An Analysis of the Spatial Reflection of Rural Housing Reconstruction after an Earthquake (Case Study: Sina Dehestan of Varzeghan County)

The aim of this study was to analyze the spatial reflection rural housing reconstruction after the earthquake in Sinai district located in the Varzeghan county aspects of infrastructure-related, social, cultural and health treatment. This study is a descriptive and analytical. The data collection was in the library field. In the field, the questionnaire is used. The sample size was 300 and the sampling method is simple random sampling. The validity of the questionnaire was evaluated based on the validity and reliability of the Guttman coefficient (split-half) which is equal to 955/0 and show the reliability of the questionnaire. After assuring the goodness of fitting the designed model, the standardized load factor test indicates that the measuring instrument has very good validation tools and shows that the coefficients and chi-square ratio are relative to the degree of freedom that has strong correlation, the relationships between hidden variables together and the relationship of obvious variables; Investigating T values of the obvious variables shows that the Infrastructure Index has the most impact on the renovation of rural housing. Which has the most effective coefficients in this case, the variables of "gas supply", "water distribution", and "transport communication network". Finally, the covariance matrix of the independent variables of the research indicates that the path estimates between all the infrastructural, cultural, social, health and physical variables are acceptable and acceptable at the upper limit. In fact, the proposed model can be proposed as a useful tool for reflecting the spatial renovation of rural housing in general, the renovation of rural housing after the earthquake in the Sina village of Varzaghan has caused the infrastructure index to have the highest spatial reflection. The study population dispersion and trouble completing the questionnaires, as well as the lack of necessary resources on SEM and LISREL software. research innovation combined two important issues the village, housing "and" disaster "and interpret the results using a combination of software LISREL structural equations and multivariable regression analysis using software SPSS.