Genocide and GIScience: Integrating Personal Narratives and Geographic Information Science to Study Human Rights

The Professional Geographer, Volume 61, Issue 4 November 2009 , pages 508 – 526

Marguerite Madden; Amy Ross

“This project combines qualitative data of personal narratives with geographic information science (GIScience) technologies to explore the potential for critical cartography in the study of mass atrocity. The case study used is northern Uganda, where millions have been affected by physical violence and hardship, displacement, and fear. Web-based virtual globes as a ready source of imagery for remote areas and derived spatial data imported to geographic information systems (GIS) provide quantified data that complement testimonials and other qualitative data from the field. Cartographic functions, geovisualization, and spatial analyses available in GIS are used to extract information from high-resolution remote sensing images documenting internally displaced persons (IDP) camps and quantifying evidence of crimes against humanity. These techniques explore spatial relationships and communicate results on the extent and impact of the atrocities in northern Uganda.”

PhD Scholarship, Institute of Geomatics and Analysis of Risk (IGAR), University of Lausanne

IGAR opens a position in the field of application of machine learning algorithms (neural networks of different architectures, support vector machines, etc.) and geostatistics for geo/environmental sciences. The main tasks concern the development, adaptation, and programming of data mining (pattern recognition) models and tools. In particular, topics to be studied include modeling of data clustering, novelty detection, feature selection, manifold learning, risk mapping, and spatio-temporal simulations. In addition to the PhD the candidate is expected to assist in teaching (French, English) and other projects at the institute.

Requires an MS degree in one of the following disciplines: statistics, machine learning, computer science, physics, geosciences, applied mathematics. Candidates should have a sound background in spatio-temporal data analysis using machine learning and geostatistical approaches. Knowledge of scientific programming languages as Matlab, C, or R is important.

GIS, Multi-criteria and Multi-factor Spatial Analysis for the Probability Assessment of the Existence of Illegal Landfills

International Journal of Geographical Information Science, Volume 23, Issue 10 October 2009 , pages 1233 – 1244

Giancarlo Biotto;  Sonia Silvestri;  Lucia Gobbo;  Elisa Furlan;  Sonia Valenti; Roberto Rosselli.

“This work deals with the identification of potentially contaminated areas using remote sensing, geographic information systems (GIS) and multi-criteria spatial analysis. The identification of unknown illegal landfills is a crucial environmental problem in all developed and developing countries, where a large number of illegal waste deposits exist as a result of fast, and relatively unregulated, industrial growth over the past century. The criteria used to perform the spatial analysis are here selected by considering the characteristics which are ‘desirable’ for an illegal waste disposal site, chiefly related to the existence of roads for easy access and to a low population density which facilitates unnoticed dumping of illegal waste materials. A large dataset describing known legal and illegal landfills and the context of their location (population, road network, etc.) was used to perform a spatial statistical analysis to select factors and criteria allowing for the identification of the known waste deposits. The final result is a map describing the likelihood of an illegal waste deposit to be located at any arbitrary location. Such a probability map is then used together with remote sensing techniques to narrow down the set of possibly contaminated sites (Silvestri and Omri, 2008), which are candidates for further analyses and field investigations. The importance of the integration of GIS and remote sensing is highlighted and represents a key instrument for environmental management and for the spatially-distributed characterization of possible uncontrolled landfill sites.”

Combating the Asian Tiger Mosquito

For the County of Mercer, New Jersey, Mosquito-Borne Diseases Pose a Serious Challenge

…from the Winter 2009/2010 issue of ArcNews

The County of Mercer, New Jersey, lies equidistant between New York City, New York, and Philadelphia, Pennsylvania. Its geography ranges from Appalachian piedmont forests to farms on the coastal plain. It hosts extensive freshwater and tidal marshes, the state’s urban capital of Trenton, and a booming interstate “edge city” near Princeton University. Such diversity provides an ideal location to study mosquitoes and arthropod-borne diseases (arboviruses).

Mercer County Mosquito Control (MCMC) monitors, inspects, and manages all mosquito-related activities within the county. Its two main responsibilities are to monitor and control nuisance mosquito populations and to protect the safety of the general public from mosquito-borne diseases, such as West Nile virus and eastern equine encephalitis. MCMC uses integrated pest management techniques, along with continual surveillance of local mosquito populations and responses to service requests generated by local residents, to keep mosquitoes at tolerable levels.