Bev Wilson PhD AICP

Cities across the United States and around the globe are embracing urban greening as a strategy for mitigating the effects of rising temperatures on human health and quality-of-life. Better understanding how the spatial configuration of tree canopy influences land surface temperature should help to increase the positive impacts of urban greening. This study applies a machine learning approach for modeling the relationship between urban tree canopy, landscape heterogeneity, and land surface temperature (LST) using data from nine cities located in nine different climate zones of the United States. We collected summer LST data from the U.S. Geological Survey (USGS) Analysis Ready Data series and processed them to derive mean, minimum, and maximum LST in degrees Fahrenheit for each Census block group within the cities considered. We also calculated the percentage of each block group comprised by the land cover designations in the 2016 or 2019 National Land Cover Database (NLCD) maintained by the USGS, depending on the vintage of the available LST data. High resolution tree canopy data were purchased for all the study cities and the spatial configuration of tree canopy was measured at the block group level using established landscape metrics. Landscape metrics of the waterbodies were also calculated to incorporate the cooling effects of waterbodies. We used a Generalized Boosted Regression Model (GBM) algorithm to predict LST from the collected data. Our results show that tree canopy exerts a consistent and significant influence on predicted land surface temperatures across all study cities, but that the configuration of tree canopy and water patches matters more in some locations than in others. The findings underscore the importance of considering the local climate and existing landscape features when planning for urban greening.

While recognition of the dangers of extreme heat in cities continues to grow, heat resilience remains a relatively new area of urban planning. One barrier to the creation and successful implementation of neighborhood-scale heat resilience plans has been a lack of reliable strategies for resident engagement. In this research, the authors designed a two-week summer STEM module for youth ages 12 to 14 in Roanoke, Virginia in the Southeastern United States. Participants collected and analyzed temperature and thermal comfort data of varying types, including from infrared thermal cameras and point sensors, handheld weather sensors, drones, and satellites, vehicle traverses, and student peer interviews. Based on primary data gathered during the program, we offer insights that may assist planners seeking to engage residents in neighborhood-scale heat resilience planning efforts. These lessons include recognizing: (1) the problem of heat in neighborhoods and the social justice aspects of heat distribution may not be immediately apparent to residents; (2) a need to shift perceived responsibility of heat exposure from the personal and home-based to include the social and landscape-based; (3) the inextricability of solutions for thermal comfort from general issues of safety and comfort in neighborhoods; and (4) that smart city technologies and high resolution data are helpful “hooks” to engagement, but may be insufficient for shifting perception of heat as something that can be mitigated through decisions about the built environment.

Place-based structural inequalities can have critical implications for the health of vulnerable populations. Historical urban policies, such as redlining, have contributed to current inequalities in exposure to intra-urban heat. However, it is unknown whether these spatial inequalities are associated with disparities in heat-related health outcomes. The aim of this study is to determine the relationships between historical redlining, intra-urban heat conditions, and heat-related emergency department visits using data from 11 Texas cities. At the zip code level, the proportion of historical redlining was determined, and heat exposure was measured using daytime and nighttime land surface temperature (LST). Heat-related inpatient and outpatient rates were calculated based on emergency department visit data that included ten categories of heat-related diseases between 2016 and 2019. Regression or spatial error/lag models revealed significant associations between higher proportions of redlined areas in the neighborhood and higher LST (Coef. = 0.0122, 95% CI = 0.0039–0.0205). After adjusting for indicators of social vulnerability, neighborhoods with higher proportions of redlining showed significantly elevated heat-related outpatient visit rate (Coef. = 0.0036, 95% CI = 0.0007–0.0066) and inpatient admission rate (Coef. = 0.0018, 95% CI = 0.0001–0.0035). These results highlight the role of historical discriminatory policies on the disparities of heat-related illness and suggest a need for equity-based urban heat planning and management strategies.

Problem, research strategy, and findings: Historical patterns of discrimination and disinvestment have shaped the current landscape of vulnerability to heat in U.S. cities but are not explicitly considered by heat mitigation planning efforts. Drawing upon the equity planning framework and developing a broader conceptualization of what equity means can enhance urban heat management. Here I ask whether areas in Baltimore (MD), Dallas (TX), and Kansas City (MO) targeted for disinvestment in the past through practices like redlining are now more exposed to heat. I compare estimates of land surface temperature (LST) derived from satellite imagery across the four-category rating system used to guide lending practices in cities around the United States, summarize the demographic characteristics of current residents within each of these historical designations using U.S. Census data, and discuss the connection between systematic disinvestment and exposure to heat. LST and air temperatures are not equivalent, which makes it difficult to reconcile existing research on the human health impacts of heat exposure that rely on a sparse network of air temperature monitoring stations with more granular LST data. Areas of these cities that were targeted for systematic disinvestment in the past have higher mean land surface temperatures than those that received more favorable ratings. Poor and minority residents are also overrepresented in formerly redlined areas in each of the three study cities.

Takeaway for practice: By examining areas that have experienced sustained disinvestment, cities may be able to more quickly narrow the focus of heat mitigation planning efforts while furthering social equity. Efforts to mitigate the negative impacts of rising temperatures in U.S. cities must be tailored to the local climate, built environment, and sociodemographic history. Finally, geospatial data sets that document historical policies are useful for centering and redressing current inequalities when viewed through an equity planning lens.

While cities across the United States are developing urban forest plans to capitalize on a myriad of ecosystem services provided by trees, the tools that are used to support this planning do not necessarily meet the needs and achieve the desired results of these municipal initiatives. This paper highlights the disconnect between how planners and academics are approaching the task of planning and managing the urban forest for climate change adaptation. Although planners tend to focus on maximizing canopy coverage, academics utilize microclimate simulation to evaluate the impact of different interventions on heat island mitigation. We argue that while the i-Tree suite is the most commonly used tool and is immensely helpful for urban forest planners, cities and communities concerned with heat island mitigation should also leverage microclimate simulation tools to better understand the likely impacts of a plan. This study provides a general critique of the i-Tree tool for planning and illustrates how, when used with a microclimate simulation tool, the siting of street trees can have a greater impact on heat island mitigation. The focus here is on the community-scale impact of trees and a microclimate simulation model of a neighborhood in Chicago, Illinois where tree canopy coverage is less than 10% and consistently declining is presented. We find that when microclimate is considered during the tree siting process, trees have a greater impact on surface and air temperatures during the summer. Reduced ground surface temperatures of around 3 ℃ to 7.5 ℃ were achieved around 13:00 (1:00 pm), whereas the impact of trees from shading had the greatest impact (0.75 ℃ to 7.75 ℃) on nearby buildings in the mid-afternoon (15:00 to 17:00 h).

In this paper, we develop an approach for identifying the location of populations most vulnerable to extreme heat events and how those locations change over time. We scan the literature on measuring vulnerability, especially sensitivity and adaptive capacity of populations. We employ Census data for metropolitan Chicago for the years 1990, 2000, and 2010, and maximum likelihood factor analysis to derive an index and map the distribution of Census tracts where residents exhibit greater sensitivity and/or lower adaptive capacity to extreme heat. Our findings show a pattern of de-concentration and decentralization of these populations within the city and region over time, with gentrification and the suburbanization of poverty trends observed in many US metropolitan regions as possible contributing factors. Finally, we discuss the implications of these findings for planning efforts in the study area and offer suggestions for further research.

In the last two decades, a variety of digital technologies have proliferated in cities. Urban planning educators must respond to this given local resources, constraints, and options. This commentary reviews curricular innovations being undertaken by planning faculty at five diverse institutions to advance pedagogy beyond analytics. Our contribution is to identify three general approaches to expand teaching on digital technologies: (1) undertake reforms within accredited planning programs, (2) develop new educational offerings, and (3) teach through engaged learning programs. We urge broader curricular innovation in the planning field to ensure the field’s relevance and impact in an increasingly technical future.

While the number of open government data initiatives has increased considerably over the past decade, the impact of these initiatives remains uncertain. Recent studies have been critical of the “bias toward the supply side” and lack of “sufficient attention to the user perspective” in the way that open government data initiatives are implemented. This article asks: (1) who is using municipal open government data resources and for what purposes? and (2) what impact are municipal open government data having in cities where they have been implemented? We performed a qualitative analysis of 26 semi-structured telephone interviews conducted with government staff, civic technologists, and private sector stakeholders in nine cities around the United States. Each of these 30 to 45-minute telephone interviews were transcribed and analyzed to distill insights regarding the use and impact of municipal open government data in the nine cities considered. We find that the array of actors within open government data ecosystems at the local level is expanding as distinctions between the public and private sectors becomes increasingly blurred and that the demands of managing and sustaining these initiatives has led to changes in the services offered by local government, as well as in the duties of government staff. The impact of these data resources has been primarily felt within local government itself, although the lack of monitoring mechanisms makes it difficult to systematically evaluate their broader effects. We conclude that open government data initiatives should be coordinated and better integrated with digital equity and digital inclusion efforts in order to advance their political and social goals.

Cities in the United States are increasingly embracing open data as a means of advancing a variety of interests. Promoting transparency, facilitating public engagement, proactively managing records requests, and fostering innovation in the public and private sectors are among the commonly cited motivations for this phenomenon. While there is an extensive literature on the benefits and challenges of open government data, there are far fewer empirical studies that explore and document how these initiatives are unfolding at the local government scale. This article asks what kinds of data are being made open in U.S. cities and to what extent do open data policies and related regulatory actions matter in shaping the content and structure of public-facing repositories. The authors conclude that population size and regulatory actions exert a positive influence on the amount and variety of datasets provided through municipal open data portals. Implications for the design and governance of open government data initiatives at the local level are also discussed.

Civic technology is an emerging field that typically leverages open data—and sometimes open source software—to address challenges that may be invisible to or neglected by government in a collaborative, problem-centered way. This article describes the goals and values of civic technology, identifies its raw materials and products, and outlines its most visible modalities. We use key informant interviews with stakeholders in Chicago’s robust civic technology ecosystem and a brief discussion of the Array of Things (AoT) project to evaluate claims that civic technology can be an effective mechanism for democratizing the Smart City. We conclude with recommendations for urban planners interested in engaging with civic technology to enhance quality of life and further social equity.

Informal settlements exist in a legally contested space and the quality of– and access to– information about them has been historically limited. The open data movement promises to address this gap by offering alternative sources for information and free or low cost analytical platforms. However, in order to use open data effectively, urban managers and planners need guidance to navigate these new data sources, software, and server platforms, as well as acquire the necessary skills. In this paper, we begin to address these issues by developing a framework that organizes the sprawling and rapidly evolving world of open urban data. Our framework includes three broad categories (1) inputs and resources, (2) activities and outputs, and (3) outcomes. We then identify and describe the key subcomponents under each, and list the prominent products and resources available to urban managers and planners. For example, under inputs and resources, we discuss open urban data sources such as Open Street Maps, cyberinfrastructure for web hosting, application deployment, and data processing, and open source software that can be used to analyze and visualize collected or derived data. We also identify the key resources available to planners for training and discuss the complementary opportunities presented by conventional datasets such as census and open urban data. Finally, using examples from ongoing activities in Mumbai, we show how open data resources can be useful for understanding urbanization and better integrating informal settlements into formal urban management and planning processes. We suggest that urban managers and planners working in informal settlements should take greater advantage of open data resources in order to both better address current challenges as well as for shaping a better future for the communities they serve.

Physical activity offers significant mental health and social wellbeing benefits but social distancing and quarantine requirements during the pandemic along with disparities in access to parks and other urban green spaces limited the ability of some to pursue outdoor activities like recreational walking, jogging, or hiking. This paper asks: (1) how access to different types of urban green space varies race and income, (2) how green space usage changed, and (3) how greenspace usage impacted mental health all during the early phase of the pandemic. Using data from a household survey as well as anonymized GPS data generated by mobile devices, we explore these question in Richmond, Virginia. At the pandemic’s onset, visits to green spaces declined regionally, but increased for low-income groups as a proportion of all visits. Structural equation modeling results suggest that mental health was directly influenced by social cohesion and race, with evidence of an indirect effect of greenspace usage on mental health through its impact on social cohesion. Social cohesion’s effect on mental health was positive while respondents who identified as White were less likely to report positive mental health. We also find a strong, positive effect of greenspace use and satisfaction on social cohesion.

There is a disconnect between the urgency of responding to the threats posed by climate change and the existing resources, technical capacity, and political will necessary to engage in resilience planning and climate action. A variety of frameworks for bridging these gaps exists but few have emerged through an iterative process of piloting and refining strategies to bridge these gaps on the ground. The Resilience Adaptation Feasibility Tool (RAFT) framework builds upon decades of experience designing and implementing community engagement processes and offers an alternate model for advancing resilience and climate adaptation planning that leverages social learning to build consensus around shared values and community priorities. This paper documents the RAFT process, situates it within the context of climate adaptation planning research, and articulates its specific strengths as a flexible and portable model for engaging the public in preparing for climate change impacts.

This study explores the impacts of multimodal accessibility to green space on housing price. Quantifying the benefits of green space accessibility is important for supporting green infrastructure planning and guiding land use development. In this study, we calculate multimodal travel times (walking and driving) from each residential property in Cook County (Chicago metro), Illinois to each articulated (public or significant private) green space. A gravity-model based method is used to compute accessibility (by travel mode), which considers the access to multiple green spaces and weights prioritization. Green spaces are divided into seven categories depending on their type and size to differentiate their potential benefits. Hedonic models using housing structural features, locational attributes, socio-economic factors and green space accessibility as explanatory variables, are used to evaluate housing price (using housing transactions records from 2010 sales in the county). The spatial effects of green space accessibilities on housing prices are explored by an Ordinary Least Squares (OLS) regression, with and without fixed locational effects, and a Geographically Weighted Regression (GWR). Results show walking and driving accessibility to all sizes of recreational, medium conversational and private green spaces present positive impacts on housing price, with some negative impacts to larger (and smaller) conservation areas. The relationship also exhibits different heterogeneous spatial pattern over the study area between walking and driving accessibility to green space, possibly related to economic variation.

The nature of urban space has long-drawn geographers’ interest and David Harvey’s conceptual framework of multiple spaces (i.e., absolute, relative, and relational) within cities has been widely adopted and developed. With its high spatial and temporal resolution, geospatial big data plays an increasingly important role in our understanding of urban structure. Taxi trajectory data is particularly useful in travel purpose estimation and allows for more granular insights into urban mobility due to the door-to-door nature of these trips. This article utilizes taxi trajectory data and explores the interaction among absolute space, relative space, and relational space in Harvey’s framework using Structural Equation Modeling (SEM). Through an empirical study of Shanghai’s downtown area, this paper highlights the importance of Harvey’s framework in understanding cities’ dynamic structure and argues for changes in urban planning and development to better coordinate land use and travel demand. We find an insignificant relationship between relative and relational space in Shanghai due to a mismatch between urban mobility and the built environment. This mismatch concentrates the transportation flow near the city’s core area, transforming the polycentric structure of Shanghai’s built environment in absolute space to a single-node structure in relational space. After identifying the contributing factors to this problem in Shanghai, this article suggests combining Harvey’s conceptual framework of multiple spaces with geospatial big data to inform planning strategies that address the challenges of rapid urbanization.

This article examines the relationship between renovation activity and housing prices in Milwaukee, Wisconsin, and whether the spatial distribution of renovation activity matters for housing appreciation. We hypothesize that the renovation of housing units is a spatially diffusive process and that proximity to renovated properties increases sale price, even after accounting for renovations to the property sold and neighborhood characteristics. By adopting a modeling approach that incorporates hedonic and repeat sales methods, we find strong evidence that proximity to renovation activity exerts a positive influence on housing appreciation and that this effect extends further in space than previously believed. Our findings lend support to policy interventions that are geographically targeted and suggest that cultivating clusters of renovated housing can be a valuable lever for neighborhood stabilization and revitalization. Though appreciation was more likely in tracts with a higher poverty rate, an analysis of annual sales volume data suggests that displacement of owner-occupier households as a result of gentrification was not widespread during the study period. However, further research to better understand spatially concentrated renovation activity as a potential contributor to the displacement of existing residents is needed.

While recent research has recognized the importance of considering social vulnerability, the changing patterns of social vulnerability within cities and the climate adaptation challenges these shifts pose have yet to receive much attention. In this article, we evaluate the changing patterns of social vulnerability in three coastal cities (Houston, New Orleans, and Tampa) over a thirty-year time period (1980–2010) and integrate neighborhood change theories with theories of social vulnerability to explain those patterns. Through this analysis, we highlight emerging dimensions of vulnerability that warrant attention in the future adaptation efforts of these cities.

Despite an overall decrease in residential mobility after the 2007 housing crisis, many households, particularly those that are low income, continue to move in pursuit of a better life. Traditional theories of residential mobility suggest that mobility will occur when housing and transportation costs are cumulatively greater than the cost of moving to a new location. At the same time, the influence of these factors is not likely to be uniform across geographic contexts or for moves up or down the metropolitan hierarchy. Our analysis examines how well affordability measures explain patterns of county-level residential mobility. Specifically, we contrast conventional measures of affordability focused on the ratio of income to housing expense with measures of location affordability that factor in both housing and transportation costs. We find that whereas households tend to move from lower to higher cost locations, transit affordability at the destination plays an important role in mobility decisions.