ANALYSIS OF VARIOUS DISABILITIES AND ITS TREND OVER THE YEARS

OBJECTIVE Disabled Population Trend across India, US and Japan.

Trend Analysis based on:

Age wise.

Education.

Employment.

Education, employment and gender.

Predicting disabled population for year 2021 and 2031.

Social outlook.

CHANNELS CHOSEN TO ANALYSE SOCIAL OUTLOOK Census of India -2001 and 2011 Twitter Facebook RSS feeds

INPUT DATA SOURCE India Data Collection Source:

http://www.censusindia.gov.in https://data.gov.in/ http://punarbhava.in/

Japan Data Collection Source: http://www.unescapsdd.org/ http://www.disabled-world.com/news/asia/japan/japan.php http://www.dinf.ne.jp/doc/english/asia/resource/z00ap/003/z00ap00309.html http://

www.hurights.or.jp/archives/focus/section2/2002/09/rights-of-disabled-persons-and-japan.html

US Data Collection source:https://www.disabilitystatistics.org/http://www.disabled-world.com/disability/statistics/

DISABLED CENSUS WITH BASE POPULATION Percentage wise disabled Population for India, Japan and US.

Disable percentage in developed country is higher than developing nation like India.

This is a clear indicator that reporting percentage of disabled population in India is negligible as compared to developed countries like US and Japan.

India India-Rural India-Urban USA0.00%1.00%2.00%3.00%4.00%5.00%6.00%7.00%8.00%9.00%

2.13% 2.21%1.93%

6.01%

2.22% 2.24% 2.17%

7.90%

20012011

TREND ANALYSIS: AGE WISE Total Disabled population in

2001 : 21.9 million.

Total Disabled population in 2011 :

26.8 million Maximum disabled population is

in age group [10 – 29] for 2001 and 2011.

Age group 0-19 :Approximately 8% fall in disability percentage.(2001 vs 2011)

The decreasing trend in age group 0-9 can be attributed to immunization coverage of polio eradication.

Age group 19 and above: There is steady rise in disability percentage across all groups.

The increasing rates among young adults could be due to factors such as accidents, on the road or at work.

TREND ANALYSIS:AREA WISE

Disability percentage is more in urban areas as compared to rural areas. Key reasons being1. Awareness in urban areas.2. India’s affluent middle classes with changing lifestyles are at greater risk of acquired impairments.

Total Disability Age wise for Rural India Total Disability Age wise for Urban India

DISABILITY TREND : GENDER WISE

Disability percentage is more in males as compared to females. Key reasons being1. Employment factor : 2. Social stigma especially in rural areas (disabilities for females)

Female Disability Age-Wise Trend

DISABILITY TREND : INDIA DISABILITY WISE

In see

ing

In Hear

ing

In Sp

eech

In Move

ment

Mental

Retarda

tion

0.004.008.00

12.00 8.822.05

0.77

6.382.15

10.40

1.71 0.927.09

3.166.95

0.54 1.39 4.340.87

2001 Male Different Disability wise

0-19 Male 20-49 Male 50 & above Male

In see

ing

In Hear

ing

In Sp

eech

In Move

ment

Mental

Retar

dation

0.006.00

7.64 8.221.25 0.71

3.34 1.966.52

0.45 1.41 2.76 0.71

2001 Female Different Disability wise

0-19 Female 20-49 Female 50 & above Female

3.82

0.61.92 1.35

In see

ing

In Hear

ing

In Speec

h

In Move

ment

Mental

Retardati

on

Mental

Illness

Any O

ther

Multiple

Disabili

ty0.003.006.00

2.83 3.14 1.44 2.381.29 0.30

3.471.50

3.57 3.991.93

5.92

1.57 0.914.48 1.443.45 2.86

0.81

4.27

0.39 0.34 2.23 1.40

2011 Male Different Disability wise

0-19 Male 20-49 Male 50 & above Male

In see

ing

In Hear

ing

In Sp

eech

In Move

ment

Mental

Retar

dation

Mental

Illness

Any O

ther

Multiple D

isability

0.002.004.00

2.44 2.801.11 1.52 0.93 0.21

2.951.03

2.85 3.361.53

3.101.12 0.61

3.431.00

3.652.76

0.633.08

0.32 0.331.82 1.53

2011 Female Different Disability wise

0-19 Female 20-49 Female 50 & above Female

Disability-wise comparison cannot be done directly for 2001 and 2011 as types of disability increased in 2011.

As per 2001 data, ‘in seeing’ is the most among males & females. However, in 2011, ‘in-movement’ is observed the most among males & a similar pattern is observed in females as well, only with slight higher percent for ‘in-seeing’ among them. This is true for both Rural & Urban as well.

DISABILITY TREND : RURAL INDIA DISABILITY WISE

In see

ing

In Hear

ing

In Sp

eech

In Move

ment

Mental

Retarda

tion

0.006.00

12.00 8.842.11 0.82

6.702.04

9.69

1.70 0.986.97

2.927.240.55 1.54

4.480.83

2001 Rural India Male Different Types of Disabilities

0-19 Male 20-49 Male 50 & above Male

In see

ing

In Hear

ing

In Sp

eech

In Move

ment

Mental

Retarda

tion

0.006.00 7.69

1.53 0.60 4.10 1.407.68

1.25 0.76 3.25 1.846.91

0.46 1.54 2.89 0.69

2001 Rural India Female Different Types of Disabilities

0-19 Female 20-49 Female 50 & above Female

In see

ing

In Hear

ing

In Sp

eech

In Move

ment

Mental

Retardati

on

Mental

Illness

Any O

ther

Multiple

Disabili

ty0.004.008.00

2.80 3.101.49 2.68 1.31 0.30

3.481.59

3.24 3.541.73

6.171.49 0.88

4.141.463.73 2.93

0.734.58

0.37 0.342.19 1.58

2011 Rural India Male Different Types of Disabilities

0-19 Male 20-49 Male 50 & above Male

In see

ing

In Hear

ing

In Spe

ech

In Move

ment

Mental

Retarda

tion

Mental

Illness

Any O

ther

Multiple D

isability

0.003.002.43 2.76

1.121.71 0.95 0.22

2.961.09

2.56 2.991.36

3.151.06 0.60

3.111.00

4.04 2.880.58

3.360.31 0.32

1.80 1.78

2011 Rural India Female Different Types of Disabilities

0-19 Female 20-49 Female 50 & above Female

It is clear that among the total population, 20-49 has the maximum disability as it is the working age group of the total population.

This means the exposure to various equipment/tools in the industry could be a possible reason for this group to more disabled.

DISABILITY TREND : URBAN INDIA DISABILITY WISE

In see

ing

In Hear

ing

In Speec

h

In Move

ment

Mental

Retarda

tion

0.0010.00 8.74

1.86 0.605.43

2.48

12.511.74 0.71

7.44 3.876.110.53 0.95 3.94 0.99

2001 Urban India Male Different Types of Disabilities

0-19 Male 20-49 Male 50 & above Male

In seeing In Hearing In Speech In Movement Mental Retardation0.005.00

10.0015.00

7.51

1.38 0.44 3.51 1.73

9.81

1.27 0.583.62 2.31

5.360.41 1.04 2.35 0.76

2001 Urban India Female Different Types of Disabilities

0-19 Female 20-49 Female 50 & above Female

In see

ing

In Hear

ing

In Sp

eech

In Move

ment

Mental

Retardati

on

Mental

Illness

Any O

ther

Multiple D

isability

0.003.006.002.87 3.24 1.35 1.69 1.24 0.28

3.431.28

4.31 5.022.40

5.341.76 0.97

5.261.402.81 2.68

1.003.57

0.41 0.372.32

0.98

2011 Urban India Male Different Types of Disabilities

0-19 Male 20-49 Male 50 & above Male

In see

ing

In Hear

ing

In Speec

h

In Move

ment

Mental

Retarda

tion

Mental

Illness

Any O

ther

Multiple D

isability

0.002.004.002.46

2.901.07 1.09 0.88 0.19

2.930.89

3.50 4.201.93 2.99

1.24 0.63

4.18

0.992.75 2.50

0.752.45

0.33 0.341.87

0.97

2011 Urban India Female Different Types of Disabilities

0-19 Female 20-49 Female 50 & above Female Compared to the rural, the urban population disability percent is much higher because the population has migrated from the rural areas to the urban areas for better employment opportunities.

In 2011, the number of factories/job opportunities has gone higher as compared to 2001 and hence we see an increase in the disability percent as compared to 2001. Also, in 2011, ‘Any other’ type of disability is seen the most in urban areas for both the genders.

PREDICTION OF DISABLED POPULATION AFTER NEXT 10 YEARS AND 20 YEARS

The total population is in the form of an exponential distribution.

Assuming the distribution of disabled population is also an exponential distribution.

Predicting the values of disabled population for the next 10 and 20 years knowing the rate of increase from 2001 to 2011 by using formula

Predicted value = N(1+rate of increase)^nYear Predicted Disabled

population2021 32.8 million2031 40.71 million

IMPACT ANALYSIS & FORECASTING Basic Education Facility Number of NGOs(31 lakhs) have increased in the past decade.

(source:http://southasia.oneworld.net/news/india-more-ngos-than-schools-and-health-centres) Better Government policies & Plans for the Disabled Few examples like Sarvashiksha Abhiyan(2001),RTE ACT2009,Comprehensive

Education Plan for Inclusion education in children(2005) (source:http://www.sightsaversindia.in/wp-content/uploads/2014/06/17581_Policies-and-Schemes-of-Central-and-

State-Governments-for-People-with-Disabilities.pdf)(Source: Research paper: Education of Children with Disabilities in India :Concern and Policy Perspective)

IMPACT ANALYSIS & FORECASTING Health Care(Life-long) Early detection of disabilities effectively contribute in reducing

number of life-long disabilities Medical trends are helping to increase the Life Span. Issue of Universal ID cards to help the disable get treatment. (Source:http://www.outlookindia.com/news/article/people-with-disabilities-to-get-universal-id-cards- govt/857100) Resource centres manufacture low-cost furniture and other aids

carefully designed to meet the requirements of each handicapped person(Source:http://www.indiaenvironmentportal.org.in/content/13166/empowering-the-disabled)

IMPACT ANALYSIS & FORECASTING Social output

The National Handicapped Finance and Development Corporation NHFDC was set up in 1997 to promote PWDs, but by December 2008, it had supported a mere 36,000 people, and half of its funds remained unspent.

The central government wants to revise the law with below updates including “non-discrimination”, “equal opportunity”, “grievance address” and so on.

The new regulations may make life easier for PWDs who work in formal settings which go along with social protections and health insurance.

However, they have no bearing on the informal sector, which is where more than 90 % of the Indian labour force and more than 95 % of the PWDs work.

(Source:http://www.dandc.eu/en/article/consequences-disability-indias)

KEYWORD'S USED-SENTIMENTAL ANALYSIS

TWITTER-SENTIMENT ANALYSIS Data Collection: Collecting tweets regarding the Disabled people via Twitter API

using R. Data Processing: segment the tweets continent wise. R Code Snippet dis=searchTwitter("disability",1000) try= twListToDF(dis) write.csv(try, file="trail.csv") To find the location of the person tweeted: try1=twListToDF(dis)$screenName try2=twListToDF(lookupUsers(try1))$location write.csv(try2, file="trail2.csv")

WORD CLOUD ON TWEETS

GRAPHS

09-10-2015

10-10-2015

11-10-2015

12-10-2015

13-10-2015

14-10-2015

15-10-2015

16-10-2015

17-10-2015

18-10-2015

0

200

400

600

800

1000

1200

1400

1600

1800

1045

805 872

14151493 1547 1540

1221

844

496

Total

Total

Tweets made regarding the disabilities over the last 10 days

Distribution of tweets continent wise

Africa1%

asia3%

Aus-tralia15%

Europe46%

NorthAmerica35%

South America0%

Africa asia AustraliaEurope NorthAmerica South America

COUNTRY WISE TWEETS

Facebook Sentiment Analysis R Code Snippetrequire("Rfacebook")fb_oauth <- fbOAuth(app_id=“XXXXXXXXX", app_secret=“yyyyyyyyyyy",extended_permissions = FALSE)save(fb_oauth, file="fb_oauth")load("fb_oauth")s1=searchPages("Disability",fb_oauth)names(s1);attach(s1)s2=subset(s1,s1$country != "NA", select = c(country));s2$countrywrite.csv(s2,"s5.csv") s3=getPage(216681151713909,fb_oauth)

Limitations1. Sampling : Very less pages are public, we cannot extract data easily. Facebook

has deprecated Singular links API.

FB PAGES COUNTRY-WISE(DISABILITY)

Australia9%

Canada4%

Ireland2%

New Zealand3%

Nigeria1% Sri Lanka

1%

Thailand1%

Ukraine1%

United Kingdom

31%

United States48%

Zambia1%

TotalAustraliaCanadaIrelandNew ZealandNigeriaSri LankaThailandUkraineUnited KingdomUnited StatesZambia

RSS FEEDS: SENTIMENT ANALYSIS To Find the no. of URLS in RSS feed links:1. Create lists of RSS feeds country-wise & Read every RSS feed2. Count no. of articles in every feed using Regular Expression evaluation3. Compare the no. of articles and its percentages 4. Evaluate the position of India in the ranking Python Code Snippet

RSS FEEDS: COUNTRY-WISE FEEDS DISTRIBUTION

RSS FEEDS: COUNTRY-WISE FEEDS DISTRIBUTION

SENTIMAL ANALYSIS CONCLUSIONS India has a significantly smaller number of News feeds/tweets/Posts across the social

media to discuss the awareness and issues for the disabled population Mental barrier still remains as a major concern to express views openly in India. Developing countries are still lagging behind if we compare with developed countries

in the discussions for making the life of disabled persons better.