Muki Haklay, Extreme Citizen Science

About people going out, not sitting at home in front of their computer, e.g. the Christmas bird count, climate modelling data from weather observations. Has been going on a long time, but evolving into cyberscience, e.g. setting up a self-activated camera to monitor wildlife, or a crab survey that requires sheet from the internet.

Can take information out of things like Flickr, Picasa Web, Panoramio and Geograph (project to take photos of the whole of the UK).  There is a concentration of photos in cities, but when you control for population, you see hotspots in tourist areas, and blindspots in suburbs.

OpenStreetMap, 30k active volunteers contributions. Completeness can be tested by comparing to OS data. By March 2010, OSM is now significantly better than Meridian 2 in most of the UK.

Control for the name of a street, because you need to be in the place for that, you see cities stronger. Where there’s higher population, OSM completes faster.

Compare to index of deprivation, and deprived areas are not covered as well as wealthy areas.

Citizen science in perspective

  • Citizen scientists
    • Collect data
    • Act as an inelligent platform for sensors
    • CPU cycles
    • Basic classifications
  • Geographical distribution, bias to highly populated, central places, toursits
  • Bias towrars affluent areas and participants
  • Demographic analysis shows high levels of education and interest in the domain

In that way, citizen science is missing a trick.

Literacy: still have a lot of people who are non-literate who are excluded. Almost of projects benefit from growth of higher education in the 60s, and that number will increase over next 10 years. Look at penetration of computers, 70% already have PCs, number increasing. Broadband, seeing much wider bandwidth which will allow us to do more interesting things. What would ike to suggest is tht there is a potential for ‘extreme cit sci’.

Users: currently focusing on highly educated and domain knowledge, but want everyone to be able to participate regardless of literacy level.

Location: Want to include everyone everywhere.

Role: Get participatory and collaborative mode where people help shape the problem.

Already happening, e.g. EPSRC project SuScit, as peoplea re discussing and able to shape how the science is done.

Noise mapping, working on Isle of Dogs, talked to community to shape what they want to do, said they were bothered by airplane noise. Volunteers in the area. Move away from computers, use paper. Track noise levels, and map to say where they are.

But then they can enter data on their website, and can provide photos e.g. to show that there’s a stack over heathrow, and a lot of airplanes in the sky at once.

During Eyjafjallajökull, community was monitoring throughout the flight ban, so the noise pollution was reduced.

Worked in Deptford, in social housing, and nearby scrapyard has made their life hell. There is also a community centre and a nursery nearby. Worked with community to monitor the noise. volunteers spread through whole area, and showed noise map, which then used in discussion with local authority about what needs to be done. Community had been complaining for 6 years, but after the local authority saw the evidence, they revoked the scrap yard’s licence.

CyberTracker, working with Bushmen in Africa to gather data. They have iconic representation of information on a GPS device in order to monitor wildlife.

Another project, working with hunter-gatherers to identify things that are important to them, e.g. trees used for food so that they won’t get cut down.

Opportunities are exciting:

  • Interfaces suitable for non-literate users
  • Bundles of sensors, data collection and analysis tools that can be applied in different contexts
  • Understand patterns of use, motivations and incentives – the science of citizen science