analysis of the spatial variation of network-constrained phenomena represented by a link attribute using a hierarchical bayesian model

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ID: 253183
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.
Reference Key
wang2017isprsanalysis Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Zhensheng Wang;Yang Yue;Qingquan Li;Ke Nie;Changbin Yu
Journal población y desarrollo
Year 2017
DOI
10.3390/ijgi6020044
URL
http://www.mdpi.com/2220-9964/6/2/44
Keywords Keywords not found

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