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ISPRS Int. J. Geo-Inf. 2017, 6(2), 44; doi:10.3390/ijgi6020044

Analysis of the Spatial Variation of Network-Constrained Phenomena Represented by a Link Attribute Using a Hierarchical Bayesian Model

1,2,3,4
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1,2,* , 1,2
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4
and
5
1
Shenzhen Key Laboratory of Spatial Smart Sensing and Services, College of Civil Engineering, Shenzhen University, Shenzhen 518060, China
2
Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation, Shenzhen University, Shenzhen 518060, China
3
College of Information Engineering, Shenzhen University, Shenzhen 518060, China
4
Key Laboratory of Urban land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen 518034, China
5
School of Management, Ningbo Institute of Technology, Zhejiang University, Ningbo 315010, China
*
Author to whom correspondence should be addressed.
Academic Editors: Arpad Barsi and Wolfgang Kainz
Received: 13 October 2016 / Revised: 23 January 2017 / Accepted: 10 February 2017 / Published: 14 February 2017
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Abstract

The spatial variation of geographical phenomena is a classical problem in spatial data analysis and can provide insight into underlying processes. Traditional exploratory methods mostly depend on the planar distance assumption, but many spatial phenomena are constrained to a subset of Euclidean space. In this study, we apply a method based on a hierarchical Bayesian model to analyse the spatial variation of network-constrained phenomena represented by a link attribute in conjunction with two experiments based on a simplified hypothetical network and a complex road network in Shenzhen that includes 4212 urban facility points of interest (POIs) for leisure activities. Then, the methods named local indicators of network-constrained clusters (LINCS) are applied to explore local spatial patterns in the given network space. The proposed method is designed for phenomena that are represented by attribute values of network links and is capable of removing part of random variability resulting from small-sample estimation. The effects of spatial dependence and the base distribution are also considered in the proposed method, which could be applied in the fields of urban planning and safety research. View Full-Text
Keywords: network-constrained; points of interests; hierarchical Bayesian model; attribute-based method; local indicators of network-constrained clusters (LINCS) network-constrained; points of interests; hierarchical Bayesian model; attribute-based method; local indicators of network-constrained clusters (LINCS)
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Wang, Z.; Yue, Y.; Li, Q.; Nie, K.; Yu, C. Analysis of the Spatial Variation of Network-Constrained Phenomena Represented by a Link Attribute Using a Hierarchical Bayesian Model. ISPRS Int. J. Geo-Inf. 2017, 6, 44.

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