Interaction between Road Network Connectivity and Spatial Pattern

Sreelekha, M.G.; Karuppanagounder, Krishnamurthy; Anjaneyulu, M. V. L. R.

doi:10.1016/j.protcy.2016.05.019

Cited by 52 publications

(32 citation statements)

References 6 publications

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“…They found that the road network density is better suited to predict road network fractal dimension even though the data contain some amount of dispersion. (Sreelekha et al, 2016). However, the similarity between Seelekha's study and the current study is shown in the zonal analysis, but the current study differs in the use of Map Algebra tool.…”

Section: Connectivity Indices Calculation By Gis Toolssupporting

confidence: 50%

“…The new method is also more detailed and clear than the method used by (Nagne et al, 2013). Additionally, this study goes further than the study of (Sreelekha et al, 2016); the latter concluded that they found that the roads network density is better suited to predict road network connectivity. While the current study combined all indicators, including density of the links and the density of the nodes to reach more accurate results.…”

Section: Resultsmentioning

confidence: 69%

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Evaluating the connectivity level of Fayoum city roads network: a new method using GIS

Saleh

Abdu

Ali

2018

PKG

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Road networks within cities are a vital component of the socioeconomic development processes. Many countries have sought to examine and evaluate the efficiency of road networks within urban areas because of the increasing demand for transportation. Thus, networks should be evaluated to guide the development of transport systems and infrastructure within cities. This study evaluates the connectivity level of the road network parts in Fayoum city, Egypt, through the application of some indicators of connectivity measurement, such as Beta, Alpha, Gamma, Connectivity degree, and the density of edges and nodes. The study follows a new method for evaluation; it does not apply the indicators on the network as a whole; however, it divides the city into zones and calculates the indicators of each zone using the Arc GIS 10.2.1 program. The authors applied the map Algebra method to map the results of the six indicators in one index called the Total Connectivity Index (TCI). The TCI gives more consistent results than other indices, and it is more related to different spatial patterns of roads. According to this indicator, most of the city road network seem to have a good connectivity level, whereas the city marginal areas prove to have a poor connectivity level. Therefore, any efforts to develop or construct new roads should be in the marginal areas of the city.

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Section: Connectivity Indices Calculation By Gis Toolssupporting

confidence: 50%

Section: Resultsmentioning

confidence: 69%

Evaluating the connectivity level of Fayoum city roads network: a new method using GIS

Saleh

Abdu

Ali

2018

PKG

View full text Add to dashboard Cite

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“…The search for a relationship between street network structure and urban land use has become a major research trend [13]. Researching hierarchical and functional structure, Southworth and Ben-Joseph applied spatial syntax theory to analyze the connection and accessibility of street Brindle proposed that there are only two major types of street network structures: the grid network and the tree network, distinguished by the degree of connectivity of the roads [2].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of Urban Street Networks Based on Tree-Like Network Features

Han

Sun

et al. 2020

Sustainability

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Urban street networks derive their complexity not only from their hierarchical structure, but also from their tendency to simultaneously exhibit properties of both grid-like and tree-like networks. Using topological indicators based on planning parameters, we develop a method of network division that makes classification of such intermediate networks possible. To quantitatively describe the differences between street network patterns, we first carefully define a tree-like network structure according to topological principles. Based on the requirements of road planning, we broaden this definition and also consider three other types of street networks with different microstructures. We systematically compare the structure variables (connectivity, hierarchy, and accessibility) of selected street networks around the world and find several explanatory parameters (including the relative incidence of through streets, cul-de-sacs, and T-type intersections), which relate network function and features to network type. We find that by measuring a network’s degree of similarity to a tree-like network, we can refine the classification system to more than four classes, as well as easily distinguish between the extreme cases of pure grid-like and tree-like networks. Each indicator has different distinguishing capabilities and is adapted to a different range, thereby permitting networks to be grouped into corresponding types when the indicators are evaluated in a certain order. This research can further improve the theory of interaction between transportation and land use.

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“…In [8], a study was carried out to analyse the road network connectivity and spatial pattern in Calicut city in India based on spatial techniques. It was observed that the transport network fractality is very related with connectivity and coverage; the network density is the best indicator for predicting the fractality of a road network, therefore the level of road network development and the network spatial structure are extremely dependant.…”

Section: Introductionmentioning

confidence: 99%

Study of the US road network based on social network analysis

2017

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The complexity of big data structures and networks demands more research in terms of analysing and representing data for a better comprehension and usage. In this regard, there are several types of model to represent a structure. The aim of this article is to use a social network topology to analyse road network for the following States in the United States (US): California, Pennsylvania and Texas. Our approach mainly focuses on clustering of road network data in order to create "communities".

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Interaction between Road Network Connectivity and Spatial Pattern

Cited by 52 publications

References 6 publications

Evaluating the connectivity level of Fayoum city roads network: a new method using GIS

Evaluating the connectivity level of Fayoum city roads network: a new method using GIS

Classification of Urban Street Networks Based on Tree-Like Network Features

Study of the US road network based on social network analysis

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