Dr James Williams

Ambient air pollution significantly contributes to respiratory illnesses, yet little is known about how industrial emissions are linked to preventable hospitalizations across atmospheric basins in middle-income countries. This study develops a basin-based geo-matics framework to examine the spatial and temporal relationship between industrial pollutants and age- and sex-adjusted avoidable hospitalizations for community-acquired pneumonia (PQI 11) in Mexico from 2013 to 2020. Using state-level data grouped into eight macro-regions, we combine bivariate choropleth maps, Pearson correlations, linear regression, and longitudinal time-series analysis to identify spatial clusters of high risk and to estimate regional sensitivities to changes in PM2.5, SO2, NOx, and volatile organic compound emissions. The findings reveal notable regional differences: northern border states and the Mexico City metropolitan basin form persistent high–high clusters where elevated emissions coincide with high PQI 11 rates, while coastal and peninsular regions show lower hospitalization burdens despite medium emission levels. Although national industrial PM2.5 emissions decreased over the study period, several macro-regions—particularly CDMX_Edomex, Centro, and Centro Norte—experienced significant increases in avoidable hospitalizations and decoupled emission–health patterns. Correlation matrices and regression slopes suggest that the strength and even direction of links between pollutants and PQI 11 vary across macro-regions, with emission-responsive patterns in Centro Norte and weak or inverse relationships in Peninsula and Pacifico Sur. These findings demonstrate that national averages obscure critical spatial disparities and highlight the value of basin-based geomatics approaches for regional air-quality governance, spatial decision support, and primary-care planning aimed at reducing preventable respiratory hospitalizations.

Introduction: Quantifying urban walkability at fine spatial resolution is essential for evidence-based active travel planning. Yet, existing indices typically rely on coarse administrative units or proprietary data, which impedes cross-city comparisons. This study presents the Hexagonal Walkability Surface (HWS) framework: a configuration-driven pipeline for computing multi-resolution walkability indices from geospatial data. Methods: HWS employs the H3 discrete global grid to tessellate city extents at four hierarchical resolutions (levels 8-11). Five pedestrian-environment features are extracted per hexagon: (1) footway and path density (pedestrian edge length per km²); (2) point-of-interest accessibility, combining in-cell counts with an inverse-distance-weighted proximity score; (3) land-use diversity as Shannon entropy of intersecting land-use classes; (4) greenspace proximity, integrating coverage proportion with distance decay; and (5) road-safety ratio of pedestrian-friendly to total edge length. Street network and land-use data are sourced from OpenStreetMap; place data is augmented with Overture Maps (2026-02-18) via DuckDB S3 queries. Features are min-max normalised and combined as a weighted composite score (0-100). Spatial autocorrelation (Getis-Ord Gi*) is used to identify clusters of walkability. Results: In Nottingham and Bristol, United Kingdom, the framework produces 1,178-57,700 and 2,581-126,518 hexagons per city, respectively, across resolutions 9-11. Across the resolutions, agreement is high (Spearman ρ = 0.963-0.971 between adjacent levels, all p < 0.001), confirming multi-scale consistency. At resolution 9 (roughly neighbourhood scale), intra-urban variation is observed in both cities (σ = 11.4 and 10.1 score points respectively), with city-centre hexagons clustering as hotspots and peripheral areas as coldspots. Gini coefficients indicate greater inequality in walkability in Nottingham (0.36) than in Bristol (0.26). Conclusions: HWS provides a reproducible, data-agnostic methodology extensible to any city with OSM coverage. The multi-resolution design could support both strategic (neighbourhood) and street-block planning decisions. By making fine-grained walkability assessment accessible to researchers and planners without proprietary data or bespoke infrastructure, HWS lowers the barrier to evidence-based active travel policy at the urban scale.

A unified, open point-of-interest (POI) dataset for Australia containing 1,735,980 POIs produced by the AnythingPOI pipeline, which fuses OpenStreetMap and Overture Maps Foundation data using H3-indexed spatial conflation, Jaro-Winkler name matching, and multi-signal confidence scoring. Source breakdown: OSM-only: 320,149 (18.4%) — from OpenStreetMap contributors (ODbL 1.0) Overture-only: 1,364,195 (78.6%) — from Overture Maps Foundation (CDLA-Permissive-2.0) Conflated (both sources matched): 51,636 (3.0%) Top categories: Professional & Business, Retail, Food & Beverage, Transportation, Healthcare. Full taxonomy: 18 Tier-1 categories, 196 Tier-2 subcategories. Contents: GeoParquet files (one per Tier-1 category), PMTiles v3 for interactive map visualisation, and coverage statistics CSVs. Each POI carries a confidence_score (0.01–0.99) reflecting the strength of the conflation evidence across spatial, name, website, phone, postcode, and Wikidata signals. Attribution: This dataset contains information from OpenStreetMap (© OpenStreetMap contributors, ODbL 1.0 — openstreetmap.org/copyright) and Overture Maps Foundation (CDLA-Permissive-2.0 — overturemaps.org). License: Open Database License (ODbL) 1.0. Any public use of this database or works produced from it must include the above attribution. Derivative databases must also be released under ODbL.

A unified, open point-of-interest (POI) dataset for Canada containing 5,565,256 POIs produced by the AnythingPOI pipeline, which fuses OpenStreetMap and Overture Maps Foundation data using H3-indexed spatial conflation, Jaro-Winkler name matching, and multi-signal confidence scoring. Source breakdown: OSM-only: 451,872 (8.1%) — from OpenStreetMap contributors (ODbL 1.0) Overture-only: 5,037,979 (90.5%) — from Overture Maps Foundation (CDLA-Permissive-2.0) Conflated (both sources matched): 75,405 (1.4%) Top categories: Professional & Business, Retail, Food & Beverage, Healthcare, Services. Full taxonomy: 18 Tier-1 categories, 196 Tier-2 subcategories. Contents: GeoParquet files (one per Tier-1 category), PMTiles v3 for interactive map visualisation, and coverage statistics CSVs. Each POI carries a confidence_score (0.01–0.99) reflecting the strength of the conflation evidence across spatial, name, website, phone, postcode, and Wikidata signals. Attribution: This dataset contains information from OpenStreetMap (© OpenStreetMap contributors, ODbL 1.0 — openstreetmap.org/copyright) and Overture Maps Foundation (CDLA-Permissive-2.0 — overturemaps.org). License: Open Database License (ODbL) 1.0. Any public use of this database or works produced from it must include the above attribution. Derivative databases must also be released under ODbL.

A unified, open point-of-interest (POI) dataset for Germany containing 6,763,796 POIs produced by the AnythingPOI pipeline, which fuses OpenStreetMap and Overture Maps Foundation data using H3-indexed spatial conflation, Jaro-Winkler name matching, and multi-signal confidence scoring. Source breakdown: OSM-only: 2,420,344 (35.8%) — from OpenStreetMap contributors (ODbL 1.0) Overture-only: 4,129,920 (61.1%) — from Overture Maps Foundation (CDLA-Permissive-2.0) Conflated (both sources matched): 213,532 (3.2%) Top categories: Professional & Business, Transportation, Retail, Food & Beverage, Other / Uncategorized. Full taxonomy: 18 Tier-1 categories, 196 Tier-2 subcategories. Contents: GeoParquet files (one per Tier-1 category), PMTiles v3 for interactive map visualisation, and coverage statistics CSVs. Each POI carries a confidence_score (0.01–0.99) reflecting the strength of the conflation evidence across spatial, name, website, phone, postcode, and Wikidata signals. Attribution: This dataset contains information from OpenStreetMap (© OpenStreetMap contributors, ODbL 1.0 — openstreetmap.org/copyright) and Overture Maps Foundation (CDLA-Permissive-2.0 — overturemaps.org). License: Open Database License (ODbL) 1.0. Any public use of this database or works produced from it must include the above attribution. Derivative databases must also be released under ODbL.