IDEAS home Printed from https://ideas.repec.org/a/kap/jgeosy/v27y2025i1d10.1007_s10109-024-00453-0.html
   My bibliography  Save this article

A scale-adaptive estimation for mixed geographically and temporally weighted regression models

Author

Listed:
  • Zhimin Hong

    (Inner Mongolia University of Technology
    Analysis Theory for Life Data and Neural Network Modeling)

  • Zhiwen Wang

    (Inner Mongolia University of Technology)

  • Huhu Wang

    (Inner Mongolia Autonomous Region Center for Disease Control and Prevention)

  • Ruoxuan Wang

    (Inner Mongolia University of Technology)

Abstract

Mixed geographically and temporally weighted regression (GTWR) models, a combination of linear and spatiotemporally varying coefficient models, have been demonstrated as an effective tool for spatiotemporal data analysis under global homogeneity and spatiotemporal heterogeneity. Simultaneously, multiscale estimation for GTWR models has also attracted wide attention due to its scale flexibility. However, most of the existing estimation methods for the mixed GTWR models still have the limitation that either all of regression relationships operate at the same spatiotemporal scale, or each of coefficients is estimated using back-fitting procedures that are very time-consuming. In order to improve the estimation accuracy and alleviate the computation burden, we propose a multiscale method with the adaptive bandwidth (short for scale-adaptive) for calibrating mixed GTWR (say mixed MGTWR) models. In the proposed multiscale estimation approach, a two-step method is used to estimate the constant coefficients and varying coefficients, and then each of the varying coefficients is again estimated by back-fitting procedures with different bandwidth sizes. In addition, we also address the calculation of “hat matrix” in the multiscale estimation for GTWR model and then derive the hat matrix of the complete MGTWR model. Simulation experiments assess the performance of the proposed scale-adaptive calibration method. The results show that the proposed method is much more efficient than existing estimation methods with regard to estimation accuracy and computation efficiency. Moreover, the proposed scale-adaptive method can also correctly reflect the inherent spatiotemporal operating scales of the explanatory variables. Finally, a real-world example demonstrates the applicability of the proposed scale-adaptive method.

Suggested Citation

  • Zhimin Hong & Zhiwen Wang & Huhu Wang & Ruoxuan Wang, 2025. "A scale-adaptive estimation for mixed geographically and temporally weighted regression models," Journal of Geographical Systems, Springer, vol. 27(1), pages 85-111, January.
  • Handle: RePEc:kap:jgeosy:v:27:y:2025:i:1:d:10.1007_s10109-024-00453-0
    DOI: 10.1007/s10109-024-00453-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10109-024-00453-0
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s10109-024-00453-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Daisuke Murakami & Narumasa Tsutsumida & Takahiro Yoshida & Tomoki Nakaya & Binbin Lu, 2020. "Scalable GWR: A Linear-Time Algorithm for Large-Scale Geographically Weighted Regression with Polynomial Kernels," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 111(2), pages 459-480, August.
    2. Jean Dub� & Di�go Legros, 2014. "Spatial econometrics and the hedonic pricing model: what about the temporal dimension?," Journal of Property Research, Taylor & Francis Journals, vol. 31(4), pages 333-359, December.
    3. Mauricio Sarrias, 2019. "Do monetary subjective well-being evaluations vary across space? Comparing continuous and discrete spatial heterogeneity," Spatial Economic Analysis, Taylor & Francis Journals, vol. 14(1), pages 53-87, January.
    4. Todd Kuethe & Valerien Pede, 2011. "Regional Housing Price Cycles: A Spatio-temporal Analysis Using US State-level Data," Regional Studies, Taylor & Francis Journals, vol. 45(5), pages 563-574.
    5. David Wheeler & Michael Tiefelsdorf, 2005. "Multicollinearity and correlation among local regression coefficients in geographically weighted regression," Journal of Geographical Systems, Springer, vol. 7(2), pages 161-187, June.
    6. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
    7. A. Fotheringham & Ricardo Crespo & Jing Yao, 2015. "Exploring, modelling and predicting spatiotemporal variations in house prices," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 54(2), pages 417-436, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zolnik, Edmund, 2021. "Geographically weighted regression models of residential property transactions: Walkability and value uplift," Journal of Transport Geography, Elsevier, vol. 92(C).
    2. Yigong Hu & Binbin Lu & Yong Ge & Guanpeng Dong, 2022. "Uncovering spatial heterogeneity in real estate prices via combined hierarchical linear model and geographically weighted regression," Environment and Planning B, , vol. 49(6), pages 1715-1740, July.
    3. Mauricio Sarrias, 2020. "Random Parameters and Spatial Heterogeneity using Rchoice in R," REGION, European Regional Science Association, vol. 7, pages 1-19.
    4. Olaru, Doina & Mulley, Corinne & Smith, Brett & Ma, Liang, 2017. "Policy-led selection of the most appropriate empirical model to estimate hedonic prices in the residential market," Journal of Transport Geography, Elsevier, vol. 62(C), pages 213-228.
    5. Paul Harris & Bruno Lanfranco & Binbin Lu & Alexis Comber, 2020. "Influence of Geographical Effects in Hedonic Pricing Models for Grass-Fed Cattle in Uruguay," Agriculture, MDPI, vol. 10(7), pages 1-17, July.
    6. Xuan Liu & Chunwu Zhu & Minghui Kong & Lirong Yin & Wenfeng Zheng, 2024. "The Value of Political Connections of Developers in Residential Land Leasing: Case of Chengdu, China," SAGE Open, , vol. 14(2), pages 21582440241, April.
    7. Sisman, S. & Aydinoglu, A.C., 2022. "A modelling approach with geographically weighted regression methods for determining geographic variation and influencing factors in housing price: A case in Istanbul," Land Use Policy, Elsevier, vol. 119(C).
    8. Chen, Chao & Feng, Tao & Ding, Chuan & Yu, Bin & Yao, Baozhen, 2021. "Examining the spatial-temporal relationship between urban built environment and taxi ridership: Results of a semi-parametric GWPR model," Journal of Transport Geography, Elsevier, vol. 96(C).
    9. repec:ags:aolpei:337997 is not listed on IDEAS
    10. Paliska, Dejan & Drobne, Samo, 2020. "Impact of new motorway on housing prices in rural North-East Slovenia," Journal of Transport Geography, Elsevier, vol. 88(C).
    11. Ghislain Geniaux, 2024. "Speeding up estimation of spatially varying coefficients models," Journal of Geographical Systems, Springer, vol. 26(3), pages 293-327, July.
    12. Yujiao Chen & Zhengbo Luo, 2022. "Hedonic Pricing of Houses in Megacities Pre- and Post-COVID-19: A Case Study of Shanghai, China," Sustainability, MDPI, vol. 14(17), pages 1-21, September.
    13. Girum D. Abate & Luc Anselin, 2016. "House price fluctuations and the business cycle dynamics," CREATES Research Papers 2016-06, Department of Economics and Business Economics, Aarhus University.
    14. Cima, Elizabeth Giron & da Rocha-Junior, Weimar Freire & Uribe-Opazo, Miguel Angel & Dalposso, Gustavo Henrique, 2023. "An Analysis of the Gross Domestic Product of Municipalities: a Spatial Glance into the State of Paraná-Brazil," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 15(2), June.
    15. Zhou Huang & Ganmin Yin & Xia Peng & Xiao Zhou & Quanhua Dong, 2023. "Quantifying the environmental characteristics influencing the attractiveness of commercial agglomerations with big geo-data," Environment and Planning B, , vol. 50(9), pages 2470-2490, November.
    16. Mehmet Burak Kaya & Onur Alisan & Alican Karaer & Eren Erman Ozguven, 2024. "Assessing Tornado Impacts in the State of Kentucky with a Focus on Demographics and Roadways Using a GIS-Based Approach," Sustainability, MDPI, vol. 16(3), pages 1-27, January.
    17. Alexis Comber & Khanh Chi & Man Q Huy & Quan Nguyen & Binbin Lu & Hoang H Phe & Paul Harris, 2020. "Distance metric choice can both reduce and induce collinearity in geographically weighted regression," Environment and Planning B, , vol. 47(3), pages 489-507, March.
    18. Cynthia Sin Tian Ho & Mats Wilhelmsson, 2022. "Geographical accessibility to bank branches and its relationship to new firm formation in Sweden via multiscale geographically weighted regression," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 42(2), pages 191-218, August.
    19. Mühlematter, Dominik J. & Wiedemann, Nina & Xin, Yanan & Raubal, Martin, 2024. "Spatially-aware station based car-sharing demand prediction," Journal of Transport Geography, Elsevier, vol. 114(C).
    20. Cui, Wencong & Li, Jianyi & Xu, Wangtu & Güneralp, Burak, 2021. "Industrial electricity consumption and economic growth: A spatio-temporal analysis across prefecture-level cities in China from 1999 to 2014," Energy, Elsevier, vol. 222(C).
    21. Guanwei Zhao & Zhitao Li & Yuzhen Shang & Muzhuang Yang, 2022. "How Does the Urban Built Environment Affect Online Car-Hailing Ridership Intensity among Different Scales?," IJERPH, MDPI, vol. 19(9), pages 1-25, April.

    More about this item

    Keywords

    Geographically and temporally weighted regression; Multiscale estimation; Spatiotemporal scale; Bandwidth;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:jgeosy:v:27:y:2025:i:1:d:10.1007_s10109-024-00453-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.