Urban land price is the primary indicator of land development in urban areas. Land prices in holly cities have rapidly increased due to tourism and religious activities. Public agencies are usually facing challenges in managing land prices in religious areas. Therefore, they require developed models or tools to understand land prices within religious cities. Predicting land prices can efficiently retain future management and develop urban lands within religious cities. This study proposed a new methodology to predict urban land prices within holy cities. The methodology is based on two models, Linear Regression (LR) and Support Vector Regression (SVR), and nine variables (land price, land area, distance to a river, distance to main roads, distance to heritage locations, distance to historical mosques, distance to commercial locations, distance to educational locations, and distance to hospital and clinics. Our findings showed that the SVR model had outperformed the LR model, where SVR achieved an accuracy of 82.9%.In contrast, LR has achieved 75.40%. Therefore, the presented models can assess land prices in holly cities like Al-Kufa. Furthermore, this tool can retain land pricing, land management, and urban planning in Iraq.
اتسعت دائرة استخدام نظم المعلومات بشكل كبير بحيث دخلت كل مفاصل حياة الانسان المختلفة مما جعل فرص انتهاك واختراق تلك الانظمة امراً لامناص منه. لذلك فان الحاجة تستدعي ايجاد وسائل تقنية واطر جوهرية للتصدي لظاهرة حدوث أي خلل في نظم المعلومات من خلال مراجعة وتدقيق دورة حياة النظام.
يتطرق البحث الى جملة ادوات واساليب تقنية تساعد المدققين والمراجعين من تحديد الخلل وطبيعته وايجاد حل له . كما توفر
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Receipt date:06/23/2020 accepted date:7/15/2020 Publication date:12/31/2021
This work is licensed under a Creative Commons Attribution 4.0 International License
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Poverty phenomenon is very substantial topic that determines the future of societies and governments and the way that they deals with education, health and economy. Sometimes poverty takes multidimensional trends through education and health. The research aims at studying multidimensional poverty in Iraq by using panelized regression methods, to analyze Big Data sets from demographical surveys collected by the Central Statistical Organization in Iraq. We choose classical penalized regression method represented by The Ridge Regression, Moreover; we choose another penalized method which is the Smooth Integration of Counting and Absolute Deviation (SICA) to analyze Big Data sets related to the different poverty forms in Iraq. Euclidian Distanc
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