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Developing a Continuous Space Representation of a Simulated Population

Author

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  • John Cullinan

Abstract

Abstract Spatial microsimulation models typically match census of population data with survey data in order to simulate synthetic populations of individuals and households within small-scale geographic areas. For most spatial microsimulation applications this level of spatial precision is satisfactory. For others, more precise information on the location of simulated units may be required. To this end this paper develops a continuous space representation of a simulated population. It presents a statistical matching approach for assigning simulated households from a spatial microsimulation model to unique spatially-referenced residential locations. The allocation is based on a random assignment after splitting the simulated households into two groups: those predicted to reside in apartments and those predicted to reside in houses. The resulting ‘geohouseholds’ have a range of potential applications in economic and spatial analysis. Création d'une représentation spatiale continue d'une population stimulée Résumé Les modèles de microsimulation spatiale assortissent généralement les données de recensement de la population à des données de sondages, afin de simuler des populations synthétiques de particuliers et de foyers au sein de régions géographiques à échelle restreinte. Dans la plupart des applications de microsimulation spatiale, ce niveau de précision spatiale est satisfaisant. Dans d'autres, des informations plus précises sur l'emplacement d'unités simulées pourront s'avérer nécessaires. A cette fin, la présente communication crée une représentation spatiale continue d'une population simulée. Elle présente une méthode de correspondance statistique permettant d'affecter des foyers simulés, issus d'un modèle de microsimulation spatiale à des lieux résidentiels unique à référence spatiale. Cette allocation est basée sur une affectation aléatoire après la subdivision des foyers simulés en deux groupes : ceux dont on prévoit qu'ils résideront en appartement, et ceux dont on prévoit qu'ils résideront dans un maison. Les « géofoyers » résultants présentent toute une série d'applications potentielles pour les analyses économiques et spatiales. Desarrollo de una representación espacial continua de una población simulada Extracto Típicamente, los modelos de microsimulación espacial emparejan el censo de datos de la población con datos de encuestas, con objeto de simular poblaciones sintéticas de individuos y hogares dentro de áreas geográficas a pequeña escala. Para la mayoría de las aplicaciones de microsimulación espacial este nivel de precisión espacial es satisfactorio. Para otras, podría requerirse información más precisa sobre la ubicación de unidades simuladas. Con este objetivo, este trabajo desarrolla la representación espacial continua de una población simulada. Presenta un planteamiento de emparejamiento estadístico para asignar hogares simulados procedentes de un modelo de microsimulación espacial a ubicaciones residenciales únicas referenciadas espacialmente. La colocación se basa en una asignación al azar después de dividir los hogares simulados en dos grupos: los que se predice que residirán en apartamentos y los que se predice que residirán en casas. Los ‘geohogares’ resultantes ofrecen una gama de aplicaciones en potencia en el análisis económico y espacial.

Suggested Citation

  • John Cullinan, 2010. "Developing a Continuous Space Representation of a Simulated Population," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(3), pages 317-338.
  • Handle: RePEc:taf:specan:v:5:y:2010:i:3:p:317-338
    DOI: 10.1080/17421772.2010.493954
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    Cited by:

    1. Cullinan, John & Hynes, Stephen & O'Donoghue, Cathal, 2011. "Using spatial microsimulation to account for demographic and spatial factors in environmental benefit transfer," Ecological Economics, Elsevier, vol. 70(4), pages 813-824, February.

    More about this item

    Keywords

    Spatial microsimulation; statistical matching; spatial analysis; recreation demand modelling; R2; C63; Q26;

    JEL classification:

    • R2 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • Q26 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Recreational Aspects of Natural Resources

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