Population Catchment Analysis | PPCA by Urban Geo Analytics

What is PPCA?

Population Potential on Catchment Areas (PPCA) is an open-source, Python-based protocol for estimating how many people are accessible on foot within specific proximity zones around streets. Designed for global use, PPCA requires only a bounding box as input and performs spatial population analysis based on freely available data. Moreover, PPCA follows a modular, four-step toolchain that integrates geospatial data from both OpenStreetMap (OSM) and Global Human Settlement (GHS) sources. As a result, PPCA is particularly suited for applications in urban planning, accessibility studies, and population exposure modeling.

Key Features

Works anywhere in the world with just a bounding box
Uses open data: OSM and GHS
Includes building classification, height estimation, and population modeling
Outputs geospatial layers ready for mapping and analysis
Built with Python, QGIS, and Google Earth Engine
Fully documented and open source

The 4-Step Workflow

Step 1: Data Acquisition & Filtering

PPCA starts by acquiring and preparing geospatial data for your selected area:

Downloads GHS raster data via Google Earth Engine
Converts raster to vector format using QGIS
Extracts and filters OSM data (buildings, streets, land use)
Computes morphometric indicators (e.g., area, perimeter, elongation)

Output: Cleaned and filtered GeoPackage files for buildings, streets, and population

Step 2: Building Classification

This module assigns each building a category:

Classifies buildings as Residential or Mixed-use (1), or Non-residential (2)
Fills missing values using a Decision Tree Classifier trained on morphometric features

Output: Classified building layer with a "type" attribute

Step 3: Floor Estimation

PPCA estimates floor counts and total floor area with a hybrid approach:

Uses available height attributes when present
Predicts missing values using a Decision Tree Classifier

Output: Building data enriched with estimated floors and floor area

Step 4: Population Estimation & Catchment Analysis

In the final step, PPCA distributes population and maps accessibility:

Allocates population values from GHS data to buildings using floor area as a weight
Aggregates values onto a pedestrian network
Defines catchment areas based on walking distances (e.g., 400m, 800m)
Computes population potential per segment and point

Output: Population maps at the building, point, and street levels

Optional: Slope & Cluster Analysis (Appendix A1)

For advanced insights, you can run an optional clustering script:

Calculates slope (rate of population change over distance)
Clusters observations into spatial profiles
Visualizes results using dendrograms and silhouette scores

Output: Catchment points labeled by cluster with slope metrics

Who is it for?

PPCA serves a diverse range of users — from urban planners and geographers to researchers focused on accessibility and mobility. NGOs, municipalities, and developers working on spatial tools and dashboards also benefit from its flexibility. Whether you’re analyzing a single neighborhood or comparing cities worldwide, you can integrate PPCA’s modular components directly into your existing workflows. Alternatively, you can run the full pipeline to conduct comprehensive, scalable population analyses. Designed for both operational and scientific applications, PPCA adapts easily to your goals.

Get Started

PPCA is available on GitHub:
👉 https://github.com/perezjoan/Population-Potential-on-Catchment-Area—PPCA-Worldwide

Run the scripts in a Python environment with QGIS or on Google Colab. Full documentation is included in each module’s notice.

Citations & Publications

If you use PPCA in your work, please cite:

Perez, J. & Fusco, G. (2025). Population potential on catchment area (PPCA): A Python-based tool for worldwide geospatial population analysis. SoftwareX, Volume 23, 102245.
DOI: 10.1016/j.softx.2025.102245

Read the publication

Perez, J. & Fusco, G. (2024). Potential of the 15-Minute Peripheral City: Identifying Main Streets and Population Within Walking Distance. In: O. Gervasi, B. Murgante, C. Garau, D. Taniar, A.M. Rocha & M.N. Faginas Lago (eds.), Computational Science and Its Applications – ICCSA 2024 Workshops. Lecture Notes in Computer Science, vol. 14817. Cham: Springer, pp. 50–60.
DOI: 10.1007/978-3-031-65238-7_4