Author: miller.81@osu.edu

Ghost Neighborhoods project demo at Poindexter Village event

At a public event at the Union Grove Baptist Church on the Near East Side neighborhood of Columbus, the Center for Urban and Regional Analysis (CURA) teamed up with the Sharpe Field Library to demonstrate the 3D urban models of historically Black neighborhoods being created by CURA’s Ghost Neighborhoods of Columbus project.  CURA is developing these 3D models to give people an immersive and visceral feeling of what Black neighborhoods were like before highway construction, disinvestment due to redlining and urban renewal projects. Participants were able to experience a VR simulation of Poindexter Village in 1940, and interacted with the model of Mt. Vernon Ave in 1951 on iPads. Participants also provided some very valuable feedback on the model interfaces, and the historical authenticity of the representations.

The Ohio History Connection organized the event to highlight plans for a new Columbus African American history museum, opening in February 2028. CURA’s 3D models will be featured in the museum. Harvey Miller, Jordan Swaim-Fox, Karyn Kerdolff, Summer Ha (CURA) and Adam Tjoelker (Sharpe Field Library) were at the event.

Inclusive accessibility: Analyzing socio-economic disparities in perceived accessibility

New paper!  Kar, A., Xiao, N., Miller, H.J. and Le, H.T.K. “Inclusive accessibility: Analyzing socio-economic disparities in perceived accessibility,” Computers, Environment and Urban Systems,  114, 102202

Highlights

  • Inclusive access considers both physical and perceptual barriers to travel.
  • Perceptions of safety and comfort vary with people’s socio-economic statuses.
  • Existing infrastructure and services yield unequal access across communities.
  • High-income white populations experience transportation privileges over others.
  • Low-income communities with greater risk exposures perceive higher mobility barriers.

Abstract

Existing accessibility measures mainly focus on the physical limitations of travel and ignore travelers’ perceptions, behavior, and socio-economic differences. By integrating approaches in time geography and travel behavior, this study introduces a bottom-up inclusive accessibility concept that aggregates individual-level travel perceptions across socio-economic groups to evaluate their multimodal access to opportunities. We classify accessibility constraints into hard constraints (physical space-time limitations to travel) and soft constraints (perceptual factors influencing travel, such as safety perceptions, comfort, and willingness to travel). We categorize travelers into 12 mutually exclusive socio-economic groups from a mobility survey dataset of 477 travelers. We apply a support vector regressor-based ensemble algorithm to estimate network-level walking perception scores as soft constraints for each social group. We derive group-specific inclusive accessibility measures that consider space-time limitations from transit and sidewalk networks as hard constraints and minimize the group-specific soft constraint to a certain threshold. Finally, we demonstrate the effectiveness of group-specific inclusive accessibility by comparing it with the classic access measure. Our study provides scientific evidence on how people of varying socio-economic statuses perceive the same travel environment differently. We find that socio-economically disadvantaged communities experience higher mobility barriers and lower accessibility while walking and using transit in Columbus, OH. Our study demonstrates a transition from person- to place-based accessibility measures by sequentially quantifying mobility perceptions for individual travelers and aggregating them by social groups for a large geographic scale, making this approach suitable for equity-oriented need-specific transportation planning.

 

Constructing Valid Geospatial Tools for Environmental Justice: New Report

Decades of research have shown that the most disadvantaged communities exist at the intersection of high levels of hazard exposure, racial and ethnic marginalization, and poverty. Geospatial environmental justice (EJ) tools, such as the White House Council on Environmental Quality-developed Climate and Economic Justice Screening Tool (CEJST), are designed to integrate different kinds of health, social, environmental, and economic data to identify disadvantaged communities and to aid policy and investment decisions that address the pervasive, persistent, and largely unaddressed problems associated with environmental disparities in the United States.  Constructing Valid Geospatial Tools for Environmental Justice, a new report from the National Academies of Sciences, Engineering, and Medicine, offers recommendations for developing environmental justice tools that reflect the experiences of the communities they measure.

I am very proud to co-chair this consensus study committee and grateful to work with an excellent study committee and the first-rate National Academies staff on a report that I sincerely hope leads to environmental justice for all in the US.