TY - JOUR
T1 - Weighted network Voronoi Diagrams for local spatial analysis
AU - She, Bing
AU - Zhu, Xinyan
AU - Ye, Xinyue
AU - Guo, Wei
AU - Su, Kehua
AU - Lee, Jay
N1 - Funding Information:
The research is funded by the National Natural Science Foundation of China (Grant number 41271401 ), and the National Science and Technology Pillar Program (Grant number 2012BAH35B03 ). The authors would like to thank the anonymous reviewers for their valuable comments and suggestions.
Publisher Copyright:
© 2015 Elsevier Ltd.
PY - 2015/7/1
Y1 - 2015/7/1
N2 - Detection of spatial clusters among geographic events in a planar space often fails in real world practices. For example, events in urban areas often occurred on or along streets. In those cases, objects and their movements were limited to the street network in the urban area. This deviated from what a set of freely located points could represent. Consequently, many of the spatial analytic tools would likely produce biased results. To reflect this limitation, we developed a new approach, weighted network Voronoi diagrams, to modeling spatial patterns of geographic events on street networks whose street segments can be weighted based on their roles in the events. Using kernel density estimation and local Moran's index statistics, the frequency of events occurring on a street segment can be used to produce a weight to associate with the street segment. The weights can then be normalized using a predefined set of intervals. The constructed weighted Voronoi network explicitly takes into account the characteristics of how events distribute, instead of being limited to assessing the spatial distribution of events without considering how the structure of a street network may affect the distribution. This approach was elaborated in a case study of Wuhan City, China. Constructing weighted network Voronoi diagrams of these partitioned networks could assist city planners and providers of public/private services to better plan for network-constrained service areas.
AB - Detection of spatial clusters among geographic events in a planar space often fails in real world practices. For example, events in urban areas often occurred on or along streets. In those cases, objects and their movements were limited to the street network in the urban area. This deviated from what a set of freely located points could represent. Consequently, many of the spatial analytic tools would likely produce biased results. To reflect this limitation, we developed a new approach, weighted network Voronoi diagrams, to modeling spatial patterns of geographic events on street networks whose street segments can be weighted based on their roles in the events. Using kernel density estimation and local Moran's index statistics, the frequency of events occurring on a street segment can be used to produce a weight to associate with the street segment. The weights can then be normalized using a predefined set of intervals. The constructed weighted Voronoi network explicitly takes into account the characteristics of how events distribute, instead of being limited to assessing the spatial distribution of events without considering how the structure of a street network may affect the distribution. This approach was elaborated in a case study of Wuhan City, China. Constructing weighted network Voronoi diagrams of these partitioned networks could assist city planners and providers of public/private services to better plan for network-constrained service areas.
KW - Kernel density estimation
KW - Local Moran's I
KW - Urban street networks
KW - Weighted Voronoi network
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U2 - 10.1016/j.compenvurbsys.2015.03.005
DO - 10.1016/j.compenvurbsys.2015.03.005
M3 - Article
AN - SCOPUS:84927549432
SN - 0198-9715
VL - 52
SP - 70
EP - 80
JO - Computers, Environment and Urban Systems
JF - Computers, Environment and Urban Systems
ER -