067-Front-Back-5x8-Paperback-Book-COVERVAULT
See Full Article
published 
DOI

http://dx.doi.org/10.25098/3.1.9
Issue 
Vol 3- Issue 1

JUN 2019

 

Huda Mohammad Saeed1, Aras Jalal Mhamad2,3, Renas Abubaker Ahmed4

1,2,4Statistic & Informatics Dep., College of Administration & Economics, Sulaimani University, Sulaimani City, Kurdistan Region – Iraq

3Accounting Dep., College of Administration & Economics, Human Development University, Sulaimani City, Kurdistan Region – Iraq

[email protected]2

 

 

 

Received :  16-1-2019                               Revised:29-1-2019

Accepted :  22-5-2019                                 Published :30-6-2019

 

Abstract

The monthly family’s expenditure is one of the important economic situation that appeared in the society especially in Sulaimani governorate in Kurdistan region – Iraq. The objective of this study is to classify family’s monthly expenditures according to families who live inside (urban) and outside (rural) of Sulaimani city for year 2011 using the modern style in analyzing of classification which is (Classification and regression tree – CART) method, regression tree models are trained in a two-stage procedure, i.e. using recursive binary partitioning to make a tree structure, by a process of pruning to removing non-significant leaves, with the possibility of assigning multivariate functions to terminal leaves to improve generalization. CART was used to identify rank outcome  explanatory s by determining monthly family’s expenditures according to families who live in urban or in rural. The study found that the important variable is (Housing Rental) between eight variables for both rural and urban area at Sulaimani governorate, also of the 3210 families: 66.7% (2270) lived in Sulaimani city, and 33.3% (1131) lived in rural area in Sulaimani governorate according to housing rental variable, while 64.4% (1963) from families who lived in Sulaimani their housing rental is zero, and 87.0% (307) of families who lived urban area in Sulaimani city their housing renal between (201000 – 400000). Although the most families expenditure who live in Sulaimani city to food was ranged (151000 – 600000), household (101000 – 225000), education serves (101000 – 225000), health field (101000 – 150000), and transportation serves (251000 – 300000), while the most families expenditure who live in rural area in Sulaimani city for transportation serves (101000 – 150000), clothing (151000 – 200000), education serves (151000 – 225000), health field (76000 – 100000), and food (600000 – 750000).

 

Keywords: classification, CART model, statistical modeling

 

الملخص

    نفقات الأسرة الشهرية هي واحدة من الحالات الاقتصادية المهمة التي ظهرت في المجتمع وخاصة في محافظة السليمانية في إقليم كردستان – العراق. الهدف من هذه الدراسة هو تصنيف المستوى النفقات الشهرية للأسرة وذالك وفقًا للعائلات التي تعيش داخل المدن (المركز) وخارج المدن (الريف) في مدينة السليمانية لعام 2011 باستخدام الأسلوب الحديث (التصنيف و الانحدار الشجري – كارت)، عملية تجريب النماذج الانحدار الشجري يتم بمرحلتين ، أي يتم استخدام تجزئة ذو الحدين لإنشاء هيكلية الشجرة ، من خلال عملية إزالة  المستوايات غير معنوية ، مع إمكانية بناء دالة متعدد المتغيرات للوصل إلى حالة التعميم. باستخدام CART تم التعرف على رتبة توضيحية للنتائج من خلال تحديد نفقات الأسرة الشهرية وفقًا للعائلات التي تعيش في داخل المدن أو الريف. وجدت الدراسة أن أهم متغير هو (تأجير المساكن) بين ثمانية متغيرات لكل من المناطق الريفية و داخل المدن في محافظة السليمانية ، أيضًا من 3210 أسرة: 66.7٪ (2270) يعيشون في مدينة السليماني ، و 33.3 ٪ (1131) يعيشون في المناطق الريفية في محافظة السليمانية وفقا لمتغير تأجير المساكن ، في حين أن 64.4 ٪ (1963) من العائلات التي كانت تعيش في السليمانية إيجارها السكني هو صفر ، و 87.0 ٪ (307) من الأسر التي تعيش في داخل المدن في مدينة السليمانية سكنهم الكلي ما بين (201000 – 400000) دينار. على الرغم من أن معظم نفقات الأسر التي تعيش في مدينة السليمانية على الغذاء تراوحت ما بين (151000 – 600000) دينار، الأسرة (101000 – 225000) دينار، يخدم التعليم (101000 – 225000) دينار، المجال الصحي (101000 – 150000) دينار، ويخدم النقل (251000 – 300000) دينار، في حين أن معظم إنفاق العائلات الذين يعيشون في المناطق الريفية في مدينة السليماني للنقل يخدم (101000 – 150000) دينار، والملابس (151000 – 200000) دينار، والتعليم (151000 – 225000) دينار، والمجال الصحي (76000 – 100000) دينار، والغذاء (600000 – 750000) دينار.

مفاتيح الكلمات– التصنيف، النماذج  CART، النمذجة الاحصائية

 پوخته‌:

خه‌رجى مانگانه‌ى خێزان به‌یه‌كێك له‌ بواره‌ گرنگه‌كانى ئابوورى داده‌نرێت له‌كۆمه‌ڵگادا به‌تایبه‌ت له‌ پارێزگاى سلێمانى هه‌رێمى كوردستانى عێراق. ئامانج له‌م توێژینه‌وه‌یه‌ بریتیه‌ له‌ پۆلێنكردنى خه‌رجى مانگانه‌ى خێزان، بۆ ئه‌و خێزانانه‌ى له‌ناوه‌وه‌و ده‌ره‌وه‌ى شارى سلێمانیدا ژیاون له‌ ساڵى 2011 به‌به‌كارهێنانى شێوازێكى تازه‌ى لێكۆلینه‌وه‌ كه‌ بریتیه‌ له‌ ڕێگاى پۆلێن و چه‌ماوه‌ی لاری (CART). مۆدێلى چه‌ماوه‌ی لاری به‌ دوو قۆناغ جێبه‌جێ ده‌كرێت, به‌كارهێنانى دابه‌شكردنى دووانى بۆ دروستكردنى شێوه‌ى دره‌ختێك، به‌ پرۆسه‌ى هه‌ره‌سهێنانی ئه‌و گه‌ڵایانه‌ى كه‌ گرنگ نین لاده‌برێن، له‌گه‌ڵ ته‌رخانكردنى مۆدێلى فره‌ گۆڕاو بۆ گه‌ڵاكانى كۆتایى به‌مه‌به‌ستى به‌ره‌وپێشبردنى گشتگیركردن. CART به‌كارهێنراوه‌ بۆ پله‌به‌ندى شیكردنه‌وه‌ى ئه‌نجامه‌كان به‌دیاریكردنى خه‌رجى مانگانه‌ى خێزان بۆ ئه‌و خێزانانه‌ى كه‌ له‌ ناوه‌وه‌و ده‌ره‌وه‌ى شارى سلێمانى ده‌ژین. توێژینه‌وه‌كه‌ ده‌ریخست كه‌ گرنگترین گۆڕاو بریتیه‌ له‌ كرێ خانوو له‌ نێوان هه‌شت گۆڕاودا بۆ هه‌ردوو ناوچه‌كانى ناوه‌وه‌و ده‌ره‌وه‌ى پارێزگاى سلێمانى, هه‌روه‌ها بۆ 3210 خێزان: له‌ سه‌دا 66,6 (2270) یان له‌ناو شارى سلێمانى و له‌سه‌دا 33,3 (1131) یان له‌ده‌ره‌وه‌ى شارى سلێمانیدا ژیاون به‌پێى گۆڕاوى كرێی خانوو,له‌كاتێكدا له‌سه‌دا 64,4 (1963) ى ئه‌و خێزانانه‌ى له‌ناو شارى سلێمانیدا ژیاون خاوه‌نى خانووى خۆیانن وه‌ له‌سه‌دا 87 (307) ى ئه‌و خێزانانه‌ى له‌ده‌ره‌وه‌ى شارى سلێمانى ژیاون كرێ خانوویان له‌نێوان (201000-400000) دینار هه‌زاردا بووه‌. له‌گه‌ڵ ئه‌مانه‌شدا ئه‌و خێزانانه‌ى له‌ناو شارى سلێمانیدا ژیاون زۆربه‌ى خه‌رجى مانگانه‌یان بۆ خۆراك بووه‌ كه‌ له‌ نێوان (151000-600000) دینار، وه‌ بۆ پێداویستى ناوماڵ له‌نێوان (101000-225000) دینار، بۆ خوێندن له‌نێوان (101000-225000) دینار، بۆ كه‌رتى ته‌ندروستى له‌نێوان (101000-150000) دینار وه‌ بۆ خزمه‌تگوزارى گواستنه‌وه‌ له‌نێوان (251000-300000) دیناردا بووه‌. له‌كاتێكدا زۆربه‌ى خه‌رجى مانگانه‌ى ئه‌و خێزانانه‌ى له‌ ده‌ره‌وه‌ى شارى سلێمانى ژیاون بۆ خزمه‌تگوزارى گواستنه‌وه‌ له‌نێوان (101000-150000) دینار، بۆ جلوبه‌رگ له‌نێوان (151000-200000) دینار، بۆ خوێندن له‌ نێوان (151000-225000) دینار، بۆ كه‌رتى ته‌ندروستى له‌نێوان (76000-100000) دینار وه‌ بۆ خواردن له‌نێوان (600000-750000) دیناردا بووه‌.

  1. References
  2. Abdul Kareem S., Raviraja S., Awadh N. A., Kamaruzaman A. & Kajindran A. (2010) “Classification and Regression Tree In Prediction Of Survival Of AIDS Patients” Malaysian Journal of Computer Science, Vol. 23(3), 2010.
  3. Bae H., Olson B. H., Hsu K. & Sorooshian S. (2010) “Classification and regression tree (CART) analysis for indicator bacterial concentration prediction for a Californian coastal area” Water Science & Technology—WST | 61.2 | 2010, doi: 10.2166/wst.2010.842.
  4. Bevilacqua, M., Braglia, M., Montanari, R., 2003. The classification and regression tree approach to pump failure rate analysis. Reliab. Eng. Syst. Saf. 79 (1), 59–67.
  5. Cascini, L., Ciurleo,M., Di Nocera, S., Gulla, G., 2015. A new-old approach for shallow landslide analysis and susceptibility zoning in fine-grained weathered soils of southern Italy. Geomorphology 241, 371–381.
  6. Cheng Z. MD MPH, Nakatsugawa M. Phd, Hu Ch. Phd, Robertson S. P. Phd, Hui X. MD MS, Moore J. A. Phd, Bowers M. R. BS, Kiess A. P. MD Phd, Page B. R. MD, Burns L. BSN, Muse M. BSN, Choflet A. MS RN OCN, Sakaue K. MS, Sugiyama Sh. MS, Utsunomiya K. MS,  Wong J. W. Phd, McNutt T. R. Phd and Quon H. MD MS (2018) “Evaluation of classification and regression tree (CART) model in weight loss prediction following head and neck cancer radiation therapy” Advances in Radiation Oncology (2018) 3, 346–355. https://doi.org/10.1016/j.adro.2017.11.006.
  7. Conoscenti, C., Ciaccio, M., Caraballo-Arias, N.A., Gomez-Gutierrez, A., Rotigliano, E., Agnesi, V., 2015. Assessment of susceptibility to earth-flow landslide using logistic regression and multivariate adaptive regression splines: a case of the Bence River basin (western Sicily, Italy). Geomorphology 242, 49–64.
  8. Steinberg, P.L. Colla, CART: Tree-structured nonparametric data analysis, Salford Systems, San Diego, CA, 1995.
  9. Daniels S., and Glorieux I. (2015) “Convenience, food and family lives. A socio-typological study of household food expenditures in 21st-century Belgium” Appetite. Volume 94, 1 November 2015, Pages 54-61. https://doi.org/10.1016/j.appet.2015.04.074.
  10. Dehnavi, A., Aghdam, I.N., Pradhan, B., Varzandeh, M.H.M., 2015. A new hybrid model using step-wise weight assessment ratio analysis (SWAM) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. Catena 135, 122–148.
  11. Felicísimo, Á.M., Cuartero, A., Remondo, J., Quirós, E., 2013. Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides 10 (2), 175–189.
  12. Guo, C.B., Montgomery, D.R., Zhang, Y.S., Wang, K., Yang, Z.H., 2015. Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan plateau, China. Geomorphology 248, 93–110.
  13. Hosmer, D.W., Lemeshow, S., 2000. Applied Logistic Regression. A Wiley-Interscience Publication, New York.
  14. Hussin, H.Y., Zumpano, V., Reichenbach, P., Sterlacchini, S., Micu, M., van Westen, C., Balteanu, D., 2016. Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model. Geomorphology 253, 508–523.
  15. Iliev I. P., Voynikova D. S., & Gocheva-Ilieva S. G. (2013) “Application of the Classification and Regression Trees for Modeling the Laser Output Power of a Copper Bromide Vapor Laser” Hindawi Publishing Corporation http://dx.doi.org/10.1155/2013/654845.
  16. Josephine W. Mburu, Leonard Kingwara, Magiri Ester, Nyerere Andrew, 2018.” Use of classification and regression tree (CART), to identify hemoglobin A1C (HbA1C) cut-off thresholds predictive of poor tuberculosis treatment outcomes and associated risk factors”, J Clin Tuberc Other Mycobact Dis, https://doi.org/10.1016/j.jctube.2018.01.002, journal homepage: www.elsevier.com/locate/jctube.
  17. Koon, S., Petscher, Y., 2015. Comparing Methodologies for Developing an Early Warning System: Classification and Regression Tree Model versus Logistic Regression. REL 2015–077. Southeast, Regional Educational Laboratory.
  18. Li Yong, 2006. “Predicting materials properties and behavior using classification and regression trees”, Materials Science and Engineering A 433, 261–268, doi:10.1016/j.msea.2006.06.100.
  19. Mahjoobi, J., Etemad-Shahidi, A., 2008. An alternative approach for the prediction of significant wave heights based on classification and regression trees. Appl. Ocean Res. 30 (3), 172–177.
  20. Malinowska, A., 2014. Classification and regression tree theory application for assessment of building damage caused by surface deformation. Nat. Hazards 73 (2), 317–334.
  21. Park T. (2018) “Health care utilization and expenditures among adults with rheumatoid arthritis using specialty pharmaceuticals” Research in Social and Administrative Pharmacy. https://doi.org/10.1016/j.sapharm.2018.09.003.
  22. Pasipanodya JG, Gumbo T. (2011). A new evolutionary and pharmacokinetic pharmacodynamic scenario for rapid emergence of resistance to single and multiple antituberculosis drugs. Curr Opin Pharmacol;11:457–63.
  23. Pouliakis A., Karakitsou E., Chrelias Ch., Pappas A., Panayiotides I., Valasoulis G., Kyrgiou M., Paraskevaidis E. and Karakitsos P. (2015) “The Application of Classification and Regression Trees for the Triage of Women for Referral to Colposcopy and the Estimation of Risk for Cervical Intraepithelial Neoplasia” Hindawi Publishing Corporation, http://dx.doi.org/10.1155/2015/914740.
  24. Roger, J. (2000). An introduction to classification and regression tree (CART) analysis, Emergency Medicine Dep., Torrance, California Annual Meeting of the society for academic emergency medicine in San Francisco.
  25. Salari M. and Javid R. J. (2017) “Modeling household energy expenditure in the United States” Renewable and Sustainable Energy Reviews. Volume 69, March 2017, Pages 822-832. https://doi.org/10.1016/j.rser.2016.11.183.
  26. sampurno, Fanny. (2006). identifying risk factors associated with new onset cardiovascular disease in patients with type I diabetes using classification tree, Melbourne University, Honours Thesis, Mathematics and statistics Dep.
  27. Sarti S., Terraneo M. and Bordogna M. T., (2017) “Poverty and private health expenditures in Italian households during the recent crisis” Health Policy. Volume 121, Issue 3, March 2017, Pages 307-314. https://doi.org/10.1016/j.healthpol.2016.12.008.
  28. Soo Yeon Choi, Il Won Seo,2018” Prediction of Fecal Coliform using Logistic Regression and Tree-based Classification, Journal of Hydro-environment Research Volume 21, October 2018, Pages 96-108, https://doi.org/10.1016/j.jher.2018.09.002.
  29. Tien Bui, D., Lofman, O., Revhaug, I., Dick, O., 2011. Landslide susceptibility analysis in the Hoa Binh province of Vietnamusing statistical index and logistic regression. Nat. Hazards 59 (3), 1413–1444.
  30. Tien Bui, D., Nguyen, Q.-P., Hoang, N.-D., Klempe, H., 2016a. A novel fuzzy K-nearest neighbor inference model with differential evolution for spatial prediction of rainfall- induced shallow landslides in a tropical hilly area using GIS. Landslides http:// dx.doi.org/10.1007/s10346-016-0708-4.
  31. Wang, L.-J., Guo, M., Sawada, K., Lin, J., Zhang, J., 2015a. Landslide susceptibility mapping in Mizunami City, Japan: a comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models. Catena 135, 271–282.
  32. Yang, T., Gao, X., Sorooshian, S., Li, X., 2016. Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled cross-validation scheme. Water Resour. Res. 52 (3), 1626–1651.
  33. Yoo K., Yoo H., Lee J. M., Shukla S. K. & Park J. (2018) “Classification and Regression Tree Approach for Prediction of Potential Hazards of Urban Airborne Bacteria during Asian Dust Events” Scientific Reports | (2018) 8:11823 | DOI: 10.1038/s41598-018-29796-7.
  34. Zhao Yannan, Li Yuan, Zhang Lifen, Wang Qiuliang, 2016,” Groundwater level prediction of landslide based on classification and regression tree”, geodesy and geodynamics, vol. 7, no.5, 348-355, http://dx.doi.org/10.1016/j.geog.2016.07.005.
  35. Zimmerman R. K., Balasubramani G. K., Nowalk M. P., Eng H., Urbanski L., Jackson M. L., Jackson L. A., McLean H. Q., Belongia E. A., Monto A. S., . Malosh R. E., Gaglani M., Clipper L., Flannery B. and Wisniewsk S. R. (2016) “Classification and Regression Tree (CART) analysis to predict influenza in primary care patients” Zimmerman et al. BMC Infectious Diseases (2016) 16:503 https://doi.org/10.1186/s12879-016-1839-x.
Views: 48