International Journal of Science and Advanced Technology (ISSN 2221-8386)
Volume 4 No 2 February 2014
http://www.ijsat.com
The Spatial Analysis of Population in Marmara
Region (Turkey) By Using Geographic
Information Systems (GIS)
İlyas İŞCAN
Assoc. Prof. Dr. Fikret TUNA
Department of Geography
Fatih University, 34500 – Buyukcekmece
Istanbul, Turkey
[email protected]
Department of Geography
Fatih University, 34500 – Buyukcekmece
Istanbul, Turkey
[email protected]
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.
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.
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].
Keywords- Spatial Analysis, Central Object, Mean Center,
Hot Spot, Standard Deviation Ellipse, Kernel Density
I.
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].
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.
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].
Marmara Region, which constitutes the northwest of
Turkey including Istanbul and several large cities, takes
Today, it is possible to show the methods of “center
object, mean center, hotspot, standard deviation ellipses and
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International Journal of Science and Advanced Technology (ISSN 2221-8386)
Volume 4 No 2 February 2014
http://www.ijsat.com
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].
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:
“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].
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).
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
FIGURE 1. THE TRANSFER OF POPULATION DATA TO MICROSOFT ACCESS IN “.MDB” FORMAT
21
International Journal of Science and Advanced Technology (ISSN 2221-8386)
Volume 4 No 2 February 2014
http://www.ijsat.com
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.
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 3)
that appears in order to produce maps by years.
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
c
b
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
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International Journal of Science and Advanced Technology (ISSN 2221-8386)
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FIGURE 5. STANDARD DEVIATION ELLIPSE AND KERNEL DENSITY ANALYZE DIALOG WINDOWS
III.
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).
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).
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.
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
TABLE 1. LOCATIONS OF CENTER FEATURE AND MEAN CENTER
Years
Population
Center Feature
1965
1985
2000
2010
102,874
93,383
403,508
292,430
Zeytinburnu
Eminönü
Fatih
Zeytinburnu
Mean Center
Coordinates
X
Y
545467
4928915
567372
4943980
569785
4943449
559738
4942211
Distance
(Km)
40.19
20.83
19.99
21.06
Direction (From
mean center to
center feature)
Northeast
Northeast
North
Northeast
TABLE 2. COORDINATES AND DISPLACEMENT OF MEAN CENTERS
Mean Center
1965
1985
2000
2010
Coordinate X
545467
567372
569785
559738
Coordinate Y
4928915
4943980
4943449
4942211
23
Distance
26.58 Km
2.47 Km
10.12Km
Dİrection
Northeast
West
Southwest
International Journal of Science and Advanced Technology (ISSN 2221-8386)
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FIGURE 6. CENTRAL FEATURES AND MEAN CENTERS IN MARMARA REGION
FIGURE 7. CENTRAL FEATURES AND MEAN CENTERS IN MARMARA REGION (CLOSE-UP)
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International Journal of Science and Advanced Technology (ISSN 2221-8386)
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FIGURE 8. 1965 POPULATION’S WEIGHTED HOT SPOTS
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).
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).
TABLE 4. WEIGHTED STANDARD DISTANCES
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
1965
1985
2000
2010
Center
X
545467
567372
569785
559741
Y
4928915
4943980
4943449
4942119
Standard Distance
(Radius)
111332.507052
76041.443178
73362.869167
90235.713412
Years
Center
X
Center Y
Standard
Distance
X
Standard
Distance
Y
Angle
1965
1985
2000
2010
545467
567372
569785
559738
4928915
4943980
4943449
4942210
87659
63474
62005
104964
130788
86807
83183
72628
86,43
85,83
88,46
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).
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 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,
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
25
International Journal of Science and Advanced Technology (ISSN 2221-8386)
Volume 4 No 2 February 2014
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Osmangazi, Inegöl and Yıldırım, Kocaeli, Izmit, Derince,
Gebze and Gölcük were among high populated districts.
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).
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,
FIGURE 9. 2010 POPULATION’S WEIGHTED HOT SPOTS
FIGURE 10. STANDARD DISTANCES
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FIGURE 11. WEIGHTED STANDARD ELLIPSES
FIGURE 12. 1965 POPULATION KERNEL DENSITY MAP
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International Journal of Science and Advanced Technology (ISSN 2221-8386)
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FIGURE 13. 2010 POPULATION KERNEL DENSITY MAP
IV.
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.
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.
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.
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.
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The Spatial Analysis of Population in Marmara Region (Turkey)