Visualising collective spatial knowledge in GIS

Marco Salatino and Massimo Zupi Department of Territorial Planning, University of Calabria

Via P. Bucci Cubo 46B 87036 Rende, Italy {m.salatino, m.zupi}@unical.it

SUMMARY The study proposes a methodology in visualising collective spatial knowledge by means of a GIS tool. We have conducted a survey, in which 188 engineering undergraduates students were asked to list urban places they think are most representative of the image of the city. Then, we georeferenced these data in GIS and produced a synthetic map of collective spatial knowledge. This way, we avoid using of sketch maps which usually requires a large amount of work to digitalise graphic their objects. Finally, we show the graphic results, explaining why the less perceived places are all clustered in the east part of the study area. KEYWORDS: Cognitive maps, Data capture, Social spatial knowledge INTRODUCTION Cognitive maps are the most powerful tool in exploring social spatial knowledge, since they describe the way in which people perceive and memorise the environment. Edward C. Tolman (1948) originally coined the term ‘cognitive maps’ to explain rats’ ability of looking for food in a maze. Reginald G. Golledge and Robert J. Stimson (1996) stated that cognitive maps are the most basic form of memory pattern resulting from a process of spatial cognition, the recognition of internal experiencing of objects, and their spatial relationship, which, in turn, results from a process of environmental cognition (according to Horan, 1999). Just like in complex systems, the social spatial knowledge is more than the simple sum of individual spatial knowledge, since it is partly given by the way in which one experience the environment, and partly given by interpersonal cultural exchanges. Thus, exploring data from cognitive maps represents an opportunity to investigate not also people’s spatial perception, but also to make light of spatial knowledge’s transmission. Cognitive maps became popular in 1960s, when Kevin Lynch realised that they could be usefully used in urban planning. In his seminal work ‘The Image of the City’ (1960), Lynch used cognitive maps to represent Boston, New Jersey City and Los Angeles, in order to answer to the question of “what make some places better remembered than others”. The so-called sketch tests (or sketch maps) are the most immediate way of obtaining useful cognitive maps. A sketch map is just a schematic map drawn by the memory of a person. Thus, graphical signs in sketch map may furnish not only information about the drawer’s own cognitive map, but also about places that he or she easily remembers. In addition, a number of previous studies have extended this concept by introducing a wide range of additional tests, such as spatial cued response tests, fill-in-the-blank tests and so on (e.g. Kitchin, 1996). After K. Lynch, a number of studies have been accomplished to extracting information about social spatial knowledge from sketch maps (see for example Kitchin, 1995; Horan, 1999; Pinheiro, 1998; Kim, 2001). Such a methodology requests interpretation of sketch maps by researchers. Interpreting sketch maps is not easy, since each respondent uses an own way in representing the same place by means of different types of signs. Moreover, cognitive maps quality is affected by errors in representation. Finally, researchers have to digitalise and elaborate a huge amount of heterogeneous data, since each object can be represented with different signs. This task usually requires a lot of work, unless researchers are well set up with hardware made on this specific purpose, as in the method proposed by Blaser (2000, 2002).

In this paper, we suggest a novel methodology in order to replace sketch maps with simple listing of landmarks and representing them by means of a GIS tool. Using a GIS in extracting cognitive maps from lists should permit to kill two birds with one stone. On the one hand, data acquisition process is much more simple and objective. On the other hand, we are able to generate map of the social spatial knowledge automatically. FROM LISTS TO MAPS: THE PROPOSED METHODOLOGY Only three simple stages make up the proposed methodology. First, respondents are asked to list a fixed number of places they think are more significant in the study area. Joining all data in the lists, one obtains a single list with two information for each quoted place, namely frequency of quotation and average position in the lists. It is possible to synthesise these two information in a single index. For a given place, such an index increases when both the frequency of quotation and the ranking of the place in the lists rise. Second, each place quoted in the lists is georeferenced, with the above mentioned index as an attribute. This is also the most laboured stage, since one has to locate each single place by identifying its exact position on the map. Third, these data are elaborated by means of a GIS tool, in order to obtain a cartographic representation of the social cognitive map for the study area. To this end, it is necessary to have two maps. We propose drowning the former by interpolating the index values, in order to turn punctual values into bidimensional data. The latter measures all distances between every point of the study area and the places quoted by respondents. Merging these two maps, it is possible to obtain a cartographic representation of the social cognitive map for the study area. Then, we can draw such map by representing the places’ degree of perception with different colours, or by means of a contour map, as it is shown in the following paragraph. It must be emphasised that each single step in the procedure can be easily performed by means of common GIS softwares. METHODOLOGY APPLICATION We tested the proposed methodology in Cosenza-Rende, an urban area of approximately 110,000 inhabitants in Southern Italy. The study area measures about 5 x 10 km and includes the city centre and some suburbs (fig. 1). Cosenza-Rende’s population rapidly grew during past decades, because of the foundation of the University of Calabria, which today is attended by approximately 30,000 students.

Figure 1: Cosenza-Rende’s conurbation.

We collected cognitive maps from a sample of 188 students of the University. This is not the first time in which students are used in acquiring spatial cognitive information. For example, F. Saarinen and C. L. McCabe collected 3,568 maps drawn by first-year geography students (according to Horan, 1999); M. Horan (1999) collected 219 maps of Pennsylvania State University’s Library from 12 classes; R.M. Kitchin (1996) proposed several sketch tests to 279 geography undergraduates in order to explore their spatial knowledge of Swansea, Wales. The respondents used in the investigation consisted of second year engineering undergraduates. Twenty of them were from Cosenza-Rende, the others had been resident in the study area for approximately 20 months. Respondents quoted 133 landmarks that we divided in six categories embracing open spaces, religious, transportation facilities, monuments, and commercial uses. In order to apply the proposed methodology, the available cartography has been converted in a regular squared grid with cells of 10 m per side (raster representation). Each single site (at least quoted once in lists) was located on the map, by specifying each pair of co-ordinates manually. Then, we obtain a set of output maps by applying the procedure previously described. The distance map (fig. 2) shows distances from all 133 places quoted in lists by the respondents.

Figure 2: Distances from places quoted in survey results.

Figure 3: Representation of the collective spatial knowledge (the real city is depicted in black).

Subsequently, the social spatial knowledge has been represented in fig. 3, where darker shades of grey identify more perceived places. To make the output more readable, we drawn a building layer (in black) on this social spatial knowledge map. Interpreting the results is not possible without introducing some consideration about the urban structure of the studied area. First, unless the considered area is almost urbanised without interruption, it is not homogeneous, since we can detect two different cities. Cosenza, in the south, is more compact and has a well structured urban fabric. On the opposite, Rende is the Cosenza’s northern expansion, with a considerably lower residential density and a much more fragmented urban fabric. Moreover, since Cosenza has an ancient historical core, many landmarks are located in its centre. Because Rende has only developed along two long parallel axes, people have to travel more to reach destinations, thus, pedestrian trips are strongly inhibited by distances. As this particular modal split of passenger transport, people’s most cited landmarks in Rende are car-oriented sites, like shopping centres or petrol stations. Contrariwise, people living in Cosenza often use to walk, because destinations are within walking distance. In addition, local authorities try to discourage driving by means of parking and traffic restrictions, especially in the historical centre of the city. As a consequence, respondents living in Cosenza prefer to cite pedestrian-oriented places, like squares and parks. Finally, we classified developed area in two classes: less and more perceived zones. To this end, it is essential to define developed or built area. Such a question involves the complex and much discussed problem of identifying city’s boundaries. Borruso (2003), for instance, takes in account road network’s density to accomplish this task. We identify urban boundaries by only considering gross population density. Fixing an arbitrary threshold (1000 inhabitants/squared kilometre), it is possible to discriminate between developed and suburban areas, see fig. 4. Once developed areas have been extracted from the study area, we divide them into less and more perceived zones. Again, we perform this elaboration by fixing an arbitrary threshold, say 40 points contour line, in the ‘collective spatial knowledge’ map. Areas above this contour line become ‘more perceived areas’, while the residuary developed area were classified as ‘less perceived’ area. The result of this elaboration is shown in fig. 4.

Figure 4: More (in black) and less (in grey) perceived urban areas in Cosenza-Rende’s conurbation.

CONCLUSION Sketch tests could aid cognitive map researchers in exploring the perceived urban space. Unfortunately, digitising sketch maps and comparing sketch data requires is difficult, since it requires a large amount of work in interpreting signs drawn by respondents. Despite the preliminary nature of this study, the obtained results demonstrate that cognitive maps can be computed by means of GIS

tools. Moreover, visualising social spatial knowledge by means of GIS is a powerful decision support systems for planners, geographers and environmental psychologists. For example, using cognitive maps, planners can identify areas in need of renovation. Since there are several types of urban decay, cognitive maps are useful in detecting urban decay, when decay is not either physic or visible. Less perceived areas in cognitive maps are poor in landmarks, then they are less attractive for new residents. Further developments will include adapting the sampling process to describe different situations, and developing new indicators. The next stage of the process is to adding other questions to the questionnaire. For example, we may ask respondents to describe the way in which they move across the urban area. Furthermore, we may diversify study area’s dimension, in order to test the proposed methodology at different scales. Finally, it is possible to focus attention to the natural environment, not also urbanised space. Indeed, the European Landscape Convention (signed in Florence, 2000) look at the landscape as perceived by people and local communities. According to this statement of the question, the proposed methodology is able to provide a large amount of data assessing the perception of the natural landscape. BIBLIOGRAPHY Blaser A.D., Sester M., Egenhofer M.J., 2000, Visualization in an Early Stage of the Problem Solving

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