Geographically weighted correspondence matrices for local error reporting and change analyses: mapping the spatial distribution of errors and change

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Comber, Alexis ; Brunsdon, Chris ; Charlton, Martin ; Harris, Paul (2016)

This letter describes and applies generic methods for generating local measures from the correspondence table. These were developed by integrating the functionality of two existing R packages: gwxtab and diffeR. They demonstrate how spatially explicit accuracy and error measures can be generated from local geographically weighted correspondence matrices, for example to compare classified and reference data (predicted and observed) for error analyses, and classes at times t1 and t2 for change analyses. The approaches in this letter extend earlier work that considered the measures derived from correspondence matrices in the context of generalized linear models and probability. Here the methods compute local, geographically weighted correspondence matrices, from which local statistics are directly calculated. In this case a selection of the overall and categorical difference measures proposed by Pontius and Milones (2011) and Pontius and Santacruz (2014), as well as spatially distributed estimates of kappa coefficients, User and Producer accuracies. The discussion reflects on the use of the correspondence matrix in remote sensing research, the philosophical underpinnings of local rather than global approaches for modelling landscape processes and the potential for policy and scientific benefits that local approaches support.
  • References (13)
    13 references, page 1 of 2

    Bicheron, P., Defourny, P., Brockmann, C., Schouten, L., Vancutsem, C., Huc, M., Bontemps, S., Leroy, M., Achard, F., Herold, M., Ranera, F., Arino, O. 2008. Glob-Cover: Products Description and Validation Report, 18, Toulouse, France. URL: Description_Validation_Report_I2.1.pdf Brunsdon, C.F., Charlton, M. and Harris, P. 2016. Geographically Weighted Cross-Tabulation, https: //, available 2 July 2016.

    Campbell, J. 1981. “Spatial correlation effects upon accuracy of supervised classification of land cover”.

    Photogrammetric Engineering of Remote Sensing 47: 355-364.

    Comber, A., Mooney, P., Purves, R.S., Rocchini, D. and Walz, A. 2016a. “Crowdsourcing: It Matters Who the Crowd Are. The Impacts of between Group Variations in Recording Land Cover”. PlosONE 11(7): e0158329 Khormi, H. M. and Kumar, L. 2011. “Modeling dengue fever risk based on socioeconomic parameters, nationality and age groups: GIS and remote sensing based case study”. Science of the Total Environment 409(22): 4713-4719.

    Lesiv, M., Moltchanova, E., Schepaschenko, D., See, L., Shvidenko, A., Comber, A. and Fritz, S. 2016.

    Remote Sensing 8: 261 doi:10.3390/rs8030261 Loveland, T.R., Reed, B.C., Brown, J.F., Ohlen, D.O., Zhu, Z., Yang, L., Merchant, J.W., 2000.

    “Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data”. International Journal of Remote Sensing, 21 (6-7): 1303-1330.

    McGwire, K. C., and Fisher, P. (2001). “Spatially variable thematic accuracy: Beyond the confusion matrix”. In C. T. Hunsaker, M. F. Goodchild, M. A. Friedl, & T. J. Case (Eds.), Spatial uncertainty in ecology: Implications for remote sensing and GIS applications (pp. 308-329). New York: SpringerVerlag.

    Fritz, S., McCallum, I., Schill, C., Perger, C., See, L., Schepaschenko, D., van der Velde, M., Kraxner, F. and Obersteiner, M. 2012. “Geo-Wiki: An online platform for land cover validation and the improvement of global land cover”. Environmental Modelling and Software 31: 110-123.

    Pontius Jr, R.G. and Millones, M., 2011. Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32(15), pp.4407-4429.

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