The Random Walk Value for Ranking Spatial Characteristics in Road Networks

Abstract

This study proposes a new network index at both nodal and link levels to rank the spatial characteristics of individual network components. The objective is to create a network metric that captures socioeconomic characteristics in urban environments. Because this index is based on the random walk simulation modeling strategy, it is coined the random walk value (RWV). An algorithm and an associated software tool were developed to calculate the RWVs of network components. Compared with other popular network indices, the unique advantage of the RWV is that this index considers not only spatial structural or topological characteristics, but also physical characteristics of network components. Two case study cities, the Chinese city of Wuhan and the U.S. city of Atlanta, were chosen to test the utility of the RWV. These two case studies yield several findings. First, the RWV is highly consistent with some of the most widely used network measures, such as closeness and connectivity measures, which was evidenced by strong correlations between RWV and other networks. Second, the RWV has been proven to be a good indicator of spatial importance and a better predictor of socioeconomic variables in urban environments. The RWV outperforms all other network indices in terms of its correlations with important socioeconomic variables and its ability to predict some of them. Third, both case studies confirm that the RWV can be a good substitute for some important socioeconomic variables, such as population density and job density, in spatial modeling. This finding is significant for studies when population and job data are not available and for studies that attempt to predict future scenarios.

Geospatial analysis requires a different way of thinking: the problem of spatial heterogeneity

“…In this regard, the topological representation to be introduced in the following text has enabled us to uncover the underlying scaling pattern of street networks. Four of the special issue papers (Gil 2014, Lerman et al 2014, Mohajeri and Gudmundsson 2014, Wei and Yao 2014) have adopted or are well connected to the topological representation, which is powerful for understanding street hierarchies or geographic forms and processes in general.Conventionally, geographic space is considered as a continuous Euclidian space that is divided or subdivided into different areas, which authorities often define and delineate administratively and legally. Data collected on geographic space are assigned into individual areas, and are therefore assumed to be homogenous in each area.…”

“…In this regard, the topological representation to be introduced in the following text has enabled us to uncover the underlying scaling pattern of street networks. Four of the special issue papers (Gil 2014, Lerman et al 2014, Mohajeri and Gudmundsson 2014, Wei and Yao 2014) have adopted or are well connected to the topological representation, which is powerful for understanding street hierarchies or geographic forms and processes in general.…”

Essential Means for Urban Computing: Specification of Web-Based Computing Platforms for Urban Planning, a Hitchhiker’s Guide

“…The use of the last category of data models is relatively newer than the other types of the models and is used for modelling the dynamics of diffusion flows and Markov Processes in networks (Nourian, 2016;Nourian, Rezvani, Sariyildiz, & van der Hoeven, 2016;Volchenkov & Blanchard, 2007;Wei & Yao, 2014). Performing spectral analyses requires using a computational linear algebra library such as NumPy 12 .…”

Multi‐Domain User‐Generated Content Based Model to Enrich Road Network Data for Multi‐Criteria Route Planning

“…For example, people might take beautiful scenery pictures (Okuyama and Yanai 2013) or save their points of interest (POIs) spatial coordinates by using Global Positioning System (GPS) devices. Consequently, by mining these data, extracting knowledge about road segments as an important component in urban planning applications would be possible (Jiang and Okabe 2014;Wei and Yao 2014). Some studies extracted attributes, such as name, number of band lines, class, type, and surface of road segments, from UGC (Lauer, Billen, and Zipf 2013;Pourabdollah et al 2013;Ding et al 2014;Li et al 2014Li et al , 2015.…”