The Role of Public Contribution in Restoration of Urban Decay in Jahrom City

Introduction
Once a suitable living space for citizens, older districts in cities, can no longer have the same performance due to technological improvements and changes in environmental, social, and economic needs. While they were the heart of wealth and power of cities in the past, under current conditions (in almost every city) and because of their poor infrastructure and urban services  they are considered as disorganized from a physical point of view. Research on historical centers of cities as well as their restoration, or in other words, improvement of urban decays, the tangible need of which has attracted officials’ attention, is caused by different inferences drawn from insight, culture, tradition, art, architecture, and in a sense lifestyles during last three centuries. During the last century, Iranian lifestyle has undergone fundamental changes, causing the impact of their past long culture and rich civilization no longer to be felt nowadays. Different studies conducted on older districts of Iranian cities show that most dealing with these areas are based on mere conservation and repair of valuable monuments, while little attention has been given to special planning with the aim of revitalization and restoration of socioeconomic and cultural life in these areas. Experience of urban management and planning for restoration of older city districts in Iran dates back to 1921. In early 1971, the role and significance of historical areas increased through holding seminars and introducing scientific books and articles. After the Islamic Revolution, attempts in this regard decreased. Since 1985, through conducting research projects, publishing scientific books and articles, and holding seminars, activities in the field expedited. Thus, the current study seeks to investigate the role of public contribution in restoration of Jahrom city urban decay.
Methodology
This study was conducted with an applied-developmental purpose, using research theoretical foundations via a library method. Moreover, data collection was done through field observation and questionnaires. Once the questionnaire got developed, it was completed through random sampling. The geographical area of this study was consisted of the older distrcits with a population and size of 104 and 22375, respectively. Sample size was calculated, using Cochrane formula. Also, by means of SPSS and Excel software programs, the data got analyzed in statistical descriptive methods (Tables of Frequency Distribution) as well as inferential statistics (Factor Analysis, Pearson).
Results and Discussion
Research findings were recorded in two parts: at first, individual characteristics of the respondents and data got analyzed, and then the indices were prioritized by means of Factor and Heuristic analyses, thus investigating the four physical (eight items), social (six items), economic (five items), and environmental (four items) indices. According to research findings, the fact that initial variable became the superior factor was because of Varimax rotation. In order to conduct a satisfactory Factor Analysis the value of KMO should be larger than 0.5. As Table 1 shows, results from the test stood higher than 0.5, having a significance level of 0.00. Therefore, the correlation between variables could be proven by a 99% likelihood.
Table 1- KMO and Bartlett’s Tests





KMO


 


0.653




Bartletts Test of Sphericity


Chi-Square


876/425




d.f


435




p


0.000





Results from analysis and value of every factor
Indices in each factor that were above 0.5 constituted one factor by themselves, while those that could not be added to them, formed other factors. What is more, the sum of the variance of the four mentioned factors was 66.154%, the largest of which (17.798%) became the first factor. The variance value of 66.154% shows that the results analysis has been satisfactory. Moreover, the results show that in this analysis, 17.798% of the variance was defined by the first factor, with the second, third, and fourth factors calculating 17.689%, 16.252%, and 14.145% of the variance, respectively. According to research findings and above tables, physical factor, or more obviously physical problems, and social problems such as social disorders presented in the mentioned old districts, are considered the most influential issues. Therefore, in order to organize the distrcit, the mentioned factor could be useful in region improvement planning.
Also, in order to investigate the degree of contribution and satisfaction, Pearson Coefficient Correlation was utilized with the findings showing that considering value of Pearson Correlation Coefficient (0.632), with a confidence of 0.99 and an error level below 0.01, there was a meaningful statistical relation between the two degrees of contribution and trust variables. In other words, the more the trust in government officials, the greater the contribution.
Conclusion
Results obtained from Factor Analysis conducted on four public contribution variables prove helpful and effective in restoring urban decay. More than 91% of the dwellers in overall level of urban decay region are contributive. This is in such a way that if dwellers’ contribution to urban decay restoration and improvement become organized, its effect will be doubled. Investigation of the overall level of urban decay region shows that 31.09% of the dwellers are organized as region councils and associations. Moreover, dwellers’ opinions show that in case facilities and motivating policies are provided, about 72.42% of them willingly welcome the restoration and improvement of residential buildings. For instance, according to the first factor with variance values of 3.738 and 17.798, the most important factor in Factor Analysis, physical factor is the most significant one in urban decay. Second significant factor is the social one, with economic and environmental factors in the next ranks. Results show that a Pearson Correlation Coefficient of 0.168, confidence of 0.99, and error level below 0.01, poses a meaningful statistical relation between the two variables of contribution and trust. In other words, the greater the people’s trust in government officials, the higher their contribution.