Geographically weighted temporally correlated logistic regression model

Liu, Yang; Lam, Kwok–Fai; Wu, Joseph T.; Lam, Tommy Tsan-Yuk

doi:10.1038/s41598-018-19772-6

Cited by 25 publications

(16 citation statements)

References 25 publications

(28 reference statements)

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“…Multifactorial dynamic relationships are common in complex traffic systems where some predictors cannot be observed or addressed easily. Regression analysis is widely used to study the correlation between dependent and independent variables, e.g., linear regression, logistic regression, and log linear regression [24]. The OLS regression is the most representative model among statistical methods for revealing the complicated relationship between urban environment and transit ridership.…”

Section: Geographically Weighted Regression Modelmentioning

confidence: 99%

Spatiotemporal Influence of Urban Environment on Taxi Ridership Using Geographically and Temporally Weighted Regression

Zhang

Huang

Zhu

2019

IJGI

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Taxicabs play an important role in urban transit systems, and their ridership is significantly influenced by the urban built environment. The intricate relationship between taxi ridership and the urban environment has been explored using either conventional ordinary least squares (OLS) regression or geographically weighted regression (GWR). However, time constitutes a significant dimension, particularly when analyzing spatiotemporal hourly taxi ridership, which is not effectively incorporated into conventional models. In this study, the geographically and temporally weighted regression (GTWR) model was applied to model the spatiotemporal heterogeneity of hourly taxi ridership, and visualize the spatial and temporal coefficient variations. To test the performance of the GTWR model, an empirical study was implemented for Xiamen city in China using a set of weekday taxi pickup point data. Using point-of-interest (POI) data, hourly taxi ridership was analyzed by incorporating it to various spatially urban environment variables based on a 500 × 500 m grid unit. Compared to the OLS and GWR, the GTWR model obtained the best performance, both in terms of model fit and explanatory accuracy. Moreover, the urban environment was revealed to have a significant impact on taxi ridership. Road density was found to decrease the number of taxi trips in particular places, and the density of bus stops competed with taxi ridership over time. The GTWR modelling provides valuable insights for investigating taxi ridership variation as a function of spatiotemporal urban environment variables, thereby facilitating an optimal allocation of taxi resources and transportation planning.

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Section: Geographically Weighted Regression Modelmentioning

confidence: 99%

Spatiotemporal Influence of Urban Environment on Taxi Ridership Using Geographically and Temporally Weighted Regression

Zhang

Huang

Zhu

2019

IJGI

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“…The regression analysis is the most commonly used statistical method to examine and explore the spatial relationships among the variables. In spatial data analysis, several regression techniques have been described and formulated over the years such as linear mixed models (LMM) (Zhang and Gove 2005 ), generalized additive models (GAM) (Zhang and Gove 2005 ), geographically temporal weighted regression (GTWR) (Liu et al 2016 ), geographically weighted temporally correlated logistic regression (GWTCLR) (Liu et al 2018 ), geographically weighted ordinal regression (GWOR) (Dong et al 2018 ) and multiscale GWR (MGWR) (Mollalo et al 2020 ). OLSR is a generalized linear regression technique.…”

Section: Methodsmentioning

confidence: 99%

Geographically Weighted Method Integrated with Logistic Regression for Analyzing Spatially Varying Accuracy Measures of Remote Sensing Image Classification

Mishra

Kumar

Prasad

et al. 2021

J Indian Soc Remote Sens

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The accuracy of thematic information extracted from remote sensing image is assessed recurrently using the confusion matrix method. But the accuracies have been criticized as a consequence of its aspatial nature. The work presented here describes a geographically weighted method combined with logistic regression for producing and visualizing the spatially distributed accuracy measures across the landscape. The outcomes compare the standard confusion matrix-based accuracy measures with those that have been permitted to differ locally. Furthermore, statistical parameters, i.e. Akaike information criterion, adjusted squared correlation coefficient (R 2 ) and residual sum of squares (RSS) were employed to compare the performance of geographically weighted logistic regression (GWLR) with global ordinary least square regression technique. The GWLR technique was found to provide more reliable performance in estimating spatially varying accuracy measures. The results demonstrated that the geographically weighted approach offers additional and valuable insights for examining spatial variation in the context of landscape mapping accuracy.

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“…The first four are all concerned with capturing autocorrelation effects into the space-time models and may not be suited to analyses of big spatio-temporal data, because of the problem of statistical inference and significance testing (Brunsdon, 2017;Spicer & Gangloff, 2016). Others, such as geographically and temporally weighted regression (Fotheringham et al, 2015;Huang, Wu, & Barry, 2010;Liu, Lam, Wu, & Lam, 2018), focus on relationship heterogeneity where attribute relationships are viewed as nonstationary in both space and time.…”

Section: Lesson 2: Informed Methods Tools and Techniques (If You mentioning

confidence: 99%

Considering spatiotemporal processes in big data analysis: Insights from remote sensing of land cover and land use

Comber

Wulder

2019

Transactions in GIS

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Data are increasingly spatio‐temporal—they are collected some‐where and at some‐time. The role of proximity in spatial process is well understood, but its value is much more uncertain for many temporal processes. Using the domain of land cover/land use (LCLU), this article asserts that analyses of big data should be grounded in understandings of underlying process. Processes exhibit behaviors over both space and time. Observations and measurements may or may not coincide with the process of interest. Identifying the presence or absence of a given process, for instance disentangling vegetation phenology from stress, requires data analysis to be informed by knowledge of the process characteristics and, critically, how these manifest themselves over the spatio‐temporal unit of analysis. Drawing from LCLU, we emphasize the need to identify process and consider process phase to quantify important signals associated with that process. The aim should be to link the seriality of the spatio‐temporal data to the phase of the process being considered. We elucidate on these points and opportunities for insights and leadership from the geographic community.

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Geographically weighted temporally correlated logistic regression model

Cited by 25 publications

References 25 publications

Spatiotemporal Influence of Urban Environment on Taxi Ridership Using Geographically and Temporally Weighted Regression

Spatiotemporal Influence of Urban Environment on Taxi Ridership Using Geographically and Temporally Weighted Regression

Geographically Weighted Method Integrated with Logistic Regression for Analyzing Spatially Varying Accuracy Measures of Remote Sensing Image Classification

Considering spatiotemporal processes in big data analysis: Insights from remote sensing of land cover and land use

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