Skip to content
News

Department: Urban Design and Planning

How Urban Ecological Land Affects Resident Heat Exposure: Evidence from the Mega-urban Agglomeration in China

Feng, R., Wang, F., Liu, S., Qi, W., Zhao, Y., & Wang, Y. (2023). How Urban Ecological Land Affects Resident Heat Exposure: Evidence from the Mega-urban Agglomeration in China. Landscape and Urban Planning, 231.

View Publication.

‌

Abstract

Resident heat exposure (RHE) is becoming more severe in the coming decades owing to rapid urbanization and climate change. Urban ecological land (UEL) provides important ecosystem services, such as mitigating the urban heat islands effect. However, the impacts of UEL on RHE remain poorly understood. This study quantifies the effects of UEL and its interaction with the natural-anthropogenic environment on RHE in the Guangdong-Hong Kong-Macao Greater Bay Area, a mega-urban agglomeration in China. The results showed a tight spatial–temporal coupling between the UEL and RHE: UEL transitioned from degradation-fragmentation in 2000–2010 to recovery-agglomeration in 2010–2020, while the RHE distribution evolved from intensification-expansion-inequity to mitigation-contraction-equity. The average explanatory power (q value) of UEL and its structure on RHE also increased by 75.99% and 70.79%, respectively. UEL patch diversity gradually dominated the RHE distribution, and the spatial marginal effect of UEL dominance increased by 234.97%. Moreover, RHE shifted from being dominated by UEL and anthropogenic heat emissions interactions to being jointly driven by UEL and natural-anthropogenic factors (especially the interaction of patch fragmentation with topography and built-up land expansion). The results of this study provide valuable information for nature-based (i.e., UEL) landscape planning and management to develop “human-centric” RHE mitigation strategies.

Keywords

Urban ecological land; Resident heat exposure; Spatial-temporal effects; Natural-anthropogenic factors; Interaction effect; Mega-urban agglomeration

Rachel Berney and Jeff Hou contribute to new book on social justice in urban design

“Just Urban Design: The Struggle for a Public City” (MIT Press 2022) features a collection of chapters and case studies that apply a social justice lens to the design of urban environments. Sixteen contributors, including Rachel Berney of Urban Design & Planning and Jeff Hou of Landscape Architecture, examine topics ranging from single-family zoning and community capacity building to immigrant street vendors and the right to walk. The book is open-access and can be downloaded from MIT Press here.

Helping Rural Counties to Enhance Flooding and Coastal Disaster Resilience and Adaptation

In the United States, flooding is a leading cause of natural disasters, with congressional budget office estimates of $54 billion in loss each year. Although both urban and rural areas are highly vulnerable to flood hazards, most natural disaster resilience studies have focused primarily on urban areas, overlooking rural communities. One such area that has been overlooked are the numerous rural communities bordering the Great Lakes. These communities face unprecedented challenges due to rising water levels, particularly since 2012, which have resulted in increased coastal flood hazard. Despite their flooding risk, they continue to lack flood hazard assessments and inundation maps, exacerbating their vulnerability. The Federal Emergency Management Agency (FEMA) commonly recommend counties to use a freely available tool—called HAZUS to develop hazard mitigation plans and enhance community resilience and adaptation. However, the usage of HAZUS for rural communities is challenging  due to existing data gaps that limit the analytical potential of HAZUS in these communities. Continued use of standard datasets for HAZUS analysis by rural counties could likely leave the communities underprepared for future flood events. The proposed project’s vision is to develop methods that use remote sensing data resources and citizen engagement (crowdsourcing) to address current data gaps for improved flood hazard modeling and visualization that is scalable and transferable to rural communities.

The results of the project will expand the traditional frontiers of preparedness and resilience to natural disasters by drawing on the expertise and backgrounds of investigators working at the interface of geological engineering, civil engineering, computer science, marine engineering, urban planning, social science, and remote sensing. Specifically, the proposed research will promote intellectual discovery by i) improving our understanding of remote sensing data sources and open-source processing methods to assist rural communities in addressing the data gaps in flood hazard modeling, ii) developing sustainable geospatial visualization tools for communicating hazards to communities, iii) advancing our understanding of the utility of combining remote sensing and crowdsourcing to flood hazard delineation, iv) understanding ways to incentives the crowd for greater participation and accuracy in hazard in addressing natural disasters, and v) identifying critical community resilience indicators through crowdsourcing. These advancements will lead to prepared and resilient rural communities that can effectively mitigate hazards related to lake level rise and flooding.

Assessing the Expectations Gap – Impact on Critical Infrastructure Service Providers’ and Consumers’ Preparedness, and Response

While community lifeline service providers and local emergency managers must maintain coordinated response and recovery plans, their timelines may not match expectations of local consumers of lifeline services. Indeed, it is quite likely consumers have unrealistic expectations about lifeline restoration, which could explain current inadequate levels of disaster preparedness. This hypothesized expectation gap has received little attention because engineering research typically addresses providers’ capacities, whereas disaster research addresses household and business preparedness. Our project will address this neglected issue by assessing consumers’ (households, business owners/managers, nonprofit managers) expectations about lifeline system performance, and comparing them to lifeline provider capacity in a post-hazard event scenario (following a Cascadia subduction zone earthquake of 9.0 magnitude or greater) in two communities—Kirkland and Shoreline, WA (likely to experience most shaking in this scenario).

Our research will assess the role of the expectations gap in influencing consumers’ and providers’ preparedness as well as response. First, we estimate the gap between consumers and providers expectations using an earthquake scenario in two case study communities. We posit that low consumer preparedness for lifeline disruption is in part a function of low expectations that lengthy disruption will occur. Next, we test the effect of providing consumers and providers with information about this gap. Our proposed sharing estimates of lifeline restoration times should change these beliefs if our assumption about this specific basis for low preparedness is correct and if our audiences attend to, process, and act upon this information. In our longitudinal research, consumers (households, businesses, and nonprofits) and lifeline providers will complete two questionnaires each. Besides lifeline provider surveys, we will collect information about lifeline providers’ capabilities and work with them to estimate restoration times using an expert elicitation-based estimation framework. We will address the following research questions:

  1. What do consumers think is the likely level of critical lifeline disruption from an earthquake and the timeline for restoration?
  2. What are consumers’ current levels of preparedness for lifeline interruption?
  3. What do lifeline providers and an independent engineering expert think are providers’ capabilities to maintain and restore lifeline services?
  4. How do consumers’ expectations compare with providers’ capabilities (expectations gap)?
  5. How will this study’s feedback about the expectations gap affect consumers’ and providers’ lifeline resilience expectations, as well as their mitigation and preparedness intentions?

A Global Horizon Scan for Urban Evolutionary Ecology

Verrelli, Brian C.; Alberti, Marina; Des Roches, Simone; Harris, Nyeema C.; Hendry, Andrew P.; Johnson, Marc T. J.; Savage, Amy M.; Charmantier, Anne; Gotanda, Kiyoko M.; Govaert, Lynn; Miles, Lindsay S.; Rivkin, L. Ruth; Winchell, Kristin M.; Brans, Kristien I.; Correa, Cristian; Diamond, Sarah E.; Fitzhugh, Ben; Grimm, Nancy B.; Hughes, Sara; Marzluff, John M.; Munshi-south, Jason; Rojas, Carolina; Santangelo, James S.; Schell, Christopher J.; Schweitzer, Jennifer A.; Szulkin, Marta; Urban, Mark C.; Zhou, Yuyu; Ziter, Carly. (2022). A Global Horizon Scan for Urban Evolutionary Ecology. Trends In Ecology & Evolution, 37(11), 1006-1019.

View Publication

Abstract

Research on the evolutionary ecology of urban areas reveals how human-induced evolutionary changes affect biodiversity and essential ecosystem services. In a rapidly urbanizing world imposing many selective pressures, a time-sensitive goal is to identify the emergent issues and research priorities that affect the ecology and evolution of species within cities. Here, we report the results of a horizon scan of research questions in urban evolutionary ecology submitted by 100 interdisciplinary scholars. We identified 30 top questions organized into six themes that highlight priorities for future research. These research questions will require methodological advances and interdisciplinary collaborations, with continued revision as the field of urban evolutionary ecology expands with the rapid growth of cities.

Keywords

Urban Ecology; Sustainability; Cities & Towns; Ecosystem Dynamics; Urban Growth; Ecosystem Services; Urban Research; Climate Change; Sociopolitical; Urban Evolution; Urbanization; Human Health; Biodiversity; Adaptation; Challenges; Dynamics; Management; Invasion; Science

A Water Quality Prediction Model for Large-scale Rivers Based on Projection Pursuit Regression in the Yangtze River

Yi, Ze-ji; Yang, Xiao-hua; Li, Yu-qi. (2022). A Water Quality Prediction Model for Large-scale Rivers Based on Projection Pursuit Regression in the Yangtze River. Thermal Science, 26(3), 2561-2567.

View Publication

Abstract

In recent decades, the Yangtze River Basin, which carries hundreds of millions of people and a substantial economic scale, has been plagued by water quality dete-rioration, threatening considerably sustainable development. In this paper, a sample set is established based on the water quality indexes of chemical oxygen demand and dissolved oxygen obtained by week-by-week monitoring on the main stream of the Yangtze River in Panzhihua, Yueyang, Jiujiang, and Nanjing from 2006 to 2018. The twelve characteristic variables are selected by random forest technique, and the week-by-week dynamic prediction models of chemical oxygen demand and dissolved oxygen at each section of main stream are established by the projection pursuit regression, which can effectively predict the water quality dynamics of the Yangtze River main stream.

Keywords

Pollution; Water Quality; Dynamic Prediction Model; Random Forest; Projection Pursuit Regression; Yangtze River

Associations between Neighborhood Built Environment, Residential Property Values, and Adult BMI Change: The Seattle Obesity Study III

Buszkiewicz, James H.; Rose, Chelsea M.; Ko, Linda K.; Mou, Jin; Moudon, Anne Vernez; Hurvitz, Philip M.; Cook, Andrea J.; Drewnowski, Adam. (2022). Associations between Neighborhood Built Environment, Residential Property Values, and Adult BMI Change: The Seattle Obesity Study III. SSM-Population Health, 19.

View Publication

Abstract

Objective: To examine associations between neighborhood built environment (BE) variables, residential property values, and longitudinal 1-and 2-year changes in body mass index (BMI). Methods: The Seattle Obesity Study III was a prospective cohort study of adults with geocoded residential addresses, conducted in King, Pierce, and Yakima Counties in Washington State. Measured heights and weights were obtained at baseline (n = 879), year 1 (n = 727), and year 2 (n = 679). Tax parcel residential property values served as proxies for individual socioeconomic status. Residential unit and road intersection density were captured using Euclidean-based SmartMaps at 800 m buffers. Counts of supermarket (0 versus. 1+) and fast-food restaurant availability (0, 1-3, 4+) were measured using network based SmartMaps at 1600 m buffers. Density measures and residential property values were categorized into tertiles. Linear mixed-effects models tested whether baseline BE variables and property values were associated with differential changes in BMI at year 1 or year 2, adjusting for age, gender, race/ethnicity, education, home ownership, and county of residence. These associations were then tested for potential disparities by age group, gender, race/ethnicity, and education. Results: Road intersection density, access to food sources, and residential property values were inversely associated with BMI at baseline. At year 1, participants in the 3rd tertile of density metrics and with 4+ fast-food restaurants nearby showed less BMI gain compared to those in the 1st tertile or with 0 restaurants. At year 2, higher residential property values were predictive of lower BMI gain. There was evidence of differential associations by age group, gender, and education but not race/ethnicity. Conclusion: Inverse associations between BE metrics and residential property values at baseline demonstrated mixed associations with 1-and 2-year BMI change. More work is needed to understand how individual-level sociodemographic factors moderate associations between the BE, property values, and BMI change.

Keywords

Body-mass Index; Physical-activity; Food Environment; Socioeconomic-status; Weight-gain; Health; Quality

Back to the Future: Reintegrating Biology to Understand How Past Eco-evolutionary Change Can Predict Future Outcomes

Thompson, Cynthia L.; Alberti, Marina; Barve, Sahas; Battistuzzi, Fabia U.; Drake, Jeana L.; Goncalves, Guilherme Casas; Govaert, Lynn; Partridge, Charlyn; Yang, Ya. (2022). Back to the Future: Reintegrating Biology to Understand How Past Eco-evolutionary Change Can Predict Future Outcomes. Integrative And Comparative Biology, 61(6), 2218-2232.

View Publication

Abstract

During the last few decades, biologists have made remarkable progress in understanding the fundamental processes that shape life. But despite the unprecedented level of knowledge now available, large gaps still remain in our understanding of the complex interplay of eco-evolutionary mechanisms across scales of life. Rapidly changing environments on Earth provide a pressing need to understand the potential implications of eco-evolutionary dynamics, which can be achieved by improving existing eco-evolutionary models and fostering convergence among the sub-fields of biology. We propose a new, data-driven approach that harnesses our knowledge of the functioning of biological systems to expand current conceptual frameworks and develop corresponding models that can more accurately represent and predict future eco-evolutionary outcomes. We suggest a roadmap toward achieving this goal. This long-term vision will move biology in a direction that can wield these predictive models for scientific applications that benefit humanity and increase the resilience of natural biological systems. We identify short, medium, and long-term key objectives to connect our current state of knowledge to this long-term vision, iteratively progressing across three stages: (1) utilizing knowledge of biological systems to better inform eco-evolutionary models, (2) generating models with more accurate predictions, and (3) applying predictive models to benefit the biosphere. Within each stage, we outline avenues of investigation and scientific applications related to the timescales over which evolution occurs, the parameter space of eco-evolutionary processes, and the dynamic interactions between these mechanisms. The ability to accurately model, monitor, and anticipate eco-evolutionary changes would be transformational to humanity's interaction with the global environment, providing novel tools to benefit human health, protect the natural world, and manage our planet's biosphere.

Keywords

Rapid Evolution; Ecological Interactions; Niche Construction; Climate-change; Phenotype; Community; Selection; Fitness; Consequences; Variability