THE EFFECT OF SPATIAL CONFIGURATION ON LAND USE AND LAND VALUE IN SEOUL 080 Kyoung Mi Min Sejong University Jong Min Moon Sejong University Young Ook Kim Sejong University Keywords: Land use Location-allocation Space syntax Accessibility Land price Kyoung Mi Min Gunja-dong, Gwangjin-gu, Seoul 143-747, Korea realmjm@empal.com Jong Min Moon Gunja-dong, Gwangjin-gu, Seoul 143-747, Korea smufpet@hotmail.com Young Ook Kim Gunja-dong, Gwangjin-gu, Seoul 143-747, Korea yokim@sejong.ac.kr Abstract Urban space changes according to differentiated space use with the passage of time, as seen in land use, location, and land value distribution. In this context, this study aims to identify the influence that spatial configuration exerts on the location of different types of businesses in terms of land use. In other words, the aim is to analyze the characteristics of location and land use in terms of building usage from the perspective of spatial configuration. First, a statistical analysis is performed on the relationship between spatial configuration and location intensity and coefficient, which shows location preference in downtown areas. Then, in regards to land value, which forms the basis of land use, the influence that attributes of spatial configuration have on land use is defined by identifying the relationship between those attributes and land value distribution in urban areas. Finally, the degree of influence that spatial configuration has on the location characteristics of buildings according to different types of businesses is identified. In order to analyze the location characteristics according to business types in terms of land use, the location activities of unit buildings inside the analyzed area are used as the analytical unit, and the attributes of spatial configuration and multiple variables of land use are validly applied to discriminant analysis, a methodology for analyzing multivariate data. The analysis of the correlation between the attributes of spatial configuration, location intensity, and location coefficient showed that global integration is closely related to the location of commercial facilities. The correlation between officially announced land price and each attribute of spatial configuration turned out to be very high, with the regression coefficient of determination at 0.563 (approximately 56% predictability). Characteristics of building location according to types of businesses are analyzed by using global integration. The results showed that, with the singular attribute of global integration, the location of facilities in urban areas showed 63% distinctiveness, along with business facilities at 55%, commercial facilities at 41%, and housing locations at 91%. This indicates that as spatial configuration system is formed by organic interactive reactions of the entire spatial structure, the characteristics of location according to type of business do not have a local location system but react to the entire city system. As mentioned above, in downtown Seoul, land use patterns, land value structure, and location inclinations of competitive business types in terms of internal characteristics of spatial configuration are all closely related. Therefore, the characteristics of spatial configuration in downtown areas discussed in this study have significance in understanding urban phenomena regarding practical land usage. The results of this study will help to interpret and predict land use and, at the same time, contribute to establishing urban policy, including the land use initiative.
080-02 Introduction Land use plan allocates human activities that should occur on limited land, and determines the level of those activities. It is the resulting combination of many of the spatial elements themselves within the city, opposing and competing relationships, and changes to the outside environment. Moreover, as the social activities of people accumulate in the city, the efficient use of the spaces is needed, and a fixed rule for locating high-profit activities in each fixed area is being applied. In order to apply this rule, although considering the characteristics and efficiency of every use, location, and scale is important, considering the accessibility of the land is also an important factor. Accessibility is a term that is frequently used in the land use and transportation fields. Nevertheless, since the concept is abstract, it is defined differently according to the researcher or research aim. Hansen defined accessibility as the potential strength of the spatial reciprocal action opportunity. Accessibility is generally related to nearness or proximity, and the easiness of spatial reciprocal action or potentiality. In network theory, accessibility is defined as the extent of overcoming friction that acts spatially and the characteristics of the related space itself. Here, the friction that is acted upon spatially can be the necessary travel time, travel cost, and travel distance to pass between two nodes. Shimbel said that in a transportation network, the accessibility of a certain node could be analyzed by adding up the connecting road sections between nodes. This concept of accessibility has been utilized as an important factor in determining the location of various types of urban facilities. Bach, by fixing and establishing the location-allocation model of city planning, understood that the optimum location for these facilities is determined by the accessibility and opportunity for access between the facility and users. Bach showed that accessibility could be used as a standard to judge spatial efficiency and balance. Up until now, research on land use and location-allocation considered time, cost, and distance as principal factors for analysis, and based on accessibility. However, in this study, attention is directed towards the space syntax method (Hillier et al, 1993) that indexes accessibility by using the street network. Space syntax observes the street network that determines the spatial morphology of the city. Thus space syntax analysis of spatial configuration calculates accessibility (spatial integration in its own term) according to the street network. In a study that used this space syntax concept, De Syllas (1999) qualitatively describes the changes in city structure and changes for land use in Berlin by using the spatial integration index of space syntax method. Yim et al (2003) use Seoul as a subject in a study that clarifies the close relation between land price and spatial integration. Space syntax analysis also demonstrates that the gross floor area of the unit building, which is one of the scales that show the integration of land use, has a close relationship with spatial integration (Sohn, 2002). When these fragmentary research results are put together, a hypothesis can be formed to analyze and understand land use and location-allocation patterns through the attributes of the spatial configuration itself. However, although there has been fragmentary research that analyzed land use, studies that comprehensively used location-allocation spatial configuration for analysis are almost nonexistent. In this context, the influence spatial configuration exerts on the location-allocation for each use of the building is closely examined in this research. Namely, this study aims to examine whether or not the location-allocation of a building can be analyzed using space syntax index of Integration which measures how much a street is integrated
or segregated within a street network, and if it can, further analyze the function of spatial integration in this context. Theoretical Background and Preceding Research When dividing the concept of land use into categories of activity and its effect, activity can be represented by function and effect can be represented by physical space. The functional factors of land use refer to the type and amount of activity occurring on the land and express their preference in economic activities. The competitiveness of location increases as usage becomes higher along with land productivity. In the process of competition between the uses, land productivity, location characteristics, and scale are determined, and the use in which these three factors are predominant obtains the land in question. Location, a physical factor of land use, can be represented by accessibility. Accessibility is a scale that evaluates the degree of ease with which contact between similar economic activities in one area can be made. Alonso (1968) asserts that the regional and transportation expenses are mutually exclusive and location is determined when these two are in a balanced state. These influences of accessibility are otherwise expressed as land value. These functional factors and physical factors are mutually related, and for land use, both factors exert influence at the same time. For example, land use efficiency is higher when it is closer to the heart of the city, where land productivity is higher and the extent of integration is larger. This level of efficiency is explained with land use intensity. Land use intensity is a scale measuring how much usage is distributed and how much of the land is being used at the location of the given land, and to calculate this, a prior investigation on location, classification of usage, and scale of building must be conducted. Generally, land use intensity of commercial use can be measured by the number of employees per unit, the sales per unit, and the number of enterprises per unit of activity that uses the land and the gross floor area of a building in relation to the land area. An index that shows that land use intensity explains the extent of land use. Research on such land use is being actively carried out. Kim (1998) analyzed land use, the physical conditions of land, and the distribution of land value in the central part of the city, which has the most active changes in land use among all the spaces of the city. The results showed that they are closely related, specifically, a close relationship was discovered between the distribution characteristics of the land in the center part of the city, that is to say, between the location, forms, and scale of the roads. For research related to the characteristics of location, Yeo (2002) used three central areas that show a high distribution of offices each year as the subjects of the study which explained that factors for locating office facilities are different in each region, and business facilities tend to locate in each area through the analysis of characteristics for location by principal variables, such as the land price, ground shape, coverage ratio, floor area ratio, and distance to subway station. Research on land use has been advanced through the use of space syntax. Shin & Kim (2003) showed that as walled cities continuously spread out, the extent of Integration between the space in the central commercial area and the adjacent areas of the old center of the city, or the decrease of syntactic accessibility, is connected to the phenomenon of decline in the old center of the city. Yim et al (2002) analyzed the mutual relationship between the characteristics of Integration and land value. Their study revealed that spatial integration is an important factor that can predict land price and syntactic accessibility is efficient index for land use plan. Physical concepts, such as the distance from the center of the city, road width, 080-03
080-04 and the present condition of location are the principal attributes in forming land value, and they are inherent in spatial configuration. Gu (2001) showed that at the initial stage in which commercial facilities are created, that process is closely connected to Integration. He determined that in case the commercial facilities are expanding, the connectivity of space and Integration (3) act as principal factors. However, these preceding researches are limited because they cannot manage the characteristics of the spatial configuration itself, which determines land usage, as these studies emphasized the current situation for land use. In other words, while research that closely examines the current situation for land use or its present characteristics has been very actively advanced, research that closely examines probability with the characteristics of spatial integration for the space itself is almost non-existent. Even in papers that researched land use and syntax characteristics of spatial configuration by using the space syntax technique, there is not any research that closely examined the characteristics of land use considering the locations of each type of industry. Therefore, analyzing the locations of each type of industry according to the characteristics of spatial integration can be said to be equally important in the aspect of land use that analyzes the influence of spatial configuration or in the aspect that establishes the land use plan. Method Setting up Analytical Headings of Land Use The setting up of analytical headings considered the characteristics of case study area that are to be analyzed out of the headings shown in preceding researches, and selected headings that could be controlled by physical plans. The analytical headings that were chosen through a study of the previous researches are: land value, plot area, width of all roads, distance to public transportation facilities, number of floors of a building, gross floor area, and the space syntax properties such as Integration and Local Integration i.e., Integration (3), connectivity, and depth. Criteria for Selecting Case Study Area The case study area should represent regional characteristics of having many types of industry, a commercial industry related to land use, a housing area, and also show various characteristics of land use in terms of having various land usages and densities of buildings. Moreover, the easiness of the investigation and the usefulness of investigation results are considered in the selection. Accordingly, 10,683 lots of 66 administrative dongs or neighborhoods that fall under Jongno and Junggu, which are central areas of Kangbuk, or north of Han River, and can be said to be the traditional center of Seoul, were investigated. For data related to the use of buildings, the building register data of Seoul was used. For investigation of the use of buildings, data was constructed through field investigation based on the building register. Criteria of Classifying Land Use For classifying the use of buildings, uses that fell under the central function, which was the subject of this research, were sorted by function out of the 30 large classification uses and 100 small classification uses that were presented in the attachment to the Korean Building Act Enforcement Ordinance. The location characteristics of the center of the city was analyzed based on the five uses that suit the aim of the research according to the classifying criteria, as shown in Table 1: business facilities, commercial facilities, marketing facilities, neighborhood living facilities, and housing facilities.
Table 1: Classification of use according function 080-05 Selecting a Location Analysis Index Location intensity Location intensity can analyze not only changes according to the increase and decrease of individual uses but also location of superior uses as a location scale obtained by each administrative area use. This shows the number of enterprises of each use for land unit by each administrative region, and the number shows how enterprises of each use are located with how much intensity in each region. Using such index, the leading location tendency of appropriate uses for each region can be learned. LI ij = where, LUN LON ij j LI ij, location intensity of i use of j district or j road, LUN, total number of enterprises of i use of j district or j road, ij LON, total number of lands of j district or j road j Location coefficient Since location intensity is limited to the corresponding road or region, it is hard to compare and analyze location characteristics between each use. Therefore in this research, the location coefficient index is introduced in order to compare and analyze the comparative location propensity of the specific region that is to be analyzed out of all the regions. Location coefficient established 1 as the standard value. When the numeric value is smaller than 1, it means the location intensity on the corresponding region is smaller than the overall average, and it can be said that there is no location propensity for the corresponding region. On the other hand, when the location coefficient is greater than 1, it is judged that the location propensity for a specific use in that
080-06 region is greater than for other regions. The comparative location tendency of each use is closely examined through this location coefficient. Here, the location coefficient of each use by the appropriate region is calculated as the equation below. LQ ijk where, LQ ijk N ijk = N N ijk LUN ik LUN jk k, location coefficient of i use in j district of k total district, LUN N ik, total number of enterprises with i use in j district of k total district, jk, total number of enterprises in j district of k total district,, total number of enterprises in i use in k total district LUN, total number of enterprises in k total district k Analysis Procedure First, the physical characteristics of basic land use are examined in order to understand the current situation of land use. The analysis is done through document investigations and field investigations on such things as plot area, width of roads, distance from public transportation facilities, numbers of floors of buildings, gross floor area, and land price. Second, the location intensity and location coefficient are calculated in order to discover the intensity of the location of specific business types in each region, and the characteristics of land use and land price are investigated. Third, the syntactic properties are analyzed by using the space syntax technique in order to measure syntactic accessibility from street network. Fourth, as a step to uncover what relationship spatial integration has with land use, regression analyses are done to discover the correlations between integration and integration (3), and land use, location intensity, location coefficient, and land price. Lastly, discriminant analysis is used to closely examine the location characteristics of each type of business. Analysis of the Characteristics Land Use in the Case Study Area The areas surrounding City Hall is the center of Seoul, a central business district (CBD) of the highest rank that acts as a direct support region for the whole area of Seoul and the whole metropolitan area. Table 2: Gross floor area and land distribution in the central part of Kangbuk Table 2 shows the gross floor area distribution of buildings and number of lands on the individual lands of the 66 dongs of Junggu and Jongno districts. The gross floor area, as a representative item that shows the quantitative part of the physical use of lands, becomes a direct index of the three-dimensional capacity of space. In this study,
the gross floor area distribution was classified into five uses suitable for the aim of the study and investigated. As a result, the central part of Kangbuk shows a direct aspect of the business function as it was found that a majority of the area was used by business facilities, with the gross floor area of business facilities at 63%. Relatively, the housing function is weak and the service function shows a comparatively high amount of spatial use. It showed that the dominating spatial function of service function is sales, food, and comfort service industry. It can be said that the place can carry out the function of being the CBD area of Seoul because the current condition of land use for commercial use and business use amounted to 82%. Plot numbers are effective when analyzing the component ratio of the current condition of land use. The largest number of lands was housing facilities at 41% and next was service facilities at 24%. Gross floor area taken by business facilities was the largest, but showed that the number of buildings was the smallest with the number of lands at 8%. This shows that business facilities are usually distributed on large lands. For the current condition of land use, regular commercial areas occupy the center part of the city at 80.37%, regular housing areas take up 17.12%, and quasi-housing areas take up 2.51%. Space Syntax Analysis The 1:10,000 parcel map of Seoul is used to analyze syntactic properties of the areas surrounding City Hall. A space syntax model of the area with a radius about 2.5 km that centers round where City Hall is located is constructed and analyzed. As seen in Figure 1, a street with high Integration is centered round City Hall, and the Jongno axis of the northern direction of City Hall has the highest Integration of 2.207. Next highest is Sejongno at 2.198, followed by Euljiro at 2.086. The Integration pattern of the areas surrounding City Hall is greatly influenced by Jongno, Euljiro, and Sejongno. Analysis results show that the surrounding areas that are currently carrying out the CBD function in Seoul are also high in Integration. Figure 1: Integration of the surrounding City Hall 080-07
080-08 Figure 2: Location intensity of the Junggu and Jongnogu Correlation of Spatial Integration and Allocation of Superior Land Use Location Intensity and Location Coefficient Analysis Results of location intensity analysis of Junggu and Jongro-gu showed that the business facilities ratio in Jongno Seorin-dong was the highest and the ratios of marketing facilities, neighborhood facilities, and housing facilities were very low. This reveals that the location of Seorin-dong is in a more advantageous location than any other dongs in terms of having accessibility or degree of recognition. The ratio of service facilities was shown to be high in Sogong-dong and Jongno 1,2,3-ga, and the ratio of housing facilities was shown to be high in Namsan-dong 2,3-ga, Hoehyeon-dong 2,3-ga, and Sajik-dong. Results of the location coefficient analysis showed that the location of business facilities currently had a superior condition of location in Mugyo-dong, Seosomun-dong, Euljiro 1-ga, and Seorin-dong, and service facilities were shown to have a high superior location in Namdaemun 4-ga, Sogong-dong, Janggyo-dong, and Jongno 3-ga. For marketing facilities, it was analyzed that Namdaemun 3-ga, Namchang-dong, Myeongdong 1-ga, Euljiro 2-ga, and Chungmuro 1- ga had a high superior location. Housing facilities had a high superior location in Namsan-dong 2,3-ga, Sunhwa-dong, Juja-dong, Hoehyeon-dong 2-ga, and Sajik-dong.
080-09 Correlation between Spatial Integration and Superior Location Use In this section, the correlation between location intensity of each type of industry and location coefficient, and Integration and Integration(3) are analyzed. As seen in Figure 4a, analysis results showed that, with a significance standard of 0.05, there is a high correlation between the location intensity of other uses that excludes the location intensity of neighborhood living facilities and Integration. The correlation coefficients between Integration and the location intensity of commercial facilities, service facilities, and housing facilities were analyzed to be high, each being 0.735, 0.624, -0.838, respectively. For Integration (3), the correlation coefficients between commercial facilities, service facilities, housing facilities, and location intensity were analyzed to be 0.675, 0.619, -0.812, respectively. In other words, it was discovered that commercial facilities and service facilities were located at a place with high Integration and housing facilities were located at a place with low Integration. The location coefficient of commercial facilities, as in the analysis results of Figure 4b, was analyzed to be high at a significant level of 0.05, more than the correlation coefficient of Integration and Integration(3) at 0.76. Moreover, the correlation coefficients between Figure 3: Location coefficient of the Junggu and Jongnogu
Figure 4: Correlation analysis between integration and location intensity the location coefficient of housing facilities and Integration, Integration (3), also showed a high correlation with a value higher than 0.78. The correlation of business facilities, service facilities, and Integration are also significant with a value higher than 0.55. However, the correlation between the location coefficient of neighborhood living facilities and Integration was shown to be very low at 0.321. For neighborhood living facilities, it is hinted that a syntax analysis of a smaller spatial range is needed because they are facilities located in the scope of the neighborhood area rather than facilities located at a city level. 080-10
Based on the analysis results of the above, as seen in Figure 5, the Integration value was set as a dependent variable and a regression analysis was carried out on location coefficient. The regression equation shown allows the prediction of location tendency by land use when the horizontal spatial system changes or land use plans are established. The regression equations of Integration on the location coefficient of commercial facilities, service marketing facilities, and housing facilities are each calculated as follows. Y = -1.162 + 1.448 X₁: R²= 0.739 (Y = location coefficient of commercial facilities, X₁= Integration) Y = -0.904 + 1.219 X₁: R²= 0.388 (Y = location coefficient of service and marketing facilities, X₁= Integration) Y = 4.943-2.608 X₁: R²= 0.710 (Y = location coefficient of housing facilities, X₁= Integration) Figure 5: The result of the regression analysis 080-11 Analysis results showed a model with a very high predictive power of about 74% with the regression coefficient of determination (R²) value of the location of commercial facilities and Integration at 0.739. For service facilities and marketing facilities, a model with a predictive power of about 40% is shown with the regression coefficient of determination (R²) value at 0.388, and for housing facilities, a model with high predictive power of about 71% is shown with the regression coefficient of determination (R²) value at 0.706. The results above suggest that the location tendency of commercial facilities and housing facilities can comparatively be analyzed exactly with a single variable called Integration. Correlation between Integration and Land Value An analysis of the correlation coefficient between syntactic properties and average land price of each plot within the study area was carried out. The results show that a comparatively high correlation exists between syntactic properties and the average land price. For Integration, with the significance level at 0.01, the correlation coefficient shows the highest correlation at 0.750. Table 3: Analysis of correlation coefficient between the declared land price and syntactic properties
Referring to the analysis results of the correlation between variables, the Integration value was set as a dependent variable and a regression analysis on land price was carried out. The regression equation of Integration on land price is as follows. Analysis results show that a high mutual relationship exists between the Integration of the central area of the city and land value, and the model shows a predictive power of about 56%. Therefore, it can be confirmed that the Integration is a variable that significantly influences the formation of land value. Y = 5.622 + 0.705*X₁: R²= 0.563 (Y = logged land price, X₁= Integration) 080-12 Analysis of Location Characteristics of Each Type of Industry through Stepwise Discriminant Analysis In this section, Integration was set as the variable by using the SPSS11, and the characteristics of location of each type of industry for each plot are analyzed through the discriminant analysis. Physical factors that influence land use, which were discovered in previous research, are considered in stages and analyzed. Analysis of Land Use Influencing Factors through Correlation Coefficient Analysis As shown in chapter 3, for the physical variables of land use, distance to subway stations, distance to boulevards, distance to bus stations, and width of all roads were selected, and for the building variables, gross floor area, number of floors, and land price were chosen. All together, 418 lots were chosen, and they were classified into business facilities, service facilities, marketing facilities, neighborhood facilities, and housing facilities. Figure 6 shows the results of the correlation analysis between syntactic properties and the variables above for the selection of variables. Variables that have a correlation with Integration showed that the logged land price is 0.750, width of all logged roads is 0.640, distance of boulevards is 0.452, and distance to bus stations is 0.430. In the analysis results mentioned above, variables that have a high correlation with syntactic properties are land price, width of all roads, distance to boulevards, and distance to bus stations. In addition, variables that have a correlation with land price are syntactic properties, width of all roads, distance to the subway, and gross floor area. In the relationship of syntactic properties and land use, it is assumed that the variable of distance to subway stations shows the lowest correlation because the difference of distance from each land is comparatively small, as many subway stations of the downtown area are located in numerous areas. Discriminate Analysis Discriminate analysis is a useful statistic technique in analyzing differences between groups. When there is information about the independent variable value in a case, the range of where the case belongs to can be predicted by calculating the discrimination point. In this study, a compound of variables were sought for that can clearly explain location characteristics through differences of groups by finding a function with an independent variable according to the dependent variables of two categories that are divided by classification of uses and a compound of variables involving Integration and land price. The number of samples should generally be ten times the value of multiplying the number of independent variables and number of groups. The total number of samples of this research is 418, and the number of samples excluding the
neighborhood living facilities is 307, which satisfies the condition of number of samples for discriminant analysis. 080-13 Facilities are largely sorted into two categories for discriminant analysis and are analyzed by four variable compounds for each category. As mentioned above, since the location of neighborhood living facilities has a low explanation ability using Integration, the classification of uses in the first category are sorted into four types of industries, excluding only the neighborhood living facilities, and analyzed. The classification of uses in the second category integrates service facilities and marketing facilities into commercial facilities and sorts them. The analysis centers round Integration and land price (declared land value), which are location characteristic variables that show a high correlation through the correlation coefficient analysis shown in Figure 6. Through the method shown in Table 4, distance to boulevards and distance to subway stations are selected as variables when the analysis centers round Integration and stepwise discriminant analysis is attempted. Moreover, when the analysis centers round land price, the width of all roads and distance to subway stations are chosen as variables and analyzed. For the estimate of the discriminant function model, the method of adding variables by stages was used, and the significance of the model was statistically tested using the Wilks' lambda method. The Wilks' lambda value signifies the ratio that is divided by the total variance within the group, and as the value becomes smaller, the ability to explain becomes higher. The Eigenvalue of the canonical discriminant function is a statistical value used when analyzing the content regarding how much of explanation ability the discriminant function has. It can be said that when the eigenvalue is large, the explanation ability of the discriminant function is high, and that when it is small, the function s explanation ability is weak. Moreover, when the eigenvalue is larger than 0.4, it can be seen generally to have predictive explanation ability to some degree. The analysis results showed that the eigenvalues of each stage of the two categories are both higher than 0.74 and have high predictive explanation abilities. Figure 6: Correlation coefficient analysis of the land use influencing factors
Table 4: Method of stepwise 080-14 discriminant analysis Table 5: Results of stepwise discriminant analysis Table 5 shows the results of the stepwise discriminant analysis. When the significance level was set at 0.05, the discriminant functions were shown to be meaningful. The canonical correlation coefficient shows the discriminating ability of the drawn discriminant function, and as it gets closer to 1, it signifies that the discriminating ability is higher. It can be said that the canonical correlation coefficients are discriminant functions with superior discriminating abilities according to the results of their analysis. As the model with fewer independent variables is generally seen to be superior assuming it has similar explanation abilities in a statistical model, it can be said that the Method 1, which has Integration as a single independent variable, is a superior model. Conclusion In this study, it was proven that Integration, a characteristic of spatial configuration, is an influential factor that can reveal the locationallocation for each type of industry. When the results of the research are summarized, they are as follows. Firstly, the results of analyzing the location coefficient of each type of industry in administrative dong units within the case study area and its correlation with syntactic properties showed that Integration is a factor that greatly influences the location of commercial facilities. The business facilities and service facilities showed a high location tendency when the Integration and Integration (3) values were larger, and as for housing facilities, they showed a high location tendency when the Integration value was lower. Secondly, the correlation between the Integration and the average declared land price was analyzed to be very high. This shows that the Integration value exerts very significant influence on land value, which reflects the results of land use. Thirdly, in the results of analyzing the location propensity of each facility using only Integration, the analysis on the location of all facilities showed a discriminating ability of about 58%. Moreover, business facilities showed a predictive power of about 55%, commercial facilities about 41% and housing facilities about 91%.
As observed above, in analyzing the land use pattern of the center part of Seoul, it was confirmed that Integration, which is a syntactic property of spatial configuration, has a close correlation with land value and the location propensity of leading types of industries. It can be said that such results present a new methodological possibility in researches related to land use. In the researches of analyzing land use that usually adopt traditional accessibility concept, street network index of spatial configuration have not been a subject of consideration. In other words, while in preceding researches, time, cost, and distance were considered as factors determining accessibility, in this study, the syntactic properties of street network were applied as the concept of accessibility, which was different from preceding researches, and this study proved that such characteristic is a principal variable in determining land use. These results will not only redefine the concept of accessibility, but will also allow a new theoretical analysis in closely examining land use pattern. Reference Alonso, W., 1968, Location And Land Use: Toward A General Theory of Land Rent, Harvard University Press, Cambridge Mass. Cho, K.H., 2001, The Effect of Street Network on the Change of Land-use in Residential Area, M.A. Thesis, Seoul National University. Desyllas, J., 1999, When Downtown Moves: Quantifying Representing and Modelling the Spatial Variable in Office Rents, F.R.B. Holanda, L. Amorim, F. Dufaux (Eds.), Proceedings, 2 nd International Space Syntax Symposium, Universidade de Brasilia, Brasilia. Gu, B.O., 2001, A Study on the Birth and Growth of Building Program in the Context of the Street Layout, M.A. Thesis, Seoul National University. Hillier, B., 1996, Space is the Machine: A Configurational Theory of Architecture, Cambridge University Press, Cambridge Hillier, B., Hanson, J., 1984, The Social Logic of Space, Cambridge University Press, Cambridge. Kim, H. R., 1999, The Interpretation of the Chang in Dominant Shop Use in Insa-dong, Seoul, The Journal of Korea Planners Association, pp. 507-519. Kim, H. S,, Im, J. H., Song, H. S., 1998, A Study On The Situation Of The Land Use And The Factor of Determining The Land-Price in C.B.D, Journal of Architectural Institute of Korea, Vol.14, No.11, pp.259-268. Lee, Y.J., 1995, A Multivariate Analysis Using of the SPSS/PC, Seok-Jeong Press. Lim, H.S., 2002, A Study on the Relationship between Spatial Configuration and Land Prices, Journal of Architectural Institute of Korea, Vol.18, No.7, pp.133-141. Ray M.,1979, Urban Geography, 2nd ed, John Wiley & Sons press, pp. 265. Shin, H.W., 2003, Growth and Transition of the Urban Spatial Configuration, M.A. Thesis, Sejong university, Seoul. Yuh, H.K., Jeong, S.A., 2002, Spatial Distribution and Locational Characteristics of Offices in Seoul, The Journal of Korea Planners Association, Vol. 37, No.7, pp.117-137. 080-15
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