Spatial data requirements for animal disease management in New Zealand : a dissertation in partial fulfilment of the requirements for the degree of Master of Veterinary Studies (Epidemiology) at Massey University
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Date
1998
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Massey University
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Abstract
The science of geology has given rise to techniques for managing and analysiing spatial data. The techniques often deal with samples that represent a continuum, such as mineral samples taken from various locations. Some animal health data is similar in nature to geo-statistical data, such as climate data or soil samples from various points on a farm. Animal health data is commonly discrete rather than continuous in space. Farms are represented as point or area features and attributes of the farm are attached to the features. Spatial analysistechniques were reviewed and comment made about their usefulness and validity in disease management.
The spatial data available in New Zealand for managing diseases was examined. Spatial data at a farm level is available in the national database management system Agribase, which records details of rural enterprises. The level of data completeness in Agribase was determined. The number of farms without spatial references varied from 10 to 18 percent, depending on the method used to update Agribase. Spatial data is available for cattle and deer herds in the National Livestock Database (NLDB). The number of herds without spatial data varied from 8 to 15 percent. Changes in the management of land information in New Zealand are resulting in an improvement in the quality and completeness of spatial data.
In summary for the management of endemic and exotic diseases, farms should be represented as area features. Point coverage's can be generated from these area features and used in some applications, such as simulation models, and for labelling purposes. To function acceptable the applications tested required that 85% of farms or herds were represented spatially.
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Keywords
Veterinary epidemiology, Livestock disease management, Spatial data, Animal health data