1 Spatial Modelling of the Terrestrial Environment - Georeferencial

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210 Spatial Modelling of the Terrestrial Environment level, the null hypothesis cannot be rejected and the spatial distribution of x in terms of n is considered to be random. If I < 0.0 and z I is significant, on the other hand, it may be concluded that there is no significant spatial autocorrelation of n in terms of x (i.e., x exhibits significant spatial dispersion). Finally, if I > 0.0 and z I is significant, n exhibits significant spatial autocorrelation in terms of x (i.e., x exhibits significant spatial clustering, such that similar values of x are likely to be found close to one another). In the analysis that follows, a standard 1 weighting and a significance level of 0.001 are used. d 10.3 Study Area and Data Pre-Processing The town of Orpington in the London Borough of Bromley is used to evaluate the builtform connectivity approach outlined above. Orpington contains numerous different types of urban land use within a relatively small area. These include several different residential districts, ranging in age from turn-of-the-century terraced houses (1900s), through semi-detached developments built during the 1920s/1930s and 3–4-storey blocks of flats constructed in the 1960s/1970s, to modern townhouses (1980s) and detached 3–4 bedroom residences (1990s). There are also several major schools in the area, as well as a large hospital complex. The existence of a 1:25 000 scale land-use map of the town and its environs, generated for a previous study (Barnsley and Barr, 1996), allows an objective evaluation of the analyses performed here. The primary dataset employed in this study isa1mspatial resolution land-cover map that has been generated from Ordnance Survey (OS) 1:1250 scale Land-Line.93+ digital map data (Figure 10.5). This covers an area of 2 km × 2 km. The original Land-Line.93+ data were provided by the OS as sixteen 500 m × 500 m tiles in standard National Transfer Format (NTF). Each tile contains vector data for up to 32 feature codes. These are processed using ARC/INFO to select the feature codes of interest from each tile, to mosaic the resultant data together into a single 2 km × 2 km coverage, and generate a topologically structured vector dataset. Each of the polygons in the resultant coverage is assigned a land-cover label drawn from the set ROAD, BUILT, TREE (representing areas of continuous, extensive tree cover), OPEN SPACE (representing all other extensive areas of non-wooded open space) and WATER (representing all open water bodies, such as lakes and rivers). This coverage is then transferred to the Grid module of ARC/INFO and resampled to a spatial resolution of 1 m to generate a raster land-cover map, before being exported from ARC/INFO and converted to the image format used by SAMS. Based on the data shown in Figure 10.5, an XRAG is compiled for the spatial, topological relation containment and the morphological properties area, compactness ( perimeter2 ) 4πarea and geographical centroid. Containment is selected as it is the key spatial relation used by Kruger (1979a) to recognize built-form constellations. The three morphological properties, outlined above, provide a means by which to evaluate region (building) size and shape. 10.4 Recognition of Built-Form Constellations To identify the built-form constellations present in Figure 10.5, we must first analyse the containment relation for the corresponding node set in the XRAG. Figure 10.6 shows a
Characterizing Land Use in Urban Systems via Built-Form Connectivity Models 211 1980s 1970s 1930s 1990s Schools 1980s Hospital complex 1920s–1930s 1970s 1900s Figure 10.5 2 km × 2 km raster (1 m) land-cover dataset centred on Locksbottom in the town of Orpington, London Borough of Bromley, annotated with examples of the principal land-use categories in the study area. The data were produced from selected vector features derived from Ordnance Survey 1:1,250 Land-Line.93+ digital map data c○ Crown Copyright Ordnance Survey visualization of the containment graph for the data presented in Figure 10.5, generated using the Vgraph software incorporated in SAMS. It is clear from this that there is a pronounced structure to the containment graph, such that distinct, localized subgraphs 2 can be visually distinguished. These correspond to Kruger’s built-form constellations (Kruger, 1979a). 2 Strictly speaking, a graph Ḡ = ( ¯ N, ¯R) is a subgraph of the graph G = (N, R) iff ¯N ⊆ N and ¯R ⊆ R (Piff, 1992). In the context of the current discussion, however, a subgraph is defined more loosely as a collection of nodes and edges that form a distinct, localized, spatial pattern within a larger graph.
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Spatial Modelling of the Terrestria
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Copyright C○ 2004 John Wiley & So
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Contents List of Contributors Prefa
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Contributors Jonathan L. Bamber Sch
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List of Contributors xi George Perr
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xiv Preface in theory and applicati
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2 Spatial Modelling of the Terrestr
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Editorial: Spatial Modelling in Hyd
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Editorial: Spatial Modelling in Hyd
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2 Modelling Ice Sheet Dynamics with
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Modelling Ice Sheet Dynamics by Sat
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36 Spatial Modelling of the Terrest
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4 Using Coupled Land Surface and Mi
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Coupled Land Surface and Microwave
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100 Spatial Modelling of the Terres
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Editorial: Terrestrial Sediment and
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Editorial: Terrestrial Sediment and
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6 Remotely Sensed Topographic Data
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Remotely Sensed Topographic Data fo
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7 Modelling Wind Erosion and Dust E
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Modelling Wind Erosion and Dust Emi
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8 Near Real-Time Modelling of Regio
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Page 204 and 205: PART III SPATIAL MODELLING OF URBAN
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Page 232 and 233: 11 Modelling the Impact of Traffic
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Page 248 and 249: PART IV CURRENT CHALLENGES AND FUTU
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268 Index Bi-spectral InfraRed Dete
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270 Index floodplain maps, UK 92 fl
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272 Index Long-Term Ecological Rese
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274 Index snow depth measurement ne
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276 Index urban land use in remotel
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1 Spatial Modelling of the Terrestrial Environment - Georeferencial

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