The document describes a study that used linear regression to model urban growth in Irbid, Jordan from 2002-2013. It aimed to determine the relationship between growth in built-up area and factors like population, density, building permits, and construction costs. The best variable to represent growth was found to be the total number of building permits, which correlated negatively with increases in population and density. Urban growth occurred primarily to the north and east of the city. Population increased 22% while built-up area rose 70% during the study period.
"مدينة معرفة حديثة ذات تخطيط جيد وتنمية مستدامة تحتضن تراثها و تشكل محور تعليم إقليمي؛ مزدهرة اقتصاديا وتقوم بالاستثمار بشكل نشط في مواردها الطبيعية والبشرية."
تنبثق رؤية اربد من المجتمع ذاته، إذ تعكس الرؤية احتياجات المدينة وتطلعاتها؛ فهي رؤية شاملة لكافة طبقات المجتمع من أدناها إلى أعلاها من أصحاب المتاجر والأطفال وربات البيوت ورجال الأعمال الذين يستثمرون في مستقبل اربد.
لمزيدمن المعلومات www.ammaninstitute.com
"مدينة معرفة حديثة ذات تخطيط جيد وتنمية مستدامة تحتضن تراثها و تشكل محور تعليم إقليمي؛ مزدهرة اقتصاديا وتقوم بالاستثمار بشكل نشط في مواردها الطبيعية والبشرية."
تنبثق رؤية اربد من المجتمع ذاته، إذ تعكس الرؤية احتياجات المدينة وتطلعاتها؛ فهي رؤية شاملة لكافة طبقات المجتمع من أدناها إلى أعلاها من أصحاب المتاجر والأطفال وربات البيوت ورجال الأعمال الذين يستثمرون في مستقبل اربد.
لمزيدمن المعلومات www.ammaninstitute.com
CITY IN PROGRESS
Feature:
82 Global Concepts, Local Initiative The Amman Institute for Urban Development in a nutshell.
Progress
92 ‘A Park for Abdoun’
96 ‘The New Ras El-Ein’
100 ‘Restoring Faisal’
Spotlight
106 ‘Why So Serious?’
In 2008, the first ever Amman Stand-Up Comedy Festival in the Arab world burst onto the city’s cultural scene, taking us all by storm; two years and two festivals later, the laughs just keep getting louder.
تحليل قطعة أرض في جامعة آل البيت في مدينة المفرق - الأردن
من أجل مشروع بناء كلية علوم جديدة
تصميم معماري 4
إعداد المجموعة ، تنسيق أسماء السعود
د. فهد الخصاونة
مشروع تطوير ميدان لاظوغلي بالقاهرة و دراسة الوضع الراهن و محددات الموقع بالإضافة الى النذة التاريخية للميدان و تديد المشكلات و حلها في مقترح متكامل
مقرر التصميم العمراني - قسم الهندسة المعمارية
كلية الهندسة جامعة طنطا - مصر
تخطيط المجاورة السكنية
تخطيط عمرانى
الفرقة الثالثة عمارة
المنطقة السكنية#
تخطيط الطرق#
مسارات المشاه#
تصميم الفراغات#
تخطيط الطرق#
تصميم المجاورة#
تخطيط عمرانى#
CITY IN PROGRESS
Feature:
82 Global Concepts, Local Initiative The Amman Institute for Urban Development in a nutshell.
Progress
92 ‘A Park for Abdoun’
96 ‘The New Ras El-Ein’
100 ‘Restoring Faisal’
Spotlight
106 ‘Why So Serious?’
In 2008, the first ever Amman Stand-Up Comedy Festival in the Arab world burst onto the city’s cultural scene, taking us all by storm; two years and two festivals later, the laughs just keep getting louder.
تحليل قطعة أرض في جامعة آل البيت في مدينة المفرق - الأردن
من أجل مشروع بناء كلية علوم جديدة
تصميم معماري 4
إعداد المجموعة ، تنسيق أسماء السعود
د. فهد الخصاونة
مشروع تطوير ميدان لاظوغلي بالقاهرة و دراسة الوضع الراهن و محددات الموقع بالإضافة الى النذة التاريخية للميدان و تديد المشكلات و حلها في مقترح متكامل
مقرر التصميم العمراني - قسم الهندسة المعمارية
كلية الهندسة جامعة طنطا - مصر
تخطيط المجاورة السكنية
تخطيط عمرانى
الفرقة الثالثة عمارة
المنطقة السكنية#
تخطيط الطرق#
مسارات المشاه#
تصميم الفراغات#
تخطيط الطرق#
تصميم المجاورة#
تخطيط عمرانى#
Shops front sign boards & adds billboards location jordan irbid the uni...Shomou' Aljizawi
IRBID JORDAN,Visual pollution is an aesthetic issue referring to the impacts of pollution that impair one's ability to enjoy a view
Dr. Nasser Abu Anzeh
Done by:
Noor Qteshat
Shomou al Jizawi
Urban planning is the most essential requirement for any city to develop.This analysis shows the results of built-up area growth relative to the population at that period.
Urban Sprawl and its Impact on Urban EnvironmentIOSR Journals
This Paper an attempt has been made to examine the urban sprawl of Gorakhpur City through the land sat Images . Remote sensing and GIS to analyze the urban sprawl mapping and detect changes of urban sprawl of Gorakhpur city through different year. Satellite data are found to be useful in mapping and quantifying the extent of urban area in different time periods. New urban region development growing largely towards north, north-west and south-west direction along the main transport route of the city. New urban development occurs mainly on vegetation and agricultural land. This study provides a methodology for better estimation of urban growth and population using various land sat images with time. Geographical information system(GIS) and satellite images have been used in this study to provide spatial inputs and test the statistical model describing growth. This is useful for the urban planning in Developing Countries where land use data is not available regularly. GIS and Rescan help a lot in monitoring urban sprawl compared to Conventional technique
URBAN SPRAWL AND ITS CHANGING PARADIGMS A CASE STUDY OF JAIPUR CITYJournal For Research
There is widespread concern about understanding and curbing urban sprawl, which has been cited for its negative impacts on natural resources, economic development and quality of life of the society. There is not, however, a universally accepted definition of urban sprawl. It has been described using quantitative measures, qualitative terms, attitudinal explanations, and landscapes patterns. The increasing pace of urbanization is usually associated with and driven by the population concentration in an urban area over the periods. The extent of urbanization and its growth drives the change in land use land cover patterns results to urban areas continue to expand over the periods. So, this present study deals with the quantifying the spatial patterns in Jaipur city, analysis based on primary and secondary data collected from different sources, using the spatial analysis technique like Entropy Model for the detection of change in spatial and temporal variability of urban sprawl and the degree of spatial concentration or dispersion of geospatial variable. The boundary less cities are the new paradigms of development and Jaipur is no exception. The process of urban sprawl has been resulted due to the continuous industrial and economic development in the rural – urban fringe of the study area.
Urban Expansion & Its Association with Population Growth: A Case Study of Raj...washifahmednaqib
Population growth refers to the increase in the population of any city, region, or country. Urbanization refers to the population shift from rural to urban areas, the corresponding decrease in the proportion of people living in rural areas, and the ways in which societies adapt to this change.
The aim of the study is to find out the association between urban expansion and population growth in Rajshahi city.
Multi-Scale Urban Analysis Using Remote Sensing and GISCSCJournals
India experienced a high rate of urbanization during the last five decades leading to concentration of population in the main cities. One of the main city is Hyderabad in India is a sprawling metropolis and an incipient megacity facing structural, environmental, social and economic problems. The objective of this study is to investigate the current pattern of land use to monitor the trends of urban growth in Hyderabad between 1997, 2007 and 2013 using satellite images and GIS. Second object is to enable a highly detailed structural characteristics of specific neighborhoods’, thus a multi-scale analysis of the urban area by remote sensing provides up-todate data of the urban morphology. This enables a value-added and more holistic view to understand urban workflows and their dependencies.
Environmental Impacts of Urban Growth From an Integrated Dynamic Perspective:...Kam Raju
India is home to one-fifth of the world’s population and that population is increasingly urban.
Although there are studies that focus on specific elements of urban growth, there is very little empirical work that incorporates feedbacks and linkages to assess the interactions between the dynamics of urban growth and their environmental impacts.
A multi-scale Urban Analysis Using Remote Sensing and GISWaqas Tariq
Urban planning was very much a design and engineering exercise with the state as a single stake holder. Mega cities with millions of population, has undergone a series of physical as well as socio-economic changes over the last 60 years. In India, Hyderabad experienced a high rate of urbanization facing structural, environmental, social and economic problems. To provide a holistic perspective on the urban characteristics, an interdisciplinary research approach is used. GISGeographic Information System and Remote Sensing provide the advance techniques and methods for studying urban land development and assist urban planning.
Factors Determining the Continuous Price Appreciation of Condominium Units in...Premier Publishers
The rapid growth rate of Addis Ababa’s population has resulted in a growing demand for residential housing. Hence, the Ethiopian government reacted to launch condominium program to improve the housing problems of the poor. However, the continuous appreciation of this condominium transaction price and unaffordability of the units for the poor were some of the challenges to this housing program after transfer. This study aimed to explore the major factors responsible for price appreciation correlated to the demand. Descriptive research method and mixed research approach have employed on three pilot study areas through, purposive selection focusing on two municipal districts in Addis Ababa. Both primary and secondary sources of data were used by means of questionnaires, interviews and a review of relevant documents. The OLS estimators have resulted highly statistically significant for the expected variables of year, area and location in determining the price suggesting further contributing factors from the actual findings. Actual findings thus identified; lack of information, illegitimate role of brokers and monopolistic housing supply as major determinants. Finally, this study has recommended passing regulations and directives can minimize the incremental rate of the condominium transaction price considering all the challenging factors of the sector having clear and reasonable valuation methods.
Analysis of factors affecting urban per capita housing area in ChinaIJAEMSJORNAL
Housing problems have become one of the hottest topics, influencing people's livelihood and national economy. This paper intends to re-analyze the per capita housing area, which characterizes the residents' happiness index, in order to measure the basic living condition. Taking into account of the large expansion of the floating population in the process of urbanization, we choose “urban resident population” to amend the “registration population”, which is the denominator of the index. We selected the data of residential investment, urban residents' consumption level and residential completion area from 1978 to 2015 to analyze the influence of independent variables on the per capita housing area, we found the volatility of housing price, which reduces the average level of urban per capita housing empirically.
Urban resilience in the digital age: The influence of Information-Communicati...AgboolaPaul3
In the pursuit of advancing urban sustainability within the unique backdrop of Nigeria’s built environment and
its environmental challenges, this study presents the significance of information and communication technology
(ICT). Undertaking this research holds immense importance in illuminating the possibilities inherent in
leveraging ICT to foster urban sustainability. The study’s objectives encompass a comprehensive investigation
into the multifaceted contributions of ICT to urban sustainability, while also delving into its impact on stakeholder
engagement and participation in these sustainability endeavors. Addressing an identified gap, the study
sheds light on the critical nexus between stakeholders’ active involvement and the resulting impact on urban
sustainability. This connection serves as a crucial yet under-explored avenue within the broader discourse on
leveraging ICT for sustainable urban development. The study employs structural equation modeling (SEM) to
evaluate a proposed model and analyze empirical data. The results of the study highlight the critical role of ICTs
in urban sustainability (β = 0.614, R2 = 0.85), demonstrating its capacity to enhance efficiency; which promotes
sustainability practices, and improves the quality of life for urban residents. The findings of this study have
significant implications, as they suggest the potential for optimizing the impact of ICT-based urban environments
to meet the diverse needs and priorities of society as a whole. By leveraging ICT effectively, countries can create a
robust smart environment that contributes to sustainable development and addresses environmental concerns.
To leverage the benefits of ICT, however, appropriate attention should be committed to the execution of smart
urban sustainability through stakeholder participation and involvement. The implication of the study enables the
possibility to optimize the impact of an ICT-based urban environment, thereby creating sustainable and resilient
communities that meet the needs and priorities of all members of society.
In our newly released Data Insights Report: Planning Applications, we revisited the wealth of environmental, land and property data that we gather, manage and supply to the property industry on a daily basis, and analysed relevant datasets to provide a summary of planning trends between 2012 and 2021. Overall, we saw that, year on year, the rate of planning applications in the UK is increasing. In fact, in every region – except for London – the number of planning applications submitted per 100,000 people was at its highest in 2021, since 2012.
As well as pinpointing the regions with the highest and lowest application volumes, the report draws on data that reveals:
Planning application types
Granted/refusal rates
Approval rates for renewable energy projects
Approval rates in flood zones
Essential amenities per approved planning application
The report also features expert commentary from Landmark’s Chris Loaring, Managing Director (Legal), who shares his observations on planning trends beyond ‘the Covid effect’ and highlights the growing implications of the journey to net zero in future planning policy. Piers Edgell, Landmark’s Client Director (Geodata) closes with a powerful argument for how planning data and the levelling up agenda are inextricably linked:
“… planning is about so much more than simply where developments will or will not be built. Every planning decision impacts the immediate environment, and so planning data could and should become a critical tool for informing and shaping social policies that aim to improve – or level up – communities. What, for example, could the kinds of planning data shown in this report tell us, when mapped against data on increasing property values or household incomes? What could the data we have summarised on amenities within the vicinity of planning applications tell us about location-specific standards of living and local needs?”.
Similar to Irbid growth using regression modle.2003to2013 (20)
DONE IN 2014 Al Damtha city Jordan population growth. QUANTITATIVE ASSESSMENT OF LAND USE . Comparison of per capita share (m²/person) with other local and international standards
Al zarqa river pollution causes, actions and revival ………rectangular of pollutionShomou' Aljizawi
Environment Impact Assessment for Al Zarqa River in Jordan, Rectangular Of Pollution , done by Eng Shomou + Eng Noor and supervised by Dr. Nasser Abu Anzeeh
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERSveerababupersonal22
It consists of cw radar and fmcw radar ,range measurement,if amplifier and fmcw altimeterThe CW radar operates using continuous wave transmission, while the FMCW radar employs frequency-modulated continuous wave technology. Range measurement is a crucial aspect of radar systems, providing information about the distance to a target. The IF amplifier plays a key role in signal processing, amplifying intermediate frequency signals for further analysis. The FMCW altimeter utilizes frequency-modulated continuous wave technology to accurately measure altitude above a reference point.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
block diagram and signal flow graph representation
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
6.
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.
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