District Level Report Mahesana

Ayush Patel

2022-08-23

Introduction - Mahesana

This report aims to provide a birds eye view of the district through the lens of village amenities data released by census in 2011. Before moving towards the descriptive insights from the census data, here is what pops up when a search is executed for Mahesana district Gujarat on wikipedia.



Summary Statistics

Total Number of Villages: 606

Total Number of Gram Panchayat: 539

Total Number of Sub Districts: 9

Total Population : 1.520734^{6}

Statistical Summaries at the subdistrict level

Sub District Total Population Total SC Population Total ST Population Total Area of Vilalges Total Area Sown (Net)
Becharaji 87014 7494 107 40578.32 29091.15
Kadi 260003 24638 685 81375.52 66871.65
Kheralu 111935 9722 218 29725.35 24158.25
Mahesana 328076 22781 904 79037.30 65197.89
Satlasana 89546 8134 243 30824.95 14432.37
Unjha 118431 9665 183 28254.62 23768.11
Vadnagar 117655 7206 73 27006.06 20391.49
Vijapur 222581 14861 599 56427.07 47815.35
Visnagar 185493 14209 132 47670.78 36047.14

Population and Geographical Area

It is of interest to look into which are the most densely populated villages. We can do this by creating a simple scatter plot between population of village and the total geographical area of a village.


Irrigation for Agriculture

The census provides the net area sown (hectares) in a village along with area irrigated with water source in hectares. The area under irrigation may be affected by several factors.

Area Sown vs Area under Irrigation


A distribution for the percentage of area irrigated will be interesting to look at.

Understanding what drives area under irrigation

Much is heard about rain fed agriculture in India. There are several factors that can affect area under irrigation - ranging from government supports, demographics, distance from urban clusters and several known and unknown variables. With the given data we can check if the following variables have any relation with area under irrigation:

  • Percentage of Marginalised group population in village
  • Distance from Major government offices
  • Distance from urban center
  • Total population of a village

A simple Linear regression to see if the above explanation has any merit

Dependent variable:
perc_irrigated_over_net_sown
total_population_of_village -0.001
(0.0004)
perc_marginalised_pop 0.051
(0.133)
district_head_quarter_distance_in_km -0.034
(0.096)
sub_district_head_quarter_distance_in_km -0.019
(0.233)
nearest_statutory_town_distance_in_km -0.071
(0.284)
sub_district_nameKadi 5.704
(3.982)
sub_district_nameKheralu 19.271***
(4.891)
sub_district_nameMahesana 9.029**
(4.061)
sub_district_nameSatlasana 19.752***
(5.597)
sub_district_nameUnjha 19.566***
(5.095)
sub_district_nameVadnagar 8.154*
(4.766)
sub_district_nameVijapur 18.531***
(4.378)
sub_district_nameVisnagar 17.446***
(4.363)
Constant 66.029***
(4.642)
Observations 584
R2 0.083
Adjusted R2 0.062
Residual Std. Error 21.720 (df = 570)
F Statistic 3.951*** (df = 13; 570)
Note: p<0.1; p<0.05; p<0.01

Model Diagnostic plots

Distribution of Redsiduals

Distribution of Redsiduals


Residuals vs Fitted

Residuals vs Fitted

Note

This is to serve as a minimal example of creating parameterised reports with .rmd/.qmd files. This document is in no way analytically or statistically rigorous.