URISA is pleased to announce that submissions are now being accepted for the 2013 Student Competition. URISA hosts an annual Student Competition to encourage students in a variety of academic settings and disciplines to write and publish papers and posters to share with the URISA membership and others in the geospatial technologies industry. The submission may include research projects, case studies, projects, or any type of methodology in which geospatial technology and skills were used or could be used.
Submissions will be accepted until June 3, 2013 in two categories:
PAPERS – Students are invited to submit a paper for the competition and possible inclusion in the URISA Journal. The papers will undergo a review process under the direction of an esteemed panel of academic and practitioner members of URISA. Lead authors of the top papers receive an award of a one-year membership in URISA and free registration to GIS-Pro 2013 (along with an opportunity to present at the conference). Students are encouraged to submit essays on geospatial issues as well as technical research papers, relating to any field geospatial field. Students should display original thought and creativity in the development of the papers.
POSTERS – Community College and GIS Certificate students are specifically invited to submit posters for the competition. The posters will undergo a review process under the direction of an esteemed panel of academic and practitioner members of URISA. The top ten posters will receive an award of a one-year membership in URISA.
For details and submission guidelines, visit http://www.urisa.org/student_competition
[Source: URISA press release]
PLOS Computational Biology, 17 January 2013
Corentin M. Barbu, Andrew Hong, Jennifer M. Manne, Dylan S. Small, Javier E. Quintanilla Calderón, Karthik Sethuraman, Víctor Quispe-Machaca, Jenny Ancca-Juárez, Juan G. Cornejo del Carpio, Fernando S. Málaga Chavez, César Náquira, and Michael Z. Levy
“With increasing urbanization vector-borne diseases are quickly developing in cities, and urban control strategies are needed. If streets are shown to be barriers to disease vectors, city blocks could be used as a convenient and relevant spatial unit of study and control. Unfortunately, existing spatial analysis tools do not allow for assessment of the impact of an urban grid on the presence of disease agents. Here, we first propose a method to test for the significance of the impact of streets on vector infestation based on a decomposition of Moran’s spatial autocorrelation index; and second, develop a Gaussian Field Latent Class model to finely describe the effect of streets while controlling for cofactors and imperfect detection of vectors. We apply these methods to cross-sectional data of infestation by the Chagas disease vector Triatoma infestans in the city of Arequipa, Peru.
Spatial distribution of Triatoma infestans presence in households of Paucarpata, Arequipa, Peru. Map of the study area. Black indicates infested households, white non-infested households, and grey non-inspected households. The area encircled by dashes was used to fit the Gaussian Field Latent Class model; the remaining area was used as a validation dataset. The close-up shows the urban grid underneath and the aggregation of vectors within city blocks.
“Our Moran’s decomposition test reveals that the distribution of T. infestans in this urban environment is significantly constrained by streets (p<0.05). With the Gaussian Field Latent Class model we confirm that streets provide a barrier against infestation and further show that greater than 90% of the spatial component of the probability of vector presence is explained by the correlation among houses within city blocks. The city block is thus likely to be an appropriate spatial unit to describe and control T. infestans in an urban context. Characteristics of the urban grid can influence the spatial dynamics of vector borne disease and should be considered when designing public health policies.”