Quantifying the spatial heterogeneity of points

Shu, Huan; Pei, Tao; Ci, Song; Ma, Ting; Du, Yunyan; Fan, Zide; Guo, Sihui

doi:10.1080/13658816.2019.1577432

Cited by 11 publications

(9 citation statements)

References 48 publications

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“…This method can be divided into four steps, as shown in Figure 2. First, we determine whether the flow set is homogeneous by using several quantitative indices proposed in our previous work (such as NLH * , A-w) [47].…”

Section: Methodsmentioning

confidence: 99%

“…Each sparse flow is seen as noise, while each dense flow can be generated into flow clusters based on the density-connected clustering concept [36] in the final step. Since our previous work [47] provides the details of the first step, we introduce only the remaining three steps.…”

Section: Methodsmentioning

confidence: 99%

“…In this experiment, each dataset is processed by our method, and a significance test is designed to validate all the identified flow clusters. Through the test, the A-w statistic of flow clusters [47] is used to test whether the density of flow clusters is different from that of noise clusters. These simulated datasets are also evaluated by three other flow clustering methods for comparison: a hierarchical clustering method, a density-based clustering method and a spatial statistics-based clustering method.…”

Section: Simulation Experimentsmentioning

confidence: 99%

“…The objective of these methods is usually to find the subset of flows with the optimal values of statistical measures. These methods can usually create a significant description of spatial homogeneity, thereby providing a standard measure for comparing flow clusters [45]- [47].…”

Section: Introductionmentioning

confidence: 99%

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Identifying Flow Clusters Based on Density Domain Decomposition

Pei

Shu

2020

IEEE Access

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Flow clustering is one of the most important data mining methods for the analysis of origin-destination (OD) flow data, and it may reveal the underlying mechanisms responsible for the spatial distributions and temporal dynamics of geographical phenomena. Existing flow clustering approaches are based mainly on the extension of traditional clustering methods to points by redefining basic concepts or some spatial association indictors of flows and the implementation of classic clustering processes, such as aggregating, collecting or searching. However, current techniques still suffer from two main problems: poor identification accuracy and complicated parameter selection processes. To resolve these problems, a new clustering method is proposed in this study for arbitrarily shaped flow clusters based on the density domain decomposition of flows. Simulation experiments based on our method and existing methods show that our method outperforms the three most commonly used methods in terms of the overall identification rate and almost all F1 measures, and it does not require any manual adjustments during the parameter selection process. Finally, a case study is conducted on taxi trip data from Beijing. Several flow clusters are identified to represent different types of residents' travel behaviors, including daily commuting, return travel, tourism and behaviors on special days. INDEX TERMS Origin-destination (OD) flow, flow space, flow clustering, density domain decomposition, point process.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Simulation Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identifying Flow Clusters Based on Density Domain Decomposition

Pei

Shu

2020

IEEE Access

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“…When a local repulsive cluster is present in a complex heterogeneous point process (with multiple heterogeneous components), the commonly used statistical measurements for quantifying heterogeneity may be invalid in some situations. For example, as seen in Figure 2, although the statistical measurement A [33][34] effectively quantifies the heterogeneity of two point processes, as seen in Figure 2a,b, that contain homogeneous components, it cannot effectively quantify the heterogeneity of a complex point process with heterogeneous components, as seen in Figure 2c with one repulsive cluster and one aggregative cluster; In this case, an incorrect CSR (complete spatial randomness) distribution judgment may be made. Specifically, a repulsive pattern is not subject to the same specific probability distribution function as homogeneous components in a point process.…”

Section: Introductionmentioning

confidence: 99%

Decomposition of Repulsive Clusters in Complex Point Processes with Heterogeneous Components

Pei

2019

IJGI

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The decomposition of a point process is useful for the analysis of spatial patterns and in the discovery of potential mechanisms of geographic phenomena. However, when a local repulsive cluster is present in a complex heterogeneous point process, the traditional solution, which is based on clustering, may be invalid for decomposition because a repulsive pattern is not subject to a specific probability distribution function and the effects of aggregative and repulsive components may be counterbalanced. To solve this problem, this paper proposes a method of decomposing repulsive clusters in complex point processes with multiple heterogeneous components. A repulsive cluster is defined as a set of repulsive density-connected points that are separated by a certain distance at a small scale and aggregated at a large scale simultaneously. The H-function is used to identify repulsive clusters by determining the repulsive distance and extracting repulsive points for further clustering. Through simulation experiments based on three datasets, the proposed method has been shown to effectively perform repulsive cluster decomposition in heterogeneous point processes. A case study of the point of interest (POI) dataset in Beijing also indicates that the method can identify meaningful repulsive clusters from types of POIs that represent different service characteristics of shops in different local regions.

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Heterogeneous spatial models in R: spatial regimes models

Piras

Sarrias

2023

J Spat Econometrics

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This paper presents the progress made so far in the development of the R package hspm. The package hspm aims at implementing a variety of models and methods to control for heterogeneity in spatial models. Spatial heterogeneity can be specified in different ways, ranging from exogenous (or endogenous) spatial regimes models, to models with coefficients that potentially vary for each observations (i.e., continuous heterogeneity). We focus on a few R functions that allow for the estimation of a general spatial regimes model, as well as all of the nested specifications deriving from it. The models are estimated by instrumental variables and generalized method of moments techniques.

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Quantifying the spatial heterogeneity of points

Cited by 11 publications

References 48 publications

Identifying Flow Clusters Based on Density Domain Decomposition

Identifying Flow Clusters Based on Density Domain Decomposition

Decomposition of Repulsive Clusters in Complex Point Processes with Heterogeneous Components

Heterogeneous spatial models in R: spatial regimes models

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