Replicated point processes with application to population dynamics models

Favretti, Marco

doi:10.1016/j.tpb.2019.04.002

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…A residual G ‐function (Baddeley, Rubak, & Moller, 2011) is adopted to test the independence of points, smoothed residuals (Baddeley, Moller, & Pakes, 2008) are commonly used to test fitted intensity, and parameter influence analysis (Baddeley, Rubak, & Turner, 2019) is applied to test model sensitivity to data. The spatial point process has been applied in many research areas, such as population dynamics (Favretti, 2019), missing‐data analysis (Peng, Tong, & Weng, 2019; Tucker, Shand, & Lewis, 2019), disease (Li, Li, Hu, Chen, & Dai, 2014), plant distribution (Genet & Pothier, 2013; Lister & Leites, 2018), and urban transportation planning (Zhai, Baran, & Wu, 2018; Zhang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Understanding the correlation between destination distribution and urban built environment from taxi GPS data

Liu

Tang

et al. 2022

Transactions in GIS

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Exploring the relationship between travel destinations and urban built environments can help to conduct reasonable measurements in urban transportation planning. Destination distribution is affected by various factors, and quantitative description of the spatial effects is a challenge in factor correlation analysis. In this study, inhomogeneous Poisson point processes are proposed to model the correlation between destination distribution and related categories of variables, including land use, trip origin distribution, demographic information, and transport networks. First, five models based on inhomogeneous Poisson point processes are constructed, considering different combinations of variables. Second, diagnostic methods, including the G-function, smoothed residuals, and parameter influence function, are applied to evaluate the models' performance. Finally, in experiments, the taxi GPS trajectory data collected in Shenzhen City, China, are used to validate the effectiveness of the proposed method. Moreover, in the discussion, the variations of variable coefficients in different time periods (workdays, weekends, and holidays) are analyzed and explained. The results of this study may enhance understanding of the correlation between travel patterns and urban structure.

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