Call for Participation: Workshop on Analyzing Movement and Mobility Within Geographic Context
The Ohio State University
11-12 May 2017
Abstract submission deadline: Monday, 20 February 2017
The wealth of mobile objects data (MOD) generated by GPS, radiolocation, proximity sensors and other location-aware technologies comes with a significant cost – the lack of path semantics such as the motivations and activities associated with the mobility behavior. Consequently, methods for analyzing MOD focus on the morphology of the object’s path in space with respect to time. Ignoring the geographic context is a major weakness since this can help researchers infer among different behaviors that are consistent with the same mobility behavior, such as whether apparently coordinated movement is coincidental or indicative of a shared activity. A vital research frontier is developing new MOD analytical techniques that go beyond the movement pattern to include the geographic context within which movement occurs.
We invite broad participation from researchers at any level and from any field of study (e.g., transportation, sociology, animal movement ecology, public health, GIS) with interests in measuring and analyzing animal and/or human movement within its geographic context. Selected participants will receive an award for travel expenses reimbursement ranging from $500-$1000, with priority to students and unfunded scholars.
This is the second of two NSF-sponsored workshops bringing 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:
- Measuring and interpreting interactions between and among moving objects (UT – Austin, November 2016);
- Analyzing movement and mobility within geographic context (OSU, May 2017).
For more information and abstract submission, please see: cura.osu.edu/may17
- Harvey J. Miller (The Ohio State University)
- Jennifer A. Miller (University of Texas at Austin)
- Gil Bohrer (The Ohio State University)
- 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)
Miller H.J. and Tolle K. (2016), “Big Data for healthy cities: Using location-aware technologies, open data and 3D urban models to design healthier built environments,” Built Environment, 42, 441-456.
Abstract: A healthy city is a built environment that encourages physical, mental and social wellbeing. Few neighbourhoods and communities in the United States and increasingly elsewhere in the world are healthy places. A major factor is changes in built environments and lifestyles that have not only eliminated physical activity from daily lives but can also make physical activity unpleasant, unhealthy and unsafe. The development and deployment of sensors connected to location-aware technologies are improving the scientific understanding of built environment characteristics that facilitate healthy and safe physical activity. This paper argues that integrating data from these with new sources of urban data can allow for deeper understanding of the intricate relationships between individuals, environments and healthy places. We discuss the need for an integrated, ecological approach to understanding healthy places and the role of location aware technologies, open data and 3D urban models in facilitating this approach. We also identify major challenges to this approach, including privacy protection.
Special Issue of Built Environment on ‘Big Data and the City’; Volume 42, Number 3, Autumn 2016
- Editorial: Big Data, Cities and Herodotus – Batty, Michael
- Big Data and the City – Batty, Michael
- From Origins to Destinations: The Past, Present and Future of Visualizing Flow Maps – Claudel, Matthew; Nagel, Till; Ratti, Carlo
- Towards a Better Understanding of Cities Using Mobility Data – Lenormand, Maxime; Ramasco, José J.
- Finding Pearls in London’s Oysters – Reades, Jonathan; Zhong, Chen; Manley, ED; Milton, Richard; Batty, Michael
- A Classification of Multidimensional Open Data for Urban Morphology – Alexiou, Alexandros; Singleton, Alex; Longley, Paul A.
- User-Generated Big Data and Urban Morphology – Crooks, A.T.; Croitoru, A.; Jenkins, A.; Mahabir, R.; Agouris, P.; Stefanidis, A.
- Sensing Spatiotemporal Patterns in Urban Areas: Analytics and Visualizations Using the Integrated Multimedia City Data Platform – Thakuriah, Piyushimita; Sila-Nowicka, Katarzyna; Paule, Jorge Gonzalez
- Playful Cities: Crowdsourcing Urban Happiness with Web Games – Quercia, Daniele
- Big Data for Healthy Cities: Using Location-Aware Technologies, Open Data and 3D Urban Models to Design Healthier Built Environments – Miller, Harvey J.; Tolle, Kristin
- Improving the Veracity of Open and Real-Time Urban Data – Mcardle, Gavin; Kitchin, Rob
- Wise Cities: ‘Old’ Big Data and ‘Slow’ Real Time – Carrera, Fabio
- Collecting and Visualizing Real-Time Urban Data through City Dashboards –Gray, Steven; O’Brien, Oliver; Hügel, Stephan