Weighted network Voronoi Diagrams for local spatial analysis

She, Bing; Zhu, Xinyan; Ye, Xinyue; Guo, Wei; Su, Kehua; Lee, Jay

doi:10.1016/j.compenvurbsys.2015.03.005

Cited by 31 publications

(22 citation statements)

References 54 publications

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“…Moreover, urban facilities are generally expected to form spatial clusters in geographical space because of the inherent association between various types of urban facilities. For example, financial facilities, commercial and consulting facilities, and retail shops are often clustered in central business districts (CBD), which is an important issue for urban planning [40]. However, in the case study, we did not account for the types of individual urban facility POIs (e.g., banks, retail stores and cinemas) nor the relationship between different types of POIs (e.g., schools and residential areas).…”

Section: Discussionmentioning

confidence: 99%

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

Wang

Yue

et al. 2017

IJGI

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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.

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Section: Discussionmentioning

confidence: 99%

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

Wang

Yue

et al. 2017

IJGI

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“…Overall, 26.3% road segments belong to low-low aggregation model, 1.2% presents low-high aggregation, 10.7% exhibits high-high aggregation and 0.2% is high-low aggregation. The z values calculated by the Moran's I were in the interval [−1.168, 22.464] [40]. To detect the urban fire centers, the NKDE-ILINCS method using kernel density as input disclose and identify high-high road segments successfully compared with in planar space, which is more reasonable and approximate to the reality.…”

Section: Ilincs Based On Nkdementioning

confidence: 99%

An Integrated Spatial Clustering Analysis Method for Identifying Urban Fire Risk Locations in a Network-Constrained Environment: A Case Study in Nanjing, China

Xia

Yue-hong

2017

IJGI

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Abstract:The spatial distribution of urban geographical events is largely constrained by the road network, and research on spatial clusters of fire accidents at the city level plays a crucial role in emergency rescue and urban planning. For example, by knowing where and when fire accidents usually occur, fire enforcement can conduct more efficient aid measures and planning department can work out more reasonable layout optimization of fire stations. This article proposed an integrated method by combining weighted network-constrained kernel density estimation (NKDE) and network-constrained local Moran's I (ILINCS) to detect spatial cluster pattern and identify higher-risk locations of fire accidents. The proposed NKDE-ILINCS weighted a set of crucial non-spatial attributes of point events and links, and considered the impact factors of road traffic states, intersection roads and fire severity in NKDE to reflect real urban environment. This method was tested using the fire data in 2015 in Nanjing, China. The results demonstrated that the method was appropriate to detect network-constrained fire cluster patterns and identify high-high road segments. Besides, the first 14 higher-risk road segments in Nanjing are listed. These findings of this case study enhance our knowledge to more accurately observe where fire accidents usually occur and provide a reference for fire departments to improve emergency rescue effectiveness.

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“…The study of Voronoi diagrams can be traced back to the seminal work by Voronoi [49]. Consider some d-dimensional space in which a number of given points (sometimes …”

Section: Voronoi Diagrams Used To Compare the Distance Extensionmentioning

confidence: 99%

Integrating Spatial and Attribute Characteristics of Extended Voronoi Diagrams in Spatial Patterning Research: A Case Study of Wuhan City in China

Miao

Chen

Zhang

et al. 2016

IJGI

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Abstract:Rapid urbanization has caused numerous problems, and the urban spatial structure has been a hot topic in sustainable development management. Urban spatial structure is affected by a series of factors. Thus, the research model should synthetically consider the spatial and non-spatial relationship of every element. Here, we propose an extended Voronoi diagram for exploring the urban land spatial pattern. In essence, we first used a principal component analysis method to construct attribute evaluation indicators and obtained the attribute distance for each indicator. Second, we integrated spatial and attribute distances to extend the comparison distance for Voronoi diagrams, and then, we constructed the Voronoi aggregative homogeneous map of the study area. Finally, we make a spatial autocorrelation analysis by using GeoDA and SPSS software. Results show that: (1) the residential land cover aggregation is not significant, but spatial diffusion is obvious; (2) the commercial land cover aggregation is considerable; and (3) the spatial agglomeration degree of the industrial land cover is increased and mainly located in urban fringes. According to the neo-Marxist theory, we briefly analyzed the driving forces for shaping the urban spatial structure. To summarize, our approach yields important insights into the urban spatial structure characterized by attribute similarity with geospatial proximity, which contributes to a better understanding of the urban growth mechanism. In addition, it explicitly identifies ongoing urban transformations, potentially supporting the planning for sustainable urban land use and protection.

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Weighted network Voronoi Diagrams for local spatial analysis

Cited by 31 publications

References 54 publications

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

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

An Integrated Spatial Clustering Analysis Method for Identifying Urban Fire Risk Locations in a Network-Constrained Environment: A Case Study in Nanjing, China

Integrating Spatial and Attribute Characteristics of Extended Voronoi Diagrams in Spatial Patterning Research: A Case Study of Wuhan City in China

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