week-04-notes
Spatial Data & GIS Operations in R
P1 Data Fundamentals
Vector Data Model
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.)
sf Package
Spatial data is just data.frame + geometry column
Common spatial data formats:
Shapefiles (.shp + supporting files) GeoJSON (.geojson) KML/KMZ (Google Earth) Database connections (PostGIS)
P2 Spatial Operations
Spatial Subsetting:Extract features based on spatial relationships
P3 Geometry Operations
Buffer Operations
Create zones around features
P4 Coordinate Reference Systems
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
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 pole
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 Widely used in national-scale thematic maps
Summary
Policy Analysis Workflow
1.Load data → Get spatial boundaries and attribute data 2.Check projections → Transform to appropriate CRS 3.Join datasets → Combine spatial and non-spatial data 4.Spatial operations → Buffers, intersections, distance calculations 5.Aggregation → Summarize across spatial units 6.Visualization → Maps and charts 7.Interpretation → Policy recommendations