Jigsaw Academy Digital India Contest - Andhra Pradesh & Karnataka

Submission for Jigsaw
Contest
on Digital Inclusion
- Asha Vishwanathan

Approach
 Data From Census considered for this analysis
◦ Household Data
◦ PCA Data
◦ DCHB Village Data
◦ DCHB Town Data
 Note : For the purposes of this study, Telangana villages have been
considered as part of Andhra Pradesh as per data from 2011 census
 Approach
◦ A metadata driven approach for all data loading & processing
◦ An excel sheet called “Import_Metadata.xls” was maintained which stored
list of all excels available for the states of Karnataka & Andhra Pradesh
◦ The sheet stores the folder location of the actual excels and also stores
the column names for the different file types
◦ The sheet “Import_Metadata.xls” drives the entire loading of files into R.
◦ It also maintains the column names for all the data that was imported for
the different types of files. Apart from Columns, it stores some processing
requirements which were used to identify relevant columns for processing,

Approach Contd.
◦ In Housing data, all data was deleted where Town
Code / Village Code was “000000”. As this indicated
district / sub district level data
◦ In Housing data, all data where ward was equal to
“0000” was removed, as ward level data was to be
deleted.
◦ PCA Data – Data was retained only for Levels in
Village / Town. All other data was removed
◦ PCA Data - Duplicates were found. Many Towns /
Village codes had multiple records with different sub-
districts.
◦ For 8 codes, which were replicated many times, data
from http://vlist.in/ (Village listing site of India) was
cross checked and a single record retained
◦ For all the other duplicates present for Urban areas,
the Area names with “OG” was discarded to remove
duplicates.

Approach Contd.
◦ 1943 records were present in Karnataka which had 0
households/population. Such records were discarded
◦ Duplicate data found in Karnataka Town DCHB excels for
803080 and 803160 was discarded by removing the ones
mapped to Sub District Code 99999
Merging
◦ Housing data was merged with PCA data using
Town/Village Code and Sub district code. This removed the
duplicate town/village codes mapped to different sub
districts from Housing data as well.
◦ Merged Housing-PCA data was further merged with Village
level data to generate a merged Village level dataset
◦ And merged Housing-PCA data was merged with Town
level data to generate a merged Town level dataset
◦ Data was merged without removing any fields. Fields are
narrowed down progressively.

Computer Access & Mobile
Penetration
 Computer Access is defined as the number of Households having
access to Computers with or without internet
 Mobile Penetration is defined as the number of Households having
access to Mobiles (Mobile Only / Mobile+Landline)
• Internet Access is defined as the number of Households having
access to Internet (Computers with Internet )
• Landline is defined as number of households having landline (
Landline Only / Mobile+Landline)
• Percentages data for some variables in Housing was converted to
actual numbers by multiplying the percentage to number of
households from PCA data to allow roll ups to higher levels
 Sub-district , district & state level information is rolled up from the
village & town level information

Processing
 New features were added to the data. All such fields are
listed in Import_Metadata, Columns sheet- under
categories New_Features_Common &
New_Features_Village
 All columns in the dataset were marked as
Keep_in_dataset(Yes/No). Those marked Yes were
retained in the final data set post processing
 All columns were also tagged to different categories like
House Condition, Conveniences etc. This category field
was used during visualizations and filtering based on a
set of related fields.
 Distance encoding of 1:4 was done for fields indicating
Nearest facility distance in kms ( a,b,c,d)
 Some of them were clubbed to indicate Nearest Facility
Distance (Nearest college, Nearest Training Institute)

Correlations & Associations
 All data that was marked relevant for
checking correlations and associations in
the Import_Metadata sheet were fed into
the correlation / association checking
process.
 Variables that had more than 6% values
missing data, were not considered.
 The relevant correlation / association
significance numbers was generated and
output as a csv.
 This was converted in .xlxs format and
used to display the numbers in Tableau
reports.

Plots
 Various plots were generated – scatter
plots, box plots.
 Only the relevant plots showing any
significant characteristic have been
shared in the presentation
 Most of the plot generation was
controlled by the metadata sheet

Details of Scripts
R Scripts:
- DigitalInclusionAnalysis.R ( Script which invokes all other scripts)
- GenericFunctions.R (Script with some generic functions)
- MergeCensusData.R (Script to merge initial data sets)
- ProcessCensusData.R (Script to process, add new features and generate the
processed sheets)
- MissingValuesCheck.s.R (Script to check missing values)
- MapConcentrations.R ( Script to create map concentrations)
- CheckCorrelations.R (Script to check correlations with continuous variables)
- CheckAssociations.R (Script to check ANOVA for categorical variables)
- Plots.R (Script to create all plots)
- DescriptiveAnalysis.R (Scripts with some basic descriptive checks on data)
Pre-requisites:
- Required folder structures with downloaded census data and metadata to be in place
before running the scripts. /Plots/Correlations, /Plots/Maps, /Plots/StateDistributions
folders need to be present in working directory to enable saving of plots.
- IND_adm2.rds to be in R working directory to allow map creations
- Import_Metadata sheet to be updated with all information

Output Files
Intermediate Files
- After Merge of Housing+PCA :
<StateName>HousingPCA.csv
- After Merge of Housing+PCA + Village :
<StateName>Village.csv
- After Merge of Housing+PCA + Town :
<StateName>Town.csv
Final Files for Analysis
- After Processing & adding new features :
<StateName>VillageProcessed.csv ,
<StateName>TownProcessed.csv
- Merging across States :
- KarAP_Villages.csv , KarAP_Towns.csv
- All_Correlations.csv
- All_Associations.csv
All plots are generated and saved in the relevant folders

Tableau Reports
 District Level Report -
https://public.tableau.com/profile/asha.v#!/vizhome/DistrictLevelDigit
alInclusionNumbers/Sheet1
 Village Level Report -
https://public.tableau.com/profile/asha.v#!/vizhome/VillageLevelDigit
alInclusion/Sheet1
 Correlation Reports –
◦ https://public.tableau.com/profile/asha.v#!/vizhome/Correlations-
Landline/LandlineCor
InternetAccess/InternetAccessCor
MobilePenetration/MobilePenetrationCor
ComputerAccess/ComputerAccessCor
 ANOVA Report –
https://public.tableau.com/profile/asha.v#!/vizhome/ANOVA-
DigitalInclusionFactors/Sheet1

DIGITAL INCLUSION
STATUS IN VILLAGES

Quick Stats
 Total Villages considered: 27,343
 Total Households : 79,45,160
 Households with Computer Access :
4,43,472 (5.5% of Total)
 Households with Internet Access: 56,723
(0.71% of Total)
 Households with Mobiles: 44,71,210
(56% of Total)
 Households with Landlines : 9,29,952
(11.7 % of Total)

Karnataka Villages –
Household Computer Access %
◦ District level Computer Access % Varies from 4% -
11%. ( See Table – 1,Slide - 17)
◦ Highest Computer access % in Bangalore & Bidar
districts
◦ Mean Computer Access across villages in Karnataka
stands at 5.26% and Standard Deviation at 12.36%
◦ District level Box Plots & Index Plots (Slide-17)
indicates a lot of spread in villages level % across all
districts.
◦ More than 25% Household computer access %
seems to be outliers which has around 1162 villages
◦ Interestingly, there are 144 villages with 100%
households having Computer Access and 285
villages with more than 75% households having
Computer Access

Table – 1 : Digital Inclusion Numbers – Karnataka Districts (Rural )
https://public.tableau.com/profile/asha.v#!/vizhome/DistrictLevelDigitalInclusionNumbers/S

Table – 2 : Digital Inclusion Numbers – Karnataka Village / Town Level
https://public.tableau.com/profile/asha.v#!/vizhome/VillageLevelDigitalInclusion/
Sheet1

Household level Computer
Access %

Household Internet Access %
◦ Internet access % for districts varies from 0 to about 3%
◦ Highest Internet access % is in Bangalore
◦ Mean Internet Access across villages in Karnataka stands
at 0.62 % and Standard Deviation at 2.34 %
◦ Internet Access in general is extremely low, with a few
outliers. There are 5 villages reporting more than 75%
Internet Access and 3 villages with 100% ( Villages : Bedli,
Chikkahonasettihalli, Hungunbad)
◦ Only about 12.7 % of households having computer access
also have internet
◦ 143 Villages with more than 10% Internet access also look
like outliers
◦ 1812 villages have internet CSC ~ 6.63 % of total villages.
1563 villages have the facility within 5 Kms, 5883 villages
have within 5-10 kms and 18085 villages have to travel
beyond10 kms.

Household level Internet Access
%

Household Mobile Penetration %
◦ Mobile Penetration % for districts varies from
43% to about 78%
◦ High mobile % is seen in Udupi,Bangalore
and Dakshina Kannada districts
◦ Mean mobile penetration across villages in
Karnataka stands at 54.4% and standard
deviation at 19.6 %
◦ 305 villages report a 100% mobile
penetration in households and 270 villages
have reported 0% mobile penetration
◦ 22806 villages have a mobile phone
coverage with only 4537 villages reporting no
mobile phone coverage which is about 16.6%

Household Mobile Penetration
%

Household level Mobile
Penetration %

Household Landline Penetration
%◦ Household Landline Penetration % for districts varies
from 4 % to 36%
◦ High landline % is seen in Dakshina Kannada and
Kodagu districts
◦ Mean landline penetration across villages in
Karnataka stands at 13.11% and standard deviation
at 15.3 %
◦ 1360 villages fall beyond the 95% range
◦ 61 villages show a 100% landline penetration in
households and 1980 villages show 0% landline
penetration
◦ 160 villages have more than 75% households having
landline percent which look like outliers
◦ 25566 villages have Landline facility in villages ~ 93.5
%, 862 villages have it at a distance of within 5 kms
and 915 at a distance of 5-10 kms

Household Landline Penetration
%

ANALYSIS OF FACTORS
AFFECTING DIGITAL
INCLUSION

Analyzing Inter-relation of
parameters of Digital Inclusion
 Internet Access has a weak correlation of
0.3 with Computer Access
 Remaining correlations are not significant
Table 3 : Tableau Reports :
https://public.tableau.com/profile/asha.v#!/vizhome/Correlations-
ComputerAccess/ComputerAccessCor
Landline/LandlineCor
InternetAccess/InternetAccessCor
MobilePenetration/MobilePenetrationCor

Correlations with variables
bucketed into categories

Quick overview
 Correlations were calculated between all variables and the
digital inclusion parameters.
 All parameters were classified into categories for easy
understanding.
 The categories are color coded and each correlation is one
point
 Most of the correlations don’t seem very significant for
Computer Access %. Only 1 variable seems to have some
amount of significant impact
 3 variables seem to have a weak to moderate impact on
Internet Access %
 There is 1 variable which seems to have a moderately
negative correlation with Mobile penetration and some 3-4
variables seem to have a weak to moderate positive
correlation
 With respect to landline percentage also, 1 variable seems to
have a high correlation and remaining few have weak to
moderate correlation

House Condition
New features like Pucca
structures were also added
All variables related to
house condition were
tagged with a category of
“House Condition”.
None of them seem to
have any significant
correlation to any of the
digital inclusion parameters
in Karnataka Villages.

House Facilities
 Cooking facilities, lighting source,
drinking source seem to have no
significant effect on digital inclusion
factors in Karnataka villages
 Latrine facility within premises has a
weak correlation to landline facility
 House ownership features don’t show
any significant correlations
 House size (4 rooms and above) show
weak correlations with landline

Household Conveniences
 Availability of all assets, white goods
and transport in a household is
moderately correlated with computer
access, weakly with internet access
 Not having any assets showed a
moderate negative correlation with
mobile

Household & Village
Demographic profile
 Married couple numbers showed no
significant correlations
 Literacy rates and Female Literacy
rates have a correlation with landline
of 0.408 and 0.419
 Gender ratio was not significant for
any digital inclusion parameters

Village Education
Infrastructure
 School / College numbers themselves don’t
appear correlated to any of the digital inclusion
parameters
 Availability of private schools, govt schools in the
village appears significant
 Distance to schools & Distance to colleges is
significant
 Availability of different levels of school
(Schools_1_5 : 0-none,1 –pre-primary, 2-primary,
3-middle, 4-senior, 5-sr. secondary) is significant
 Nearness to any training instt appears quite
significant to all other than computer access
 Availability of govt. schools and nearness to
schools seems to have lesser significance in
mobile

Table 4 : Tableau Reports :
https://public.tableau.com/profile/asha.v#!/vizhome/ANOVA-
DigitalInclusionFactors/Sheet1

Village Health Infrastructure
 Nearness to primary health center,
primary health sub center seems to be
significant for all digital inclusion
parameters other than Computer
Access
 Nearest mobile health clinic distance
code seems to be significant for
Computer access
 Number of hospitals etc don’t show

Village – Internet, Mobile
,Telecom , Power Infra
 Distance to Nearest Internet CSC and
availability of Internet Cafes CSC seems
to be significant for all parameters
 Mobile & Telephone infrastructure like
mobile phone coverage status, nearest
mobile phone coverage distance,
nearest telephone landlines coverage
distance, is significant for landline &
mobile.
 Availability of Power Supply for all users
is significant for computer access

Village Water and Sanitation
Infra
 Availability of Treated tap water is
significant for mobile, landline &
computer
 Availability of Hand pump seems to be
significant for computer access
 Whether the area is covered under the
Total Sanitation campaign is
significant for all parameters

Village facilities- Postal, Banking,
PDS and others
 Nearest private courier distance & availability
of post office is significant for all
 Access to PDS shops and distance is
significant for all except internet access
 SHG Distance & Availability was significant to
all
 ATM Availability, Comm. Bank availability &
distance to bank was significant for all except
ATM Availability was not significant for
landline
 Availability and nearness to community
centers was found significant for all except
internet access
 Availability of ASHA centers was significant
for all except for internet

Village Connectivity
 Availability and nearness to public bus
service was significant for all except
availability of public bus service was
not significant for internet

DIGITAL INCLUSION
STATUS IN TOWNS

Quick Stats
 Total Towns considered: 347
12,59,450 (23 % of Total)
 Households with Internet Access:
5,83,166 (10.8 % of Total)
 Households with Mobiles: 41,24,143 (77
% of Total)
(19.9 % of Total)

Karnataka Towns –
Computer Access %
◦ District level Computer Access % Varies
from 9% - 35%. ( See Table – 1,Slide -
49)
◦ Highest Computer access % is in
Bangalore districts
◦ Mean Computer Access across villages in
Karnataka stands at 11.19 % and
Standard Deviation at 6.1 %
◦ District level Box Plots & Index Plots
(Slide-52) indicates a few towns (27)
outside the 20% limit

Table – 1 : Digital Inclusion Numbers – Karnataka Districts (Urban)

District level Computer Access %

Karnataka Towns –
Internet Access %
◦ Internet access % for districts varies from
2 to about 20%
◦ Highest Internet access % is in Bangalore
◦ Mean Internet Access across towns in
Karnataka stands at 3.1 % and Standard
Deviation at 2.7 %
◦ Internet Access in general is low, with a
few outliers. There are a 29 towns above
7 %
◦ 46. 3 % of households having computer
access also have internet

District level Internet Access
%

Karnataka Towns –
Mobile Penetration %
60% to about 86%
◦ High mobile % is seen in
Hassan,Udupi,Kodagu, Bangalore and
Dakshina Kannada districts
◦ Mean mobile penetration across towns in
Karnataka stands at 72.76% and standard
deviation at 9.2 %
◦ There are 7 towns which report mobile
penetration below 55% belonging to different
districts
◦ There are no towns having 100 % mobile
penetration

District level Mobile Penetration
%

Karnataka Towns –
Landline %
◦ Landline Penetration % for districts varies
from 7 % to 35%
◦ High landline % is seen in Dakshina
Kannada and Udupi districts
◦ Mean landline across towns in Karnataka
stands at 14.82% and standard deviation
at 9.16 %
◦ 32 Towns are present with Landline %
more than 30%

District level Landline
Penetration %

 Karnataka towns show a strong
correlation (0.78) of computer access
with internet access and weak
correlations of 0.36 with
landline/mobile
 Internet access has a moderate
correlation to Landline in AP towns
(0.51)
 Ref Table 3, slide 31

Quick overview
 There are a few factors in the
moderate to high correlation for
computer access & internet access
 There are quite a few factors which
show up as moderate to strong
correlation to mobile penetration &
landline

House Condition
 Roof material (Concrete) and Floor
material(Mosaic floor) have moderate
correlation with Computer Access
 Floor material(Mosaic floor), Roof
material (Concrete) show the max
correlation with internet access at 0.5-0.6
 In Karnataka towns, Wall material (Stone
packed with mortar) seems to have the
strongest correlation of 0.58 with
Landline
 Floor material Pucca(Burnt brick,
cement, stone,mosaic) shows the max
correlation with Mobile penetration

House facilities
 Cooking facilities (LPG,PNG) has a moderate correlation of
0.614 on mobile penetration and 0.542 on Internet access
 Cooking with firewood has a negative correlation (-0.5) with
both mobile and internet access
 Lighting with electricity has a moderate correlation with
mobile penetration
 Drinking water source within premises has a moderate
correlation (0.6) with mobile penetration & landline
 Bathing facility and latrine facility within premises have a
correlation of around 0.6 to mobile and around 0.3-0.4 for
Internet access, 0.4-0.5 for landline
 Piped sewer and closed drainage have a moderate
correlation with internet access and weak correlation to
computer access
 House ownership features don’t show any significant
correlations
 House sizes ( 3 rooms and above) show a moderate to strong
correlation with landline/mobile. Household size 4 and below
shows a weak correlation with internet

 Availability of all assets, white goods and
transport in a household is strongly
correlated with computer access &
internet access, moderately correlated
with mobile & Landline
 Availability of scooter/moped moderately
correlated with computer access,
Car/Jeep/Van is moderately correlated
with computer access, internet access &
landline. TV, is strongly correlated with
mobile
 Households availing banking facility is
also moderately correlated with landline
facility

Household & Town Demographic
profile
 Married couple none have a weak correlation to mobile
penetration & landline
 Literacy rates and Female Literacy rates have a correlation ~
0.5 with landline and ~ 0.3-0.4 with internet & computer
access. Female literacy with slightly higher correlation
 Main OT population % has a weak correlation with Internet
access
 Main & Marginal Agri labour %, Main Cultivar pop % has a
weak negative correlation with Internet access, moderate
negative correlation with Mobile penetration. Marginal worker
population % shows a weak negative with mobile penetration.
Main Cultivar pop % shows a weak negative with Computer
access
 SC/ST % has a weak negative correlation with mobile
penetration
 Gender ratio was not significant for any digital inclusion
parameters

Quick Stats
 Total Villages considered: 26,264
6,29,994 ( 4.4% of Total)
 Households with Internet Access: 65,346
(0.45% of Total)
(51.5 % of Total)
(5.5 % of Total)

Andhra Villages –
Computer Access %
from 3% - 6%. ( See Table – 1,Slide - 75)
◦ Highest Computer access % in
Adilabad,Rangareddy & Mahbubnagar
districts
◦ Mean Computer Access across villages
stands at 4.6 % and Standard Deviation at
10.58%
◦ There’s a lot of variation in villages and
there are 1211 villages with more than
20% computer access

Table – 1 : Digital Inclusion Numbers – Andhra Districts (Rural )

Table – 2 : Digital Inclusion Numbers – Andhra Village / Town Level
https://public.tableau.com/profile/asha.v#!/vizhome/VillageLevelDigitalInclusion/
Sheet1

Andhra Villages –
Internet Access %
◦ Internet access % is quite low for districts and varies
from 0 to about 1%
◦ There are very few districts with internet access and
highest is seen in Rangareddy
◦ Mean Internet Access across villages stands at 0.36
% and Standard Deviation at 1.8 %
◦ Internet Access in general is extremely low, with a few
outliers. There are 167 villages having more than
5%.3 villages report 100% internet access
(Chalamala (G),Cheedigaruvu, Vasudevapuram)
◦ Only about 10.37 % of households having computer
access also have internet
◦ 947 villages have internet CSC ~ 3.6 % of total
villages. 2793 villages have the facility within 5 Kms,
8494 villages have within 5-10 kms and 14030
villages have to travel beyond10 kms.

Andhra Villages –
36 % to about 70%
◦ High mobile % is seen in Rangareddy
◦ Mean mobile penetration across villages in
stands at 45.57 % and standard deviation at
22.08 %
◦ 1478 villages report 0% mobile penetration
and around 2815 have less than 10% mobile
penetration
◦ There’s wide variation between different
districts and good amount of scatter within
the same district as well
◦ 87.1% of villages have Mobile phone
coverage

Andhra Villages –
Landline %
from 3.12 % to 10.24 %
◦ High landline % is seen in Krishna
◦ Mean landline % across villages stands at
4.31 % and standard deviation at 5.5 %
◦ There are 222 villages more than 25%
and 9 villages with landline % as 100
◦ 19061 of villages have landlines ~ 72.5%,
1969 have within 5 kms, 2722 have in 5-
10 kms and 2512 have beyond 10 kms

 Computer Access % doesn’t seem to
have significant correlation to any of
the other digital inclusion parameters
Ref Table 3, slide 31

Quick overview
 Only 1 variable seems to have a
moderate correlation with Computer
access
 2 variables seem to have moderate to
strong influence on internet access
 A lot of variables seem to have moderate
to strong correlation with mobile
penetration with one having a strong
negative correlation
 Only 1 variable has a strong correlation
with landline % while many have weak to
moderate correlation

House Condition
 Floor Material Pucca, which is a combination of Burnt Brick,
Cement, Stone and MosaicTiles, has the maximum
correlation with Mobile Penetration of 0.508
 Some more related features like Wall material pucca
(Stone_not_packed_with_mortar_pct,Stone_packed_with_mortar_p
ct , Burnt_brick_pct,Concrete_pct), Household with permanent
structures also show correlations with Mobile Penetration in
the region of 0.4-0.5
 Similar features like floor material-mud show a moderate
negative correlation with Mobile penetration (-0.512)
 For Computer Access, Internet Access and Landline ,none of
the features in house condition have any significant
correlations
(Ref Table 3, slide 31, for complete set of reports)

House facilities
 Cooking with LPG/PNG has a weak correlation with
mobile and landline
 Cooking with firewood has a negative weak correlation
with mobile / landline
 Lighting with electricity has a moderate correlation with
mobile penetration
 Drinking water source within premises has a weak
correlation with computer access & mobile
 Bathing facility available – has a moderate correlation to
mobile penetration, latrine facility within premises has a
weak correlation to landline
correlations
 House size and household size have no significant
correlation with digital inclusion factors

 Availability of all assets, white goods
and transport in a household is
moderately correlated with computer
access & internet access
 Availability of TV / Scooter or
Motorcycle is moderately correlated
with mobile
 Availability of car/jeep/van is weakly
correlated with internet access

Household & Village
Demographic profile
 Married couple numbers showed no
 Literacy rates and Female Literacy rates
have a correlation ~ 0.3 with landline
with female literacy with slightly higher
correlation. Literacy rates correlation ~
0.4 with mobile
 Main OT population % shows a weak
positive correlation with mobile and
SC/ST % shows a moderately negative
correlation with mobile
 Gender ratio was not significant for any
digital inclusion parameters

Village Education
Infrastructure
 School / College numbers themselves don’t appear
correlated to any of the digital inclusion parameters
 Availability of private schools, govt schools in the village
appears significant in general
 Availability of Private schools does not seem very
significant for Computer Access
 Availability of Govt schools does not seem very
significant for Internet access
 Distance to schools & Distance to colleges is significant
 Availability of different levels of school (Schools_1_5 :
0-none,1 –pre-primary, 2-primary, 3-middle, 4-senior, 5-
sr. secondary) is significant
 Nearness to any training instt appears quite significant
to all other parameters than computer access

Village Health Infrastructure
 Nearness to primary health center,
primary health sub center seems to be
significant for all digital inclusion
parameters other than Computer
Access
 Nearest mobile health clinic distance
code seems to be significant for all
other than Internet access
 Number of hospitals etc don’t show

Village – Internet, Mobile,
Telecom & Power Infra
 Distance to Nearest Internet CSC and
availability of Internet Cafes CSC seems
to be significant for all parameters other
than computer access
 Distance to nearest telephone landline &
and nearest public call office is
significant for all
 Mobile phone coverage status is
significant for all other than Internet
access
 Availability of Power Supply for all users
is significant for all factors

Village Water and Sanitation
Infra
 Availability of Treated tap water is
significant for mobile, landline &
internet
 Availability of Hand pump seems to be
significant for computer access
 Availability of Closed drainage is
significant for all
 Availability of Community Toilet
Complex is significant for all

Village facilities- Postal, Banking,
PDS and others
 Nearest private courier distance & availability
of post office is significant for all except
computer access
 Access to PDS shops and distance is
 SHG Distance & Availability was significant to
all
 ATM Availability, Comm. Bank availability &
distance to bank was significant for all except
Computer Access
 Availability and nearness to community
centers was found significant for all
 Availability of ASHA centers was significant
for all except for internet

Village Connectivity
 Availability and nearness to public bus
service was significant for all except
availability was not significant for
internet
 Nearness to pucca road was found
significant for all

Quick Stats
 Total Towns considered: 352
4,50,535 ( 11.04 % of Total)
 Households with Internet Access:
1,47,265 (3.6 % of Total)
(71.5 % of Total)
(10.36 % of Total)

Andhra Towns –
Computer Access %
from 6.8 % - 36%. ( See Table – 1,Slide -
x)
◦ Highest Computer access % in
Hyderabad & Rangareddy districts
◦ Mean Computer Access across villages in
stands at 9.31 % and Standard Deviation
at 6.07 %
◦ There are 22 towns more than 20%

Table – 1 : Digital Inclusion Numbers – Andhra Districts (Urban)

Andhra Towns–
Internet Access %
◦ Internet access % for districts varies from
1.31 to about 21.55%
◦ Highest Internet access % is in Hyderabad
and its way ahead of the next one
Rangareddy at 7.36%
◦ Mean Internet Access across towns stands at
9.311% and Standard Deviation at 6.06 %
◦ Internet Access in general is low, with a few
outliers. There are 19 towns with more than
7%
◦ 32 % of households having computer access
also have internet

Andhra Towns–
◦ Mobile Penetration % for districts varies
from 62.6% to about 81.87%
◦ Highest mobile % is seen in Rangareddy
district
◦ Mean mobile penetration across towns in
stands at 70.42% and standard deviation
at 9.78 %
◦ There are 8 towns with less than 50%
mobile penetration which look like outliers

Andhra Towns–
Landline %
from 6.03 % to 28.92 %
◦ Highest landline % is seen in Hyderabad
which is way ahead than its next West
Godavari at 13 %
◦ Mean landline penetration across towns
stands at 8.8 % and standard deviation at
4.6 %
◦ There are 8 villages more than 20% with
2 of them belonging to Hyderabad

Household Level Landline
Penetration %

 There is strong correlation between
Internet access and Computer Access
in AP towns with a correlation of 0.85
 Computer access has a moderate
correlation (0.64) with Landline
penetration and a weak correlation
(0.45) with Mobile penetration
 Internet access has a relatively good
correlation (0.68) to Landline in AP
towns
 Ref Table 3, slide 31

Quick overview
 Andhra towns – a lot of factors seem
to have weak to strong correlations
with the digital inclusion parameters
 Some 2-3 factors seem to have a very
strong correlation to computer access
and internet access

House Condition
 Floor Material (Mosaic floor, any other
material) have correlations in the 0.4-0.5
range for Computer access and 0.5-0.6 for
Internet access. Permanency of structures
like good residence, concrete roof material,
permanent structures seem to have some
weak to moderate relation on Internet
access. Rest don’t seem to have any
 Floor Material (Mosaic floor, any other
material) have correlations in the 0.3-0.5
range on Landline penetration
 Temporary structures have a moderate
negative correlation with Mobile penetration
of -0.54

House facilities
 Cooking with firewood has a negative correlation on all digital
inclusion factors in the range of -0.4 to -0.5
 Kitchen inside house & cooking fuel LPG/PNG has a 0.4-0.5
correlations with most of the digital inclusion factors
 Lighting with Kerosene has a weak negative correlation with
Internet access and moderately negative with mobile
penetration
 Drinking water source within premises has a weak correlation
with computer access & mobile
 Bathing facility and latrine facility within premises have a
correlation of around 0.4 to all the digital inclusion factors
 Flush tank to piped sewer showed a 0.62 correlation with
Internet access and 0.52 with mobile penetration, Similar
waste water outlet to closed drainage showed 0.5-0.6
correlation with Internet,mobile and computer access
correlations

 House size ( 4 and 5 rooms) show a
moderate correlation with Mobile
penetration

 Availability of all assets, white goods and
transport in a household is strongly
correlated with computer access &
internet access, moderately correlated
with Landline
 Availability of scooter/moped is strongly
correlated with mobile. TV, Car/Jeep/Van
is moderately correlated with mobile
 Households availing banking facility is
also moderately correlated to mobile
 Availability of none of the assets
exhibited a negative correlation from
weak to strong for all digital inclusion
factors.

Household & Town Demographic
profile
 Married couple 2 and below have a weak correlation to
computer access & internet access, moderate correlation to
mobile access
 Literacy rates and Female Literacy rates have a correlation ~
0.5 with landline and ~ 0.4 with internet access.Female
literacy correspond with slightly higher correlation
 SC/ST % , population below 6 % has a negative correlation
of 0.4 with landline
 Main OT population % has a correlation of ~ 0.4 with landline
% and ~ 0.3 with Mobile
 Main & Marginal Agri labour %, Main Cultivar pop % has a
weak negative correlation with landline, Main agri labour has
a weak negative correlation with Internet access & mobile &
Main agri labour % has a weak negative correlation with
computer access
 Gender ratio was not significant for any digital inclusion
parameters

State level comparison
 Karnataka is marginally higher in almost all digital inclusion
parameters across both towns and villages
 Towns perform better than villages in all parameters
Village Comparison
 5.5% of households in Karnataka Villages have computer
access versus 4.4 % In AP villages. There are more high
density villages spread across districts in Karnataka which
take the district means higher
 0.71% households in Karnataka Villages have internet access
vs 0.45% in AP villages
 56 % households in Karnataka Villages have mobile vs
51.5% in AP villages
 11.7 % households in Karnataka Villages have landline vs
5.5% in AP villages. Landline penetration in general seems
quite low even though a sizeable % of the villages has access
to landline/phone lines

Town Comparison
 23% of households in Karnataka Towns have
computer access versus 11.04 % In AP
Towns
 10.8 % households in Karnataka Towns have
internet access vs 3.6% in AP Towns. The
mean across towns in AP is higher than
Karnataka due to some high values in
Hyderabad district. Bangalore district in
comparison doesn’t show as high internet %.
 77 % households in Karnataka Towns have
mobile vs 71.5% in AP Towns
 19.9 % households in Karnataka Towns have
landline vs 10.36% in AP Towns

House condition
 In Villages, Floor Material Pucca (
Stone+Concrete+Mosaic + Brick)
seems to be a good indicator of house
condition to consider for digital
inclusion.
 Whereas in towns, floor material
mosaic seems to be stronger indicator
for digital inclusion
 Karnataka villages seem to be
unaffected by any of house condition

House facilities
 Cooking facilities (LPG,PNG) has a moderate
correlation with digital inclusion in towns and
weak correlation in Andhra villages. Karnataka
villages don’t show any correlation with any
cooking factors.
 Lighting with electricity shows a moderate
correlation in Andhra towns / villages and
Karnataka towns.With little impact on Karnataka
villages
 Bathing facility and latrine facility within premises
in general had a weak to moderate correlation on
Andhra towns and villages & Karnataka towns.
Karnataka villages showed a weak correlation
with latrine facility within premises and landline

Household conveniences
 Availability of all assets, white goods and transport
in a household was found related to most of the
digital inclusion factors across both towns and
villages
 % of households availing banking facility was found
correlated to mobile in both Karnataka and AP
towns.

House & Village/Town
Demographics
 House sizes seem to have moderate to strong impact in both
Karnataka and Andhra towns. They seem to have not much impact
in villages.
 House ownership profile showed no significant correlations
 Married couple 2 and below had a weak correlation only in Andhra
Towns. Household profile otherwise didn’t show any significant
correlations
 General Literacy rates and female literacy rates showed weak to
moderate correlations in villages and slightly higher correlations in
towns across both Andhra & Karnataka
 Gender ratio had no significance in any place
 SC/ST % showed a moderately negative correlation in Andhra towns
and a weakly negative correlation in Karnataka Towns. They
seemed to have not much significance in Karnataka villages
 Main & Marginal Agri Labour %, Main Cultivar %, Marginal Worker %
all showed weak to moderately negative correlations in Towns in
both Andhra and Karnataka. Main OT % showed a weakly positive
correlation in Andhra Towns & Villages and Karnataka Towns.

Village Infra
 Availability of different levels of school
(Schools_1_5 : 0-none,1 –pre-primary,
2-primary, 3-middle, 4-senior, 5-sr.
secondary) was found to be significant
for both Karnataka and Andhra
villages
 Availability of schools (any) and
nearness to school was significant
 Nearness to training institute was also
found to be significant.

Village Infra
 In both Karnataka & Andhra villages, nearness to
primary health center, primary health sub center was
found to be significant for all digital inclusion
parameters other than Computer Access
 Nearest mobile health clinic distance code seems to be
significant for all other than Internet access
 Distance to Nearest Internet CSC and availability of
Internet Cafes CSC seems to be significant for all
parameters
 Mobile & Telephone infrastructure like mobile phone
coverage status, nearest mobile phone coverage
distance, nearest telephone landlines coverage
distance is significant for landline & mobile
 Availability of Power Supply for all users was significant
for all factors in Andhra Villages, but was found only
significant for computer access in Karnataka Villages

Village Infra
 Availability of Treated tap water was
significant for mobile, landline & internet
across villages in both states
 Availability of Hand pump was significant
for computer access in both states
 Availability of Closed drainage &
Availability of Community Toilet Complex
was significant in Andhra villages
 In Karnataka villages, whether the area
was covered under the Total Sanitation
campaign was significant

Village Infra
 Nearest private courier distance & availability of post
office was found to generally significant across villages
in both states
 Access to PDS shops and distance to the shop was
 SHG Distance & Availability was significant to all
 ATM Availability, Comm. Bank availability & distance to
bank was significant for most of the digital inclusion
parameters
 Availability and nearness to community centers,
Availability of ASHA centers was significant for all
except for internet
 Availability and nearness to public bus service was
significant for most of the digital inclusion parameters.
However, availability of public bus service was not
significant for internet

Jigsaw Academy Digital India Contest - Andhra Pradesh & Karnataka

Jigsaw Academy Digital India Contest - Andhra Pradesh & Karnataka

Recommended

Recommended

More Related Content

Similar to Jigsaw Academy Digital India Contest - Andhra Pradesh & Karnataka

Similar to Jigsaw Academy Digital India Contest - Andhra Pradesh & Karnataka (20)

More from Jigsaw Academy

More from Jigsaw Academy (8)

Recently uploaded

Recently uploaded (20)

Jigsaw Academy Digital India Contest - Andhra Pradesh & Karnataka