NTC 2015 - Reuse of Open & Big Data for Sustainable Services for Social Good

Big Data and Open Data Reuse

by Nonprofits for the Creation of

Sustainable Social Services

Nonprofit Technology Conference, Austin TX

Wed March 4, 2015 10:30 AM

Schedule: http://sched.co/1z1r

Eval: 15NTCSessionEval?c=1208

Hashtag: #15NTCReuseData

http://sched.co/1z1r

https://www.surveymonkey.com/s/15NTCSessionEval?c=1208

https://twitter.com/search?q=#15NTCReuseData

Who We Are – TechSoup Global

March 4 2015Open Data Reuse by Nonprofits #15NTCreusedata2

TechSoup Global is a nonprofit serving

the nonprofit community worldwide.

We have built nonprofit sector capacity through

technology for 25 years.

We are working toward a time when every social benefit

organization on the planet has the technology,

resources, and knowledge they need to operate at their

full potential.

Who We Are


• Steve Nagoski - Data Scientist

• Michael Enos - Director of Community and Platform

Who You Are & What You Care About

How do we Sustainably Connect our Information & Insights?

• Stories of Success – Collaboration Panel

• Questions About Open Data & Sustainability

Use #15NTCreusedata & Question Cards & Q&A

Data Reuse by Nonprofits

• Big Data & Open Data Trends

• Open Data Concerns

• Case Study: Balkans Data Academy

• Case Studies: Digital Humanitarians

• Data Science and Machine Learning

• Case Study: Hunger Index

• Sustainability of Open Information Initiatives


“The purpose of computing is insight,

not numbers.”

-Richard Hamming, 1961


Data Trends – Long Term

“What a computer is to me is it’s the most

remarkable tool that we’ve ever come up with,

and it’s the equivalent of a bicycle for our minds.”

- Steve Jobs, 1990

Big Data Trends - Global

• # of orgs and governments operating “Data Driven” grows every year, instrumenting & collecting broader data to make smarter decisions

• Online connectivity:

─ 350B SMS Messages/mo

─ 1.5T App Messages/mo (Whatsapp)

─ 15T Tweets/mo

─ 30B unique Facebook shares/mo

─ 3B Internet Users worldwide (40%), growing 8% YoY

• Cloud Storage makes storing 100PB/org affordable

─ Facebook, Microsoft, Amazon, Twitter, Thousands more.

─ Millions in the next 2 years

• New Analysis Tools are Efficient at those sizes


Open Data Trends - Global

• 2013 : G8 signs Open Data Charter

• 2014 : G20 pledge:

─ advance open data as weapon against corruption

• 2014 : UN recognizes need for “Data Revolution”

Still a LONG way to go

• 8% of participating countries publish spending figures

• 6% publish open data on government contracts

• 3% publish open data on ownership of companies

• Many Open Data initiatives not yet sustaining, growing

─ OpenDataBarometer.org, Jan 2015


http://opendatabarometer.org

Open Data Trends - US

• White House hires first Chief Data Scientist @dpatil

• Obama keynotes O’Reilly Strata conference Feb 2015

─ “Understanding and Innovating with Data has the potential to change the way we do almost anything for the better”https://www.youtube.com/watch?v=vbb-AjiXyh0

• 135,000 open govt datasets available at Data.gov

─ Weather, Maps, Healthcare, Political Funding, Census

• Collaboration between NGOs (Why) & Data Scientists (How) & Analysts/Engineers (What) to deliver stronger insights


http://twitter.com/dpatil

https://www.youtube.com/watch?v=vbb-AjiXyh0

Open Data Concerns - US

• Privacy vs Accountability & Transparency

─ Most open data Anonymous for Privacy Census

Public Services Usage Info

Driving Traffic Patterns

─ Some must be detailed for Accountability Health Inspection Data for Restaurants

Campaign Finance data for Politicians

─ Some we have committed to record for Accountability but have not put collection/access systems in place Police Shootings and/or Deaths Records

Public Access to Police Event Video


Open Data Concerns

• Misuse of Open Data and Misinterpretation

• Correlation != Causation


“The temptation to form

premature theories upon

insufficient data is the

bane of our profession.”

– Sherlock Holmes

“Torture the data, and it

will confess to anything.”

– Ronald Coase








• Sustainability of Open Data Initiatives


Balkans Data Academy : Why / Why Not?

• 1 week Hackathon in Sarajevo Aug 2014

─ expose Bosnian election data to voters

• Project managed by TechSoup Foundation + Local Civic

Activists ZastoNe https://www.youtube.com/watch?v=BcxgAOCFppY

• Team– 15 people from 7 different Nonprofit Orgs w/

different skills + 1 common goal

• Set up framework for future Data Academies, expand

footprint, enable more local NGOs to expand project


http://zastone.ba/en/ca-why-not/

https://www.youtube.com/watch?v=BcxgAOCFppY

Balkans Data Academy : What

• Outcomes – Success!

─ Database & API Created, Open Source Project - Github

─ Data now easy to reload and expand

─ Website Created

─ Introduction Video created

• Next Steps

─ Use for live data in October 2014 Election

─ Collaborate & Train to expand local nonprofit capabilities in

future Academies


Digital Humanitarians

Feb 2015, Dr. Patrick Meier

• The Rise of Digital Humanitarians

• The Rise of Big Crisis Data

• Crowd Computing Satellite & Aerial Imagery

• Artificial Intelligence applied to Disaster Response

• Verifying Big Crisis Data – Dealing with False Data

• Dictators vs Digital Humanitarians (Egypt, China, Iran)

http://iRevolution.net http://DigitalHumanitarians.com #DigitalJedis


http://iRevolution.net

http://DigitalHumanitarians.com

https://twitter.com/search?q=#digitaljedis

Digital Humanitarians – Haiti Earthquake 2010


Digital Humanitarians – Philippines 2012


HDX – Ebola, West Africa, Feb 2015


Resistance to AI / Machine Learning


• Oct 2010: Crowdsourcerer vs Muggles

“How Harry Potter Explains Humanitarian Crowd-Sourcing”

What is Machine Learning + AI Today

• Predictive Modeling + Threshold Automation

• Abuse prevention in Financial Svcs, Social Media

– Spam

– Personal/Community Abuse

– Fraud

– AML - Anti Money Laundering

– ATO - Account Take Over detection

• Detecting False Data

• Stitching Many sources to get the truest picture

• Constantly Adjusting, Measuring, Improving– Learning from False Positives, Negatives, most valuable Measures


Applying Machine Learning to #OpenData

• Counting Tents in Refugee Camp Satellite Images

• Stitching together area images from UAV cameras

• Translation Services for Global Responses

• Identifying unreliable/false posts in Social Media

• Smart Geolocation with minimal input metadata











Hunger Index - What problems are we trying to solve?

• Are Food Assistance Providers achieving our goals?

• How do we forecast and communicate the need for food?

• How can food assistance programs make better decisions

about programs and investments.


Total Meals

Required

MealsPurchased

Food Assistance

Missing Meals

What is the Hunger Index?

• An aggregate measure of the need for food by the most

vulnerable member of a community.

• An index for comparing performance year-to-year and

region-to-region.

• A measure of how well we are serving those in need in

our community.

• Began in 2007 in Santa Clara and San Mateo Counties,

expanding to Alameda, Sonoma and Santa Cruz Counties


Hunger Index Methodology: Components

Scope – Community, Income and Time Range

TMR – Total Meals Required

MP – Meals Purchased

FAP – Food Assistance Provided

TNF – Total Need for Food Assistance

MM – Missing Meals

HI – Hunger Index

• Counties


The Hunger Index


Hunger Index Methodology: Vulnerable Population

Scope

Geography

Time range

Income Demographics

http://www.census.gov/acs/www/


http://www.census.gov/acs/www/

Hunger Index Methodology: TMR

TMR: Total Meals Required

• Households with Incomes < $50K

• Average Household Size– Table B25010

– Santa Clara County 2010 = 2.94 persons/household

• Number of Meals per year =

1095/person/year


Hunger Index Example: TMR, Santa Clara County 2010


Annual Income HouseholdsMeals Required

(millions)

0 thru $10,000 26,848 86.4

$10,000 to $20,000 38,863 125.1

$20,000 to $30,000 40,182 129.4

$30,000 to $40,000 38,351 123.5

$40,000 to $50,000 40,967 131.9

Total 185,211 596.3

Methodology: Meals Purchased (MP)

• From Consumer Expenditure Survey

–http://www.bls.gov/cex/csxstnd.htm

• No. of Households * Average Annual

Expenditure per household

• Important Correction: Subtract SNAP

purchases. http://www.cdss.ca.gov/research/PG352.htm

• Divide by Cost of a Meal to get Meals

Purchased http://www.cnpp.usda.gov/usdafoodcost-home.htm


http://www.bls.gov/cex/csxstnd.htm

http://www.cdss.ca.gov/research/PG352.htm

http://www.cnpp.usda.gov/usdafoodcost-home.htm

Example MP Data: Santa Clara County 2010


Annual Income

(000)Households

Average Annual Expenditure

on Food

0 thru $10 26,848 $3,189

$10 to $20 38,863 $3,413

$20 to 30 40,182 $4,008

$30 to 40 38,351 $4,883

$40 to 50 40,967 $5,515

Methodology: Food Assistance Provided (FAP)

• Data in different formats normalized to

meals

• Time range

• For SC and SM Counties

– Food Banks, SNAP, WIC, Government School Meal

Programs Senior Nutrition, CACFP


Example FAP: Santa Clara County 2010


SourceMeals

(millions)

SNAP 81.4

Second Harvest Food Bank 24.7

School meals 21.3

WIC 14.1

CACFP 4.7

Other 1.6

Total (FAP) 147.8

Final Calculations

TNF: Total Need for Food Assistance

TNF = TMR – MP

296.6M = 596.2M – 299.6M

MM: Missing Meals

MM = TNF – FAP

148.8.M = 296.6M - 147.8M

HI: Hunger Index

HI = MM/TNF

0.502 or 50.2%


Example Final Calc: Santa Clara County 2010


TMR: Total Meals Required 596.2

MP: Meals Purchased 299.6

FAP: Food Assistance Provided 147.8

TNF: Total Need for Food 296.6

MM: Missing Meals 148.8

HI: Hunger Index 0.502

Findings and Implications


Analysis

– Compare against

previous year

– Look for major shifts in

components

– Trends

Collateral benefits

– Understanding of need

• Who, where, when

– Understanding of Food

Assistance

• Who, where, when

– Use of data in other contexts

– How is the population,

demographics and economics

changing over time



How many households are vulnerable and

how much food do they need to be healthy?

Year Households Meals Needed

2010 173,000 564 million

2011 185,000 596 million

Growth 7% 5.7%



Purchased300

Food Assistance

148.8

Missing Meals 147.8

Santa Clara County 2011596 Million Meals185,000 households

CalFresh55%

Food Bank17%

School meals14%

WIC10%

Other4%

Food Assistance in

Santa Clara 2011Total Food Assistance: 149 million meals

Santa Clara County Hunger Index


109.5136.6 147.8

110.4

137.1148.8

0

50

100

150

200

250

300

350

2009 2010 2011

Food Assistance Provided Missing Meals

Santa Clara County Hunger Index 2011


• Hunger Index indicates agencies still struggling to

catch up.

• Vulnerable households increased by more than

7% and need grew by over 8%

• Food Assistance grew by just over 8%.

• Most growth: CalFresh and WIC

• 149 million meals missing last year – enough to

feed 136,000 people for one year, more than the

population of Santa Clara.

What does the Hunger Index tell us?


• Households are spending less on food and

using more food assistance

• It will be a challenge for food assistance

programs to keep up

• We need to continue to work together to make

a difference










Sustainability of Open Data Initiatives

• Sustainability through Collaboration

• Collaboration Panel – share Successes

• Q&A on Open Data opportunities to Panel

• Questions from #NTC15reusedata


Date post:	18-Jul-2015
Category:	Data & Analytics
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NTC 2015 - Reuse of Open & Big Data for Sustainable Services for Social Good

Data & Analytics