the role of citizen science in environment management

Luigi Ceccaroni

The role of citizen science in environment management

The role of citizen science

in environment management

Forecasting based on

citizens’ observatories

Luigi Ceccaroni (1000001 Labs)

3rd International Marine Protected Areas Congress, Marseille (France), October 22, 2013 Università di Bologna – Scienze Ambientali, Ravenna (Italy), December 17, 2015, 2 pm

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• Knowledge of the field of citizen science and current

trends that influence it

• Understand the principles and practical aspects of a

citizen science project

• Learn about data quality in citizen science

Learning outcomes 2

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• Current activities in the area of citizen science online

and offline

• Typology of engagement in citizen science

• Citizen Science in a historical perspective and

underlying trends

• The case for low resolution in environmental

monitoring

• Forecasting based on citizens’ observatories

Content: an introduction to citizen science 3

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Acquisition, processing, delivery: a new way 4

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• Citizen science involving people on training cruises, on

sailing races, on holiday (e.g., scuba diving) in data

collection

Citizens’ participation via smart devices

Tall Ship Regatta

21st Sept. - 4th October 2013:

Barcelona - Toulon - La Spezia

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Barcelona World

Race

2013-2015: around

the world sailboat

race



collection

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collection

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Information acquisition 8

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Information delivery (Citclops) 9

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Information delivery (WaterWatch) 11

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Information delivery (WaterWatch) 12

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• Water-color, oil-spill, river-status measurement

• Water transparency via phone pictures and

Secchi disc

• Retrieval of sensor measurements from low-

cost moorings

Applications 15

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• Improvement of scuba-diving activities

• Best beaches’ ranking

• Early-warning systems for HABs and bio-

chemical hazards

Applications 16

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• Andromeda

• Citclops

• CITI-SENSE

• Coastwatch

• COBWEB

• SeaWatchers

• Socientize

• WaterWatch

• WeSenseIt

European citizen-science projects: other examples 17

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Coastwatch and Citclops 18

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SeaWatchers 19

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COBWEB, CITI-SENSE, WeSenseIt, Omniscientis 20

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Andromeda project 21

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Andromeda project 22

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citizen science n. scientific work undertaken by

members of the general public, often in collaboration

with or under the direction of professional scientists and

scientific institutions.

citizen scientist n. (a) a scientist whose work is

characterized by a sense of responsibility to serve the

best interests of the wider community (now rare); (b) a

member of the general public who engages in scientific

work, often in collaboration with or under the direction

of professional scientists and scientific institutions; an

amateur scientist.

Citizen Science (Oxford English Dictionary 2014) 23

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Indicators (of environment quality/perturbation) can be

measured by citizens in a more or less

voluntary,

active or

conscious way.

Typology of engagement in citizen science 24

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• Involvement of citizens in: archaeology, astronomy,

ornithology, conservation, meteorology…

• No recognition, viewing volunteers as ‘untrustworthy’

contributors, that are better replaced by automated

instruments

Historical perspective: the era of professional

science (late 1800s – 1900s)

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• Societal trends:

• Education and qualifications

• Leisure

• Sharing economies / peer production systems

• Technological trends:

• Internet access (broadband)

• Mobile devices

• Collaborative Web

• DIY electronics

Historical perspective: the era of opening science

(2000s)

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• World (data by UNESCO): 95 M – 199 M

Increased level of education and qualifications 27

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• Italy (data by UNESCO): 1.8 M - 2.0 M - 1.9 M

• Spain (data by UNESCO): 1.8 M - 1.8 M - 2.0 M

Increased level of education and qualifications 28

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• Italy (data by OECD): 1850 - 1730

• Spain (data by OECD): 1750 - 1690

Increased level of leisure

1650

1700

1750

1800

1850

1900

1950

2000

2050

2000 2002 2004 2006 2008 2010 2012 2014 2016

Israel

Italy

Spain

OECD countries

Average Annual Hours Worked (per person)

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• In many areas, especially in production and sharing of

information

Increased level of sharing economies 30

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Increased (fixed) broadband affordability

Data by the International

Telecommunication Union

(ITU)

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Increased (mobile) broadband affordability

Data by the International Telecommunication Union (ITU)

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Increased mobile-subscription affordability

Data by the International Telecommunication Union (ITU)

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Collaborative Web A map of the world, created by people like you and free to use under an open license

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DIY electronics

Citclops’s KdUINO monitoring buoy based on Arduino

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The case for low resolution 36

with Jaume Piera

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Monitoring: resolution, accuracy μM = 10−6 mol/dm3 = 10−3 mol/m3

In situ ultraviolet spectrophotometer to

measure nutrients

Temporal and spatial variability?

High accuracy

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Monitoring: frequency

120 100 90 80 130 140 110 150

20

15

5

10

25

0

Nitrate (μM)

Julian days

1 sample every month

(at 10 am on the first day of the month)

1 sample every week

(at 10 am on the first day of the week)

Monterey Bay, California

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The sunlight cycle

30 35 40

0

50

100

150

200

250

Julian days

Sunlight intensity (W/m2h)

Day

Night

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30 35 40

0

50

100

150

200

250

Julian days


1 sample every week (at 4 pm on the first day)

This is a typical frequency in

biological domains

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30 35 40

0

50

100

150

200

250

Julian days


1 sample every day (at 4 pm)

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30 35 40

0

50

100

150

200

250

Julian days


2 samples every day (at 4 pm and 4 am)

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1.very high

2.high

3.low

4.very low

5.no sunlight

Monitoring: resolution

vs.

Questionnaire to citizens

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Monitoring: resolution

0

50

100

150

200

250

Julian days


1 sample every day (at 4 pm) Sunlight intensity

25 27 29 31 33 35 37 39 41

very high

high

low

very low

no sunlight

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Forecasting based on citizens’ observatories 45

with Filip Velickovski

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• Extract data from source

• Convert to data array

Parsing

• Convert data into feature vectors + target value

Feature preparation • Select feature

variables, amount of past time points

Feature Selection

• Train and test a set of models (decision trees, SVM, Bayesian)

Model selection • Evaluate best

model on unseen test set

Final evaluation

Machine-learning pipeline 47

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Converting to three class classification problem 48

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Using mean daily values (example: TSM)

6th May 30th Apr 24th May 12th May 18th May 2014

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Using mean daily values, sometimes few data points

(example: TSM)

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18th Dec 14th Dec 22nd Dec 26th Dec

2013

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Short-term correlation to tide (Dominant semi-diurnal

lunar tidal component (M2) with a period of 12:25 h)

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28th Sep 1st Oct 4th Oct 2013

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Knowledge coming from pre-processing 52

• Clear relation among color, tides, and other drivers:

wind (which steers both waves and currents),

river/sluice discharge and biology

• Short-term forecasting (within a tidal period): not

possible without de-tiding the signals

• Longer-term forecasting: possibly improved by de-

tiding the signals

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Feature and target-variable preparation

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FU

Chl-a

TSM

Feature configuration

Number of examples for training

ML technique

Support Vector Machine • C (soft-margin) : 1.0 • Kernel : radial basis

Distribution of target FU classes

415

stable

decrease increase

27% 48%

Blind predictor benchmark

48%

25%

Accuracy (10-fold cross- validation)

53%

Wave height

Forecasting 54

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FU

Chl-a

TSM



ML technique

Random Forest • n_trees: 10

359

33 % 32 %

35%

35 % 52 %

Wave height

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stable

decrease increase Accuracy (10-fold cross- validation)

Forecasting

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FU

Chl-a

TSM



ML technique

Decision Tree • max depth: 10

345

34 % 31 %

35%

35 % 45 %

Wave height

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stable

decrease increase Accuracy (10-fold cross- validation)

Forecasting

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http://www.1000001labs.org/

http://www.citclops.eu/

http://eyeonwater.org/

http://ecsa.citizen-science.net/

http://www.coastwatch.org/

http://creekwatch.researchlabs.ibm.com/

http://www.ecy.wa.gov/programs/eap/fw_riv/index.html

http://projectbaseline.org

http://crowd.cs.umass.edu/

https://it.wikipedia.org/wiki/Citizen_science

http://senseable.mit.edu/

http://research.cens.ucla.edu/aquatic/

http://www.secchidipin.org/

http://www.earthobservations.org/geo_me_201211_geo9_ec.shtml

http://www.nurp.noaa.gov/Spotlight/Observatory.htm

Resources 57

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58

the role of citizen science in environment management

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