Irbid growth using regression modle.2003to2013
- 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
- 7. 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
- 8. 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
- 9. 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
- 10. 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.
- 11. 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.
- 12. 2004 8002
- 13. 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.
- 14. 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
- 15. 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
- 16. 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
- 17. 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
- 18. 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
- 19. 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
- 20. 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
- 21. 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
- 22. 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
- 23. 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
- 24. 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
- 25. 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
- 26. 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
- 27. 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
- 28. 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
- 29. 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
- 30. 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
- 31. 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
- 32. 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
- 33. 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
- 34. 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
- 35. 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
- 36. 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