Fitting Planar Proximity Graphs on Real Street Networks

2016 ◽  
pp. 11-20
Author(s):  
Dimitris Maniadakis ◽  
Dimitris Varoutas
Keyword(s):  
Proximity Graphs ◽  
Street Networks

Deviant Street Networks: Prostitution in New York City.Bernard Cohen

1982 ◽  
Vol 87 (5) ◽  
pp. 1230-1232 ◽  
Author(s):  
Gale Miller
Keyword(s):  
New York ◽  
Street Networks

scholarly journals Urban Form Resilience: A Comparative Analysis of Traditional, Semi-Planned, and Planned Neighborhoods in Shiraz, Iran

Urban Science ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 18
Author(s):  
Ayyoob Sharifi ◽  
Maryam Roosta ◽  
Masoud Javadpoor
Keyword(s):  
Urban Form ◽  
Green Spaces ◽  
Economic Factors ◽  
Urban Resilience ◽  
Future Studies ◽  
Street Networks ◽  
The Past ◽  
Extreme Heat Events ◽  
Physical Form ◽  
Heat Events

As cities are exposed to a portfolio of risks, the concept of resilience has risen to prominence over the past two decades. Consequently, a large volume of research has been published on different aspects of urban resilience. However, urban form resilience is still relatively understudied. As a step toward filling this gap, this study examines resilience of nine selected neighborhoods from Shiraz, an old Iranian city. The selected cases represent three different urban form patterns, namely, traditional, semi-planned, and planned. Different indicators related to the physical configuration of lots, blocks, open and green spaces, and street networks are used to examine resilience of each neighborhood to three major stressors, namely, earthquakes, extreme heat events, and floods. Additionally, a combination of Shannon entropy and the VIKOR (VlseKriterijumska Optimizcija I Kaompromisno Resenje in Serbian) method is used to rank the resilience of each neighborhood to each of the three stressors. Results show that, overall, the physical form of the planned neighborhoods is more conducive to urban resilience. In contrast, the urban form of traditional neighborhoods was found to be less resilient. There were, however, some variations depending on the type of stressor considered. The paper concludes by emphasizing the need to consider social and economic factors in future studies of urban form resilience.

Detecting inconsistent information in crowd-sourced street networks based on parallel carriageways identification and the rule of symmetry

2021 ◽  
Vol 175 ◽  
pp. 386-402
Author(s):  
Xiang Zhang ◽  
Tianfu Wang ◽  
Delin Jiao ◽  
Zhiying Zhou ◽  
Jianwei Yu ◽  
...  
Keyword(s):  
Street Networks ◽  
Inconsistent Information

scholarly journals Data on cut-edge for spatial clustering based on proximity graphs

Data in Brief ◽  
2020 ◽  
Vol 28 ◽  
pp. 104899 ◽  
Author(s):  
Alper Aksac ◽  
Tansel Ozyer ◽  
Reda Alhajj
Keyword(s):  
Spatial Clustering ◽  
Cut Edge ◽  
Proximity Graphs

DEVELOPING PROXIMITY GRAPHS BY PHYSARUM POLYCEPHALUM: DOES THE PLASMODIUM FOLLOW THE TOUSSAINT HIERARCHY?

2009 ◽  
Vol 19 (01) ◽  
pp. 105-127 ◽  
Author(s):  
ANDREW ADAMATZKY
Keyword(s):  
Foraging Behavior ◽  
Delaunay Triangulation ◽  
Spanning Tree ◽  
Physarum Polycephalum ◽  
Minimum Spanning Tree ◽  
Nearest Neighbour ◽  
Minimal Spanning Tree ◽  
Proximity Graphs ◽  
Relative Neighborhood Graph ◽  
Plasmodium Of Physarum Polycephalum

Plasmodium of Physarum polycephalum spans sources of nutrients and constructs varieties of protoplasmic networks during its foraging behavior. When the plasmodium is placed on a substrate populated with sources of nutrients, it spans the sources with protoplasmic network. The plasmodium optimizes the network to deliver efficiently the nutrients to all parts of its body. How exactly does the protoplasmic network unfold during the plasmodium's foraging behavior? What types of proximity graphs are approximated by the network? Does the plasmodium construct a minimal spanning tree first and then add additional protoplasmic veins to increase reliability and through-capacity of the network? We analyze a possibility that the plasmodium constructs a series of proximity graphs: nearest-neighbour graph (NNG), minimum spanning tree (MST), relative neighborhood graph (RNG), Gabriel graph (GG) and Delaunay triangulation (DT). The graphs can be arranged in the inclusion hierarchy (Toussaint hierarchy): NNG ⊆ MST ⊆ RNG ⊆ GG ⊆ DT . We aim to verify if graphs, where nodes are sources of nutrients and edges are protoplasmic tubes, appear in the development of the plasmodium in the order NNG → MST → RNG → GG → DT , corresponding to inclusion of the proximity graphs.

scholarly journals Different Ways of Thinking about Street Networks and Spatial Analysis

2014 ◽  
Vol 46 (4) ◽  
pp. 341-344 ◽  
Author(s):  
Bin Jiang ◽  
Atsuyuki Okabe
Keyword(s):  
Spatial Analysis ◽  
Street Networks ◽  
Ways Of Thinking

What determines a boundary for navigating a complex street network: evidence from London taxi drivers

2021 ◽  
pp. 1-20
Author(s):  
Eva-Maria Griesbauer ◽  
Ed Manley ◽  
Daniel McNamee ◽  
Jeremy Morley ◽  
Hugo Spiers
Keyword(s):  
Urban Space ◽  
Large Scale ◽  
Route Planning ◽  
Taxi Drivers ◽  
Street Networks ◽  
Planning Models ◽  
Key Factor ◽  
Efficient Planning ◽  
Regent's Park ◽  
Insight Into

Abstract Spatial boundaries play an important role in defining spaces, structuring memory and supporting planning during navigation. Recent models of hierarchical route planning use boundaries to plan efficiently first across regions and then within regions. However, it remains unclear which structures (e.g. parks, rivers, major streets, etc.) will form salient boundaries in real-world cities. This study tested licensed London taxi drivers, who are unique in their ability to navigate London flexibly without physical navigation aids. They were asked to indicate streets they considered as boundaries for London districts or dividing areas. It was found that agreement on boundary streets varied considerably, from some boundaries providing almost no consensus to some boundaries consistently noted as boundaries. Examining the properties of the streets revealed that a key factor in the consistent boundaries was the near rectilinear nature of the designated region (e.g. Mayfair and Soho) and the distinctiveness of parks (e.g. Regent's Park). Surprisingly, the River Thames was not consistently considered as a boundary. These findings provide insight into types of environmental features that lead to the perception of explicit boundaries in large-scale urban space. Because route planning models assume that boundaries are used to segregate the space for efficient planning, these results help make predictions of the likely planning demands of different routes in such complex large-scale street networks. Such predictions could be used to highlight information used for navigation guidance applications to enable more efficient hierarchical planning and learning of large-scale environments.

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