Python for GIS Course

Lesson 1 • Setting Up the Python GIS Environment

Install and configure Python, conda environments, and key GIS libraries. Establishes the technical foundation every subsequent chapter depends on.

Lesson 2 • Collections and Control Flow

Teaches lists, dictionaries, loops, and conditionals for iterating over spatial datasets. Directly enables batch processing of geographic features.

Lesson 3 • Functions and Modules

Defines reusable functions and imports external modules to organize GIS code. Prepares students to structure multi-step spatial analysis scripts.

Lesson 4 • File I/O and Error Handling

Reads and writes text and CSV files and handles runtime errors gracefully. Enables robust scripts that process real-world, imperfect spatial data.

Lesson 1 • Multi-Geometry and Collections

Handles MultiPoint, MultiLineString, MultiPolygon, and GeometryCollection types. Prepares students for real datasets where features have complex, multi-part shapes.

Lesson 2 • Geometric Measurements and Predicates

Computes area, length, distance, and spatial relationships between geometries. Connects geometric math to real GIS analysis questions.

Lesson 3 • Geometry Primitives with Shapely

Creates Point, LineString, and Polygon objects and accesses their properties. Introduces the geometry model underlying all vector GIS operations.

Lesson 1 • Working with GeoJSON and KML

Parses and generates GeoJSON and KML formats for web and desktop GIS exchange. Broadens format fluency beyond binary formats.

Lesson 2 • Raster Data with rasterio

Reads raster bands, profiles, and transforms and writes processed arrays to disk. Connects NumPy array operations to georeferenced raster data.

Lesson 3 • Spatial Databases and OGR

Connects to PostGIS and SpatiaLite databases and reads layers via OGR. Enables workflows that source data from enterprise spatial databases.

Lesson 4 • Vector Data with Fiona

Opens shapefiles and GeoJSON files, reads features, and writes new datasets. Establishes the read/write pattern used throughout vector workflows.

Lesson 1 • Attribute Queries and Data Cleaning

Filters rows, handles missing values, and renames and recasts attribute columns. Prepares attribute data for reliable spatial analysis.

Lesson 2 • Proximity and Distance Analysis

Computes buffers, nearest-neighbor distances, and distance matrices between feature sets. Enables site selection, service-area, and accessibility analyses.

Lesson 3 • GeoDataFrame Fundamentals

Creates GeoDataFrames from files, dictionaries, and databases and inspects their structure. Establishes the primary data container for all GeoPandas workflows.

Lesson 4 • Geoprocessing Workflows with GeoPandas

Chains multiple GeoPandas operations into reproducible, end-to-end geoprocessing scripts. Demonstrates professional workflow design and output documentation.

Lesson 1 • Terrain Analysis from DEMs

Derives slope, aspect, and hillshade from digital elevation models using array gradients. Builds foundational terrain products used in hydrology and land analysis.

Lesson 2 • Raster-Vector Integration

Clips rasters to vector boundaries and extracts pixel values at point locations. Bridges raster and vector workflows for combined analysis.

Lesson 3 • Raster Resampling and Reprojection

Resamples rasters to new resolutions and reprojects them to target CRS using rasterio. Ensures raster alignment for multi-source analysis.

Lesson 4 • NumPy Arrays for Raster Data

Loads raster bands into NumPy arrays and applies element-wise math and masking. Connects array computing to spatial raster analysis.

Lesson 1 • Interactive Maps with Folium

Builds interactive Leaflet maps in Python using Folium with popups and layer controls. Enables shareable web-based spatial outputs.

Lesson 2 • Raster Visualization and Styling

Renders single-band and multi-band rasters with custom color maps and stretch options. Produces clear visual representations of elevation, imagery, and classified data.

Lesson 3 • Interactive Visualization with Plotly and Kepler.gl

Creates animated and 3D spatial charts with Plotly and large-scale maps with Kepler.gl. Extends visualization to exploratory and presentation contexts.

Lesson 4 • Thematic and Choropleth Mapping

Applies classification schemes and color ramps to create choropleth and proportional symbol maps. Communicates spatial patterns in attribute data effectively.

Lesson 1 • Spatial Interpolation Methods

Interpolates continuous surfaces from point samples using IDW and kriging. Produces estimated raster surfaces from sparse measurement data.

Lesson 2 • Exploratory Spatial Data Analysis

Computes spatial autocorrelation, Moran's I, and LISA statistics using PySAL. Identifies clustering and dispersion patterns before deeper analysis.

Lesson 3 • Point Pattern Analysis

Analyzes point distributions using kernel density estimation and nearest-neighbor statistics. Detects spatial clustering in event and incident datasets.

Lesson 4 • Network Analysis with OSMnx

Downloads street networks, computes shortest paths, and calculates centrality metrics. Enables routing and accessibility analysis on real road networks.

Lesson 1 • Scheduling and Monitoring Pipelines

Schedules recurring spatial data jobs with cron and monitors execution with logging. Delivers operational pipelines that run without manual intervention.

Lesson 2 • Workflow Orchestration with Snakemake

Defines reproducible multi-step pipelines as Snakemake rules with dependency tracking. Ensures that only outdated steps re-run when inputs change.

Lesson 3 • Batch Processing Spatial Data

Processes entire directories of spatial files using loops and multiprocessing. Scales single-file workflows to large dataset collections efficiently.

Lesson 4 • Testing and Validating GIS Workflows

Writes unit tests for spatial functions and validates output data quality automatically. Prevents silent errors from propagating through production pipelines.