Spatial analysis of visual environmental information in urban landscapes: attempting to detect homogeneous areas through a GIS

Transcription

1 Sustainable Development and Planning II, Vol Spatial analysis of visual environmental information in urban landscapes: attempting to detect homogeneous areas through a GIS A. Tsouchlaraki 1 & G. Achilleos 2 1 Hellenic Open University, Greece 2 National Technical University of Athens, Greece Abstract The spatial analysis of an urban landscape presents many difficulties due to the various elements that have to be recorded, to the multiple interactions between them, and also due to the fact that these data are spatially distributed and some times temporal. A subdivision of an urban landscape to visually homogeneous areas would be very useful in understanding problems that exist in urban environments and in decision-making. Also detecting similarities and differences between neighbourhoods would be very useful for urban sustainable development, for real estate purposes, and for tourism activities. The designers of urban transport systems and road infrastructure should also be aware of the landscape patterns that exist in an urban area. This kind of spatial analysis can be faced using a GIS, as this is the only system which includes all those methodologies and techniques of the spatial analysis science. This paper presents a spatial analysis of visual environmental information that took place in Zografos Municipality in Athens. The collected data were entered into a spatial database and the total area was classified into three homogeneous sub-areas. Further on an attempt was made for the detection of similarities and differences within six neighbourhoods. The results of the analysis are discussed and suggestions for further investigation are made. Keywords: urban landscape, visual elements, spatial analysis, GIS, classification.

2 1600 Sustainable Development and Planning II, Vol. 2 1 Introduction The analysis and registration of the visual elements in an urban landscape can contribute to structural interventions in urban planning, such as: 1. Identifying geographical areas that are suitable for urban development and controlling the capacity and capability of certain areas threatened by additional burden due to urbanisation. 2. Analysing the existing road network in urban areas and spatially arranging and planning new networks or modifying existing ones. 3. Formulating decisions as to which elements should remain and which ones should be modified in a region or neighbourhood. 4. Controlling undesirable land uses susceptible of creating visual environmental problems (orientation problems, traffic accidents due to poor visibility). 5. Spatially arranging and specially planning building constructions, in order to achieve harmonisation in the materials and colours used, as well as in the magnitudes of the elements that are usually visible [1]. It can also contribute to the understanding of concepts such as neighbourhood and sub-area. If a researcher examines an urban study area, he can discriminate within this area some homogenous sub-areas differentiated by certain visual elements (variables) [2]. These variables are in many of the cases clearly distinguished and in some others difficult to be recognised. The spatial analysis of these urban visual elements and especially the powerful statistical modules offered by the GI Systems can support the researcher to classify the study area and define such homogenous sub-areas [3, 4, 5, 6]. It is obvious from the above that the registration and analysis of the visual resources of the urban environment may prove to be useful in many applications, either in order to determine the extent and the magnitude of the various problems within the urban area or to become a useful aid for further analysis and management [7, 8, 9, 10, 11, 12]. This paper presents a spatial analysis of visual environmental information that took place in Zografos Municipality in Athens. The collected data were entered into a spatial database and the total area was classified into three homogeneous sub-areas. Further on an attempt was made for the detection of similarities and differences within six neighbourhoods [2, 6]. The results of the analysis are discussed and suggestions for further investigation are made. 2 Spatial analysis of visual elements using a GIS 2.1 Determination, collection and spatial analysis of urban visual elements In order to be able to describe and represent the features and properties of the area examined in the best possible way, the processes used for the inventory have to be planned and prioritised in advance. Stages that are critical in every registration process are: (i) determining the observation posts that will be used; (ii) the elements that will be collected for describing the visual environment, which vary depending on the purpose and aim of the application; (iii) the

3 Sustainable Development and Planning II, Vol presentation means that will be chosen to document the description; and (iv) the method that will be used for the spatial analysis and evaluation of the visual quality [1, 4]. A successful practice for selecting the observation posts is the shooting of photographs using a camera (analogue or preferably digital), from crossroads or areas presenting movement or gatherings of people, such as shopping centres, churches, parks, museums, etc. A basic requirement for the proper selection of the observation posts is for the researcher to have a priori a clear overview of the study area. This can be achieved with the use of topographic diagrams, aerial photography and preliminary visits on site. In addition to the pictures taken from the selected observation posts, the observer must take note also of other features and visual elements of the environment, such as: visibility elements (viewing angle, viewer distance, view elevation); the form of paths (grid, irregular, radial); the degree of enclosure (or the non-enclosure) of the landscape; the architectural patterns (horizontal and vertical distribution of buildings, shape, colour, materials); roadside vegetation elements (vertical distribution of trees, density, foliage); open areas and their uses (pedestrian streets, squares, parks, parking areas, roads, etc.); activity pattern or population movement, as observed on the roads at the time of surveying. The above visual elements [1] can be enriched depending on the purpose of the application and are noted during their registration on tables well designed or diagrams, while the observer can also add personal remarks, as he or she deems appropriate. The visual elements that have been used for the inventory, using the abovementioned techniques, can be combined and represented on thematic and combination maps depicting the information on the area, in detailed and concise form [3]. Statistical processing of these elements can subsequently lead to results on the features and properties of the urban area examined [2]. 2.2 The contribution of a GI system A GIS specifically for visual resources analysis can play the role of a special GIS, a Visual Environmental Geographical Information System. A system like this may play the role of the advisor and the presenter for many applications such as real estates, land planning and land developing, urban design and many more [3, 5]. This specific GIS may also include data of non visual resources content in its spatial database (demographic data, housing distribution, economic data, future plans data, transportation, infrastructure data, urban planning geometric data and building rules data), in order to combine them with the spatial visual resources data for supporting decision makers.

4 1602 Sustainable Development and Planning II, Vol. 2 The development of environmental data collection technology and especially remote sensing, digital photogrammetry, laser scanning of the landscape with over satisfactory accuracy, palmtop computers with GIS applications incorporating GPS and Mobile technology, plays the master role to this GIS contribution in the visual elements spatial analysis. 3 Spatial analysis of the urban landscape of Zografos municipality 3.1 The study area collection of urban visual elements A visual element collection has been carried out in a part of the Zografos Municipality in Attica (Figures 1, 2). The collection of the data was the subject of the first phase of the research, which was described in detail in an older publication [13]. The background used for the research planning was the Roadmap of Athens (Kapranidis editions [14]). The area, owning to its dense construction, was divided into six parts (Figure 2), namely neighbourhoods, which in turn, each one was divided into thirty-six smaller quadrangular subareas (100mx100m approximately) namely pixels, an order of magnitude that has been observed to ensure homogeneity in terms of characteristics. The visual elements were collected for each quadrangular sub-area and entered within a GI System s database so as to represent them on thematic and combination maps and to proceed to statistical processes as necessary, with the aim to identify the visual characteristics and standards that prevail in the area. Figure 1: Zografos municipality area. Figure 2: Study area. This area was selected, because it presents local differentiations regarding its urban environment and the visual elements that characterise it, as these elements are defined for the purpose of this research. These differentiations are due probably to the area s intense relief (continuing uphills downhills, flat parts), to the fact that the area is located at the environs of the Municipality of Athens with

5 Sustainable Development and Planning II, Vol the physical border being the mountainous volume of Imittos and it has undergone major construction and economic activities during the last two decades, when the Athens University and the NTU s Campus were constructed at its borders. The construction development was mainly integrated in the existing older neighbourhoods, which were constructed on the basis of other criteria and construction terms, thus resulting to the creation of distinct homogenous neighbourhoods in the area of the Municipality of Zografou [15]. Ten variables were selected to describe the visual characteristics of the urban environment, which are presented in Table 1 (Table 1). Each variable was divided into maximum four categories. These variables were then used to distinguish homogenous areas. Table 1: Visual variables. VARIABLES V1 Open Space enclosed moderate monumental ---- V2 Viewer Distance close intermediate far ---- V3 Roadside Trees Distribution regular irregular none ---- V4 Path Pattern grid irregular radial ---- V5 Degree of Enclosure 2:1 1:3 1:4 >1:n V6 Viewing Angle 0 90 deg deg deg ---- V7 Disturbance (noisy area) medium heavy V8 Viewer Elevation superior normal inferior ---- V9 Land Use pedestrian vehicular V10 Building Distribution aligned uneven missing Analysis of the study area at pixel level In a first approach of the study, the study area is regarded as a single entity, for which there is registered information on each pixel. This effort aims at the comprehension of the area and its characteristics, at the investigation of the visual variables recorded for this area and their distribution in space, as well as at an effort to classify the area into general categories, within which the visual variables examined present homogeneity Description of the study area Table 2 presents statistical descriptive data of the visual variables that were recorded in the study area (Table 2). We can observe in this table that the variables took values within the entire possible range. The results for each variable are presented in Figures 3, 4, 5, 6, 7, 8, 9, 10, 11, on which we can see a variety in the distribution of the variables values (Figures 3, 4, 5, 6, 7, 8, 9, 10, 11). The results of variable V9: Land Use are not presented on a map, because only 5 pixels of the study area were registered as pedestrian, while the remaining pixels were registered with vehicular use. The correlations among the variables range from to +0.30, which practically means that the variables are independent on one another. Therefore, we may conclude at this point that the existence of one visual characteristic in an area does not depend on the existence of other characteristics.

6 1604 Sustainable Development and Planning II, Vol. 2 Table 2: Descriptive statistics. Open Space Viewer Distance Roadside Trees Distr. Path Pattern Degree of Enclosure Viewing Angle Disturbance Viewer Elevation Land Use Building Distr. Minimum Maximum Mean Std. Deviation Classifying the study area into homogenous areas Following this, an attempt was made to classify the study area into three generalised categories of homogenous characteristics, as to the visual variables in the GIS s database. Figure 3: V1. Figure 4: V2. Figure 5: V3. For this purpose, we used the k-means cluster classification algorithm, which by calculating geometrical distances between the data of the variables, identified centres that determined the three homogenous categories. On the basis of these centres the pixels in the categories were classified [2, 3, 4]. An important question is about the number of categories that are desirable by the classification. The number of categories practically lies on the researcher s decision, however, it must be limited in order for the classification to be functional and the categories to present the necessary distinction among them [3]. The methods for determining category intervals are classified into four classes: (i) exogenous; (ii) arbitrary; (iii) idiographic; and (iv) serial. Each of the

7 Sustainable Development and Planning II, Vol four classes presents advantages and disadvantages and of course meets the individual needs of an investigation. In this research the number of categories was defined at an arbitrary way (three), and then the limits of the variables values for the three classes were determined ideographically through the k-means cluster analysis. Figure 12 presents the classification result and Table 3 includes the statistical results of this classification. Moreover, Table 4 includes the total numbers of the pixels that participate in each classification category of the study area. Figure 6: V4. Figure 7: V5. Figure 8: V6. Figure 9. V7. Figure 10: V8. Figure 11: V10.

8 1606 Sustainable Development and Planning II, Vol. 2 Figure 12: Total area classification map (3 categories). Table 3: Total area classification: final cluster centers. Final Cluster Centers Open Space Viewer Distance Roadside Trees Distr. Path Pattern Degree of Enclosure Viewing Angle Disturbance Viewer Elevation Land Use Building Distr. Cluster Table 4: Total area classification: No of cases per cluster. Number of cases in each cluster cluster % cluster % cluster % %

9 Sustainable Development and Planning II, Vol Table 3 shows that the distinction among the three categories is mainly based on the variables regarding (i) the visibility to open spaces (V1); (ii) the trees distribution on the left and the right side of the road (V3); (iii) the degree of enclosure that depends on the roads width and the buildings height (V5); and (iv) the viewer s relative elevation as to the objects he/she observes (V8). The remaining variables remain stable in the three generalised categories. In other words, throughout the study area and regardless of the classification category prevail moderate lengths and small angles in the viewsheds (V2=2, V6=1), irregular form of the road network (V4=2), common disturbance form (V7=1), vehicular land use for the linear elements (roads) (V9=2) and irregular distribution in the buildings heights (V10=2). As to the generalised categories that derived from the classification, the first is the one that prevails and covers 70.27% of the study area. In this category there is no visibility to open spaces (V1=1), the trees distribution on the roadsides is irregular (V3=2), there is a great degree of enclosure, i.e. the buildings heights are larger than the roads widths (V5=1) and the observer is at an equal relative elevation with the objects (V8=2). The second category of classification that covers 16.76% of the study area differs from the first one as to the visibility to open spaces and to the trees distribution. Therefore, in this category there is a moderate visibility to open spaces (V1=2) and the trees are evenly settled on the right and the left side of the roads (V3=2). As depicted in Figure 12, this category includes large avenues with open space and green areas. The third category differs from the first as to the trees distribution, the degree of enclosure and the viewer s relative elevation. In this category trees are distributed on a regular and continuous pattern (V3=2), the degree of enclosure is small, which means that the roads widths are smaller than the buildings heights (V5=3) and the viewer is on a lower relative elevation as to the objects observed. This category appears in areas neighbouring with big buildings (hospitals, sheltered fields, University s campus). The results from the classification applied are considered to be satisfactory, taking into account that we were able to identify the general standards prevailing in the study area. Moreover, the number of three categories that was selected for the classification is also satisfactory, since it manages to distinguish areas with homogenous characteristics. It is obvious that in this case a classification with more categories would be practically purposeless, since the differentiations to be identified would have been very small. 3.3 Spatial analysis at neighbourhood level Further to this, the research attempts to analyse the study area at neighbourhood level. In this investigation neighbourhood is defined as a rectangular part with dimensions of 6x6 pixels which practically corresponds to an area of 600mΧ600m. This area was estimated by taking into consideration the everyday human activities (mini markets, playgrounds, churches), at neighbourhood level, which usually take place in such distances. In this way, the entire study area is sub-divided into 6 neighbourhoods, as depicted in Figure 2.

10 1608 Sustainable Development and Planning II, Vol. 2 This investigation is important, since one Municipality (administrative unit) does not present the same visual quality in all its positions. Therefore, it is important to present the way neighbourhoods are correlated and distinguished one from the other as to their visual characteristics Comparison of characteristics among the neighbourhoods In order to compare the neighbourhoods, the mean values of the ten visual variables were calculated from the 6x6 pixels that define each neighbourhood. From these mean values, which are presented in detail in Table 5, the correlation coefficients among the neighbourhoods were then calculated (Table 6). The last column of Table 5 presents also the mean values of the visual variables from the entire study area. Table 5: Mean data of the six neighbourhoods and the study area Mean of Study Area Open Space Viewer Distance Roadside Trees Distr Path Pattern Degree of Enclosure Viewing Angle Disturbance Viewer Elevation Land Use Building Distr *bold numbers of neighbourhoods exceed mean value of study area. Table 6: Correlations among the six neighbourhoods. N1 N2 N3 N4 N5 N6 MEAN N N N N N N MEAN 1 Based on the data of Table 6 we observe very high correlations among certain neighbourhoods. This is the case of the 1 st and the 3 rd neighbourhood (rxy=+0.740), where the irregular appearance of trees and the large buildings (hospital, student s campus) affect the visibility to open spaces and the degree of enclosure (Table 6). The 3 rd and 6 th neighbourhood present also a very high correlation coefficient (rxy=+0.803). The common characteristic of these two neighbourhoods is the dense and irregular construction, which also affects the viewsheds offered to the viewer (Table 6). Moreover, a high correlation

11 Sustainable Development and Planning II, Vol (rxy=+0.763) is present in the 4 th and 5 th neighbourhood, where big green open areas are observed. According to the above, it is obvious that there are certain characteristics that differentiate neighbourhoods as to the quality of the visual environment offered, although they belong to the same administrative area. This conclusion gave rise to the discrimination analysis that follows, which aims to investigate whether the visual characteristics examined could classify the pixels in the 6 neighbourhoods Attempting to discriminate the neighbourhoods All the classification methods have been developed in order to classify observations for which we do not know in advance the group they belong to. However, they are frequently used in order to verify the accuracy of a classification. This means that they are applied to observations whose grouping is known, so as to calculate the percentage of the correctly classified data. This percentage verifies the accuracy of the process and the separation degree of the groups [2, 4]. This is the purpose of the following analysis. The pixels, in our case are already classified in six neighborhoods. Thus the percentage of the correctly classified pixels, using visual characteristics shows the ability of the visual characteristics to classify automatically the pixels to neighborhoods. The classification was made using the linear discrimination method and with the use of the SPSS package. Table 7 presents the results from the application of the linear discrimination method with the use of all the visual variables. The percentage of the successful classification achieved is 68.1%, something that proves that visual characteristics may be used at a high success percentage in order to distinguish the neighbourhoods. Table 7: Discriminant analysis of neighbourhoods. Original Count % "Neighbourhood" a. 68.1% of original grouped cases correctly classified. Classification Results a Predicted Group Membership Total This analysis would have been of greater importance, in case the neighbourhoods originated from different administrative areas. In this case, we could have attempted to develop a classification model of the various administrative areas, on the basis of the visual characteristics of the environment. The significance of such a model is not to predict the administrative area to

12 1610 Sustainable Development and Planning II, Vol. 2 which a pixel belongs, because this can be easily found on the map of administrative borders. The value of such a model is understanding those characteristics that lead the various administrative areas to give a different visual impression to an observer and affect his/her preference. The possibility to develop such models could be very useful in urban planning, but also in the determination of the real estate value. 4 Remarks conclusions further research The spatial analysis of an urban landscape usually presents many difficulties due to the various elements that have to be recorded, to the complicated analyses that have to be applied taking into account all these multiple interactions between the visual data, and also due to the fact that these data are spatially distributed. A subdivision of an urban landscape to visually homogeneous areas would be very useful in understanding problems that exist in urban environments and in decision-making for planning for these environments. Also the detection of similarities and differences between neighbourhoods and sub-areas would be very useful for urban sustainable development, for real estate purposes, and for tourism activities. The designers of urban transport systems and road infrastructure should also be aware of the landscape patterns that exist in an urban area [15]. The course among the neighbourhoods that are characterised by complexity in the visual patterns disturbs both the driver and the passengers of transport systems. This disturbance may be just an aesthetic matter or a difficulty in movement and orientation throughout the course. Another very useful approach of this kind of analyses would be the attempt to detect relationships between the construction terms and the visual characteristics of urban environment, in order to predict unpleasant effects on the visual environment at the phase of its planning. Moreover, further research could lead to the correlation of the visual characteristics to the area s economic data, as for example the fiscal value of the land and the commercial value of real estate. This kind of spatial analysis can be carried out using a GIS, as this is the only system, which includes all those methodologies and techniques of the spatial analysis science [3, 4, 5]. References [1] Smardon R., Castello T., Egging Η., "Urban Visual Description and Analysis", Foundations for Visual Project Analysis, John Willey & Sons, N.Υ.,1986. [2] Therrien C., Decision Estimation and Classification: An Introduction to Pattern Recognition and Related Topics, John Wiley & Sons, N.Y., [3] Burrough P., Principles of Geographical Information Systems for Land Resources Assessement, Clarendon Press, Oxford, [4] Koutsopoulos K., GEOGRAPHY: Space analysis methodology and methods, Symmetria editions, Athens 1990 (in Greek).

13 Sustainable Development and Planning II, Vol [5] Koutsopoulos K. Geographical Information Systems and Space Analysis, Papassotiriou editions, Athens 2002 (in Greek). [6] Rogers A., Statistical Analysis of Spatial Dispersion, Pion Limited, London, [7] Ananiadou, M., Tzimopoulou. Designing the Landscape and Urban Outdoor Areas in Thessaloniki, Technika Chronika, A, vol. 12, no. 4, 1992 (in Greek). [8] Appleyard D., Lynch Κ. and Meyer J.R., The view from the road, Cambridge: ΜΙΤ Press,1964. [9] Aravantinos, A. Urban Planning: Towards a sustainable development of the built environment, Published by SYMMETRIA, Athens, 1997 (in Greek). [10] Economou, D. Mental Representations of the Athens City Centre: First Survey Findings, Technika Chronika, A, vol. 10, no. 1, 1990 (in Greek). [11] Karamanou, Z., Koukopoulos, S. Nomikos, M. Utilising the Construction Reserve. A Different View of Residential Development, Technika Chronika, A, vol. 11, no. 2, 1991 (in Greek). [12] Lynch Κ., The Image of the City, ΜΙΤ Press, Cambridge, [13] Tsouchlaraki A., Achilleos G., Visual Elements Inventory in Urban Landscapes: Collecting Data for a Visual Environmental GIS, Proceedings of the 4 th International Conference: Management Information Systems: Incorporating GIS and Remote Sensing, (Editor: C. A. Brebbia), Wessex Institute of Technology Press, U.K., 2004, p.p [14] Kapranidis S., Fotis, N., Roadmap of Athens, Piraeus and the Suburbs, Ellinikes Touristikes & Hartografikes ekdosis, Athens 2004 (in Greek). [15] Vlastos, Th., Siolas, A. The Contribution of Transportation Networks in the Strategy of Articulating and Reuniting Urban Entities for Restructuring Towns: The case of Western Athens, Technika Chronika, A, vol. 14, no. 1, 1994 (in Greek).