Analysis of visibility within urban landscapes (external and/or internal visibility) raises additional questions and problems. With buildings and streets represented as 2D polygons, rather than 3D grids, a first level of visibility analysis can be carried out – this kind of analysis is generally described as isovist analysis (Benedikt, 1979). Applications of such analysis include questions such as where to place CCTV cameras or security guards in order to ensure all points are visible from the selected locations. This in turn involves an NP-hard optimization problem (see further Section 7.1.4, Algorithms and computational complexity theory), that of determining the minimum number of such points to ensure complete coverage. The problem also extends in a natural manner to 3D environments.
Rana (2004b) has developed efficient heuristic procedures for solving such problems based on a combination of systematic evaluation of visibility from a fine grid of initial observer positions, and then reducing this set by a ranking rule (Ranking and Overlap Elimination, or ROPE procedure). By stochastically iterating this procedure using randomly selected observer positions (S-ROPE) the final count of observer points may be reduced and thus approach optimality. An ArcView add-on (Isovist Analyst) is available to perform this operation. The procedure is illustrated in Figure 6‑24 for an area of central London (Aldwych). A sample isovist region is shown in green (street areas), based on one of the initial test observer locations shown as a red dot symbol. The blue dotted box symbols indicate the 27 locations identified by the program using the S-ROPE algorithm with around 8000 starting points as the (approximately) optimal positions for surveillance cameras, ensuring 100% coverage. Restricting the initial solution set to medial axis regions determined by simple distance transform methods (as described in Section 4.4.2, Cost distance) shows an immediate further improvement of these results (Rana, 2006). It is interesting to consider how difficult it would be for architects, surveillance specialists or traffic monitoring units to perform such analysis manually.
Figure 6‑24 Isovist analysis, Street network, central London
Closely related to isovist analysis is the much broader topic known as space syntax. This research area was developed initially by Hillier et al. (1993) in the context of architectural design and movement behavior in small-scale regions of the built environment, but has been extended to apply to much larger urban areas. More details can be found at UCL Space Syntax Laboratory: http://www.bartlett.ucl.ac.uk/space-syntax/research
The earliest work in this field focused on so-called axial lines. These lines are defined as the longest visibility lines (or ‘lines of direct movement’) in the 2D public urban space. By analyzing the geometry of streets, open spaces and buildings the 1st, 2nd, 3rd etc. longest lines of visibility can (in principle) be identified and drawn. A set of such lines covering an entire area will contain many lines and many intersections of these lines. Together they define an ‘axial map’. The axial lines with the highest number of intersections are said to have high ‘connectivity’. Figure 6‑25 illustrates these basic elements using part of the streetscape of the town of Gävle, in Sweden. Here the axial lines with the highest connectivity are colored red, with the lowest being colored blue.
Figure 6‑25 Axial lines and connectivity
Source: Axwoman software, Dr B Jiang
A set of additional measures can be computed once a network of axial lines has been constructed. These include examining the hierarchy of axial lines, for example determining the number of other axial lines within a given number of steps (e.g. 3 steps) distance – this is known as the depth of the axial map. However, the computation of axial lines and maps is complex, slow and not an exact process. It also focuses on spatial elements that are not incorporated as standard objects within GIS datasets. For this reason Jiang and Claramunt (2002) proposed identifying ‘characteristic points’ within urban GIS data, and then using the connectivity of these key points to derive similar urban morphology measures to those produced using the space syntax approach. These characteristic points are relatively simple to identify (e.g. road intersections/network nodes, combined with additional vertices included to handle curved routes). Once identified it is then possible to construct a set of urban morphology measures that are strongly correlated with many of those produced using axial lines. The example in Figure 6‑25 was generated using Jiang’s Java‑based axial line software; ArcGIS extensions for urban morphology analysis (Axwoman) and axial line generation (Axialgen) are also available from Prof Jiang.
The concepts behind space syntax and isovist analysis are combined in Turner’s Depthmap software (Turner, 2004). This uses a similar approach to that of Rana (2004b) described in the preceding subsection (Section 6.3.3, Line of sight). First, a vector file (e.g. a DXF file) of the building or street layout to be analyzed is loaded. Then a fine grid of points/cells is overlain on the parts of the diagram or map that is to be analyzed. The program computes the Euclidean inter-visibility between each pair of points in the set (essentially a visibility graph) and from this can, for example, color code each cell in a raster view of the map according to the number of other points it is visible from (a form of global visibility ranking). With this starting point, a range of visibility and connectivity measures can be computed. Furthermore, using a similar starting point with a greedy algorithm, Depthmap can also automatically generate an axial map and related measures. Figure 6‑26 provides simple illustration of these concepts. This shows the internal space of an art gallery with its rooms and connecting doors. Locations that have the highest levels of overall visibility are colored red and yellow, and the computed axial lines, with color coding, are also displayed.
Figure 6‑26 Depthmap — Gallery space visibility map