INTELLIGENT GENERALISATION OF URBAN ROAD NETWORKS. Alistair Edwardes and William Mackaness

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1 INTELLIGENT GENERALISATION OF URBAN ROAD NETWORKS Alistair Edwardes and William Mackaness Department of Geography, University of Edinburgh, Drummond Street, EDINBURGH EH8 9XP, Scotland, U.K. Tel /8163: BIOGRAPHY Alistair Edwardes is a geography graduate, who completed an MSc in GIS at Edinburgh in 1997 and then worked on an ESRC funded project on data compression of census boundary data. He is currently on the ESPRIT funded AGENT project exploring intelligent mapping technologies and preparing a PhD on map generalisation. William Mackaness is a lecturer in geography at the University of Edinburgh. His key areas of research are in autonomous design in automated mapping and the application of agent based methodologies in geography. INTRODUCTION The paper reports on results produced in the area of cartographic generalisation of urban form. Specifically, the characterisation and generalisation of an urban street network. A number of researchers have explored ways of automating the reduction in level of detail of road networks as part of the automated map generalisation process. The broad context of this research is illustrated in Figure 1 - namely to reduce detail at changing scales, deriving multiple themed data at a range of scales from a single detailed database. Figure 1: (a) 1: (b) 1: (Copyright of the IGN). This research reports on a technique that reduces the level of detail typically found in urban regions whilst retaining the essential defining qualities of a road network; qualities such as those defined in (Mackaness and Beard 1993; Mackaness and Mackechnie 1999). 1

2 A range of approaches have been proposed that reduce the complexity of the network whilst retaining its connectivity. These have included ranking of importance based on line of sight (Mackaness 1995) building on the work of (Penn 1993), use of graph theory and attribute data (Thomson and Richardson 1995), and techniques based on the 'continuity' of the line (Thomson and Richardson 1999). In all the approaches the representation of the network is considered as consisting of one of two types of entity. Either collections of areas (urban blocks), e.g Peng and Muller (1996) and Ruas (1999), produced from cycles of roads or collections of lines (roads), e.g. Thomson and Richardson (1999) and Reynes (1997). These two types of phenomena provide dual representations of the network, each with their own characteristics advantageous for generalisation. This research follows these representations, but deals with both representations simultaneously. METHODOLOGY This research builds on the work of Thomson and Richardson (1999) who developed a technique for ranking the importance of a road based on its continuous form. Figure 2 illustrates this idea for an abstract set of lines. When such lines are viewed as roads, those that are short, discontinuous or cul-de-sacs are less important to the definition of the essential patterns and connections of the city in the way that the arterial roads are. Figure 2: The role of lines in defining the essential pattern of the city, the darker lines represent the lines exhibiting the property of 'good continuity'. The technique used by Thomson and Richardson (1999) was to group roads in terms of the perceptual property of 'good continuation'. That is, by the concatenation of lines into chains of lines that do not branch and for which, at any crossing point of two or more lines, the continuation of any of line is the best approximation of a straight line with respect to the other lines of the crossing. In their approach the continuous chains, termed 'strokes', generated by this grouping process where also sorted by semantic similarity and then ranked in order of importance, based on semantics and length. The generalisation of the network was performed by the successive attenuation of the strokes in order of the ranking, in such a way as to ensure that the network remained connected at all times. Their approach produced some interesting results (Figure 3). 2

3 Figure 3. Network Generalisation using the good continuation method of Thomson and Richardson (1999 pp. 158) source data at 1:20,000. Highlighted roads represent network at 1:200,000 The authors admitted that the approach could be improved - for example in the removal of unwanted 'hanging' streets generated by the process. The algorithm reported in this paper uses the process of grouping described by Thomson and Richardson, and enriches the approach by describing a method to enforce symmetry in the concatenation of lines into a chain. However, unlike the approach of Thomson and Richardson the generation of chains is used here as a method of analysis, a measure of the network, rather than as an end in itself. This is done since, it is argued in this paper, as a technique for generalisation of the network, using the continuous lines on their own will inevitably generate unwanted 'hanging' streets and also provides no obvious way to link essential cartographic constraints to the generalisation process. Instead, the dual of the network, a surface that partitions the network into areas, was generated from the cycles of roads and this used as the primary data structure for input into the algorithm. Generalisation of this structure was by the aggregation of adjacent cycles, when one of the cycles was less than the minimum perceptible area. This allowed the cartographic constraint for minimum levels of detail to be linked to the algorithm. Aggregation in this manner, with a couple of significant special cases, is guaranteed not to disconnect the network; the proof is detailed in the paper. This satisfies the cartographic constraint that the network must remain connected and also that the cyclical nature of an urban network is maintained. The process of aggregation was directed using the information obtained from the analysis of 'good continuation'. Where an area was too small, it was removed by aggregating it with one of its neighbouring areas, the selection of which was made by considering each of the common boundaries between a neighbour and the area. The neighbour chosen was the one with the common boundary that had the weakest 'stroke' value. This meant that the most important strokes were preserved during the generalisation process and hence that global contextual information for the entire network was integrated into the local processing for individual cycles. Graph theoretic techniques where used in order to ensure that every area was visited in an appropriate order and hence ensure that the aggregation process was applied in a consistent manner. A weighted graph was created, where the links represented the adjacency relationships between areas, the nodes, and the weights of the links was determined from the stroke values of the boundaries between adjacent areas. A 3

4 minimal spanning tree algorithm was used to traverse every node of this tree, areas where aggregated when they were found to be less than the minimum size. Without aggregation, the approach of using a minimal spanning tree on an adjacency graph weighted in this way also provided a novel and valuable technique for the characterisation of urban forms. The algorithm has been implemented on Laser Scan's LAMPS2 GIS system (Hardy 1999). The combined use of 'strokes' and MST has enabled the close control of cul-de-sacs and better retained the essence of the road network. Figure 4 shows the stages of the processing of the network and describes the results for generalisation between scales of 1:15000 and 1: a) b) c) d) Figure 4. a) The ungeneralised city. b) The city limit is defined and the strokes analysed. c) The partitions are generated within the city limit. d) the partitions are generalised. (Copyright of the IGN). 4

5 Figure 5: Road network displayed at 1:50,000; before (left) and after (right) generalisation (Copyright of the IGN). CONCLUSION The technique represents a significant contribution to the fields of map generalisation and to the study and visualisation of urban structure. It improves on the sorts of visual problems raised by (Mackaness and Mackechnie 1999) and refines the results achieved by Thomson and Richardson (1999). This sort of research contributes to research into contextual generalisation (Ruas 1998), and is part of the research into more intelligent methods of automated map generalisation (Lamy et al. 1999). REFERENCES Hardy, P. (1999), Active Object Techniques for Production of Multiple Map and GeoData Products for a Spatial Database. Proceedings of the19th International Cartographic Conference, (Ottawa), Lamy, S., Ruas, A., Demazeau, Y., Baeijs, C., Jackson, M., Mackaness, W., and Weibel, R. (1999), AGENT Project: Automated Generalisation New Technology. Proceedings of thepaper presented at the 5th EC-GIS Workshop, Stresa Italy. Mackaness, W. A. (1995), Analysis of Urban Road Networks to Support Cartographic Generalization. Cartograpy and Geographic Information Systems, 22, Mackaness, W. A., and Beard, M. K. (1993), Use of Graph Theory to Support Map Generalization. Cartography and Geographic Information Systems, 20, Mackaness, W. A., and Mackechnie, G. (1999), Detection and Simplification of Road Junctions in Automated Map Generalization. GeoInformatica, 3, Molenaar, M An Introduction to the Theory of Spatial Object Modelling for GIS: (London:Taylor and Francis). Peng, W. and Muller, J. C., (1996) A Dynamic Decision Tree Structure Supporting Urban Road Network Automated Generalisation. The Cartographic Journal. Vol 33 (1) pp Penn, A. (1993), Intelligent Analysis of Urban Space Patterns: Graphical Interfaces to Precedent Databases for Urban Design. Proceedings of theauto Carto 11 Proceedings, (Minneapolis),

6 Reynes J.L. (1997) Selection du reseau routeir en vue de la selection. DESS Report, University Paris VI, COGIT, Ruas, A. (1998), Constraint Modelling to Automate Urban Generalisation Process. Proceedings of the8th International Symposium on Spatial Data Handling, (Canada), Ruas A. (1999) Modèle de généralisation de données géographiques à base de contraintes et d autonomie. Thèse de doctorat, Université de Marne-la-Vallée, avril Thomson, R. C., and Richardson, D. E. (1995), A Graph Theory Approach to Road Network Generalization. Proceedings of theproceedings of the 17th ICA Meeting, (Barcelona Spain), Thompson, R. C, and Richardson, D. E. (1999) The 'Good Continuity' Principle of Perceptual Organisation applied to the Generalisation of Road Networks. Proceeding of the 19th International Cartographic Conference, Ottwawa pp