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    Estimating probability surfaces for geographical point data: An adaptive kernel algorithm


    Brunsdon, Chris (1995) Estimating probability surfaces for geographical point data: An adaptive kernel algorithm. Computers and Geosciences, 21 (7). pp. 877-894. ISSN 0098-3004

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    Abstract

    The statistical analysis of spatially referenced information has been acknowledged as an important component of geographical data processing. With the arrival of GIS there has been a need to devise statistical methods that are compatible with, and relevant to, GIS-based methodologies. Here an algorithm is presented which estimates a “risk surface” from a set of point-referenced events. Such a surface may be viewed as an object embedded in three-dimensional space, or as a contour map. In addition to this view, it is possible to incorporate these surfaces into a broader based GIS framework, allowing the mapping of these patterns in conjunction with other data, overlay analysis, and spatial query. The technique is adaptive, in the sense that parameters which control the surface estimation are adjusted over geographic space, allowing for local variations in point pattern characteristics. The paper is concluded with an example based on probabilistic mapping using data taken from Californian Redwood seedling data.

    Item Type: Article
    Keywords: Density; estimation; Spatial analysis; Kernel; GIS;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG
    Item ID: 6183
    Depositing User: Prof. Chris Brunsdon
    Date Deposited: 10 Jun 2015 14:11
    Journal or Publication Title: Computers and Geosciences
    Publisher: Elsevier
    Refereed: Yes
    URI:

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