scholarly journals Urban Street Network Analysis in a Computational Notebook

2020 ◽  
Author(s):  
Geoff Boeing
Keyword(s):  
Network Analysis ◽  
Spatial Data ◽  
Street Network ◽  
Urban Street ◽  
Street Networks ◽  
Points Of Interest ◽  
The World ◽  
Study Sites ◽  
Python Package

Computational notebooks offer researchers, practitioners, students, and educators the ability to interactively conduct analytics and disseminate reproducible workflows that weave together code, visuals, and narratives. This article explores the potential of computational notebooks in urban analytics and planning, demonstrating their utility through a case study of OSMnx and its tutorials repository. OSMnx is a Python package for working with OpenStreetMap data and modeling, analyzing, and visualizing street networks anywhere in the world. Its official demos and tutorials are distributed as open-source Jupyter notebooks on GitHub. This article showcases this resource by documenting the repository and demonstrating OSMnx interactively through a synoptic tutorial adapted from the repository. It illustrates how to download urban data and model street networks for various study sites, compute network indicators, visualize street centrality, calculate routes, and work with other spatial data such as building footprints and points of interest. Computational notebooks help introduce methods to new users and help researchers reach broader audiences interested in learning from, adapting, and remixing their work. Due to their utility and versatility, the ongoing adoption of computational notebooks in urban planning, analytics, and related geocomputation disciplines should continue into the future.

scholarly journals Urban Street Network Analysis in a Computational Notebook

REGION ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. 39-51 ◽  
Author(s):  
Geoff Boeing
Keyword(s):  
Network Analysis ◽  
Spatial Data ◽  
Street Network ◽  
Urban Street ◽  
Street Networks ◽  
Points Of Interest ◽  
The World ◽  
Study Sites ◽  
Python Package

Computational notebooks offer researchers, practitioners, students, and educators the ability to interactively run code and disseminate reproducible workflows that weave together code, visuals, and narratives. This article explores the potential of computational notebooks in urban analytics and planning, demonstrating their utility through a case study of OSMnx and its tutorials repository. OSMnx is a Python package for working with OpenStreetMap data and modeling, analyzing, and visualizing street networks anywhere in the world. Its official demos and tutorials are distributed as open-source Jupyter notebooks on GitHub. This article showcases this resource by documenting the repository and demonstrating OSMnx interactively through a synoptic tutorial adapted from the repository. It illustrates how to download and model street networks for various study sites, compute network indicators, visualize street centrality, calculate routes, and work with other spatial data such as building footprints and points of interest. Computational notebooks can empower guides for introducing methods to new users and can help researchers reach broader audiences interested in learning from, adapting, and remixing their work. Due to their utility and versatility, the ongoing adoption of computational notebooks in urban planning, analytics, and related geocomputation disciplines should continue into the future.

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

2020 ◽  
Vol 12 (2) ◽  
pp. 628 ◽  
Author(s):  
Baorui Han ◽  
Dazhi Sun ◽  
Xiaomei Yu ◽  
Wanlu Song ◽  
Lisha Ding
Keyword(s):  
Street Network ◽  
Urban Street ◽  
Network Function ◽  
Street Networks ◽  
The World ◽  
Road Planning ◽  
Network Patterns ◽  
Degree Of Similarity ◽  
Network Type

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.

scholarly journals A New Methodology to Study Street Accessibility: A Case Study of Avila (Spain)

2021 ◽  
Vol 10 (7) ◽  
pp. 491
Author(s):  
Manuel Curado ◽  
Rocio Rodriguez ◽  
Manuel Jimenez ◽  
Leandro Tortosa ◽  
Jose F. Vicent
Keyword(s):  
Network Analysis ◽  
The Other ◽  
Centrality Measures ◽  
Innovative Behavior ◽  
Street Network ◽  
Commercial Activity ◽  
Determining Factors ◽  
Combine Network ◽  
The City

Taking into account that accessibility is one of the most strategic and determining factors in economic models and that accessibility and tourism affect each other, we can say that the study and improvement of one of them involved the development of the other. Using network analysis, this study presents an algorithm for labeling the difficulty of the streets of a city using different accessibility parameters. We combine network structure and accessibility factors to explore the association between innovative behavior within the street network, and the relationships with the commercial activity in a city. Finally, we present a case study of the city of Avila, locating the most inaccessible areas of the city using centrality measures and analyzing the effects, in terms of accessibility, on the commerce and services of the city.

The Development of Pompeii’s Urban Street Network

2017 ◽  
Author(s):  
Eric E. Poehler
Keyword(s):  
Urban Form ◽  
Formal Analysis ◽  
Street Network ◽  
Urban Street ◽  
Street Networks ◽  
The City ◽  
Present Understanding

Chapter 2 explores the present understanding of Pompeii’s evolution by disassembling the apparent patchwork of grids across the city and reconsiders the presumed awkwardness in their adhesion. To do this, the traditional tools of formal analysis—street alignments and block shapes—are employed with and critiqued by the stratigraphic evidence recovered in the last three decades of excavation below the 79 CE levels. The result is an outline of the development of Pompeii’s urban form as a series of street networks: from the archaic age, through the period of the “hiatus” of the fifth and fourth centuries BCE, to a reorganization of the city’s space so profound that it can genuinely be considered a refoundation, and finally to the adjustments of a refounded city in the Colonial, Augustan, and post-earthquake(s) periods.

scholarly journals Detection of silent subsidence over extensive area by SBAS DInSAR: a case study of Southern Bali, Indonesia

2020 ◽  
Vol 153 ◽  
pp. 02003
Author(s):  
Putu Edi Yastika ◽  
Norikazu Shimizu ◽  
Ni Nyoman Pujianiki ◽  
I Gede Rai Maya Temaja ◽  
I Nyoman Gede Antara ◽  
...  
Keyword(s):  
Land Subsidence ◽  
Groundwater Extraction ◽  
Severe Damage ◽  
Human Needs ◽  
Points Of Interest ◽  
Linear Behavior ◽  
The World ◽  
Spatio Temporal ◽  
The City

Numerous cities around the world are facing the problem of land subsidence. In many cases, it is the excessive groundwater extraction to meet human needs that leads to this subsidence. Since land subsidence rates are very slow (a few centimeters per year), the subsidence usually remains unnoticed until it has progressed to the point of causing severe damage to buildings, houses, and/or other infrastructures. Therefore, it is very important to detect the presence of subsidence in advance. In this study, screening for the presence of land subsidence in the city of Denpasar, Bali, Indonesia is conducted. The Sentinel-1A/B SAR dataset, taken from October 2014 to June 2019, is processed using the SBAS DInSAR method. Subsidence is found in the districts of Denpasar Selatan, Denpasar Barat, and Kuta, which falls in the range of -100 mm to -200 mm in an area of about 93.03 ha. All the extracted points of interest show the subsidence having linear behavior. The spatio-temporal behavior of the subsidence in Denpasar is presented clearly. However, the mechanism and the deriving factors of the subsidence remain unclear. Therefore, further studies are needed.

scholarly journals Multifractal scaling analyses of urban street network structure: The cases of twelve megacities in China

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246925
Author(s):  
Yuqing Long ◽  
Yanguang Chen
Keyword(s):  
Fractal Dimension ◽  
Clustering Algorithm ◽  
Scaling Law ◽  
Central Area ◽  
Direct Determination ◽  
Traffic Networks ◽  
Street Network ◽  
Urban Street ◽  
Street Networks ◽  
Multifractal Scaling

Traffic networks have been proved to be fractal systems. However, previous studies mainly focused on monofractal networks, while complex systems are of multifractal structure. This paper is devoted to exploring the general regularities of multifractal scaling processes in the street network of 12 Chinese cities. The city clustering algorithm is employed to identify urban boundaries for defining comparable study areas; box-counting method and the direct determination method are utilized to extract spatial data; the least squares calculation is employed to estimate the global and local multifractal parameters. The results showed multifractal structure of urban street networks. The global multifractal dimension spectrums are inverse S-shaped curves, while the local singularity spectrums are asymmetric unimodal curves. If the moment order q approaches negative infinity, the generalized correlation dimension will seriously exceed the embedding space dimension 2, and the local fractal dimension curve displays an abnormal decrease for most cities. The scaling relation of local fractal dimension gradually breaks if the q value is too high, but the different levels of the network always keep the scaling reflecting singularity exponent. The main conclusions are as follows. First, urban street networks follow multifractal scaling law, and scaling precedes local fractal structure. Second, the patterns of traffic networks take on characteristics of spatial concentration, but they also show the implied trend of spatial deconcentration. Third, the development space of central area and network intensive areas is limited, while the fringe zone and network sparse areas show the phenomenon of disordered evolution. This work may be revealing for understanding and further research on complex spatial networks by using multifractal theory.

scholarly journals The structure, centrality, and scale of urban street networks: Cases from Pre-Industrial Afro-Eurasia

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259680
Author(s):  
Mark Altaweel ◽  
Jack Hanson ◽  
Andrea Squitieri
Keyword(s):  
Urban Systems ◽  
Network Centrality ◽  
Street Network ◽  
Transport Costs ◽  
Urban Centers ◽  
Urban Settings ◽  
Urban Street ◽  
Street Networks ◽  
Linear Manner ◽  
Urban Sites

Cities and towns have often developed infrastructure that enabled a variety of socio-economic interactions. Street networks within these urban settings provide key access to resources, neighborhoods, and cultural facilities. Studies on settlement scaling have also demonstrated that a variety of urban infrastructure and resources indicate clear population scaling relationships in both modern and ancient settings. This article presents an approach that investigates past street network centrality and its relationship to population scaling in urban contexts. Centrality results are compared statistically among different urban settings, which are categorized as orthogonal (i.e., planned) or self-organizing (i.e., organic) urban settings, with places having both characteristics classified as hybrid. Results demonstrate that street nodes have a power law relationship to urban area, where the number of nodes increases and node density decreases in a sub-linear manner for larger sites. Most median centrality values decrease in a negative sub-linear manner as sites are larger, with organic and hybrid urban sites’ centrality being generally less and diminishing more rapidly than orthogonal settings. Diminishing centrality shows comparability to modern urban systems, where larger urban districts may restrict overall interaction due to increasing transport costs over wider areas. Centrality results indicate that scaling results have multiples of approximately ⅙ or ⅓ that are comparable to other urban and road infrastructure, suggesting a potential relationship between different infrastructure features and population in urban centers. The results have implications for archaeological settlements where urban street plans are incomplete or undetermined, as it allows forecasts to be made on past urban sites’ street network centrality. Additionally, a tool to enable analysis of street networks and centrality is provided as part of the contribution.

scholarly journals SOCIAL METRICS APPLIED TO SMART TOURISM

2016 ◽  
Vol IV-4/W1 ◽  
pp. 117-124 ◽  
Author(s):  
O. Cervantes ◽  
E. Gutiérrez ◽  
F. Gutiérrez ◽  
J. A. Sánchez
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Semantic Network ◽  
Graph Model ◽  
Points Of Interest ◽  
Centrality Metrics ◽  
Social Metrics ◽  
The City ◽  
Smart Tourism

We present a strategy to make productive use of semantically-related social data, from a user-centered semantic network, in order to help users (tourists and citizens in general) to discover cultural heritage, points of interest and available services in a smart city. This data can be used to personalize recommendations in a smart tourism application. Our approach is based on flow centrality metrics typically used in social network analysis: flow betweenness, flow closeness and eccentricity. These metrics are useful to discover relevant nodes within the network yielding nodes that can be interpreted as suggestions (venues or services) to users. We describe the semantic network built on graph model, as well as social metrics algorithms used to produce recommendations. We also present challenges and results from a prototypical implementation applied to the case study of the City of Puebla, Mexico.

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