عنوان مقاله [English]
Changes in land use will also cause changes in environmental conditions. In 2007, for the first time in human history, the world's urban population exceeded from rural population. Now world's urban population is growing faster than the world's population and more than half the world's population (54% in 2014) lives in urban areas. Most of this growth occurred in developing countries and in these countries the growth of urban settlements is five times of the developed countries. Intense migration of rural population to the cities and the rapid growth of urban population caused uncontrolled physical expansion of urban around the world. Therefore, to mitigate the effects of urban growth, it is essential to manage the population to the optimum areas. To determine appropriate areas for urban growth is among the useful solutions in this field. For this purpose, land-use suitability analysis is applied. Land-use suitability analysis is a very important work to city planners and managers to determine the most appropriate spatial pattern for future land use. Tabriz city in the recent decades has high population growth and uncontrolled migration. Also, by increase in use of cars and high consumer culture, a lot of land around the city has been under urban growth. The area of city in 1982 was 7220 hectares while in 2009 this value increased to 22,346 hectare. Indeed, during the past 27 years the city has experienced growth more than 3 times. The city population in 1984 was about 957 thousand and it reached to 1,336 thousand in 2011. Indeed, during the past 27 years the city's population increased only 1.3 times. Therefore, the urban growth conduction and land-use suitability is essential for decrease of social, economic and environmental adverse effects. One of the effective methods for land-use suitability analysis is multi-criteria evaluation (MCE). Ordered Weighted Averaging (OWA) as a MCE method has capability of risk taking/ risk averse and trade off relations.
In this research, we used ENVI4.7 software for the processing of UTM+ landsat image of Tabriz city. The image classification was the maximum likelihood supervised and the rate of overall accuracy and Kappa coefficient is 97.61 and 0.96, respectively. Then, the data entered into IDRISI Selva software and constraint and factor maps were created. Constraint maps were urban and industrial lands and water levels with 50 meter buffer. Factor maps were land-use capabilities for urban development map, distance from urban, roads, industries, water, agricultural, barren and fault lands, elevation, slope and aspect. Factor maps were used in processing stage and standardized with FUZZY operators. Finally, factor maps with AHP method are weighted. Then, for assessing compensation rates of factor maps and control of risk taking/averse we used OWA. Suitable regions for future development of Tabriz city is created by the analysis.
Results and Discussion
To make AHP-OWA model operational in assigning suitable areas for urban growth, all criteria are combined and weighted. Constraint or Boolean maps as a mask or cover have two values of 0 and 1. Indeed, the regions as constraint with zero value are not calculated in processing and only those pixels with one value are included in the analysis. All factor maps were reclassified with values ranged 0- 255, that pixels are zero or close to zero. They have very low potential for urban development and those pixels with 255 or close to that have very high potential to urban development. The criteria are from different types and reclassified after the standardization. For example, in the factor map of distance from urban lands with contiguity to urban lands the potential of lands for urban development are very high, while in the factor map of distance from fault this relation is unlike. After standardization, the factor maps were weighted with AHP model. Preferences and priorities of the factor maps by pairwise comparison matrix are based on prior theoretical researches in the land-use suitability analysis. In this research, the great importance of factor maps is given to distance from urban lands, distance from fault and distance from roads. Also, factor maps of elevation, slope and land-use capabilities are in the next place with lower importance relative to the factor maps of distance from urban lands, distance from fault and distance from roads. The consistency ratio is 0.03 that was in the acceptable range. This shows that the weighing of factor maps is accurate. After weighting the factor maps by AHP model, the third set of weighting is given by OWA model. This weighting gives us possibility of control level of trade off relations and also risk level between factors. Weighting are established in triangular spatial decision making. OWA method operates in this decision making space and allow us to survey different strategies or decision scenarios and even inconsistent. Weighting in OWA model applied on a pixel by pixel basis to factor scores as determined by their rank ordering across factors at each location (pixel). In order to perform a weighting by AHP, we have 11 order weights. Order weight 1 is assigned to the lowest ranked factor for that pixel (i.e., the factor with the lowest score), order weight 2 to the next higher ranked factor for that pixel, and so forth. Thus, it is possible that a single order weight could be applied to the pixels from any of the various factors depending upon their relative rank order. Finally, land-use suitability for urban development is produced and output map is displayed in 0 – 255 range.
Rapid increase in population growth in recent decades in Iran caused the urban sprawl. The Iranian cities settled in proximity of rivers and favorable regions surrounded by agricultural lands. Then, urban growth by the recent decades converted these agricultural lands to urban areas. Tabriz city in the recent decades has rapid growth. This growth often occurred in high slope areas, proximity to Tabriz fault and in agricultural lands. Therefore, it is essential to use efficient ways to find the suitable lands for urban growth. The AHP-OWA model can be efficient method for this type of analysis.