Abstract:
مسکن به عنوان اصلیترین نیاز تشکیلدهنده یک شهر محسوب میشود. توزیع نابرابر درآمد و ضعف نظام برنامهریزی بهمنظور حمایت از گروههای هدف در بخش مسکن در شرایط رشد قیمت زمین و مسکن سیاستهای تأمین مسکن مناسب برای گروههای آسیبپذیر را مشکل ساخته است. تجسم کالبدی این امر در شهرها بهصورت رشد و توسعه محلات غیررسمی و فقیرنشین و پیدایش نواحی مسکونی جدید در حاشیه شهرها ظهور کرده است. بر این اساس هدف پژوهش شناسایی و تحلیل شاخص های موثر در مسکن گروه های آسیب پذیر می باشد. روش این تحقیق از نظر هدف کاربردی و از نوع توصیفی و پیمایشی می باشد. یافته های تحقیق نشان می دهد شاخص های اجتماعی و اقتصادی تاثیر بسزایی در امر مسکن گروه های کم در آمد دارد. نتایج نشان می دهد، 22 شاخص در عامــل اول با واریانس 37 بارگذاری شده اند. 4 شاخص مربـوط بـه شـاخص های درصدجمعیت نسبت به کل شهری، بعدخانوار، تعداد نفر در هر اتاق و درصد مهاجرت جمعیتی، میباشند. بـا توجـه بـه اینکـه شاخص های گوناگونی در این عامل بارگذاری شده اند، برای نامگذاری به شاخص هایی که دارای عدد بارگذاری بیشتری هستند توجه میکنیم که شاخص اقتصادی، اجتماعی میباشند.
Tabriz city has been done. The analysis of quantitative and qualitative social issues is done
through tools that are variables called social indicators. Social indicators are a key tool for
drawing the future perspective of the state of the province and planning.
Results and Discussion
Oribe era provides better output than Varimax era. Two matrices are generated, a pattern
matrix and a structure matrix. The difference between low and black factor loadings is more
evident in the pattern matrix. Therefore, this matrix is interpretable. Factor loadings in the
model matrix show the unique relationship between the factor and the variable. The pattern
has less complex variables and a simpler structure. The last step in factor analysis is to
determine how many factors should be determined for interpretation in order to assign labels
to them. The number of factors that should be divided depends largely on the main purpose of
the analysis. It is more appropriate to have only one factor in this research. Because according
to the table, the first factor had an eigenvalue of 8.14. while the second, third, and fourth
factors had eigenvalues of 2.15, 1.64, and 0.99, respectively. Also, the Pebble diagram
confirms the dominance of the first factor that was involved with 15 items.
Conclusion The results show that the mentioned 22 indicators are loaded in the first factor
with a variance of 37. The above 4 indicators are related to the indicators of the percentage of
the population in relation to the whole city, the size of the household, the number of people in
each room and the percentage of population migration. Due to the fact that various indicators
are loaded in this factor, for naming, we pay attention to the indicators that have more loading
numbers, which are economic and social indicators.After completing the mentioned steps, in
the last step, using the factor scores obtained in the previous step, factor scores are calculated
for each region and finally, the ranking of the regions is done. For this purpose, a composite
index obtained from the algebraic sum of the scores of the five factors for each region is
calculated. At the end, by arranging the table based on the numerical value of the combined
indicators of the regions, they can be ranked. Table 8 shows the numerical value of the
combined indicators of each region and the rank of the regions.