Environment and Planning B: Urban Analytics and City Science

Although a growing body of literature has examined a variety of planning support systems, few studies have been conducted to understand emerging planning support apps for mobile participation and its impact on collaborative planning. This research develops a conceptual framework for assessing different phases of collaborative planning processes and the added value of planning support apps in stakeholder interaction and management. The case studies include four Dutch regeneration projects, which are ongoing pilot projects of the new Environment and Planning Act and supported by a variety of planning support apps. The data for each case study were collected from multiple sources, including policy documents, interviews with stakeholders and online information. The results show that the apps support to engage many participants, provide real-time communication and facilitate effective interactions between the stakeholder managers and local residents. However, their performance is largely dependent on the user-friendliness of the system. Besides, a good consensus building process in the planning phase has a positive effect on stakeholder management and the performance of the apps in the execution phase.

This paper reports on the development trajectory of an empirical tool for transit-oriented development planning in Flanders, Belgium. The tool, StationsRadar, draws on a branch of empirical railway station assessment tools that aim to support transit-oriented development planning processes by visualizing the performance of station locations for a range of transport (‘node’) and land use (‘place’) accessibility indicators. At the root of this paper lies the observation that, while the vast majority of reviewed studies highlight the relevance of the developed tools for planning practice, little work is undertaken to systematically verify that claim. Against this backdrop, we invoke an experiential research strategy as recently proposed in the field of planning research; we organize a series of experiential workshops in which we probe the tool’s added value for regional planning in Flanders. In the process, we specifically work towards a qualitative appraisal of tool ‘usability’ and discuss how our findings bear relevance to the well-rehearsed practice of developing empirical transit-oriented development support tools. Additionally, we elaborate on and illustrate the ramifications of our findings in terms of the subsequent/iterative technical revision of the tool. We conclude this paper by putting forward three major usability recommendations pertaining to: interactive and diversified data visualizations, actor-mobilizing momentum in light of data transparency, and the integration of ‘hard’ and ‘soft’ data in light of crowdsourcing aspirations. We reflect on the broader technical and methodological challenges that come with implementing these in practice.

Informal settlement growth is a vital challenge for developing countries, which requires monitoring and assessment by urban planners and city managers. Rural migration to urban areas leads to the unplanned expansion that grows within and beyond the city’s official boundaries. Although informal housing (IH) is growing fast, very little attention is oriented toward exploring tools and procedures for predicting its future expansion. Many studies have shown that informal housing is widespread and represents one of the most dominant features of urbanization in Egypt. Modern simulation and modeling technologies provide new methodologies to explore the complexity of urban growth. As a result, many planning models were developed and successfully used to simulate the spread of planned settlements in developed nations. However, the implementation of these models rarely achieves realistic simulation in the case of unplanned growth due to the developer’s field of study and the available resources. The main objective is to simulate the expected informal housing by modeling its causative land use factors in the Greater Cairo Region. This paper develops a predictive model that anticipates the spatial distribution of unplanned growth and where informal housing is likely to occur over a period based on known growth factors. The proposed Informal Housing Growth Model derives its principles from cellular automata and geographic information system technologies. This model uses a multi-criteria concept, including parameters and conditions related to informal growth, and can be adapted to other growth factors.

This work presents a study on the urban configuration of a number of Italian metropolitan areas and their development over time, with the aim of evaluating the size and shape of urban areas expansion. Raster data are used, produced by the European Environmental Agency within the COoRdination of INformation on the Environment land cover project. The study is based on a version of spatial entropy measures proposed and validated by a recent series of papers, aimed at the evaluation of spatial data heterogeneity; the methods assess the efficiency of the spatial configuration of urban areas. An innovative combination of two entropy measures is the tool for evaluating the urban development in Italy. Results allow both conclusive comments about each metropolitan area and comparisons across areas over space and time.

Driving restriction is used to mitigate traffic congestion and improve air quality. A partial bridge restriction policy is created in Chongqing, China since the bridges are natural network bottlenecks due to the local river system. Is such a strategy really capable of reducing air pollution and further improving local air quality? Employing an integration of principal component analysis and a regression-discontinuity design approach, this study examines the short-run effect of the partial driving restrictions on the local air quality index in Chongqing, China. The examination is first conducted to the city level, and then its eight administrative districts are tested separately. The findings reveal that the air quality index of the whole city area has experienced deterioration after the introduction of restrictions in Chongqing. Among eight districts, Yuzhong is the only one experiencing an improvement of air quality index.

Fine particulate matter (PM_2.5) is harmful to human health. Although the relationship between urban land use and PM_2.5 has been studied in recent years, there has been little consideration of the relationship between land use structure and PM_2.5 spatiotemporal patterns at the microscale. Based on mobile monitoring PM_2.5 data and point of interest data, this paper explored their relationship with a classification and regression tree model. The results showed that PM_2.5 exhibits spatiotemporal heterogeneity at the microscale. The neighborhoods’ land use structure can explain 60.4% of the PM_2.5 spatiotemporal patterns. Transportation and ecology are the two most significant land use types that correlated with PM_2.5 spatiotemporal patterns. Fourteen rules of neighborhood land use structures with different land use types are identified land use structure which leads to different spatiotemporal patterns of PM_2.5. The higher the PM_2.5 risk, the stronger the correlation with neighborhood land use structure is. The classification and regression tree model can be effectively used to judge the relationship between neighborhood land use structure and PM_2.5 spatiotemporal patterns. The results provide a basis for developing appropriate measures, based on local conditions, to predict PM_2.5 pollution levels at the microscale, and reduce the risk of neighborhood exposure to PM_2.5.

Currently, many cities worldwide are changing current existing and mostly outdated lighting situation systems from inefficient lamps to light emitting diodes (LEDs). Providing the opportunity of energy savings, they can help in preventing influences to the night sky and furthermore issues for human health, wildlife and environment. This work simulates a potential LED conversion for the megacity of Mexico City and investigates impacts to conservation areas. Modelled for the whole visible spectrum, the analysis places special focus on the effects of applying various colour temperatures. Additionally, a highly polluted atmosphere was included as theoretical model, something applying to megacities in particular, to see impacts on skyglow of such an environmental contingency. In general, results show that the night sky brightness increases significantly with increasing colour temperature of LEDs if the lumen output is kept constant. It is shown that a potential conversion requires a thorough adjustment, otherwise negative impacts on environment and health might rise. Furthermore, an increased aerosol optical thickness ends in producing more diffuse light, identifying a major concern for the environment. The results obtained in this paper may be a strong motivation to ascertain measurements conducted in other large urban areas correlated to the computational results presented here.

The ever-growing online corpus of images of the built environment, on social media and mapping platforms, offers a new kind of archive of the built environment. Recent advances in computer vision, specifically convolutional neural networks, offer new ways of querying and analyzing large image corpuses. In this paper, we propose a new method by which historians of the built environment can use these vast image corpuses in their study, enabling new research questions. To demonstrate proof of need, we report on an ongoing case study in Tel Aviv that attempts to show the feasibility of our proposed method for enabling a Historic Urban Landscapes (HUL)-based approach to the study of the built environment. In so doing, we show how such image corpuses could potentially form a new type of archive for architectural and urban history.

The patterns of syntactic differentiation and their causes and effects are fundamental to space syntax analysis. Often, however, differentiation is taken for granted with no reference to the dynamic process that brings it about. Here, we first show that by measuring the amount of syntactic differentiation, we can better distinguish between types of street networks. We then show that repeated local transformations of a regular street grid lead to different yet largely predictable trajectories of differentiation depending upon the rules used. Finally, we show that different paths to differentiation entail different costs in terms of undesirable properties. This allows us to better assess the likely consequences of design moves and their appropriateness relative to design intentions.

The study of human activity–travel patterns for urban planning has evolved a long way in theories, methodologies, and applications. However, the scarcity of data has become a major barrier for the advancement of research in the field. Recently, the proliferation of urban sensing and location-based devices generates voluminous streams of spatio-temporal registered information. In this study, we propose an approach using the linear-chain Conditional Random Fields (CRFs) model to learn the spatio-temporal correspondences of different types of activities and the inter-dependencies among sequential activities from training dataset such as the household travel or time use surveys, and to infer the hidden activity types associated with urban sensing data. The performance of the CRFs model is compared against the Random Forest (RF) model, which has been used in a number of existing studies. The results show that the linear-chain CRFs models generally outperform the RF counterparts with respect to classification accuracy of activity types, in particular for those travelers having more outdoor daily activities. The proposed methodology is demonstrated by reconstructing the activity landscape of the surrounding area of a major Mass Rail Transit station in Singapore using the transit smart card transaction data. The inferred activities from the transit smart card data are expected to complement the ground surveys and improve our understanding of the interactions of different components of activities/travels as well as the relationship between urban space and human activities.

In order to reduce the atmospheric pollution in urban areas, an enhanced approach is proposed in this paper for the traffic congestion analysis. The approach is formulated as bi-level optimization program considering additional constraints in the traffic assignment problem. To respect the required eco-friendly threshold constraint, the travel demand between several origin–destination pairs was categorized in two classes: old polluting cars and modern (less) nonpolluting cars. The validity of the formulation was verified by optimality conditions. Two network examples are discussed to explain the properties and advantages of the suggested technique. It is found that for the both examples, the proposed optimal solution displays better results as compared to the common user equilibrium route choice policies. As a result, the enhanced approach leads to traffic network congestion relief with minimum air pollution and maximum use of routes network.

Urban planners have a stake in preserving restaurants that are unique to local areas in order to cultivate a distinctive, authentic landscape. Yet, over time, chain restaurants (i.e. franchises) have largely replaced independently owned restaurants, creating a landscape of placelessness. In this research, we explored which (types of) locales have an independent food culture and which resemble McCities: foodscapes where the food offerings can be found just as easily in one place as in many other (often distant) places. We used a dataset of nearly 800,000 independent and chain restaurants for the Continental United States and defined a chain restaurant using multiple methods. We performed a descriptive analysis of chainness (a value indicating the likelihood of finding the same venue elsewhere) prevalence at the urban area and metropolitan area levels. We identified socioeconomic and infrastructural factors that correlate with high or low chainness using random forest and linear regression models. We found that car-dependency, low walkability, high percentage voters for Donald Trump (2016), concentrations of college-age students, and nearness to highways were associated with high rates of chainness. These high chainness McCities are prevalent in the Midwestern and the Southeastern United States. Independent restaurants were associated with dense pedestrian-friendly environments, highly educated populations, wealthy populations, racially diverse neighborhoods, and tourist areas. Low chainness was also associated with East and West Coast cities. These findings, paired with the contribution of methods that quantify chainness, open new pathways for measuring landscapes through the lens of unique services and retail offerings.

The elderly may have unique, daily travel behaviour characteristics compared to other age groups, associated with age and physical ability. Defining these characteristics can inform urban infrastructure construction and planning. In this study, 20 elders aged between 60 and 70 years, living in the city centre of Tianjin, were selected to complete the survey. A total of 2232 hours of participant travel behaviour were collected via GPS equipment from July to August 2019. Data were used to create a space–time cube. Based on a statistical analysis of the GPS data, results indicated that the elderly mainly had six kinds of daily travel behaviours: visiting, shopping, outdoor exercise, eating out, going to the hospital and picking up and dropping off grandchildren. The main activity time was from 10:00 a.m. to 9:00 p.m. Their travel mode was mostly pedestrian-based, with an average single travel distance of about 1.01 km, and an average single travel time of about 0.5 hours. Using the space–time cube, characteristics of elderly daily travel behaviour were visualised. In addition, a typical space–time cube was summarised and presented. Data and methods from this study can provide reference and support for future-related research.

Space, time and the social realm are intrinsically linked. While an array of studies have tried to untangle these factors and their influence on human behaviour, hardly any have taken their effects into account at the same time. To disentangle these factors, we try to predict future encounters between students and assess how important social, spatial and temporal features are for prediction. We phrase our problem of predicting future encounters as a link-prediction problem and utilise set of Random Forest predictors for the prediction task. We use data collected by the Copenhagen network study; a study unique in scope and scale and tracks 847 students via mobile phones over the course of a whole academic year. We find that network and social features hold the highest discriminatory power for predicting future encounters.

Residential income segregation is a spatial manifestation of social inequality and is an important factor that influences access to resources, services, and amenities. In general, past research analyzing income segregation has applied index-based methods to describe the separation of low-income households at one spatial scale; however, existing studies have not yet considered how income segregation varies across multiple income classes, spatial scales, and local contexts. This study applies a multilevel multigroup modeling approach to explore the global and local patterns of income segregation between dissemination areas (micro-scale), census tracts (meso-scale), and neighborhoods (macro-scale) in Toronto, Canada. A global model that estimates the overall multiscale segregation of five income classes finds that the most affluent families had the highest levels of segregation and that the segregation of all income classes was strongest at the macro- and micro-scales. A local model that allows the micro-scale segregation measures to vary geographically shows that higher-income families were less segregated in the city center than in the inner suburbs, that middle-income families were highly segregated in areas serviced by public transit, and that almost all income classes had high levels of segregation in disadvantaged neighborhoods prioritized for investment by local policymakers. The methodological and substantive contributions of this study for understanding the complex patterns of income segregation are discussed.

Labour market areas (LMAs) are a type of functional region (FR) defined on commuting flows and used in many countries to serve as the territorial reference for regional studies and policy making at local levels. Existing methods rely on manual adjustments of the results to ensure high quality, making them difficult to be monitored, hard to apply to different territories, and onerous to produce in terms of required work-hours. We propose an approach to automatise all stages of the delineation procedure and improve the final results, building upon a state-of-the-art stochastic search procedure that ensures optimal allocation of municipalities/counties to LMAs while keeping good global indicators: a pre-processing layer clusters adjoining municipalities with strong commuting flows to constrain the initial search space of the stochastic search, and a multi-criteria heuristic corrects common deficiencies that derive from global maximisation approaches or simple greedy heuristics. It produces high quality LMAs with optimal local characteristics. To demonstrate this methodology and assess the improvement achieved, we apply it to define LMAs in Spain based on the latest commuting data.

Based on government appointed specialists’ (Antiquities Advisory Board or AAB’s) assessments of the heritage value of more than 1400 heritage items in the Hong Kong Special Administrative Region, we found no evidence of nationalistic bias against British or Japanese built military heritage buildings and structures after the handover of Hong Kong to China in July 1997. We also found no evidence of bias in favour of imperial Chinese architecture in the postcolonial period. Incidentally, we found some evidence that suggests AAB’s assessments of heritage value for military heritage buildings and structures have increased while those for imperial Chinese architecture have decreased after 1997, which is somewhat puzzling and merits further investigation. The reasons for the results are discussed in terms of the governance of the AAB as a government appointed committee.

Accessibility is fundamentally thought to be related to functional, economic, and social performances of cities and geographical systems and, therefore, constitutes an essential aspect for spatial planning. Previous studies focused on cities or metropolitan scales, often disregarding their position within regional and national systems, which can greatly affect their performance. Although accessibility at various spatial scales has been examined, the studies focused on accessibility patterns at different scales, with no reference to the level of accessibility of cities over local, regional, and national scales simultaneously, i.e. multiscale accessibility. This study aims to elucidate the multiscale accessibility level of individual cities and examine its relationship to urban performance in the urban system of Israel. Spatial accessibility was analyzed using the space syntax methodology for the entire national road network across multiple geographic scales—from the local to the national scale. Based on three distinct spatial accessibility systems identified, a unique multiscale accessibility profile was created for individual cities in Israel. Subsequently, each city’s multiscale accessibility profiles were examined against urban performance indicators determined from urban scaling theory. We found that the superiority of cities characterized by high accessibility level plays a role not only for a specific scale but also over scales and spatial systems. Moreover, most urban performance indicators related to the multiscale accessibility profiles of cities, while some multiscale accessibility profiles can be related to over- or under-performance of cities. The findings suggest that pervasive accessibility across spatial scales is inherently connected to urban performance and may indicate on the implementation and interpretation of accessibility. These findings may assist in various aspects of spatial planning at various scales.

The new availability of big data sources provides an opportunity to revisit our ability to predict neighborhood change. This article explores how data on urban activity patterns, specifically, geotagged tweets, improve the understanding of one type of neighborhood change—gentrification—by identifying dynamic connections between neighborhoods and across scales. We first develop a typology of neighborhood change and risk of gentrification from 1990 to 2015 for the San Francisco Bay Area based on conventional demographic data from the Census. Then, we use multivariate regression to analyze geotagged tweets from 2012 to 2015, finding that outsiders are significantly more likely to visit neighborhoods currently undergoing gentrification. Using the factors that best predict gentrification, we identify a subset of neighborhoods that Twitter-based activity suggests are at risk for gentrification over the short term—but are not identified by analysis with traditional census data. The findings suggest that combining Census and social media data can provide new insights on gentrification such as augmenting our ability to identify that processes of change are underway. This blended approach, using Census and big data, can help policymakers implement and target policies that preserve housing affordability and protext tenants more effectively.

Recent work on ‘anti-adaptive’ neighbourhoods has highlighted a number of common features, including scale of design, number of designers, mono-functionality, percentage of public space, planning rules and system of ownership. This article aims to provide a more general conceptual analysis of adaptability and anti-adaptability in terms of degrees of individual choice, where an individual’s choice set is understood as a combination of individual freedoms, both physical and normative, and of individual normative powers. Individual choice is constitutive of adaptability, and its ‘non-specific’ value helps to explain why adaptability is itself seen in a positive light. Thus, the article points to a potentially unifying explanatory factor that can help us to better understand the various common features of anti-adaptive neighbourhoods highlighted in the recent literature. The final part of the article discusses some of the implications of this reasoning for policy and design.

Urban planning implementation evaluation (UPIE) is an important tool for the supervision and inspection of the outcomes, processes and effectiveness of planning implementation. Conformance-based evaluation refers to the exploration of the degree of coincidence between actual situations and planning contents. Decision-centred performance evaluation focuses on exploring the role and purpose of plans in the implementation process. Although consensus has been reached on conformance and decision-centred performance evaluation, goal-oriented performance evaluation needs further development. Therefore, the integrated theoretical framework of UPIE, which emphasizes social effectiveness, has been constructed with goal-oriented performance in mind. It is suggested that performance-based evaluation that focuses on social effectiveness reveals the gap between actual situations and planning goals and can be established by the degree of realization of planning goals. Meanwhile, it has put forward a service-capabilities-based evaluation methodology which relies on the spatial matching between service supply and user group demands. Taking the Urban Master Plan of Xi’an (2008–2020) as an example, the results of UPIE show that this blueprint needs further revisions and adjustments. Through a goal-oriented methodology, the integrated theoretical framework can attract more attention to social effectiveness.

Mini-parks are becoming a popular form of outdoor recreational space in densely populated areas, largely because their small size makes site selection easier than for ordinary parks. However, existing studies on mini-parks are limited because most of them rely on data collected through traditional surveys, which are severely restricted by space and time. In this study, we utilised Tencent user density data – a type of space–time synchronous data with high spatial resolution – to trace mini-park visitation in the main city of Yancheng, China, and we integrated data about land use, points of interest, transportation, demographics and housing prices to measure the parks’ surrounding features. We investigated how factors relating to the parks’ spatial and internal attributes, surrounding physical features and surrounding socio-economic features affected the number of park visits during the week versus during the weekend by establishing a series of multiple linear regression models. The results showed that higher resident population, more surrounding public toilets and larger open site promoted mini-park visits while distance to the city centre, surrounding large parks and main roads discouraged mini-park visits. This study also found that the effects of weekend visitation factors were more complex than those of weekday visitation factors. These findings can help urban green space planners and decision makers to efficiently allocate mini-parks to areas where they will be most effective.