Computational Geography Laboratory

Series Overview

The Computational Geography Laboratory is an open-ended collection of applied models spanning the full breadth of quantitative geography. Where the foundational series (1–6) develop core mathematical tools through single-domain problems, the Laboratory applies those tools to more complex, cross-domain challenges — coastal processes, urban climate, water quality, geomorphology, land use dynamics, and geographic machine learning.

Models are numbered continuously from earlier series and organised into lettered clusters. Each cluster is self-contained: you can work through a cluster independently once you have the relevant prerequisites from the foundational series.

How to Use This Series

By cluster — Browse the list below. Each cluster covers a coherent topic and typically contains 2–4 models. Prerequisites are noted per cluster.

By series order — Work sequentially from model 56 onward for a comprehensive progression through advanced geographic modelling.

Clusters

Code	Topic	Models
T	Radar Remote Sensing	56–57
U	Geophysical Remote Sensing	58–59
AA	Weathering and Hillslope Processes	71–73
AB	Fluvial Geomorphology	74–75
AC	Coastal Processes	76–78
AJ	Urban Climate and Air Quality	95–96
AK	Transportation and Accessibility	97–98
AL	Land Use and Demographic Dynamics	99–101
AR	Reservoir Operations and Water Supply	102–103
AS	Water Quality and Eutrophication	104–106
AT	Groundwater Contamination and Remediation	107–108
Z	Foundations of Geographic Machine Learning	109–111
AU	Ensemble Methods and Data Assimilation	112–114