30 June, 2014
Today marks the publication of the report ‘crowdsourced geographic information in government‘. The report is the result of a collaboration that started in the autumn of last year, when the World Bank Global Facility for Disaster Reduction and Recovery(GFDRR) requested to carry out a study of the way crowdsourced geographic information is used by governments. The identification of barriers and success factors were especially needed, since GFDRR invest in projects across the world that use crowdsourced geographic information to help in disaster preparedness, through activities such as the Open Data for Resilience Initiative. By providing an overview of factors that can help those that implement such projects, either in governments or in the World Bank, we can increase the chances of successful implementations. To develop the ideas of the project, Robert Soden (GFDRR) and I run a short workshop during State of the Map 2013 in Birmingham, which helped in shaping the details of project plan as well as some preliminary information gathering. The project team included myself, Vyron Antoniou, Sofia Basiouka, and Robert Soden (GFDRR). Later on, Peter Mooney (NUIM) and Jamal Jokar (Heidelberg) volunteered to help us – demonstrating the value in research networks such as COST ENERGIC which linked us.
The general methodology that we decided to use is the identification of case studies from across the world, at different scales of government (national, regional, local) and domains (emergency, environmental monitoring, education). We expected that with a large group of case studies, it will be possible to analyse common patterns and hopefully reach conclusions that can assist future projects. In addition, this will also be able to identify common barriers and challenges.
We have paid special attention to information flows between the public and the government, looking at cases where the government absorbed information that provided by the public, and also cases where two-way communication happened.
Originally, we were aiming to ‘crowdsource’ the collection of the case studies. We identified the information that is needed for the analysis by using few case studies that we knew about, and constructing the way in which they will be represented in the final report. After constructing these ‘seed’ case study, we aimed to open the questionnaire to other people who will submit case studies. Unfortunately, the development of a case study proved to be too much effort, and we received only a small number of submissions through the website. However, throughout the study we continued to look out for cases and get all the information so we can compile them. By the end of April 2014 we have identified about 35 cases, but found clear and useful information only for 29 (which are all described in the report). The cases range from basic mapping to citizen science. The analysis workshop was especially interesting, as it was carried out over a long Skype call, with members of the team in Germany, Greece, UK, Ireland and US (Colorado) while working together using Google Docs collaborative editing functionality. This approach proved successful and allowed us to complete the report.
Some ideas take long time to mature into a form that you are finally happy to share them. This is an example for such thing.
I got interested in the area of Philosophy of Technology during my PhD studies, and continue to explore it since. During this journey, I found a lot of inspiration and links to Andrew Feenberg’s work, for example, in my paper about neogeography and the delusion of democratisation. The links are mostly due to Feenberg’s attention to ‘hacking’ or appropriating technical systems to functions and activities that they are outside what the designers or producers of them thought.
In addition to Feenberg, I became interested in the work of Albert Borgmann and because he explicitly analysed GIS, dedicating a whole chapter to it in Holding on to Reality. In particular, I was intrigues by his formulation to The Device Paradigm and the notion of Focal Things and Practices which are linked to information systems in Holding on to Reality where three forms of information are presented – Natural Information, Cultural Information and Technological Information. It took me some time to see that these 5 concepts are linked, with technological information being a demonstration of the trouble with the device paradigm, while natural and cultural information being part of focal things and practices (more on these concepts below).
I first used Borgmann’s analysis as part of ‘Conversations Across the Divide‘ session in 2005, which focused on Complexity and Emergence. In a joint contribution with David O’Sullivan about ‘complexity science and Geography: understanding the limits of narratives’, I’ve used Borgmann’s classification of information. Later on, we’ve tried to turn it into a paper, but in the end David wrote a much better analysis of complexity and geography, while the attempt to focus mostly on the information concepts was not fruitful.
The next opportunity to revisit Borgmann came in 2011, for an AAG pre-conference workshop on VGI where I explored the links between The Device Paradigm, Focal Practices and VGI. By 2013, when I was invited to the ‘Thinking and Doing Digital Mapping‘ workshop that was organise by ‘Charting the Digital‘ project. I was able to articulate the link between all the five elements of Borgmann’s approach in my position paper. This week, I was able to come back to the topic in a seminar in the Department of Geography at the University of Leicester. Finally, I feel that I can link them in a coherent way.
So what is it all about?
Within the areas of VGI and Citizen Science, there is a tension between the different goals or the projects and identification of practices in terms of what they mean for the participants – are we using people as ‘platform for sensors’ or are we dealing with fuller engagement? The use of Borgmann’s ideas can help in understanding the difference. He argues that modern technologies tend to adopt the myopic ‘Device Paradigm’ in which specific interpretation of efficiency, productivity and a reductionist view of human actions are taking precedence over ‘Focal Things and Practices’ that bring people together in a way meaningful to human life. In Holding On to Reality (1999), he differentiates three types of information: natural, cultural and technological. Natural information is defined as information about reality: for example, scientific information on the movement of the earth or the functioning of a cell. This is information that was created in order to understand the functioning of reality. Cultural information is information that is being used to shape reality, such as engineering design plans. Technological information is information as reality and leads to decreased human engagement with fundamental aspects of reality. Significantly, these categories do not relate to the common usage of the words ‘natural’, ‘cultural and ‘technological’ rather to describe the changing relationship between information and reality at different stages of socio-technical development.
When we explore general geographical information, we can see that some of it is technological information, for example SatNav and the way that communicate to the people who us them, or virtual globes that try to claim to be a representation of reality with ‘current clouds’ and all. The paper map, on the other hand, provide a conduit to the experience of hiking and walking through the landscape, and is part of cultural information.
Things are especially interesting with VGI and Citizen Science. In them, information and practices need to be analysed in a more nuanced way. In some cases, the practices can become focal to the participants – for example in iSpot where the experience of identifying a species in the field is also link to the experiences of the amateurs and experts who discuss the classification. It’s an activity that brings people together. On the other hand, in crowdsourcing projects that grab information from SatNav devices, there is a demonstration of The Device Paradigm, with the potential of reducing of meaningful holiday journey to ‘getting from A to B at the shortest time’. The slides below go through the ideas and then explore the implications on GIS, VGI and Citizen Science.
Now for the next stage – turning this into a paper…
During the symposium “The Future of PGIS: Learning from Practice?” which was held at ITC-University of Twente, 26 June 2013, I gave a talk titled ‘Keeping the spirit alive’ – preservations of participatory GIS values in the Geoweb, which explored what was are the important values in participatory GIS and how they translate to the Geoweb, Volunteered Geographic Information and current interests in crowdsourcing. You can watch the talk below.
To see the rest of the presentations during the day, see https://vimeo.com/album/2475389 and details of the event are available here http://www.itc.nl/Pub/Events-Conferences/2013/2013-June/Participatory-GIS-Symposium.html
8 July, 2013
The term ‘Citizen Science’ is clearly gaining more recognition and use. It is now get mentioned in radio and television broadcasts, social media channels as well as conferences and workshops. Some of the clearer signs for the growing attention include discussion of citizen science in policy oriented conferences such as UNESCO’s World Summit on Information Society (WSIS+10) review meeting discussion papers (see page ), or the Eye on Earth users conference (see the talks here) or the launch of the European Citizen Science Association in the recent EU Green Week conference.
Another aspect of the expanding world of citizen science is the emerging questions from those who are involved in such projects or study them about the efficacy of the term. As is very common with general terms, some reflections on the accuracy of the term are coming to the fore – so Rick Bonney and colleagues suggest to use ‘Public Participation in Scientific Research‘ (significantly, Bonney was the first to use ‘Citizen Science’ in 1995); Francois Grey coined Citizen Cyberscience to describe projects that are dependent on the Internet; recently Chris Lintott discussed some doubts about the term in the context of Zooniverse; and Katherine Mathieson asks if Citizen Science is just a passing fad. In our own group, there are also questions about the correct terminology, with Cindy Regalado suggestions to focus on ‘Publicly Initiated Scientific Research (PIScR)‘, and discussion on the meaning of ‘Extreme Citizen Science‘.
One way to explore what is going on is to consider the evolution of the ‘hype’ around citizen science through ‘Gartner’s Hype Cycle‘ which can be seen as a way to consider the way technologies are being adopted in a world of rapid communication and inflated expectations from technologies. leaving aside Gartner own hype, the story that the model is trying to tell is that once a new approach (technology) emerges because it is possible or someone reconfigured existing elements and claim that it’s a new thing (e.g. Web 2.0), it will go through a rapid growth in terms of attention and publicity. This will go on until it reaches the ‘peak of inflated expectations’ where the expectations from the technology are unrealistic (e.g. that it will revolutionize the way we use our fridges). This must follow by a slump, as more and more failures come to light and the promises are not fulfilled. At this stage, the disillusionment is so deep that even the useful aspects of the technology are forgotten. However, if it passes this stage, then after the realisation of what is possible, the technology is integrated into everyday life and practices and being used productively.
So does the hype cycle apply to citizen science?
If we look at Gartner cycle from last September, Crowdsourcing is near the ‘peak of inflated expectations’ and some descriptions of citizen science as scientific crowdsourcing clearly match the same mindset.
There is a growing evidence of academic researchers entering citizen science out of opportunism, without paying attention to the commitment and work that is require to carry out such projects. With some, it seems like that they decided that they can also join in because someone around know how to make an app for smartphones or a website that will work like Galaxy Zoo (failing to notice the need all the social aspects that Arfon Smith highlights in his talks). When you look around at the emerging projects, you can start guessing which projects will succeed or fail by looking at the expertise and approach that the people behind it take.
Another cause of concern are the expectations that I noticed in the more policy oriented events about the ability of citizen science to solve all sort of issues – from raising awareness to behaviour change with limited professional involvement, or that it will reduce the resources that are needed for activities such as environmental monitoring, but without an understanding that significant sustained investment is required – community coordinator, technical support and other aspects are needed here just as much. This concern is heightened by statements that promote citizen science as a mechanism to reduce the costs of research, creating a source of free labour etc.
On the other hand, it can be argued that the hype cycle doesn’t apply to citizen science because of history. Citizen science existed for many years, as Caren Cooper describe in her blog posts. Therefore, conceptualising it as a new technology is wrong as there are already mechanisms, practices and institutions to support it.
In addition, and unlike the technologies that are on Gartner chart, academic projects within which citizen science happen benefit from access to what is sometime termed patient capital without expectations for quick returns on investment. Even with the increasing expectations of research funding bodies for explanations on how the research will lead to an impact on wider society, they have no expectations that the impact will be immediate (5-10 years is usually fine) and funding come in chunks that cover 3-5 years, which provides the breathing space to overcome the ‘through of disillusionment’ that is likely to happen within the technology sector regarding crowdsourcing.
And yet, I would guess that citizen science will suffer some examples of disillusionment from badly designed and executed projects – to get these projects right you need to have a combination of domain knowledge in the specific scientific discipline, science communication to tell the story in an accessible way, technical ability to build mobile and web infrastructure, understanding of user interaction and user experience to to build an engaging interfaces, community management ability to nurture and develop your communities and we can add further skills to the list (e.g. if you want gamification elements, you need experts in games and not to do it amateurishly). In short, it need to be taken seriously, with careful considerations and design. This is not a call for gatekeepers , more a realisation that the successful projects and groups are stating similar things.
Which bring us back to the issue of the definition of citizen science and terminology. I have been following terminology arguments in my own discipline for over 20 years. I have seen people arguing about a data storage format for GIS and should it be raster or vector (answer: it doesn’t matter). Or arguing if GIS is tool or science. Or unhappy with Geographic Information Science and resolutely calling it geoinformation, geoinformatics etc. Even in the minute sub-discipline that deals with participation and computerised maps that are arguments about Public Participation GIS (PPGIS) or Participatory GIS (PGIS). Most recently, we are debating the right term for mass-contribution of geographic information as volunteered geographic information (VGI), Crowdsourced geographic information or user-generated geographic information.
It’s not that terminology and precision in definition is not useful, on the contrary. However, I’ve noticed that in most cases the more inclusive and, importantly, vague and broad church definition won the day. Broad terminologies, especially when they are evocative (such as citizen science), are especially powerful. They convey a good message and are therefore useful. As long as we don’t try to force a canonical definition and allow people to decide what they include in the term and express clearly why what they are doing is falling within citizen science, it should be fine. Some broad principles are useful and will help all those that are committed to working in this area to sail through the hype cycle safely.
20 October, 2012
The Spatial Data Infrastructure Magazine (SDIMag.com) is a relatively new e-zine dedicated to the development of spatial data infrastructures around the world. Roger Longhorn, the editor of the magazine, conducted an email interview with me, which is now published.
In the interview, we are covering the problematic terminology used to describe a wider range of activities; the need to consider social and technical aspects as well as goals of the participants; and, of course, the role of the information that is produced through crowdsourcing, citizen science, VGI with spatial data infrastructures.
27 November, 2011
This post continues to the theme of the previous one, and is also based on the chapter that will appear next year in the book:
The post focuses on the participatory aspect of different Citizen Science modes:
Against the technical, social and cultural aspects of citizen science, we offer a framework that classifies the level of participation and engagement of participants in citizen science activity. While there is some similarity between Arnstein’s (1969) ‘ladder of participation’ and this framework, there is also a significant difference. The main thrust in creating a spectrum of participation is to highlight the power relationships that exist within social processes such as urban planning or in participatory GIS use in decision making (Sieber 2006). In citizen science, the relationship exists in the form of the gap between professional scientists and the wider public. This is especially true in environmental decision making where there are major gaps between the public’s and the scientists’ perceptions of each other (Irwin 1995).
In the case of citizen science, the relationships are more complex, as many of the participants respect and appreciate the knowledge of the professional scientists who are leading the project and can explain how a specific piece of work fits within the wider scientific body of work. At the same time, as volunteers build their own knowledge through engagement in the project, using the resources that are available on the Web and through the specific project to improve their own understanding, they are more likely to suggest questions and move up the ladder of participation. In some cases, the participants would want to volunteer in a passive way, as is the case with volunteered computing, without full understanding of the project as a way to engage and contribute to a scientific study. An example of this is the many thousands of people who volunteered to the Climateprediction.net project, where their computers were used to run global climate models. Many would like to feel that they are engaged in one of the major scientific issues of the day, but would not necessarily want to fully understand the science behind it.
Therefore, unlike Arnstein’s ladder, there shouldn’t be a strong value judgement on the position that a specific project takes. At the same time, there are likely benefits in terms of participants’ engagement and involvement in the project to try to move to the highest level that is suitable for the specific project. Thus, we should see this framework as a typology that focuses on the level of participation.
At the most basic level, participation is limited to the provision of resources, and the cognitive engagement is minimal. Volunteered computing relies on many participants that are engaged at this level and, following Howe (2006), this can be termed ‘crowdsourcing’. In participatory sensing, the implementation of a similar level of engagement will have participants asked to carry sensors around and bring them back to the experiment organiser. The advantage of this approach, from the perspective of scientific framing, is that, as long as the characteristics of the instrumentation are known (e.g. the accuracy of a GPS receiver), the experiment is controlled to some extent, and some assumptions about the quality of the information can be used. At the same time, running projects at the crowdsourcing level means that, despite the willingness of the participants to engage with a scientific project, their most valuable input – their cognitive ability – is wasted.
The second level is ‘distributed intelligence’ in which the cognitive ability of the participants is the resource that is being used. Galaxy Zoo and many of the ‘classic’ citizen science projects are working at this level. The participants are asked to take some basic training, and then collect data or carry out a simple interpretation activity. Usually, the training activity includes a test that provides the scientists with an indication of the quality of the work that the participant can carry out. With this type of engagement, there is a need to be aware of questions that volunteers will raise while working on the project and how to support their learning beyond the initial training.
The next level, which is especially relevant in ‘community science’ is a level of participation in which the problem definition is set by the participants and, in consultation with scientists and experts, a data collection method is devised. The participants are then engaged in data collection, but require the assistance of the experts in analysing and interpreting the results. This method is common in environmental justice cases, and goes towards Irwin’s (1995) call to have science that matches the needs of citizens. However, participatory science can occur in other types of projects and activities – especially when considering the volunteers who become experts in the data collection and analysis through their engagement. In such cases, the participants can suggest new research questions that can be explored with the data they have collected. The participants are not involved in detailed analysis of the results of their effort – perhaps because of the level of knowledge that is required to infer scientific conclusions from the data.
Finally, collaborative science is a completely integrated activity, as it is in parts of astronomy where professional and non-professional scientists are involved in deciding on which scientific problems to work and the nature of the data collection so it is valid and answers the needs of scientific protocols while matching the motivations and interests of the participants. The participants can choose their level of engagement and can be potentially involved in the analysis and publication or utilisation of results. This form of citizen science can be termed ‘extreme citizen science’ and requires the scientists to act as facilitators, in addition to their role as experts. This mode of science also opens the possibility of citizen science without professional scientists, in which the whole process is carried out by the participants to achieve a specific goal.
This typology of participation can be used across the range of citizen science activities, and one project should not be classified only in one category. For example, in volunteer computing projects most of the participants will be at the bottom level, while participants that become committed to the project might move to the second level and assist other volunteers when they encounter technical problems. Highly committed participants might move to a higher level and communicate with the scientist who coordinates the project to discuss the results of the analysis and suggest new research directions.
12 May, 2011
GIS Research UK (GISRUK) is a long running conference series, and the 2011 instalment was hosted by the University of Portsmouth at the end of April.
During the conference, I was asked to give a keynote talk about Participatory GIS. I decided to cover the background of Participatory GIS in the mid-1990s, and the transition to more advanced Web Mapping applications from the mid-2000s. Of special importance are the systems that allow user-generated content, and the geographical types of systems that are now leading to the generation of Volunteer Geographic Information (VGI).
The next part of the talk focused on Citizen Science, culminating with the ideas that are the basis for Extreme Citizen Science.
Interestingly, as in previous presentations, one of the common questions about Citizen Science came up. Professional scientists seem to have a problem with the suggestion that citizens are as capable as scientists in data collection and analysis. While there is an acceptance about the concept, the idea that participants can suggest problems, collect data rigorously and analyse it seems to be too radical – or worrying.
What is important to understand is that the ideas of Extreme Citizen Science are not about replacing the role of scientists, but are a call to rethink the role of the participants and the scientists in cases where Citizen Science is used. It is a way to consider science as a collaborative process of learning and exploration of issues. My own experience is that participants have a lot of respect for the knowledge of the scientists, as long as the scientists have a lot of respect for the knowledge and ability of the participants. The participants would like to learn more about the topic that they are exploring and are keen to know: ‘what does the data that I collected mean?’ At the same time, some of the participants can become very serious in terms of data collection, reading about the specific issues and using the resources that are available online today to learn more. At some point, they are becoming knowledgeable participants and it is worth seeing them as such.
The slides below were used for this talk, and include links to the relevant literature.