Dr James Williams.
Geospatial AI & Data Science
for Human Geography
Building AI systems that move beyond coordinates — capturing how people experience, narrate, and give meaning to geographic space.
AWS 
Anaconda 
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Android Studio 
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Arduino 
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AWK 
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C 
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GitHub 
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Zustand AWS Anaconda Android Android Studio Apache Arduino Astro AWK Bootstrap Bulma C Chart.js C++ C# CSS3 D3.js Django DuckDB Elasticsearch Express Expo FastAPI Firebase Flask Git GitHub Go Handlebars HTML5 JavaScript Jekyll JSON Keras LaTeX Matplotlib MATLAB MongoDB MySQL Next.js Node.js npm Pandas PHP Plotly PostgreSQL Python pytest R Raspberry Pi React Redux Redis Rust Sass Supabase TypeScript Vite YAML Zustand
Mapping the unmappable —
memory, place, and lived experience.
My research develops AI systems that move beyond coordinates and polygons to capture the subjective, narrative quality of geographic space. From platial clustering of urban functions to geospatial analysis of forced displacement, I combine cutting-edge machine learning with human-centred geographic methods.
Macro-Regional Spatial Patterns of Ambient Air Pollution and Avoidable Hospitalizations for Community-Acquired Pneumonia in Mexico (2013–2020)
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.
Geo-Narratives
Capturing stories, memories, and emotions attached to places using linked video and walking data.
Spatial AI
Machine learning for vague and fuzzy geographic boundaries and urban functional regions.
Interactive Systems
Tangible interfaces and participatory tools for community mapping and engagement.
Published software libraries
PlaceCrafter
A web-based geospatial framework for identifying and visualizing 'platial' functional regions by clustering OpenStreetMap Points of Interest.
Teaching
Module materials, lecture slides, and open resources for GIS and geospatial AI courses.
Leisure Walking Framework
A comprehensive, grounded-theory framework for curating personalised leisure walking experiences, creating a bridge between subjective human narratives and computational routing systems.
Read Case StudySelected Publications
Macro-Regional Spatial Patterns of Ambient Air Pollution and Avoidable Hospitalizations for Community-Acquired Pneumonia in Mexico (2013–2020)
C. Hernandez-Nava, M. Mata-Rivera, R. Zagal-Flores, J. 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.
Geospatial Experience-Oriented Notation (GEON): A Semantic Format for LLM-Native Spatial Intelligence
J. Williams
Existing geospatial data formats such as GeoJSON, Well-Known Text (WKT), and CityGML, are optimised for geometric computation and rendering. While effective for Geographic Information Systems (GIS), these approaches present limitations when used with Large Language Models (LLMs). For example, coordinate arrays carry no inherent semantic meaning, spatial relationships require computational geometry to extract, and the human experience of place is usually absent. This manuscript introduces foundational work on Geospatial Experience-Oriented Notation (GEON), a text-based format that bridges machine-optimised geospatial data and human-readable spatial descriptors. GEON encodes identity, geometry, purpose, experiential qualities, spatial relationships, temporal patterns, and data provenance in a readable and structured syntax designed for human comprehension and LLM reasoning. This manuscript presents the initial specification, reference implementations in Python, Rust, and JavaScript, and an empirical evaluation demonstrating how GEON achieves 20\% fewer tokens than equivalent GeoJSON files, while encoding 31\% more semantic facts per token. This manuscript explores the implementation and how LLMs reason about place-making, urban design interventions, and spatial intelligence tasks that existing formats struggle to support.
Diabetes Disparities in Mexico: A Spatio-Temporal and Marginalization Index Analysis
C. Hernandez-Nava, J. Williams, S. Flores-Hernandez, M. Mata-Rivera
Understanding the geospatial and temporal distribution of diabetes mellitus in Mexico can be an essential tool in supporting vulnerable populations and addressing health inequalities. This article presents a spatio-temporal investigation of patients aged 18 years and older with diabetes mellitus in Mexico, associated with geographical area and a temporal range from 2005 to 2022. This approach includes calculating diabetes-related hospitalizations and deaths and its association with the margination index segmented into eight geographical areas of Mexico. Furthermore, this research stratifies based upon age group and type of medical institute of the health services in Mexico. The main contribution of this research is to explore the relationship between diabetes-related hospitalizations, deaths, geographical area, age, sex, and margination index of populations to support preventive action. The results highlight that adults between the ages of 45 and 64 years old who live in areas with a high margination index have a greater likelihood of suffering complications related to diabetes. The age-adjusted rate of DRAH shows that the Peninsula has the highest values among geographical areas. Research will now continue to explore mapping interventions to specific states and external datasets, to further extrapolate the results of the analysis.