Geographical Citizen Science

Geographical Citizen Science Dr Muki Haklay Department of Civil, Environmental and Geomatic Engineering, UCL & Co-founder ‘Mapping for Change’ Povesham.wordpress.com MappingForChange.org.uk

Oh, No! Why do we need another term? What’s wrong with Volunteered Geographic Information (VGI), Crowdsourced Geographic information, User Generated Geographic Information or any other similar term that is already in use? Well, terms are useful to highlight specific aspects that might be otherwise overlooked...

Outline • Contexts for VGI: – Web 2.0 – Geographical Information production – Citizen Science/Citizen Cyberscience • Geographical Citizen Science – Practical challenges: coverage, quality, motivations – Conceptual challenges: trust and elitism, heterogeneity, uncertainty, lab and reality

Non-spatial Crowdsourcing/ User Generated Content Non-spatial applications - anywhere, anyone, anytime. It is possible to collaborate, update and keep in touch regardless of where you are.

Spatially Implicit Crowdsourcing/User Generated Content Spatially Implicit applications – place matters: if you’re not there, you can’t take a picture

Spatial Crowdsourcing/User Generated Content

Context I: Web 2.0 and VGI • Demographic and technological trends: education, broadband & computer use, applications for sharing media, social networking • Identifying VGI in spatially explicit and implicit applications, understanding the wider trends that influence it

VGI in geographic information production (Image source: OpenStreetMap)

Context II: Geographic data collection and VGI • ‘Crowdsourcing’ – reducing costs of production through free labour • Active vs. Passive Crowdsourcing • Ensuring standards and integration with existing systems

Citizen Science Volunteer rainfall observer Rick Grocke checks the rain gauge at Tanami Downs cattle station in the Northern Territory of Australia Source: Audubon Cal. Source: WMO–No. 919

Citizen Science Source: BioScience 58(3) p. 195 Source: BioScience 58(3) p. 192

Non-spatial citizen (cyber)science

Context III: Geographical Citizen Science and VGI • The goal of the project is the production of scientific knowledge • Role of participants – from passive volunteering resources (CPU cycles) to ‘distributed thinking’ (identifying galaxies) • Participants have an interest in the topic, some become experts

Hanny van Arkel. “The Dutch schoolteacher and Queen admirer who discovered Hanny‟s Voorwerp”.

Aspects of Geographical Citizen Science • Coverage – which part of the world can we expect to cover? • Participants – who is likely to participate, for how long and what will motivates such a person to continue and contribute? • Culture – how established science view citizen science activities?

Geograph Panoramio Picasa Web Flickr Source: Vyron Antoniou, UCL

Spatial pattern • Not only urban areas are captured, but also national parks are emerging as popular places to capture information

England – March 2008 Completeness can be tested in comparison to official datasets from the Ordnance Survey

England – March 2010

Change in completeness Mar 2008 – Mar 2010

England – March 2010 • The test for completeness with attributes checks that roads and streets names have been completed • Until the release of Ordnance Survey data in 1st April 2010, this was a good indication for ground survey of an area

Change in completeness with attributes

Population and completeness Length of OSM / Length of Meridian 2 <= 0.20 Length of OSM / Length of Meridian 2 > 1.00 (both datasets with attributes) (both datasets with attributes) 16000 7000 Number of 'Untouched' Grid Cells 14000 Number of 'Complete' Grid Cells 6000 12000 5000 10000 OSM 0308 4000 OSM 0308 8000 OSM 0309 OSM 0309 OSM 1009 3000 OSM 1009 6000 2000 4000 2000 1000 0 0 <= 14.00 15.00 - 40.00 41.00 - 137.00 138.00 - 820.00 821.00+ <= 14.00 15.00 - 40.00 41.00 - 137.00 138.00 - 820.00 821.00+ Population / 1km Grid Square Population / 1km Grid Square

Prosperity and completeness

Geographical Citizen Science – geographic patterns • Highly populated and central places • Tourist attractions: national parks and coastal zones • Bias towards affluent areas (and participants) • Potential for rapid coverage of large areas by dedicated volunteers

Cultural problems • Citizen Scientists are perceived as untrustworthy – No quality control – Elitism – demarcation of ‘professional scientists’ from ‘amateurs’ • Evaluation mechanisms assume unskilled workers such as classifying each object three times (in Citizen Cyberscience) • But volunteer effort can improve the data

Positional accuracy in complete tiles Mean 9,57m Standard Deviation 6,51m Y-axis: Length difference between OSM and Meridian 2 X-axis: Average positional accuracy in the Tile Average positional accuracy is 9.57m with St. Dev. = 6.51m Source: Vyron Antoniou, UCL

Positional accuracy in incomplete tiles Mean 11,72m Standard Deviation 7,73m Y-axis: Length difference between OSM and Meridian 2 X-axis: Average positional accuracy in the Tile Average positional accuracy is 11.72m (almost 22.5% less accurate) with a greater St. Dev. = 7.73m Source: Vyron Antoniou, UCL

Positional accuracy for complete and incomplete tiles Source: Vyron Antoniou, UCL

Time for a paradigm shift? Standard GI view VGI/Citizen Science From expectation for a To explicit acceptance of universal and heterogeneity homogenous coverage From expectation of high To acceptance that modern level of knowledge instrumentation and enthusiasm are enough From attempting to To accepting the replicate laboratory uncertainty of reality conditions in the field From elitism To participatory science

‘Extreme’ Citizen Science „Normal‟ Citizen „Extreme‟ Citizen Science Science Users Educated, usually Everyone, regardless with some domain of level of literacy knowledge Location Affluent, populated Everywhere and popular Role Data collection and Shaping the problem, entry analysing the data Mode of Crowdsourcing Collaborative and work participatory science

‘Extreme’ Citizen Science • Citizens can participate in defining the problems, envisioning possible projects and participate in the analysis as in the UK EPSRC project SuScit • Can (and should) participate in the discussion of the results of what they’ve collected • Often, they are best placed to analyse the data

Noise Mapping

Sound Readings - Normal Flight Operations Sound Readings - Period of No Flights 90 90 80 80 70 70 60 60 50 50 dBA 40 40 30 30 20 20 10 10 0 0 00:00 04:48 09:36 14:24 19:12 00:00 04:48 00:00 04:48 09:36 14:24 19:12 00:00 Eyjafjallajökull

Conclusions • Geographical Citizen Science – Great potential for data collection, analysis as well as awareness – Requires careful consideration of the limitations that are imposed by participants locations, motivations and availability – ‘Cyborg’ view of participants with their personal knowledge, mobile phones, and sensors – Change in mindset: heterogeneity, collaboration, skill development Povesham.wordpress.com MappingForChange.org.uk

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Geographical Citizen Science

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