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Quantifying the spatial heterogeneity of points
Shu, Hua1,2; Pei, Tao1,2,3; Song, Ci1,2; Ma, Ting1; Du, Yunyan1; Fan, Zide1; Guo, Sihui1,2
2019-07-03
Source PublicationINTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
ISSN1365-8816
Volume33Issue:7Pages:1355-1376
Corresponding AuthorPei, Tao(peit@lreis.ac.cn)
AbstractVariation in the spatial heterogeneity of points reflects the evolutionary process or mechanism of geographical events. The key to depicting this variation is quantifying spatial heterogeneity. In this paper, the spatial heterogeneity of a point pattern is defined as the degree of aggregation-type deviation from complete spatial randomness. In such a case, a goodness-of-fit-type statistic based on the distribution of nearest-neighbor distances called the level of heterogeneity (LH*) is regarded as a standard measurement, and a normalized version called the normalized level of heterogeneity (NLH*) is proposed for datasets with different point numbers and study region areas. Considering the complex integration calculation of LH* and NLH*, simulation experiments are implemented to test the capability of some classic nearest-neighbor statistics in quantifying spatial heterogeneity. The results showed that except for the standard LH* statistic, only Clark and Evans' statistic (A-w) and Byth and Ripley's statistic (H-xw) are robust. Statistics NLH*, (A-w) and (H-xw) are validated by quantifying the spatial heterogeneity of two-dimensional crime events, three-dimensional earthquake events and four-dimensional origin-destination (OD) events. The results indicate that these statistics all have a reasonable explanation in quantifying spatial heterogeneity for real-world geographical events of different types and with different dimensions. Compared with NLH*, Clark and Evans' (A-w) statistic and Byth and Ripley's (H-xw) statistic are recommended from the perspective of accessibility.
KeywordPoint pattern spatial heterogeneity spatial statistics nearest-neighbor statistics
DOI10.1080/13658816.2019.1577432
WOS KeywordSTATISTICAL-ANALYSIS ; CRIME CONCENTRATION ; NEAREST-NEIGHBOR ; PATTERN-ANALYSIS ; INHOMOGENEITY ; SEGREGATION ; EARTHQUAKES ; DENSITY
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41525004] ; National Natural Science Foundation of China[41421001] ; National Key R&D Program of China[2017YFB0503604]
Funding OrganizationNational Natural Science Foundation of China ; National Key R&D Program of China
WOS Research AreaComputer Science ; Geography ; Physical Geography ; Information Science & Library Science
WOS SubjectComputer Science, Information Systems ; Geography ; Geography, Physical ; Information Science & Library Science
WOS IDWOS:000468585300005
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/59267
Collection中国科学院地理科学与资源研究所
Corresponding AuthorPei, Tao
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing, Jiangsu, Peoples R China
Recommended Citation
GB/T 7714
Shu, Hua,Pei, Tao,Song, Ci,et al. Quantifying the spatial heterogeneity of points[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2019,33(7):1355-1376.
APA Shu, Hua.,Pei, Tao.,Song, Ci.,Ma, Ting.,Du, Yunyan.,...&Guo, Sihui.(2019).Quantifying the spatial heterogeneity of points.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,33(7),1355-1376.
MLA Shu, Hua,et al."Quantifying the spatial heterogeneity of points".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 33.7(2019):1355-1376.
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