Spatial Data & GIS Operations in R

Week 4: CPLN 5920-MUSA 5080

Allison Lassiter

2026-02-10

Today’s Agenda

What We’ll Cover

Part 1. Why Spatial Data Analysis Matters

Identifying patterns
Finding spatial biases
Creating spatially-informed policies

Part 2. Spatial Data Fundamentals

Vector data model and sf objects
Reading and inspecting spatial data
Basic spatial data operations

Part 3. Spatial Operations

Spatial subsetting and filtering
Spatial joins and relationships
Measuring distances and areas

Part 4. Geometry Operations

Buffers, intersections, and unions
Spatial aggregation

Part 5. Coordinate Systems

Understanding projections
Transforming between coordinate systems

Part 6. Putting It All Together

Policy analysis workflow

Part 1: Why Spatial Analysis Matters

From Last Week’s Census Data…

You’ve been working with:

County-level demographic data
Income, education, population patterns
Data quality and reliability analysis

Missing piece: WHERE are these patterns occurring?

This week: Add the spatial dimension to understand:

Geographic clustering of problems
Spatial relationships between communities
Access to services and resources

Real Policy Questions Need Spatial Answers

“Which communities have the lowest income?”

→ Are they clustered? Isolated? Near resources?

“Where should we locate a new health clinic?”

→ Optimize access for underserved populations

“How do school districts compare?”

→ Account for geographic boundaries and spillovers

“Is there environmental justice concern?”

→ Do pollution sources cluster near vulnerable communities?

Spatial Analysis and Algorithmic Bias

Geographic bias in algorithms:

Training data may under-represent certain areas
Spatial autocorrelation violates independence assumptions
Service delivery algorithms may reinforce geographic inequities

Examples:

Rideshare algorithms avoiding certain neighborhoods
Crime prediction concentrating enforcement in specific areas
Social service algorithms missing rural communities

Your role: Understand spatial patterns to design fairer systems

Part 2: Spatial Data Fundamentals

Vector Data Model

Real world → Simplified geometric representations

Three basic types:

Points → Locations (schools, hospitals, crime incidents)
Lines → Linear features (roads, rivers, transit routes)
Polygons → Areas (census tracts, neighborhoods, service areas)

Each feature has:

Geometry → Shape and location
Attributes → Data about that feature (population, income, etc.)

The sf Package: Simple Features for R

Why sf?

Modern replacement for older spatial packages
Integrates with tidyverse workflows
Follows international standards
Fast and reliable

Key principle: Spatial data is just data frame (or tibble) + geometry column

Loading Spatial Data

Common spatial data formats:

Shapefiles (.shp + supporting files)
GeoJSON (.geojson)
KML/KMZ (Google Earth)
Database connections (PostGIS)

Examples in corresponding code

Part 3: Spatial Operations

Spatial Subsetting

Extract features based on spatial relationships

examples in code

Key functions: st_filter(), st_intersects(), st_touches(), st_within()

Understanding the .predicate Parameter

The .predicate tells st_filter() what spatial relationship to look for:

Basic structure:
st_filter(data_to_filter, reference_geometry, .predicate = relationship)

Different questions need different relationships:

“Which counties border Allegheny?” → st_touches
“Which tracts are IN Allegheny?” → st_within
“Which tracts overlap a metro area?” → st_intersects

Default: If no .predicate specified, uses st_intersects

Why .predicate Matters: Visual Example

For county neighbors, why st_touches not st_intersects?

example in code

st_intersects includes the reference feature itself!

Spatial Relationships: When to Use Each

Predicate	Definition	Policy Use Case
`st_intersects()`	Any overlap at all	“Counties affected by flooding”
`st_touches()`	Share boundary, no interior overlap	“Neighboring counties”
`st_within()`	Completely inside	“Schools within district boundaries”
`st_contains()`	Completely contains	“Districts containing hospitals”
`st_overlaps()`	Partial overlap	“Overlapping service areas”
`st_disjoint()`	No spatial relationship	“Counties separate from urban areas”

Most common: st_intersects() (any overlap) and st_touches() (neighbors)

Predicate Examples with Code

Spatial Joins

Combine datasets based on spatial relationships

examples in codes

Distance Calculations

examples in codes

Area Calculations

examples in codes

Important: Units depend on coordinate reference system!

A note about that (.)

The dot (.) is a placeholder that represents the data being passed through the pipe (%>%).

pa_counties <- pa_counties %>% mutate( area_sqkm = as.numeric(st_area(.)) / 1000000 )

The . refers to pa_counties - the data frame being passed through the pipe. So this is equivalent to:

pa_counties <- pa_counties %>% mutate( area_sqkm = as.numeric(st_area(pa_counties)) / 1000000 )

Part 4: Geometry Operations

Buffer Operations

Create zones around features

`{r buffers} # 10km buffer around all hospitals hospital_buffers <- hospitals %>% st_buffer(dist = 10000) # 10,000 meters

Different buffer sizes by hospital type

hospital_buffers <- hospitals %>% mutate( buffer_size = case_when( type == “Major Medical Center” ~ 15000, type == “Community Hospital” ~ 10000, type == “Clinic” ~ 5000 ) ) %>% st_buffer(dist = .$buffer_size) `

Policy application: Service accessibility zones, environmental impact areas

Intersection Operations

Find overlapping areas

The Key Difference st_filter() with predicates: Selects complete features (keeps or removes entire rows)

st_intersection() and st_union(): Modifies geometries (creates new shapes)

When to Use Each

Use st_filter() when:

“Which census tracts touch hospital service areas?”
You want to select/identify features based on location
You need complete features with their original boundaries
You’re counting: “How many tracts are near hospitals?”

Use st_intersection() when:

“What is the area of overlap between tracts and service zones?”
You need to calculate areas, populations, or other measures within specific boundaries
You’re doing spatial overlay analysis
You need to clip data to a study area

Union Operations

Combine multiple features into one

Spatial Aggregation

Summarize data across spatial boundaries

Part 5: Coordinate Reference Systems

Why Projections Matter

The Earth is round, maps are flat

Problems:

Can’t preserve area, distance, and angles simultaneously
Different projections optimize different properties
Wrong projection → wrong analysis results!

Example: Measuring areas in latitude/longitude gives wrong answers

Geographic vs. Projected Coordinates

Geographic Coordinate Systems (GCS):

Latitude/longitude coordinates
Units: decimal degrees
Good for: Global datasets, web mapping
Bad for: Area/distance calculations

Projected Coordinate Systems (PCS):

X/Y coordinates on a flat plane
Units: meters, feet, etc.
Good for: Local analysis, accurate measurements
Bad for: Large areas, global datasets

Common Coordinate Reference Systems

WGS84 (EPSG:4326)

GPS standard, global coverage
Geographic system (lat/lon)
Good for web mapping, data sharing

Web Mercator (EPSG:3857)

Web mapping standard
Projected system
Heavily distorts areas near poles

State Plane / UTM zones

Local accuracy
Different zones for different regions
Optimized for specific geographic areas

Albers Equal Area

Preserves area
Good for demographic/statistical analysis

Checking and Setting CRS

To simply check current CRS st_crs(pa_counties)

To set CRS (ONLY if missing) pa_counties <- st_set_crs(pa_counties, 4326)

Transform to different CRS

Pennsylvania South State Plane (good for PA analysis)

pa_counties_projected <- pa_counties %>% st_transform(crs = 3365)

Transform to Albers Equal Area (good for area calculations) pa_counties_albers <- pa_counties %>% st_transform(crs = 5070) `

When to Transform

Transform when:

Calculating areas or distances
Creating buffers
Doing geometric operations
Working with local/regional data

Live Demo: Projection Effects

Part 6: Putting It All Together

Policy Analysis Workflow

Typical spatial analysis steps:

Load data → Get spatial boundaries and attribute data
Check projections → Transform to appropriate CRS
Join datasets → Combine spatial and non-spatial data
Spatial operations → Buffers, intersections, distance calculations
Aggregation → Summarize across spatial units
Visualization → Maps and charts
Interpretation → Policy recommendations

Example: Healthcare Access Analysis

Research question: Which communities have poor access to healthcare?

Step 1: Load data

Step 2: Transform to local projection

counties <- st_transform(counties, 3365) hospitals <- st_transform(hospitals, 3365)

Step 3: Calculate access

hospital_buffers <- hospitals %>% st_buffer(dist = 25000) # 25km access zone

county_access <- counties %>% mutate( has_hospital = st_intersects(., st_union(hospital_buffers), sparse = FALSE), distance_to_nearest = as.numeric(st_distance(st_centroid(.), hospitals)) )

Step 4: Join with demographic data and analyze