Ohio State Geography is hiring! Tenure-track assistant professorship in Geospatial Data Science

The Department of Geography in the College of Arts and Sciences at The Ohio State University invites applications for a tenure track position at the assistant professor level, commencing autumn semester 2018. The position is contingent on budgetary approval. We seek a scholar with expertise in areas such as spatial-temporal data analytics, spatial simulation and modeling, cyberGIS and high performance computing, and/or geovisualization. Preferred application domains include scholars who address issues surrounding sustainability, resilience and social equity in areas that include urban science, transportation and/or public health. The successful candidate will also be required to teach classes in the department’s GIS program.

This position is partially funded by Ohio State’s Discovery Themes Initiative, a significant faculty hiring investment in key thematic areas in which the university can build on its culture of academic collaboration to make a global impact. The successful candidate will join a highly collaborative interdisciplinary community of scholars in the Sustainable and Resilient Economy (SRE) program including faculty from social and behavioral sciences, environmental sciences, engineering, business, public health, and policy. The SRE program seeks to advance sustainability science by developing a more holistic understanding of sustainable and resilient production and consumption systems, human-environment interactions, and innovations in sustainable technologies and governance. Successful applicants will be expected to participate in or lead collaborative teams and interdisciplinary research on sustainability and resilience topics.

 

Qualifications:

A Ph.D. in GIScience or a closely related field is required. All applicants are expected to have very strong and fundable research programs and to contribute to both graduate and undergraduate supervision and instructions. Preferred qualifications include experience developing or working in interdisciplinary research teams, university teaching experience and experience mentoring members of underrepresented groups.  Appointment is contingent on the university’s verification of credentials and other information required by law and/or university policies, including but not limited to a criminal background check.

About Columbus:

The Ohio State University campus is located in Columbus, the capital city of Ohio. Columbus is the center of a rapidly growing and diverse metropolitan area with a population of over 1.5 million. The area offers a wide range of affordable housing, many cultural and recreational opportunities, excellent schools, and a strong economy based on government as well as service, transportation and technology industries. Columbus has consistently been rated as one of the Top U.S. cities for quality of life, and was selected as one of the Top 10 cities for African Americans to live, work, and play by Black Enterprise magazine. Additional information about the Columbus area is available at http://www.columbus.org.

Application Instructions:

Apply to Academic Jobs Online at: https://academicjobsonline.org/ajo/jobs/9712.  A complete application consists of a cover letter including teaching, research and service credentials, a curriculum vitae, up to three representative publications, and three letters of references. Inquiries may be directed to Morton O’Kelly at okelly.1@osu.edu. Applications received prior to November 15, 2017 will receive priority consideration.

The Ohio State University is committed to establishing a culturally and intellectually diverse environment, encouraging all members of our learning community to reach their full potential. We are responsive to dual-career families and strongly promote work-life balance to support our community members through a suite of institutionalized policies. We are an NSF Advance Institution and a member of the Ohio/Western Pennsylvania/West Virginia Higher Education Recruitment Consortium (HERC).

The Ohio State University is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation or identity, national origin, disability status, or protected veteran status.

Green accessibility: Estimating the environmental costs of network-time prisms

Network-time prisms are powerful measures of space-time accessibility within transportation networks.  However, they fail to capture the environmental costs of potential mobility.  In this paper, we present a method for estimating the expected energy consumption and emissions associated with network time prisms.  We also verify our method using data from instrumented vehicles moving within an experimental network-time prism in the Phoenix AZ road network.

Song, Y., Miller, H.J., Stempihar, J. and Zhou, X. (2017), “Green accessibility: Estimating the environmental costs of network-time prisms for sustainable transportation planning,” Journal of Transport Geography, 64, 109-119.

Abstract.  Accessibility, or the ease to participate in activities and obtain resources in a given environment, is crucial for evaluating transportation systems. Greater accessibility is often achieved by increasing individuals’ potential mobility. However, potential mobility, if realized by motorized modes, can also generate negative environmental impacts such as fossil fuel consumption and greenhouse gas (GHG) emissions. While the negative environmental impacts of greater mobility are acknowledged, there has been a lack of research to validate those impacts using empirical data, especially considering variations in individuals’ mobility levels. This paper presents a method for estimating the expected environmental costs of accessibility represented by a network-time prism (NTP). A NTP delimits all accessible locations within a network and the available time for an individual to present at each location given a scheduled trip origin and destination, a time budget and the maximum achievable speeds along network edges. Estimating the expected environmental costs of a NTP involves three steps: (1) semi-Markov techniques to simulate the probabilities to move along network edges at given times; (2) the speed profiles for reachable edges, and (3) a cost function that translates speeds into environmental impacts. We focus on air quality and employ the motor vehicle emission simulator MOVESLite to estimate the CO2 emissions at both the edge and prism levels. We calibrate and validate the methods for experimental NTPs defined within the Phoenix, AZ, USA road and highway network using vehicles instrumented with GPS-enabled onboard diagnostic devices (OBD). We demonstrate the effectiveness of our method through two scenarios and investigate the impact of changes in mobility levels on the expected CO2 emissions associated with the experimental NTPs.

Keywords: Space-time accessibility; Network-time prism; Emissions

Kinetic prisms: Incorporating acceleration limits into space–time prisms

Space-time prisms are physically impossible since they assume infinite acceleration and deceleration. This paper is a first step in resolving this issue.  A theoretical description of kinetic prism geometry, but provides insights that can lead to moving objects data analytics with practical applications (e.g., animal movement, vehicle energy consumption and emissions, bicycles and other forms of active transportation)

Kuijpers, B., Miller, H.J. and Othman, W., (2017) “Kinetic prisms: incorporating acceleration limits into space-time prisms,” International Journal of Geographic Information Science, 31, 2164-2194.

Abstract:  Presently, data concerning moving objects abound. These data mainly consist of time-stamped geographical locations, which are collected by location aware devices, such as Global Positioning System receivers. Space–time prisms are used to model the spatio-temporal space of potential movement in between measured locations (called anchors). They rely on the knowledge of the maximal speed of travel of an object and they capture all space–time paths that respect this speed limit. However, the classic space–time path and prism model is not physically realistic, in the sense that it contains spatio-temporal paths of moving objects can alter their direction and speed instantaneously. Since this is physically impossible, the classical model is not acceptable in applications where mechanics and kinetics are vital. We propose a more realistic version of space–time prisms, in which not only speed but also acceleration is bounded. This additional bound results in a physically realistic model, which we refer to as kinetic prisms. Furthermore, we study how imposing constraints on the speed and heading at anchor points affects the geometry of kinetic prisms. In this paper, we give analytical descriptions of kinetic prisms and algorithms for their construction for movement in one- and two-dimensional space.

Keywords: Spatio-temporal data models, moving-object data, space–time prisms