Exploring the most affecting factors on resident's migration in historic city center of Tehran and strategies for attracting residents, using the Q factor analysis.

Document Type : Research Paper


1 school of architecture and environmental design, Iran university of science and technology, Tehran, Iran

2 Associate professor, School of Architecture and Environmental Design, Iran University of Science and Technology, Tehran, Iran

3 Associate Professor, School of Architecture and Environmental Design, Iran University of Science and Technology, Tehran, Iran.

4 Associate professor, Department of Educational Sciences, Tarbiat Dabir Shahid Rajaei University, Tehran. Iran


Exploring the most affecting factors on resident's migration in historic city centers and strategies for attracting residents, using the Q factor analysis. (The case of Tehran historical center)

Introduction: Population changes in the country and increasing migration to cities in the last few decades have resulted in the development of cities in the countryside and caused irreparable environmental damage. On the other hand, the migration of inhabitants from the historic city centers has led to numerous difficulties in these neighborhoods. Due to old residents' exodus, low-income groups were replaced and land use changed to warehouses and workshops. Conducting population and capital to uninhabited central areas could result in using the potential of city centers to settle the different categories of society.

This study has been done to answer the following questions: Why residents of historic city centers have migrated from the neighborhood and what strategies can prevent them from leaving and attracting different categories of society to these places.

Methodology: The study is based on qualitative, quantitative, and Delphi surveys. The required data were collected through library studies, interviews with experts, and two Delphi survey phases. Using the document content analysis, open coding and axial coding, and Q factor analysis were analyzed.

Thus, after extracting the key concepts of the interviews and forming a goal-content table, the questions of the questionnaire were designed for survey Delphi and distributed among the second group of experts in two stages.

Q factor analysis is a powerful tool for understanding the values, tastes, concerns, and typologies of individual perspectives. In this analysis, respondents are categorized instead of responses.

After library studies in the field of research, the Delphi method was used to expand the subject beyond what is available in the subject literature. The theoretical sampling method was used to select the specialists for the first person and the snowball sampling method was used to select the other individuals. Fifteen experts participated in the interview section and 20 experts participated in the survey section of the questionnaire for factor Q analysis. The interviews continued until the theoretical saturation and knowledge network were completed. The first three interviews were unstructured and the rest were semi-structured. The number of interviewees was confirmed according to the KMO test in the next steps.

After extracting the key concepts of interviews and forming the target-content table, questions of the questionnaire were designed to survey the Delphi and distributed among the second group of experts in two stages. The questions were designed in the form of a Likert scale from zero to 9. The results of the questionnaire were analyzed using the Q factor analysis method.

Cronbach's alpha coefficient was used for measuring the reliability of the questionnaire. Since this number is equal to 0.97, it can be said that the questions have good reliability. Also, the Test-Retest method was used to check the validity of the questionnaire. Thus, by repeating the test in similar people, the same results were obtained.

Results and discussion: Total Variance Explained shows that out of 20 specialists, 7 factors have been identified. The highest variance explained is related to the first factor of 15.6. The second factor is 12.7, the third factor is 9.6, the fourth factor is 9.5, the fifth factor is 8.4, the sixth factor is 6.7 and the seventh factor is 6.4. The cumulative percentage of total factors is 69.2, which indicates that about 69.2 respondents' opinion was common. About 30.8% of those thoughts are personal, which may be due to personal awareness, inclinations, and desires. This means that external reality has existed and has been able to capture 69.2% of the respondents' thinking and shape their common theories.

The Rotated Component Matrix Shows the factor load for each of the variables after rotation. Any person with a factor load greater than 0.3 + _ was significant and is categorized in that factor.

The first factor, which is the first class of respondents, consists of 7 experts, the second factor consists of 3 experts, the third factor consists of 2 experts, the fourth factor consists of 3 experts, and finally the fifth and sixth factors each consist of two experts.

To find the common mental pattern of experts in each factor, answers with scores of 0, 1, 8, and 9 are selected from each of the experts in that factor, which have half or more repetitions in the constituents of that factor.

Then, the selected questions of each factor have been considered as a suggested label. The titles have been offered to 5 experts for approval. These titles make up the content and the goals are the questions that remain.

After re-testing the questionnaire of experts and reaching the same or almost the same answer, the work is completed at this stage. Finally, labeling on the main concept of questions extracted on each category was selected as effective factors on the exodus of residents and strategy to preserve and attract residents, according to the expert’s opinion.

Conclusion: The Q factor analysis of experts led to the identification of five mental patterns. These patterns were divided into two main categories of exit causes and adsorption strategies. The first category, which includes two perspectives, points out that the problem of quantity, quality, construction, and infrastructure on the one hand and the attractiveness of the new development is the other most important effects on resident migration from the old context. The second category considers components such as participatory approach, change of urban management on a micro and macro scale, and improvement of the mental image affect the retention of remaining residents and reverse migration from new areas to the old context.

Keywords: Historic city centers, Intra urban migration, Inner-city vacancies, Delphi method, Q factor analysis.


Main Subjects

Articles in Press, Accepted Manuscript
Available Online from 05 December 2021
  • Receive Date: 16 July 2021
  • Revise Date: 01 December 2021
  • Accept Date: 05 December 2021
  • First Publish Date: 05 December 2021