounding box. Numeric.
bbox_ymax. Maximum vertical coordinate of bounding box. Numeric.
bbox_xmin. Minimum horizontal coordinate of bounding box. Numeric.
bbox_xmax. Maximum horizontal coordinate of bounding box. Numeric.
For reverse geocoding (reverse=TRUE):
osm_name. Full display name from OpenStreetMap. Character string.
osm_adm0_code. ISO country code. Character string.
osm_adm0. Country name. Character string.
osm_adm1. State or first-level administrative division. Character string.
osm_adm2. District or county. Character string.
osm_adm3. Municipality. Character string.
osm_adm4. Borough or village. Character string.
# 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) # Reverse geocode multiple coordinates (city level) geocode_osm_batch( reverse = TRUE, lon = c(-83.743, -84.556, -82.999), lat = c(42.278, 42.701, 39.961), zoom = 10 )# 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) # Reverse geocode multiple coordinates (city level) geocode_osm_batch( reverse = TRUE, lon = c(-83.743, -84.556, -82.999), lat = c(42.278, 42.701, 39.961), zoom = 10 )
Function to download data files through the SUNGEO API. Function produces a data.table object, corresponding to the user's choice of countries, topics, sources, and spatial and temporal units.
get_data( country_names = NULL, country_iso3 = NULL, geoset = "GADM", geoset_yr = 2018, space_unit = "adm1", time_unit = "year", topics = NULL, year_min = 1990, year_max = 2017, print_url = TRUE, print_time = TRUE, error_stop = FALSE, by_topic = TRUE, skip_missing = TRUE, cache_param = FALSE, short_message = TRUE )get_data( country_names = NULL, country_iso3 = NULL, geoset = "GADM", geoset_yr = 2018, space_unit = "adm1", time_unit = "year", topics = NULL, year_min = 1990, year_max = 2017, print_url = TRUE, print_time = TRUE, error_stop = FALSE, by_topic = TRUE, skip_missing = TRUE, cache_param = FALSE, short_message = TRUE )
country_names |
Country name(s). Character string (single country) or vector of character strings (multiple countries). |
country_iso3 |
Country code (ISO 3166-1 alpha-3). Character string (single country) or vector of character strings (multiple countries). |
geoset |
Name of geographic boundary set. Can be one of |
geoset_yr |
Year of geographic boundaries. See |
space_unit |
Geographic level of analysis. Can be one of |
time_unit |
Temporal level of analysis. Can be one of |
topics |
Data topics. See |
year_min |
Time range of requested data: start year. See |
year_max |
Time range of requested data: end year. See |
print_url |
Print url string of requested data to console? Default is |
print_time |
Print processing time for API query to console? Default is |
error_stop |
Error handling. If |
by_topic |
Break query down by topic and country? If |
skip_missing |
Skip missing data topics? If |
cache_param |
Store cached query on server? This can speed up processing for repeated queries. Default is |
short_message |
Shorten error messages? If |
data.table object, with requested data from SUNGEO API.
# Single country, single topic out_1 <- get_data(country_name="Afghanistan",topics="Demographics:Population:GHS") out_1 out_2 <- get_data( country_name=c("Afghanistan","Moldova"), topics=c("Demographics:Ethnicity:EPR","Demographics:Population:GHS")) out_2 # Other boundary sets, spatial and time units out_3 <- 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_3# Single country, single topic out_1 <- get_data(country_name="Afghanistan",topics="Demographics:Population:GHS") out_1 out_2 <- get_data( country_name=c("Afghanistan","Moldova"), topics=c("Demographics:Ethnicity:EPR","Demographics:Population:GHS")) out_2 # Other boundary sets, spatial and time units out_3 <- 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_3
This function reports the availability of data files on the SUNGEO server, searchable by country and topic.
get_info(country_names = NULL, country_iso3s = NULL, topics = NULL)get_info(country_names = NULL, country_iso3s = NULL, topics = NULL)
country_names |
Country name(s). Character string (single country) or vector of character strings (multiple countries). |
country_iso3s |
Country code (ISO 3166-1 alpha-3). Character string (single country) or vector of character strings (multiple countries). |
topics |
Data topics. See |
list object, with three slots: 'summary', 'topics', and 'geoset'.
# Get list of all available data out_1 <- get_info() out_1["summary"] out_1["topics"] out_1["geosets"] # Get list of available data for a single country out_2 <- get_info(country_names="Afghanistan") out_2 # Get list of available data for a single topic out_3 <- get_info(topics="Elections:LowerHouse:CLEA") out_3 # Get list of available data for a multiple countries and topics out_4 <- get_info( country_names=c("Afghanistan","Zambia"), topics=c("Elections:LowerHouse:CLEA","Events:PoliticalViolence:GED")) out_4# Get list of all available data out_1 <- get_info() out_1["summary"] out_1["topics"] out_1["geosets"] # Get list of available data for a single country out_2 <- get_info(country_names="Afghanistan") out_2 # Get list of available data for a single topic out_3 <- get_info(topics="Elections:LowerHouse:CLEA") out_3 # Get list of available data for a multiple countries and topics out_4 <- get_info( country_names=c("Afghanistan","Zambia"), topics=c("Elections:LowerHouse:CLEA","Events:PoliticalViolence:GED")) out_4
2.5 arc-minute resolution raster of estimates of human population (number of persons per pixel), consistent with national censuses and population registers, for the year 2010.
data(gpw4_deu2010)data(gpw4_deu2010)
class : SpatRaster dimensions : 186, 220, 1 (nrow, ncol, nlyr) resolution : 0.04166667, 0.04166667 (x, y) extent : 5.875, 15.04167, 47.29167, 55.04167 (xmin, xmax, ymin, ymax) coord. ref. : lon/lat WGS 84 (EPSG:4326) source(s) : memory name : gpw_v4_population_count_rev11_2010_2pt5_min min value : 0.00 max value : 92915.66
Gridded Population of the World (GPW) v4: Population Count, v4.11 doi:10.7927/H4JW8BX5.
Regular hexagonal grid of 0.5 degree diameter cells, covering territory of Germany (2020 borders).
data(hex_05_deu)data(hex_05_deu)
Simple feature collection with 257 features and 3 fields. geometry type: POLYGON. dimension: XY. bbox: xmin: 5.375001 ymin: 46.76568 xmax: 15.375 ymax: 55.13726. epsg (SRID): 4326. proj4string: +proj=longlat +datum=WGS84 +no_defs.
Unique cell identifier. Character.
Longitude of cell centroid. Numeric.
Latitude of cell centroid. Numeric.
SUNGEO
Roads thematic layer from Digital Chart of the World. Subset: divided multi-lane highways.
data(highways_deu1992)data(highways_deu1992)
Simple feature collection with 1741 features and 5 fields. geometry type: MULTILINESTRING. dimension: XY. bbox: xmin: 5.750933 ymin: 47.58799 xmax: 14.75109 ymax: 54.80712 epsg (SRID): 4326. proj4string: +proj=longlat +datum=WGS84 +no_defs.
Is the road a divided multi-lane highway with a median? Character string.
Primary or secondary route? Character string.
Feature code description (road or trail). Character string.
ISO 3166-1 alpha-3 country code. Character string.
Country name. Character string.
Defense Mapping Agency (DMA), 1992. Digital Chart of the World. Defense Mapping Agency, Fairfax, Virginia. (Four CD-ROMs). Available through DIVA-GIS: https://diva-gis.org/data.html (accessed May 19, 2026).
Function automatically calculates the Local G hot spot intensity measure for spatial points or spatial polygons. Uses RANN for efficient nearest neighbor calculation (spatial points only); users can specify the number of neighbors (k). Users can specify the neighborhood style (see spdep::nb2listw) with default being standardized weight matrix (W).
hot_spot( insert, variable = NULL, style = "W", k = 9, remove_missing = TRUE, NA_Value = 0, include_Moran = FALSE )hot_spot( insert, variable = NULL, style = "W", k = 9, remove_missing = TRUE, NA_Value = 0, include_Moran = FALSE )
insert |
Spatial point or spatial polygon object. Acceptable formats include |
variable |
Column name or numeric vector containing the variable from which the local G statistic will be calculated. Must possess a natural scale that orders small and large observations (i.e. number, percentage, ratio and not model residuals). |
style |
Style can take values |
k |
Number of neighbors. Default is 9. Numeric. |
remove_missing |
Whether to calculate statistic without missing values. If |
NA_Value |
Substitute for missing values. Default value is 0. Numeric. |
include_Moran |
Calculate local Moran's I statistics. Default is |
If input is sf, SpatialPolygonsDataFrame or SpatialPointsDataFrame object, returns sf object with same geometries and columns as input, appended with additional column containing Local G estimates (LocalG). If input is RasterLayer object, returns RasterBrick object containing original values (Original) and Local G estimates (LocalG).
# Calculate Local G for sf point layer data(clea_deu2009_pt) out_1 <- hot_spot(insert=clea_deu2009_pt, variable = clea_deu2009_pt$to1) class(out_1) plot(out_1["LocalG"]) # Calculate Local G for sf polygon layer (variable as numeric vector) data(clea_deu2009) out_2 <- hot_spot(insert=clea_deu2009, variable = clea_deu2009$to1) summary(out_2$LocalG) plot(out_2["LocalG"]) # Calculate Local G for sf polygon layer (variable as column name) out_3 <- hot_spot(insert=clea_deu2009, variable = "to1") summary(out_3$LocalG) plot(out_3["LocalG"]) # Calculate Local G for a SpatialPolygonsDataFrame object (variable as column name) out_4 <- hot_spot(insert=as(clea_deu2009,"Spatial"), variable = "to1") summary(out_4$LocalG) plot(out_4["LocalG"])# Calculate Local G for sf point layer data(clea_deu2009_pt) out_1 <- hot_spot(insert=clea_deu2009_pt, variable = clea_deu2009_pt$to1) class(out_1) plot(out_1["LocalG"]) # Calculate Local G for sf polygon layer (variable as numeric vector) data(clea_deu2009) out_2 <- hot_spot(insert=clea_deu2009, variable = clea_deu2009$to1) summary(out_2$LocalG) plot(out_2["LocalG"]) # Calculate Local G for sf polygon layer (variable as column name) out_3 <- hot_spot(insert=clea_deu2009, variable = "to1") summary(out_3$LocalG) plot(out_3["LocalG"]) # Calculate Local G for a SpatialPolygonsDataFrame object (variable as column name) out_4 <- hot_spot(insert=as(clea_deu2009,"Spatial"), variable = "to1") summary(out_4$LocalG) plot(out_4["LocalG"])
Function for basic geometry calculations on polyline features, within an overlapping destination polygon layer.
line2poly( linez, polyz, poly_id, measurez = c("length", "density", "distance"), outvar_name = "line", unitz = "km", reproject = TRUE, na_val = NA, verbose = TRUE )line2poly( linez, polyz, poly_id, measurez = c("length", "density", "distance"), outvar_name = "line", unitz = "km", reproject = TRUE, na_val = NA, verbose = TRUE )
linez |
Source polyline layer. |
polyz |
Destination polygon layer. Must have identical CRS to |
poly_id |
Name of unique ID column for destination polygon layer. Character string. |
measurez |
Desired measurements. Could be any of "length" (sum of line lengths by polygon), "density" (sum of line lengths divided by area of polygon) and/or "distance" (distance from each polygon to nearest line feature). Default is to report all three. Character string or vector of character strings. |
outvar_name |
Name (root) to be given to output variable. Default is |
unitz |
Units of measurement (linear). Defaul is |
reproject |
Temporarily reproject layers to planar projection for geometric operations? Defaul is |
na_val |
Value to be assigned to missing values (line lengths and densities only). Defaul is |
verbose |
Print status messages and progress? Default is |
An sf polygon object, with summary statisics of linez features aggregated to the geometries of polyz.
If measurez = "lengths", contains fields with suffixes
"_length". Sum of line lengths within each polygon, in km or other units supplied in unitz.
If measurez = "density", contains fields with suffixes
"_length". Sum of line lengths within each polygon, in km or other units supplied in unitz.
"_area". Area of each polygon, in km^2 or the square of linear units supplied in unitz.
"_density". Sum of line lengths divided by area of each polygon, in km/km^2 or other units supplied in unitz.
If measurez = "distance", contains fields with suffixes
"_distance". Distance from each polygon to nearest line feature, in km or other units supplied in unitz.
If measurez = c("length","density","distance") (default), contains all of the above.
# Road lengths, densities and distance from polygon to nearest highway data(hex_05_deu) data(highways_deu1992) out_1 <- line2poly(linez = highways_deu1992, polyz = hex_05_deu, poly_id = "HEX_ID") plot(out_1["line_length"]) plot(out_1["line_density"]) plot(out_1["line_distance"]) # Replace missing road lengths and densities with 0's, rename variables out_2 <- line2poly(linez = highways_deu1992, polyz = hex_05_deu, poly_id = "HEX_ID", outvar_name = "road", na_val = 0) plot(out_2["road_length"]) plot(out_2["road_density"]) plot(out_2["road_distance"])# Road lengths, densities and distance from polygon to nearest highway data(hex_05_deu) data(highways_deu1992) out_1 <- line2poly(linez = highways_deu1992, polyz = hex_05_deu, poly_id = "HEX_ID") plot(out_1["line_length"]) plot(out_1["line_density"]) plot(out_1["line_distance"]) # Replace missing road lengths and densities with 0's, rename variables out_2 <- line2poly(linez = highways_deu1992, polyz = hex_05_deu, poly_id = "HEX_ID", outvar_name = "road", na_val = 0) plot(out_2["road_length"]) plot(out_2["road_density"]) plot(out_2["road_distance"])
Function to create a table of consecutive dates, in SUNGEO-compliant format.
make_ticker( date_min = 19000101, date_max = as.integer(gsub("-", "", as.Date(Sys.Date()))) )make_ticker( date_min = 19000101, date_max = as.integer(gsub("-", "", as.Date(Sys.Date()))) )
date_min |
Start date, in YYYYMMDD format. Default is |
date_max |
End date, in YYYYMMDD format. Default is today. Integer. |
data.table object, with seven columns:
DATE. Date in YYYYMMDD format. Integer.
DATE_ALT. Date in Date (YYYY-MM-DD) format. Date.
TID. Date ID, in consecutive integer format. Integer.
YRWK. Week in YYYYWW format. Integer.
WID. Weed ID, in consecutive integer format. Integer.
YRMO. Month in YYYYMM format. Integer.
MID. Month ID, in consecutive integer format. Integer.
YEAR. Year in YYYY format. Integer.
# All dates from January 1, 1900 to today out_1 <- make_ticker() out_1 # All dates from January 1, 1200 to today out_2 <- make_ticker(date_min=12000101) out_2 # All dates from January 1, 1500 to December 31, 1899 out_3 <- make_ticker(date_min=15000101, date_max=18991231) out_3# All dates from January 1, 1900 to today out_1 <- make_ticker() out_1 # All dates from January 1, 1200 to today out_2 <- make_ticker(date_min=12000101) out_2 # All dates from January 1, 1500 to December 31, 1899 out_3 <- make_ticker(date_min=15000101, date_max=18991231) out_3
Function that finds a set of common columns in a list of tables, and merges the tables on these columns.
merge_list(lst)merge_list(lst)
lst |
List of tables to be merged. List object. |
data.table object
# Merge list of three tables with different common variables A <- data.table::data.table(month=month.name,year=rep(1991:1992,each=12),A=rnorm(24)) B <- data.table::data.table(year=c(1991,1992),B=rbeta(2,1,1)) C <- data.table::data.table(month=month.name,C=runif(12)) out_1 <- merge_list(list(A,B,C)) out_1# Merge list of three tables with different common variables A <- data.table::data.table(month=month.name,year=rep(1991:1992,each=12),A=rnorm(24)) B <- data.table::data.table(year=c(1991,1992),B=rbeta(2,1,1)) C <- data.table::data.table(month=month.name,C=runif(12)) out_1 <- merge_list(list(A,B,C)) out_1
Function to calculate relative scale and nesting metrics for changes of support from a source polygon layer to an overlapping (but spatially misaligned) destination polygon layer.
nesting( poly_from = NULL, poly_to = NULL, metrix = "all", tol_ = 0.001, by_unit = FALSE )nesting( poly_from = NULL, poly_to = NULL, metrix = "all", tol_ = 0.001, by_unit = FALSE )
poly_from |
Source polygon layer. |
poly_to |
Destination polygon layer. Must have identical CRS to |
metrix |
Requested scaling and nesting metrics. See "details". Default is "all". Character string or vector of character strings. |
tol_ |
Minimum area of polygon intersection, in square meters. Default is 0.001. Numeric. |
by_unit |
Include a by-unit decomposition of requested nesting metrics (if available)? Default is FALSE. Logical. |
Currently supported metrics (metrix) include:
Relative scale ("rs"). Measures whether a change-of-support (CoS) task is one of aggregation or disaggregation, by calculating the share of source units that are smaller than destination units. Its range is from 0 to 1, where values of 1 indicate pure aggregation (all source units are smaller than destination units) and values of 0 indicate no aggregation (all source units are at least as large as destination units). Values between 0 and 1 indicate a hybrid (i.e. some source units are smaller, others are larger than target units).
Relative nesting ("rn"). Measures how closely source and destination boundaries align, by calculating the share of source units that cannot be split across multiple destination units. Its range is from 0 to 1, where values of 0 indicate no nesting (every source unit can be split across multiple destination units) and values of 1 indicate full nesting (no source unit can be split across multiple destination units).
Relative scale, symmetric ("rs_sym"). Alternative measure of "rs", which ranges from -1 to 1. It calculates a difference between two proportions: the share of source units that is smaller than destination units (i.e. "rs" from standpoint of source units), and the share that is larger (i.e. "rs" from standpoint of destination units). Values of -1 indicate pure disaggregation (all source units are larger than destination units), 1 indicates pure aggregation (all source units are smaller than destination units). Values of 0 indicate that all source units are the same size as target units.
Relative nesting, symmetric ("rn_sym"). Alternative measure of "rn", which ranges from -1 to 1. It calculates a difference between two components: the nesting of source units within destination units (i.e. "rn" from standpoint of source units), and the nesting of destination units within source units (i.e. "rn" from standpoint of destination units. Values of 1 indicate that source units are perfectly nested within destination units; -1 indicates that destination units are perfectly nested within source units.
Relative scale, alternative ("rs_alt"). Alternative measure of "rs", rescaled as a proportion of destination unit area. This measure can take any value on the real line, with positive values indicating aggregation and negative values indicating disaggregation.
Relative nesting, alternative ("rn_alt"). Alternative measure of "rn", which places more weight on areas of maximum overlap. The main difference between this measure and "rn" is its use of the maximum intersection area for each source polygon instead of averaging over the quadratic term. Two sets of polygons are considered nested if one set is completely contained within another, with as few splits as possible. If none or only a sliver of a source polygon area falls outside a single destination polygon, those polygons are "more nested" than a case where half of a source polygon falls in destination polygon A and half falls into another polygon B.
Relative scale, conditional ("rs_nn"). Alternative measure of "rs", calculated for the subset of source units that are not fully nested within destination units.
Relative nesting, conditional ("rn_nn"). Alternative measure of "rn", calculated for the subset of source units that are not fully nested within destination units.
Proportion intact ("p_intact"). A nesting metric that requires no area calculations at all. This measure ranges from 0 to 1, where 1 indicates full nesting (i.e. every source unit is intact/no splits), and 0 indicates no nesting (i.e. no source unit is intact/all are split).
Proportion fully nested ("full_nest"). A stricter version of "p_intact". This measure ranges from 0 to 1, where 1 indicates full nesting (i.e. every source unit is intact/no splits AND falls completely inside the destination layer), and 0 indicates no nesting (i.e. no source unit is both intact and falls inside destination layer).
Relative overlap ("ro"). Assesses extent of spatial overlap between source and destination polygons. This measure is scaled between -1 and 1. Values of 0 indicate perfect overlap (there is no part of source units that fall outside of destination units, and vice versa). Values between 0 and 1 indicate a "source underlap" (some parts of source polygons fall outside of destination polygons; more precisely, a larger part of source polygon area falls outside destination polygons than the other way around). Values between -1 and 0 indicate a "destination underlap" (some parts of destination polygons fall outside of source polygons; a larger part of destination polygon area falls outside source polygons than the other way around). Values of -1 and 1 indicate no overlap (all source units fall outside destination units, and vice versa). This is a theoretical limit only; the function returns an error if there is no overlap.
Gibbs-Martin index of diversification ("gmi"). Inverse of "rn", where values of 1 indicate that every source unit is evenly split across multiple destination units, and 0 indicates that no source unit is split across any destination units.
It is possible to pass multiple arguments to metrix (e.g. metrix=c("rn","rs")). The default (metrix="all") returns all of the above metrics.
The function automatically reprojects source and destination geometries to Lambert Equal Area prior to calculation, with map units in meters.
Values of tol_ can be adjusted to increase or decrease the sensitivity of these metrics to small border misalignments. The default value discards polygon intersections smaller than 0.001 square meters in area.
Named list, with numeric values for each requested metric in metrix. If by_unit==TRUE, last element of list is a data.table, with nesting metrics disaggregated by source unit, where the first column is a row index for the source polygon layer.
# Calculate all scale and nesting metrics for two sets of polygons data(clea_deu2009) data(hex_05_deu) 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# Calculate all scale and nesting metrics for two sets of polygons data(clea_deu2009) data(hex_05_deu) 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
Function for assigning values from a source point layer to a destination polygon layer, using simple point-in-polygon overlays
point2poly_simp( pointz, polyz, varz, char_varz = NULL, funz = list(function(x) { sum(x, na.rm = TRUE) }), na_val = NA, drop_na_cols = FALSE )point2poly_simp( pointz, polyz, varz, char_varz = NULL, funz = list(function(x) { sum(x, na.rm = TRUE) }), na_val = NA, drop_na_cols = FALSE )
pointz |
Source points layer. |
polyz |
Destination polygon layer. Must have identical CRS to |
varz |
Names of variable(s) to be assigned from source polygon layer to destination polygons. Character string or vector of character strings. |
char_varz |
Names of character string variable(s) in |
funz |
Aggregation function to be applied to variables specified in |
na_val |
Value to be assigned to missing values. Defaul is |
drop_na_cols |
Drop columns with completely missing values. Defaul is |
Assignment procedures are the same for numeric and character string variables. All variables supplied in varz are passed directly to the function specified in funz. If different sets of variables are to be aggregated with different functions, both varz and funz should be specified as lists (see examples below).
Returns a sf polygon object, with variables from pointz assigned to the geometries of polyz.
# Assignment of a single variable (sums) data(hex_05_deu) data(clea_deu2009_pt) out_1 <- point2poly_simp(pointz=clea_deu2009_pt, polyz=hex_05_deu, varz="vv1") plot(out_1["vv1"]) # Replace NA's with 0's out_2 <- point2poly_simp(pointz = clea_deu2009_pt, polyz = hex_05_deu, varz = "vv1", na_val = 0) plot(out_2["vv1"]) # Multiple variables, with different assignment functions out_3 <- point2poly_simp(pointz = clea_deu2009_pt, polyz = hex_05_deu, varz = list( c("to1","pvs1_margin"), c("vv1"), c("incumb_pty_n","win1_pty_n")), funz = list( function(x){mean(x,na.rm=TRUE)}, function(x){sum(x,na.rm=TRUE)}, function(x){paste0(unique(na.omit(x)),collapse=" | ") }), na_val = list(NA_real_,0,NA_character_)) plot(out_3["pvs1_margin"])# Assignment of a single variable (sums) data(hex_05_deu) data(clea_deu2009_pt) out_1 <- point2poly_simp(pointz=clea_deu2009_pt, polyz=hex_05_deu, varz="vv1") plot(out_1["vv1"]) # Replace NA's with 0's out_2 <- point2poly_simp(pointz = clea_deu2009_pt, polyz = hex_05_deu, varz = "vv1", na_val = 0) plot(out_2["vv1"]) # Multiple variables, with different assignment functions out_3 <- point2poly_simp(pointz = clea_deu2009_pt, polyz = hex_05_deu, varz = list( c("to1","pvs1_margin"), c("vv1"), c("incumb_pty_n","win1_pty_n")), funz = list( function(x){mean(x,na.rm=TRUE)}, function(x){sum(x,na.rm=TRUE)}, function(x){paste0(unique(na.omit(x)),collapse=" | ") }), na_val = list(NA_real_,0,NA_character_)) plot(out_3["pvs1_margin"])
Function for interpolating values from a source point layer to a destination polygon layer, using Voronoi tessellation and area/population weights.
point2poly_tess( pointz, polyz, poly_id, char_methodz = "aw", methodz = "aw", pop_raster = NULL, varz = NULL, pycno_varz = NULL, char_varz = NULL, char_assign = "biggest_overlap", funz = function(x, w) { stats::weighted.mean(x, w, na.rm = TRUE) }, return_tess = FALSE, seed = 1 )point2poly_tess( pointz, polyz, poly_id, char_methodz = "aw", methodz = "aw", pop_raster = NULL, varz = NULL, pycno_varz = NULL, char_varz = NULL, char_assign = "biggest_overlap", funz = function(x, w) { stats::weighted.mean(x, w, na.rm = TRUE) }, return_tess = FALSE, seed = 1 )
pointz |
Source points layer. |
polyz |
Destination polygon layer. Must have identical CRS to |
poly_id |
Name of unique ID column for destination polygon layer. Character string. |
char_methodz |
Interpolation method(s) for character strings. Could be either of "aw" (areal weighting, default) or "pw" (population weighting). See "details". Character string. |
methodz |
Interpolation method(s) for numeric covariates. Could be either of "aw" (areal weighting, default) and/or "pw" (population weighting). See "details". Character string or vector of character strings. |
pop_raster |
Population raster to be used for population weighting, Must be supplied if |
varz |
Names of numeric variable(s) to be interpolated from source polygon layer to destination polygons. Character string or list of character strings. |
pycno_varz |
Names of spatially extensive numeric variables for which the pycnophylactic (mass-preserving) property should be preserved. Character string or vector of character strings. |
char_varz |
Names of character string variables to be interpolated from source polygon layer to destination polygons. Character string or vector of character strings. |
char_assign |
Assignment rule to be used for variables specified in |
funz |
Aggregation function to be applied to variables specified in |
return_tess |
Return Voronoi polygons, in addition to destinaton polygon layer? Default is |
seed |
Seed for generation of random numbers. Default is 1. Numeric. |
This function interpolates point data to polygons with a two-step process. In the first step (tessellation), each point is assigned a Voronoi cell, drawn such that (a) the distance from its borders to the focal point is less than or equal to the distance to any other point, and (b) no gaps between cells remain. The second step (interpolation) performs a polygon-in-polygon interpolation, using the Voronoi cells as source polygons.
Currently supported integration methods in the second step (methodz) include:
Areal weighting ("aw"). Values from poly_from weighted in proportion to relative area of spatial overlap between source features and geometries of poly_to.
Population weighting ("pw"). Values from poly_from weighted in proportion to relative population sizes in areas of spatial overlap between source features and geometries of poly_to. This routine uses a third layer (supplied in pop_raster) to calculate the weights.
When a list of variables are supplied and one methods argument specified, then the chosen method will be applied to all variables.
When a list of of variables are supplied and multiple methods arguments specified, then weighting methods will be applied in a pairwise order. For example, specifying varz = list(c("to1","pvs1_margin"), c("vv1")) and methodz = c('aw', 'pw') will apply areal weighting to the the first set of variables (to1 and pvs1_margin) and population weighing to the second set (vv1).
Interpolation procedures are handled somewhat differently for numeric and character string variables. For numeric variables supplied in varz, "aw" and/or "pw" weights are passed to the function specified in funz. If different sets of numeric variables are to be aggregated with different functions, both varz and funz should be specified as lists (see examples below).
For character string (and any other) variables supplied in char_varz, "aw" and/or "pw" weights are passed to the assignment rule(s) specified in char_assign. Note that the char_varz argument may include numerical variables, but varz cannot include character string variables.
Currently supported assignment rules for character strings (char_assign) include:
"biggest_overlap". For each variable in char_varz, the features in poly_to are assigned a single value from overlapping poly_from features, corresponding to the intersection with largest area and/or population weight.
"all_overlap". For each variable in char_varz, the features in poly_to are assigned all values from overlapping poly_from features, ranked by area and/or population weights (largest-to-smallest) of intersections.
It is possible to pass multiple arguments to char_assign (e.g. char_assign=c("biggest_overlap","all_overlap")), in which case the function will calculate both, and append the resulting columns to the output.
If return_tess=FALSE, returns a sf polygon object, with variables from pointz interpolated to the geometries of polyz.
If return_tess=TRUE, returns a list, containing
"result". The destination polygon layer. sf object.
"tess". The (intermediate) Voronoi tessellation polygon layer. sf object.
# Interpolation of a single variable, with area weights data(hex_05_deu) data(clea_deu2009_pt) out_1 <- point2poly_tess(pointz = clea_deu2009_pt, polyz = hex_05_deu, poly_id = "HEX_ID", varz = "to1") plot(out_1["to1_aw"]) # Extract and inspect tessellation polygons out_2 <- point2poly_tess(pointz = clea_deu2009_pt, polyz = hex_05_deu, poly_id = "HEX_ID", varz = "to1", return_tess = TRUE) plot(out_2$tess["to1"]) plot(out_2$result["to1_aw"]) # Interpolation of multiple variables, with area and population weights data(gpw4_deu2010) gpw4_deu2010 <- terra::rast(gpw4_deu2010) # unwrap PackedSpatRaster out_3 <- point2poly_tess(pointz = clea_deu2009_pt, polyz = hex_05_deu, poly_id = "HEX_ID", methodz = c("aw","pw"), varz = list( c("to1","pvs1_margin"), c("vv1") ), pycno_varz = "vv1", funz = list( function(x,w){stats::weighted.mean(x,w)}, function(x,w){sum(x*w)} ), char_varz = c("incumb_pty_n","win1_pty_n"), pop_raster = gpw4_deu2010) plot(out_3["vv1_pw"])# Interpolation of a single variable, with area weights data(hex_05_deu) data(clea_deu2009_pt) out_1 <- point2poly_tess(pointz = clea_deu2009_pt, polyz = hex_05_deu, poly_id = "HEX_ID", varz = "to1") plot(out_1["to1_aw"]) # Extract and inspect tessellation polygons out_2 <- point2poly_tess(pointz = clea_deu2009_pt, polyz = hex_05_deu, poly_id = "HEX_ID", varz = "to1", return_tess = TRUE) plot(out_2$tess["to1"]) plot(out_2$result["to1_aw"]) # Interpolation of multiple variables, with area and population weights data(gpw4_deu2010) gpw4_deu2010 <- terra::rast(gpw4_deu2010) # unwrap PackedSpatRaster out_3 <- point2poly_tess(pointz = clea_deu2009_pt, polyz = hex_05_deu, poly_id = "HEX_ID", methodz = c("aw","pw"), varz = list( c("to1","pvs1_margin"), c("vv1") ), pycno_varz = "vv1", funz = list( function(x,w){stats::weighted.mean(x,w)}, function(x,w){sum(x*w)} ), char_varz = c("incumb_pty_n","win1_pty_n"), pop_raster = gpw4_deu2010) plot(out_3["vv1_pw"])
Function for interpolating values from a source polygon layer to an overlapping (but spatially misaligned) destination polygon layer, using area and/or population weights.
poly2poly_ap( poly_from, poly_to, poly_to_id, geo_vor = NULL, methodz = "aw", char_methodz = "aw", pop_raster = NULL, varz = NULL, pycno_varz = NULL, char_varz = NULL, char_assign = "biggest_overlap", funz = function(x, w) { stats::weighted.mean(x, w, na.rm = TRUE) }, seed = 1 )poly2poly_ap( poly_from, poly_to, poly_to_id, geo_vor = NULL, methodz = "aw", char_methodz = "aw", pop_raster = NULL, varz = NULL, pycno_varz = NULL, char_varz = NULL, char_assign = "biggest_overlap", funz = function(x, w) { stats::weighted.mean(x, w, na.rm = TRUE) }, seed = 1 )
poly_from |
Source polygon layer. |
poly_to |
Destination polygon layer. Must have identical CRS to |
poly_to_id |
Name of unique ID column for destination polygon layer. Character string. |
geo_vor |
Voronoi polygons object (used internally by |
methodz |
Area interpolation method(s). Could be either of "aw" (areal weighting, default) and/or "pw" (population weighting). See "details". Character string or vector of character strings. |
char_methodz |
Interpolation method(s) for character strings. Could be either of "aw" (areal weighting, default) or "pw" (population weighting). See "details". Character string. |
pop_raster |
Population raster to be used for population weighting, Must be supplied if |
varz |
Names of numeric variable(s) to be interpolated from source polygon layer to destination polygons. Character string or vector of character strings. |
pycno_varz |
Names of spatially extensive numeric variables for which the pycnophylactic (mass-preserving) property should be preserved. Character string or vector of character strings. |
char_varz |
Names of character string variables to be interpolated from source polygon layer to destination polygons. Character string or vector of character strings. |
char_assign |
Assignment rule to be used for variables specified in |
funz |
Aggregation function to be applied to variables specified in |
seed |
Seed for generation of random numbers. Default is 1. Numeric. |
Currently supported integration methods (methodz) include:
Areal weighting ("aw"). Values from poly_from weighted in proportion to relative area of spatial overlap between source features and geometries of poly_to.
Population weighting ("pw"). Values from poly_from weighted in proportion to relative population sizes in areas of spatial overlap between source features and geometries of poly_to. This routine uses a third layer (supplied in pop_raster) to calculate the weights.
It is possible to pass multiple arguments to methodz (e.g. methodz=c("aw","pw")), in which case the function will calculate both sets of weights, and append the resulting columns to the output.
Interpolation procedures are handled somewhat differently for numeric and character string variables. For numeric variables supplied in varz, "aw" and/or "pw" weights are passed to the function specified in funz. If different sets of numeric variables are to be aggregated with different functions, both varz and funz should be specified as lists (see examples below).
For character string (and any other) variables supplied in char_varz, "aw" and/or "pw" weights are passed to the assignment rule(s) specified in char_assign. Note that the char_varz argument may include numerical variables, but varz cannot include character string variables.
Currently supported assignment rules for character strings (char_assign) include:
"biggest_overlap". For each variable in char_varz, the features in poly_to are assigned a single value from overlapping poly_from features, corresponding to the intersection with largest area and/or population weight.
"all_overlap". For each variable in char_varz, the features in poly_to are assigned all values from overlapping poly_from features, ranked by area and/or population weights (largest-to-smallest) of intersections.
It is possible to pass multiple arguments to char_assign (e.g. char_assign=c("biggest_overlap","all_overlap")), in which case the function will calculate both, and append the resulting columns to the output.
sf polygon object, with variables from poly_from interpolated to the geometries of poly_to.
# Interpolation of a single variable, with area weights data(clea_deu2009) data(hex_05_deu) out_1 <- poly2poly_ap(poly_from = clea_deu2009, poly_to = hex_05_deu, poly_to_id = "HEX_ID", varz = "to1" ) plot(out_1["to1_aw"]) # Interpolation of multiple variables, with area weights out_2 <- poly2poly_ap( poly_from = clea_deu2009, poly_to = hex_05_deu, poly_to_id = "HEX_ID", varz = list( c("to1","pvs1_margin"), c("vv1") ), pycno_varz = "vv1", funz = list( function(x,w){stats::weighted.mean(x,w)}, function(x,w){sum(x*w)} ), char_varz = c("incumb_pty_n","win1_pty_n") ) plot(out_2["vv1_aw"]) # Interpolation of a single variable, with population weights data(gpw4_deu2010) gpw4_deu2010 <- terra::rast(gpw4_deu2010) # unwrap PackedSpatRaster out_3 <- poly2poly_ap(poly_from = clea_deu2009, poly_to = hex_05_deu, poly_to_id = "HEX_ID", varz = "to1", methodz = "pw", pop_raster = gpw4_deu2010) plot(out_3["to1_pw"]) # Interpolation of a single variable, with area and population weights out_4 <- poly2poly_ap(poly_from = clea_deu2009, poly_to = hex_05_deu, poly_to_id = "HEX_ID", varz = "to1", methodz = c("aw","pw"), pop_raster = gpw4_deu2010) plot(out_4["to1_aw"])# Interpolation of a single variable, with area weights data(clea_deu2009) data(hex_05_deu) out_1 <- poly2poly_ap(poly_from = clea_deu2009, poly_to = hex_05_deu, poly_to_id = "HEX_ID", varz = "to1" ) plot(out_1["to1_aw"]) # Interpolation of multiple variables, with area weights out_2 <- poly2poly_ap( poly_from = clea_deu2009, poly_to = hex_05_deu, poly_to_id = "HEX_ID", varz = list( c("to1","pvs1_margin"), c("vv1") ), pycno_varz = "vv1", funz = list( function(x,w){stats::weighted.mean(x,w)}, function(x,w){sum(x*w)} ), char_varz = c("incumb_pty_n","win1_pty_n") ) plot(out_2["vv1_aw"]) # Interpolation of a single variable, with population weights data(gpw4_deu2010) gpw4_deu2010 <- terra::rast(gpw4_deu2010) # unwrap PackedSpatRaster out_3 <- poly2poly_ap(poly_from = clea_deu2009, poly_to = hex_05_deu, poly_to_id = "HEX_ID", varz = "to1", methodz = "pw", pop_raster = gpw4_deu2010) plot(out_3["to1_pw"]) # Interpolation of a single variable, with area and population weights out_4 <- poly2poly_ap(poly_from = clea_deu2009, poly_to = hex_05_deu, poly_to_id = "HEX_ID", varz = "to1", methodz = c("aw","pw"), pop_raster = gpw4_deu2010) plot(out_4["to1_aw"])
This function takes in an sf spatial object (polygon or point) and returns a regularly spaced RasterLayer. Reverse translation option allows users to create an sf polygon object from the regularly spaced RasterLayer. This function can also conver the sf object into a cartogram with a user-specified variable name.
sf2raster( polyz_from = NULL, pointz_from = NULL, input_variable = NULL, reverse = FALSE, poly_to = NULL, return_output = NULL, return_field = NULL, aggregate_function = list(function(x) mean(x, na.rm = TRUE)), reverse_function = list(function(x) mean(x, na.rm = TRUE)), grid_dim = c(1000, 1000), cartogram = FALSE, carto_var = NULL, message_out = TRUE, return_list = FALSE )sf2raster( polyz_from = NULL, pointz_from = NULL, input_variable = NULL, reverse = FALSE, poly_to = NULL, return_output = NULL, return_field = NULL, aggregate_function = list(function(x) mean(x, na.rm = TRUE)), reverse_function = list(function(x) mean(x, na.rm = TRUE)), grid_dim = c(1000, 1000), cartogram = FALSE, carto_var = NULL, message_out = TRUE, return_list = FALSE )
polyz_from |
Source polygon layer. |
pointz_from |
Source point layer. |
input_variable |
Name of input variable from source layer. Character string. |
reverse |
Reverse translation from raster layer to |
poly_to |
Destination polygon layer for reverse conversion. Must be specified if |
return_output |
Return output for reverse conversion. Must be specified if |
return_field |
Return field for reverse conversion. Must be specified if |
aggregate_function |
Aggregation function to be applied to variables specified in |
reverse_function |
Aggregation function for reverse conversion. Must be specified if |
grid_dim |
Dimensions of raster grid. Numerical vector of length 2 (number of rows, number of columns). Default is |
cartogram |
Cartogram transformation. Logical. Default is |
carto_var |
Input variable for cartogram transformation. Must be specified if |
message_out |
Print informational messages. Logical. Default is |
return_list |
Return full set of results, including input polygons, centroids and field raster. Default is |
If return_list=FALSE (default) and reverse=FALSE (default), returns RasterLayer object, with cell values corresponding to input_variable.
If return_list=TRUE and input layer is polygon, returns a list containing
"return_output". Output raster, with values corresponding to input_variable. RasterLayer object.
"return_centroid". Raster of centroids, with values corresponding to input_variable. RasterLayer object.
"poly_to". Source polygons, with columns corresponding to input_variable and auto-generated numerical ID Field. sf object.
"return_field". Output raster, with values corresponding to auto-generated numerical ID Field. RasterLayer object.
If return_list=TRUE and input layer is points, returns a list containing
"return_output". Output raster, with values corresponding to input_variable. RasterLayer object.
"return_point". Source points, with column corresponding to input_variable.
If reverse=TRUE, returns an sf polygon layer, with columns corresponding to input_variable and auto-generated numerical ID Field.
# Rasterization of polygon layer. data(clea_deu2009) out_1 <- sf2raster(polyz_from = utm_select(clea_deu2009), input_variable = "to1") terra::plot(out_1) # Rasterization of point layer data(clea_deu2009_pt) out_2 <- sf2raster(pointz_from = utm_select(clea_deu2009_pt), input_variable = "to1", grid_dim = c(25,25)) terra::plot(out_2) # Cartogram (vote turnout scaled by number of valid votes) out_3 <- sf2raster(polyz_from = utm_select(clea_deu2009), input_variable = "to1", cartogram = TRUE, carto_var = "vv1") terra::plot(out_3) # Polygonization of cartogram raster out_4a <- sf2raster(polyz_from = utm_select(clea_deu2009), input_variable = "to1", cartogram = TRUE, carto_var = "vv1", return_list = TRUE) out_4 <- sf2raster(reverse = TRUE, poly_to = out_4a$poly_to, return_output = out_4a$return_output, return_field = out_4a$return_field) terra::plot(out_4)# Rasterization of polygon layer. data(clea_deu2009) out_1 <- sf2raster(polyz_from = utm_select(clea_deu2009), input_variable = "to1") terra::plot(out_1) # Rasterization of point layer data(clea_deu2009_pt) out_2 <- sf2raster(pointz_from = utm_select(clea_deu2009_pt), input_variable = "to1", grid_dim = c(25,25)) terra::plot(out_2) # Cartogram (vote turnout scaled by number of valid votes) out_3 <- sf2raster(polyz_from = utm_select(clea_deu2009), input_variable = "to1", cartogram = TRUE, carto_var = "vv1") terra::plot(out_3) # Polygonization of cartogram raster out_4a <- sf2raster(polyz_from = utm_select(clea_deu2009), input_variable = "to1", cartogram = TRUE, carto_var = "vv1", return_list = TRUE) out_4 <- sf2raster(reverse = TRUE, poly_to = out_4a$poly_to, return_output = out_4a$return_output, return_field = out_4a$return_field) terra::plot(out_4)
Function to round numerical values with minimal information loss (e.g. to avoid "0.000" values in tables).
smart_round(x, rnd = 0, return_char = TRUE)smart_round(x, rnd = 0, return_char = TRUE)
x |
Vector of values to be rounded. Numeric. |
rnd |
Requested number of decimal places. Default is 0. Non-negative integer. |
return_char |
Return rounded values as character string? Default is TRUE. Logical. |
Rounds the values in its first argument to the specified number of decimal places (default 0). If brute-force rounding produces zero values (e.g. "0.00"), the number of decimal places is expanded to include the first significant digit.
If return_char=TRUE, returns a character string of same length as x. If return_char=FALSE, returns a numerical vector of same length as x.
# Round a vector of numbers, character string output (best for tables) out_1 <- smart_round(c(.0013,2.3,-1,pi),rnd=2) out_1 # Round a vector of numbers, numerical output out_2 <- smart_round(c(.0013,2.3,-1,pi),rnd=2,return_char=FALSE) out_2# Round a vector of numbers, character string output (best for tables) out_1 <- smart_round(c(.0013,2.3,-1,pi),rnd=2) out_1 # Round a vector of numbers, numerical output out_2 <- smart_round(c(.0013,2.3,-1,pi),rnd=2,return_char=FALSE) out_2
SUNGEOSub-National Geospatial Data Archive System: Geoprocessing Toolkit
See the README on [GitHub](https://github.com/zhukovyuri/SUNGEO#readme)
Function to update the coordinates of the bounding box of sf vector data objects (e.g. after cropping or subsetting).
update_bbox(sfobj)update_bbox(sfobj)
sfobj |
Layer to be updated. |
sf object, with corrected bounds.
# Update bbox for subset of sf object data(clea_deu2009) out_1 <- update_bbox(clea_deu2009[clea_deu2009$cst_n%in%c("Berlin"),]) out_1 # Bounding box of full dataset data.table::as.data.table(clea_deu2009)[,sf::st_bbox(geometry)] # Bounding box of subset (incorrect) data.table::as.data.table(clea_deu2009)[cst_n%in%c("Berlin"),sf::st_bbox(geometry)] # Corrected bounding box data.table::as.data.table(out_1)[,sf::st_bbox(geometry)]# Update bbox for subset of sf object data(clea_deu2009) out_1 <- update_bbox(clea_deu2009[clea_deu2009$cst_n%in%c("Berlin"),]) out_1 # Bounding box of full dataset data.table::as.data.table(clea_deu2009)[,sf::st_bbox(geometry)] # Bounding box of subset (incorrect) data.table::as.data.table(clea_deu2009)[cst_n%in%c("Berlin"),sf::st_bbox(geometry)] # Corrected bounding box data.table::as.data.table(out_1)[,sf::st_bbox(geometry)]
Function to automatically convert simple feature, spatial and raster objects with geographic coordinates (longitude, latitude / WGS 1984, EPSG:4326) to planar UTM coordinates. If the study region spans multiple UTM zones, defaults to Albers Equal Area.
utm_select(x, max_zones = 5, return_list = FALSE)utm_select(x, max_zones = 5, return_list = FALSE)
x |
Layer to be reprojected. |
max_zones |
Maximum number of UTM zones for single layer. Default is 5. Numeric. |
return_list |
Return list object instead of reprojected layer (see Details). Default is |
Optimal map projection for the object x is defined by matching its horizontal extent with that of the 60 UTM zones. If object spans multiple UTM zones, uses either the median zone (if number of zones is equal to or less than max_zones) or Albers Equal Area projection with median longitude as projection center (if number of zones is greater than max_zones).
Re-projected layer. sf or RasterLayer object, depending on input.
If return_list=TRUE, returns a list object containing
"x_out". The re-projected layer. sf or RasterLayer object, depending on input.
"proj4_best".proj4string of the projection. Character string.
# Find a planar projection for an unprojected (WSG 1984) hexagonal grid of Germany data(hex_05_deu) sf::st_crs(hex_05_deu) out_1 <- utm_select(hex_05_deu) sf::st_crs(out_1) # Find a planar projection for a raster data(gpw4_deu2010) gpw4_deu2010 <- terra::rast(gpw4_deu2010) # unwrap PackedSpatRaster sf::st_crs(gpw4_deu2010) out_2 <- utm_select(gpw4_deu2010) sf::st_crs(out_2)# Find a planar projection for an unprojected (WSG 1984) hexagonal grid of Germany data(hex_05_deu) sf::st_crs(hex_05_deu) out_1 <- utm_select(hex_05_deu) sf::st_crs(out_1) # Find a planar projection for a raster data(gpw4_deu2010) gpw4_deu2010 <- terra::rast(gpw4_deu2010) # unwrap PackedSpatRaster sf::st_crs(gpw4_deu2010) out_2 <- utm_select(gpw4_deu2010) sf::st_crs(out_2)