NETWORK ANALYSIS IN GEOGRAPHY. By Peter Haggett and Richard J. Chorley.

Hosking, Peter L.

doi:10.1111/j.1745-7939.1972.tb00681.x

Cited by 2 publications

(3 citation statements)

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“…The urban-rural gradient analysis of the KDE is further characterized by the obvious "density island effect" of road networks, with a turning point (e.g., 12 km) beyond which the KDE value stabilized at around 1 (Figure 10). This finding matches not only the above-SA result [61], but also the central-place theory, demonstrating that transport infrastructures are more frequently distributed in higher-order places (i.e., towns), but fewer are located in lower-order places (i.e., suburban or rural areas) [45]. Therefore, we confirmed the previous outcomes [27] indicating that the road network was an effective proxy for the structural information of the region that can also be used to segment urban areas [14].…”

Section: Delimitation Of the Urban Boundarysupporting

confidence: 91%

Spatial Paradigms in Road Networks and Their Delimitation of Urban Boundaries Based on KDE

Lin

et al. 2020

IJGI

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An in-depth analysis of urban road network distribution plays a critical role in understanding the urbanization process. However, effective ways to quantitatively analyze the spatial paradigms of road networks are still lacking, and few studies have utilized road networks to rapidly identify urban areas of a region. Thus, using a fast-developing region in the south-eastern costal region of China, Fuzhou City, as a case, we introduced kernel density estimation (KDE) to characterize road networks and quantified the area’s spatial heterogeneity using exploratory spatial data analysis (ESDA) and semivariance analysis (SA). The results show that there is an uneven spatial distribution of the networks both at the regional and downtown levels. At the regional level, there is a conspicuous polarization in the road distribution, with the KDE being much higher in the urban areas than in the rural areas; at the downtown level, the KDE gradually decreases from the center to the periphery. Quantitatively, the ranges of the spatial dependence of the networks are approximately 25 km for the entire study region and 12 km for the downtown area. Additionally, the spatial variations vary among different directions, with greater variations in the northeast–southwest and the southeast–northwest directions compared with the other directions, which is in line with the urban sprawl policy of the study area. Both the qualitative and quantitative results show that the distribution of road networks has a clear urban–rural dual structure, which indicates that road networks can be an active tool in identifying the urban areas of a region. To this end, we propose a quick and easy method to delimit urban areas using KDE. The extraction results of KDE are better than those of the index-based built-up index (IBI), indicating the effectivity and feasibility of our proposed method to identify the urban areas in the region. This research sheds new light on urbanization development research.

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Section: Delimitation Of the Urban Boundarysupporting

confidence: 91%

Spatial Paradigms in Road Networks and Their Delimitation of Urban Boundaries Based on KDE

Lin

et al. 2020

IJGI

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“…It is used to detect networks with "good expansion" properties. A small spectral gap would probably indicate the presence of articulations points or bridges that might cause serious disruptions of the flow in the network when removed [14] ∆𝜆 (7) where:…”

Section: Hub Dependence Of the Networkmentioning

confidence: 99%

“…Nevertheless, the integration of complex transport networks is very recent [6]. In overall terms, these approaches have had a strong emphasis on routing optimization and transport costs [7]. To evaluate the proprieties of the transport system, a network representation must be done, this representation changes over time and over the kind of transport network subject to analysis.…”

Section: Introductionmentioning

confidence: 99%

Structural Analysis in Transit System Using Network Theory Case of Guadalajara, Mexico

Barraza

Estrada

2021

Urban Science

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Structural analysis in a transit network is a key aspect used to evaluate in a planning process. In this sense, the use of network science was applied in this work to generate a framework of the main structural features of a transport network. In this case, an alternative transport network in Guadalajara, Mexico was taken as an example. The network properties selected were grade of accessibility, spatial friction, and vulnerability. In the case of the grade of accessibility, this propriety makes reference to the efficiency of the travel time that the network gives due to its structural features. The spatial friction measures how direct in terms of distance the trips that the network provides are, and the vulnerability relates to the ease with which the network can comprise its performance by affectations to their nodes or links. In this sense, this work presents a detailed methodology and a set of open-source tools that can be used to measure these key structural elements for decision making.

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NETWORK ANALYSIS IN GEOGRAPHY. By Peter Haggett and Richard J. Chorley.

Cited by 2 publications

References 0 publications

Spatial Paradigms in Road Networks and Their Delimitation of Urban Boundaries Based on KDE

Spatial Paradigms in Road Networks and Their Delimitation of Urban Boundaries Based on KDE

Structural Analysis in Transit System Using Network Theory Case of Guadalajara, Mexico

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