Paper Info
Title
Analysis of the Spatial Variation of Network-Constrained Phenomena Represented by a Link Attribute Using a Hierarchical Bayesian Model.
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.
Year
DOI
Venue
2017
10.3390/ijgi6020044
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
Keywords
Field
DocType
network-constrained,points of interests,hierarchical Bayesian model,attribute-based method,local indicators of network-constrained clusters (LINCS)
Spatial analysis,Data mining,Spatial dependence,Bayesian inference,Euclidean space,Urban planning,Spatial variability,Point of interest,Geography,Spatial ecology
Journal
Volume
Issue
Citations 
6
2
1
PageRank 
References 
Authors
0.36
5
5
Name
Order
Citations
PageRank
Zhensheng Wang110.36
Yang Yue27413.76
Qingquan Li31181135.06
Ke Nie411.03
Changbin Yu584575.14