Forum on Inn vative Data Approaches to SDGs

31 May - 2 June 2017 Holiday Inn, Songdo, Incheon, Republic of Korea

Innovative Data Approaches for Capturing and Analyzing Data

to Achieve the SDGs

Dr. Soenke Ziesche 31 May 2017

Holiday Inn, Songdo, Incheon, Republic of Korea

Overview of the presentation

• SDGs, targets, indicators and tiers • Big data • Internet of things • Artificial intelligence • Overview of approaches • Challenges • Opportunities • Conclusion

SDGs, targets, indicators and tiers

2030 Agenda for Sustainable Development

Sustainable Development Goals • 17 goals, • 169 targets, • 244 indicators. (232 indicators without duplicates.) Millennium Development Goals 8 goals, 21 targets, 60 indicators.

Indicators

Tier 1 Indicator is conceptually clear and has an internationally established methodology and standards are available. In addition, data are regularly produced by countries for at least 50 per cent of countries […]. Tier 2 Indicator is conceptually clear, has an internationally established methodology and standards are available, but data are not regularly produced by countries. Tier 3 No internationally established methodology or standards are yet available for the indicator, but methodology/standards are being (or will be) developed or tested.

Five indicators with more than one tier assigned. Almost two thirds of the indicators are tier 2 or 3.

Tier 1 2

3

Indicator 82 61 84

Percentage 36% 27% 37%

Big data

Three Vs: • Volume: amount of data • Variety: different types and sources • Velocity: often real-time availability Robert Kirkpatrick, Director of UN Global Pulse: MDG data: Mostly collected and owned by Governments. SDG data: Partly produced passively by people, collected by machines and owned by corporations.

Taxonomy of Big Data:

Exhaust data Passively collected data from people’s use of digital services. Pentland: “It's the little data breadcrumbs that you leave behind you as you move around in the world.” Examples • Mobile phone data • Financial transactions • Online search and access logs • Citizen card • Postal data

Taxonomy of Big Data:

Sensing data Internet of Things and Global Positioning Systems (GPS) Aim: to reduce the information gap between world and internet.

Examples • Satellite and unmanned aerial vehicle imagery • Sensors in cities, transport and homes • Sensors in nature, agriculture and water • Wearable technology (human and animals) • Biometric data

Taxonomy of Big Data:

Digital content Content actively produced by people as well as Governments. Unstructured data, unlike exhaust and sensing data, can include text and multimedia content, e.g. images, videos or audio -> AI analysis Examples • Social media data • Web scraping • Participatory sensing / crowdsourcing • Health records • Radio content

Internet of things

• Sensors: (Often small) objects, which detect changes in its

environment and potentially quantify the extent of the change. • Machine-to-machine communication and AI decision making. • Prevention of negative incidents, e.g. disasters or illnesses,

through early detection.

• Example healthcare: “My car, my airplane, my computer know more about their health status than I do.” Peter Diamandis

Artificial intelligence

“Intelligence measures an agent’s ability to achieve goals in a wide range of environments.” Legg and Hutter (2007)

Artificial general intelligence: - equals human intelligence. - not yet been developed.

Successes were in specialized fields. Examples: Deep Blue (1997), AlphaGo (2016)

Machine and deep learning

Synergies

Overview of big data approaches

Category Source

Exhaust data Mobile phone data Financial transactions Online search and access logs Citizen card Postal data

Sensing data Satellite and unmanned aerial vehicle imagery Sensors in cities, transport and homes Sensors in nature, agriculture and water Wearable technology Biometric data

Digital content Social media data Web scraping Participatory sensing / crowdsourcing Health records Radio content

Exhaust data Mobile phone data

Containing the Ebola Outbreak – the Potential and Challenge of Mobile Network Data SDG targets: 3.3, 3.d Countries: Guinea, Liberia, Sierra Leone

Exhaust data Financial transactions

Scanner data in the Swiss Consumer Price Index: An alternative to price collection in the field SDG indicator: 2.c.1 Country: Switzerland Also: Japan, ROK

Exhaust data Online search and access logs

Big Data: Google Searches Predict Unemployment in Finland SDG target: 8.5 Country: Finland

Exhaust data Citizen card

Oyster card SDG indicator: 11.2.1 SDG target: 11.2 Country: UK Also: China, India

Exhaust data Postal data

Building Proxy Indicators of National Wellbeing with Postal Data 187 countries

Sensing data Satellite and

unmanned aerial vehicle imagery

Japan Aerospace Exploration Agency, Greenhouse gases observing satellite "IBUKI" SDG target: 13.2 Anywhere

Sensing data Sensors in cities,

transport and homes

Sensors to monitor bridges SDG target: 9.1 Country: Sweden

Sensing data Sensors in nature,

agriculture and water

Halt illegal logging in rainforests SDG targets: 15.1, 15.2, 15.b Country: Indonesia

Sensing data Wearable technology

VitalHerd SDG targets: 2.3, 2.4 Anywhere

Sensing data Biometric data

Biometric Cash Assistance SDG targets: 5.a, 8.10 Country: Jordan

Digital content Social media data

Tweeting Supertyphoon Haiyan: Evolving Functions of Twitter during and after a Disaster Event SDG target: 11.5 Country: Philippines

Digital content Web scraping

HealthMap SDG target: 3.3 Anywhere

Digital content Participatory sensing /

crowdsourcing Citizen Feedback Monitoring Program SDG indicators: 16.6.2 SDG targets: 16.6 Country: Pakistan

Digital content Health records

Khushi Baby SDG targets: 3.8, 3.b Country: India

Digital content Radio content

Supporting decision – making through analysis of public radio content Country: Uganda

AI applications towards SDGs without big data

ZenRobotics Recycler SDG target: 12.5 Country: Finland

Challenges

Exhaust data

• Often proxy indicators: Risk of inaccuracy and apophenia.

Sensing data

• Analysis bottleneck, connectivity, interoperability.

Digital content

• Veracity, cleanliness, standards, sustainability.

Overall

• Privacy and human rights

Opportunities

Exhaust data • For human activities that can’t be measured by sensors, e.g. online, financial. Sensing data • Precise and scientific. In the near future many more details of the world and

our daily lives will be measured. Digital content • Innovative tools towards more democracy and maxim “leave no one behind”.

Recommendations

• Governments to explore including big data, internet of things and

artificial intelligence in SDGs National Action Plans.

• Involve private sector, including start-ups, academic research institutions and NGOs to further foster public-private collaboration and knowledge sharing

• Use real time features to reduce the gaps between planning and

evaluation of SDGs activities.

• Pilot appropriate approaches to see whether they may be useful in national contexts.

Group discussion