Smart Citizens measurement strategies - Do's and Don'ts

Measurement StrategiesMay 9, 2016

Rio de Janeiro

#ResilientRio

• Personal interest• Raising awareness• Open data city policy• Activism• Gain knowledge• Concerns• For fun

Why measure?

Experience in Amsterdam

Topics & Scenarios

Amsterdam Smart Citizen project

Smart Citizen Kit

Reasons to join the project

Other7%

Social experiment18%

Technology20%

Air polution55%

Interview results prior to participation:

Biggest hurdles

Other7%

Social experiment18%

Technology20%

Air polution55%

CO/NO2sensornetwerk

website

Lessons learned

• Sensitivity / Specificity• Location• Sample rate• Open Hardware• Affordability• How to involve others• Calibration• Comparison• Dependencies• Think non-sensor too• What other resources do we have?• Data is story telling

Sensitivity / Specificity

Location

Sample rate

If you can’t open it…

Different ways to achieve the same goal

Instructables

PublicLab http://www.cs.cmu.edu/~zhuxj/astro/html/spectrometer.html

Affordable

K30 CO2 sensorGroove Air Quality Sensor

How can others help?

Calibration

https://en.wikipedia.org/wiki/Calibration_curve

Comparison

http://citydashboard.waag.org/

Dependencies

DHT11 - Temperature Humidity sensor

Think non-sensor

Similar projects?

http://datacanvas.org/sense-your-city/

What other resources do you have?

• Knowledge of citizens• Official measuring institutes• Historical data

Resource: WHO

http://www.who.int/topics/environmental_pollution/en/

Resource: SMAC

http://www.rio.rj.gov.br/web/smac#

Resource: crowd sensing

https://www.waag.org/nl/project/making-sense

• scistarter.com

Platforms

Data is story telling!

Be creative!

http://genomicgastronomy.com/work/2011-2/smog-tasting/

Do’s

• Sensitivity / Specificity: choose the right sensor• Location: pick a stable environment• Sample rate: measure often enough• Open Hardware: make something hackable• Affordability: enable others to join in• How to involve others: enable others to join in• Calibration: expose the sensor to extremes• Comparison: compare with other sources• Dependencies: measure secondary factors too• Think non-sensor too: use the wisdom of the crowd• Data is story telling: make data meaningful

Iterative hacking process

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