District Level Report Sabar Kantha

Ayush Patel

2022-08-23

Introduction - Sabar Kantha

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 Sabar Kantha district Gujarat on wikipedia.



Summary Statistics

Total Number of Villages: 1376

Total Number of Gram Panchayat: 680

Total Number of Sub Districts: 13

Total Population : 2.064869^{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)
Bayad 181292 11316 1084 55996.72 45833.50
Bhiloda 223142 10366 133882 70667.86 31614.69
Dhansura 106733 5950 1127 39105.50 30477.43
Himatnagar 224436 25111 2034 74826.26 48986.35
Idar 215598 36991 10865 74984.62 54629.82
Khedbrahma 268142 7057 216917 80807.03 33662.46
Malpur 91460 5047 3782 35741.94 19949.41
Meghraj 155752 5248 61107 53914.50 33127.36
Modasa 154977 15898 5575 59091.68 40720.20
Prantij 137683 12498 304 38121.66 27568.73
Talod 136126 10846 386 40650.86 33291.54
Vadali 71711 8738 3464 30226.47 22420.19
Vijaynagar 97817 4148 79676 45030.26 13760.94

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***
(0.0005)
perc_marginalised_pop 0.020
(0.026)
district_head_quarter_distance_in_km -0.107**
(0.049)
sub_district_head_quarter_distance_in_km -0.862***
(0.226)
nearest_statutory_town_distance_in_km 0.734***
(0.245)
sub_district_nameBhiloda 4.017
(3.412)
sub_district_nameDhansura 0.426
(3.903)
sub_district_nameHimatnagar -5.524
(4.185)
sub_district_nameIdar 3.927
(3.554)
sub_district_nameKhedbrahma -13.466***
(3.427)
sub_district_nameMalpur 2.276
(3.092)
sub_district_nameMeghraj -43.484***
(2.982)
sub_district_nameModasa -2.873
(3.361)
sub_district_namePrantij 6.033
(4.322)
sub_district_nameTalod -9.390**
(3.969)
sub_district_nameVadali -3.411
(3.896)
sub_district_nameVijaynagar -19.567***
(3.876)
Constant 76.518***
(4.114)
Observations 1,348
R2 0.318
Adjusted R2 0.309
Residual Std. Error 22.361 (df = 1330)
F Statistic 36.421*** (df = 17; 1330)
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.