Transactions in GIS, August 2011, Volume 15, Issue 4
Jane Law and Ping W. Chan
“This article studies Bayesian hierarchical spatial modelling that monitors the changes of residual spatial pattern (structure) of the outcome variable for exploring unknown risk factors in small-area analysis. Spatially structured random effects (SRE) and unstructured random effects (URE) terms added to the conventional logistic regression model take into account overdispersion and residual spatial structure, which if unaccounted for could cause incorrect identification of risk factors.
Decomposed maps of spatial random effects (exp(S)) showing progressive diminishing of residual spatial structure as significant covariates were added to Model 3
“Mapping and/or calculating the ratio of random effects that are spatially-structured monitor the extent of residual spatial structure. The monitoring provides insights into identification of unknown covariates that have similar spatial structures to those of SRE. Adding such covariates to the model has the potential to diminish the residual spatial structure, until possibly all or most of the spatial structure can be explained. Risk factors identified are the added covariates that have statistically significant regression coefficients. We apply the methods to the analysis of domestic burglaries in Cambridgeshire, England. Small-area analysis of crime where data often display apparent spatial structure would particularly benefit from the methodologies. We discuss the methodologies, their relevancy in our analysis of domestic burglaries, their limitations, and possible paths for future research.”
Journal of Coastal Research, Volume 27, Issue 6A), November 2011: 44-51
Nathan Crowell, Timothy Webster, and Nelson J. O’Driscoll
“Exposure to solar radiation and tidal inundation are important factors for a wide variety of chemical and ecological processes in coastal ecosystems. Accurate quantification of these factors is often difficult on a local scale. To address this research gap, a remote-sensing approach was developed to model inundation and radiation characteristics within an intertidal zone located in the Minas Basin (Bay of Fundy, Nova Scotia, Canada). A light detection and ranging (LIDAR)–derived elevation model was subjected to tidal modelling based on hourly sea level predictions and solar modelling based on sunrise and sunset times for 2009. Model results indicated an intertidal zone of 145.8 km2 with an elevation between −6.9 m and 6.8 m. The intertidal zone was determined to contain three unique wetland classes: (1) 4.4 km2 of high salt marsh, dominated by Spartina patens; (2) 5.0 km2 of low salt marsh, dominated by Spartina alterniflora; and (3) 63.1 km2 of nonvegetated marine flat (73.3 km2 unclassified intertidal).
Orthorectified imagery for a region of intertidal wetland separated from agricultural land by a protective dyke to the NE of Wolfville, Nova Scotia, Canada taken in 2006. The model results were overlaid to depict (B) delineated wetland classes based on dominant vegetation, (C) atmospheric exposure characteristics of the intertidal zone based on hydrographic modelling which examined topography and hourly tidal predictions for 2009, and (D) solar exposure characteristics of the intertidal zone based on hydrographic modelling and astronomical predictions.
“Detailed exposure characteristics were calculated for each of the classes within the intertidal zone at 10-cm vertical intervals. Exposure calculations for 2009 showed that an average of 4.2 km2 of salt marsh were exposed to solar radiation and 8.4 km2 were exposed to the atmosphere each hour. Similarly, 11.7 km2 of marine flat were exposed to solar radiation and 22.9 km2 were exposed to the atmosphere each hour. The developed remote-sensing techniques successfully established intertidal zones, uniquely identified wetland classes, and modelled inundation and solar exposure characteristics within the study area.”
Annual Conference of the Transportation Association of Canada, 2011
Brandt Denham, George Eguakun, and Kwei Quaye
“Saskatchewan Government Insurance (SGI) is responsible for collecting and maintaining a comprehensive database of traffic accidents. This data is used by SGI and other safety partners for monitoring, decision making and the evaluation of traffic safety program initiatives in Saskatchewan. The GeoTAIS project was launched in July of 2010 in an effort to enhance the quality of Saskatchewan’s traffic accident database to keep up with cutting edge traffic safety analysis/research and to facilitate the provision of well informed traffic safety programs in Saskatchewan.
Actual Wildlife Accident Locations on Highway 16, Control Section 24
“The overarching goal of the project is to develop a Geographic Information System (GIS) that would allow for the visual representation of the traffic accident data captured in the SGI’s Traffic Accident Information System (TAIS) and SGI’s claims information systems in a spatial format. The second goal of the project is to deploy guidelines from the recently published AASHTO Highway Safety Manual combined with the spatial data from GeoTAIS to develop Safety Performance Functions (SPFs) for all provincial highways in Saskatchewan. The final goal of the project is to utilize the spatial data, SPFs and the Empirical Bayes (EB) method to visually identify collision hotspots and areas in the provincial road network with high potential for safety improvements. The success of the project will help ensure that traffic safety problem identification, investments, monitoring, and program evaluation in Saskatchewan are informed by the best data in a speedy and efficient manner. This paper discusses the development of the GeoTAIS project and its application in identifying hazardous wildlife crash locations as part of the ongoing efforts to improve traffic safety on Saskatchewan’s provincial highways.”