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Spatially Weighted Interaction Models (SWIM)

Author

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  • Maryam Kordi
  • A. Stewart Fotheringham

Abstract

One of the key concerns in spatial analysis and modeling is to study and analyze the processes that generate our observations of the real world. The typical global models employed to do this, however, fail to identify spatial variations in these processes because they assume that the processes being investigated are spatially stationary. In many real-life situations, spatial variations in relationships seem plausible and at least worth examining so that the assumption of global stationarity is, at best, unhelpful and, at worst, unrealistic. In contrast, local spatial models allow for potential variations in relationships over space leading to greater insights into the data-generating processes. In this study, a framework for localizing spatial interaction models, based on geographically weighted techniques, is developed. Using the framework, we construct a family of spatially weighted interaction models (SWIM) that can help in detecting, visualizing, and analyzing spatial nonstationarity in spatial interaction processes. Using custom-built algorithms, we apply both traditional interaction models and SWIM to a journey-to-work data set in Switzerland. The results of the model calibrations are explored using matrix visualizations, which suggest that SWIM provide useful information on the nature of spatially nonstationary processes leading to spatial patterns of flows.

Suggested Citation

  • Maryam Kordi & A. Stewart Fotheringham, 2016. "Spatially Weighted Interaction Models (SWIM)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 106(5), pages 990-1012, September.
    • Handle: RePEc:taf:raagxx:v:106:y:2016:i:5:p:990-1012
    DOI: 10.1080/24694452.2016.1191990
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    Cited by:

    1. Kar, Armita & Le, Huyen T. K. & Miller, Harvey J., 2021. "What is essential travel? Socio-economic differences in travel demand during the COVID-19 lockdown," OSF Preprints qtkhb, Center for Open Science.
    2. Felipa de Mello-Sampayo, 2020. "Spatial Interaction Model for Healthcare Accessibility: What Scale Has to Do with It," Sustainability, MDPI, vol. 12(10), pages 1-19, May.
    3. Yu, Haijing & Shen, Shaowei & Han, Lei & Ouyang, Jian, 2024. "Spatiotemporal heterogeneities in the impact of the digital economy on carbon emission transfers in China," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    4. Zhi Li & Jinsong Liu, 2023. "Evolution Process and Characteristics of Multifactor Flows in Rural Areas: A Case Study of Licheng Village in Hebei, China," Sustainability, MDPI, vol. 15(4), pages 1-16, February.
    5. Oshan, Taylor M., 2022. "Spatial Interaction Modeling," OSF Preprints m3ah8, Center for Open Science.
    6. Oshan, Taylor M., 2020. "The spatial structure debate in spatial interaction modeling: 50 years on," OSF Preprints 42vxn, Center for Open Science.
    7. Liu, Baoju & Tang, Zhongan & Deng, Min & Shi, Yan & He, Xiao & Huang, Bo, 2024. "Estimation of travel flux between urban blocks by combining spatio-temporal and purpose correlation," Journal of Transport Geography, Elsevier, vol. 116(C).
    8. Yang, Xiping & Fang, Zhixiang & Xu, Yang & Yin, Ling & Li, Junyi & Lu, Shiwei, 2019. "Spatial heterogeneity in spatial interaction of human movements—Insights from large-scale mobile positioning data," Journal of Transport Geography, Elsevier, vol. 78(C), pages 29-40.

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