Modelling Geographical Systems pp 33-44 | Cite as
Using Local Statistics for Boundary Characterization
- 286 Downloads
Abstract
Geographic boundary analysis involves the definition and evaluation of boundaries in spatial patterns (see Jacquez et al., 2000, for a review). In this paper, we are concerned with the evaluation of polygon boundaries which are recorded in choropleth maps of thematic properties. In such situations, the polygons may have been determined independently of the mapped phenomena (Mark and Csillag, 1989). Further, the same set of polygons may be used to display a number of different variables. For example, in forestry, the same set of photo-interpreted polygons may be used to display mean dominant height, stand density, species composition, etc. Thus, when we view a single map of a given variable, it is possible that adjacent polygons may have the same value (see Figure 1 for an example). In such circumstances, we may wish to determine how distinctive any given boundary is relative to other boundaries in the mapped pattern. We characterize this as the strength (certainty) of the cartographic boundary.
Keywords
Strong Boundary Dominant Height Boundary Strength Exploratory Spatial Data Analysis Real LandscapePreview
Unable to display preview. Download preview PDF.
References
- Amrhein, C.G. and H. Reynolds (1996), ‘Using spatial statistics to assess aggregation effects.’ Geographical Systems, 2, 143–158.Google Scholar
- Amrhein, C. and H. Reynolds (1997), ‘Using the Getis statistic to explore aggregation effects in Metropolitan Toronto Census data.’ The Canadian Geographer, 41 (2), 137–149.CrossRefGoogle Scholar
- Boots, B. and M. Tiefelsdorf (2000), ‘Global and local spatial autocorrelation in bounded regular tessellations.’ Journal of Geographical Systems, 2 (4), 319–348.CrossRefGoogle Scholar
- De Groeve, T. and K. Lowell (2000), ‘Boundary uncertainty assessment from a single forest type map.’ Photogrammetric Engineering and Remote Sensing, 67, 717–726.Google Scholar
- Derksen, C., M. Wulder, E. LeDrew and B. Goodison (1998), ‘Associations between spatially derived autocorrelation patterns of SSM/I-derived prairie snow cover and atmospheric circulation.’ Hydrological Processes, 12, 2307–2316.CrossRefGoogle Scholar
- Fortin, M-J. (1994), ‘Edge detection algorithms for two-dimensional ecological data.’ Ecology, 75, 956–965.CrossRefGoogle Scholar
- Fortin, M-J. and P. Drapeau (1995), ‘Delineation of ecological boundaries: comparison of approaches and significance tests.’ Oikos, 72, 323–332.CrossRefGoogle Scholar
- Fortin, M-J. and G. Edwards (2001), ‘Accuracy issues related to the delineation of vegetation boundaries.’ In C. Hunsaker, M. Goodchild, M. Friedl and T. Case (eds.), Spatial Uncertainty in Ecology. Springer-Verlag, Berlin, pp. 158–174.CrossRefGoogle Scholar
- Fortin, M-J., R.J. Olson, S. Ferson, L. Iverson, C. Hunsaker, G. Edwards, D. Levine, K. Butera and V. Klemas (2000), ‘Issues related to the detection of boundaries.’ Landscape Ecology, 15, 453–466.CrossRefGoogle Scholar
- Fotheringham, A.S. (1997), ‘Trends in quantitative methods I: Stressing the local.’ Progress in Human Geography, 21 (1), 88–96.CrossRefGoogle Scholar
- Fotheringham, A.S. (1999), ‘Guest editorial: local modelling.’ Geographical and Environmental Modelling, 3(1), 5–7.Google Scholar
- Fotheringham, A.S. (2000), ‘Context-dependent spatial analysis: a role for GIS?’ Journal of Geographical Systems, 2 (1), 71–76.CrossRefGoogle Scholar
- Fotheringham, A.S. and C. Brunsdon (1999), ‘Local forms of spatial analysis.’ Geographical Analysis, 31 (4), 340–358.CrossRefGoogle Scholar
- Getis, A. and J.K. Ord (1996), ‘Local spatial statistics: an overview.’ In P. Longley and M. Batty (eds.), Spatial Analysis: Modelling in a GIS Environment. Geoinformation International, Cambridge, pp. 261–277.Google Scholar
- Griffith, D.A. (1996), ‘Spatial autocorrelation and eigenfunctions of the geographic weights matrix accompanying geo-referenced data.’ The Canadian Geographer, 40, 351–367.CrossRefGoogle Scholar
- Griffith, D.A. and C.G. Amrhein (1997), Multivariate Statistical Analysis for Geographers. Prentice Hall, Upper Saddle River, N.J.Google Scholar
- Haines-Young, R. and M. Chopping (1996), ‘Quantifying landscape structure: a review of landscape indices and their application to forested landscapes.’ Progress in Physical Geography, 20 (4), 418–445.CrossRefGoogle Scholar
- Hargis, C.D., J.A. Bissonette and J.L. David (1997), ‘Understanding measures of landscape pattern.’ In J.A. Bissonette (ed.), Wildlife and Landscape Ecology: Effects of Pattern and Scale. Springer-Verlag, New York, pp. 231–261.CrossRefGoogle Scholar
- Hargis, C.D., J.A. Bisonette and J.L. David (1998), ‘The behavior of landscape metrics commonly used in the study of habitat fragmentation.’ Landscape Ecology, 13, 167–186.CrossRefGoogle Scholar
- Jacquez, G.M., S. Maruca and M-J. Fortin (2000), ‘From fields to objects: a review of geo-graphic boundary analysis.’ Journal of Geographical Systems, 2, 221–241.CrossRefGoogle Scholar
- Mark, D.M. and F. Csillag (1989), ‘The nature of boundaries on area-class’inaps.’ Cartographica, 26, 65–78.CrossRefGoogle Scholar
- Ord, J.K. and Getis, A. (1995), Local spatial autocorrelation statistics: distributional issues and an application.’ Geographical Analysis, 27, 286–306.CrossRefGoogle Scholar
- Turner, S.J., R.V. O’Neill, W. Conley, M.R. Conley and H.C. Humphries (1991), ‘Pattern and scale: statistics for landscape ecology.’ In M.J. Turner and R.H. Gardner (eds.), Quantitative Methods in Landscape Ecology: The Analysis and Interpretation of Landscape Heterogeneity. Springer-Verlag, New York, pp. 17–76.CrossRefGoogle Scholar
- Unwin, A. and Unwin, D. (1998), ‘Exploratory spatial data analysis with local statistics.’ Journal of the Royal Statistical Society: Series D (The Statistician),47(3), 415–421.Google Scholar
- Wulder, M. and B. Boots (1998), ‘Local spatial autocorrelation characteristics of remotely sensed imagery assessed with the Getis statistic.’ International Journal of Remote Sensing, 19(11), 2223–2231.CrossRefGoogle Scholar
- Wulder, M. and B. Boots (2001), ‘Local spatial autocorrelation characteristics of Landsat TM imagery of a managed forest.’ Canadian Journal of Remote Sensing, 27 (1), 67–75.Google Scholar