Some of the mighty transportation geographers (and alumni) at The Ohio State University.
(L-R): Ruochen Yin (PhD student), Ahmad Tokey (PhD student), Aniket Sangwan (PhD student), Huyen T.K. Le (Assistant Professor), Armita Kar (PhD, 2023; post-doc at Nationwide Children’s Hospital, Columbus), Luyu Liu (PhD, 2023; post-doc at University of Florida), Abdirashid Dahir (PhD student), Harvey Miller (Professor)
Not shown but in our hearts: Sara Johnson (PhD student), John Layman (Masters student), Manhoush Mostafavi Sabet (PhD student)
New paper: Willberg, E., Tenkanen, H., Miller, H.J., Pereira, R. H. M. and Toivonen, T. (2023) “Measuring just accessibility within planetary boundaries,” Transport Reviews, DOI: 10.1080/01441647.2023.2240958.
Abstract. Our societies struggle to provide a good life for all without overconsuming environmental resources. Consequently, scholarly search for approaches to meet environmental and social goals of sustainability have become popular. In transport research, accessibility is a key tool to characterise linkages between people, transport, and land use. In the current paper, we propose a conceptual framework for measuring just accessibility within planetary boundaries. We reviewed transport studies and discovered a substantial literature body on accessibility and social disadvantage, much vaster compared to the literature around environmental and ecological impacts of accessibility. We also show a gap in approaches that have integrated these two perspectives. Building on the review, we suggest a conceptual framework for incorporating environmental and social sustainability goals in accessibility research. We conclude the paper by pointing to key challenges and research avenues related to the framework, including (i) dealing with uncertainty and complexity in socio-ecological thresholds, (ii) integrating environmental limits into the conceptualisations of transport equity, (iii) measuring accessibility through other costs than travel time, and (iv) integrating both quantitative and qualitative data.
New paper: Lin Y, Li J, Porr A, Logan G, Xiao N, Miller HJ (2023) “Creating building-level, three-dimensional digital models of historic urban neighborhoods from Sanborn Fire Insurance maps using machine learning.” PLoS ONE 18(6): e0286340. https://doi.org/10.1371/journal.pone.0286340.
Abstract. Sanborn Fire Insurance maps contain a wealth of building-level information about U.S. cities dating back to the late 19th century. They are a valuable resource for studying changes in urban environments, such as the legacy of urban highway construction and urban renewal in the 20th century. However, it is a challenge to automatically extract the building-level information effectively and efficiently from Sanborn maps because of the large number of map entities and the lack of appropriate computational methods to detect these entities. This paper contributes to a scalable workflow that utilizes machine learning to identify building footprints and associated properties on Sanborn maps. This information can be effectively applied to create 3D visualization of historic urban neighborhoods and inform urban changes. We demonstrate our methods using Sanborn maps for two neighborhoods in Columbus, Ohio, USA that were bisected by highway construction in the 1960s. Quantitative and visual analysis of the results suggest high accuracy of the extracted building-level information, with an F-1 score of 0.9 for building footprints and construction materials, and over 0.7 for building utilizations and numbers of stories. We also illustrate how to visualize pre-highway neighborhoods.