A multidimensional spatial scan statistics approach to movement pattern comparison

Gao, Yizhao; Li, Ting; Wang, Shaowen; Jeong, Myeong‐Hun; Soltani, Kiumars

doi:10.1080/13658816.2018.1426859

Cited by 50 publications

(23 citation statements)

References 35 publications

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“…Motivated by clustering spatial flows, multiple approaches were introduced to calculate the similarity between flow vectors (Gao, Li, Wang, Jeong, & Soltani, ; Tao & Thill, ; Yao et al, ; Zhu & Guo, ). However, direction is rarely explicitly considered in these, which could cause misinterpretation of the pattern (see detailed discussions in Section 4.1).…”

Section: Where Direction Has Been Consideredmentioning

confidence: 99%

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Making direction a first‐class citizen of Tobler's first law of geography

Zhu

Janowicz

Mai

2019

Transactions in GIS

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Waldo Tobler frequently reminded us that the law named after him was nothing more than calling for exceptions. This article discusses one of these exceptions. Spatial relations between points are frequently modeled as vectors in which both distance and direction are of equal prominence. However, in Tobler's first law of geography, such a relation is described only from the perspective of distance by relating the decreasing similarity of observations in some attribute space to their increasing distance in geographic space. Although anisotropic versions of many geographic analysis techniques, such as directional semivariograms, anisotropy clustering, and anisotropic point pattern analysis, have been developed over the years, direction remains on the level of an afterthought. We argue that, compared to distance, directional information is still under‐explored and anisotropic techniques are substantially less frequently applied in everyday GIS analysis. Commonly, when classical spatial autocorrelation indicators, such as Moran's I, are used to understand a spatial pattern, the weight matrix is only built from distance, without direction being considered. Similarly, GIS operations, such as buffering, do not take direction into account either, with distance in all directions being treated equally. In reality, meanwhile, particularly in urban structures and when processes are driven by the underlying physical geography, direction plays an essential role. In this article we ask whether the development of early GIS, data (sample) sparsity, and Tobler's law lead to a theory‐induced blindness for the role of direction. If so, is it possible to envision direction becoming a first‐class citizen of equal importance to distance instead of being an afterthought only considered when the deviation from a perfect circle becomes too obvious to be ignored?

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Section: Where Direction Has Been Consideredmentioning

confidence: 99%

“…Motivated by clustering spatial flows, multiple approaches were introduced to calculate the similarity between flow vectors (Gao, Li, Wang, Jeong, & Soltani, 2018;Tao & Thill, 2016;Yao et al, 2018;Zhu & Guo, 2014).…”

Section: Direction In Spatial Interactionsmentioning

confidence: 99%

Making direction a first‐class citizen of Tobler's first law of geography

Zhu

Janowicz

Mai

2019

Transactions in GIS

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“…This method can aggregate flows with different lengths by tuning the parameter used in the distance metrics; however, the selection of a parameter is still a problem that has to be seriously considered. Gao et al [19] presented a multidimensional spatial scan statistics approach to detect highly concentrated flow clusters with a new spatial data model that integrates each OD flow into a 4D point. The obvious ISPRS Int.…”

Section: Introductionmentioning

confidence: 99%

Tree-Based and Optimum Cut-Based Origin-Destination Flow Clustering

Xiang

2019

IJGI

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Data about the movements of diverse objects, including human beings, animals, and commodities, are collected in growing amounts as location-aware technologies become pervasive. Clustering has become an increasingly important analytical tool for revealing travel patterns from large-scale movement datasets. Most existing methods for origin-destination (OD) flow clustering focus on the geographic properties of an OD flow but ignore the temporal information preserved in the OD flow, which reflects the dynamic changes in the travel patterns over time. In addition, most methods require some predetermined parameters as inputs and are difficult to adjust considering the changes in the users' demands. To overcome such limitations, we present a novel OD flow clustering method, namely, TOCOFC (Tree-based and Optimum Cut-based Origin-Destination Flow Clustering). A similarity measurement method is proposed to quantify the spatial similarity relationship between OD flows, and it can be extended to measure the spatiotemporal similarity between OD flows. By constructing a maximum spanning tree and splitting it into several unrelated parts, we effectively remove the noise in the flow data. Furthermore, a recursive two-way optimum cut-based method is utilized to partition the graph composed of OD flows into OD flow clusters. Moreover, a criterion called CSSC (Child tree/Child graph Self-Similarity Criterion) is formulated to determine if the clusters meet the output requirements. By modifying the parameters, TOCOFC can obtain clustering results for different time scales and spatial scales, which makes it possible to study movement patterns from a multiscale perspective. However, TOCOFC has the disadvantages of low efficiency and large memory consumption, and it is not conducive to quickly handling large-scale data. Compared with previous works, TOCOFC has a better clustering performance, which is reflected in the fact that TOCOFC can guarantee a balance between clusters and help to fully understand the corresponding patterns. Being able to perform the spatiotemporal clustering of OD flows is also a highlight of TOCOFC, which will help to capture the differences in the patterns at different times for a deeper analysis. Extensive experiments on both artificial spatial datasets and real-world spatiotemporal datasets have demonstrated the effectiveness and flexibility of TOCOFC. flow data make the visualization blurry, which drives the development of new approaches to more effectively discover the unknown information implicit in the data itself.A series of enhanced OD visualization methods [3][4][5][6][7][8][9][10][11] have been investigated to improve the ability to analyze OD flows from multiple research perspectives. With the purpose of eliminating the occlusion problem, Buchin [3] applied a spiral tree to the flow map, which makes the flow map very neat and clear by merging lines smoothly, although predefined study areas such as TAZs or administrative areas are needed. Selassie [4] presented a divided edge bundling method that bu...

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“…Another reason we have seen rapid growth of Eulerian data in academic research is the privacy concerns associated with individual tracking (see Paul et al 2018), but Eulerian data pose similar but unique challenges for maintaining individual privacy. The methods for studying Eulerian data presented in this issue (Gao et al 2018, Guo et al 2018, Tao et al 2018 offer new avenues for further analysis in the Eulerian domain.…”

Section: Lagrangian Vs Eulerian Movement Analysismentioning

confidence: 99%

“…The modelling framework is easy to comprehend and makes the contribution of defining how transaction and presence-based sensors can be combined into a single analysis. Gao et al (2018) demonstrate an extension to the popular spatial scan statistic (Kulldorff 1997) for movement flows between regions. The method is appropriate for both aggregate flows (e.g.…”

Section: Summaries Of the Articles Featured In This Special Issuementioning

confidence: 99%