COMPASS – An open-source software package for creating multi-level representative nested synthetic populations for small areas
Simulation models, such as agent-based or microsimulation models capturing urban contexts, increasingly draw on spatially explicit, attribute-rich synthetic population datasets as real-world data inputs. Despite their growing relevance, the creation of such datasets via simulated annealing still faces significant limitations. Addressing these limitations, we present the open-source software package Combinatorial Optimisation for Microdata Population Assembly and Spatial Synthesis (COMPASS) . The algorithm implemented in COMPASS enables: aggregate-level representativeness at different levels of counts (e.g., households and individuals); the processing of nested input data (e.g., retaining survey household and kinship structures); and systematic monitoring of uncertainty arising at creation stage. We provide COMPASS as a compiled multi-platform software package, suitable for reproducible workflows via R and Python. This paper introduces COMPASS and illustrates one potential workflow using open access data created to reflect an artificial population – transferable to many national contexts.
Authors
- Andreas Höhn (ORCID: https://orcid.org/0000-0002-7170-1205)
- Corinna Elsenbroich (ORCID: https://orcid.org/0000-0003-1153-4326)
- Nik Lomax (ORCID: https://orcid.org/0000-0001-9504-7570)
- Petra Sylvia Meier (ORCID: https://orcid.org/0000-0001-5354-1933)
- Ricardo Colasanti
- Hugh Rice
- Alison Heppenstall
Institutions
- University of Leeds (GB)
- The Alan Turing Institute (GB)
- University of Glasgow (GB)
Publication Details
- Journal
- Environment and Planning B Urban Analytics and City Science
- Published
- 2026-10-07
- DOI
- https://doi.org/10.1177/23998083261484652
- Primary Topic
- demographic modeling and climate adaptation
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- UK Research and Innovation
- National Institute for Health and Care Research
- Economic and Social Research Council
- Public Health Research Programme