Spatial Modelling of Disease using Data- and Knowledge-driven Approaches

Spatial and Spatio-temporal Epidemiology, Available online 19 July 2011

Kim B. Stevens and Dirk U. Pfeiffer

“The purpose of spatial modelling in animal and public health is three-fold: describing existing spatial patterns of risk, attempting to understand the biological mechanisms that lead to disease occurrence and predicting what will happen in the medium to long-term future (temporal prediction) or in different geographical areas (spatial prediction). Traditional methods for temporal and spatial predictions include general and generalized linear models (GLM), generalized additive models (GAM) and Bayesian estimation methods. However, such models require both disease presence and absence data which is not always easy to obtain. Novel spatial modelling methods such as maximum entropy (MAXENT) and the genetic algorithm for rule production (GARP) require only disease presence data and have been used extensively in the fields of ecology and conservation, to model species distribution and habitat suitability. Other methods, such as multicriteria decision analysis (MCDA), use knowledge of the causal factors of disease occurrence to identify areas potentially suitable for disease. In addition to their less restrictive data requirements, some of these novel methods have been shown to outperform traditional statistical methods in predictive ability (Elith, et al., 2006). This review paper provides details of some of these novel methods for mapping disease distribution, highlights their advantages and limitations, and identifies studies which have used the methods to model various aspects of disease distribution.”

Positional Accuracy of TIGER 2000 and 2009 Road Networks

Transactions in GIS

Transactions in GIS, August 2011, Volume 15, Issue 4

Paul A. Zandbergen, Drew A. Ignizio and Kathryn E. Lenzer

“The Topologically Integrated Geographic Encoding and Referencing (TIGER) data are an essential part of the US Census and represent a critical element in the nation’s spatial data infrastructure. TIGER data for the year 2000, however, are of limited positional accuracy and were deemed of insufficient quality to support the 2010 Census. In response the US Census Bureau embarked on the MAF/TIGER Accuracy Improvement Project (MTAIP) in an effort to improve the positional accuracy of the database, modernize the data processing environment and improve cooperation with partner agencies. Improved TIGER data were released for the entire US just before the 2010 Census. The current study characterizes the positional accuracy of the TIGER 2009 data compared with the TIGER 2000 data based on selected road intersections. Three US counties were identified as study areas and in each county 100 urban and 100 rural sample locations were selected. Features in the TIGER 2000 and 2009 data were compared with reference locations derived from high resolution natural color orthoimagery.

Comparison of TIGER data and local street centerlines for a rural area in Bernalillo County, NM

Comparison of TIGER data and local street centerlines for a rural area in Bernalillo County, NM

“Results indicate that TIGER 2009 data are much improved in terms of positional accuracy compared with the TIGER 2000 data, by at least one order of magnitude across urban and rural areas in all three counties for most accuracy metrics. TIGER 2009 is consistently more accurate in urban areas compared with rural areas, by a factor of at least two for most accuracy metrics. Despite the substantial improvement in positional accuracy, large positional errors of greater than 10 m are relatively common in the TIGER 2009 data, in most cases representing remnant segments of minor roads from older versions of the TIGER data. As a result, based on the US Census Bureau’s suggested accuracy metric, the TIGER 2009 data meet the accuracy expectation of 7.6 m for two of the three urban areas but for none of the three rural areas. The suggested metric is based on the National Standard for Spatial Data Accuracy (NSSDA) protocol and was found to be very sensitive to the presence of a small number of very large errors. This presents challenges during attempts to characterize the accuracy of TIGER data or other spatial data using this protocol.”

Spatial Analysis of Sap Consumption by Birds in the Chaco Dry Forests from Argentina

Emu - Austral OrnithologyEmu – Austral Ornithology, Published 17 August 2011

Leandro Macchi, Pedro G. Blendinger, and M. Gabriela Núñez Montellano

“Sap is a resource of high energy content that is usually inaccessible to birds, although woodpeckers have the ability to drill into living trees to obtain sap. Because spatial patterns of resource availability influence avian abundance, we explored how spatial patterns of sap availability determine the spatial distribution of two sap-feeding species in the semiarid Chaco of Argentina. We studied the White-fronted Woodpecker (Melanerpes cactorum), which obtains sap by drilling holes into tree trunks, and the Glittering-bellied Emerald (Chlorostilbon aureoventris), which can obtain sap only from active woodpecker holes; 12 other bird species also exploited the sap flows from holes drilled by White-fronted Woodpeckers. The abundance of tree species used for sap feeding did not explain the spatial patterns of territorial groups of White-fronted Woodpeckers. However, within each territory, the abundance of Woodpeckers was centred on a single tree from which sap was obtained. The abundance of the Emeralds was strongly associated with the availability of trees with active sap-holes. During the dry season, sap is a major component in the diet of White-fronted Woodpeckers and Glittering-bellied Emeralds. However, the spatial distribution of these two consumers in relation to the availability of sap was species-specific. This species-specific response was closely related to the ecology and life history of each species. The abundance of woodpeckers could be determined by local mechanisms, such as location of a single sap tree in their small territories, whereas non-territorial hummingbirds would be able to track sap wells at a larger scale than the territory of a single territorial group of Woodpeckers. Our results show the importance of spatial analysis in identifying the ecological determinant of habitat selection and niche differentiation within species.”