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Comber, A; Brunsdon, CF; Charlton, M; Harris, P (2017)
Publisher: Taylor & Francis
Languages: English
Types: Article
Subjects:
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.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 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: http://due.esrin.esa.int/files/GLOBCOVER_Products_ Description_Validation_Report_I2.1.pdf Brunsdon, C.F., Charlton, M. and Harris, P. 2016. Geographically Weighted Cross-Tabulation, https: //github.com/chrisbrunsdon/gwxtab, available 2 July 2016.
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    • 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.
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    • 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.
    • Pontius Jr, R.G. and Santacruz, A. 2014. “Quantity, exchange and shift components of difference in a square contingency table”. International Journal of Remote Sensing 35 (21): 7543-7554.
    • Pontius Jr, R.G. and Santacruz, A. 2015. Package 'diffeR': Metrics of Difference for Comparing Pairs of Maps. https://cran.r-project.org/web/packages/diffeR/diffeR.pdf [available 18 July 2016] Propastin, P. 2012. “Modifying geographically weighted regression for estimating aboveground biomass in tropical rainforests by multispectral remote sensing data”. International Journal of Applied Earth Observation and Geoinformation 18: 82-90.
    • Tobler, W.R. 1970. “A computer movie simulating urban growth in the Detroit region. Economic Geography*, 46(sup1): 234-240.
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  • Discovered through pilot similarity algorithms. Send us your feedback.

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