District Level Report Vadodara

Ayush Patel

2022-08-23

Introduction - Vadodara

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 Vadodara district Gujarat on wikipedia.



Summary Statistics

Total Number of Villages: 1537

Total Number of Gram Panchayat: 832

Total Number of Sub Districts: 12

Total Population : 2.099855^{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)
Chhota Udaipur 215590 6205 205129 76144.64 35680.17
Dabhoi 129278 8275 51917 60836.95 50469.44
Jetpur Pavi 253561 4879 214779 80034.41 52004.01
Karjan 137174 8096 39422 58616.30 49107.69
Kavant 200449 3805 192345 60572.21 33790.09
Nasvadi 147367 774 134556 53186.51 31425.82
Padra 219241 13361 7417 52189.57 40605.58
Sankheda 182449 4936 91419 70780.22 53129.07
Savli 236542 12497 17713 77617.91 59397.48
Sinor 65440 4359 22154 29251.54 23916.21
Vadodara 186902 11419 22195 40438.69 30823.55
Vaghodia 125862 4496 41553 53565.79 40984.85

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 0.002***
(0.0005)
perc_marginalised_pop -0.080**
(0.034)
district_head_quarter_distance_in_km -0.237***
(0.056)
sub_district_head_quarter_distance_in_km -0.399***
(0.103)
nearest_statutory_town_distance_in_km -0.095
(0.112)
sub_district_nameDabhoi 37.457***
(5.713)
sub_district_nameJetpur Pavi 26.639***
(3.107)
sub_district_nameKarjan 40.788***
(5.458)
sub_district_nameKavant -2.270
(2.877)
sub_district_nameNasvadi 8.192***
(2.958)
sub_district_namePadra 6.532
(6.443)
sub_district_nameSankheda 26.126***
(4.180)
sub_district_nameSavli 18.685***
(5.832)
sub_district_nameSinor 43.843***
(6.058)
sub_district_nameVadodara -16.113**
(7.016)
sub_district_nameVaghodia 11.231*
(6.448)
Constant 61.992***
(7.209)
Observations 1,514
R2 0.484
Adjusted R2 0.479
Residual Std. Error 23.350 (df = 1497)
F Statistic 87.899*** (df = 16; 1497)
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.