Urban

Turning old maps into 3D digital models of lost neighborhoods

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.

 

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Why is sustainable mobility so hard? Some observations on the paths forward

On April 14, I had the opportunity to give a lecture in the Mobility and Planning for Human-scale Cities lecture series organized by the Mobility Lab at the University of Tartu in Estonia, sponsored by the US Speaker Program of the US Department of State.

Mobility is central to urbanity, and urbanity is central to our common future as the world’s population crowds into urban areas. This is creating a global mobility crisis due to the unsustainability of our 20th century transportation systems for a crowded and connected 21st century world. We need to move beyond inflexible, unsustainable and brittle car-dominated mobility monocultures to flexible, sustainable and resilient mobility polycultures with a wide spectrum of integrated mobility options. This transition is hard because mobility is complex, a wicked problem and a fundamental social dilemma.

In this lecture, I address the transition towards sustainable mobility. I discuss how we can leverage the urban data revolution to resolve these challenges. In particular, I focus on the role of next generation urban observatory science that respects complexity, embraces uncertainty and conflicting values, facilitates urban experimentation and creates environments for collaboration and knowledge co-production. I identified the major scientific challenges, merits and broader impacts of the observatory approach to transportation and urban science.

A recording of the lecture is available via the link below:

Why is sustainable mobility so hard? Some observations on the paths forward – 14 April 2023

Measuring the impacts of dockless micro-mobility services on public transit accessibility

New paper:  Liu, L. and Miller, H.J. (2022) “Measuring the impacts of dockless micro-mobility services on public transit accessibility,” Computers, Environment and Urban Systems, 98, 101885.

We develop new measures of the accessibility increments to public transit afforded by dockless micromobility. We apply this to public transit and Lime scooter data for Columbus.  We find that dockless micro-mobility services such as scooters can improve public transit accessibility, but the benefits are very uneven and face substantial challenges including capacity and cost.

Abstract: Dockless micromobility services have potential as a fast and flexible solution to short-distance trips and public transit’s first-mile/last-mile (FM/LM) access problem; however, these services also have limitations, including uneven spatial distribution, low capacity, and user out of pocket expense. This can impact on the ability of micromobility to enhance public transit accessibility. We introduce accessibility increment measures – the amount by which public transit accessibility improves due to micromobility services. We apply these measures to hypothetical trips using public transit and micromobility data from Columbus, Ohio, USA. We find dockless scooters can increase accessibility by multimodal public transit trips, with increments in the first mile significantly outweighing last mile accessibility increments. Accessibility increments are highly concentrated in the city center due to the distributions of scooters and bus stops. We also find that scooters’ accessibility increment contribution is highly unequal: a small number of scooters contribute most of the accessibility increments. Monetary cost simulations show that the first-mile accessibility increment will rapidly decrease and last-mile increment slightly increase with lower willingness to pay. Capacity simulations show a group of users’ accessibility increment will rapidly decrease as the group size increases, but this depends on whether they are competing or collaborating for scooters. Our results show that despite showing promising potentials, vendors and policymakers still need to address these issues to make collaboration between public transit and dockless micromobility sustainable and equitable. The paper provides measures and evidence for future transit and micromobility planning for scooter vendors and transit authorities.