Detecting Colocation Flow Patterns in the Geographical Interaction Data

Zhang, Haiping; Zhou, Xingxing; Tang, Guoan; Zhang, Xueying; Jing, Qin; Xiong, Liyang

doi:10.1111/gean.12274

Cited by 21 publications

(5 citation statements)

References 74 publications

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“…Given that flow data have various forms, the flow data that can be applied to spatial autocorrelation analysis using the proposed method are worth discussing [43]. Flow data have been widely applied, and the OD pair is among the most important forms, and can be referred to as OD flow.…”

Section: Discussionmentioning

confidence: 99%

Flow-Data-Based Global Spatial Autocorrelation Measurements for Evaluating Spatial Interactions

Sun,

Zhang

2023

IJGI

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Spatial autocorrelation analysis is essential for understanding the distribution patterns of spatial flow data. Existing methods focus mainly on the origins and destinations of flow units and the relationships between them. These methods measure the autocorrelation of gravity or the positional and directional autocorrelations of flow units that are treated as objects. However, the intrinsic complexity of actual flow data necessitates the consideration of not only gravity, positional, and directional autocorrelations but also the autocorrelations of the variables of interest. This study proposes a global spatial autocorrelation method to measure the variables of interest of flow data. This method mainly consists of three steps. First, the proximity constraints of the origin and destination of a flow unit are defined to ensure similarity of flow units in terms of direction, distance, and position. This undertaking aims to determine the neighborhood of flow units and generate their adjacent matrices. Second, a spatial autocorrelation measurement model for flow data is constructed on the basis of the adjacent matrix generated. Artificial data sets are also employed to test the validity of the model. Finally, the proposed method is applied to the flow data analysis of population migration in central and eastern China to prove the practical application value of the model. The proposed method is universal and can be generalized to the global spatial autocorrelation analysis of any type of flow data.

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Section: Discussionmentioning

confidence: 99%

Flow-Data-Based Global Spatial Autocorrelation Measurements for Evaluating Spatial Interactions

Sun,

Zhang

2023

IJGI

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“…Methods of mining spatial colocation patterns have been widely used in urban planning [6] , industrial layout [7] and urban security [8] . According to the mining scale, existing algorithms for mining spatial colocation patterns can be roughly divided into algorithms for mining global spatial colocation patterns and algorithms for mining local spatial colocation patterns [9] , [10] .…”

Section: Literature Reviewmentioning

confidence: 99%

Mining significant local spatial association rules for multi-category point data

Cai,

Chen,

Chen

et al. 2024

Heliyon

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“…Juhász et al (2021) found both that colocated technologies are more likely to become related over time and that colocation and the complexity of technologies are conducive to intensification [ 42 ]. Zhang et al (2022) proposed a colocation pattern detection and spatial association rule discovery approach that treats origin–destination (OD) flows as Boolean spatial features while considering the spatial proximity of the origins and destinations of OD flows and their direction similarity [ 43 ].…”

Section: Introductionmentioning

confidence: 99%

Analysing the global and local spatial associations of medical resources across Wuhan city using POI data

Qiao

Cheng

2023

BMC Health Serv Res

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Background There is a sharp contradiction between the supply and demand of medical resources in the provincial capitals of China. Understanding the spatial patterns of medical resources and identifying their spatial association and heterogeneity is a prerequisite to ensuring that limited resources are allocated fairly and optimally, which, along with improvements to urban residents’ quality of life, is a key aim of healthy city planning. However, the existing studies on medical resources pattern mainly focus on their spatial distribution and evolution characteristics, and lack the analyses of the spatial co-location between medical resources from the global and local perspectives. It is worth noting that the research on the spatial relationship between medical resources is an important way to realize the spatial equity and operation efficiency of urban medical resources. Methods Localized colocation quotient (LCLQ) analysis has been used successfully to measure directional spatial associations and heterogeneity between categorical point data. Using point of interest (POI) data and the LCLQ method, this paper presents the first analysis of spatial patterns and directional spatial associations between six medical resources across Wuhan city. Results (1) Pharmacies, clinics and community hospitals show “multicentre + multicircle”, “centre + axis + dot” and “banded” distribution characteristics, respectively, but specialized hospitals and general hospitals present “single core” and “double core” modes. (2) Overall, medical resources show agglomeration characteristics. The degrees of spatial agglomeration of the five medical resources, are ranked from high to low as follows: pharmacy, clinic, community hospital, special hospital, general hospital and 3A hospital. (3) Although pharmacies, clinics, and community hospitals of basic medical resources are interdependent, specialized hospitals, general hospitals and 3A hospitals of professional medical resources are also interdependent; furthermore, basic medical resources and professional medical resources are mutually exclusive. Conclusions Government and urban planners should pay great attention to the spatial distribution characteristics and association intensity of medical resources when formulating relevant policies. The findings of this study contribute to health equity and health policy discussions around basic medical services and professional medical services.

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Detecting Colocation Flow Patterns in the Geographical Interaction Data

Cited by 21 publications

References 74 publications

Flow-Data-Based Global Spatial Autocorrelation Measurements for Evaluating Spatial Interactions

Flow-Data-Based Global Spatial Autocorrelation Measurements for Evaluating Spatial Interactions

Mining significant local spatial association rules for multi-category point data

Analysing the global and local spatial associations of medical resources across Wuhan city using POI data

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