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Flow Spatiotemporal Moran's I: Measuring the Spatiotemporal Autocorrelation of Flow Data

文献类型：期刊论文

作者Fu, Qingyang1,2; Zhou, Mengjie1,2,3; Li, Yige1; Ye, Xiang4,5; Yang, Mengjie1; Wang, Yuhui1
刊名GEOGRAPHICAL ANALYSIS
出版日期2024-03-12
页码26
ISSN号0016-7363
DOI10.1111/gean.12397
通讯作者Zhou, Mengjie(mengjiezhou@hunnu.edu.cn)
英文摘要Flows can reflect the spatiotemporal interactions or movements of geographical objects between different locations. Measuring the spatiotemporal autocorrelation of flows can help determine the overall spatiotemporal trends and local patterns. However, quantitative indicators of flows used to measure spatiotemporal autocorrelation both globally and locally are still rare. Therefore, we propose the global and local flow spatiotemporal Moran's I (FSTI). The global FSTI is used to assess the overall spatiotemporal autocorrelation degree of flows, and the local FSTI is applied to identify local spatiotemporal clusters and outliers. In the FSTI, to reflect flow spatiotemporal adjacency relationships, we establish flow spatiotemporal weights by multiplying the spatial and temporal weights of flows considering spatiotemporal orthogonality. The flow spatial weights include contiguity-based (considering first/higher-order and common border) and Euclidean distance-based weights. The temporal weights consider ordinary and lagged cases. As flow attributes may follow a long-tail distribution, we conduct Monte Carlo simulations to evaluate the statistical significance of the results. We assess the FSTI using synthetic datasets and Chinese population mobility datasets, and compare some results with those of recent flow-related methods. Additionally, we perform a sensitivity analysis to select a suitable temporal threshold. The results show that the FSTI can be used to effectively detect spatiotemporal variations in the autocorrelation degree and type.
WOS关键词PATTERNS ; MODELS
资助项目National Natural Science Foundation of China ; Natural Science Foundation of Hunan Province[2023JJ40447] ; Scientific research project of Hunan Provincial Department of Education[23B0093] ; State Key Laboratory of Resources and Environmental Information System ; [41901314] ; [42301471]
WOS研究方向Geography
语种英语
WOS记录号WOS:001182444600001
出版者WILEY
资助机构National Natural Science Foundation of China ; Natural Science Foundation of Hunan Province ; Scientific research project of Hunan Provincial Department of Education ; State Key Laboratory of Resources and Environmental Information System
源URL[http://ir.igsnrr.ac.cn/handle/311030/203619]  
专题中国科学院地理科学与资源研究所
通讯作者Zhou, Mengjie
作者单位1.Hunan Normal Univ, Sch Geog Sci, Changsha, Hunan, Peoples R China
2.Hunan Normal Univ, Hunan Key Lab Geospatial Big Data Min & Applicat, Changsha, Hunan, Peoples R China
3.Hunan Normal Univ, Key Lab Urban Rural Transformat Proc & Effects, Changsha, Peoples R China
4.Nanjing Normal Univ, Sch Geog, Nanjing, Jiangsu, Peoples R China
5.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Fu, Qingyang,Zhou, Mengjie,Li, Yige,et al. Flow Spatiotemporal Moran's I: Measuring the Spatiotemporal Autocorrelation of Flow Data[J]. GEOGRAPHICAL ANALYSIS,2024:26.
APA Fu, Qingyang,Zhou, Mengjie,Li, Yige,Ye, Xiang,Yang, Mengjie,&Wang, Yuhui.(2024).Flow Spatiotemporal Moran's I: Measuring the Spatiotemporal Autocorrelation of Flow Data.GEOGRAPHICAL ANALYSIS,26.
MLA Fu, Qingyang,et al."Flow Spatiotemporal Moran's I: Measuring the Spatiotemporal Autocorrelation of Flow Data".GEOGRAPHICAL ANALYSIS (2024):26.

入库方式： OAI收割

来源：地理科学与资源研究所

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