International Journal of

Transformation of land use-land cover change occurs due to the numbers and activities of people. Urban growth modeling has attracted authentic attention because it helps to comprehend the mechanisms of land use change and thus helps relevant policies made. This study applied logistic regression to model urban growth in the Ahvaz Metropolitan Area of Khuzestan province in IDRISI Selva software and to discover what are driving forces effective on the urban growth of Ahvaz city, and with what intensity? Historical land use and land cover data of Ahvaz were extracted from the 1991and 2006 Satellite images. The following two groups of factors were found to affect urban growth in different degrees as indicated by odd ratios: (1) Constraints Distance to the Bridge, Rural Areas, Planned town and Industry activities (all with odds ratios<1_or coefficient <0); and (2) Number of urban cells within a 5·5 cell window, Distance to the Hospitals, Main Road, High Road, Rail Line, River, CBD and Secondary centers, agriculture areas in distance more than 5km of Urban area and Vacant area (all with odds ratios>1_or coefficient >0). Relative operating characteristic (ROC) value of 0.906 indicates that the probability map is valid. It was concluded logistic regression modeling is suitable for Understanding and measuring of driving forces effect on urban growth. Second, unlike the Cellular Automata (CA) model, the logistic regression model is not temporally explicit; urban growth trend in Ahvaz isn't in the event of infill development strategy. Also, variables of sprawl based agents indicate more power than to compact base agents.

Preset paper aims to recognize the most important factors in creating a smart city in the city of Ahvaz. For achieving this, all criteria, which play an important role in creating smart cities, were collected using different resources based on descriptive-analytical method. At the next stage, a survey of a number of 40 urban planning experts was accomplished in Ahvaz city, which is the case study of the research, to rank smart city criteria and sub-criteria in terms of importance using Fuzzy TOPSIS technique. The results showed that among six criteria, “smart government” with the score of 4 percent was ranked as the most important criterion and “smart environment” with the score of around 1.5 percent was recognized as the least important criterion in the regard of creating a smart city. Moreover, of the sub-criteria, “Stable economy and ability to transform”, “Social and ethnic plurality”, “Crisis management and ability to organize human resources”, “local and regional accessibility”, “Sustainable resource management”, and “Individual Safety” were recognized as the most important factors in different aspects of making the city smart.