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This report is the result of two lab work, whereby we have been accompanied and supported by many people. We now have an opportunity to express our gratitude to all of them. We express our sincere gratitude to Ms. Anjana Vyas and Mrs. Harini Mittal for giving us an opportunity to work in the Pura project introduced by Dr. A.P.J. Abdul Kalam and for their help throughout the project.


Table of Contents


1) Introduction 2) Area of study 3) Methodology Data Used Procedure 4) Observation and Analysis 5) Conclusion

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4 5 6 6 7 9 19


Introduction The rural-urban migration is the main reason for the rapid urbanization and the unsustainable development in the rural areas; as a result there are severe environmental impacts, loss of natural resources, and many more. There are many socio-economic factors those are responsible for the migration. Lack of employment in the rural areas is one of the major economic factor responsible for the migration. The main purpose of the PURA project is to stop the rural-urban migration. For that, generation of employment in the rural areas is the essential step. To generate the employment in the rural areas, there should be certain criteria which must be fulfilled; that provide the best location for the industries and service areas. Here, we have considered the employment distribution and road network connectivity to locate the service centre. The main purpose of the study is to decide the service area and impact of industries in nearby areas and to improve the employment and to stop the migration.


Study area

The area on which we are working is Mahesana district; Satlasana, Kheralu, and Vadanagar are the Talukas of our concern in the district.


Methodology Data used Census data of Mahesana district To get the information about the major crops of the area as well as the work force distribution in the study region. Amenity data To get the information about the road connectivity and other factors like water supply and irrigation pattern. Revenue map It was used to verify the taluka and village boundaries of the study area.

Software used • Microsoft excel • Arc GIS 9.3


Procedure Summarization of the data The census data was shorted out to the area of interest with the help of filters in the Excel sheet. Data joining The data in excel sheet need to be converted into CSV format to get joined in the attribute table of the village boundaries in the Arc GIS. Crop pattern After data joining, with the help of symbology in GIS; we can get the crop pattern. In which we can see the Bajari is the dominating major crop in the study area. Employment distribution As done before, we can also have the employment distribution and compare both parameters. Best suited location for buffer stock With the help of criteria mentioned before and road connectivity, we have found the best suited area for the buffer stock. Service area Service areas were found which can be served within the time intervals of 5 min and 10 min from the service centers with the help of network analyst tool.

Route analysis By locating the cluster points and help of network analyst tool, we have found the optimum route for the connectivity of the centers.


Location for Cotton Ginning: Cash crop pattern After data joining and with the help of symbology we have generated the cash crop pattern of the study area. Rest of the procedure is similar to the process that is explained above for the search of the best suited service area.


Observations and analysis Analysis work is shown in the maps and their description bellow:


The above map shows the main commercial crop pattern of Talukas – Satlasana, Vadnager and Kheralu of Mehsana district. There are many other crops scattered all over these talukas, mainly – Cotton, Jawar, Bajri, wheat, mustard, etc. From the above map we can clearly make out that the main production of these talukas are Bajri crop.


MAP 2 This map shows the relation between the crop pattern and the employment distribution. Mailny focused on Bajri crop as it is the largest production of these 3 talukas. The portion shown in green are the areas showing the growth of Bajri. This pattern helps us to analyze the whole targeted area in terms of the employment distribution.


MAP 3 In this map we have overlapped the District Road Network to the base map. This helps us to find out the connectivity to these areas. Better the connectivity, the better the transportation of crops.


MAP 4 In this map we have shown the best possible Buffer Stock location for the Bajri crop. We have considered 3 parameters for detecting this location.

1. Connectivity of Road Networks. 2. Employment 3. Productivity of Bajri Crop. 13

MAP 5 In this map the area which is showing from brown color is service area which is reachable within 5 min from gajipur bajri buffer stock location and the area which is showing in green color is the particular service area which is reachable within 510 min from the Gajipur bajri buffer stock location. We found out these particular area with the use of network analyst tool of arc gis software.


MAP 6 We have selected random locations in the areas where Bajri is the main crop and showed the optimum possible route to reach these locations from the prime point (Gajipur) and with the help of this we have plotted a network of routs that could connect the distributed locations to the main Buffer Stock.


MAP 7 This map shows the cash crop pattern of these three talukas.


MAP 8 This map shows the distributions of the cash crops and the employment in these three talukas. The big dots shows the larger number of employed people.


MAP 9 We found out the cotton ginning area with the help of the below mentioned three parameters. 1. Connectivity of Road Networks. 2. Employment 3. Productivity of Cotton Crop.


MAP 10 The areas shown in brown in the map shows the areas that are in reach within the time period of 5 minutes from the Kajiput Cotton Ginning unit. And the polygon in green shows the area that could be covered during 5 to 10 minutes. We found out these polygons with the help of network analysis tool of ArcGIS Software.


MAP 11 This map shows the optimum 5 locations that we have selected for importing the cotton to the main location. Also it shows the routes that can be used to reach these locations faster.



By this study we can conclude that employment can be generated with the help of agriculture based industries and services by using better connectivity and all other facilities existing in the study area. And as a result migration can be minimized.