Local indicators for categorical data : Impacts of scaling decisions
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When the geographic distribution of landscape pattern varies, global indices fail to capture the spatial nonstationarity within the dataset. Methods that measure landscape pattern at a spatially local scale are advantageous, as an index is computed at each point in the dataset. The geographic distribution of local indices is used to discover spatial trends. Local indicators for categorical data (LICD) can be used to statistically quantify local spatial patterns in binary geographic datasets. LICD, like other spatially local methods, are impacted by decisions relating to the spatial scale of the data, such as the data grain (p), and analysis parameters such as the size of the local neighbourhood (m). The goal of this article is to demonstrate how the choice of the m and p parameters impacts LICD analysis. We also briefly discuss the impacts spatial extent can have on analysis; specifically the local composition measure. An example using 2006 forest cover data for a region in British Columbia, Canada where mountain pine beetle mitigation and salvage harvesting has occurred is used to show the impacts of changing m and p. Selection of local window size (m = 3,5,7) impacts the prevalence and interpretation of significant results. Increasing data grain (p) had varying effects on significant LICD results. When implementing LICD the choice of m and p impacts results. Exploring multiple combinations of m and p will provide insight into selection of ideal parameters for analysis. © / Canadian Association of Geographers / L'Association canadienne des géographes.
Long , J A , Nelson , T A & Wulder , M A 2010 , ' Local indicators for categorical data : Impacts of scaling decisions ' Canadian geographer-Geographe canadien , vol 54 , no. 1 , pp. 15-28 . DOI: 10.1111/j.1541-0064.2009.00300.x
Canadian geographer-Geographe canadien
© 2010. Canadian Association of Geographers / L'Association canadienne des géographes. This is the accepted version of the following article: Local indicators for categorical data: Impacts of scaling decisions Long, J. A., Nelson, T. A. & Wulder, M. A. 1 Jan 2010 In : Canadian geographer-Geographe canadien. 54, 1, p. 15-28 , which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/j.1541-0064.2009.00300.x/abstract
Support for this research was provided by NSERC and the Government of Canada through the Mountain Pine Beetle Program, a three-year, $100 million program administered by Natural Resources Canada, Canadian Forest Service.
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