1.
Urban growth in Irbid
Jordan using
Regression model
2002-2013
The subject: study the growth of Irbid from 2002-2013
Purpose: show the
Use regression model to estimate the growth in Irbid and the main elements that effect
the growth and the built up area.
Estimate the growth direction in Irbid by using GIS system
Done by Shomou FarouqAl Jizawi
Supervision D.r Imad al hashimy

2.
Urban growth in Irbid Jordan using
Regression model
The subject: study the growth of Irbid from 2002-2013
Purpose: show the
Use regression model to estimate the growth in Irbid and the main elements that effect
the growth and the built up area.
Estimate the growth direction in Irbid by using GIS system.
Abstract
This study applied leaner regression to model urban growth in Irbid to discover the
relationship between urban growth and the driving forces. We will use cross section
from 2002 to 2013.There are many factors affect the new building construction
growth in the city which leads to urban growth. What we will study in this paper is.
A. Find the main 'Y' that represents the growth of built up area using leaner
regressionmodel.
The probable depending Y's are:
 Total Number of Building permits each year
 Total new Building area each year
 Total building construction price each year
 Building construction price per square meter.
B. The percentage ofgrowth in Irbid city from 2004 to 2013.
 Population and density growth.
 Built up area "new building construction".
C. Using GIS to study the direction of growthin Irbid.
1. INTRODUCTION
Due to the high concentration of population in urban areas there is a rapid growth in
urban. Urban development has often the meaning of urban growth. Thus, the rapid
change in the pattern of urban within a short period of time can be seen. On the
other hand, understanding the mechanisms of urban development is crucial for
planning and urban management in order to achieve sustainable urban development.
Therefore, I will develop a model for study the urban growth. The modeling aims to
discover the relationship between urban growth regarding to the increase in the built
up area and population and density growth in Irbid.

3.
2. PROPOSED METHODOLOGY
Specification-choice the variables dependent Y and independent X.
Table 1
List of variables included in the linear regression model
Variable Meaning Nature of variable
Dependent
Y1 Total Number of Building permits each year Continuous
Y2 Total new Building area each year Continuous
Y3 Total building construction price each year Continuous
Y4 Building construction price per square meter. Continuous
Independent
X1 Population Continuous
X2 Population density (person/km2) Continuous
3. THE DATA FOR THE STUDY AREA
In this research the process of urban growth is modeled for the city of Irbid. Irbid is
the 2nd largest city in population in Jordan according to statistics provided by the
Statistical Center of Jordan, the city of Irbid, with an area of about 1,572 square
kilometers and a population of about 1.16 million. It is the city with the highest
density in Jordan. The city of Irbid, is located in the north of Jordan 320 35, to 350
48, .In this study, the satellite images shows the built up area of Irbid in the years
2004, 2008, 2011 and 2013 are used.

4.
Table 2
List for cross section data for Irbid for 2002 to 2013
4. EVALUATION AND RESULTS
Table 2
I used the SPSS program to find. The best y and x which will represent the growth of Irbid
city. The association between the variables.
Estimation for the coefficients a's and b's which is shown in the result of spss computer
program.
Give the tests results T test R2 test
X
Y
Population
X1
Density
X2
Population & density 2x's T test R2 test
Total number of
Building permits
(Y1,X1) 5.424 .618
(Y1,X2) 5.2 .617
(Y1,X1,X2) 5.427 .618
Total Building
construction area
(Y2,X1) .76 .055
(Y2,X2) .477 .237
(Y2,X1,X2) .481 .055
Total construction
price
(Y3,X1) .513 .026
(Y3,X2) .537 .027
(Y3,X1,X2) .548 .26
price per m2 (Y4,X1) .8 .24
(Y4,X2) .6 .024
(Y4,X1,X2) .6 .24
The best y is the Total number of Building permits with either one x or 2 x's
population and density.
The result the best Y and X is
1. YTotal number of Building permitswithXpopulation.
2. YTotal number of Building permitswithXdensity.
populationdensityTotal Number of
Building permits
each year
total New
Building area
each year
total newBuilding
construction price
per year
Building
construction
Price per m2
year
9507006043000430550493998001052002
9776006213036484678578997001102003
9520006053729626513764736001122004
9748006203463687155876734001172005
9968006343641821372963000001152006
10187006482925610825751000001132007
1041300662.42497604343991000001552008
1064400677.11867527856851000001512009
10881006921867547706929000001562010
11103007061867534000623570001162011
1137100723221769160062357000902012
1162300739.52418733787880544401202013
%22%22-%19%70%78%14
%of increase
between q`
2002- 2013
10395086612710.5608365.477726245121.6AVARAGE

5.
3. YTotal number of Building permitswith Xpopulation +Xdensity.
In research on how population growth affects built up area. There is a strong relation
how greater population size and density affect the growth of total Number of Building
permits each year.
The relationship between population and density growth and the total Number of
Building permits each year is negatively correlated.
What we found that the number of new building permits decrease while the
population and the built up area increased and that’s related to many reason
1- Now they built building with from 7 to 21 or more flats per building.
2- Single building like villas is less.
3- We have new malls building which area is very big.
 There is no correlation between the growth of population size and density
with the total New Building area each year.
 There is no correlation between the growth of population size and density
with the total new building construction price.
 There is no correlation between the growth of population size and density
with the building construction price per square meter
The graph for the best Y and X
The association of the variables is
Y1=a-+b X1 or Y1=a-+b X1-+b X2
Single regression
1-YTotal number of Building permits=a-+b Xpopulation
2-YTotal number of Building permits=a-+b Xdensity
Multiple regressions
3-YTotal number of Building permits=a-+b Xdensity-+bXpopulation
One of the single regression
YTotal number of Building permits=10433.9-.007Xpopulation
The elasticity is bigger than 1
The relation is elastic

6.
The percentage of growth in Irbid city from 2002 to 2013.
1. Population and density growth.
2. Built up area "new building construction".
1. Population and density growth.
The population now is 1162300 personincreased 211600 personfrom 2002 to 3013 which
representa 22% increase from 2002 which is a high percentage.
The density now is 120person per km2increased 135 person per km2from 2002 to 3013
which represent an 22% increase which is a high percentage.
950700
977600
952000
974800
996800
1018700
1041300
1064400
1088100
1110300
1162300
900000
950000
1000000
1050000
1100000
1150000
2002 2004 2006 2008 2010 2012 2014
the population persentage growth in irbid
the population persentage
growth in irbid
604
621
605
620
634
648
662.4
677.1
692
706
723
580
600
620
640
660
680
700
720
740
2000 2002 2004 2006 2008 2010 2012 2014
the density in irbid
the density in irbid

7.
2-Built up area "new building construction".
The total new building area in 2013 is 733787 m2which represent a 70% increase which is
very high percentage, with an average of 608365 and a sum of 7300385 m2 in 12 years
from 2002 to 3013.
The total number of Building permits in 2013 is 2418which represent a 19% decrease,but
in 2012 it started to increase again. Its average is 2710.5 and with sum of 32527 in 12 years
from 2002 to 3013.
0
100000
200000
300000
400000
500000
600000
700000
800000
900000
total New Building area
total New Building area
0
500
1000
1500
2000
2500
3000
3500
4000
Total Number of Building permits
Total Number of Building
permits

8.
The total new Building construction price in 2013 is 88054440 JDwhich represent a 78%
increase, with an average of 77726245 JD and a sum of 932714940 in 12 years from 2002
to 3013.
The building construction price per m2in 2013 is 120 JD which represent a 14% increase
from 2002, with an average of 121.2 JD from 2002 to 3013.
0
20000000
40000000
60000000
80000000
100000000
120000000
200220032004200520062007200820092010201120122013
Total price for building construction each year
Total price for building
construction each year
0
20
40
60
80
100
120
140
160
180
200220032004200520062007200820092010201120122013
price per m2 for building construction each year
price per m2 for building
construction each year

9.
N
D. Using GIS to study the direction of growthin Irbid.
In this paper, four satellite images of Ibid, which were taken in2004, 2008, 2012 and 2014,
are used as the base information layers to study the changes in urban growth direction of
the city of Irbid. The direction of the urban growth for the city of Irbid is to the north
toward Amman the capital of Jordan, the other direction or growth is in the direction of
Petra Street and in the center of the city which made the city very crowded. In a period of
twelve years the increase of population is 22% 211600 from 2002 with total population of
1162300 person is clear in the GIS photos. The increase in the built up area from 2002 to
2013 is 7300385 m2 which represent 70% increase from 2002.

10.
6. IMPLEMENTATION AND RESULT
In the first step of research, was obtained the variables. Then we collect the data
that is needed, the next step was by using linear regression model to find the best x's
and Y's that represent the urban growth of Irbid city regarding to the city growth.
Then we defined the direction of growth in Irbid by using GIS Arial views. Finally,
the simulated image of the urban growth was generated.
7. CONCLUSION
In this paper, at the beginning of the study I thought that the new building area or
the price are one of the main element that effect the growth and can represent Y in a
good way but after I worked on the SPSS I found out that they are not effective.
Also the price per m2 was the highest in the year's 2008, 2009, and 2010 while the
built up expansion and the new building growth was the highest in these three years
which show us that the price factor is not important and doesn’t affect the growth.
In the other hand the number of new building permits represents the best Y and
represents the growth with successful results. the number of new building permits
is decreasing from 2002 until 2013 by 14% while the growth is rapidly increasing
and that’s related to the decrease of the multi flat and level buildings, the decrease
of the villas and the increase of the huge building with thousands of m2 area like the
hypermarkets and the huge malls and shopping center which appear in the past
years. But I think there will be a limit then the number of permits will start to
increase again.
The satellite images of Irbid are used as the base information layers to study the
direction of the urban growth which is mainly to the north of Irbid in the direction
to Amman and to the east which appear after al Petra Street constructed. In my
opinion it's very important to study the urban growth, the element that affects it and
the direction of growth to be able to estimate the future expansion and its directions
to design the best future urban solutions.

11.
2004
8002

12.
DEPENDENT Y= Number of Building permits In Irbid
INDEPENDENT X= population In Irbid.
Regression
a
Variables Entered/Removed
Model Variables Entered Variables
Removed
Method
1
population In
Irbid.
. Enter
a. Dependent Variable: Number of Building permits In Irbid
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 a
.786 .618 .580 446.03847
a. Predictors: (Constant), population In Irbid
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 3224853.763 1 3224853.763 16.209 b
.002
Residual 1989503.154 10 198950.315
Total 5214356.917 11
a. Dependent Variable: Number of Building permits In Irbid
b. Predictors: (Constant), population In Irbid.

13.
a
Coefficients
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 10433.952 1922.650 5.427 .000
population In
Irbid.
-.007- .002 -.786- -4.026- .002
a. Dependent Variable: Number of Building permits In Irbid
Graph

14.
REGRESSION
DEPENDENT Y= Number of Building permits In Irbid
INDEPENDENT X= density In Irbid
Regression
a
Variables Entered/Removed
Model Variables Entered Variables
Removed
Method
1
density In
Irbid
. Enter
a. Dependent Variable: Number of Building permits In Irbid
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 a
.786 .617 .579 446.75889
a. Predictors: (Constant), density In Irbid
a
ANOVA
Model Sum of Squares df Mean Square F Sig.
1
Regression 3218421.856 1 3218421.856 16.125 b
.002
Residual 1995935.060 10 199593.506
Total 5214356.917 11

15.
a. Dependent Variable: Number of Building permits In Irbid
b. Predictors: (Constant), density In Irbid
a
Coefficients
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 10394.375 1917.836 5.420 .000
density In
Irbid
-11.624- 2.895 -.786- -4.016- .002
Graph

16.
REGRESSION
DEPENDENT Y = Number of Building permits In Irbid
INDEPENDENT X1= density In Irbid
INDEPENDENT X2= population In Irbid.
Regression
a
Variables Entered/Removed
Model Variables Entered Variables Removed Method
1 Population In Irbid . Enter
a. Dependent Variable Number of Building permits In Irbid
b. Tolerance = .000 limits reached.
ModelSummary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 a
.786 .618 .580 446.03847
a. Dependent Variable: Number of Building permits In Irbid
b. Predictors: (Constant), population In Irbid
a. Predictors: (Constant), population In Irbid
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 3224853.763 1 3224853.763 16.209 b
.002
Residual 1989503.154 10 198950.315
Total 5214356.917 11

17.
a. Dependent Variable: Number of Building permits In Irbid
Excluded Variablesa
Model Beta In t Sig. Partial
Correlation
Collinearity
Statistics
Tolerance
1 density In Irbid b
21.953 .646 .534 .211 3.511E-005
Graph
a
Coefficients
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 10433.952 1922.650 5.427 .000
population In Irbid -.007- .002 -.786- -4.026- .002
a. Dependent Variable: Number of Building permits In Irbid
b. Predictors in the Model:(Constant), population In Irbid

18.
REGRESSION
DEPENDENT Y= Building area In Irbid
INDEPENDENT X= population In Irbid.
Regression
a
Variables Entered/Removed
Model Variables Entered Variables
Removed
Method
1
Population In
Irbid
. Enter
a. Dependent Variable: Building area In Irbid
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 a
.234 .055 -.040- 113890.54104
a. Predictors: (Constant), population In Irbid
a
ANOVA
Model Sum of Squares df Mean Square F Sig.
1
Regression 7487628845.723 1 7487628845.723 .577 b
.465
Residual
129710553379.19
3
10 12971055337.919
Total
137198182224.91
7
11

19.
a. Dependent Variable: Building area In Irbid
b. Predictors: (Constant), population In Irbid
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 236210.357 490925.428 .481 .641
population In Irbid .358 .471 .234 .760 .465
a. Dependent Variable: Building area In Irbid
Graph

20.
REGRESSION
DEPENDENT Y= Building area In Irbid
INDEPENDENT X= density In Irbid.
Regression
a
Variables Entered/Removed
Model Variables Entered Variables
Removed
Method
1 density In Irbid . Enter
a. Dependent Variable: Building area In Irbid
b. All requested variables entered.
ModelSummary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 a
.237 .056 -.038- 113803.11318
a. Predictors: (Constant), density In Irbid
a
ANOVA
Model Sum of Squares df Mean Square F Sig.
1
Regression 7686696534.905 1 7686696534.905 .594 b
.459
Residual
129511485690.01
1
10 12951148569.001
Total
137198182224.91
7
11
a. Dependent Variable: Building area In Irbid
b. Predictors: (Constant), density In Irbid

21.
a
Coefficients
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 232853.287 488531.349 .477 .644
density in Irbid 568.097 737.406 .237 .770 .459
a. Dependent Variable: Building area In Irbid
Graph

22.
DEPENDENT Y= Building area In Irbid
INDEPENDENT X= density In Irbid
INDEPENDENT X= population In Irbid.
Regression
a
Variables Entered/Removed
Model Variables Entered Variables
Removed
Method
1
Population In
Irbid
. Enter
a. Dependent Variable: Building area In Irbid
b. Tolerance = .000 limits reached.
a. Predictors: (Constant), population In Irbid
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 7487628845.723 1 7487628845.723 .577 b
.465
Residual
129710553379.19
3
10 12971055337.919
Total
137198182224.91
7
11
a. Dependent Variable: Building area In Irbid
b. Predictors: (Constant), population In Irbid
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 a
.234 .055 -.040- 113890.54104

23.
a
Coefficients
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 236210.357 490925.428 .481 .641
Population In Irbid .358 .471 .234 .760 .465
a. Dependent Variable: Building area In Irbid
Excluded Variables
Model Beta In t Sig. Partial
Correlation
Collinearity
Statistics
Tolerance
1 Density In Irbid b
87.980 1.906 .089 .536 3.511E-005
a. Dependent Variable: Building area In Irbid
b. Predictors in the Model:(Constant), population In Irbid

24.
DEPENDENT Y= Building Price Per year In Irbid
INDEPENDENT X= population In Irbid.
Regression
a
Variables Entered/Removed
Model Variables Entered Variables
Removed
Method
1
population In
Irbid
. Enter
a. Dependent Variable: Building Price Per year
b. All requested variables entered.
ModelSummary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 a
.160 .026 -.072- 17006552.42486
a. Predictors: (Constant), population In Irbid
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression
76199170968913.
060
1
76199170968913.
060
.263 b
.619
Residual
28922282537943
87.000
10
28922282537943
8.700
Total
29684274247633
00.000
11
a. Dependent Variable: Building Price Per year In Irbid
b. Predictors: (Constant), population In Irbid

25.
a
Coefficients
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 40183424.486 73306781.656 .548 .596
population In
Irbid
36.116 70.362 .160 .513 .619
a. Dependent Variable: Building Price Per year In Irbid
Graph

26.
DEPENDENT Y= Building Price Per year In Irbid
DEPENDENT X= density In Irbid.
Regression
a
Variables Entered/Removed
Model Variables Entered Variables
Removed
Method
1
density In
Irbid
. Enter
a. Dependent Variable: Building Price Per year In Irbid
b. All requested variables entered.
ModelSummary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 a
.165 .027 -.070- 16992790.57774
a. Dependent Variable: Building Price Per year In Irbid
b. Predictors: (Constant), density In Irbid
a. Predictors: (Constant), density In Irbid
a
ANOVA
Model Sum of Squares df Mean Square F Sig.
1
Regression
80878108572431.
720
1
80878108572431.
720
.280 b
.608
Residual
28875493161908
68.500
10
28875493161908
6.900
Total
29684274247633
00.000
11

27.
a
Coefficients
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 39207724.718 72946254.935 .537 .603
density In
Irbid
58273.102 110107.613 .165 .529 .608
a. Dependent Variable: Building Price Per year In Irbid
Graph

28.
REGRESSION
DEPENDENT Y= Building Price Per year In Irbid
Irbiddensity In=1XDEPENDENTIN
Irbid.Population In=2XDEPENDENTIN
Regression
a
Variables Entered/Removed
Model Variables Entered Variables
Removed
Method
1
Population In
Irbid
. Enter
a. Dependent Variable: Building Price Per year In Irbid
b. Tolerance = .000 limits reached.
ModelSummary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 a
.160 .026 -.072- 17006552.42486
a. Predictors: (Constant), population In Irbid
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 76199170968913.060 1 76199170968913.060 .263 b
.619
Residual 2892228253794387.000 10 289222825379438.700
Total 2968427424763300.000 11
a. Dependent Variable: Building Price Per year In Irbid
b. Predictors: (Constant), population In Irbid

29.
a
Coefficients
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 40183424.486 73306781.656 .548 .596
population In Irbid 36.116 70.362 .160 .513 .619
a. Dependent Variable: Building Price Per year In Irbi
a. Dependent Variable: Building Price Per year In Irbid
b. Predictors in the Model:(Constant), population In Irbid
Graph
a
Excluded Variables
Model Beta In t Sig. Partial
Correlation
Collinearity
Statistics
Tolerance
1 Density In Irbid b
138.087 4.446 .002 .829 3.511E-005

30.
REGRESSION
DEPENDENT Y= Building Price Per m2 In Irbid
DEPENDENT X= population In Irbid
Regression
a
Entered/RemovedVariables
Model Variables Entered Variables
Removed
Method
1
population In
Irbid
. Enter
a. Dependent Variable: Building Price Per m2 In Irbid
b. All requested variables entered.
ModelSummary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 a
.153 .024 -.074- 21.73237
a. Predictors: (Constant), population In Irbid
a. Dependent Variable: Building Price Per m2 In Irbid
b. Predictors: (Constant), population In Irbid
ANOVA
Model Sum of Squares df Mean Square F Sig.
1
Regression 113.707 1 113.707 .241 b
.634
Residual 4722.959 10 472.296
Total 4836.667 11

31.
a
Coefficients
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 75.805 93.677 .809 .437
population In Irbid 4.412E-005 .000 .153 .491 .634
a. Dependent Variable: Building Price Per m2 In Irbid
Graph

32.
REGRESSION
DEPENDENT Y= Building Price Per m2 In Irbid
INDEPENDENT X= density In Irbid.
Regression
a
Entered/RemovedVariables
Model Variables Entered Variables
Removed
Method
1
density In
Irbid
. Enter
a. Dependent Variable: Building Price Per m2 In Irbid
b. All requested variables entered.
ModelSummary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 a
.156 .024 -.073- 21.72260
a. Dependent Variable: Building Price Per m2 In Irbid
b. Predictors: (Constant), density In Irbid
a. Predictors: (Constant), density In Irbid
a
ANOVA
Model Sum of Squares df Mean Square F Sig.
1
Regression 117.952 1 117.952 .250 b
.628
Residual 4718.715 10 471.871
Total 4836.667 11

33.
a
Coefficients
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 75.150 93.250 .806 .439
density In
Irbid
.070 .141 .156 .500 .628
a. Dependent Variable: Building Price Per m2 In Irbid
Graph

34.
REGRESSION
DEPENDENT Y= Building Price Per m2 In Irbid
INDEPENDENT X= density In Irbid
INDEPENDENT X= population In Irbid.
Regression
a
Entered/RemovedVariables
Model Variables Entered Variables
Removed
Method
1
Population In
Irbid
. Enter
a. Dependent Variable: Building.Price.Per.m2.In.irbid
b. Tolerance = .000 limits reached.
ModelSummary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 a
.153 .024 -.074- 21.73237
a. Dependent Variable: Building Price Per m2 In Irbid
b. Predictors: (Constant), population In Irbid
a. Predictors: (Constant), population In Irbid
a
ANOVA
Model Sum of Squares df Mean Square F Sig.
1
Regression 113.707 1 113.707 .241 b
.634
Residual 4722.959 10 472.296
Total 4836.667 11

35.
a
Coefficients
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 75.805 93.677 .809 .437
Population in Irbid 4.412E-005 .000 .153 .491 .634
a. Dependent Variable: Building Price Per m2 In Irbid
a. Dependent Variable: Building Price Per m2 In Irbid
b. Predictors in the Model:(Constant), population In Irbid
Graph
a
Excluded Variables
Model Beta In t Sig. Partial
Correlation
Collinearity
Statistics
Tolerance
1 Density In Irbid b
80.833 1.662 .131 .485 3.511E-005