Colburn, Gregg, and Clayton Page Aldern. Homelessness Is a Housing Problem: How Structural Factors Explain U.S. Patterns. Oakland: University of California Press, 2022.
Research Theme: Global Built Environment
Includes international scholarship as well as scholarship that has international implications
Automating building environmental assessment: A systematic review and future research directions
T.A.D.K. Jayasanka, Amos Darko, D.J. Edwards, Albert P.C. Chan, Farzad Jalaei, Automating building environmental assessment: A systematic review and future research directions, Environmental Impact Assessment Review, Volume 106, 2024, 107465, ISSN 0195-9255, https://doi.org/10.1016/j.eiar.2024.107465.
Abstract
Building environmental assessment (BEA) is critical to improving sustainability. However, the BEA process is inefficient, costly, and often inaccurate. Because automation has the potential to enhance the efficiency and accuracy of the BEA process, studies have focused on automating BEA (ABEA). Updated until now, a comprehensive analysis of prevailing literature on ABEA remains absent. This study conducts the first comprehensive systematic analysis appraising the state-of-the-art of research on ABEA. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guided to systematically analyse 91 relevant studies. Results uncover that only 29.7% of BEA systems worldwide have automated their processes, with the US LEED residing at the vanguard of automation efforts. The New Buildings scheme was mostly focused on, while largely ignoring other schemes, e.g., Existing Buildings. Five key digital approaches to ABEA were revealed, namely building information modelling (BIM) and plug-in software, BIM-ontology, data mining and machine learning, cloud-BIM, and digital twin-based approaches. Based on identified gaps, future research directions are proposed, specifically: using data mining and machine learning models for ABEA; development of a holistic cloud-based approach for real-time BEA; and digital twin for dynamic BEA. This study generates a deeper understanding of ABEA and its theoretical implications, such as major constructs and emerging perspectives, constitute a basis for holistic, and innovation in, BEA.
Change Stories project begins 4-day residential event in Bogotá, Colombia
The Change Stories project is an research project with collaboration from academics and their community-based partners in Belfast (Queen’s University), Northern Ireland, Belo Horizonte (Federal University of Minas Gerais Brazil and Observatory for Urban Health), Brazil, and Bogotá (Universidad de los Andes), Colombia. Additional collaborators include USA and Internationally-based advisory group members, who work within their communities. The project is funded by the Robert Wood Johnson Foundation. The 3 case study cities in the Change Stories project are Belfast, Northern…
Magdalena Haakenstad
Magdalena Haakenstad, Postdoctoral Scholar in Urban Planning for Health in the University of Washington Department of Urban Design and Planning in Seattle, Washington, USA. She is a cultural anthropologist interested in environmental health, means and strategies of political negotiation, visual communication in public space, and decolonizing methodologies. As a part of her research, she had an opportunity to work with historically marginalized communities in Mexico, the US and Slovakia on public art projects, storytelling, photo essays, and filmmaking to help amplify their voices. She holds a PhD in General Anthropology from Charles University in Prague, Czech Republic.
Civic Resilience and the COVID-19 Crisis in Urban Asia
Hou, Jeffrey. 2021. Civic Resilience and the COVID-19 Crisis in Urban Asia. Journal of Geographical Science, 100: 121-136. DOI: 10.6161/jgs.202112_(100).0006
Abstract
Civil society responses including self-help and mutual aid have played an important role in addressing the COVID-19 crisis around the world, including Asia. They represent a form of civic resilience, the ability of citizens and communities to cope with and adapt to social, economic, and environmental disturbances. But how exactly did communities and social groups in Asia self-organize to address challenges during the pandemic, particularly those facing the most vulnerable populations in society? What did these cases have in common? What can we learn from these civil society responses for future planning? What were the roles of researchers, spatial planning professionals, and institutions in strengthening community resilience? This article presents outcomes from a two-part webinar titled ＂Bottom-Up Resilience＂ that took place in July 2020 featuring activists, organizers, and researchers from Hong Kong, Manila, Shanghai, Singapore, Taipei, and Tokyo. Preliminary findings include contrasting responses from institutions and civil society actors, how the civil society responses have built upon and expanded trust and empathy in a given place, how civil society responses scale up, and such scalability has depended heavily on solidarity and collaboration. The article further discusses how these efforts represent a form of civic resilience, the continued barriers, and implications for spatial planning practices.
Kevin Muiruri
Research interests: project delivery methods and impact to project success; project control and construction contracts; privatization in construction and private-public partnerships; project cost management; sustainability.
M.S. Construction Management, University of Washington (2022)
B.S. Civil Engineering, Dedan Kimathi University of Technology, Kenya (2017)
Associate Professor Manish Chalana Embarking on Fulbright-Nehru Fellowship March 2024
Historic preservation (or “heritage conservation” in India) is the practice of identifying, managing, and interpreting the historical record in the built environment. For many people, the resulting presence of these tangible reminders in their day-to-day world plays a major role in shaping their perceptions of who has contributed what to their nation’s development. The magnitude and challenges of these tasks have increased dramatically in contemporary times, as the field has begun to grapple with the complexity of history. This is…
Zeyu Wang
Research Interests: Geospatial big data, travel behavior, human mobility, built environment assessment
Detecting Subpixel Human Settlements in Mountains Using Deep Learning: A Case of the Hindu Kush Himalaya 1990–2020
Chen, T.-H. K., Pandey, B., & Seto, K. C. (2023). Detecting subpixel human settlements in mountains using deep learning: A case of the Hindu Kush Himalaya 1990–2020. Remote Sensing of Environment, 294, 113625–. https://doi.org/10.1016/j.rse.2023.113625
Abstract
The majority of future population growth in mountains will occur in small- and medium-sized cities and towns and affect vulnerable ecosystems. However, mountain settlements are often omitted from global land cover analyses due to the low spatial resolution of satellite images, which cannot resolve the small scale of mountains settlements. This study demonstrates, for the first time, the potential of deep learning to detect human settlements in mountains at the sub-pixel level, based on Landsat satellite imagery. We hypothesized that adding spatial and temporal features could improve the detection of mountain settlements since spectral information alone led to inaccurate results. For spatial features, we compared a U-shaped neural network (U-Net), a deep learning algorithm that automatically learns spatial features, with a simple random forest (RF) algorithm. Then, we assessed whether temporal features would increase accuracy by comparing two input datasets, multispectral imagery and temporal features from the Continuous Change Detection and Classification (CCDC) algorithm. We evaluated each method by calculating the accuracies of (1) the binary settlement footprint, (2) the subpixel estimates of impervious surfaces, and (3) urban growth. We tested the accuracies using visually interpreted datasets from time-series Google Earth images across the Hindu Kush Himalaya that were not used for training to evaluate model transferability. The U-Net successfully improved mountain settlement mapping compared to the random forest, with a substantial discrepancy in small settlements. The time-series results from the U-Net successfully captured long-term urban growth but fewer short-term changes. Contrary to expectations, the CCDC temporal features reduced the accuracy of mountain settlement mapping due to frequent cloud cover in hilly areas. Our subpixel analysis reveals that the built-up area of the Hindu Kush Himalaya has expanded at a rate of 61 km2 per year from 1990 to 2020, which is about twice the estimate of the Global Human Settlement Layer using binary urban/non-urban classifications.
Keywords
Urban land cover; Land cover fraction; Peri-urban; Built-up area; Subpixel mapping; Machine learning; Time-series; Himalaya; CCDC
College of Built Environments Announces 2023 Inspire Fund Awards
In 2021, the College of Built Environments launched the CBE Inspire Fund to “inspire” CBE research activities that are often underfunded, but for which a relatively small amount of support can be transformative. The Inspire Fund aims to support research where arts and humanities disciplines are centered, and community partners are engaged in substantive ways. Inspire Fund is also meant to support ‘seed’ projects, where a small investment in early research efforts may serve as a powerful lever for future…