Street Network Configuration Research Articles

Understanding the role of street network configurations in the placement of illegitimately operating facilities

Understanding the role of street network configurations in the placement of illegitimately operating facilities

Understanding the role of street network configurations in the placement of illegitimately operating facilities

Understanding the role of street network configurations in the placement of illegitimately operating facilities

Understanding the role of street network configurations in the placement of illegitimately operating facilities

AbstractThe role of street networks in shaping the spatial distribution of crime has become a foundational component within environmental criminology. Most studies, however, have focused on opportunistic crime types, such as property offenses. In this study, we instead research a theoretically distinct phenomenon by examining the placement of venues that host criminal activity. In particular, we study the relationship between network structure and the placement of illicit massage businesses, which operate on the intersections of illicit and legitimate activity by hosting illicit commercial sex under the guise of legitimate massage. We model their placement as a function of two network metrics: betweenness, which measures a street's usage potential, and a variant called “local betweenness,” which measures the potential of nearby streets. Multilevel models are used to examine the importance of these street‐level metrics while accounting for tract‐level covariates. Our findings demonstrate that, unlike property crimes, illicit massage businesses tend to be located on streets that are themselves quiet but that are close to areas of high activity. Such locations seem to combine accessibility and discretion, and therefore, represent ideal conditions for such businesses to thrive. Our findings can inform problem‐oriented approaches to prevent the harms associated with illegitimately operating businesses.

Re-examining the role of street network configuration on bicycle commuting using crowdsourced data

Understanding the role of street network configuration on commuter cycling can aid city planners in assessing and evaluating interventions to promote regular cycling into people's routines. Studies examining this relationship generally build models on observed cycling counts. While this provides valuable information, it could still result in an incomplete picture because some routes with advantages (e.g., good accessibility, amenities etc) may not have been utilised as expected because of other confounding factors (e.g., safety issues, motorised traffic) or vice-versa, leading to incorrect conclusions especially in the absence of data on these confounding factors. Thus, in this study, we argue that observing higher cycling flows on a route should not be the sole criteria to examine the role of street network configuration on the cycling patterns, especially when certain confounding factors have not been controlled for. We utilise data from the activity tracking app Strava for the city of Glasgow and compare the observed cycling intensities with the modelled cycling intensities where all the cyclists take the shortest routes. We estimated three linear regression models for: Observed Strava Cycling Intensities, Modelled Strava Cycling Intensities, and the difference between these two measures. Street network configuration were incorporated using Space Syntax measures: Normalised Angular Choice and Normalised Angular Integration. The roles of these variables, along with route characteristics and natural environment factors, on commuter cyclists' trips are explored. Visual exploration and linear regression models indicate that cyclists deviated away from the well-integrated, straighter routes to aesthetically attractive routes with cycling infrastructure, and towards links within mixed land use and to the least deprived areas. These results are of interest to policy makers and assist in infrastructure planning.

Revelando desigualdades espaciais de renda e raciais e padrões de segregação com um indicador configuracional de acessibilidade potencial.

Abstract Urban segregation has been a central challenge in the pursuit of equity and sustainability, especially in Latin American cities. The existing body of research on segregation, however, has predominantly focused on cities in the United States and Europe. Most existing metrics on segregation do not usually explicitly address the spatial dimension and diversity of cities’ spatial structures. As a result, there is still a gap in the methods to properly capture the spatial relationships implied in segregation. This paper proposes and applies a network-based method for assessing the inequalities in access to opportunities for different social groups (income and race), and the patterns of socio-spatial segregation based on urban accessibility levels. We apply the method to the city of Pelotas, Brazil, combining the street network configuration with population densities and spatial distribution of urban opportunities. Results revealed that high-income white groups dominate the accessibility levels, benefiting from a concentration of opportunities in the central district, while low-income non-white groups experience poorer accessibility. The method allowed to uncover a center-periphery segregation pattern, although more complex and spatially diversified, providing valuable insights that can inform urban planning and policy-making efforts at the intraurban scale.

THE EFFECTS STREET-NETWORK CONFIGURATION IN MODELLING WALKABILITY THROUGH SPACE SYNTAX

This study investigates street network connectivity in an informal settlement, Kampong Taman Sari, Bandung. It generally refers to the informal residential area with minimum facilities and urban services. The majority of these settlements depict irregular patterns and narrow paths or alleys. Thus, kampong inhabitants mainly rely on walking, biking, and riding motorcycles to access vital urban features and functions. This purpose study is to examine the potential connectivity not only for accessibility but also for evacuation movement in an informal settlement. This investigation also aims to understand an associative relation between street-network configuration and informal settlement patterns with the probability distribution of pedestrian movement. This study utilizes computational street network analysis through the space syntax method that consists of two distinctive evaluations, such as axial analysis and visual graph analysis. The following result depicts the spatial accessibility, integration, permeability, walkability, and connectivity based on this relation. Experimenting in modelling a walkable kampong in the urban environment will conduce to urban design qualities of the street environment and street network layout for pedestrians or users.

Understanding the relationship between the spatial configuration and the crime rate of Downtown Eastside in Vancouver, Canada

As one of Vancouver's oldest neighbourhoods and the historical heart of the city, Downtown Eastside (DTES) has been home to many low-income residents and has been challenged by factors such as high crime rates, drug use, unemployment, housing issues, and loss of businesses in the community. Although a variety of studies have explored the causes and effects of DTES's social dilemma and indicated that crimes are closely related to the spatial configuration of the underlying street network, how DTES's underlying street network affects the spatial distribution of different types of crime remains unclear. Focusing on the relationship between the street network and the crime rate of DTES, the authors make three hypotheses regarding the connections between the street network of DTES and the distribution patterns of crimes, and employ the space syntax methods to analyse the street network of DTES and test the hypotheses. The results indicate that the spatial configuration of street network can be helpful in explaining where different crimes may occur, thus providing a spatial perspective to figure out approaches to addressing DTES's social issues. At last, this paper, combining spatial and social perspectives, puts forward strategies for making DTES a more liveable place, and is likely to work as a reference for academics and practitioners to further understand gentrification and urbanisation related issues.

Exploring Dynamic Street Vendors and Pedestrians through the Lens of Static Spatial Configuration in Yuncheng, China

Extensive studies have demonstrated the relationship between static street networks and dynamic pedestrian movement. Street vendors temporarily appear in a vibrant space, closely engaging with numerous pedestrians. Is street vending distribution related to street network configuration? And if so, in what ways? This study investigates the heterogeneous relationships between pedestrians, vendors and the urban and suburban places they inhabit in Yuncheng by contrasting two different types of pedestrian-driven maps. The vending distribution map was identified via semi-structured interviews with urban managers (n = 11) in 2017. The spatial network configuration was examined through space syntax segment angular analysis, containing Integration, Choice and Combined Centrality properties at macro-, meso- and micro-scales. By overlapping the distribution map to the syntactic maps in the Quantum Geographic Information System (QGIS) platform, our results show that street vending distribution is most related to the Choice property at the meso-scale. Using semi-structured interviews (n = 53) and observations at two specific sites, we further found relationships tend to be opposed in urban and suburban areas. The study provides an alternative approach to integrating quantitative and qualitative data, expanding the theoretical and empirical understanding of spatial configuration and urban street activities related to walking and vending. This understanding has the potential to contribute to the design of more liveable street spaces.

Simulation analysis of urban network performance under link disruptions: Impacts of information provisions in different street configurations

This study uses aggregated network-level operation metrics to examine the performance of three street network configurations, namely two-way (TW), two-way without left turns (TWL), and one-way networks (OW), under disruptive events in the network. Overall, a TWL network is found to be the most efficient both with and without disruptions. When there is no disruption, a TWL network shows a comparable trip completion rate as a TW network with turn pockets at intersection. Although the mean travel distance in the TWL network is about 30% higher than the TW network, its mean trip time is only 16% higher due to lower intersection delays. When disruptions take place, only TWL network is found to accommodate the most challenging ones in the central area due to its higher intersection efficiency and more evenly distributed traffic inside. The study also examined the impacts of various ITS-related strategies to provide drivers with advanced information on the disruption to mitigate its negative effects. The results revealed that providing information on disruptions that occur outside the most congested areas when vehicles start their trip might actually reduce overall network performance, since doing so may cause vehicles to reroute through heavily congested areas. For disruptions in the central region of the network, alerting drivers just one or two blocks upstream of the disruption can achieve similar delay reductions to broadcasting the disruption to 75% of road users.

Home-Based urban commute and Non-Commute trip generation in Less-Studied Contexts: Evidence from Cairo, Istanbul, and Tehran

The determinants of travel demand, i.e., commute and non-commute trip generation, in the Middle East and North Africa (MENA) region are unknown, while the main factors behind travel generation in high-income countries have been studied since the 1990 s. This paper aims at shedding light on these determinants and comparing them in Cairo, Istanbul, and Tehran as case-study cities. Three research questions were answered by this paper: (1) are there any significant differences between urban travel generation levels of the three cities? (2) which factors determine urban travel generation, including commute trips and non-work activities in the large agglomerations of the MENA region? and (3) how similar or different are these determinants in the three mentioned cities? The analysis was based on a mobility survey undertaken in 2017 in 18 neighborhoods of the three cities. The data resulted from 8284 face-to-face interviews (Cairo: 2786, Istanbul: 2781, Tehran: 2717). Weighted Least Squares models showed that 19 and 18 variables related to individual and household status, socioeconomics, attitudes, the built environment, and accessibility define commute and non-commute trip generation, respectively. The results of Analysis of Variance indicate that these determinants are almost similar in a holistic view, but there are some relative differences between them in the three cities. This study addresses the contextual differences between the determinants in the MENA region and high-income countries and concludes that travel demand is context sensitive. The findings specially show how street network configuration and neighborhood-level facilities can be a tool for urban planners to control mobility demand. Highly connected street networks with adequate neighborhood amenities are associated with the frequency of commute and non-commute trips in the case cities, thus planners and decisionmakers in the MENA region are encouraged to use land use and street connectivity to gain control on trip generation for attaining a more sustainable travel modal share.

The Relations between Street Network Configuration and Travel Behavior in Pakistan; the Optimal Level of Street Connectivity for a More Active Mobility

Urban sprawl is a particular pattern of the street network and land use. The relationship between street networks and sprawl has been discussed by urban scholars in developed and high-income countries. Nevertheless, there is a lack of research on the relationships between street connectivity and urban travel behavior, particularly among emerging markets. This paper aims to study correlations between urban mobility and street-length density as an indicator for assessing the compactness of an area by developing two hierarchical regression models and controlling for socioeconomic variables in two large Pakistani cities: Lahore and Rawalpindi. Moreover, this paper defines optimal cutoff values for street-length density and active transport. Finally, three chi-square tests were conducted to assess the differences between using different mode choices by people living in sprawled neighborhoods versus compact neighborhoods. Our findings confirm the use of different transport modes depending on the purpose of the trip (commuting or non-commuting), length of trip (within or outside the neighborhood), and starting point (sprawled neighborhood or compact area). We also find a positive correlation between street-length density around homes and commuting distance, the frequency of public transport use, and the use of private motor vehicles in commuting trips in the two cities. Street-length density around workplaces is correlated with commuting distance, the frequency of public transport use, and the use of private motor vehicles when socioeconomic variables (including age, daily activity, and monthly income) are controlled for in the two models. The behavior of Pakistani residents changes with a street-length density of 137 and 144.7 m/m2 for homes and workplaces, respectively, in terms of using active mobility.

The building blocks of walkability: Pedestrian activity in Abu Dhabi city center

This study develops an empirical approach that underlines the effect of land use mix and development grain on pedestrian movement in the streets and public spaces of urban neighborhoods. The study begins with the end in mind; it compares two urban sectors in Abu Dhabi city with broadly recognized contrasting levels of pedestrian movement. The research works backward to identify the combination of built environment attributes that result in differing levels of pedestrian activity between the eastern and western sectors of Abu Dhabi city center. Using Geographic Information Systems maps/data files, direct observations, and field metrics, the study computes various indices related to land use mix, density, and street connectivity for Abu Dhabi central business district. The findings of this study highlight the land use, spatial and street network configurations that enhance pedestrian activity in urban centers. From a local perspective, the research outcome would inform future visions aimed at transforming the city of Abu Dhabi into a global metropolis that provides its citizens and visitors with unique and vibrant urban settings conducive to intra- and inter-neighborhood walkability.

Modeling Pedestrian Flows: Agent-Based Simulations of Pedestrian Activity for Land Use Distributions in Urban Developments

Pedestrian activity is a cornerstone for urban sustainability, with key implications for the environment, public health, social cohesion, and the local economy. Therefore, city planners, urban designers, and decision-makers require tools to predict pedestrian mobility and assess the walkability of existing or planned urban environments. For this purpose, diverse approaches have been used to analyze different inputs such as the street network configuration, density, land use mix, and the location of certain amenities. This paper focuses on the location of urban amenities as key elements for pedestrian flow prediction, and, therefore, for the success of public spaces in terms of the social life of city neighborhoods. Using agent-based modeling (ABM) and land use floor space data, this study builds a pedestrian flow model, which is applied to both existing and planned areas in the inner city of Hamburg, Germany. The pedestrian flows predicted in the planned area inform the ongoing design and planning process. The flows simulated in the existing area are compared against real-world pedestrian activity data for external validation to report the model accuracy. The results show that pedestrian flow intensity correlates to the density and diversity of amenities, among other KPIs. These correlations validate our approach and also quantify it with measurable indicators.

Simulation of traffic light disruptions in street networks

Intelligent transportation systems (ITS) use information technology and control devices like traffic lights and sensors to help commuters in achieving flow coordination, improving safety and smoothness in mobility and providing valuable information to public managers. But ITS devices are not free of vulnerabilities against cyber-attacks and failures. The impacts of traffic light disruption will be different for different street network structures. Complex network analysis can be used to evaluate critical infrastructures like street networks, revealing patterns of flow distribution, path redundancy, efficiency and robustness. But, at the present moment, the correlations between street network topology and the impact of traffic light disruptions were not sufficiently addressed in the literature. This work aims to evaluate the role of topological configuration and resilience of street networks when subjected to traffic light disruption, 50000 trips were simulated using three attack strategies that prioritize traffic lights based on complex network centrality metrics. Complex street, network analysis is used to interpret the results of the trip simulation.

Examining the diffusion of coronavirus disease 2019 cases in a metropolis: a space syntax approach

BackgroundThe urban built environment (BE) has been globally acknowledged as one of the main factors that affects the spread of infectious disease. However, the effect of the street network on coronavirus disease 2019 (COVID-19) incidence has been insufficiently studied. Severe acute respiratory syndrome coronavirus 2, which causes COVID-19, is far more transmissible than previous respiratory viruses, such as severe acute respiratory syndrome coronavirus, which highlights the role of the spatial configuration of street network in COVID-19 spread, as it is where humans have contact with each other, especially in high-density areas. To fill this research gap, this study utilized space syntax theory and investigated the effect of the urban BE on the spatial diffusion of COVID-19 cases in Hong Kong.MethodThis study collected a comprehensive dataset including a total of 3815 confirmed cases and corresponding locations from January 18 to October 5, 2020. Based on the space syntax theory, six space syntax measures were selected as quantitative indicators for the urban BE. A linear regression model and Geographically Weighted Regression model were then applied to explore the underlying relationships between COVID-19 cases and the urban BE. In addition, we have further improved the performance of GWR model considering the spatial heterogeneity and scale effects by adopting an adaptive bandwidth.ResultOur results indicated a strong correlation between the geographical distribution of COVID-19 cases and the urban BE. Areas with higher integration (a measure of the cognitive complexity required for a pedestrians to reach a street) and betweenness centrality values (a measure of spatial network accessibility) tend to have more confirmed cases. Further, the Geographically Weighted Regression model with adaptive bandwidth achieved the best performance in predicting the spread of COVID-19 cases.ConclusionIn this study, we revealed a strong positive relationship between the spatial configuration of street network and the spread of COVID-19 cases. The topology, network accessibility, and centrality of an urban area were proven to be effective for use in predicting the spread of COVID-19. The findings of this study also shed light on the underlying mechanism of the spread of COVID-19, which shows significant spatial variation and scale effects. This study contributed to current literature investigating the spread of COVID-19 cases in a local scale from the space syntax perspective, which may be beneficial for epidemic and pandemic prevention.

Street Network Models and Indicators for Every Urban Area in the World

Cities worldwide exhibit a variety of street network patterns and configurations that shape human mobility, equity, health, and livelihoods. This study models and analyzes the street networks of every urban area in the world, using boundaries derived from the Global Human Settlement Layer. Street network data are acquired and modeled from OpenStreetMap with the open‐source OSMnx software. In total, this study models over 160 million OpenStreetMap street network nodes and over 320 million edges across 8,914 urban areas in 178 countries, and attaches elevation and grade data. This article presents the study’s reproducible computational workflow, introduces two new open data repositories of ready‐to‐use global street network models and calculated indicators, and discusses summary findings on street network form worldwide. It makes four contributions. First, it reports the methodological advances of this open‐source workflow. Second, it produces an open data repository containing street network models for each urban area. Third, it analyzes these models to produce an open data repository containing street network form indicators for each urban area. No such global urban street network indicator data set has previously existed. Fourth, it presents a summary analysis of urban street network form, reporting the first such worldwide results in the literature.

An analysis of the relationship between pedestrian density and street network configuration by using mobile tracking data

An analysis of the relationship between pedestrian density and street network configuration by using mobile tracking data

PUBLIC SPACES AND GENDER: TESTING THE RELATIONSHIP OF SPATIAL CONFIGURATION OF STREET NETWORKS

The purpose of this paper is to examine the spatial dominance in public spaces from a gendered and women perspectives and to analyse the relationship with the spatial configuration of street networks. In analysing the street networks in Space Syntax, the question arises; to what extent the movement and activity may explain street integration among gender-based pedestrians. The result found that there is a correlation between spatial configurative analyses and women present in the streets. In essence, the less integrated streets attract more women pedestrians and improve better quality of space. The research is relevant to spatial design interventions and policymaking to enhance gender equal access to public space.

Street Network Models and Indicators for Every Urban Area in the World

Cities worldwide exhibit a variety of street network patterns and configurations that shape human mobility, equity, health, and livelihoods. This study models and analyzes the street networks of every urban area in the world, using boundaries derived from the Global Human Settlement Layer. Street network data are acquired and modeled from OpenStreetMap with the open‐source OSMnx software. In total, this study models over 160 million OpenStreetMap street network nodes and over 320 million edges across 8,914 urban areas in 178 countries, and attaches elevation and grade data. This article presents the study’s reproducible computational workflow, introduces two new open data repositories of ready‐to‐use global street network models and calculated indicators, and discusses summary findings on street network form worldwide. It makes four contributions. First, it reports the methodological advances of this open‐source workflow. Second, it produces an open data repository containing street network models for each urban area. Third, it analyzes these models to produce an open data repository containing street network form indicators for each urban area. No such global urban street network indicator data set has previously existed. Fourth, it presents a summary analysis of urban street network form, reporting the first such worldwide results in the literature.

Resilience of Urban Street Network Configurations under Low Demands

Urban street networks are subject to a variety of random disruptions. The impact of movement restrictions (e.g., one-way or left-turn restrictions) on the ability of a network to overcome these disruptions—that is, its resilience—has not been thoroughly studied. To address this gap, this paper investigates the resilience of one-way and two-way square grid street networks with and without left turns under light traffic conditions. Networks are studied using a simplified routing algorithm that can be examined analytically and a microsimulation that describes detailed vehicle dynamics. In the simplified method, routing choices are enumerated for all possible origin–destination (OD) combinations to identify how the removal of a link affects operations, both when knowledge of the disruption is and is not available at the vehicle’s origin. Disruptions on two-way networks that allow left turns tend to have little impact on travel distances because of the availability of multiple shortest paths between OD pairs and the flexibility in route modification. Two-way networks that restrict left turns at intersections only have a single shortest-distance path between any OD pair and thus experience larger increases in travel distance, even when the disruption is known ahead of time. One-way networks sometimes have multiple shortest-distance routes and thus travel distances increase less than two-way network without left turns when links are disrupted. These results reveal a clear tradeoff between improved efficiency and reduced resilience for networks that have movement restrictions, and can be used as a basis to study network resilience under more congested scenarios and in more realistic network structures.