2010 URISA Student Competition Results Announced

URISA seeks to encourage students in a variety of academic settings and disciplines to write and publish papers and projects for the URISA membership and others in the spatial technologies industry. To promote this objective URISA has established a student competition. URISA is pleased to announce the results of its 2010 Student Competition. This year’s competition featured three tiers: Papers, Presentations and Posters.

Paper Category

Presentation Category

Poster Category

Honorable Mentions

[Source: URISA press release]

Anchor Uncertainty and Space-time Prisms on Road Networks

International Journal of Geographical Information Science, Volume 24, Issue 8 August 2010 , pages 1223 – 1248

Bart Kuijpers; Harvey J. Miller; Tijs Neutens; Walied Othman

“Space-time prisms capture all possible locations of a moving person or object between two known locations and times given the maximum travel velocities in the environment. These known locations or ‘anchor points’ can represent observed locations or mandatory locations because of scheduling constraints. The classic space-time prism as well as more recent analytical and computational versions in planar space and networks assume that these anchor points are perfectly known or fixed. In reality, observations of anchor points can have error, or the scheduling constraints may have some degree of pliability. This article generalizes the concept of anchor points to anchor regions: these are bounded, possibly disconnected, subsets of space-time containing all possible locations for the anchor points, with each location labelled with an anchor probability. We develop two algorithms for calculating network-based space-time prisms based on these probabilistic anchor regions. The first algorithm calculates the envelope of all space-time prisms having an anchor point within a particular anchor region. The second algorithm calculates, for any space-time point, the probability that a space-time prism with given anchor regions contains that particular point. Both algorithms are implemented in Mathematica to visualize travel possibilities in case the anchor points of a space-time prism are uncertain. We also discuss the complexity of the procedures, their use in analysing uncertainty or flexibility in network-based prisms and future research directions.”

Migrant Boats: A Geo-temporal Analysis and Visualization of Migrant Boats

University of British Columbia Computer Science Department project, Fall 2009

Anika Mahmud

“This particular paper tries to solve one of the mini challenges of VAST challenge 2008 where the task is to find the changing pattern of migrants using boat from Isla Del Sueno to Florida over three years. This paper focuses on geo-temporal analysis of the synthesis data and its visualization using info visualization technique.”

Spatial and Temporal Analysis of Forest Cover Changes in the Bartin Region of Northwestern Turkey

African Journal of Biotechnology, Vol. 9 (35), pp. 5676-5685, 30 August 2010

Ayhan Atesoglu and Metin Tunay

“This study analyzed the changes in the forest areas in Bartin province of Turkey and the surrounding areas using remote sensing data and GIS techniques. Three Landsat Thematic Mapper (TM) images of the study region, recorded in 1987, 1992, and 2000, were utilized. The main land-use characteristics were derived using a maximum-likelihood classification technique. The remotely sensed data allows monitoring of current land use/land cover and detection of temporal changes. Furthermore, a temporal and spatial comparison of the classified image can be performed using Geographical Information System (GIS) to show land-use changes. GIS analysis of the classification results based on reference datasets revealed that the area covered by forests decreased significantly and that the amount of the reduction corresponded mainly to increased agricultural land use. The reasons for this negative impact on forested areas were growth in the region’s population, and expansion of agricultural areas and settlements. The classification results also showed that past, afforestation work had been successful.”

Research Explores Factors in Obesity

South Dakota State University researchers are using the tools of spatial analysis to explore nationwide data for insights on what influences obesity.

“We can identify and map some of these regions or ‘hotspots’ of high and low obesity,” said associate professor Michael Wimberly of SDSU’s Geographic Information Science Center of Excellence. “Ultimately what we want to do is explain what some of the drivers are.”

SDSU postdoctoral researcher Akihiko Michimi, who is working on the project with Wimberly, said one glaring regional difference is that the rate of obesity is high in much of the rural South United States, but low in the rural West and in New England states.

Michimi and Wimberly’s first journal article about the study appeared June 29 in the American Journal of Preventive Medicine.

The SDSU study set out to map spatial patterns of obesity and risk factors nationwide by using Behavioral Risk Factor Surveillance System data from telephone surveys compiled annually by the Centers for Disease Control and Prevention. The BRFSS data includes self-reported height and weight, as well as respondents’ answers to questions about their levels of physical activity, and about fruit and vegetable consumption.

“The advantage of using BRFSS compared to a variety of other data sources is that we can get wall-to-wall national coverage. They actually do sampling in every county across the United States,” Wimberly said. “So we can map things, first of all, and we can also use various spatial statistics to test hypotheses about what the environmental correlates of obesity, physical activity, fruit and vegetable consumption are at a national level as opposed to other studies that have been more localized.”

For example, the SDSU analysis shows that the rural South and parts of the Great Plains had low proportions of people who are physically active in their leisure time, while the rural West, New England, and the upper Midwest had high proportions.

When analyzing data for another factor — the proportion of adults consuming fruits and vegetables five times or more per day — researchers found the West Coast, New England and parts of the South had the highest proportions. But the Lower Mississippi Valley, the Great Plains and the Mid-Appalachian Mountain region had low proportions.

Michimi and Wimberly said a current idea in research is that factors in society can set up “obesogenic environments” that give rise to obesity — if factors discourage physical activity or encourage eating the wrong sorts of food, for example.

One of the angles they’re currently exploring in a follow-up study is the possibility that distance from supermarkets — a possible indicator of access to nutritious foods rather than highly processed, less healthful foods — could play a role.

SDSU’s preliminary analysis of data from the 48 contiguous United States showed that the probability of obesity increased with distance from supermarkets, while consumption of five or more servings of fruits and vegetables per day decreased. The research also showed clear differences between large metropolitan areas and sparsely populated rural areas.

“Sometimes people have to drive 25 or 30 miles to get to a supermarket or grocery store,” Michimi said. “But big cities on the East Coast or West Coast have a high population density. If they have a large number of people, they have a large number of stores. So the distance to the supermarkets in general is much, much shorter compared to the distances to the supermarkets on the Great Plains.”

Wimberly said there are no easy answers about what’s responsible for obesity. But analyzing it with the tools of geography could make some less obvious factors visible.

“The geographic perspective opens up a unique window. Looking at maps, people relate very intuitively to the patterns and it really catalyzes a lot of new thought, ideas, hypotheses. That’s the power of what we refer to as ‘exploratory spatial data analysis,’ working with the data using statistical techniques that allow us to tease out real spatial trends from the underlying noise and using that as a method for hypothesis generation. We can also pull multiple sources of data together to actually test hypotheses about the underlying relationships.”

The U.S. Department of Agriculture’s National Research Initiative funded the work through a grant from its Human Nutrition and Obesity Program.

[Source: South Dakota State University press release]