Location-aware statistics: hot spots, kriging, regression and GWR, point patterns and KDE, clustering and regionalisation, accessibility and location-allocation, accuracy and trend tests.
Build Voronoi (Thiessen) polygons in Python with shapely and GeoPandas, clipped to a real boundary. Compute Thiessen-weighted catchment rainfall for the River Eden from Environment Agency rain gauge readings, check the gauges, compare with the gauge average and IDW, then compare Voronoi 'nearest fire station' areas in Cumbria with drive-time service areas from OpenStreetMap.
Batch geocode all 192 open schools in Sheffield in Python with geopy and OpenStreetMap Nominatim (1 request per second, a custom user agent and an on-disk cache) and with postcodes.io bulk postcode lookups, reverse geocode a sample, and measure every method honestly against the Department for Education's own coordinates. Covers free text against structured queries, failure modes, cross-checking two geocoders, and the licensing of geocoded results.
A MovingPandas tutorial on one day of real AIS vessel tracks in Puget Sound: 277,574 GPS fixes from 703 vessels turned into trajectories, with speed and direction, outlier cleaning, stop detection at ferry terminals, splitting into 148 trips, Douglas-Peucker against time-aware generalisation, a flow map of 319 aggregated flows, and reading and writing GPX.
Build a walking cost surface from real slope (Copernicus GLO-30) and land cover (ESA WorldCover), compute the accumulated cost surface and the least-cost path with scikit-image's MCP_Geometric, add a direction-aware Dijkstra version with SciPy, and compare the routes with the straight line and the OpenStreetMap footpaths from Pen-y-Pass to the summit of Yr Wyddfa (Snowdon).
A free, phone-friendly check for any farm: whether the rains have started, when they usually start, rain and heat for the next 10 days and how green the crops are. Examples from Lilongwe, Malawi, and Gazipur, Bangladesh.
Five new free tools for herders, district offices and cooperatives: a pasture and water bulletin, a water point check, a weekly fire count, flood water near a village today and a rainfall insurance check. Real answers from Kenya and South Sudan.
Two free tools for any district: is this season's rain and crop growth better or worse than usual, and where did it flood and how many people live there? Worked examples from Marsabit, Kenya, in 2022 and Feni, Bangladesh, in 2024.
An introduction to the TechnicalGIS climate time-series tools: five open datasets, how to draw an area on the map, worked examples from London, north Evia, Nairobi and Las Vegas, the processing limits and the terms for reusing every output.
Cluster GPS and point-of-interest data in Python with DBSCAN and HDBSCAN: use the haversine metric on latitude and longitude or plain metres on a projected grid, choose eps with a k-distance plot, handle variable density with HDBSCAN, draw convex and concave hull polygons, treat noise properly, read silhouette and DBCV with care, and scale up with a BallTree. Uses 3,950 real Overture Maps restaurants, cafés and bars in Glasgow.
Build a small geodemographic classification of Cardiff's 1,115 Census 2021 Output Areas in Python: choose and transform variables from Nomis bulk tables, standardise them, check correlations, choose k for k-means with the elbow, silhouette and stability across seeds, profile and cautiously name the clusters with index scores and radar charts, compare the result with the official 2021/2 OAC, test the MAUP at LSOA level, and contrast it with contiguous regions from spopt's SKATER and max-p.
Fit geographically weighted regression (GWR) and multiscale GWR (MGWR) in Python with the mgwr package on real West Yorkshire house prices: choose the bandwidth by AICc, map local coefficients with a multiple-testing correction, read local R² and local collinearity, and compare against global OLS, spatial lag and spatial error models.
Make a kernel density heatmap from points in Python: compare Scott/Silverman, likelihood cross-validation and a fixed planning distance, Gaussian and Epanechnikov kernels and sample weights, export an edge-corrected GeoTIFF with rasterio, show it on a basemap, and map case-control relative risk on Camden's London plane trees.
Assess a real land cover map in Python: ESA WorldCover 2021 against 8,597 Eurostat LUCAS 2022 field survey points in Ireland. Build the confusion matrix, compute overall, user's and producer's accuracy, see why kappa is discouraged, and use the Olofsson et al. (2014) stratified estimators to get error-adjusted areas with 95% confidence intervals and to plan a new stratified random sample.
Solve location-allocation problems in Python with spopt and the CBC solver: the p-median, p-center and maximal covering location problem (MCLP). Choose sites for new urgent-care or mobile clinic bases among Cornwall's community buildings, on OSMnx drive times and Census 2021 population, compare the three answers on maps and charts, draw the trade-off curve as the number of sites grows, and see how problem size drives solve time.
Test whether points are clustered with point pattern analysis in Python: a quadrat chi-squared test, the Clark-Evans nearest-neighbour index and Ripley's G, F, K and L functions with Monte Carlo CSR envelopes, edge correction, a global MAD test and a random-labelling null, on 3,292 London plane trees in Camden.
Run a pixel-wise Mann-Kendall trend test and Theil-Sen slope on 24 years of MODIS NDVI in Python. Read MOD13A1 from the Microsoft Planetary Computer, mask it with the QA layer, aggregate to growing-season means, vectorise the tests with xarray.apply_ufunc (checked against pymannkendall), compare the seasonal and modified Mann-Kendall tests, control the false discovery rate across 229,229 pixels, and map greening and browning on China's Loess Plateau.
Measure spatial accessibility to GP practices in Python with the two-step floating catchment area (2SFCA) method and its distance-decay version (E2SFCA). Build OSMnx drive times for Cornwall from OpenStreetMap, use NHS GP workforce data and Census 2021 population, compare the PySAL access package with a vectorised NumPy version, and test the catchment threshold, the decay function and edge effects.
Fit spatial regression models in Python with PySAL's spreg on real West Yorkshire house prices: test the OLS residuals for spatial autocorrelation, read the Lagrange Multiplier diagnostics, fit spatial lag and spatial error models, compare them by AIC and log-likelihood, interpret direct, indirect and total impacts, and check how much the spatial weights matter.
A practical guide to Uber's H3 hexagonal grid in Python with the h3-py v4 API: index points, choose a resolution, fill polygons, count 541,081 Overture places across London, smooth with k-rings, do the same in DuckDB's h3 extension, and compare hexagons with square grids of equal area.
A cartopy tutorial for publication-quality maps in Python: choose a projection, draw a classified choropleth of Census 2021 population density with a proper legend, add a scale bar, a true-north arrow, an inset locator map and gridlines, style it for light and dark backgrounds, export at 300 dpi to PNG, PDF and SVG, and shade the Copernicus DEM for Greater Manchester.
Convert raster to vector in Python with rasterio.features.shapes on 10 m ESA WorldCover for Greater Manchester: polygonize water and built-up land, fix invalid polygons, simplify without gaps, rasterize ONS district boundaries for land-cover shares, then write and validate a Cloud Optimized GeoTIFF with rio-cogeo and measure HTTP range reads.
Estimate rooftop solar potential in Python for 2,148 buildings in Manchester: take a 1 m Environment Agency LiDAR DSM and Overture Maps footprints, compute roof slope and aspect, trace horizon angles for shading from neighbours and trees, build a sun map from a PVGIS typical year with pvlib, check it against pvlib's Perez model and PVGIS, then estimate kWp and kWh per year per roof and rank the best roofs.
A practical guide to spatial joins in GeoPandas with real Greater Manchester data: 13,187 NaPTAN bus stops, 1,386 OpenStreetMap schools, 215 ONS wards and 1,702 LSOAs. Covers point in polygon with sjoin, predicates, counts per polygon, how='left'/'inner'/'right', sjoin_nearest with max_distance, clip and overlay, and a timing comparison of a naive loop against the spatial index.
A practical guide to coordinate reference systems in Python with pyproj, GeoPandas and rasterio, tested on real Greater Manchester data. Covers EPSG codes, WGS 84 vs British National Grid (EPSG:27700), the always_xy axis-order trap, OSTN15 vs Helmert datum shifts checked against 13,187 NaPTAN bus stops, measured area and distance errors in the wrong CRS, and reprojecting a GeoDataFrame and a Copernicus DEM raster.
Run a viewshed analysis in Python on a real DEM: compute what one wind turbine can see with gdal.ViewshedGenerate, rebuild the same line-of-sight algorithm in numpy, correct for earth curvature and refraction, build a cumulative zone of theoretical visibility (ZTV) from 26 turbines, draw line-of-sight profiles, and measure how much trees and buildings in a LiDAR DSM shrink a bare-earth viewshed.
Calculate zonal statistics in Python with exactextract and rasterstats on real data: ONS 2021 LSOA boundaries for Cumbria with GHSL population, the Copernicus DEM and ESA WorldCover. Covers partial-pixel weighting vs pixel centroids, CRS alignment, categorical land-cover fractions, a speed benchmark, GeoPackage export and choropleth maps.
Read ERA5 climate reanalysis in Python with xarray from free, login-free cloud Zarr stores, clip it to the UK, compute area-weighted means and anomalies against 1991–2020, export a Cloud Optimised GeoTIFF and check it against Met Office stations.
Run a hot spot analysis in Python with PySAL: aggregate 2025 STATS19 road collisions in Birmingham to a hexagon grid, test for clustering with global Moran's I, map Getis-Ord Gi* hot spots and LISA clusters with permutation inference and FDR correction, and see how spatial weights, the areal unit and exposure change the answer.
A practical spatial interpolation tutorial in Python: download real 2025 NO₂ annual means from the UK AURN network, fit spherical, exponential and Gaussian variograms, run ordinary kriging with PyKrige, compare it with inverse distance weighting, score both with leave-one-out cross-validation, map the kriging variance and add Sentinel-5P satellite NO₂ as a covariate.
Benchmark point-in-polygon joins, buffers, overlays and dissolves on 200,000 points with GeoPandas and Shapely 2, see why spatial indexes give a 100x+ speed-up, and compare Shapefile, GeoPackage, FlatGeobuf and GeoParquet.