Point pattern analysis of regional city distributions

Pourtaheri, Reza; Vahidi-Asl, Mohammad Q.

doi:10.1007/s11135-011-9546-6

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…The flow of interesting contributions in this field was triggered by the works of Ripley (1976), Diggle (1983), Getis (1984), and Bailey and Gatrell (1995). This increased interest extended to various disciplines such as industrial clustering (Arbia, 2001; Barff, 1987; Kosfeld, Eckey, & Lauridsen, 2011; Marcon & Puech, 2003; Sweeney & Feser, 1998) and urban clustering (Pourtaheri & Vahidi‐Asl, 2011). However, the subject of the application of Ripley's K‐function analysis to the geographical distribution of urban labour clustering under border effects remains open.…”

Section: Literature Review: Measuring Border Effectsmentioning

confidence: 99%

Alternative measure of border effects across regions: Ripley's K‐function method

Sun

2021

Papers in Regional Science

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This study uses Ripley's K‐function to examine the diversity in cross‐region labor mobility under various border effects from heterogeneous policies. Based on manufacturing labour data for Chinese cities, we emphasize the importance of border effects in the formation of regional integration with a Ripley's K‐function analysis. The statistical measures indicate that the amount of labour mobility involves distance‐decay effects of the border. However, the variety in the distance‐decay of the border effects differs considerably between each pair of adjacent regions. For this reason, transport costs caused by natural or economic geography probably explained most of the differences in border effects.

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Section: Literature Review: Measuring Border Effectsmentioning

confidence: 99%

Alternative measure of border effects across regions: Ripley's K‐function method

Sun

2021

Papers in Regional Science

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“…Economic geographers are among those who have been interested in the Ripley's K ‐function analysis and use it in order to assess the geographic distribution of economic activity because it can reveal information on the behaviour of industries (Albert et al ., ; Arbia et al ., ; Arbia et al ., ; Barff, ; Feser & Sweeney, ; Giuliani et al ., ; Kosfeld et al ., ; Kretser et al ., ; Marcon & Puech, ; Pourtaheri & Vahidi‐Asl, ; Sweeney & Feser, ) and even cities (Getis, ; Pourtaheri & Vahidi‐Asl, ) in a given territory for a long time. The location of a city may explain its economic relationship to neighbouring cities within a region and even across regions (Dobkins & Ioannides, ).…”

Section: Introductionmentioning

confidence: 99%

Geographic information system‐based edge effect correction for Ripley'sK‐function under irregular boundaries

Sun

2019

Geographical Research

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Ripley's K‐function is a test to detect geographically distributed patterns occurring across spatial scales. Initially, it assumed infinitely continuous planar space, but in reality, any geographic distribution occurs in a bounded region. Hence, the edge problem must be solved in the application of Ripley's K‐function. Traditionally, three basic edge correction methods were designed for regular study plots because of simplified geometric computation: the Ripley circumference, buffer zone, and toroidal methods. For an irregular‐shaped study region, a geographic information system (GIS) is needed to support geometric calculation of complex shapes. The Ripley circumference method was originally implemented by Haase and has been modified into a Python program in a GIS environment via Monte Carlo simulation (hereafter, the Ripley–Haase and Ripley–GIS methods). The results show that in terms of the statistical powers of clustering detection for irregular boundaries, the Ripley–GIS method is the most stable, followed by the buffer zone, toroidal, and Ripley–Haase methods. After edge effects of irregular boundaries have been eliminated, Ripley's K‐function is used to estimate the degree of spatial clustering of cities in a given territory, and in this paper, we demonstrate that by reference to the relationship between urban spatial structure and economic growth in China.

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“…Point process methodology is applied in diverse scientific fields to model and predict earthquakes, wildfires, disease occurrences, telecommunications, plant and cellular systems, and animal colonies, to name but a few examples. See, for instance, Andrews et al (2011), Eberhard et al (2012), Edelman (2012), Klaver et al (2012), Mohler et al (2011), Pourtaheri &Vahidi-Asl (2011), andWaller et al (2011) for a non-exhaustive list of some recent applications of spatial and space-time point processes. With such variety in the applied fields that use these models as well as the wide range of available models, it is important to have general and-preferably-easily applicable methods to assess goodness-of-fit and predictive performance.…”

Section: Introductionmentioning

confidence: 99%

Calibration diagnostics for point process models via the probability integral transform

Thorarinsdottir

2013

Stat

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We propose the use of the probability integral transform (PIT) for model validation in point process models. The simple PIT diagnostic tools assess the calibration of the model and can detect inconsistencies in both the intensity and the interaction structure. For the Poisson model, the PIT diagnostics can be calculated explicitly. Generally, the calibration may be assessed empirically based on random draws from the model, and the method applies to processes of any dimension.

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Point pattern analysis of regional city distributions

Abstract: Point processes, Complete spatial randomness, Maximum pseudo-likelihood estimation, MCMC maximum likelihood estimation,

Cited by 4 publications

References 12 publications

Alternative measure of border effects across regions: Ripley's K‐function method

Alternative measure of border effects across regions: Ripley's K‐function method

Geographic information system‐based edge effect correction for Ripley'sK‐function under irregular boundaries

Calibration diagnostics for point process models via the probability integral transform

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