Environment and Planning B: Urban Analytics and City Science

Immersive virtual reality is a promising technology for planning participation. The paper contributes to the literature by comparing the latest virtual reality technology using head-mounted display with conventional graphic representation (pictures of rendered three-dimensional environments in this case) in terms of the effects on the participants’ preferences for the plans and their underlying decision mechanisms. Using a stated choice experiment based on a real-world project of street renewal, we collected choice data from 48 university students from non-design majors. We found significant quantitative but limited qualitative differences between the aggregate preferences under virtual reality and conventional graphic representation, and some generally unappealing features under conventional graphic representation were more favored under virtual reality. Results of the discrete choice modeling showed the individual decision mechanisms became more homogeneous under virtual reality. Virtual reality had stronger impacts on the female participants than the male participants. The females had more aggregate preference reversals, larger preference differences, and stronger changes in the decision mechanism. But the mechanisms of the two genders converged under virtual reality. The findings can be used to design better participatory processes with virtual reality and conventional graphic representation properly applied according to their capabilities.

Urban sprawl is a phenomenon observed in most cities around the globe and especially in Latin America, where it is associated to socioeconomic segregation. In the case of Chile, sprawl has been generally based on large real estate projects. Developers target their projects to different types of consumers, which translates into submarkets with a broad range of housing-unit’s characteristics, but also different location strategies. This heterogeneity has been analyzed and measured in the literature, but quantitative studies have used exogenous or sequential methods to identify submarkets, leading to potential bias in the segmentation. In this paper, we propose an econometric model to measure location drivers for different types of real estate projects that fills this gap. The modeling framework is based on discrete-choice and latent-class models, allowing us to simultaneously identify market segmentations, and their particular location choice preferences, without the need of arbitrary or ex-ante definitions of submarkets. The model is applied to the city of Santiago, Chile. The results reveal two clearly different approaches taken by developers to produce housing, with one submarket of “exclusive” and more sprawling projects, and another submarket of “massive” and more density driven projects. Location strategies are very different between submarkets, reproducing the socio-spatial segregation already observed in the consolidated city.

In many places, streets are still primarily designed for the convenience of motorists, considering mobility function as the principal design goal. There is a scarcity of empirical evidence on the relationship between the design of a street and how it is experienced by pedestrians who use it. This work focuses on quantifying pedestrians’ perception of walkability through a stated preference survey using a dynamic 3D representation of various street designs in Toronto, Canada. The stated preference scenarios are generated through a rule-based 3D environment (Esri’s CityEngine) and animated using a gaming engine (Unity). A random sample of 600 Torontonians is used for the empirical investigation by estimating a mixed multinomial logit model. The results indicate that there is a high preference for (i) streets that include transit lanes as opposed to car-exclusive lanes, (ii) the presence of trees on the sidewalk, and (iii) two-way cycle paths on the curb lane. Furthermore, pedestrians are willing to trade sidewalk width for the presence of trees and outdoor dining. The survey’s innovative presentation mode and its findings can contribute to the development of much-needed evidence-based design tools to assess the trade-offs required between the many possible uses of roadway space, while focusing on the overlooked role of the pedestrian experience.

A large literature establishes the role of mobility in the maintenance of neighborhood social structures. Jane Jacobs famously argued that social capital is maintained through “cross-use of space,” and James Coleman formalized its dependence on the “closure” of human interactions. Since many of these interactions entail human movement, neighborhoods with higher social capital should be distinguishable by more cohesive mobility networks. I observe the mobility of Chicago residents through a large dataset of smartphone users. I construct a neighborhood-level mobility network for the city and characterize neighborhoods according to their local graph structure. Neighborhoods that are well integrated with their surroundings have higher income and educational attainment. Consistent with social capital theory and routine activity theory in criminology, higher local network integration independently predicts lower levels of violent and property crime. The methodologies presented provide a meaningful, replicable, and inexpensive approach to the structural measurement of neighborhood networks and social structure.

To explore the relationship between the objective morphological features and subjective scenic beauty preference of landscape open space units, this study improves the research method for morphology quantification, scenic beauty preference survey and relationship analysis. Fourteen morphology factors representing the features of boundary, domain and enclosure are quantified based on the point cloud data of 35 open space units. Scenic beauty evaluation is conducted online with dynamic panoramic photos. Principal component analysis is implemented to convert 14 correlated form factors into five principal components representing morphological principle. The multiple linear regression model explains the contribution of each principal component to scenic beauty preference values, showing a significance sequence of penetration, scale, naturalness, complexity and rhythm. The first three principal components have positive impacts on scenic beauty preference, while the last two principal components are negative. This work aims to reveal the regularity of public’s scenic beauty preference for open space morphology.

Traditional cell-based cellular automata (CA) models use a regular cellular grid to represent geographic space, and new approaches to CA models have explored the use of a vector representation of space instead of a regular grid to characterize urban space more realistically. However, less attention has been paid to modeling the interaction between the geospatial information and the irregular cells. To date, the majority of spatial boundaries have been created by individual agencies in an uncoordinated manner. As a consequence, the potential uses of the data collected for land-use change models are limited. In this paper, we propose a new vector-based CA model based on a new constrained irregular space representation using the theory of hierarchical spatial reasoning. For dividing the geographic space considering different items, first land patches are considered as the minimum division unit; then aggregation rules, including attribute, geometric and boundary barrier constraints, are defined; and finally different levels of spatial units are formed through land patches based on aggregation rules. The proposed model is used to simulate the land-use changes in Nanjing, Jiangsu Province, China. The performance validation and comparison illustrate the feasibility of the proposed space representation in a CA model. By using this model, it is expected that the use of the real spatial boundaries that are employed in urban planning could help provide a flexible paradigm to consider various drivers or constraints for realistically simulating land-use changes.

Evaluations of plan implementation are typically conceived in terms of plan conformance (the degree to which outcomes align with planning intent) or plan performance (the degree to which decision makers apply the plan). In this research, we consider the relationship between performance–conformance by evaluating the implementation of compact activity centre policy in greater Brisbane. We examine two decades of changes to local land use planning regulations using content analysis, quantifications of permitted development intensity, and comparisons of actual land use changes with planned intent, to identify evidence of the use of metropolitan scale activity centre policy. We find that the activity centre policy performed well and was used as intended across the metropolitan area. However, despite strong performance, the policy conformed poorly in terms of achieving the expected outcomes. These findings highlight the importance of understanding plan implementation from both performance and conformance perspectives. While conformance evaluations are essential to connect planning intent to physical outcomes, understanding plan performance is also necessary to explain the results of conformance evaluations and whether conformance successes or failures were due to deficiencies with the plan itself or how the plan was used by key actors charged with its implementation.

Nighttime light imageries are widely used for mapping the gross domestic product (GDP) over large areas. However, nighttime light imagery is inappropriate to disaggregate agricultural GDP and inadequate to differentiate the GDP from the secondary and tertiary sectors. Points-of-interest, a kind of geospatial big data with geographic locations and textual descriptions of the category, can effectively distinguish industrial and commercial areas, and therefore have the potential to improve the precise GDP mapping from secondary and tertiary sectors. In this study, a machine learning method, random forest, was used to disaggregate the 2010 county-level census GDP data of mainland China to 1 km × 1 km grids. Six Random Forest models were constructed for different economic sectors to explore the non-linear relationships between various geographic predictors and GDP from different sectors. By fusing points-of-interest of varying categories, the spatial distribution of economic activities from the secondary and tertiary sectors was effectively distinguished. Compared to previous studies, the strategy of developing specific Random Forest models for different sectors generated a more reasonable distribution of GDP. Our results highlight the feasibility of using point-of-interest data in disaggregating non-agricultural GDP by exploiting the complementary features of the different data sources.

Exploring the nature of spatial and temporal variation in house prices is important because it can help better understand such issues as affordability and equity of access to housing. In the UK, research on house price variation has been hindered by a lack of extensive data linking the prices of properties at different places and times to their physical attributes. This paper addresses this gap through using a new dataset linking Land Registry Price Paid Data to attribute data from Ordnance Survey and Energy Performance Certificates datasets. The new data are used to investigate spatial disparities in England’s house prices at four geographical scales (from local authority to individual address) between 2009 and 2016 – a period of sustained price rises after the global financial crisis of 2008. We selected two housing price measures for comparison, namely transaction price and the house price per square metre. Multilevel variance components models are used to estimate variation in the two house price measures at four different spatial scales and we compare spatial disparities in the two measures at these different scales. Our results suggest that accounting for the size of properties by using house price per square metre offers a more accurate picture of house price variation than does the use of transaction prices at the same geographic scale. Spatial disparities in house price per square metre are more apparent and are seen to be clustered at local authority level and highly clustered at Middle Layer Super Output Area level, with imbalances increasing during this eight-year period and highlighting the strong and growing influence of London on the national housing market.

U.S. cities prioritize the storage of automobiles over the safe movement of bicycles. While this generally occurs by allocating street curbs for car parking instead of bike lanes, the privileging of the automobile is even more evident in the case of angled parking, in which cars sit roughly perpendicular to the flow of traffic. Such a layout takes up nearly double the space in the right of way as does parallel parking, which leaves even less room for bike lanes. Though angled parking is defended as a traffic-calming measure, numerous studies indicate that this layout is associated with higher rates of collisions than parallel parking. In addition, angled parking also inherently increases the number of cars that can be parked along a given curb, which incentivizes automobile travel generally. However, one challenge of understanding the impact that angled parking has on safety and bicycle infrastructure is that cities do not always maintain accurate records as to where angled parking occurs. For example, San Francisco, CA has ambitious air-quality, climate-mitigation, and active transportation goals, all of which are undermined by angled parking. Yet, its supply of this parking layout is unquantified, given that parking angle was omitted from the city’s parking census. This study uses satellite imagery to resolve this data gap, and calculates that San Francisco dedicates 50 miles of street curbs to angled parking. While some assume angled parking is a planning response to San Francisco’s famed hills, the majority of it occurs on streets with no incline at all. As to angled parking’s traffic-calming effect, this benefit appears to be non-existent in San Francisco; average vehicle speeds differed by less than a half-mile per hour between angled-parking streets and adjacent non-angled streets. The angled parking identified here—particularly the four miles that overlap with the city’s bike-lane network—represents opportunities for conversion to more balanced road layouts. Overall, this methodology can serve as the basis for identifying angled parking in other cities, a configuration that should be re-evaluated by transportation planners given its car-centric effects, debatable ability to calm traffic, and preclusion of separated bicycle facilities.

As is often believed that the more centrally located a shop, the higher its sales volume, this paper analyzed relationships between the spatial clustering of retail stores, their respective transaction volumes, and the urban street networks to determine whether, and to what extent, the accessibility and density of a store’s location was correlated with its transaction volume. While this hypothesis is widely accepted, its veracity is underexplored and rarely validated using large-scale empirical datasets, possibly owing to the lack of access. Therefore, transaction datasets and accessibility indicators were first examined; a clear, positive correlation between density and revenue was found for specialty stores wherein people do “comparison shopping,” and for stores that complemented each other for activities such as “one-trip shopping,” the revenues were positively correlated when the stores were clustered. Generally, daily-use stores’ revenues were more sensitive to local access and those of non-daily-use stores were more sensitive to global access. In conclusion, these findings would not have been found using conventional methodology focused on the retail sector as a whole, because aggregate market mechanisms would have hidden the observed effects on specific store categories. Therefore, upon disaggregating the data, we found a distinct heterogeneity across the different store types for what concerns the relationship between revenue and location.

This research aimed to explain the co-presence patterns in public spaces, i.e. pedestrian movement rates, based on the analysis of spatial patterns established in street segments. Two dispersed residential neighbourhoods with different spatial characteristics – morphological configuration, land use and physical/visual permeability – were analysed in Santa Maria city. Generalized linear regression models were used to infer the relations among variables. The research question was: which spatial characteristics are related to co-presence in public spaces of dispersed residential neighbourhoods? The configurational morphological attributes had statistical significance for almost all regression models for neighbourhood 1. In neighbourhood 2, commercial activity was significant and positively related with all models. The results regarding physical and visual permeability were inconclusive for both study areas.

The rapid spread of infectious diseases is devastating to the healthcare systems of all countries. The dynamics of the spatial spread of epidemic have received considerable scientific attention. However, the understanding of the spatial variation of epidemic severity in the urban system is lagging. Using synchronized epidemic data and human mobility data, integrated with other multiple-sourced data, this study examines the interplay between disease spread of coronavirus disease (COVID-19) and inter-city and intra-city mobility among 319 Chinese cities. The results show a disease spreading process consisting of a major transfer (inter-city) diffusion before the Chinese New Year and a subsequent local (intra-city) diffusion after the Chinese New Year in the urban system of China. The variations in disease incidence between cities are mainly driven by inter-city mobility from Wuhan, the epidemic center of COVID-19. Cities that are closer to the epidemic center and with more population in the urban area will face higher risks of disease incidence. Warm and humid weather could help mitigate the spread of COVID-19. The extensive inter-city and intra-city travel interventions in China have reduced approximately 70% and 40% inter-city and intra-city mobility, respectively, and effectively slowed down the spread of the disease by minimizing human to human transmission together with other disease monitoring, control, and preventive measures. These findings could provide valuable insights into understanding the dynamics of disease spread in the urban system and help to respond to another new wave of pandemic in China and other parts of the world.

With the rapid increase of city building density, public emergency service system for providing fire services faces increasing challenge in reducing the loss of lives and property, especially for the reduction of massive casualties in fire accidents. For obtaining a higher benefit from public service facilities, GIS-based techniques such as location optimization are commonly used. However, as a special facility, fire emergency facilities are quite particular in siting and providing services, and they have their unique demands including specific response time, benefit maximization, workload balancing and cost minimization; traditional optimization methods for fire facility siting are difficult to account for all of these objectives. Furthermore, the public emergency services agencies in China are implementing a plan to establish a hierarchical fire service system by siting fire stations with different capacities, and under this context, the general covering models with the same level of facilities are limited in their effectiveness. Therefore, this paper proposes a hierarchical covering model which takes into account the different characteristics of different levels of fire facilities (i.e. macro fire station and micro fire station). The case study of Nanjing city proves that our model is effective in practical applications of emergency services optimization.

The inequalities that mark global society have been deepening worldwide. They materialize in cities, putting pressure on public transport systems for spatial and temporal supply, at the same time as mobility itself generates multifaceted inequalities. From empirical evidence of four socially and spatially distinct Brazilian cities — São Paulo, Rio de Janeiro, Curitiba, and Fortaleza — we explore how differences in scale, geography, class, and race are related to spatial segregation, leading to different levels of access to jobs by public transport in the global peripheral context. These juxtaposed and combined inequalities create highly unfair and strongly cumulative effects on some social groups, contributing to the reproduction of inequality. Based on public and open data and combining methodologies of spatial analysis to enhance comparability and reproducibility, we explore different areal units, time thresholds, and metrics in order to examine transport inequalities in different urban contexts and refine our results. Upper classes have higher accessibility than lower classes, whites have higher accessibility than blacks, and large cities are more unequal than smaller ones. However, racial inequalities combine and overlap with class and city inequalities, changing these dichotomic notions when multiple dimensions are considered. The groups that polarize social hierarchy also polarize the urban space, since the white upper class and the black lower class are more segregated, but the way segregation interacts with accessibility is not straightforward and varies according to the socio-spatial structure.

This article introduces a Spatial Livability Index based on geographically weighted principal component analysis. We study the case of 203 subzones in Singapore which are dense geographic boundaries in terms of population and built-up area. These regions share spatial correlations of objective measures of livability such as open spaces or community facilities. The proposed objective indicator captures the “hidden” patterns of livability provided by neighboring locations. Moreover, the results allow to identify atypical areas, that is geographical units that score very good/bad under the spatial approach but very bad/good under the non-spatial framework of livability.

In this study, we attempt to estimate the effects of various transportation policies on the perceived safety of the built environment. We train a convolutional neural network on a dataset of safety perception scores for Google Street View images taken in Boston, MA . We then apply the trained neural network to a large set of Google Street View images of coordinates in Montreal and Toronto to generate their respective safety perception scores. We estimate probit, logit, and ordinary least squares regression models using our cross-sectional dataset consisting of safety perception scores, as well as transportation policy variables and a set of control variables, by regressing the safety perception scores on the remaining set of variables. We answer our research question by observing the direction, magnitude, and statistical significance of the coefficient estimates associated with the policy variables across all regression models. We studied and cataloged transportation policies planned for over the past 10 years in both cities. We found that those census tracts with the poorest safety scores were the same places where planners focused their transportation investments. The study makes an important contribution to transportation planning methodologies by drawing on the novel data source of Google Street View images, to understand the safety of an area.

As an important part of the urban ecosystem, urban trees provide various benefits to urban residents. It is therefore important to examine the spatial distribution and the temporal change in urban tree canopies. Different from traditional overhead view remote sensing-based methods, street-level images, which present the most common view that people have of greenery, provide a more human-centric way to quantify street tree canopies. This study mapped and analyzed the spatial distribution and temporal change in the green view index, which represents the visibility of tree canopies along streets in New York City during the last 10 years using historical Google Street View images. Deep learning and computer vision algorithms were used to derive the quantitative information of street tree canopies from street-level images and map the spatial distribution of the green view index. This study further investigated the potential disparities in terms of green view index across different racial/ethnic groups by comparing with census data. Results show that non-Hispanic Whites tend to live in neighborhoods with higher green view index and Hispanics tend to live in neighborhoods with lower green view index. The green view index values in New York City have increased slightly in the last 10 years, and the change of green view index has no significant correlation with resident’s ethnic/racial status. This study proves the usability of historical Google Street View images for monitoring the temporal change of urban street tree canopy changes at large scale, and it also provides insights and a valuable reference for urban greening programs.

There has been recent interest in the use of network analysis to quantify bike network features and their impact on biking levels and safety. However, limited bike network indicators have been evaluated. This study introduces a list of network indicators to quantify the bike network and study its effect on bike kilometers traveled and bike–vehicle crashes. Data from the city of Vancouver, Canada, are used as a case study. Full Bayesian modeling incorporating spatial effects is employed to develop Bike Kilometers Travelled (BKT) and bike–vehicle crash models. The developed BKT models show that the bike network centrality, assortativity, and weighted slope have negative associations with BKT, while the bike network directness, length, complexity and development, and connectivity have positive associations with BKT. The developed crash models show that the bike network length, centrality, assortativity, and continuity have negative associations with bike–vehicle crashes. On the other hand, the bike network complexity and development, connectivity, and linearity have positive associations with bike–vehicle crashes. The models provide insights that can be useful for planning bike networks to increase bike traffic and improve bike safety. The models also show that some changes to a bike network to increase bike traffic should be accompanied by crash risk-mitigating measures. As well, the models can be used to identify zones within a city that require safety improvements.

Noise is an ever-growing problem in cities. Conventional noise mitigation approaches may not necessarily control noise pollution, since whether a sound is perceived as noise is largely influenced by its specific contexts. Based on an activity-centric framework, this study examines the effects of activity-related contexts and measured sound levels based on individuals’ sound evaluations as they undertake daily activities at different geographic locations and times. Data for the study were collected from 33 participants in Chicago (USA) using Global Positioning System-equipped mobile phones, portable sound sensors, and activity diaries. Multilevel logistic modeling was used to examine the relationships among measured sound levels, sound evaluations, and activity-related contexts for each recorded activity of the participants. The results indicate that activity-related contexts significantly influence individuals’ sound evaluations as they perform their daily activities. When activity-related contexts are taken into account, the measured sound levels that individuals experienced when performing an activity are no longer significant in influencing their sound evaluations. These results support the notion that sound is not only a physical feature but also a socio-psychological construct. It is crucial to adopt a human-centric and context-aware approach in urban planning through understanding the circumstances in which a sound is perceived as noise. Such an approach would help improve sound-related urban environments and construct livable and healthy cities.

Level-of-service has been widely used to measure the operational efficiency of existing highway systems categorically, based on certain ranges of traffic speeds. However, this existing method is generic for investigating urban traffic characteristics. Hence, there is a crucial knowledge gap in capturing the unique traffic speed conditions during a certain temporal duration, in a common spatial area that includes different land use clusters. This study fills this gap by modeling the link between traffic speeds and land use clusters during certain time periods, along with the given level-of-service criteria. As a case study, this study adopted the central business district in Los Angeles in the United States. A total of 1780 traffic sensor speed data on Interstate 10 East adjacent to the central business district of Los Angeles was collected and clustered by the land use designated by the zoning regulations of the city of Los Angeles. The proposed traffic time–speed curve model that integrates different land uses in a large urban core was then developed and validated statistically, using historical real-world traffic data. Finally, an illustrative example was presented to demonstrate how the proposed model can be implemented to measure critical time periods and corresponding speeds per land-use cluster, responding to the designated level-of-service criteria. This study focused on making recommendations for government transportation agencies to employ an appropriate method that can estimate critical time periods affecting the existing operational status of a highway segment in different land-use clusters within a common spatial area, while promoting an effective application of a set of traffic sensor speed data.

The lack of longitudinal studies of the relationship between the built environment and travel behavior has been widely discussed in the literature. This paper discusses how standard propensity score matching estimators can be extended to enable such studies by pairing observations across two dimensions: longitudinal and cross-sectional. Researchers mimic randomized controlled trials and match observations in both dimensions to find synthetic control groups that are similar to the treatment group and to match subjects across before- and after-treatment periods. We call this a two-dimensional propensity score matching method. This method demonstrates superior performance for improving treatment effect estimation based on Monte Carlo evidence. A near-term opportunity for such matching is identifying the treatment effect of transportation infrastructure on travel behavior.