Location & Accessibility
Who can reach what — and where the next site should go.
Location analysis asks practical questions: which people does this clinic serve, who lives too far from a pharmacy, how much trade will a new store take, where should five depots go. Each answer depends on a model of catchment, distance and choice that is easy to run and easy to misread. These guides cover catchments and accessibility measures, gravity and Huff models, weighted site suitability, and p-median and coverage optimisation — measured on a real street network and a real population, with the edge effects and solver limits attached.
Each stage makes a stronger claim than the last. A catchment says who is nearby; an accessibility measure says how easily they get there; a gravity model says where they will choose to go; a suitability model says where the next one should be. The assumptions compound, so the last guide is about testing how much the answer depends on the weights.
Start here
The cornerstone guide — the whole workflow end to end. Everything after it goes deeper into one part of it.
Step 1 Location Analysis Explained: Catchments, Accessibility and Site Selection location analysis gis explained Begin the path →The full path, in order
The concepts underneath the work, then each task, then the errors that task produces. Work down, or jump to the step you need.
- Catchment Areas Explained: Buffers, Isochrones and Voronoi Compared catchment area gis 🧭 Concept
- How to Build Voronoi Service Areas Around Facilities in Python voronoi polygons python geopandas ⚙️ How-To
- How to Count the Population Within Reach of Each Facility population within distance of facility python ⚙️ How-To
- Accessibility Measures Explained: Nearest, Cumulative and Gravity spatial accessibility measures 🧭 Concept
- How to Measure Distance to the Nearest Facility for Every Home distance to nearest facility python ⚙️ How-To
- How to Measure Access to Services with the Two-Step Floating Catchment Method 2sfca python ⚙️ How-To
- Gravity and Huff Models Explained: Predicting Where People Go huff model explained 🧭 Concept
- How to Estimate Market Share with a Huff Model in Python huff model python ⚙️ How-To
- Site Suitability Explained: Constraints, Factors and Weights site suitability analysis gis 🧭 Concept
- How to Run a Weighted Site Suitability Analysis in Python suitability analysis python raster ⚙️ How-To
- Location-Allocation Explained: p-Median, Coverage and What Each Optimises location allocation p-median explained 🧭 Concept
- How to Choose Facility Locations with a p-Median Model in Python p-median python pulp ⚙️ How-To
- How to Maximise Coverage with a Limited Number of Sites maximal covering location problem python ⚙️ How-To
- How to Find the Areas Nobody Can Reach Within a Travel Time service area coverage gaps python ⚙️ How-To
- Fixing Voronoi Polygons That Extend Forever or Miss the Study Area voronoi polygons infinite python 🔧 Fix
- Fixing a Location-Allocation Model That Never Solves pulp p-median slow 🔧 Fix
- Fixing Accessibility Scores That Are Wrong Near the Study Area Edge accessibility edge effect 🔧 Fix
- Fixing a Gravity Model That Sends Everyone to the Biggest Store huff model largest store wins everywhere 🔧 Fix
- Fixing a Suitability Map That Comes Out All One Value or All NoData suitability raster overlay nodata 🔧 Fix
Topics this path covers
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