The Spatial Analysis of Population in Marmara Region (Turkey) By Using Geographic Information Systems (GIS)

The Spatial Analysis of Population in Marmara Region (Turkey) By Using Geographic Information Systems (GIS) İlyas İŞCAN Department of Geography Fatih University, 34500 Buyukcekmece Istanbul, Turkey ilyasiscan@gmail.com Assoc. Prof. Dr. Fikret TUNA Department of Geography Fatih University, 34500 Buyukcekmece Istanbul, Turkey ftuna@fatih.edu.tr Abstract- Today, one of the most important tools of geography discipline that can be used in spatial analysis of the population is geographic information systems (GIS). The purpose of this study is to analyze the development, distribution and yearly changes of population in Marmara Region by using five different spatial and statistical methods (central object, mean center, hot spot and standard deviation ellipse and kernel density) of GIS. The study was conducted in district scale by using the population data of the years 1965, 1985, 2000 and 2010. The study revealed that the coordinates of central object and mean center changed every year and they have shifted towards Istanbul under the force of Istanbul s highest population. In hot spot analysis, the red and orange spots were generally concentrated in the districts of Istanbul. In addition, the standard deviation ellipses and standard distance circles have generally narrowed towards Istanbul. Other results and maps were given in the study in detail. Keywords- Spatial Analysis, Central Object, Mean Center, Hot Spot, Standard Deviation Ellipse, Kernel Density I. INTRODUCTION Population researches and analyses, which are among the studies of population geography, are among important issues of geography and examine the number of people, their distribution and movements in a city, country or region. In this context, population researches, which are underlying many geographical events and features, are among important issues that should be addressed in geography discipline [1, 30]. Therefore, population growth and various properties are investigated, spatial analyses are done and various problems and solutions to them are studied in population researches [5, 13, 15, 21, 22]. However, the population is a phenomena or a dynamic event that is being changed every moment. New births and deaths occur together, migration takes place every day and demographic characteristics change over time. So, the amount and nature of the population and its distribution vary dynamically [20]. Therefore, to make better use of the environment and understand population change easily, and to make a better population analysis, the population structure of a region should be known very well to deal with these changes. Marmara Region, which constitutes the northwest of Turkey including Istanbul and several large cities, takes 20 immigration more than any other regions in Turkey. Marmara Region s population of 5,697,775 in 1965 has increased to the population of 21,887,360 in 2010. Accordingly, an increase of 284% occurred in the last 45 years. Therefore, today, Marmara region is experiencing a variety of problems depending on this high population increase. Therefore, there is a need to conduct some researches on population of Marmara Region in order to detect and solve the problems caused by high population. The different views caused by the distribution of population in a region and their reasons and results are studied primarily by geography as well as various disciplines. However, analysis of the spatial distribution of population is the core subject of population geography [21]. Although the population interests many branches of science, geography s difference in approach to the subject is that geography studies population by addressing every aspect of the spatial perspective to explain the distribution [29]. Spatial analysis is described as the process of modeling all graphic and descriptive information in the space of a particular coordinate system and interpretation of the results of this model. It includes all the applications such as evaluation of the structures of geographic features, estimation of the environmental impacts of spatial events and conversion of all applications into meaningful forms [16]. It also emphasizes the importance of geographical location [2, 6, 11, 12, 19]. In short, spatial analysis is a method that explains the spatial structure, interactions, processes of existing data in a place and it is a data analysis that examines their possible relations with other spatial data events [4]. Today, one of the most important tools of geography discipline that can be used in spatial analysis of the population is geographic information systems (GIS). Nowadays, GIS has become even stronger with the addition of statistical analysis (geostatistics, spatial statistics) tools. Especially, the technology-based spatial statistical data analysis (spatial data analysis) functions that had been placed into GIS have given a new dimension to the studies of population geography since GIS provide an effective set of tools that view, manage and analyze spatial data when combined with spatial statistics methods [ 2, 3, 9, 17, 28]. Today, it is possible to show the methods of center object, mean center, hotspot, standard deviation ellipses and

the kernel density analysis as some of the most important GIS methods are being used in the analysis of the population. Central object, defines an object that has been placed in the center of an area. It is found by calculating the distances of all objects to each other. Mean center defines the center of the condensation and is defined by a point that explains the center of the condensation. Accordingly, the mean center is the average of X and Y coordinates of all objects in an area. If the analysis includes the line or area features other than the points for the objects in an area, this time the coordinates of each point is defined first and then, the averages of these points are taken into consideration for the calculation of mean center. Mean center is also considered to be the center of attraction [23]. By using this method (mean center), which is also a common method that is used for population analysis, the distribution and over time shift direction of the population are determined. So, it is used in most of the studies in which spatial distribution is aimed to be determined [8, 10, 14, 25]. Hot spot analysis or GETIS-Ord G Statistics is used to define the cold areas (cold spots) or warm areas (hot spots) [19]. A high positive z-values, shows the structure of low-value clusters. On the other hand, the opposite situation shows the high-value clusters [18]. Besides, standard deviation ellipses try to reveal whether the objects in a pattern have direction of orientation or not. So, standard distances towards X and Y coordinates are calculated independently [16]. Therefore, the standard deviation ellipse, as the standard distance, shows the degree and extent of the spread and produces results. The shape and size of the ellipses shows the degree of spread, while the axis positions reveal the spatial orientation of the population [7]. In addition, kernel density analyze explain the density of points fall within the circle having a radius and changing means away from the source. Conceptually, a smooth, soft and curved surface is defined over each point. The surface value is the highest at point locations and this value decreases with distance from the point and becomes zero at the end. This definition is applied circumferentially around [24]. In summary, the kernel density is the density of points within a certain bandwidth radius of the circle [9]. The purpose of this study is to analyze the development, distribution and yearly changes of population in Marmara Region by using five different spatial and statistical methods (central object, mean center, hot spot and standard deviation ellipse and kernel density) of GIS by using the population data of the years 1965, 1985, 2000 and 2010. For this purpose, following questions were tried to be answered: 1. What were the locations of central object and mean center in the years of 1965, 1985, 2000 and 2010? 2. What were their locations relative to each other? How do their locations change through years? 3. How did the points show distribution after hot spot analysis applied? 4. What were the properties of standard deviation ellipses and standard distance circles produced based on population distribution? 5. In which areas, did the population concentrate on within the results of Kernel Density? II. METHODOLOGY The study area of this study is Marmara Region, which is located in the northeast of Turkey, and the main data source of the study was Turkish Statistical Institute [26, 27]. In this study, which was conducted at the districts level, the population data of 1965, 1985, 2000 and 2010 were taken from TUIK s website and reorganized in Excel format. Then, after passing through a certain order, the data were transferred to ArcGIS 10.1 software with ".mdb" extension with the help of Microsoft Access (Figure 1). FIGURE 1. THE TRANSFER OF POPULATION DATA TO MICROSOFT ACCESS IN.MDB FORMAT 21

For the analysis of population data of four different years, the functions of central feature (Figure 2a), mean center (Figure 2b) and hot spot (Figure 2c) under ArcToolbox were used. After opening these functions, the data of 1965, 1985, 2000 and 2010 populations were selected as inputs and input feature class and weight in the dialog box (Figure 3) that appears in order to produce maps by years. In addition, for the analysis of population data of four different years, the functions of standard deviation ellipse (Figure 4a) and kernel density (Figure 4b) under ArcToolbox were used. After opening these functions, the data of 1965, 1985, 2000 and 2010 populations were selected as inputs and input feature class and weight in the dialog box (Figure 5) that appears in order to produce maps by years. a b c FIGURE 2. CENTRAL FEATURE, MEAN CENTER AND HOT SPOT ANALYZE UNDER ARCTOOLBOX FIGURE 3. CENTRAL FEATURE, MEAN CENTER AND HOT SPOT ANALYZE DIALOG WINDOWS a b FIGURE 4. STANDARD DEVIATION ELLIPSE AND KERNEL DENSITY ANALYZE UNDER ARCTOOLBOX 22

FIGURE 5. STANDARD DEVIATION ELLIPSE AND KERNEL DENSITY ANALYZE DIALOG WINDOWS III. FINDINGS The results of the analysis of center feature, the mean center, and hot spot on four different years population are presented below, respectively. In spatial distribution of population; the weighted center feature was centered on Zeytinburnu in 1965, Eminönü in 1985, Fatih in 2000 and again Zeytinburnu in 2010. The locations of the center vary every year. The locations, coordinates, distance and orientation of the center feature and mean center are given in a list below (Table 1). In addition, an apparent displacement in the mean center s location was examined with 26.58 km in northeast direction between the years of 1965-1985. Also, displacement in the mean center s location was examined with 2.47 km in west direction between 1985-2000 and 10.12 km in the southwest direction between 2000-2010 (Table 2). In addition, yearly center features and mean centers were given together in Figure 6. Moreover, in Figure 7, the results of both analyzes are presented in close-up. According to hot spot analysis, it was revealed that the population was concentrated in the districts of Istanbul in 1965. The reason for the concentration of red dots in Istanbul is both the amount of the high population and high number of districts in Istanbul. This event was also true for other years. It was also seen that orange dots were located in the districts of Istanbul such as Beykoz, Kartal and Adalar. White dots are located in Çatalca, Kandıra (in Kocaeli), Gemlik, Mudanya, Orhaneli, Keles, and Inegol (the districts of Bursa). Moreover, blue and light blue colors were distributed to other districts in the region (Figure 8). According to results of hot spot analysis for 1985 it was revealed that the population was concentrated in Istanbul. Red and orange colored spots were located in the districts of Istanbul. White colors were located in Çatalca, Mudanya, Orhaneli and Keles. Light blue spots were observed in Kocaeli's Gebze, Yalova, Bursa, Gemlik and Inegöl. Blue colors were distributed to the other districts. Besides, according to results of hot spot analysis for 2000 it was seen that the red and orange colors are concentrated in the districts of Istanbul again. White spots were located in Çatalca and Tuzla districts of Istanbul. Light blue spots were seen in Nilüfer, Yıldırım, Gürsu and Orhaneli districts. Moreover, the blue dots are dispersed to other districts. TABLE 1. LOCATIONS OF CENTER FEATURE AND MEAN CENTER Years Population Center Feature Mean Center Direction (From Distance Coordinates mean center to (Km) X Y center feature) 1965 102,874 Zeytinburnu 545467 4928915 40.19 Northeast 1985 93,383 Eminönü 567372 4943980 20.83 Northeast 2000 403,508 Fatih 569785 4943449 19.99 North 2010 292,430 Zeytinburnu 559738 4942211 21.06 Northeast TABLE 2. COORDINATES AND DISPLACEMENT OF MEAN CENTERS Mean Center Coordinate X Coordinate Y Distance Dİrection 1965 545467 4928915 - - 1985 567372 4943980 26.58 Km Northeast 2000 569785 4943449 2.47 Km West 2010 559738 4942211 10.12Km Southwest 23

FIGURE 6. CENTRAL FEATURES AND MEAN CENTERS IN MARMARA REGION FIGURE 7. CENTRAL FEATURES AND MEAN CENTERS IN MARMARA REGION (CLOSE-UP) 24

According to results of hot spot analysis for 2010 it was revealed that the red spots were concentrated in the districts of Istanbul again. Orange spots were seen in Istanbul s Pendik, Sultanbeyli and Çatalca districts. White spots were seen in Tuzla, Çayırova and Gürsu districts. Moreover, there were more light blue spots than previous years and blue spots were dispersed to the other districts (Figure 9). In addition, the analysis of standard deviation elipse showed that the circle radius of the standard distance is deemed to fall until 2010 (Table 3). TABLE 3. WEIGHTED STANDARD DISTANCES Years Center Standard Distance X Y (Radius) 1965 545467 4928915 111332.507052 1985 567372 4943980 76041.443178 2000 569785 4943449 73362.869167 2010 559741 4942119 90235.713412 Also, these values increased again in 2010 and has come to a position of between 1965 and 1985. Accordingly, the standard distance circles are shrinking as in Istanbul focused. This case, shows the large amount of population in Istanbul, immigration to Istanbul and Istanbul s being center city in the region (Figure 10). In addition, the weighted standard deviation ellipse can determine the extent and direction of the distribution. When looking at the prepared map, it was obvious that the population was concentrated around Marmara Sea (Table 4) and the direction of extension was the average 88-degree angle appears to be an east-west axis. In addition, the standard deviation of the ellipse narrows gradually until FIGURE 8. 1965 POPULATION S WEIGHTED HOT SPOTS 25 2010, and enlarges again in 2010. The narrowing and growth of this standard deviation ellipse is connected to the increase and decrease of standard distance numbers (Figure 11). Years TABLE 4. WEIGHTED STANDARD DISTANCES Center X Center Y Standard Distance X Standard Distance Y Angle 1965 545467 4928915 87659 130788 86,43 1985 567372 4943980 63474 86807 85,83 2000 569785 4943449 62005 83183 88,46 2010 559738 4942210 104964 72628 94,78 According to the results of Kernel (kernel core predictive) density analysis, it was revealed that most of the population was concentrated in the province of Istanbul in 1965. In particular, Fatih, Zeytinburnu, Eminonu, Besiktas, Sisli, Eyüp and Üsküdar are concentrated in the darker colors. In addition, other than Istanbul, high population was seen in Bursa and Mustafakemalpaşa, Kocaeli, Adapazarı, Balıkesir, Çanakkale s Biga, Edirne and Uzunköprü (Figure 12). In 1985, the dark colors were seen in Istanbul's Bakırköy, Beyoğlu, Eyüp, Sisli, Zeytinburnu, Fatih, Eminönü, Beşiktaş, Beşiktaş, Üsküdar, Kadıköy and Kartal districts and Bursa, Balıkesir and Adapazarı. In 2000, with the addition of new districts to Istanbul, it was seen that the population was concentrated in Bakırköy, Bahçelievler, Bağcılar, Beyoğlu, Beşiktaş, Beyoğlu, Beşiktaş, Eminönü, Fatih, Sisli, Besiktas, Eyüp, Üsküdar, Kadıköy and Kartal districts. Also, Adapazarı, Balıkesir and Bandırma, Tekirdag and Corlu, Beyoğlu, Lüleburgaz, Edirne,

Osmangazi, Inegöl and Yıldırım, Kocaeli, Izmit, Derince, Gebze and Gölcük were among high populated districts. For the year 2010, the results of weighted kernel density analysis showed that most of the population is concentrated in the province of Istanbul again. In particular, the population was concentrated in the districts of Bağcılar, Küçükçekmece, Bakırköy, Bahçelievler, Esenler, Güngören, Zeytinburnu, Bayrampaşa, Gaziosmanpaşa, Fatih, Bağcılar, Üsküdar, Kadıköy and Ümraniye. In addition, other than Istanbul, Bursa s Osmangazi, Yıldırım and Inegöl, Balikesir, Tekirdag's Central, Malkara, Çorlu and Çerkezkoy districts, Central and Suloglu districts of Edirne, Adapazarı and Kocaeli districts were among high concentrated population areas (Figure 13). FIGURE 9. 2010 POPULATION S WEIGHTED HOT SPOTS FIGURE 10. STANDARD DISTANCES 26

FIGURE 11. WEIGHTED STANDARD ELLIPSES FIGURE 12. 1965 POPULATION KERNEL DENSITY MAP 27

IV. CONCLUSIONS In this study, the development, distribution and yearly changes of population in Marmara Region was analyzed by using five different spatial and statistical methods (central object, mean center, hot spot, standard deviation ellipse and kernel density) by using the population data of years 1965, 1985, 2000 and 2010. Some important results were revealed at the end of the study. First, it was seen that, by the central object analysis, the weighted center feature was centered on Zeytinburnu in 1965, Eminönü in 1985, Fatih in 2000 and again Zeytinburnu in 2010. Accordingly, all of the central features were located in Istanbul. Undoubtedly, the main reason for this is the highest population of Istanbul in Marmara Region. In addition, the coordinates of mean center changed every year. It displaced towards Istanbul under the force of Istanbul s highest population. Moreover, as a result of the comparison of central feature and mean center, an apparent displacement was observed from mean center to central feature in northeast direction. Moreover, as a result of hot spot analysis, it was seen that red and orange spots were generally concentrated in the districts of Istanbul. In addition, white spots were located in Çatalca, Kandıra Mudanya, Gemlik, Orhaneli, Keles, Tuzla, İnegöl, Çayırova ve Gürsu, blue and light blue spots were dispersed to other districts. Moreover, the standard deviation of the ellipse narrowed gradually until 2010, and enlargeed again in 2010. Generally, the radiuses of the circles were decreased towards the centre of Istanbul because of Istanbul s high population. In addition, the focus of the ellipses around Marmara Sea showed the high populations of the districts in this area. FIGURE 13. 2010 POPULATION KERNEL DENSITY MAP 28 In the results of Kernel (kernel core predictive) density analysis, it was revealed that most of the population was concentrated in the province of Istanbul. Between 1965 and 2010, high increases in population was observed especially in Bursa and some districts tied to it, Kocaeli and its some districts, Balıkesir, Edirne, Malkara and Çorlu. In summary, as a result of this study which was conducted to analyze the development, distribution and yearly changes of population in Marmara Region using five different spatial and statistical methods of (central object, mean center, hot spot, standard deviation ellipse and kernel density) by using the population data of years 1965, 1985, 2000 and 2010, important results were revealed and mapped. In this aspect, due to data contained and the results revealed the characteristics and movement of population was presented in the study. In addition, the study is considered a guiding example for the upcoming similar studies in the different regions of Turkey due to its methods. REFERENCES [1] Akbulak, C. (2006). İznik Depresyonu nun Beşeri Ve İktisadi Coğrafya Açısından İncelenmesi. (Phd Thesis), İstanbul Üniversitesi Sosyal Bilimler Enstitüsü, İstanbul. [2] Anselin, L. (1995). Local indicators of spatial association-lisa. Geographical Analysis, 27: 93-115. [3] Anselin, L. (2003). Spatial externalities, spatial multipliers, and spatial econometrics. International Regional Science Review, 26, 153-166. [4] Bailey, T.C. & Gatrell, A.C. (1995). Interactive Spatial Data Analysis. Addison Wesley Longman Limited, Harlow. [5] Barrett, H.R. (1992). PopulationGeography. Bristol: Oliver&Boyd. [6] Boots, B. & Tiefelsdorf, M. (2000). Global and local spatial autocorrelation in bounded regular tessellations. Journal of Geographical Systems. 2, 319-348.

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