A Study on the Urban Spatial Structure - A Case Study of Jinju City -

2011 ◽  
Vol 14 (4) ◽  
pp. 92-101
Author(s):  
Jeong-Hyun Cho ◽  
Chang-Hak Lee ◽  
Tae-Kyung Baek
Keyword(s):  
Spatial Structure ◽  
Urban Spatial Structure

scholarly journals Urban spatial structure re-identification as an approach to solving urban transport problem (A case study in Malang)

2018 ◽  
Vol 181 ◽  
pp. 10006
Author(s):  
Dadang Meru Utomo ◽  
Septiana Hariyani
Keyword(s):  
Local Government ◽  
Spatial Structure ◽  
Urban Transport ◽  
Urban Spatial Structure ◽  
Spatial Structures ◽  
Index Method ◽  
Centrality Index ◽  
Effective Result

In an effort to solve the congestion problem, the local government of Malang City has taken various steps. However, unfortunately, the result has not been effective. This shows that the efforts that have been taken so far are sectoral, therefore a more comprehensive approach is needed in order to reach a more effective result. This research aims at re-evaluating the determination of Malang City's urban spatial structure through the delineation of narrower units of area using the centrality index method. The research suggests that urban spatial structures have a very clear pattern of association with congestion locations. It can be concluded that the local government of Malang City needs to put more consideration on evaluating the urban spatial structure to solve the problem of transportation which is ever-growing the in second largest urban area in East Java Province.

Modeling the Change of Urban Spatial Structure: Use Interzonal Travel Data to Estimate Urban Growth and Expansion by Hierarchical Cluster Analyses

2018 ◽  
pp. 376-408
Author(s):  
Darcin Akin ◽  
Serdar Alasalvar
Keyword(s):  
Spatial Structure ◽  
Urban Growth ◽  
Hierarchical Cluster ◽  
Circulation Pattern ◽  
Transportation System ◽  
Urban Spatial Structure ◽  
Economic Activities ◽  
The City ◽  
Hierarchical Cluster Analyses

The Urban spatial structure is affected by spatial interactions among various activity locations, and land uses in the city over the transportation system. Each city has its unique circulation pattern of passengers and freight due to its unique geographic conditions and the distribution of locations of economic activities. In that sense, it is claimed in this chapter per the authors that urban spatial structure can be modeled using interzonal (O/D) travel data. Thus, the chapter presents a case study of modeling spatial structures developed by employing Hierarchical Cluster Analysis (HCA) using travel pattern data for current and future scenarios. As a result, urban growth and expansion were estimated based on the level of interaction (represented by distance or similarity modeled based on trip interchanges) over the transportation system in terms of population and/or employment increases. The interaction was described by a measure of distance or similarity, modeled with respect to trip interchanges.

scholarly journals Low-Carbon Transportation Oriented Urban Spatial Structure: Theory, Model and Case Study

2017 ◽  
Vol 10 (2) ◽  
pp. 19 ◽  
Author(s):  
Yuyao Ye ◽  
Changjian Wang ◽  
Yuling Zhang ◽  
Kangmin Wu ◽  
Qitao Wu ◽  
...  
Keyword(s):  
Spatial Structure ◽  
Theory Model ◽  
Structure Theory ◽  
Urban Spatial Structure ◽  
Low Carbon

Book Review: Housing and Urban Spatial Structure: a case study

Urban Studies ◽  
1976 ◽  
Vol 13 (2) ◽  
pp. 210-211
Author(s):  
Colin Jones
Keyword(s):  
Spatial Structure ◽  
Urban Spatial Structure

Modeling the Change of Urban Spatial Structure: Use Interzonal Travel Data to Estimate Urban Growth and Expansion by Hierarchical Cluster Analyses

2016 ◽  
pp. 168-202 ◽  
Author(s):  
Darcin Akin ◽  
Serdar Alasalvar
Keyword(s):  
Spatial Structure ◽  
Urban Growth ◽  
Hierarchical Cluster ◽  
Circulation Pattern ◽  
Transportation System ◽  
Urban Spatial Structure ◽  
Economic Activities ◽  
The City ◽  
Hierarchical Cluster Analyses

The Urban spatial structure is affected by spatial interactions among various activity locations, and land uses in the city over the transportation system. Each city has its unique circulation pattern of passengers and freight due to its unique geographic conditions and the distribution of locations of economic activities. In that sense, it is claimed in this chapter per the authors that urban spatial structure can be modeled using interzonal (O/D) travel data. Thus, the chapter presents a case study of modeling spatial structures developed by employing Hierarchical Cluster Analysis (HCA) using travel pattern data for current and future scenarios. As a result, urban growth and expansion were estimated based on the level of interaction (represented by distance or similarity modeled based on trip interchanges) over the transportation system in terms of population and/or employment increases. The interaction was described by a measure of distance or similarity, modeled with respect to trip interchanges.

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