Presentation from the “Obscure Data” session of the “Our World, Our Data” conference held at at MIT Media Lab on April 30, 2017. Organized by Safecast with sponsorship from the Shuttleworth Fund. Many thanks to Joi Ito, Director of the MIT Media Lab, and Ethan Zuckerman and Lorrie LeJeune of The Center for Civic Media. https://www.media.mit.edu/events/our-world-our-data/

1. Data: Obscurity to Insight
Our World, Our Data Symposium
30 Apr 2017

2. AGENDA
•EDF overview
•Data existence vs data
availability
•Harvesting value from data
•Air Sensor Workgroup

3. Founded in 1967, committed to
bipartisan environmentalism
Leading international nonprofit
organization with over 2 million
members

4. About EDF
Climate and Energy
Health
Oceans
Ecosystems
Our focus areas

5. Data Existence
vs
Data Availability

6. Data Availability - Scenarios
•Data exists but no one knows about it
•Data exists and known to people but very
difficult to access

7. Air quality data use case
More than 5.5 million people worldwide
die prematurely every year

8. Air quality data use case
Why AQ data?
Neighborhood scale air quality
assessment, impact analysis - health,
productivity

9. Air quality data use case
•Regulatory-grade data spread across
siloed systems
–Aggregators: AirNow, OpenAQ, ESDR

10. Air quality data use case
•Air quality sensor data also exists
–Projects funded by grants
–Researchers and academics
–Initiatives like Array of Things, IVAN
–Sensor developer efforts like AirCasting,
PurpleAir map

11. Oil and Gas data use case
Natural gas lost as Methane emissions
every year is enough to fuel 6 million
homes

12. Oil and Gas data use case
Why O&G environmental data?
Emissions inventory, leaks, violations,
environmental risk assessments

13. Oil and Gas data use case
•Govt. agency data systems exist
–Federal
–State
•Challenges are
–Data currency
–Ease of access - time and effort

14. Harvesting Value from
Data

15. Harvesting Value from Data
•Data existence ≠ Data availability
•How does data become valuable?
–Awareness about data existence
–Ease of access
–Facilities to analyze and develop data
products

16. Harvesting Value from Data
•Technological requirements
–High volume of data
–Standardized data formats and structures
–Metadata and data dictionary
–Tools for data access, data analysis and
visualization

17. Air Sensor Workgroup

18. Air Sensor Workgroup
•Data standards
–Date and timestamp standards
–Standardized definitions, metadata
https://www.edf.org/asw

19. Air Sensor Workgroup
•Air Quality Data Platform
–Air quality data from low- and medium-cost
sensors
–Infrastructure to perform analytics
–Visualization for advanced users and citizen
scientists

20. Abhijit RS
arudrapatna@edf.org
415-293-6105
Thank You