The Determinants Of Variation Of Population Densities Within Cities Using Normal Micro-Squared LOLS And Geographical Regression GWR Methods

Authors

  • Nashwan Sh. Abdullah College of Human Sciences, Department of Geography, University of Duhok - Duhok, Kurdistan Region - Iraq

DOI:

https://doi.org/10.25007/ajnu.v6n1a10

Keywords:

OLS, Linear Regression, تحليل الكلي, تحليل الجزئي, ارتفاع وانحدار الأرض

Abstract

Geographical distribution of population inside cities and their affecting factors are the key stone to start arranging the internal structure of the city, the aim of the study is to test the role of these factors ( distance from the city center , size population , area , the rate of height and slope of surface) at the residential quarters level , on the variation of population densities inside the cities of Iraqi Kurdistan Region ( Sulaymaniyah , Erbil , Duhok) and the degree of the effect of each factor and their spatial variation , in accordance with the concept of the Linear Regression Analysis ,using two methods Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR)..

The study revealed the role of three key factors (size population, area, distance from the city center) through high significant statistic, the variation ratio was ranged in between (25-76%) according to the OLS, and (25-83%) according to the GWR. the Impact of the above factors was varied spatially progressively from the city center to the urban margin, as it was expected. In spite of the above mentioned, there was part of variation unexplained, related to the social and economic factors that were not taken in a consideration in this study.

Downloads

Download data is not yet available.

References

المصادر باللغة الانكليزية :
Andrew O. Finley, Comparing spatially-varying coefficients models for analysis of ecological data with non-stationary and anisotropic residual dependence, Methods in Ecology and Evolution 2011, 2, 143–154. doi: 10.1111/j.2041-210X.2010.00060.x
Bo Huanga, Bo Wub and Michael Barryc “Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices, International Journal of Geographical Information Science, Vol. 24, No. 3, March 2010, 383–401.
Chia-Hsien Lin and Tzai-Hung Wen, Using Geographically Weighted Regression (GWR) to Explore Spatial Varying Relationships of Immature Mosquitoes and Human Densities with the Incidence of Dengue, International Journal of Environmental Research and Public Health, 2011, 8, 2798-2815; doi:10.3390/ijerph8072798 www.mdpi.com/journal/ijerph
Chris Brunsdon , A.Stewart Fotheringham & Martin E.Charlton. Geographically Weighted Regression: A Method for Exploration Spatial Nonstationarity , Geographical Analysis ,Vol,28,No.4.1996.pp.281-298.
Chunrong Jia , Wesley James and Satish Kedia, Relationship of Racial Composition and Cancer Risks from Air Toxics Exposure in Memphis, Tennessee, U.S.A., International Journal of Environmental Research and Public Health, 2014, 11, 7713-7724; doi:10.3390/ijerph110807713. http://www.mdpi.com/journal/ijerph
Felix Ndidi Nkeki & Animam Beecroft Osirike,GIS-Based Local Spatial Statistical Model of Cholera Occurrence: Using Geographically Weighted Regression , Journal of Geographic Information System, 2013, 5, 531-532 http://dx.doi.org/10.4236/jgis.2013.56050
Felix Ndidi Nkeki , Animam Beecroft Osirike, GIS-Based Local Spatial Statistical Model of Cholera Occurrence: Using Geographically Weighted Regression , Journal of Geographic Information System, 2013, 5, 531-542. http://dx.doi.org/10.4236/jgis.2013.56050
Foody, G. (2004) spatial nonstationarity and scale-dependency in the relationship between species richness and environmental determinants for the sub-Saharan endemic avifauna. Global Ecology and Biogeography, 13, 315–320.
Gang Lin, Jingying Fu, Dong Jiang , Wensheng Hu , Donglin Dong , Yaohuan Huang , and Mingdong Zhao , Spatio-Temporal Variation of PM2.5 Concentrations and Their Relationship with Geographic and Socioeconomic Factors in China, International Journal of Environmental Research and Public Health, 2014, 11, 173-186; doi:10.3390/ijerph110100173 http://www.mdpi.com/journal/ijerph
Guo, L., Ma, Z. & Zhang, L. (2008) Comparison of bandwidth selection in application of geographically weighted regression: a case study. Canadian Journal of Forest Research, 38, 2526–2534.
J. Mart´ınez-Fern´andez , E. Chuvieco , and N. Koutsias , Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression, Natural Hazards and Earth System Sciences, 13, 311–327, 2013,doi:10.5194/nhess-13-311-2013 www.nat-hazards-earth-syst-sci.net/13/311/2013/
Jie Gao , Zhijie Zhang , Yi Hu , Jianchao Bian , Wen Jiang , Xiaoming Wang ,Liqian Sun , and Qingwu Jiang , Geographical Distribution Patterns of Iodine in Drinking-Water and Its Associations with Geological Factors in Shandong Province, China, International Journal of Environmental Research and Public Health, 2014, 11(5), 5431-5444; doi:10.3390/ijerph110505431 .http://www.mdpi.com/journal/ijerph
Khalid Al-Ahmadi , and Ali Al-Zahrani , Spatial Autocorrelation of Cancer Incidence in Saudi Arabia, International Journal of Environmental Research and Public Health, 2013, 10, 7207-7228; doi:10.3390/ijerph10127207 http://www.mdpi.com/journal/ijerph
Khalid Al-Ahmadi , and Ali Al-Zahrani , NO2 and Cancer Incidence in Saudi Arabia, International Journal of Environmental Research and Public Health, 2013, 10(11), 5844-5862; doi:10.3390/ijerph10115844 . http://www.mdpi.com/journal/ijerph
Mahdi-Salim Saib , Julien Caudeville , Florence Carre , Olivier Ganry , Alain Trugeon , and Andre Cicolella , Spatial Relationship Quantification between Environmental, Socioeconomic and Health Data at Different Geographic Levels, International Journal of Environmental Research and Public Health, 2014, 11, 3765-3786; doi:10.3390/ijerph110403765. http://www.mdpi.com/journal/ijerph
Noresah Mohd Shariff , 1Sanjay Gairola, Anita Talib . Modelling Urban Land Use Change Using Geographically Weighted Regression and the Implications for Sustainable Environmental Planning, International Environmental Modelling and Software Society (iEMSs) 2010 International Congress on Environmental Modelling and Software , Modelling for Environment’s Sake, Fifth Biennial Meeting, Ottawa, Canada , David A. Swayne, Wanhong Yang, A. A. Voinov, A. Rizzoli, T. Filatova (Eds.)
http://www.iemss.org/iemss2010/index.php?n=Main.Proceedings
Paul Holloway and Jennifer A. Miller , Exploring Spatial Scale, Autocorrelation and Nonstationarity of Bird Species Richness Patterns, International Journal of Geo-Information, 2015, 4, 783-798; doi:10.3390/ijgi4020783 http://www.mdpi.com/journal/ijgi
Pavel Propastin, Martin Kappas and Stefan Erasmi, Application of Geographically Weighted Regression to Investigate the Impact of Scale on Prediction Uncertainty by Modelling Relationship between Vegetation and Climate, International Journal of Spatial Data Infrastructures Research, 2008, Vol. 3, 73-94 . DOI: 10.2902/1725-0463.2008.03.art6
Peili Duan, Lijie Qin 1, Yeqiao Wang and Hongshi He , Spatiotemporal Correlations between Water Footprint and Agricultural Inputs: A Case Study of Maize Production in Northeast China, Water 2015, 7, 4026-4040; doi:10.3390/w708402 . http://www.mdpi.com/journal/water
S H M Arshad1, J Jaafar, M Z Z Abiden, Z A Latif and A R A Rasam , Spatial stochastic regression modelling of urban land use : 8th International Symposium of the Digital Earth (ISDE8) doi:10.1088/1755-1315/18/1/012170 http://www.healthunit.org
Shujuan Li , Hongyan Ren , Wensheng Hu 3, Liang Lu , Xinliang Xu , Dafang Zhuang andQiyong Liu , Spatiotemporal Heterogeneity Analysis of Hemorrhagic Fever with Renal Syndrome in China Using Geographically Weighted Regression Models, International Journal of Environmental Research and Public Health, 2014, 11, 12129-12147; doi:10.3390/ijerph111212129. http://www.mdpi.com/journal/ijerph
Stephen A. Matthews & Tse-Chuan Yang.Mapping the results of local statistics: Using geographically weighted regression, DEMOGRAPHIC RESEARCH ,VOLUME 26, ARTICLE 6, PAGES 151-166 ,PUBLISHED 02 MARCH 2012 http: DOI: 10.4054/DemRes.2012.26.6 www.demographic-research.org/Volumes/Vol26/6/
Tayyab Ikram Shah and Scott Bell, Exploring the Intra-Urban Variations in the Relationship among Geographic Accessibility to PHC Services and Socio-demographic Factors, Conference’HealthGIS 13, November 5–8, 2013, Orlando, Florida USA.
Ubydul Haque, Lauren M Scott, Masahiro Hashizume, Emily Fisher, Rashidul Haque, Taro Yamamoto and Gregory E Glass6 , Modeling malaria treatment practices in Bangladesh using spatial statistics, Haque et al. Malaria Journal 2012, 11:63 http://www.malariajournal.com/content/11/1/63
Wenjuan Hou, Jiangbo Gao, Shaohong Wu , and Erfu Dai .Interannual Variations in Growing-Season NDVI and Its Correlation with Climate Variables in the Southwestern Karst Region of China, remote sensing, 2015, 7, 11105-11124, doi:10.3390/rs70911105 http://www.mdpi.com/journal/remotesensing
Wenyi Sun , Jianhua Gong , Jieping Zhou , Yanlin Zhao , Junxiang Tan ,Abdoul Nasser Ibrahim and Yang Zhou , A Spatial, Social and Environmental Study of Tuberculosis in China Using Statistical and GIS Technology, International Journal of Environmental Research and Public Health, 2015, 12, 1425-1448; doi:10.3390/ijerph120201425 http://www.mdpi.com/journal/ijerph
- الكتب باللغة الانكليزية :
Christopher D.LIoyd .Local Models for spatial analysis ,CRC Press , London, 2007.
Christopher D.LIoyd .Spatial Data Analysis An Introduction for GIS users, Oxford University Press , 2010.
Coro Chasco Yrigoyen , Isabel García Rodríguez, José Vicéns Otero , MODELING SPATIAL VARIATIONS IN HOUSEHOLD DISPOSABLE INCOME WITH GEOGRAPHICALLY WEIGHTED REGRESSION1, : Instituto L.R. Klein – Centro Gauss. U.A.M. D.T. nº 15. February 2006
Fahui Wang. Quantitative Methods and Applications in GIS, CRC Press, London, 2006.
J.Keith, Spatial Autocorrelation: A Statistician's Reflections, In: Luc Anselin & Sergio J.Rey , Perspectives on Spatial Data Analysis, Springer , Heidelberg , 2010.
Lauren M. Scott & Mark V.Janikas. Spatial statistics in ArcGIS , In: Manfred M.Fischer & Arther Getis (eds). Handbook of Applied Spatial analysis, Springer, Heidelberg, 2010.
Manfred M.Fischer , Martin Reismann & Thomas Scherngerll. Spatial Interaction and Autocorrelation, In: Luc Anselin & Sergio J.Rey . Perspectives Spatial Data analysis, Springer, Heidelberg, London.2010.
Michael Jde.Smith, Michael F.Goodchild , Paul A.Longley . Geospatial analysis comprehensive guide to principles techniques and software tools ,Second Edition, Winche Sea Press, US.2007.
Mitchell, Andy.The ESRI Guide to GIS Analysis, Volume 2: Spatial Measurements & Statistics ESRI Press, Redland. California, 2005.
Peter A.Rogerson . Statistical methods for geography, SAGA, Publication, London, 2001.
Rachel Guillain and Julie Le Gallo. Employment Density in Ile-de-France : Evidence from Local Regression , In : Antonio Pa'ez, Ron N. Buliung, Julie Le Gallo, and Sandy Dall'erba . Progress in Spatial Analysis, Methods and Applications, Springer,Heidelberg, 2010 .
S. Fotheringham,A, C. Brunsdon and M. E. Charlton, “Geographically Weighted Regression, the A Spatially Varying Relationships,” John Wiley and Sons, Ltd., Hoboken, 2002.
S. Fotheringham,A, C. Brunsdon, and M. Charlton. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. John Wiley & Sons, Chichester, 2002.
S.Fotheringham,A . Brunsdon,CH.,& Cherlton,M. Quantitative Geography Perspectives on Spatial Data Analysis. SAGE Publication. Ltd. London.2007.
Sullivan, Daivd.O, & Unwin,Daivd .Geographical Information Analysis. John Wiley & Sons. New Jersey.2003.
المصادر باللغة العربية :
جمهورية العراق ،وزارة التخطيط ،الهيئة العليا للتعداد العام للسكان والمساكن (2009) ،خلاصة تحديث وحصر المباني والأسر في محافظات دهوك وابيل والسليمانية (غير منشورة).
يمان سنكري ، (اعداد وترجمة) ، التحليل الاحصائي للبيانات المكانية في نظم المعلومات الجغرافية،
المراجعة العلمية : المهندس عبد الله كامل ، شعاع للنشر والعلوم ، حلب-سوريا، 2008 .
نعمان شحادة ، التحليل الاحصائي في الجغرافيا والعلوم الاجتماعية ، دار صفاء ، عمان -الاردن, 2011.

Published

2017-08-01

How to Cite

Abdullah, N. S. (2017). The Determinants Of Variation Of Population Densities Within Cities Using Normal Micro-Squared LOLS And Geographical Regression GWR Methods. Academic Journal of Nawroz University, 6(1), 19–37. https://doi.org/10.25007/ajnu.v6n1a10

Issue

Section

Articles