Cities and urban areas present important opportunities for advancing toward sustainability and resilience goals. From a systems perspective, various targets and indicators of key outcomes reflect sustainability of urban areas, motivating consideration of local and regional context. We explore four key themes for creating sustainable urban areas: (1) urban areas are complex systems linked with diverse rural areas across the urban-rural gradient, (2) data and modeling of physical and non-physical systems can aid decision making for sustainability, (3) creating sustainable urban-rural systems requires collaboration with partner communities, and (4) change requires adaptive and transformative governance frameworks. These themes guide sustainability innovations through co-production of knowledge among communities, authorities, managers, and researchers. The sustainability of urban areas strongly depends on urban-rural connectivity regarding both physical systems and governance frameworks.

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.

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 by race and income, (2) how green space usage changed, and (3) how greenspace usage impacted mental health 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.

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.

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 exist, 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.

Recent studies have demonstrated some advantages of using advanced heuristic algorithms to identify near-Pareto-optimal future locations, types, and sizes for stormwater low-impact development and green infrastructure (LID/GI) across a given urban landscape. However, previous optimization studies did not consider social equity as an objective, which poses problems because urban green infrastructure often is distributed inequitably. Increasing access to LID/GI in historically marginalized areas is a prominent environmental justice issue, and increasingly is becoming a primary consideration when prioritizing future locations, types, and sizes of urban LID/GI. This study integrated a novel spatial social equity objective [LID/GI-Social Vulnerability Index (SVI) correlation objective, ?] into a multiobjective LID/GI optimization model. The LID/GI-SVI correlation is an objective that directs the optimization algorithm to search for LID/GI distributions that maximize the linear correlation between LID/GI implementation and subbasins with higher estimated percentages of historically marginalized people. Our analysis focused on understanding the impacts of the LID/GI-SVI correlation objective on a LID/GI optimization model. This modeling study demonstrates that (1) the LID/GI-SVI correlation objective can be used to direct optimization algorithms to search for LID/GI distributions that can achieve runoff management objectives, increase green LID/GI implementation in more marginalized areas, and explore the potential trade-offs or synergies between hydrologic and equity goals; (2) LID/GI optimization formulations that consider only hydrologic objectives likely will not result in equitable LID/GI distributions; (3) LID/GI distributions that perform well on the LID/GI-SVI correlation may be composed of different types of LID/GI than less-equitable but more hydrologically favorable LID/GI distributions; and (4) for our study area, including spatial equity as an objective resulted in modest reductions in the hydrologic performance of near-Pareto-optimal LID/GI distributions.

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 au-thors designed a two-week summer STEM module for youth ages 12 to 14 in Roanoke, Virginia in the South-eastern 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 sat-ellites, 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 inextrica-bility 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 eleven 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.

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 migrant population (also called the floating population), is an important contributor to urban population increase in China in recent decades, especially in Shanghai. This phenomenon has led many scholars to study different dimensions of the migrant population such as economic development, social equality, and political management. Education level is an important factor for measuring the qualifications of migrants and understanding their impacts. The agglomeration of migrants with different education levels can in some ways reflect the regional development level and labor market. This paper seeks to analyze the distribution of the migrant population by different education levels based on the sixth Shanghai population census. To study the distribution of the local and migrant quantitatively, a chi-square test and Mantel tests are used in each sub-district of Shanghai combined with geographic information system (GIS) visualization. Hotspot analysis is used to further understand the clusters of migrants with different education levels. This study shows that migrants in Shanghai are highly spatially correlated, of which the majority is concentrated in the suburban area. Migrants with a primary education are mainly located in the outer suburban areas. Migrants with a secondary education, including a high school or equivalent diploma, are concentrated most heavily in the industry-centralized areas of the inner suburbs, while those with a higher education are patch-distributed in areas with higher education institutions or major research centers. Compared with the local population, the overall education level of migrants is lower, and this difference is significant in peripheral area of Central Shanghai. Further, although clusters of migrants grow by the concentrated industrial zones and research parks, migrants with different education levels have their own clustering features. This phenomenon indicates that most migrants are concentrated in the suburban areas of Shanghai and the nearer to Central Shanghai, the higher proportion of migrants with higher education level. Job opportunities are the most important factor influencing the distribution and clustering of migrants in Shanghai. There is a significant difference of the percentage between local and migrant with the same education level in the central and peripheral areas of downtown Shanghai, suggesting that there might be discrimination in job market or housing market against migrants under the current hukou system.

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.

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.

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.

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.

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 deconcentration 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.

Heat waves are occurring more frequently and contributing to more deaths. While the use of geographic information systems is widespread among emergency management and disaster response professionals, the incorporation of new geospatial data sources and tools has not proceeded at the same rate with significant differences across geographic contexts and types of hazards. This chapter highlights the growing danger posed by heat waves to the health and safety of urban residents and argues that new geospatial data sources and tools as well as enhanced data integration and data sharing offer a promising way forward in addressing this issue.

Scholars have written extensively on the interaction of race and space (Neely & Samura, 2011), but the "significance of the qualities and particularities of different specific places in the unfolding of race" cannot be ignored (Delaney, 2002, p. 10). I do not claim to fully understand how race works, but I do know how it worked for me. I am also beginning to understand how it worked on me. This essay uses Warren County, North Carolina - where I grew up - as a point of departure for exploring aspects of race, racial identity, and spatial development in a rural environment with complex racial geographies. I argue that the "racialization of space and the spatialization of race" (Lipsitz, 2007) has influenced Warren County's development and, by extension, formal and informal planning efforts there.