Stray cats in Auckland, New Zealand: Discovering geographic information for exploratory spatial analysis
Farnworth, Mark; Aguilar, Glenn
Citation:Aguilar, G., & Farnworth, M. (2012). Stray cats in Auckland, New Zealand: Discovering geographic information for exploratory spatial analysis. Applied Geography, 34, 230-238. doi:10.1016/j.apgeog.2011.11.011
Permanent link to Research Bank record:http://hdl.handle.net/10652/1804
Stray cats are a common feature of urban landscapes and are associated with issues of animal welfare and negative environmental impacts. Management, planning and decision-making require readily accessible information on stray cats. However, much of the existing data is not immediately useful for a geographic information system (GIS) in terms of format, content and explicit location information. Spreadsheets we obtained from a single large shelter in the Auckland region. They contained records of stray cat pickups and admissions for an entire year (n = 8573) of which 56.4% (n = 4834) contained data that could be processed to derive relevant spatial information. The resulting data consisted of identified roads and areas of Auckland where the stray cats were found. Published census databases and shapefiles were matched with the data to build a GIS of stray cats. Global and local regression analysis was employed to discover spatial distribution characteristics including the identification of areas with relatively high and low concentrations of stray cats and to explore relationships between socioeconomic condition and stray cat density. Significant clustering is more evident in South Auckland than elsewhere in the region. Specific geographical information is valuable, not only for understanding population dynamics of stray cats, but also to allow spatial and temporal targeting of resources to minimise their impact and promote responsible ownership.
Keywords:Stray cat, Geocoding, Spatial distribution, Hot spot analysis, Exploratory GIS, GWR
ANZSRC Field of Research:090903 Geospatial Information Systems
NOTICE: this is the author’s version of a work that was accepted for publication in Applied Geography. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Nurse Education Today, doi:10.1016/j.apgeog.2011.11.011
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