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Environment and Planning B Urban Analytics and City Science· 2026Q1

COMPASS – An open-source software package for creating multi-level representative nested synthetic populations for small areas

Andreas Höhn, Hugh Rice, Ricardo Colasanti, Alison Heppenstall et al.

Short summary

The open-source software package COMPASS uses combinatorial optimization to generate multi-level representative synthetic populations for small areas, overcoming limitations of previous simulated annealing methods.

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Key points

  • Introduces COMPASS, an open-source software for creating synthetic populations.
  • Ensures aggregate-level representativeness at multiple count levels.
  • Retains nested data structures (e.g., household, kinship) from input data.
  • Monitors uncertainty during synthetic population creation.
  • Provides multi-platform executables compatible with R and Python for reproducible workflows.

AI-generated from the title and abstract; the full text is not read.

Abstract

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.

The authors' abstract, as published at the source. Environment and Planning B Urban Analytics and City Science, 2026 · DOI ↗

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Field: Management Science and Operations Research

Management Science and Operations ResearchDecision Sciences