عنوان مقاله [English]
Life in the modern cities takes shape through interaction with various environmental, socio-economic, infrastructural, health, security, political and cultural conditions. The result of this interaction shapes the quality of urban life (QOUL). Quality of lifeis a complex concept involving social, economic, environmental, physical, psychological and political aspects (El Din et al, 2013). In general, Quality of life (QOL) has been evaluatedbytwo objective and subjective points of view. Researches in this field, have mainly been conducted in the form of social studies and in the macro geographical scales of countries or cities,and less attention has been paid to the spatial differences of the life quality in the complex urban environments. In these studies, the principal components analysis (PCA) method has been the most common method used for combining and overlaying of the life quality indicators (Lo, 1998; Jun, 2006; Li and Weng, 2007; Motakan et al, 2010; HatamiNejad et al, 2014; Messer et al, 2014). But,Oneof the disadvantages of PCA is the possibility of deleting some of the useful information.Using Multi-Criteria Decision-Making (MCDM) and Fuzzy Logic methods can also be useful in spatial modeling of life quality. Moreover, QOL as one of the features of geographical environment is a dynamic concept. This means that this feature changesover time and location. The spatiotemporal modeling of this concept can help monitoringthe quality of urban life and planning for its improvement.
Data and Methods
This study offers a framework and process for spatiotemporal modeling of QOUL. For spatial modeling of QOUL, effective indiceswere taken into consideration at first. In this study,the indicators related to the urban quality of life were extracted in 3 three environmental, infrastructural/physical, and socio-economic dimensions.The Analytical Hierarchy Process (AHP) method was used for weighing the parameters(Uyan, 2013). Then, the indicators were combined with each other using the GammaFuzzyModel(Vafai, 2013) and Vikor-Fuzzy overlay technique(Huang et al, 2009). Furthermore, QOUL was modeled temporally due to the variability of environmental indicators and some of infrastructural / physical indicators during the seasons of the year. For this purpose,the cyclic model (developed based on the snapshot approach (Worboys and Duckham, 2004)) was used. In order to assess the developed framework, the quality of lifewasmodeled at urban blocks level in regions 3,6,11 of the city of Tehran.
The obtained results showed that applying multi-criteria decision-making and Fuzzy logicmodels in modeling of life quality is capable of showing the spatial difference oflife quality in urban environments. Based on the results of spatial modeling, the quality of life is more desirable in northern parts of the area (region 3) while the desirability decreases towards the southern areas (region 11). The study of Moran’s spatial autocorrelation index (greater than 0.35 for the results of both models and all seasons) emphasize on the non-randomness of the distribution method of the QOL feature in urban blocks and shows the existence of cluster pattern in the study area.The results of temporal modeling indicated that most of the blocks are more favorable in the spring and autumn seasons than in the winter and summer in terms of environmental conditions.
1- حاتمینژاد،پوراحمد،منصوریان،رجایی؛حسین،احمد،حسین،عباس؛ 1392،تحلیل مکانی شاخصهای کیفیت زندگی در شهرتهران،پژوهش های جغرافیای انسانی،دوره 45،شماره 4،زمستان 1392، 56-29.
2- متکان،پوراحمد،منصوریان،میرباقری،حسینی اصل؛علیاکبر،احمد،حسین،بابک،امین؛ 1388،سنجش کیفیت مکانهای شهری،بااستفاده از روش ارزیابی چندمتغیره درGIS (موردمطالعه: شهرتهران)،سنجش ازدوروGISایران،سال اول،شماره چهارم،زمستان 1388، 20-1.
3- Bilal, M., Nichol, J.E., Bleiweiss, M.P., and Dubois, D., (2013). A Simpli ﬁ ed high resolution MODIS Aerosol Retrieval Algorithm (SARA) for use over mixed surfaces. Remote Sensing of Environment, 136:135-145.
4- Bilal, M., Nichol, J.E., Bleiweiss, M.P., Dubois, D. (2013). A Simpliﬁed high resolution MODIS Aerosol Retrieval Algorithm (SARA) for use over mixed surfaces. Remote Sensing of Environment, 136:135-145.
5- Discoli, C., Martini, I., Juan, G.S, Barbero, D., Dicroce, L., Ferreyro, C., & Esparza, J. (2014). Methodology aimed at evaluating urban life quality levels. Sustainable Cities and Society, 10:140–148.
6- El Din, H.S., Shalaby, A., Farouh, H.E., & Elariane, S.A. (2013). Principles of urban quality of life for a neighborhood. HBRC Journal, 9:86-92.
7- Fu, P., and Rich P.M. (2002). A Geometric Solar Radiation Model with Applications in Agriculture and Forestry. Computers and Electronics in Agriculture, 37:25-35.
8- Gulliver, J., Morley, D., Vienneau, D., Fabbri, F., Bell, M., Goodman, P., Beevers, S., Dajnak, D., Kelly, F.J., Fecht, D. (2015). Development of an open -source road trafﬁc noise mode l for exposure assessment. Environmental Modelling & Software, 74:183-193.
9- Gupta, K., Kumar, P., Pathan, S.K., & Sharma, K.P. (2012). Urban Neighborhood Green Index – A measure of green spaces in urban areas. Landscape and Urban Planning 105:325–335.
10- Huang, J.J., Tzeng, G.H., and Liu, H.H. (2009). A Revised VIKOR Model for Multiple CriteriaDecision Making -The Perspective of Regret Theory. MCDM, CCIS, 35:761-768.
11- Istamto, T., Houthuijs, D., Lebret, E. (2014). Willingness to pay to avoid health risks from road-trafﬁc-related air pollution and noise across ﬁve countries. Science of the Total Environment, 497:420-429.
12- Jensen, J.R. (2005). Introductory digital image processing. Upper Saddle River: Pearson: Prentice Hall.
13- Jimenez-Munoz, J.C., Sobrino, J.A., Skokovic, D., Mattar, C., and Cristobal, J. (2014). Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data. Geoscience and Remote Sensing Letters, IEEE, 11(10): 1840-1843.
14- Joseph, J., Wang, F., & Wang, L. (2014). GIS-based assessment of urban environmental quality in Port-au-Prin ce, Haiti. Habitat International, 41:33-40.
15- Jun, B.W. (2006). Urban Quality of Life Assessment Using Satellite Image and Socioeconomic data in gis. Korean journal of remote sensing, 22:323-335.
16- Kim, K.H., Ho, D.X., Richard, B.J.C., Ch, J.M.O., Park, C.G.B., & Ryu, I.C. (2012). Some insights into the relationship between urban air pollution and noise levels. Science of the Total Environment, 424:271-279.
17- Klee, P. (2011). The core of GIScience: a process-based approach. Enschede, the Netherlands: ITC.
18- Lee, S. (2007). Application and veriﬁcation of fuzzy algebraic operators to landslide susceptibility mapping. Environ Geol, 52:615-623.
19- LI, G., and Weng, Q. (2007). Measuring the quality of life in city of Indianapolis by integration of remote sensing and census data. International Journal of Remote Sensing, 28:249–267.
20- Lo, C. P. (1997). Application of Landsat TM data for quality of life assessment im an urban environment. Computer, Environment and Urban Systems, 21:259-276.
21- Marans, R.W. (2015). Quality of urban life and environmental sustainability studies: Future linkage opportunities. Habitat International, 45:47-52.
22- Marans, R.W., Stimson, R.J. (2011). Investigating Quality of Urban Life (Theory, Methods, and Empirical Research). Melbourne: Springer.
23- Messer, L.C., Jagai, J.S., Rappazzo, K.M., and Lobdell, DT. (2013). Construction of an environmental quality index for public health research. Environmental Health, 13:39.
24- Mostafa, A.M. (2012). Quality of Life Indicators in Value Urban Areas: Kasr Elnile Street in Cairo. Procedia-Social and Behavioral Sciences, 50:254-270.
25- Pacione, M. (2003). Urban environmental quality and human Wellbeing - a social geographical perspective. Landscape and Urban Planning, 65:19-30.
26- Pradhan, B. (2010). Application of an advanced fuzzy logic model for landslide susceptibility analysis. International Journal of Computational Intelligence Systems, 3(3):370-381.
27- Sadeghi, B., and Khalajmasoumi, M. (2015). A futuristic review for evaluation of geothermal potentials using fuzzy logic and binary index overlay in GIS environment. Renewable and Sustainable Energy Reviews, 43:818-831.
28- Silverman, B.W. 1998. Density Estimation for Statistics and Data Analysis. Boca Raton, Florida: CRC.
29- Uyan, M., (2013). GIS-based solar farms site selection using analytic hierarchy process (AHP) in Karapinar region, Konya/Turkey. Renewable and Sustainable Energy Reviews, 28:11-17.
30- Vafai, F., Hadipour, V., and Hadipour, A. (2013). Determination of shoreline sensitivity to oil spills by use of GIS and fuzzy model. Case study - The coastal areas of Caspian Sea in north of Iran. Ocean and Coastal Management, 71:123-130.
31- Wang, C., Liu, Q., Ying, N., Wang, X., & Ma, J. (2013). Air quality evaluation on an urban scale based on MODIS satellite images. Atmospheric Research,132:22-34.
32- Wang, X., Li, M.H., Liu, S., and Liu, G. (2006). Fractal characteristics of soils under different land-use patterns in the arid and semiarid regions of the Tibetan Plateau, China. Geoderma, 134(1):56-61.
33- Weng, Q. (2009). Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing, 64:335-344.
34- Worboys, M., Duckham, M. (2004). GIS A Computing Perspective (Second Edition). CRC Press.
35- Yang, X. (2011). Urban Remote Sensing Monitoring, Synthesis and Modeling in the Urban Environment (Remote sensing of high resolution urban impervious surfaces). John Wile y & Sons, Ltd.
36- Zadeh, L.A. (1965). Fuzzy sets. IEEE Inf Control, 8:338-353.
37- Zenga, Y., Fengb, Z., Xianga, N. 2004. Assessment of soil moisture using Landsat ETM+ temperature/vegetation index in semiarid environment. IEEE, 6: 4306-4309.