Week 3: Vector and Raster Representations

PPOL 6805 / DSAN 6750: GIS for Spatial Data Science
Fall 2025

Class Sessions

Author

Affiliation

Jeff Jacobs

jj1088@georgetown.edu

Published

Wednesday, September 11, 2024

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Logistical Table-Setting

[Super quick] Syllabus dates, new Wednesday office hours https://jjacobs.me/meet
TA Intros!
Coding Workshops!
HW1!

TA Intros

(In alphabetical order by surname wohoo)

Christy Hsu
th1010@georgetown.edu

Coding workshop leader
Survived the fire and flames of this class in first semester after studying history 🤯

Yumi Li
xl794@georgetown.edu

Ombuds-person
Survived several Jeff classes + background in computer science (including Java!)

Relevant Word of the Day: 山 (Shān) = Mountain

Coding Workshops Starting This Friday!

Car Barn Room 230 (conference room across hallway from DSAN Suite 207), if you can make it in person!
Zoom Link if you can join remotely!
Video recording to be posted on Panopto (link TBD) once processed

HW1: Fun with Vectors and Rasters

Question 1 [Multiple Choice!]: Understanding the WKT geometries conceptually
Question 2: Using the WKT geometries to create Vectortown
Question 3: Using the raster format (today’s lecture) to create Gridtopia

How to Do Things with Geometries

HW1 \(\rightarrow\) HW2

Once you finish HW1, you’ll know how to create geometries with sf and terra
So now, what can you do with them?
For example, we’d like to be able to say things like:
- “The new lamppost cannot be placed at \((x, y)\), since there is already a building there!”
- “There are \(N_1\) lampposts in County 1, and \(N_2\) lampposts in County 2”
- “The average resident in Neighborhood A lives 2 km away from their nearest bus stop

First Things First: Loading and Saving

Note how there were no data files in HW1 😱
From HW2 onwards (and in your GIS life), we’ll:
- Download from e.g. city Open Data Portals: geo data files, but also loading on-the-fly (this week)
- Summarize/aggregate (this week and next week)
- Visualize findings (“Mapping Libraries” unit)

Vector Formats

Shapefiles (`.shp` et al.)

A shape“file” is actually (at least) three separate files bundled together:

Mandatory .shp: Containing feature geometries
Mandatory .shx: Positional indices
Mandatory .dbf: Data attributes
Optional .prj: Coordinate reference system
Optional .xml: Metadata

Shapefiles

Let’s see what’s inside the shapefile we first saw in Week 1, containing data on DC’s Census Tracts: Census Tracts in 2020

DC Census Tracts (with the Georgetown campus tract highlighted!) from OpenData.DC.gov

Shapefile Anatomy

GeoJSON / TopoJSON (`.geojson`)

JavaScript Object Notation: General cross-platform format
Useful when data is too complex for e.g. .csv
TopoJSON = Memory-efficient GeoJSON
Bonus: Inline preview on GitHub!

my_data.geojson

{
  "type": "FeatureCollection",
  "features": [
    {
      "type": "Feature",
      "geometry": {
        "type": "Polygon",
        "coordinates": [
          [
            [30, 20], [45, 40],
            [10, 40], [30, 20]
          ]
        ]
      },
      "properties": {
        "color": "green",
        "area": 3565747
      }
    },
    {
      "type": "Feature",
      "geometry": {
        "type": "Polygon",
        "coordinates": [
          [
            [15, 5], [40, 10],
            [10, 20], [5, 10], 
            [15, 5]
          ]
        ]
      },
      "properties": {
        "color": "red",
        "area": 3272386
      }
    }
  ]
}

GeoPackage (`.gpkg`)

Open-source (non-proprietary) data format standard

Raster Formats

GeoTIFF (.tif or .tiff)
- Based on TIFF format developed at NASA
NetCDF (.nc4)
- Used in earth sciences, as format for data sources measured and distributed multiple times per day over large full-country or full-continent areas.

Coordinate Reference Systems (CRS)

EPSG (European Petroleum Survey Group) Registry: Most common way to specify a CRS
- For example, 4326 is the EPSG code for the WGS84 coordinate system
PROJ: Rather than opaque numeric code like EPSG, uses plaintext “proj-strings” containing parameter info: datum, ellipsoid, projection, and units (e.g. meters). Example: PROJ4 code EPSG:4326 is represented as
```
+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs
```
WKT: Lengthy but human-readable descriptions

Geospatial Operations 1: Unary Operations

Getting the Geometries

Using rnaturalearth with mapview

Code

set.seed(6805)
library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ lubridate 1.9.4     ✔ tibble    3.3.0
✔ purrr     1.0.4     ✔ tidyr     1.3.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Code

library(sf)

Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE

Code

library(rnaturalearth)
library(mapview)
france_sf <- ne_countries(country = "France", scale = 50)
(france_map <- mapview(france_sf, label = "geounit", legend = FALSE))

Centroid of France

Code

france_cent_sf <- sf::st_centroid(france_sf)

Warning: st_centroid assumes attributes are constant over geometries

Code

france_map + mapview(france_cent_sf, label = "Centroid", legend = FALSE)

One We Already Saw: Union

Computing the union of all geometries in the sf via sf::st_union()

Code

library(leaflet.extras2)

Loading required package: leaflet

Code

africa_sf <- ne_countries(continent = "Africa", scale = 50)
africa_union_sf <- sf::st_union(africa_sf)
africa_map <- mapview(africa_sf, label="geounit", legend=FALSE)
africa_union_map <- mapview(africa_union_sf, label="st_union(africa)", legend=FALSE)
africa_map | africa_union_map

Helpful for Rasterizing: BBox

Code

africa_bbox_sf <- sf::st_bbox(africa_sf)
africa_bbox_map <- mapview(africa_bbox_sf, label="st_bbox(africa)", legend=FALSE)
africa_map | africa_bbox_map

Convex Hulls by Country

Code

africa_countries_cvx <- sf::st_convex_hull(africa_sf)
africa_countries_cvx_map <- mapview(africa_countries_cvx, label="geounit", legend=FALSE)
africa_map | africa_countries_cvx_map

Convex Hull of Continent

Use st_union() first:

Code

africa_cvx <- africa_sf |> st_union() |> st_convex_hull()
africa_cvx_map <- mapview(africa_cvx, label="geounit", legend=FALSE)
africa_map | africa_cvx_map

One We Already Saw: Centroids

Computing the centroid of all geometries in the sf via sf::st_centroid()

Code

africa_cents_sf <- sf::st_centroid(africa_sf)

Warning: st_centroid assumes attributes are constant over geometries

Code

africa_cents_map <- mapview(africa_cents_sf, label="geounit", legend=FALSE)
africa_map | africa_cents_map

Geospatial Operations 2: Binary Operations

Spatial Joins

Code

nc <- system.file("shape/nc.shp", package="sf") |>
  read_sf() |>
  st_transform('EPSG:2264')
gr <- st_sf(
         label = apply(expand.grid(1:10, LETTERS[10:1])[,2:1], 1, paste0, collapse = ""),
         geom = st_make_grid(nc))
gr$col <- sf.colors(10, categorical = TRUE, alpha = .3)
# cut, to verify that NA's work out:
gr <- gr[-(1:30),]
suppressWarnings(nc_j <- st_join(nc, gr, largest = TRUE))
par(mfrow = c(2,1), mar = rep(0,4))
plot(st_geometry(nc_j), border = 'grey')
plot(st_geometry(gr), add = TRUE, col = gr$col)
text(st_coordinates(st_centroid(st_geometry(gr))), labels = gr$label, cex = .85)
# the joined dataset:
plot(st_geometry(nc_j), border = 'grey', col = nc_j$col)
text(st_coordinates(st_centroid(st_geometry(nc_j))), labels = nc_j$label, cex = .7)
plot(st_geometry(gr), border = '#88ff88aa', add = TRUE)

Spatial Sampling

Code

# Sample random points
africa_points_list <- sf::st_sample(africa_union_sf, 10)
africa_points_sf <- sf::st_sf(africa_points_list)
africa_points_map <- mapview(africa_points_sf, label="Random Point", col.regions=cb_palette[1], legend=FALSE)

Warning in cbind(`Feature ID` = fid, mat): number of rows of result is not a
multiple of vector length (arg 1)

Code

africa_map + africa_points_map

The “Default” Predicate: `st_intersects`

Code

countries_w_points <- africa_sf[africa_points_sf,]
mapview(countries_w_points, label="geounit", legend=FALSE) + africa_points_map

Counting with `lengths()`

Code

country_inter <- sf::st_intersects(africa_sf, africa_points_sf)
# Computes point counts for each polygon
(num_intersections <- lengths(country_inter))

 [1] 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0
[39] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2

Code

africa_sf <- africa_sf |> mutate(
  num_points = num_intersections
) |> arrange(geounit)
africa_sf |> select(geounit, num_points) |> head()

geounit	num_points	geometry
Algeria	2	MULTIPOLYGON (((8.576563 36…
Angola	2	MULTIPOLYGON (((13.07275 -4…
Benin	0	MULTIPOLYGON (((1.622656 6….
Botswana	0	MULTIPOLYGON (((25.25879 -1…
Burkina Faso	0	MULTIPOLYGON (((0.9004883 1…
Burundi	0	MULTIPOLYGON (((30.55361 -2…

Plotting with `mapview`

Code

mapview(africa_sf, zcol="num_points")

Plotting with `ggplot2`

Since we’re starting to get into data attributes rather than geometric features, switching to ggplot2 is recommended!

Code

africa_sf |> ggplot(aes(fill=num_points)) +
  geom_sf() +
  theme_classic()

Getting Fancier…

To do fancier geospatial operations, we’ll need to start overthinking the different possible relationships between two or more geometries!
To this end: predicates

DE-9IM Strings

Code

library(sf)
polygon <- po <- st_polygon(list(rbind(c(0,0), c(1,0), c(1,1), c(0,1), c(0,0))))
p0 <- st_polygon(list(rbind(c(-1,-1), c(2,-1), c(2,2), c(-1,2), c(-1,-1))))
line <- li <- st_linestring(rbind(c(.5, -.5), c(.5, 0.5)))
s <- st_sfc(po, li)

par(mfrow = c(3,3))
par(mar = c(1,1,1,1))

# "1020F1102"
# 1: 1
plot(s, col = c(NA, 'darkgreen'), border = 'blue', main = expression(paste("I(pol)",intersect(),"I(line) = 1")))
lines(rbind(c(.5,0), c(.5,.495)), col = 'red', lwd = 2)
points(0.5, 0.5, pch = 1)

# 2: 0
plot(s, col = c(NA, 'darkgreen'), border = 'blue', main = expression(paste("I(pol)",intersect(),"B(line) = 0")))
points(0.5, 0.5, col = 'red', pch = 16)

# 3: 2
plot(s, col = c(NA, 'darkgreen'), border = 'blue', main = expression(paste("I(pol)",intersect(),"E(line) = 2")))
plot(po, col = '#ff8888', add = TRUE)
plot(s, col = c(NA, 'darkgreen'), border = 'blue', add = TRUE)

# 4: 0
plot(s, col = c(NA, 'darkgreen'), border = 'blue', main = expression(paste("B(pol)",intersect(),"I(line) = 0")))
points(.5, 0, col = 'red', pch = 16)

# 5: F
plot(s, col = c(NA, 'darkgreen'), border = 'blue', main = expression(paste("B(pol)",intersect(),"B(line) = F")))

# 6: 1
plot(s, col = c(NA, 'darkgreen'), border = 'blue', main = expression(paste("B(pol)",intersect(),"E(line) = 1")))
plot(po, border = 'red', col = NA, add = TRUE, lwd = 2)

# 7: 1
plot(s, col = c(NA, 'darkgreen'), border = 'blue', main = expression(paste("E(pol)",intersect(),"I(line) = 1")))
lines(rbind(c(.5, -.5), c(.5, 0)), col = 'red', lwd = 2)

# 8: 0
plot(s, col = c(NA, 'darkgreen'), border = 'blue', main = expression(paste("E(pol)",intersect(),"B(line) = 0")))
points(.5, -.5, col = 'red', pch = 16)

# 9: 2
plot(s, col = c(NA, 'darkgreen'), border = 'blue', main = expression(paste("E(pol)",intersect(),"E(line) = 2")))
plot(p0 / po, col = '#ff8888', add = TRUE)
plot(s, col = c(NA, 'darkgreen'), border = 'blue', add = TRUE)

The predicate equals corresponds to the DE-9IM string "T*F**FFF*". If any two geometries obey this relationship, they are (topologically) equal!

References

Rodrigue, Jean-Paul. 2016. The Geography of Transport Systems. Taylor & Francis.

Logistical Table-Setting

TA Intros

Coding Workshops Starting This Friday!

HW1: Fun with Vectors and Rasters

How to Do Things with Geometries

HW1 \(\rightarrow\) HW2

First Things First: Loading and Saving

Vector Formats

Shapefiles (.shp et al.)

Shapefiles

Shapefile Anatomy

GeoJSON / TopoJSON (.geojson)

GeoPackage (.gpkg)

Raster Formats

Coordinate Reference Systems (CRS)

Geospatial Operations 1: Unary Operations

Getting the Geometries

Centroid of France

One We Already Saw: Union

Helpful for Rasterizing: BBox

Convex Hulls by Country

Convex Hull of Continent

One We Already Saw: Centroids

Geospatial Operations 2: Binary Operations

Spatial Joins

Spatial Sampling

The “Default” Predicate: st_intersects

Counting with lengths()

Plotting with mapview

Plotting with ggplot2

Getting Fancier…

DE-9IM Strings

References

Shapefiles (`.shp` et al.)

GeoJSON / TopoJSON (`.geojson`)

GeoPackage (`.gpkg`)

The “Default” Predicate: `st_intersects`

Counting with `lengths()`

Plotting with `mapview`

Plotting with `ggplot2`