Spatial Interaction Modelling: A Manisfesto

Francisco Rowe, Robin Lovelace and Adam Dennett
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Francisco Rowe: University of Liverpool

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Abstract: Spatial interaction (SI) modelling is a core tool in spatial data modelling to predict spatial flows and understand their underpinning factors. SI modelling has been applied to provide data insights and support decision making in multiple settings, notably in transport, human mobility, migration and epidemiology. While considerable progress has been made on advancing the theoretical and methodological underpinnings of spatial interaction modelling, key challenges remain facilitate the application of SI models, extend existing modelling approaches, leverage the greater opportunities afforded by Big Data. We identify three key challenges: reproducibility, calibration and Big Data modelling. We propose a blueprint to tackle these challenges by identifying four areas of development: (1) to enable essential infrastructure to facilitate the training, calibration and reproducibility of SI models; (2) to embrace modelling frameworks to capture spatial, temporal and population heterogeneity; (3) to enhance statistical inference to accommodate Big Data analysis; and, (4) to integrate data science approaches to enhance. SI model-generated predictions and statistical inference.

Date: 2022-07-22
New Economics Papers: this item is included in nep-geo and nep-ure
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DOI: 10.31219/osf.io/xcdms

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