Columbus

Ghost Neighborhoods of Columbus updates

In CURA’s Ghost Neighborhoods of Columbus project, we are applying machine learning methods to extract information from historical Sanborn Fire Insurance maps to generate realistic 3D models of how neighborhoods looked in the past. Machine learning – computer techniques that can find general patterns in data – can hep unlock the incredibly rich information in the building-level maps created for fire insurance underwriting purposes. Sanborn maps date back from the late 1800s until the 1960s and are available for over 10,000 cities and towns in the United States.

In our project, we are focusing on Black and Brown neighborhoods in Columbus that have been altered and, in many cases, damaged by deliberate actions such as urban highway construction, urban renewal and redlining practices. Our intent is to raise awareness and empower restorative justice by creating evocative experiences that connect with people in a visceral manner. A longer-term objective is to build a scientific database to which we can apply high resolution morphological analytics to understand the impacts of built environments – and their large–scale alterations – on social, health and environmental justice outcomes in these communities. 

Here are some updates about our three main study sites to date: i) Hanford Village in 1961; ii) Poindexter Village in 1940; iii) Mt. Vernon Ave in 1951.

(All images can be clicked for larger versions.)

Hanford Village in 1961

Our first study site is Hanford Village, a historically Black neighborhood in Columbus that was bisected and harmed by the construction of highway I-70 starting in the late 1960s.  The image below shows our initial 3D model. The buildings are from 1961; the completed I-70 highway is superimposed on the image. Buildings in red were demolished for the highway and adjacent Alum Creek Drive (on the right side of the image, near the highway curve).  Our results show that a total of 380 buildings have been demolished in these areas, including 286 dwellings, 86 garages, 5 apartments, and 3 stores.

Note that the buildings textures (exteriors) are plausible but not accurate. We applied simple rules within ESRI City Engine to generate these textures. In our current phase, we are studying historical and current day photos to build more accurate building textures.  The image below shows a Google Street View image of a still existing 1940s Cape Cod home in the George Washington Carver Addition in Hanford Village with a 3D model of the building. We are currently working on making all of the buildings in our Hanford Village model this realistic and accurate.

Poindexter Village in 1940

Poindexter Village was one the first public housing project in Ohio and one of the first in the United States: Franklin Delano Roosevelt attended its dedication in 1940. Most of the buildings have since been demolished. The images below shows our 3D model of Poindexter Village, overlaid on a present day aerial image. The top image shows an overview; the bottom image shows building details. The only buildings that remain are the church (the building with the columns) and the two residential buildings to its left in both images.

A museum and visitor center is planned for the two remaining buildings. We are in the final stages of building this model.  We are also working in partnership with the Ohio History Connection to explore ways to incorporate this model into the planned museum and visitor center, delivered via the web, onsite digital displays or a 3D printed tabletop model.

Mt. Vernon Ave in 1951

Mt. Vernon Ave in 1951 is our newest study site and time, supported by a seed grant from the Battelle Engineering, Technology and Human Affairs (BETHA) Endowment fund at The Ohio State University and in partnership with the Columbus Landmarks Foundation and the City of Columbus Near East Area Commission (NEAC).  CLF and NEAC are pursuing historic district designation for Mt. Vernon Ave. Mt. Vernon Ave was the commercial heart of the Black community in Columbus in the mid 20th century; the construction of the I-71 highway severed this corridor from Columbus downtown. The image below shows a map of Mt. Vernon Ave in the Bronzeville neighborhood of Columbus. The dashed line indicates where the street no longer exists (the present day Columbus State Community College campus.)

Our initial study area is a three-block stretch of Mt. Vernon Ave between present-day Monroe St (to the west) and 20th Street (to the east). The images below shows a present-day aerial image with our study area demarcated at two different map scales. Based on our research, this was the largest concentration of commercial activity in 1951. It was also the site of  an ill-considered urban renewal project in 1971 under the Model Cities program

The next three images illustrate the information extracted from the Sanborn maps. The first image shows building footprints with uses, overlaid on a present-day map.  D = dwelling; F = flat (apartment); S = store (commercial); A=  automobile (garage).

This image shows the number of stories derived from the Sanborn maps:

Finally, the building construction material from the Sanborn maps:

The animation below compares the present-day buildings in 3D to the 1951 buildings in 3D. Even with this basic, simple building models, we can see the sharp decline in density and loss of commercial activity after the urban renewal project.

We are currently conducting archival research to determinate accurate building textures, especially for the commercial buildings, and adding this information to the models. We are also working with Dr. Matt Lewis to develop an in-situ augmented reality experience of Mt. Vernon Ave in 1951.

Publications

Press releases

The Ghost Neighborhoods rap

A professional MC from EventRap hired as a discussant to “rap-up” our project presentation at the OSU TDAI/SI Interdisciplinary Research Fall Forum, November 9 2023

The Ghost Neighborhoods project team (past and present)

Center for Urban and Regional Analysis (CURA)

  • Nicole Williams
  • Michelle Hooper
  • Gerika Logan
  • Harvey Miller
  • Adam Porr
  • Ningchuan Xiao

Students

  • Mostahidul Alam – PhD student, Geography
  • Troy Harbin – Undergraduate student, GIS
  • Jialin Li – PhD student, Geography
  • Yue Lin – PhD student, Geography
  • Mahnoush Mostafavi Sabet – PhD student, Geography
  • Josie Stiver – Undergraduate student, City Planning
  • Shubh Thakkar– Undergraduate student, Geography
  • Ahmad Tokey, PhD student, Geography
  • Di Wang – PhD student, History
  • Xinyi Wu – PhD student, Civil, Environmental and Geodetic Engineering

Collaborators

  • Rebecca Kemper – Columbus Landmarks Foundation
  • Matt Lewis – Department of Design and Advanced Computing Center for the Arts and Design, OSU
  • Rongjun Qin – Civil, Environmental and Geodetic Engineering, OSU
  • Jason Reece – Knowlton School, OSU
  • Joshua Sadvari – OSU Libraries
  • Shelbi Toone – Ohio History Connection

 

Understanding the spatiotemporal evolution of opioid overdose events using a regionalized sequence alignment analysis

The latest paper from the Franklin County Opioid Crisis Activity Level (FOCAL) mapping project, led by my former student Dr. Yuchen Li, in collaboration with Dr. Ayaz Hyder from OSU College of Public Health.

Li, Y., Miller, H.J., Hyder, A. and Jia, P. (2023) “Understanding the spatiotemporal evolution of opioid overdose events using a regionalized sequence alignment analysis.” Social Science & Medicine, p.116188.

Abstract

Background.  Opioid overdose events and deaths have become a serious public health crisis in the United States, and understanding the spatiotemporal evolution of the disease occurrences is crucial for developing effective prevention strategies, informing health systems policy and planning, and guiding local responses. However, current research lacks the capability to observe the dynamics of the opioid crisis at a fine spatial-temporal resolution over a long period, leading to ineffective policies and interventions at the local level.

Methods. This paper proposes a novel regionalized sequential alignment analysis using opioid overdose events data to assess the spatiotemporal similarity of opioid overdose evolutionary trajectories within regions that share similar socioeconomic status. The model synthesizes the shape and correlation of space-time trajectories to assist space-time pattern mining in different neighborhoods, identifying trajectories that exhibit similar spatiotemporal characteristics for further analysis.

Results. By adopting this methodology, we can better understand the spatiotemporal evolution of opioid overdose events and identify regions with similar patterns of evolution. This enables policymakers and health researchers to develop effective interventions and policies to address the opioid crisis at the local level.

Conclusions. The proposed methodology provides a new framework for understanding the spatiotemporal evolution of opioid overdose events, enabling policymakers and health researchers to develop effective interventions and policies to address this growing public health crisis.

Keywords: Opioid overdose epidemic; Sequential analysis; Neighborhood context; Geographic information science; Spatiotemporal pattern mining

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.

 

Media