Measuring space-time prism similarity through temporal profile curves

Harvey J. Miller, Martin Raubal and Young Jaegal (2016) “Measuring space-time prism similarity through temporal profile curves,” in T. Sarjakoski, M.Y. Santos, and L.T. Sarjakoski (Eds.) Geospatial Data in a Changing World: Selected Papers of the 19th AGILE Conference on Geographic Information Science, Springer: Lecture Notes on Geoinformation and Cartography, 51-66.

Abstract: Space-time paths and prisms based on the time geographic framework model actual (empirical or simulated) and potential mobility, respectively. There are well-established methods for quantitatively measuring similarity between space-time paths, including dynamic time warping and edit-distance functions. However, there are no corresponding measures for comparing space-time prisms.  Analogous to path similarity, space-time prism similarity measures can support  comparison of individual accessibility, prism clustering methods and retrieving prisms similar to a reference prism from a mobility database. In this paper, we introduce a method to calculate space-time prism similarity through temporal sweeping. The sweeping method generates temporal profile curves summarizing dynamic prism geometry or semantic content over the time span of the prism’s existence. Given these profile curves, we can apply existing path similarity methods to compare space-time prisms based on a specified geometric or semantic prism.  This method can also be scaled to multiple prisms, and can be applied to prisms  and paths simultaneously. We discuss the general approach and demonstrate the
method for classic planar space-time prisms.


Call for participation – NSF Workshops on Advancing Movement and Mobility Science

Recent years have witnessed the emergence of interdisciplinary scientific communities focusing on moving objects, motivated by technological advances in location-aware technologies for moving objects data (MOD) collection. In response to these challenges and opportunities, interdisciplinary communities are emerging that focus on the analysis of MOD to in order to provide new insights into complex spatio-temporal systems. However, a schism is also emerging between researchers focusing on human entities (e.g., people, vehicles, commodities) and animal entities (e.g., tigers, pandas, albatrosses, salmon).

A series of two workshops will bring together scholars working on animal movement ecology and human mobility science to generate a nascent interdisciplinary/cross-domain community focusing on the analysis of moving objects.  Specifically, the two workshops will address crucial issues that span both human mobility science and animal movement ecology:

  • Workshop 1: Measuring and interpreting interactions between and among moving objects (November 2016 in Austin, Texas)
  • Workshop 2: Analyzing moving entities within geographic context (May 2017 in Columbus, Ohio).

These workshops are supported by an award from the National Science Foundation (BCS 1560727)

Call for participation – Workshop 1: Measuring and analyzing interactions among mobile entities.   University of Texas-Austin, 10-11 November 2016

Interactions among mobile objects are a second-order but crucial property of movement.  In human mobility, interactions reflect actual or potential social interactions or shared activities, and are often the basis for the formation and maintenance of social networks and social capital.  In animal movement ecology, interactions can range from physical contact to sharing common resources, to simple awareness and are crucial for understanding spatial ecological processes and behaviors such as mating and territoriality, as well as epizootiology.

New advancements in collecting movement/location data enable more and better quality data to be collected, and have resulted in an increasing number of studies on animal or human interaction.  However, there have been few methodological advancements related to improving the ability to analyze and understand interactions. Most of the currently used interaction metrics were developed under a different paradigm of MOD collection (coarser spatial and temporal resolution) and the assumptions the metrics make (such as the way inherent expected values are calculated) are likely inappropriate in many applications to which they are applied.

We invite participation from researchers at any level who are involved in measuring and analyzing interactions among humans and/or animals.  Selected participants will receive travel reimbursement for legitimate expenses ranging from $500-$1000, with priority to students and unfunded scholars.

More information:

To be considered, submit the following:

  1. Brief cover letter indicating your intention to participate, and whether partial travel support is required
  2. A 750 word (maximum) abstract of your proposed presentation
  3. If travel support is required, a one to two sentence statement of need.
  4. A short CV (NSF style preferred; two pages maximum)

Please combine all of the above into one document (*.pdf preferred) named with your last name followed by first two initials (ex. MillerJA.pdf) and email as an attachment to

Due date: 2 September 2016.

Workshop Co-organizers

  • Harvey J. Miller (The Ohio State University)
  • Jennifer A. Miller (University of Texas at Austin)
  • Gil Bohrer (The Ohio State University)

Steering Committee

  • Somayeh Dodge (University of Minnesota)
  • Joni Downs (University of South Florida)
  • Steven Farber (University of Toronto)
  • Wayne Getz (University of California – Berkeley)
  • Trisalyn Nelson (Arizona State University)
  • Kathleen Stewart (University of Maryland)
  • May Yuan (University of Texas – Dallas)

Estimating the most likely space–time paths, dwell times and path uncertainties from vehicle trajectory data: A time geographic method

Jinjin Tang, Ying Song, Harvey J. Miller and Xuesong Zhou (2016) “Estimating the most likely space-time paths, dwell times and path uncertainties from vehicle trajectory data: A time geographic method,” Transportation Research C, 66, 176-194.

Abstract: Global Positioning System and other location-based services record vehicles’ spatial locations at discrete time stamps. Considering these recorded locations in space with given specific time stamps, this paper proposes a novel time-dependent graph model to estimate their likely space–time paths and their uncertainties within a transportation network. The proposed model adopts theories in time geography and produces the feasible network–time paths, the expected link travel times and dwell times at possible intermediate stops. A dynamic programming algorithm implements the model for both offline and real-time applications. To estimate the uncertainty, this paper also develops a method based on the potential path area for all feasible network–time paths. This paper uses a set of real-world trajectory data to illustrate the proposed model, prove the accuracy of estimated results and demonstrate the computational efficiency of the estimation algorithm.