Spatial
K Arnold
11/27/2021
Objectives:
- Identify the basic structure of geospatial data
- Identify the basic types of geospatial visualizations
- Apply data wrangling to geospatial data
References:
Cholera
# dsn <- "data/SnowGIS_SHP/"
dsn <- fs::path(fs::path_wd(), "data", "SnowGIS_SHP")
if (!dir.exists(dsn))
usethis::use_zip("http://rtwilson.com/downloads/SnowGIS_SHP.zip", destdir = "data/", cleanup = TRUE)v Downloading from 'http://rtwilson.com/downloads/SnowGIS_SHP.zip'
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Downloaded: 6.56 MB (100%)v Download stored in 'data/SnowGIS_SHP.zip'v Unpacking ZIP file into 'SnowGIS_SHP/' (23 files extracted)v Deleting 'SnowGIS_SHP.zip'st_layers(dsn)Driver: ESRI Shapefile
Available layers:
layer_name geometry_type features fields
1 Cholera_Deaths Point 250 2
2 Pumps Point 8 1cholera_deaths <- st_read(dsn, layer = "Cholera_Deaths")Reading layer `Cholera_Deaths' from data source
`C:\projects\info601\info601\materials\13geospatial\lab\data\SnowGIS_SHP'
using driver `ESRI Shapefile'
Simple feature collection with 250 features and 2 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 529160.3 ymin: 180857.9 xmax: 529655.9 ymax: 181306.2
Projected CRS: OSGB 1936 / British National Gridcholera_pumps <- st_read(dsn, layer = "Pumps")Reading layer `Pumps' from data source
`C:\projects\info601\info601\materials\13geospatial\lab\data\SnowGIS_SHP'
using driver `ESRI Shapefile'
Simple feature collection with 8 features and 1 field
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 529183.7 ymin: 180660.5 xmax: 529748.9 ymax: 181193.7
Projected CRS: OSGB 1936 / British National Gridst_bbox(cholera_deaths) xmin ymin xmax ymax
529160.3 180857.9 529655.9 181306.2 What are those?
ggplot(cholera_deaths) +
annotation_map_tile(type = "osm", zoomin = 0) +
geom_sf(aes(size = Count), alpha = 0.7)Zoom: 17Fetching 16 missing tiles
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Oops this is incorrectly projected.
st_crs(cholera_deaths)Coordinate Reference System:
User input: OSGB 1936 / British National Grid
wkt:
PROJCRS["OSGB 1936 / British National Grid",
BASEGEOGCRS["OSGB 1936",
DATUM["OSGB 1936",
ELLIPSOID["Airy 1830",6377563.396,299.3249646,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4277]],
CONVERSION["British National Grid",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",49,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",-2,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",0.9996012717,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",400000,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",-100000,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["(E)",east,
ORDER[1],
LENGTHUNIT["metre",1]],
AXIS["(N)",north,
ORDER[2],
LENGTHUNIT["metre",1]],
USAGE[
SCOPE["Engineering survey, topographic mapping."],
AREA["United Kingdom (UK) - offshore to boundary of UKCS within 49°45'N to 61°N and 9°W to 2°E; onshore Great Britain (England, Wales and Scotland). Isle of Man onshore."],
BBOX[49.75,-9,61.01,2.01]],
ID["EPSG",27700]]Project this into the EPSG 4326 / WGS84 coordinate system (standard for GPS systems and Google Earth).
cholera_4326 <- cholera_deaths %>% st_transform(4326)
st_bbox(cholera_4326) xmin ymin xmax ymax
-0.1400738 51.5118557 -0.1329335 51.5158345 But that was still wrong: we need datum. The original still was missing some data.
st_crs(cholera_deaths)$proj4string[1] "+proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +ellps=airy +units=m +no_defs"st_crs(27700)$proj4string[1] "+proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +ellps=airy +units=m +no_defs"cholera_latlong <- cholera_deaths %>%
st_set_crs(27700) %>%
st_transform(4326)
pumps_latlong <- cholera_pumps %>%
st_set_crs(27700) %>%
st_transform(4326)ggplot(cholera_latlong) +
#coord_sf(lims_method = "geometry_bbox") +
annotation_map_tile(type = "osm", zoomin = 0) +
geom_sf(aes(size = Count), alpha = 0.7) +
geom_sf(data = pumps_latlong, size = 3, color = "red")Zoom: 17Fetching 9 missing tiles
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Mapview just takes care of all of this (but it’s not as clear how to control scales etc.)
mapview(cholera_pumps, col.regions = "red", homebutton = FALSE) +
mapview(cholera_deaths, cex = 'Count', homebutton = TRUE)NC gerrymandering
if (!nzchar(system.file(package = "fec12")))
remotes::install_github("baumer-lab/fec12")
library(fec12)district_elections <- results_house %>%
mutate(district = parse_number(district_id)) %>%
group_by(state, district) %>%
summarize(
N = n(),
total_votes = sum(general_votes, na.rm = TRUE),
d_votes = sum(ifelse(party == "D", general_votes, 0), na.rm = TRUE),
r_votes = sum(ifelse(party == "R", general_votes, 0), na.rm = TRUE)
) %>%
mutate(
other_votes = total_votes - d_votes - r_votes,
r_prop = r_votes / total_votes,
d_prop = d_votes / total_votes,
winner = ifelse(r_votes > d_votes, "Republican", "Democrat")
) %>%
ungroup()`summarise()` has grouped output by 'state'. You can override using the `.groups` argument.nc_results <- district_elections %>%
filter(state == "NC")
nc_results %>%
select(district, r_prop, winner) %>%
arrange(desc(r_prop))# A tibble: 13 x 3
district r_prop winner
<dbl> <dbl> <chr>
1 3 0.631 Republican
2 6 0.609 Republican
3 5 0.575 Republican
4 11 0.574 Republican
5 10 0.570 Republican
6 13 0.568 Republican
7 2 0.559 Republican
8 8 0.532 Republican
9 9 0.518 Republican
10 7 0.499 Democrat
11 4 0.255 Democrat
12 1 0.229 Democrat
13 12 0.204 Democrat results_house# A tibble: 2,343 x 13
state district_id cand_id incumbent party primary_votes primary_percent
<chr> <chr> <chr> <lgl> <chr> <dbl> <dbl>
1 AL 01 H2AL01077 TRUE R 48702 0.555
2 AL 01 H2AL01176 FALSE R 21308 0.243
3 AL 01 H2AL01184 FALSE R 13809 0.158
4 AL 01 H0AL01030 FALSE R 3854 0.0440
5 AL 02 H0AL02087 TRUE R NA NA
6 AL 02 H2AL02141 FALSE D NA NA
7 AL 03 H2AL03032 TRUE R NA NA
8 AL 03 H2AL03099 FALSE D NA NA
9 AL 04 H6AL04098 TRUE R NA NA
10 AL 04 H2AL04055 FALSE D 10971 0.514
# ... with 2,333 more rows, and 6 more variables: runoff_votes <dbl>,
# runoff_percent <dbl>, general_votes <dbl>, general_percent <dbl>,
# won <lgl>, footnotes <chr>Data source: https://cdmaps.polisci.ucla.edu/
# src <- "http://cdmaps.polisci.ucla.edu/shp/districts113.zip"
# dsn_districts <- "data/districtShapes/"
dsn_districts <- fs::path(fs::path_wd(), "data", "districtShapes")
#st_layers(dsn_districts)
districts <- st_read(dsn_districts, layer = "districts113") %>%
mutate(DISTRICT = parse_number(as.character(DISTRICT))) %>%
janitor::clean_names()Reading layer `districts113' from data source
`C:\projects\info601\info601\materials\13geospatial\lab\data\districtShapes'
using driver `ESRI Shapefile'
Simple feature collection with 436 features and 15 fields (with 1 geometry empty)
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -179.1473 ymin: 18.91383 xmax: 179.7785 ymax: 71.35256
Geodetic CRS: NAD83districts$statename %>% unique() [1] "Louisiana" "Maine" "Maryland"
[4] "Massachusetts" "Michigan" "Minnesota"
[7] "Mississippi" "Missouri" "Montana"
[10] "Nebraska" "Nevada" "New Hampshire"
[13] "New Jersey" "Oklahoma" "Oregon"
[16] "Pennsylvania" "Rhode Island" "South Carolina"
[19] "South Dakota" "Tennessee" "Texas"
[22] "New York" "North Carolina" "Alabama"
[25] "Arizona" "Arkansas" "California"
[28] "Colorado" "Connecticut" "Delaware"
[31] "District Of Columbia" "Florida" "Georgia"
[34] "Hawaii" "Idaho" "Illinois"
[37] "Indiana" "Iowa" "Kansas"
[40] "Kentucky" "New Mexico" "North Dakota"
[43] "Ohio" "Utah" "Vermont"
[46] "Virginia" "Washington" "West Virginia"
[49] "Wisconsin" "Wyoming" "Alaska" nc_shp <- districts %>%
filter(statename == "North Carolina")
mi_shp <- districts %>% filter(statename == "Michigan")mapview(mi_shp)mi_shpSimple feature collection with 14 features and 15 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -90.41814 ymin: 41.69612 xmax: -82.4186 ymax: 48.26263
Geodetic CRS: NAD83
First 10 features:
statename id district startcong endcong districtsi county page
1 Michigan 026113114001 1 113 114 <NA> <NA> <NA>
2 Michigan 026113114002 2 113 114 <NA> <NA> <NA>
3 Michigan 026113114003 3 113 114 <NA> <NA> <NA>
4 Michigan 026113114004 4 113 114 <NA> <NA> <NA>
5 Michigan 026113114005 5 113 114 <NA> <NA> <NA>
6 Michigan 026113114006 6 113 114 <NA> <NA> <NA>
7 Michigan 026113114007 7 113 114 <NA> <NA> <NA>
8 Michigan 026113114008 8 113 114 <NA> <NA> <NA>
9 Michigan 026113114009 9 113 114 <NA> <NA> <NA>
10 Michigan 026113114010 10 113 114 <NA> <NA> <NA>
law note bestdec finalnote rnote
1 <NA> <NA> <NA> {"From US Census website"} <NA>
2 <NA> <NA> <NA> {"From US Census website"} <NA>
3 <NA> <NA> <NA> {"From US Census website"} <NA>
4 <NA> <NA> <NA> {"From US Census website"} <NA>
5 <NA> <NA> <NA> {"From US Census website"} <NA>
6 <NA> <NA> <NA> {"From US Census website"} <NA>
7 <NA> <NA> <NA> {"From US Census website"} <NA>
8 <NA> <NA> <NA> {"From US Census website"} <NA>
9 <NA> <NA> <NA> {"From US Census website"} <NA>
10 <NA> <NA> <NA> {"From US Census website"} <NA>
lastchange fromcounty geometry
1 2016-05-29 16:44:10.857626 F MULTIPOLYGON (((-84.26702 4...
2 2016-05-29 16:44:10.857626 F MULTIPOLYGON (((-86.43104 4...
3 2016-05-29 16:44:10.857626 F MULTIPOLYGON (((-85.79066 4...
4 2016-05-29 16:44:10.857626 F MULTIPOLYGON (((-85.82145 4...
5 2016-05-29 16:44:10.857626 F MULTIPOLYGON (((-84.16806 4...
6 2016-05-29 16:44:10.857626 F MULTIPOLYGON (((-86.27396 4...
7 2016-05-29 16:44:10.857626 F MULTIPOLYGON (((-85.29364 4...
8 2016-05-29 16:44:10.857626 F MULTIPOLYGON (((-84.60314 4...
9 2016-05-29 16:44:10.857626 F MULTIPOLYGON (((-83.32514 4...
10 2016-05-29 16:44:10.857626 F MULTIPOLYGON (((-82.6769 42...mi_shp %>%
inner_join(district_elections %>% filter(state == "MI")) %>%
mapview(zcol = "r_prop")Joining, by = "district"mapview(nc_shp)