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Spatial Interaction Modeling of OD Flow Data: Comparing Geographically Weighted Negative Binomial Regression (GWNBR) and OLS (GWOLSR)

1
School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
2
School of Computer Science, Central China Normal University, Wuhan 430077, China
3
Division of Geography and Environmental Management, School of Science and the Environment, Manchester Metropolitan University, Chester Street, Manchester M1 5GD, UK
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School of Geography Science, Nanjing Normal University, Nanjing 210023, China
5
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(5), 220; https://doi.org/10.3390/ijgi8050220
Received: 7 February 2019 / Revised: 23 April 2019 / Accepted: 4 May 2019 / Published: 8 May 2019
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Abstract

Due to the emergence of new big data technology, mobility data such as flows between origin and destination areas have increasingly become more available, cheaper, and faster. These improvements to data infrastructure have boosted spatial and temporal modeling of OD (origin-destination) flows, which require the consideration of spatial dependence and heterogeneity. Both ordinary least square (OLS) and negative binomial (NB) regression methods have been used extensively to calibrate OD flow models by processing flow data as different types of dependent variables. This paper aims to compare both global and local spatial interaction modeling of OD flows between traditional and geographically weighted OLS (GWOLSR) and NB (GWNBR) modeling methods. From this study with empirical data it is concluded that GWNBR outperforms GWOLSR in reducing spatial autocorrelation and in detecting spatial non-stationarity. Although, it is noted that both local modeling methods show improvement when compared against the equivalent global models. View Full-Text
Keywords: OD flows; spatial interaction modeling; geographically weighted OLS; geographically weighted negative binomial regression; Jiangsu OD flows; spatial interaction modeling; geographically weighted OLS; geographically weighted negative binomial regression; Jiangsu
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Zhang, L.; Cheng, J.; Jin, C. Spatial Interaction Modeling of OD Flow Data: Comparing Geographically Weighted Negative Binomial Regression (GWNBR) and OLS (GWOLSR). ISPRS Int. J. Geo-Inf. 2019, 8, 220.

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