18 - Ayalew. L. Yamagishi. H. Marui. H & Kanno. T. (2005). "Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications.", Engineering Geology 81. (2005). 432– 445.
19 - Ayalew,l. and Yamagishi, H. (2005):The application of GIS –based logistic regression for landslide susceptibility mapping in the Kakuda-Yaahiko Mountanins, central Japan, Geomorphology 65,15-31.
20 - Atkinson, P., Massari, R (2011). Logistic modeling susceptibility to land sliding in the Apennines, Italy Geomorphology.Vol.130.
21- Cornforth, D., H., 2005, "Landslides in practice: investigation, analysis, and remedial/preventative options in soils", Wiley, 1ed, 624 pp.
22 -Chen, Zhaohua. Wang, Jinfei (2007). Landslide hazard mapping using logistic regressionmodel in Mackenzie Valley, Canada. Geomorphology, Vol.42.
23 - Dai, F. C.& Lee, c. f. (2002): Landslide characteristics and slop instability modeling using GIS.Lantau,Hong Kong Gemorphology 42, 213-228.
24 - Das, I., Sahoo. S, Westen, A. Stein, A. Hack, A. (2010). Lanslidesusceptibility assessment using logistic regression and its comparison with a rock mass classification system, along road section in the northern Himalayas (India). Geomorphology, Vol.1147.
25 - Gregory C.Ohlmacher, John C. Davis (2003). Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Geomorphology,Vol 69.
26 - Hosseinzadeh, M., Servati, M. R., and Mansouri, A. 2009. Zonation of Mass Movements Occurring Risk using Logistic Regression Model. IRAN Geology Quarterly, 3 (11): 27-37. (in Per.)
27 - Lee, S. 2007. Application and Verification of Fuzzy Algebraic Operators to Landslide Susceptibility Mapping. Environment Geology, 52: 615-623. (In Eng.)
28 -Menard. S. (1995) Applied logistic regression analysis. Sage university Paper Series on Quantitative Applications in Social Sciences, vol. 106. Thousand Oaks. California. 98 pp.
29- Parthian, B., Lee, S. (2010). Landslide susceptibility assessment and factor effect analysis: bad propagation artificial neural networks and comparison with frequency ratio and bivariate logistic regression modeling. Geomorphology, Vol. 25.

Analysis of the relationship between objective and subjective indicators of quality of life in terms of access to urban services in the context of old zone of Urmia

amin mahmodi azar

In recent decates, the subject of quality of living in city and the satisfaction of citizens from their environments has attarced the views the most of researchers and international institutes around the world. Because of its importance, UN to publish a report annually in which different cities around the world ranked and compared through the quality of life. Quality of living talk about different indices and one of its important indices is access to the city services which in turns would decrease some of the city problems such as slums and irregular construction, land speculation and etc. This research tries to analysis the city quality of living according to the standards of services access in cities. So, quality of service access was analysed in objective and subjective sections in old zone of Urmia. The research method is descriptive-analytic and we use questioner, different models such as AHP and network analysis and fuzzy techniques, Pierson coefficient for our study. Results show that satisfaction of city-service access has a very meaningful statistic impact on the subjective factors of quality of living and as it increases more, we have more subjective quality. These results tell planners that key factors would have great impacts and roles on the satisfaction of the life of citizens.

Journal of Applied Research in Geographical Sciences

Analysis of the relationship between objective and subjective indicators of quality of life in terms of access to urban services in the context of old zone of Urmia