Modelling & prediction
Predicting values where nobody measured them, and deciding where things should go — with the limits of each model attached.
Topics in Modelling & prediction
3 topics, 55 guides. Each topic page groups the concepts, the how-to guides and the fixes for that subject.
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Spatial machine learning
Features, spatial cross-validation, prediction rasters and the area a model can honestly speak for.
4 concepts · 9 how-to · 4 fixes → -
Interpolation & surfaces
IDW, kriging, TIN and variograms — turning scattered samples into a surface, with its support attached.
5 concepts · 9 how-to · 4 fixes → -
Location & accessibility
Catchments, access to services, gravity models, site suitability and choosing where the next facility should go.
6 concepts · 9 how-to · 5 fixes →
Every guide in Modelling & prediction
Grouped by topic. A guide that spans two topics appears under both.
Spatial machine learning 17 guides
- How to Classify Land Cover from Satellite Imagery in Python classify land cover python
- Spatial Machine Learning Explained: Why Location Breaks the Usual Rules spatial machine learning explained
- Spatial Leakage Explained: Why Random Cross-Validation Lies spatial leakage explained
- Spatial Features Explained: Turning Geometry into Columns spatial features explained
- How to Sample Training Points from Rasters and Polygons sample training points python
- How to Build Spatial Features for a Machine Learning Model build spatial features python
- How to Do Spatial Cross-Validation in Python spatial cross validation python
- My Model Scores 0.95 in Testing and Fails in the Field model scores high fails in field
- How to Predict a Continuous Surface with Random Forest random forest spatial prediction python
- How to Turn Model Predictions Back into a Raster predictions to raster python
- My Prediction Raster Is Striped, Blocky or Full of NoData prediction raster artefacts
- How to Evaluate a Spatial Model Honestly evaluate spatial model python
- Extrapolation in Space Explained: The Area of Applicability area of applicability explained
- Feature Importance Says Coordinates Are the Best Predictor coordinates dominate feature importance
- How to Handle Class Imbalance in a Spatial Classifier class imbalance spatial classifier
- How to classify crop types from a satellite time series crop type classification python
- A crop classifier confuses two crops every year crop classifier confuses classes
Interpolation & surfaces 18 guides
- Spatial Interpolation Explained: Guessing Between the Samples spatial interpolation explained
- Sample Design Explained: Where to Measure sample design explained
- How to Interpolate Points to a Grid with IDW in Python idw interpolation python
- My IDW Surface Is Full of Bullseyes idw bullseyes
- The Variogram Explained: How Far Does a Measurement Reach variogram explained
- How to Fit a Variogram and Krige a Surface in Python kriging python
- My Kriging Fit Fails or Returns a Flat Surface kriging fit fails flat surface
- How to Build a TIN and Interpolate Elevations tin interpolation python
- IDW, Kriging, Splines or TIN? Choosing an Interpolator choosing an interpolator explained
- How to Cross-Validate an Interpolated Surface cross validate interpolation python
- How to Choose a Cell Size for an Interpolated Surface choose cell size interpolation
- My Interpolated Surface Extends Far Beyond the Data interpolation extends beyond data
- How to Interpolate to Polygons Instead of a Grid areal interpolation python
- Regridding Explained: Bilinear, Conservative and Nearest for Gridded Data regridding explained conservative bilinear
- How to Compare Census Years Across Changed Boundaries census crosswalk python
- How to Redistribute Population with Dasymetric Mapping dasymetric mapping python
- Fixing Census Totals That Do Not Add Up After Aggregation census totals don't match
- How to lay out a soil sampling grid in Python soil sampling grid python
Location & accessibility 20 guides
- Location Analysis Explained: Catchments, Accessibility and Site Selection location analysis gis explained
- Catchment Areas Explained: Buffers, Isochrones and Voronoi Compared catchment area gis
- How to Build Voronoi Service Areas Around Facilities in Python voronoi polygons python geopandas
- How to Count the Population Within Reach of Each Facility population within distance of facility python
- Accessibility Measures Explained: Nearest, Cumulative and Gravity spatial accessibility measures
- How to Measure Distance to the Nearest Facility for Every Home distance to nearest facility python
- How to Measure Access to Services with the Two-Step Floating Catchment Method 2sfca python
- Gravity and Huff Models Explained: Predicting Where People Go huff model explained
- How to Estimate Market Share with a Huff Model in Python huff model python
- Site Suitability Explained: Constraints, Factors and Weights site suitability analysis gis
- How to Run a Weighted Site Suitability Analysis in Python suitability analysis python raster
- Location-Allocation Explained: p-Median, Coverage and What Each Optimises location allocation p-median explained
- How to Choose Facility Locations with a p-Median Model in Python p-median python pulp
- How to Maximise Coverage with a Limited Number of Sites maximal covering location problem python
- How to Find the Areas Nobody Can Reach Within a Travel Time service area coverage gaps python
- Fixing Voronoi Polygons That Extend Forever or Miss the Study Area voronoi polygons infinite python
- Fixing a Location-Allocation Model That Never Solves pulp p-median slow
- Fixing Accessibility Scores That Are Wrong Near the Study Area Edge accessibility edge effect
- Fixing a Gravity Model That Sends Everyone to the Biggest Store huff model distance decay parameter
- Fixing a Suitability Map That Comes Out All One Value or All NoData suitability raster overlay nodata