Spatial Data Science · Urban Analytics · GIS
MSc Urban Analytics, University of Glasgow · BArch, Tecnológico de Monterrey
I use spatial data, Python and GIS to study urban and regional problems. My recent work has examined neighbourhood deprivation change, public transport reliability, urban environmental conditions and travel behaviour, using methods ranging from spatial statistics and GIS to machine learning.
Before specialising in urban analytics, I worked in data consultancy and business systems across retail, healthcare, manufacturing and environmental engineering.
Spatial & GIS: ArcGIS Pro · GeoPandas · PySAL · Shapely · QGIS (developing) Python & Data: Python · Pandas · scikit-learn · DuckDB · SQL Visualisation: Tableau · Power BI Workflow: Git · GitHub
Investigating whether neighbourhood deprivation change across Glasgow's 746 data zones can be predicted from socioeconomic, housing and built-environment characteristics. The project uses spatial statistics, machine learning and geographically separated model validation to examine how well predictive models generalise across the city.
Analysis of approximately 310,000 GTFS-Realtime vehicle-position records along Manchester's Oxford Road/Wilmslow Road corridor, examining how bus performance and reliability vary spatially along the route.
Graph-based modelling of PM2.5 air pollution across 25,053 London Output Areas using GraphSAGE, PyTorch Geometric and a KNN spatial graph constructed from neighbourhood relationships and census characteristics.