Transportation geography group at The Ohio State University

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)

Inclusive accessibility: Integrating heterogeneous user mobility perceptions into space-time prisms,

New paper: Kar, A., Le, H.T.K. and Miller, H.J. (2023) “Inclusive accessibility: Integrating heterogeneous user mobility perceptions into space-time prismsAnnals of the American Association of Geographers, online first.

Abstract. Travelers’ day-to-day mobility depends on their perceptions, experiences, and personal characteristics. Many accessibility measures overlook perceptual factors and mainly consider space–time limitations of mobility, overestimating travelers’ potential mobility. We introduce a novel inclusive accessibility concept that advances time-geographic accessibility measures in light of travel behavior theories. We conceptualize inclusive accessibility as a subset of the classic space–time prism (STP) that incorporates hard constraints (e.g., limited infrastructure and services and time) and soft constraints (e.g., perceptions of safety and comfort toward the built environment and infrastructure and travel time preferences). We collected survey data on individual-level mobility perceptions and applied machine learning algorithms to predict personalized soft constraints for walking. Considering public transit and walking, we model and compare three network-based STPs: classic STP with hard constraints, inclusive STP with soft spatial constraints, and inclusive STP with soft spatial and temporal constraints. Our method demonstrates heterogeneities in individuals’ mobility perceptions. We illustrate that the individual’s level of accessibility shrinks substantially as we approach more conservative measures that include travel perceptions. Our method highlights the differences between travelers’ physically and psychologically accessible space depending on their travel choices and exposure.

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