Aadhaar enrollment data

about_data.md

About data

Aadhaar enrollments on a given date and pincode combination are grouped together and represented in the dataset.

Here, the enrollment data is divided into X no. of buckets by the aadhaar data operator. Each row represents information about a single enrollment (of a person) except the last four columns. The value in mobile_number_provided and email_provided for a row will always be less than or equal to the value in aadhaar_generated or rejected column. Consider the following example row from the dataset:

20150420,Allahabad Bank,A-Onerealtors Pvt Ltd,Uttar Pradesh,Ambedkar Nagar,Akbarpur,224155,F,22,5,0,0,5

Here, the last four columns represent: aadhaar_generated, rejected, mobile_number_provided, email_provided. 5 for aadhaar_generated field represents 5 aadhaar generations, 0 rejections, 0 enrollments with mobile number and 5 enrollments with email information.

If this is the case, will the information in the last four columns be repeated with other four (apart from the F/22 one mentioned here) enrollments?

Data issues

aadhaar_generated/rejected fields

---

I looked up the unique number of values for the 'rejected' column. There seem to be 728 unique values in this column. My assumption was that the column had values 0 or 1 (not rejected or rejected). Similarly there are 1296 unique values for 'aadhaar_generated' column. Which raises the question does 1 alone represent being rejected (or accepted in the case of aadhaar_generated field)? Do we need to understand what these unique values represent?

pincode

---

Few entries have Others in the pincode column.

data_questions.md

Questions

Once the data is well understand, the following are some of the questions to be considered. Visualizations can be made specific to a particular question. A question is posed and a corresponding visualization is proposed.

1. Enrollment rejections

Q1.1) Which states register more rejections? If so, is it because of any special drives by the govt?

V1.1.1) India map visual that shows rejection rate by color. Rejection rate can also be adjusted specific to gender.

V1.1.2) As the data is granular at pincode level, we can consider coloring pincode specific regions subject to the availability of shape file.

Q1.2) Is there any trend in the rejected enrollments w.r.t to mobile and email availability?

V1.2.1) Depending on the granularity of the data, we can choose a bar graph.

Q1.3) Are some registrars more strict than others (rejection rate)?

V1.3.1) Tabular format, restricted to top ten.

2. Enrollment generations

Q2.1) Do some districts see higher aadhaar enrollments? Could this be associated to the efficiency of the local government officers or corporators? (How to validate this?)

Q2.2) Are there states with higher female/male enrollments?

V2.2.1) Similar visual as to V1.1.1)

Q2.3) Do some registrars have higher aadhaar generation rates in specific states (or are same registrars uniform in enforcing rules across states)?

Q2.4) What is the trend across different age groups?

V2.4.1) Heatmap of states/enrollment generations in age-specific brackets.

3. Do elections catalyze aadhaar enrollments?

Q3.1) Layer elections (local/state/general) data over enrollment data to check elections (future promise of change) is a driver for enrollments

enrollment_state_gender_wise.txt

state	total	female	male
Andaman and Nicobar Islands	128117	61444	66666
Andhra Pradesh	31427847	15655798	15764298
Arunachal Pradesh	159514	76864	82644
Assam	251296	74892	176402
Bihar	27383356	12781710	14601100
Chandigarh	478403	218979	259396
Chhattisgarh	6432922	3239289	3193309
Dadra and Nagar Haveli	93078	40465	52591
Daman and Diu	66642	29272	37366
Delhi	8442508	3883532	4557023
Goa	920025	450755	468849
Gujarat	21875410	10298280	11576079
Haryana	12295317	5880582	6414282
Himachal Pradesh	3103380	1463151	1640032
Jammu and Kashmir	1736678	798368	938277
Jharkhand	11848531	5685359	6162465
Karnataka	26049015	12845961	13200028
Kerala	18367093	9290020	9071390
Lakshadweep	26908	12840	14065
Madhya Pradesh	30621885	14523308	16096839
Maharashtra	46609470	22103532	24502336
Manipur	562283	269222	292833
Meghalaya	22864	9022	13842
Mizoram	162497	80922	81574
Nagaland	431101	211893	219072
Odisha	10054344	4841563	5209311
Others	416412	202496	212814
Puducherry	435455	218302	217027
Punjab	13422914	6293201	7128878
Rajasthan	29864357	14226001	15637188
Sikkim	191025	91599	99411
Tamil Nadu	20453271	10204772	10244135
Telangana	1001171	486021	515089
Tripura	735003	359434	375453
Uttar Pradesh	55995801	26289579	29703917
Uttarakhand	3239644	1527760	1711768
West Bengal	18852128	9091851	9753526

enrollment_state_gender_wise_queries.sql

# using google bigquery

# state-wise enrollment - includes different values in rejected/aadhaar_generated fields
SELECT state, count(*) as c FROM [aadhaar_enrollment_data.aed] group by state order by state

# gender-wise enrollment - includes different values in rejected/aadhaar_generated fields
SELECT state, count(*) as c FROM [aadhaar_enrollment_data.aed] where gender='F' group by state order by c state

enrollment_state_generated_rejected.txt

state	total	generated	rejected
Andaman and Nicobar Islands	128117	74219	8779
Andhra Pradesh	31427847	17318303	5304895
Arunachal Pradesh	159514	77831	9779
Assam	251296	189328	9017
Bihar	27383356	16607351	2327206
Chandigarh	478403	345814	49512
Chhattisgarh	6432922	3171165	646566
Dadra and Nagar Haveli	93078	43427	11077
Daman and Diu	66642	43305	8564
Delhi	8442508	4718559	1628588
Goa	920025	567320	145813
Gujarat	21875410	13219193	1673380
Haryana	12295317	7439905	1283989
Himachal Pradesh	3103380	2142149	236708
Jammu and Kashmir	1736678	810625	154519
Jharkhand	11848531	6913462	1542452
Karnataka	26049015	16140284	3024848
Kerala	18367093	10472822	2710854
Lakshadweep	26908	14138	3048
Madhya Pradesh	30621885	18808650	3549492
Maharashtra	46609470	29478948	4748208
Manipur	562283	344028	37237
Meghalaya	22864	16275	1012
Mizoram	162497	86569	9584
Nagaland	431101	180797	79079
Odisha	10054344	4734576	1644510
Others	416412	61466	89179
Puducherry	435455	223398	86905
Punjab	13422914	8534197	1436032
Rajasthan	29864357	18852727	3424914
Sikkim	191025	125432	11779
Tamil Nadu	20453271	10732809	2216639
Telangana	1001171	685957	167570
Tripura	735003	464338	117798
Uttar Pradesh	55995801	31920342	6417335
Uttarakhand	3239644	1955636	313961
West Bengal	18852128	8712184	2312243

enrollment_state_generated_rejected_queries.sql

# using google bigquery

# rejected enrollments per state
SELECT state, count(*) as c FROM [aadhaar_enrollment_data.aed] where rejected='1' group by state order by state

# generated enrollments per state
SELECT state, count(*) as c FROM [aadhaar_enrollment_data.aed] where aadhaar_generated='1' group by state order by state

Thanks for the comment Anand. That clears my question.