The main purpose of the paper is to provide a greater understanding of the factors that influence heterogeneous distributions of crime. The authors focus on the comparative analysis of three quantitative approaches: Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR) and Data Envelopment Analysis (DEA). The above methods are then applied to explore the structural theories of violence in Cincinnati, Ohio by block group. To predict assault rates between January and June of 2008 the authors use 2006 population density from Caliper Corporation, density of alcohol outlets from the Ohio Division of Liquor Control, and social disorganization calculated from socio-economic disadvantage, female headed-households, and residential instability, as independent variables.

The presentation can be accessed here and the more detailed review can be accessed here.

Bibliography

Grubesic, T. H., Mack, E. A., Kaylen, M. T. (2012). Comparative modeling approaches for understanding urban violence. Social Science Research, 41(1), 92-109.doi:10.1016 /j.ssresearch.2011.07.004

In this paper the authors combined RS and GIS techniques to assess the environmental factors influencing Sin Nombre virus (SNV) contraction in deer mice, the primary rodent host. 119 field sites sampled in the Walker River Basin in western Nevada and east-central California in 1995, 1996 and 1998 were used. Spatial patterns and statistical relationships between site characteristics and infection rates were analyzed to retroactively classify rodent infection status and estimate prediction accuracy. Results can be applied to identify landscape characteristics with greater human risk from SNV, the agent associated with Hantavirus pulmonary syndrome.

The presentation can be accessed here and a detailed review of the paper can be accessed here.

Bibliography

Boone, J. D., McGwire, K. C., Otteson, E. W., DeBaca, R. S., Kuhn, E. A., Villard, P….St. Jeor, S. C. (2000). Remote Sensing and Geographic Information Systems: Charting Sin Nombre Virus Infections in Deer Mice. Emerging Infectious Diseases, 6(3), 248-258. https://dx.doi.org/10.3201/eid0603.000304.

In this paper, the authors chronicle an experiment which models and predicts invasive expansion patterns of giant hogweed (Heracleum mantegazzianum) in a central European geographic context. Giant hogweed is a dangerous invasive species: it can outcompete native plants, turning a diverse ecosystem into a monoculture. The plant’s sap contains toxins which react with light causing severe skin burns. The invasion simulation used a cellular automaton defined by a life-cycle matrix model, mechanistic local and corridor dispersal and randomly determined long-distance dispersal. Landscape configurations corresponded to the real-world suitable habitats and corridors of eight 1km² study areas. These simulations were then compared with monitoring data from 2002 to 2009 to determine the simulation modelling accuracy. With an accurate model, researchers can quantify the relative importance of different processes for large scale spread and impacts of the invasive species, aiding in determining effective pest management strategies.

The presentation can be accessed here and a more detailed review here.

Bibliography

Moenickes, Sylvia & Thiele. (2012). What shapes giant hogweed invasion? Answers from a spatio-temporal model integrating multiscale monitoring data. Biol Invasions, 15, 62-73. doi:10.1007/s10530-012-0268-z