CURA is hiring!

Are you a geospatial geek who wants to make cities and regions more sustainable, equitable and resilient places? Do you have technical and management skills combined with a passion for engaged urban science? Would you like to build a career at dynamic, interdisciplinary urban research and outreach center at a highly collaborative and comprehensive university?

The Center for Urban and Regional Analysis (CURA) at The Ohio State University is seeking a Consulting Manager to join our team. CURA engages in a diverse portfolio of data-intensive research projects focused on urban and regional issues, with a special emphasis on geospatial data science. Many of our projects involve close collaboration with community partners in Central Ohio and beyond. The Consulting Manager is responsible for managing these projects and ensuring that they have the necessary resources and provides direct supervision for the center’s student research associates. The Consulting Manager also helps to define the strategic direction of the organization and to identify and develop new project opportunities.

Required Qualifications:

Bachelor’s degree in Geography, Geographic Information Science, Computer Science, or a related field.  Experience in research involving geospatial analysis or data science is required, as well as experience using Python or R in this context.

Desired Qualifications:

Bachelor’s degree and 5 years’ experience or Master’s degree and 3 years’ experience in a related field. Detailed understanding of urban and regional issues, as demonstrated by possession of a related degree, professional experience, or extensive personal engagement.  Supervisory experience is desired, with preference for supervision of students. Experience in a consulting service or similar is desired.

More information and to apply:

External Career Site:

Internal Employee Career Site:$392530/9925$97204.htmld

Internal Student Career Site:$392530/9925$97206.htmld

Realizable accessibility: evaluating the reliability of public transit accessibility using high‑resolution real‑time data

New paper!  Liu, L., Porr, A. and Miller, H.J. (2022) “Realizable accessibility: Evaluating the reliability of public transit accessibility using high-resolution real-time data,” Journal of Geographical Systems, online first.

Take home message:

We develop a refined time geographic measure of accessibility via public transit using real-time vehicle location data. We also show how to use this measure with schedule data to analyze the reliability of public transit accessibility at the urban scale. To be published in a special issue on “Time Geography in the Age of Mobility Analytics” in the Journal of Geographical Systems.


The widespread availability of high spatial and temporal resolution public transit data is improving the measurement and analysis of public transit-based accessibility to crucial community resources such as jobs and health care. A common approach is leveraging transit route and schedule data published by transit agencies. However, this often results in accessibility overestimations due to endemic delays due to traffic and incidents in bus systems. Retrospective real-time accessibility measures calculated using real-time bus location data attempt to reduce overestimation by capturing the actual performance of the transit system. These measures also overestimate accessibility since they assume that riders had perfect information on systems operations as they occurred. In this paper, we introduce realizable real-time accessibility based on space–time prisms as a more conservative and realistic measure. We, moreover, define accessibility unreliability to measure overestimation of schedulebased and retrospective accessibility measures. Using high-resolution General Transit Feed Specification real-time data, we conduct a case study in the Central Ohio Transit Authority bus system in Columbus, Ohio, USA. Our results prove that realizable accessibility is the most conservative of the three accessibility measures. We also explore the spatial and temporal patterns in the unreliability of both traditional measures. These patterns are consistent with prior findings of the spatial and temporal patterns of bus delays and risk of missing transfers. Realizable accessibility is a more practical, conservative, and robust measure to guide transit planning.