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GEPOC Parameters -- Open Source Parametrisation and Validation for Austria, Version 2.0

Martin Bicher, Maximilian Viehauser, Daniele Giannandrea, Hannah Kastinger, Dominik Brunmeir, Claire Rippinger, Christoph Urach, Niki Popper

TL;DR

This work provides an end-to-end, open-data-driven parametrisation for the GEPOC population model focused on Austria, detailing data harmonisation, disaggregation, and Farr-based probability derivation to yield ABM-ready parameters. It presents ABM, Geography, and IM parameter definitions, regionalisation schemes, and a rigorous validation framework that benchmarks synthetic populations, births, deaths, and migrations against national statistics and forecasts. The study demonstrates that the parametrised GEPOC ABM can closely track observed and forecasted demographic trajectories across multiple spatial scales, with explicit evaluation of internal migration models and sensitivity to time-step and aggregation choices. The work thus enables reproducible, policy-relevant population simulations for Austria, supporting scenario analysis and forecasting with transparent data provenance and validation metrics.

Abstract

GEPOC, short for Generic Population Concept, is a collection of models and methods for analysing population-level research questions. For the valid application of the models for a specific country or region, stable and reproducible data processes are necessary, which provide valid and ready-to-use model parameters. This work contains a complete description of the data-processing methods for computation of model parameters for Austria, based exclusively on freely and publicly accessible data. In addition to the description of the source data used, this includes all algorithms used for aggregation, disaggregation, fusion, cleansing or scaling of the data, as well as a description of the resulting parameter files. The document places particular emphasis on the computation of parameters for the most important GEPOC model, GEPOC ABM, a continuous-time agent-based population model. An extensive validation study using this particular model was made and is presented at the end of this work.

GEPOC Parameters -- Open Source Parametrisation and Validation for Austria, Version 2.0

TL;DR

This work provides an end-to-end, open-data-driven parametrisation for the GEPOC population model focused on Austria, detailing data harmonisation, disaggregation, and Farr-based probability derivation to yield ABM-ready parameters. It presents ABM, Geography, and IM parameter definitions, regionalisation schemes, and a rigorous validation framework that benchmarks synthetic populations, births, deaths, and migrations against national statistics and forecasts. The study demonstrates that the parametrised GEPOC ABM can closely track observed and forecasted demographic trajectories across multiple spatial scales, with explicit evaluation of internal migration models and sensitivity to time-step and aggregation choices. The work thus enables reproducible, policy-relevant population simulations for Austria, supporting scenario analysis and forecasting with transparent data provenance and validation metrics.

Abstract

GEPOC, short for Generic Population Concept, is a collection of models and methods for analysing population-level research questions. For the valid application of the models for a specific country or region, stable and reproducible data processes are necessary, which provide valid and ready-to-use model parameters. This work contains a complete description of the data-processing methods for computation of model parameters for Austria, based exclusively on freely and publicly accessible data. In addition to the description of the source data used, this includes all algorithms used for aggregation, disaggregation, fusion, cleansing or scaling of the data, as well as a description of the resulting parameter files. The document places particular emphasis on the computation of parameters for the most important GEPOC model, GEPOC ABM, a continuous-time agent-based population model. An extensive validation study using this particular model was made and is presented at the end of this work.

Paper Structure

This paper contains 95 sections, 4 theorems, 44 equations, 75 figures, 19 tables, 4 algorithms.

Table of Contents

  1. Introduction
  2. Model Parameters
  3. GEPOC ABM
  4. GEPOC ABM Geography
  5. GEPOC ABM IM
  6. Demographic Terminology and Relations
  7. Regional-Level and Identifiers
  8. Census
  9. Population P
  10. Births B, Deaths D, Emigrants E, Immgrants I.
  11. Births by Mother Bm.
  12. Internal Migrants M and Net Internal Migration DM.
  13. Aggregation
  14. Event Probabilities and Rates
  15. Event Probabilities Xp
  16. ...and 80 more sections

Key Result

Theorem 3.1

Let $1-\alpha(a)$ stand for the expected year-of-life spent by a person in its age cohort, given that the person is going to die, then is a good model for the probability of of death $D^p(y,r,s,a)$ as given by Definition def:probability. Hereby, $P_{avg}$ stands for the average population over the course of year $y$.

Figures (75)

  • Figure 1: Concept of the Advanced Migration Matcher Algorithm via strings connecting pins on a wall.
  • Figure 2: Overview of the time periods covered by the different data-sources. The darker the colour the higher the resolution of the corresponding data, red for spatial resolution, blue for age & sex resolution. Several data-sources are only used for a part of the time-frame which they cover, indicated by a green rectangle.
  • Figure 3: Fit of the Gaussian bell curve through the relative births per female inhabitant ($S_i^b/1000$)
  • Figure 4: Activation functions $f_1, f_2$ and $f_3$ used to parametrise the distribution.
  • Figure 5: Fit of the parametrised curve through the death probabilities given in the data. The fit is better the more recent the data which justifies its use for the forecast.
  • ...and 70 more figures

Theorems & Definitions (11)

  • Definition 3.1: regional-level, region-id
  • Definition 3.2: fine/coarse
  • Definition 3.3: area-status
  • Definition 3.4: probability of an event
  • Definition 3.5: average rate of an event
  • Definition 3.6: total fertility rate $TFR$ and mean age at childbearing $MAC$
  • Theorem 3.1: Farr's Death Rate Formula (modern version)
  • Corollary 3.1: Farr Formula (model parametrisation)
  • Definition 3.7: Life Expectancy Formula
  • Corollary 3.2: Overall Population Balance
  • ...and 1 more