نوع مقاله : مقاله علمی پژوهشی
نویسندگان
1 استادیار گروه جغرافیا، دانشگاه سیدجمال الدین اسدآبادی، اسدآباد، ایران
2 دکتری جغرافیا و برنامه ریزی شهری، دانشگاه اصفهان، اصفهان، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Introduction
Poverty in developing countries is rapidly urbanizing, as it is referred in such terms as "urbanization in the face of poverty" and "urbanization under poverty". According to the statistics, issued by the Ministry of Roads and Urban Development, more than 20 million people live in slums in Iran, eleven million of whom are resettled in informal settlements with the remaining 9 million, residing in worn-out tissues. Most urban slums are located in Tehran province, forming a crescent around Tehran Metropolis from Karaj to Varamin. Meanwhile, like many largest metropolises in the world, Tehran has experienced significant growth over the last five decades. The population of the city has increased from 2.7 million in 1966 to 8.8 million in 2011. Also, it has grown from 4,600 hectares to more than 61,000 hectares. In other words, the extent of Tehran has increased more than 13 times over a period of seventy years. As a consequence of this situation, urban poverty zones has grown inside and around the city. According to the surveys, there are 3,269 hectares of worn-out tissues in Tehran, 593 of which is located in central Tehran. District 12 and adjacent districts such as 11, 13, 15, and 16 are in this area. Accordingly, the present research tries to determine urban poverty zones of Tehran Metropolitan District 12 so as to empower the inhabitants, organizing and enhancing the quality of their lives and living spaces.
Methodology
This research was an applied one, using a quantitative approach with regard to the investigated components. The research statistical population was the 12th district of Tehranmetropolitan in 2016. Necessary information was extracted from statistical blocks of IRAN in 2016. Indexing was done by means of the database information in Arc / GIS software, Arc / View. Then, the outputs got extracted from the indices and transferred to Excel. Once the above steps were performed, the indices were transferred to SPSS software program, where they got classified into 4 factors through factor analysis model. Eigenvalues, percentages of variance, cumulative variance, as well as coefficient of difference (gap between blocks) were calculated for each factor. Considering each of the extraction factors, the city blocks were classified into five groups: very wealthy, wealthy, moderate, poor, and very poor.
Results and Discussion
Based on the study findings, the first factor got classified into 9 indices, namely net residential density, total residential density, residential population density, area population density, net residential per capita, employment rate, task coefficient, population burden, and economic participation. This factor had the most influence among the four factors. As for the second factor, ten indices were loaded, while in case of the third and fourth factor, there were only 4. According to the first factor, there were 137 very poor blocks, 337 poor, 390 moderate, 173 wealthy, and 24 very wealthy. In other words, the spatial distribution of urban poverty in terms of economic-physical factors in District 12 was as follows: 13% very poor, 32% poor, 37% moderate, 16% wealthy, and 2% very wealthy. As for the second factor, this district had 76 very poor, 277 poor, 444 moderate, 232 wealthy, and 32 very wealthy blocks. Therefore, the spatial distribution of urban poverty from the perspective of socio-economic and cultural factors in this district was as follows: 3% very wealthy,22% wealthy, 42% moderate, 26% poor, and 7% very poor. According to the third factor, 55 blocks were very poor; 372 blocks, poor; 393 ones, moderate; 188 blocks, wealthy; and 53 ones, very wealthy. This means that 5% of the blocks were very wealthy; 18%, wealthy; 37%, moderate; 35%, poor; and 5%, very poor. According to the fourth factor, fifty blocks were very poor; 220 blocks were poor; 490 ones,moderate; 276 wealthy; and 25 ones, very wealthy. As a result, 2% were very wealthy, 26% wealthy, 46% moderate, 21% poor, and 5% very poor, from a socioeconomic perspective. By combining the above four factors together as a combined index, the results weould be as follows: 53 blocks (5%) were very wealthy; 277 ones (26%), wealthy; 401 blocks (38%), moderate; 257 ones (24%), poor; and 73 ones (7%), very poor.
Conclusion
Results from this research showed that31% of the population of District 12 were poor, while 38% belonged to the middle class. Thus, the social polarization phenomenon has occurred in District 12. In fact, inequality has been formed among the city blocks and social, economic, and physical differences among them is clearly visible. These results are in line with the findings of Rustaii and Karbasi (2017), Farhadikhah et al. (2017), and Bozorgvar et al. (2017), according to whom cities such as Maragheh, Mashhad, and New City of Hashtgerd have moved towards social polarization. In addition, the results of this study are in agreement with the findings of Anderson (2004). To a large extent, geographical polarization has been formed in terms of combining different economic, social, and physical characteristics in the 12th district of Tehran. In geographic polarization, individuals or households are concentrated in particular neighborhoods. Indeed, certain neighborhoods are clustered as the focus of the poor. Poverty in the neighborhoods of District 12 has intensified geographically. Poverty is most prevalent in central, southern, and northern neighborhoods such as Sirus, Shush, Pamnar Ark, Baharestan Saadi, and Ferdowsi-Lalehzar. In other neighborhoods such as Amin, Kowsar, Mokhtari Takhti, Ghiam, Sanglj, and Shemiran, it has also taken root less severely. The important point is that there is a direct correlation between poverty and worn-out tissue indices. The highest concentration of worn-out textures could be found in neighborhoods such as Shush, Sirus, Mokhtari Takhti, Sanglaj, Pamnar, Amin, Baharestan, and parts of Shemiran. Therefore, the poor zones overlap with the worn texture zones.
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