Dataset

Final Dataset is stored as DATASET.csv in the root folder. Feel free to use in your projects.

Example:

State	District	Area (sq km)	Households	Total Population	Total Males	Total Females	Literate Population	Literate Males	Literate Females	Illiterate Population	Males Illiterates	Female Illiterates	Total Working Population	Total Working Males	Total Working Females	Unemployed Population	Unemployed Males	Unemployed Females	ST	SC	Hindus	Muslims	Sikhs	Buddhists	Jains	Others_Religions	Religion_Not_Stated	Households.1	Rural_Households	Urban_Households	Households_with_Internet	MPI HCR
ANDHRA PRADESH	Adilabad	16105.0	649849	2741239	1369597	1371642	1483347	856350	156831	1257892	513247	744645	1323667	748939	574728	1417572	620658	796914	495794	488596	2399901	275970	1377	25510	617	322	22120	817714	597466	220248	5512	27.12%
UTTAR PRADESH	Agra	4041.0	710566	4418797	2364953	2053844	2680510	1614594	127908	1738287	750359	987928	1389844	1119701	270143	3028953	1245252	1783701	7255	991325	3922718	411313	12057	4049	21508	384	36692	903823	496971	406852	27183	32.83%

Estimation of National Poverty using Data Analysis

This project attempts to analyse and estimate poverty indicators at the Indian district level. We consider various parameters from the 2011 census data that may be relevant to the estimation of poverty in a district, and join them with their relevant poverty indicators.

To estimate and mesaure poverty we use the multi-dimensional poverty index (MPI) based on the 2023 NITI Aayog report which has the headcount of persons under the MPI line for the years 2014 and 2019. We will be using the head-count of the 2014 MPI for each district. This data will be scraped out of the report PDF file (resources/NITI_2023.pdf), and be stored as DATASET.csv and datasets/final_dataset.csv

This project is made in dash-plotly to create a responsive dashboard fully in a python environment.

Dashboard

The plotly-based dashboard created looks like that. It provides 3 crucial graphs that give us crucial insights on to how the demographics of that state look, both aggregated and over the different districts.

• A scatterplot indicating if there is a correlation b/w literate population of a district and the poverty level of the same.

• A grouped bar graph, that compares the population, literate population, and workign population for the state segregated by sex.

• A line plot that shows the MPI HCR level for all the districts in the state in decreasing order.

You can choose any of the administrative states or union territories as of census data of 2011. At the time of the 2011 census, there were a recorded 640 districts. The HCR values will be considered for only these districts. There is no available data for a poverty indicator at the district level in the year 2011.