Back to Search
Start Over
Analysis of the Spatial Variation of Network-Constrained Phenomena Represented by a Link Attribute Using a Hierarchical Bayesian Model
- Source :
- ISPRS International Journal of Geo-Information, Vol 6, Iss 2, p 44 (2017), ISPRS International Journal of Geo-Information; Volume 6; Issue 2; Pages: 44
- Publication Year :
- 2017
- Publisher :
- MDPI AG, 2017.
-
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.
- Subjects :
- Point of interest
network-constrained
Geography, Planning and Development
0507 social and economic geography
points of interests
lcsh:G1-922
computer.software_genre
Bayesian inference
01 natural sciences
010104 statistics & probability
Earth and Planetary Sciences (miscellaneous)
0101 mathematics
Computers in Earth Sciences
Spatial dependence
Spatial analysis
hierarchical Bayesian model
attribute-based method
local indicators of network-constrained clusters (LINCS)
Euclidean space
05 social sciences
Base (topology)
Geography
Spatial ecology
Spatial variability
Data mining
050703 geography
computer
lcsh:Geography (General)
Subjects
Details
- Language :
- English
- ISSN :
- 22209964
- Volume :
- 6
- Issue :
- 2
- Database :
- OpenAIRE
- Journal :
- ISPRS International Journal of Geo-Information
- Accession number :
- edsair.doi.dedup.....b1a6a89f683ec7cb175602cb2bab1e68