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    Geographically weighted methods and their use in network re-designs for environmental monitoring


    Harris, Paul and Clarke, Annemarie and Juggins, Steve and Brunsdon, Chris and Charlton, Martin (2014) Geographically weighted methods and their use in network re-designs for environmental monitoring. Stochastic Environmental Research and Risk Assessment, 28. pp. 1869-1887. ISSN 1436-3240

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    Abstract

    Given an initial spatial sampling campaign, it is often of importance to conduct a second, more targeted campaign based on the properties of the first. Here a net- work re-design modifies the first one by adding and/or removing sites so that maximum information is preserved. Commonly, this optimisation is constrained by limited sampling funds and a reduced sample network is sought. To this extent, we demonstrate the use of geographically weighted methods combined with a location-allocation algorithm, as a means to design a second-phase sampling campaign in univariate, bivariate and multivariate contexts. As a case study, we use a freshwater chemistry data set covering much of Great Britain. Applying the two-stage procedure enables the optimal identification of a pre- specified number of sites, providing maximum spatial and univariate/bivariate/multivariate water chemistry informa- tion for the second campaign. Network re-designs that account for the buffering capacity of a freshwater site to acidification are also conducted. To complement the use of basic methods, robust alternatives are used to reduce the effect of anomalous observations on the re-designs. Our non-stationary re-design framework is general and provides a relatively simple and a viable alternative to geostatistical re-design procedures that are commonly adopted. Particu- larly in the multivariate case, it represents an important methodological advance.

    Item Type: Article
    Keywords: Non-stationarity; Summary statistics; PCA; Location-allocation; Robust; � Acidification
    Academic Unit: Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG
    Item ID: 5896
    Identification Number: https://doi.org/10.1007/s00477-014-0851-1
    Depositing User: Prof. Chris Brunsdon
    Date Deposited: 21 May 2015 10:27
    Journal or Publication Title: Stochastic Environmental Research and Risk Assessment
    Publisher: Springer Verlag
    Refereed: Yes
    URI:

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