Environment and Planning B: Urban Analytics and City Science

While collaborative planning has gained popularity in addressing conflicts of interest in urban renewal, the development of information communication technologies has provided creative tools in participatory planning. To accommodate the needs of participatory e-planning, we designed a digital collaborative platform composed of four modules to establish a framework for all stakeholders to participate in the urban renewal process in China. By taking a village as a case study (hereafter referred to as village X), this research introduces the application of a digital platform in the renewal of an old village facilitated by a third party. Although the application of e-planning participation in the renewal of X village has been effective, its further application has encountered challenges, such as willingness, the capacity of the residents, and credibility, due to the lack of an institutionalized arrangement. Compared with the West, China has a long way to go in promoting public participation in urban renewal through institutional arrangements. Educating the public and deepening the understanding of local governments in the necessity of public participation should be the future task of urban planners in China. The limitations and further applications of digital platforms are also discussed.

Despite the importance of urban forms to the dispersion of particulate matter (PM), only a few studies exist on the relationship between them due to the limitations of the data and methodology. Thus, this study used a deep learning-based investigation of the impact of urban form on PM_2.5 concentration. Autoencoder, long short-term memory (LSTM), and the random forest model were used to analyze their relationship. The random forest model showed that urban form variables predict PM_2.5 concentration with a 95.66% accuracy, confirming that urban form characteristics significantly impact PM_2.5 concentrations. Among the urban form variables, floor area ratio turned out to be the most important, suggesting the need for more detailed efforts to reduce PM_2.5 in locations with high floor areas. The effect on PM_2.5 prediction accuracy was evaluated with root mean square error. It was difficult to accurately predict PM_2.5 in areas with large building coverage areas and low-rise residential areas. This study improved the accuracy of results on the influence of urban form by using PM_2.5 non-aggregated data measured hourly over 4 years, which expanded the applicability of deep learning-based urban analysis.

Latin America’s intensive urbanization processes are triggering rapid peri-urban transformations and the expansion of cities. These include accelerated metropolization processes, urban sprawl, and the emergence of new conurbations. These changes parallel the expansion of highly profitable agricultural activities and plantations linked to international markets. This paper aims to analyze land use/cover changes between 1990 and 2050 in the Quillota Province, Valparaíso Region, Chile. Specific objectives considered (1) analyzing changes in land use/cover trajectories between 1990 and 2017, (2) simulating changes in land use/cover based on three scenarios of territorial planning to 2050 (trending, ecological planning, and spatial planning), and (3), identifying the areas most likely to be modified by urbanization and agricultural activity as a result of biodiversity loss in the study area. The Dyna-CLUE model implemented was complemented with GIS techniques for the analysis of land use/cover trajectories that allowed classifying and characterizing the most dynamic land uses/cover within the Quillota Province, such as urban land uses. The results of simulations to 2050 show a probable conurbation of medium-sized cities of Quillota-La Cruz-Calera, and future land use conflicts between peri urban-agricultural land use and plantation-natural conservation land use. The results suggest that it is essential to choose scenarios to ensure sustainable land use planning to control urban and peri-urban sprawl and protect areas of high natural value.

Improving the built environment to support walking is a popular strategy to increase urban sustainability and walkability. In the past decade alone, many US cities have implemented crosswalk visibility enhancement programs as part of road safety improvements and active transportation plans. However, there are no systematic ways of measuring and monitoring the presence of key built environment attributes that influence the safety and walkability of an area, such as marked crosswalks. Furthermore, little is known about how these attributes change over time at a national scale. In this paper, we introduce an innovative approach using a deep learning-based computer vision model on Street View images to identify changes in intersection-level marked crosswalks around more than 4,000 US transit stations over a 14-year period. We found an increase in the overall number of marked crosswalks at intersections. Furthermore, high-visibility crosswalks became more common, as they replaced existing parallel-line crosswalks. We further examine crosswalks around transit stations in New York City and San Francisco to illustrate geographic variations and compare associations with other characteristics of the built environment as reported in the Smart Location Database. Areas with increases in high-visibility crosswalks focused on high density residential areas and areas with a higher percent of zero-vehicle households. However, geographic variations exist. For example, in San Francisco, transit station areas outside downtown or major corridors (South and Southwest of the city) had the lower prevalence of marked crosswalks. This analysis confirms important gaps in crosswalk visibility that call for safety enhancements and opens the door for additional research involving these data. We conclude by discussing the limitations and future research opportunities using computer vision to automatically detect large-scale transportation infrastructure changes at a relatively low cost.

Parking is often overlooked by urban researchers even though parking consumes large proportions of a city’s physical footprint and imposes a significant impediment to more sustainable travel. Underpinning this lack of attention is suitable data and methods capable of capturing the complex dynamics of parking. Here we redress this gap by drawing on an emergent source of parking data and deploying empirical techniques to unpack this complexity. Data from 3542 on-street parking sensors observed over a 9-year period are used to delineate the first typology of parking routines before using a fixed-effects logistic regression model to explain how nearby land-use types and land-use mix shapes tempo and timing of parking utilisation. The benefit of our approach lies in its capacity to discriminate broad types of temporal rhythms associated with parking dynamics at particular places, how these change over time and how these rhythms are associated with different types and mixes of nearby land use. This knowledge is important to inform policies seeking to optimise the use of on-street parking and invoke more sustainable patterns of mobility.

Proliferation of Short Term Rental (STR) in cities has generated considerable debate as it was found associated with negative externalities, such as gentrification. Nonetheless, it signals urban qualities working as attractors at different geographical scales. STRs’ relation with urban form remains largely understudied. In this paper, we explore how urban form relates to STRs registered by the Airbnb platform in Amsterdam (NL). First, we identify urban types (homogenous patterns of form) through an ‘urban morphometric’ approach. Second, we assess the relation between urban types and density of Airbnbs via a composite machine learning (ML) technique. Third, we provide profiles of the urban types most strongly associated with it. Fifteen urban types explain up to 44% of Airbnb density’s variance. Compact and diverse urban types relate more strongly with Airbnbs. Conversely, repetitive, sparse and uniform urban types are inversely related. The proposed morphometric-based method is robust, replicable and scalable, offering a novel way to study the intricate relation between urban form, STRs and, in fact, any other measurable urban dynamics at an unprecedented scale. By identifying spatial features related to urban attractiveness, it can inform evidence-based design codes incorporating place-making qualities in existing and new neighbourhoods.

Any analytical study of a neighbourhood must begin with an accurate definition of the geographic region that contains it. For a long time, there has been an interest in taking surveys of neighbourhood extents, but this can generate numerous haphazardly sketched polygons. Researchers typically face the challenges of using boundary polygons reported by each participant and unifying these polygons into one representative boundary. Over the years, several researchers have reported their findings on methods for unifying these boundaries. We present and compare the following five methods (two existing, one modified and two new): Dalton radial average, Bae–Montello average, a vectorised version of the Bae–Montello raster grid overlay, a vectorised derivative inspired by the Wenhao kernel density axis method maximum kernel density axis and a new k-medians clustering method. A crowd-sourced evaluation method is presented. N=42 raters ranked the five methods of aggregating real boundary data based on the results from three study areas. We found that the boundary aggregation method derived from the Bae–Montello grid, closely followed by the Dalton radial average method, provided the most reasonable results. This paper outlines the reasons for these results and illustrates how this knowledge may point to the ability of future algorithms to improve the presented methods. The paper ends with a recommendation that neighbourhood boundaries should utilise boundaries derived from the Bae–Montello raster grid overlay method and/or the Dalton radial average method to facilitate comparisons in the field.

Plans can only impact practice when elected officials adopt, enact, and approve funding for specific strategies. We explore ways to track implementation from the planning documents to elected officials’ priorities and to their voting patterns to identify the consistencies and gaps that may limit the impact of plans. We use Twitter data mining, text content analysis, and voting records from the digitized council minutes in Calgary, Alberta, between the 2017 municipal election and the last quarter of 2020. We connect the expressed preferences to votes for each councilor over the study period. On the two most salient topics—transit and affordable housing—those who expressed support on Twitter also supported investments. With one exception of an anti-tax councilor, over time, the rest of the councilors reached agreements on public investments (supra-local funding lightened the financial burdens for the city facilitating “yes” votes). Planners can derive meaningful information from the elected officials’ social media communication, such as concerns and support for specific planning initiatives, to promote successful plan implementation. This information can also enhance voters’ awareness of local officials’ views and actions on planning initiatives.

The arrangement of buildings along roads creates one of the most fundamental patterns of three-dimensional streetscape skeletons, primarily defined as a set of building heights and setbacks in a district. Under zoning regulations, building heights and setbacks are indirectly controlled by the building coverage ratio (BCR) and the floor area ratio (FAR). Variations in the BCR result in variations in streetscape skeletons. Moderate complexity of streetscape skeletons is a necessary condition for aesthetic streetscape. Understanding the relationships between variations in the BCR, building heights and setbacks is thus important in order to harmonise streetscape skeletons, smaller variations in building heights, and setbacks, however, this relationship has yet to be theoretically investigated due to the complex relationship between buildings. The objective of this paper is therefore to formulate the relationship between variations in building heights and setbacks as a function of the standard deviation of BCR, and, based on this formulation, to discuss how to indirectly harmonise variations in streetscape skeletons under zoning regulations. This formulation enables us to analytically investigate the relationship between these two functions and the standard deviation of a BCR. An indirect scheme for harmonising variations in streetscape skeletons under zoning regulations is proposed on the basis of this formulation. The external diseconomies of inharmonious streetscape skeletons are quantitatively defined in order to incentivise plot owners to harmonise streetscape skeletons. The optimal building height and setback criteria are computed, which minimises the social cost of inharmonious building heights and building setbacks in a district. This scheme for incentivising plot owners to reduce their social costs is expected to contribute to indirectly harmonising streetscape skeletons.

Over the past half century, the Seoul metropolitan area (SMA) has experienced rapid urbanization. Urban development and population growth within the SMA have caused various problems, such as a lack of affordable housing, traffic congestion, and socioeconomic inequality between the SMA and the rest of the country. As a solution, growth control was adopted, but it resulted in increasing housing prices within Seoul. In late 2018, skyrocketing housing prices forced Seoul’s government to abandon its growth-control policy and announce large-scale “new-town” projects planned outside of the city’s urban growth boundary. The primary purpose of this research is to predict future urbanization dynamics by utilizing the long short-term memory (LSTM)–based prediction model. The secondary purpose is to identify the influential driving factors in urbanization that can help policy makers develop evidence-based, informed strategies. To predict future urbanization’s spatial patterns in the SMA, LSTM models have been estimated under two scenarios: (A) assuming that current urbanization trends and contributing factors will remain consistent in the future and (B) considering new development plans’ impacts. A comparison of the modeling results indicates that the government-driven new-town projects will help urbanize 55.8% more land by 2030. The variable influence analysis also reveals that strong growth-control measures may be necessary for areas with higher employment and homeownership rates to control rapid urbanization. However, housing supply and economic growth–related policies in Seoul’s suburbs would help attract the city’s population to the outskirts. The LSTM-based model yields an accurate and reliable spatial prediction in the form of visual maps, and its graphic results will assist policy makers greatly in developing effective strategies for smart urban growth management.

New tools have enabled “civic hackers” and transportation researchers to map previously uncharted transit networks previously confined to the purview of locals and insiders. These new datasets reveal the extent of these systems and their role in providing access to the city. In this paper, we describe the methodology regarding the mapping process of Bogotá’s semi-formal SITP Provisional bus system, accomplished using smartphones, cloud-based data management systems, and GIS software. By visualizing the semi-formal SITP Provisional system alongside the centrally planned bus system, we provide the first complete picture of Bogotá’s transit system. We also develop two types of data analysis based on General Transit Feed Specification (GTFS) data generated from this mapping process. Using spatial algorithms, we identify parallelism between the semi-formal transit routes and the formal transit network. We then visualize the degree of access to job opportunities that each system provides. We find that integrating semi-formal and formal transit services, that is, the entire network, increases accessibility levels for workers, especially at urban peripheries. Results suggest the importance of considering semi-formal transit services in transportation planning, the services often neglected in the planning process, and the advantages of integrating them into the network to increase accessibility to opportunity areas. We recommend that other cities harness GPS-enabled apps to map transit systems, generate GTFS data, and empower local actors to make use of the data. Based on this bottom-up approach, semi-formal transit networks can provide additional inputs for urban transportation planning processes regarding the transportation user´s accessibility needs.

Precise building height is indispensable for evaluating variability in building heights. However, relevant data are not always available. Conventionally, building height is approximated as the product of the number of building storeys and floor height, called approximated building height. However, there is no consensus on how floor height, a key determinant of approximated building height, should be set. In Japan, increasingly precise building height data are becoming available as an open 3D urban model. This provides the motivation for answering the following research questions, in the context of the Tokyo metropolitan region: (1) What is the difference between approximated and precise building height? (2) How should we set floor height to minimise the difference between approximated and precise building height? The results show that (1) the average difference is −3.46 m if floor height, c, is 5 m, −0.87 m if c is 4 m and 1.71 m if c is 3 m; (2) c = 4 can effectively estimate building height from the number of storeys; and (3) the greatest difference between approximated and precise building height is spatially clustered where commercial zones and industrial zones are allocated, with their degree being dependent on how floor height is set. Furthermore, it is found that (1) in commercial zones and industrial districts, the optimal floor height, defined as the optimal solution which minimises the sum of squared differences between approximated and precise building height, is greater than 4 m; (2) in mid/high rise residential districts, the optimal floor height ranges from 3 to 3.4 m; and (3) in low-rise residential districts, the optimal floor height ranges from 3.5 to 3.9 m. The findings of this paper can help urban planners find the optimal floor height in each district and understand spatial variations in building and floor heights.

Developing 15-minute cities, where people can access to living essentials within a 15-minute trip, has become a global effort. In addition to practical exercise, researchers have paid attention to the evaluation of 15-minute cities using home-based accessibility approaches. However, existing approaches do not account for human mobility, an important indicator of how people access and interact with urban amenities. In this study, we propose a novel network-based framework that assesses a 15-minute city considering human mobility patterns. We assume that there exists an optimal mobility network, which would maximize human mobility under the constraints of the current distribution of amenities. Locations where the provision of urban amenities does not match local needs are first identified based on the comparison between optimal mobility patterns and their actual counterparts. Built environment, demographic, and network structure factors that contribute to identified mismatch issues are then examined. The empirical study of Nanjing, China, suggests that the proposed framework could enable a dynamic evaluation of 15-minute cities and could provide important insights on policies and intervention strategies of planning and developing 15-minute cities.

Agent-Based Models (ABMs) are being increasingly used to evaluate urban systems, urban policies and environmental impacts. One prerequisite for using the ABM framework consists of generating a synthetic population representative of the actual population, featuring the appropriate attributes with respect to model objectives. A precise spatial positioning of the synthetic population agents is often key to ensuring ABM modeling quality. This paper considers the problem of allocating synthetic population agents to a finer spatial scale. Such an allocation process is performed from a higher-level statistical area where a synthetic population can be generated, that is, a container statistical area (CSA), to several nested non-overlapping elementary statistical areas (ESAs), where only marginals are available. This allocation step relies not only on common attributes between CSA and ESA, but also on additional discriminatory attributes, that is, attributes of interest, estimated from external data sources. The case study examined herein is based on French census and fiscal data. Common attributes include eight socio-demographic variables, totaling 17 modalities. An additional attribute of interest, that is, income, has also been added. The allocation problem at hand is modeled as an integer quadratic programming problem. An exact algorithm is first applied to solve the problem; the applicability of this algorithm proves to be limited to small-size synthetic populations. A heuristic is proposed to handle the allocation of larger-size synthetic populations. Tests carried out on the case study show that this heuristic yields near-optimal solutions; it is also computationally efficient and may fulfill the needs of a majority of users.

Recently, we have seen new developments in our understanding of the emergence and organization of cities and urban systems, including application of scaling laws to urban areas. A recent wave of studies has observed consistent behavior of multiple urban measures that scale with city size across geographic and sectoral contexts. However, the extant evidence is lacking in two important ways: first, a wide variety of urban measures still remain unexplored, and second, there is limited evidence from developing countries. This paper offers new evidence on both these fronts: i) applying scaling laws to predict slum population in cities, an urban measure that remains largely unexplored, and ii) applying them in the context of a developing country, India. Results suggest that population alone is not sufficient to predict slum population in India. Conversely, I use empirical results from scaling laws to test established slum growth theories that have influenced policymaking globally for decades, despite having limited empirical evidence to support them. I also show that scaling exponents are sensitive to the way we define urban systems, of which cities are a part, an issue that has been raised in the ongoing methodological debate on urban scaling laws. I believe that findings presented in this paper have implications for advancement of slum theories as well as urban scaling laws by offering new empirical evidence and mechanisms through which such scaling might happen in the context of slums.

This paper describes and visualises the data contained within the {TTS2016R} data package created in R, the statistical computing and graphics language. {TTS2016R} contains home-to-work commute information for the Greater Golden Horseshoe area in Canada retrieved from the 2016 Transportation Tomorrow Survey (TTS). Included are all Traffic Analysis Zones (TAZ), the number of people who are employed full-time per TAZ, the number of jobs per TAZ, the count of origin destination (OD) pairs and trips by mode per origin TAZ, calculated car travel time from TAZ OD centroid pairs and associated spatial boundaries to link TAZ to the Canadian Census. To illustrate how this information can be analysed to understand patterns in commuting, we estimate a distance-decay curve (i.e. impedance function) for the region. {TTS2016R} is a growing open data product built on R infrastructure that allows for the immediate access of home-to-work commuting data alongside complimentary objects from different sources. The package will continue expanding with additions by the authors and the community at-large by requests in the future. {TTS2016R} can be freely explored and downloaded in the associated Github repository where the documentation and code involved in data creation, manipulation and all open data products are detailed.

Graffiti as an urban phenomenon comes in different forms and materials, from simple spray slogans to wall paintings and art, containing multi-thematic content. Despite the contradictory nature of various literature opinions, reports of a positive association between wall-graffiti and fear of crime or streetscape value have emerged. However, comparative urban studies registering graffiti locations are non-existent, thereby hindering the benchmarking of urban liveability. In this work, the spatial patterns of graffiti-vandalism across 30 European city centres were investigated, using Google Street View–derived observations. A significant variation in graffiti presence across Europe was recorded, ranging from about 3%–9% of street segments in London, Oslo and Vienna, to roughly 70%–76% in Madrid, Athens and Sofia. In addition, their spatial polarisation that reflects the presence of potential socio-spatial inequities requiring further attention was demonstrated. Overall, the created geo-visualisations could enable European policymakers to facilitate better-informed response strategies and researchers to delve into the effects of graffities on urban systems and societies.

There is a long tradition of understanding globalization by measuring the “world city-ness”, and there are two distinctive frameworks concerning the interrelations of the “world city network”, one of which builds upon the notion of worldwide corporate organizations and the other on the infrastructure of transport. Despite that, more studies on a single city are still required, since most cities participated in the globalization process on their own terms. Thus, the aim of this study is to visualize the patterns of origins of foreign visitors using data from mobile phones in order to better understand how China's global cities contribute to globalization in a cartogram. It is found that the strength of Shanghai’s global connections are concentrated overtly in the countries of the Asia-Pacific region, but Beijing still has the dominant role in sustaining China’s global links.