District Level Report Banas Kantha

Author

Ayush Patel

Published

August 23, 2022

Introduction - Banas 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 Banas Kantha district Gujarat on wikipedia.



Summary Statistics

Total Number of Villages: 1237

Total Number of Gram Panchayat: 722

Total Number of Sub Districts: 12

Total Population : 2.705591^{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)
Amirgadh 132354 3545 77462 60966.98 21724.14
Bhabhar 101258 6266 96 40285.37 35031.21
Danta 207086 6269 124849 84333.56 22977.34
Dantiwada 115221 11996 5966 43179.63 27067.65
Deesa 471969 46898 10545 143740.70 117510.84
Deodar 163007 16908 3802 57071.07 50263.11
Dhanera 201163 24332 19867 79965.02 63951.99
Kankrej 257553 17669 617 78187.52 38928.22
Palanpur 278542 31239 13187 74019.16 51310.96
Tharad 299335 40865 6960 133574.49 115984.29
Vadgam 231947 37879 5489 55910.00 38647.58
Vav 246156 44071 2215 169467.78 122421.39

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 0.0001
(0.0003)
perc_marginalised_pop -0.023
(0.027)
district_head_quarter_distance_in_km -0.193***
(0.047)
sub_district_head_quarter_distance_in_km 0.073
(0.070)
nearest_statutory_town_distance_in_km 0.048
(0.055)
sub_district_nameBhabhar 0.736
(5.808)
sub_district_nameDanta 10.493***
(2.984)
sub_district_nameDantiwada 5.703
(3.895)
sub_district_nameDeesa 30.746***
(3.494)
sub_district_nameDeodar 40.646***
(4.662)
sub_district_nameDhanera 7.733*
(4.205)
sub_district_nameKankrej 9.358**
(4.319)
sub_district_namePalanpur 2.424
(3.489)
sub_district_nameTharad 12.871***
(4.710)
sub_district_nameVadgam 6.137*
(3.361)
sub_district_nameVav -39.484***
(5.422)
Constant 62.849***
(3.575)
Observations 1,222
R2 0.554
Adjusted R2 0.548
Residual Std. Error 19.710 (df = 1205)
F Statistic 93.639*** (df = 16; 1205)
Note: p<0.1; p<0.05; p<0.01

Model Diagnostic plots

Distribution of Redsiduals


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.