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1. Drivers of Tree Canopy Loss in a Mid-Sized Growing City: Case Study in Portland, OR (USA)

   https://doi.org/10.3390/su16051803  

   Ock, YunJae; Shandas, Vivek; Ribeiro, Fernanda; Young, Noah (March 2024, Sustainability)

   The benefits of the urban tree and tree canopy (UTC) are increasingly crucial in addressing urban sustainability. Yet, increasingly evident from earlier research is the distributional inequities of UTC and active efforts to expand tree plantings. Less is known about the dynamics of UTC loss over time and location. This study aims to understand the dynamics of UTC change, especially canopy loss, and to investigate the drivers of the loss. This study draws on a high–resolution dataset of an urban canopy in Portland, Oregon, USA, assessing changes in UTC from 2014 to 2020. By integrating demographic, biophysical, and policy data with UTC information, we use a spatial autoregressive model to identify the drivers of UTC loss. The results reveal an unexpected spatial distribution of UTC change: less gain in the neighborhoods with the least UTC, and greater loss in the neighborhoods with moderate UTC. This study identifies four primary drivers of UTC loss: socioeconomic characteristics, urban form, activities on trees, and residential status. Factors such as population density, race, and income have an impact on canopy loss, as well as the building footprint and the number of multifamily housing units; residential statuses, such as the proportion of owner-occupied housing and residential stability, impact canopy loss. 

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2. An Analysis of the Spatial Variations in the Relationship Between Built Environment and Severe Crashes

   https://doi.org/10.3390/ijgi13120465  

   Alisan, Onur; Ozguven, Eren Erman (December 2024, ISPRS International Journal of Geo-Information)

   Traffic crashes significantly contribute to global fatalities, particularly in urban areas, highlighting the need to evaluate the relationship between urban environments and traffic safety. This study extends former spatial modeling frameworks by drawing paths between global models, including spatial lag (SLM), and spatial error (SEM), and local models, including geographically weighted regression (GWR), multi-scale geographically weighted regression (MGWR), and multi-scale geographically weighted regression with spatially lagged dependent variable (MGWRL). Utilizing the proposed framework, this study analyzes severe traffic crashes in relation to urban built environments using various spatial regression models within Leon County, Florida. According to the results, SLM outperforms OLS, SEM, and GWR models. Local models with lagged dependent variables outperform both the global and generic versions of the local models in all performance measures, whereas MGWR and MGWRL outperform GWR and GWRL. Local models performed better than global models, showing spatial non-stationarity; so, the relationship between the dependent and independent variables varies over space. The better performance of models with lagged dependent variables signifies that the spatial distribution of severe crashes is correlated. Finally, the better performance of multi-scale local models than classical local models indicates varying influences of independent variables with different bandwidths. According to the MGWRL model, census block groups close to the urban area with higher population, higher education level, and lower car ownership rates have lower crash rates. On the contrary, motor vehicle percentage for commuting is found to have a negative association with severe crash rate, which suggests the locality of the mentioned associations. 

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3. Widespread Race and Class Disparities in Surface Urban Heat Extremes Across the United States

   https://doi.org/10.1029/2021EF002016  

   Benz, Susanne_Amelie; Burney, Jennifer_Anne (July 2021, Earth's Future)

   Abstract Here we use remotely sensed land surface temperature measurements to explore the distribution of the United States’ urban heating burden, both at high resolution (within cities or counties) and at scale (across the whole contiguous United States). While a rich literature has documented neighborhood‐level disparities in urban heat exposures in individual cities, data constraints have precluded comparisons across locations. Here, drawing on urban temperature anomalies during extreme summer surface temperature events from all 1,056 US counties with more than 10 developed census tracts, we find that the poorest tracts (and those with lowest average education levels) within a county are significantly hotter than the richest (and more educated) neighborhoods for 76% of these counties (54% for education); we also find that neighborhoods with higher Black, Hispanic, and Asian population shares are hotter than the more White, non‐Hispanic areas in each county. This holds in counties with both large and small spreads in these population shares, and for 71% of all counties the significant racial urban heat disparities persist even when adjusting for income. Although individual locations have different histories that have contributed to race‐ and class‐based geographies, we find that the physical features of the urban environments driving these surface heat exposure gradients are fairly uniform across the country. Systematically, the disproportionate heat surface exposures faced by minority communities are due to more built‐up neighborhoods, less vegetation, and—to a lesser extent—higher population density. 

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4. The spatial and social correlates of neighborhood morphology: Evidence from building footprints in five U.S. metropolitan areas

   https://doi.org/10.1371/journal.pone.0299713  

   Durst, Noah J; Sullivan, Esther; Jochem, Warren C (April 2024, PLOS ONE)

   Xu, Gang (Ed.)

   Recent advances in quantitative tools for examining urban morphology enable the development of morphometrics that can characterize the size, shape, and placement of buildings; the relationships between them; and their association with broader patterns of development. Although these methods have the potential to provide substantial insight into the ways in which neighborhood morphology shapes the socioeconomic and demographic characteristics of neighborhoods and communities, this question is largely unexplored. Using building footprints in five of the ten largest U.S. metropolitan areas (Atlanta, Boston, Chicago, Houston, and Los Angeles) and the open-source R package,foot, we examine how neighborhood morphology differs across U.S. metropolitan areas and across the urban-exurban landscape. Principal components analysis, unsupervised classification (K-means), and Ordinary Least Squares regression analysis are used to develop a morphological typology of neighborhoods and to examine its association with the spatial, socioeconomic, and demographic characteristics of census tracts. Our findings illustrate substantial variation in the morphology of neighborhoods, both across the five metropolitan areas as well as between central cities, suburbs, and the urban fringe within each metropolitan area. We identify five different types of neighborhoods indicative of different stages of development and distributed unevenly across the urban landscape: these include low-density neighborhoods on the urban fringe; mixed use and high-density residential areas in central cities; and uniform residential neighborhoods in suburban cities. Results from regression analysis illustrate that the prevalence of each of these forms is closely associated with variation in socioeconomic and demographic characteristics such as population density, the prevalence of multifamily housing, and income, race/ethnicity, homeownership, and commuting by car. We conclude by discussing the implications of our findings and suggesting avenues for future research on neighborhood morphology, including ways that it might provide insight into issues such as zoning and land use, housing policy, and residential segregation. 

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5. Uneven vulnerability: characterizing population composition and change in the low elevation coastal zone in the United States with a climate justice lens, 1990–2020

   https://doi.org/10.3389/fenvs.2023.1111856  

   Tagtachian, Daniela; Balk, Deborah (August 2023, Frontiers in Environmental Science)

   Fine scale data collection on vulnerability metrics is necessary for just policy outcomes. Those most likely to be disproportionately affected by specific climate risks should be identified early so that the needs of vulnerable communities (especially historically marginalized communities) can be addressed and mitigated in accordance with climate justice principles. While there is a growing body of event-specific and place-based studies, systematic studies on coastal populations at risk have typically not applied equity principles and have often ignored attributes such as race and ethnic composition, age structure, urban/rural classification, and housing tenure. Additionally, assumptions about future population trends depend on understanding past spatial patterns of change, as well as demographic and socioeconomic characteristics of the populations at risk, especially considering increasing coastal hazards. Yet, with few exceptions, research on coastal vulnerability has not analyzed changes in exposure over time and has not systematically addressed implications for communities of color over time. This paper seeks to fill these gaps. In this paper, using an equity lens and spatial demographic methods with the finest-resolution data available (census blocks), we estimate the extent of exposure and population change from 1990 to 2020 in the low elevation coastal zone in the continental United States. We find that the population of the LECZ has increased during this period, primarily by the growth of the urban population which has risen from about 22 million to 31 million persons. From 2000 to 2020, the urban population consistently grew at higher rates inside the LECZ than outside of it, reversing the pattern from the decade prior. We also examine changes in the population by race and Hispanic origin, urban and rural status, and a set of more expansive vulnerability themes. Our estimates, tabulated by counties and states, reveal the concentration and characteristics of exposure and changes to it over the past 30 years. Key findings include: residents of the LECZ are much older than average; Black residents are overrepresented in renter-occupied housing units in the urban LECZ; and from 2000 to 2020, Hispanic population growth was much higher in urban LECZ areas than urban areas elsewhere. These systematic insights into the demographic attributes of the populations most at risk of sea-level rise and associated coastal hazards can be used to ensure adaptation, mitigation, and disaster-related policies are tailored to the specific needs of these communities and actors at local, regional, and national scales. It also showcases how spatial methods can be used to understand demographic change and be put in place for future estimates of population in non-traditional units (e.g., coastal zones or other environmentally-vulnerable areas). 

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