Investigation of spatial-temporal changes of women's employment index in rural areas of Isfahan province (1996-2011)

Introduction
One of the indicators of the modernization of the national economy and social development is the participation of women and their role in economic structures. Simultaneously with the development of women's issues and their role in development, the impact of rural women's employment on development was considered. The present study explores the spatial-temporal processes and patterns of rural women's employment index in Isfahan province in 1996, 2006 and 2011. employment of women status in the Iran has spatial variations, exploring of patterns and spatial behavior of women have a most important to dynamic and family planning in rural area in Isfahan. At this paper we use an exploration spatial data analysis methodology.
Theories:
The role of women's employment in the dynamics of human life is undeniable, so that it can be considered the center of human and social communication. Women's employment can affect independence, self-esteem, self-reliance, social adjustment and health, academic achievement, continuing education, self-efficacy, and accountability. Theories and hypotheses, such as the theory of the promotion of the role of role development theory, have evaluated the hypothesis of the accumulation of the role of women's employment in the development process as positive and significant. Theories in this field include the theory of welfare, the views of neoclassical economists, the theory of the disintegration of the labor market, and the theory of empowerment.
Methodology:
The purpose of this study is to explore spatial patterns of employment rate of rural women in Isfahan province and to study the trend and rate of spatial-temporal changes during the years 1375-1390. To conduct this research, the data of the rural areas of the Statistics Center census were used in the years 1375, 1385, 1390. The employment rate of rural women has been calculated and extracted. To measure the employment rate, the number of employees is divided by the working age population and then the percentage is presented. In this study, in order to discover spatial patterns of analytical techniques of spatial statistics; G General ord-Geties and Autocorrelation Spatial have been used.
Moran's statistic is one of the classic ways to measure spatial autocorrelation. Moran is calculated as follows:

Many planners and analysts are interested in studying where the most common occurrences in an event are in demographic analysis or economic activity. Hot spot analysis is one of the most important and best analyzes that can answer these questions. In principle, this statistic is the same as hot spots and cold spots; Which is as follows:

Modeling changes
Data are divided into two categories depending on whether or not they contain the spatial dimension of the tolls: a) Spatial data that includes both feature and spatial information of the toll; For example, the rate of building density for a place in different time periods is non-spatial data, but the rate of building density in different locations is spatial data and the spatial component of the data may be very useful in understanding why building rates change. The difference between spatial and non-spatial data is important because many statistical techniques formed for non-spatial data to analyze spatial data are not reasoned. Spatial data and local analysis have unique properties and problems that require the use of different sets of statistical modeling techniques and methods.
In this paper, in order to obtain changes in the employment rate of rural women, the employment rate of all three periods was calculated and the maps of hot spots of each period were obtained. it was prepared. The percentage formula for the values of spatial-temporal changes is as follows:
Percentage of Change=(Values of final year-Values of initial)/Values of initial*100
Data: Employment data are recorded in the population and housing census. We use the employment data of rural women in Isfahan province in 2006, 1996 and 2011.
Procedure of research:
1- Calculating the employment rate of women
2- Calculating the index of Moran and G
3- Preparing a map of hot spots
4- Calculate the rate of two-dimensional changes relative to each other
5- Preparing a map of spatial-temporal changes and chart of changes
6- Cartography
7- Data analysis

Results and discussion:
Major changes between 1996, 2006 and 2011 can be seen in the Hot Spot maps of the employment index. In the maps related to the spatial pattern of women's employment in low values, a large area with a north-south direction has been formed from rural areas of northern cities of Isfahan to rural areas of Semirom city. In 2006, this area was divided into two parts. The western area, which includes the rural areas of Golpayegan, Khansar, Frieden and Fereydunshahr. The larger area still covers the central and southern parts of the province. In 2011, this cold spot in the form of a circle covered the rural areas around Isfahan's Kalatshahr and neighboring cities. Hot spots can still be seen in the eastern part of Isfahan and Khoro Biabank, Ardestan and Natanz. The research findings indicate spatial changes in women's employment over time, around the metropolis of Isfahan and the cities around the low employment zone. More than 60 rural areas of the province have been unchanged in the employment index of women, and the villages of the west and south have had the most positive changes, and some villages of Nain, Ardestan and Isfahan have had negative changes.
Conclusion:
What is clear from the spatial model of women's employment is that the central part of Isfahan province with its dominant industrial and service characteristics has affected its surrounding villages and the villages of these areas have mainly taken on an urban function, which has caused women to have less share than men. Have employment opportunities. This is self-evident and confirms the lack of structural changes for the employment of rural women in these areas, which will lead to individual dissatisfaction and ultimately social dissatisfaction with the increase in women's literacy.