The first of three GIS status update reports commissioned by PiHG:
Miller, H. J. (2017) “Geographic information science I: Geographic information observatories and opportunistic GIScience,” Progress in Human Geography. Online publication date: May-15-2017. DOI: 10.1177/0309132517710741
Abstract: Geographic information observatories (GIOs) extend the capabilities for observatory-based science to the broad geographic data associated with a place or region. GIOs are a form of scientific instrumentation that affords a holistic view of geographic data. This potentially could lead to new insights about geographic information, as well as the human and coupled human-natural systems described by this information. In this report, I discuss GIOs in light of a timely question – what new types of GIScience should we be doing with big geographic data? I argue that GIOs also allow for a new type of opportunistic geographic information science that leverages real-world events via ongoing observation, experimentation and decision-support.
Keywords: data science, geographic information observatory, natural experiments, opportunistic science, spatial decision support
Forbes looked at Bureau of Labor Statistics data that predicted which jobs would have the highest growth between 2014 – 2024. The top twenty fastest growing jobs can be found at this website: http://www3.forbes.com/business/the-20-fastest-growing-jobs-in-america/. Two careers on the list are:
- #15: cartographer
- #8: statistician
Geographic Information Systems (GIS) combines cartography and statistics, leading to career opportunities with tremendous growth potential!
Where should one prepare for a career in GIS? You can’t go wrong at the Department of Geography, The Ohio State University! The geography program at Ohio State is consistently listed as one of the top programs in the USA for a spatial career.
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)