The 3 days of the Royal Geographical Society (with IBG) or RGS/IBG annual conference are always valuable, as they provide an opportunity to catch up with the current themes in (mostly human) Geography. While I spend most of my time in an engineering department, I also like to keep my ‘geographer identity’ up to date as this is the discipline that I feel most affiliated with.
Since last year’s announcement that the conference will focus on ‘Geographies of Co-Production‘ I was looking forward to it, as this topic relate many themes of my research work. Indeed, the conference was excellent – from the opening session to the last one that I attended (a discussion about the co-production of co-production).
Just before the conference, the participatory geographies research group run a training day, in which I run a workshop on participatory mapping. It was good to see the range of people that came to the workshop, many of them in early stages of their research career who want to use participatory methods in their research.
In the opening session on Tuesday’s night, Uma Kothari raised a very important point about the risk of institutions blaming the participants if a solution that was developed with them failed. There is a need to ensure that bodies like the World Bank or other funders don’t escape their responsibilities and support as a result of participatory approaches. Another excellent discussion came from Keri Facer who analysed the difficulties of interdisciplinary research based on her experience from the ‘connected communities‘ project. Noticing and negotiating the multiple dimensions of differences between research teams is critical for the co-production of knowledge.
By the end of this session, and as was demonstrated throughout the conference, it became clear that there are many different notions of ‘co-production of knowledge’ – sometime it is about two researchers working together, for others it is about working with policy makers or civil servants, and yet for another group it means to have an inclusive knowledge production with all people that can be impacted by a policy or research recommendation. Moreover, there was even a tension between the type of inclusiveness – should it be based on simple openness (‘if you want to participate, join’), or representation of people within the group, or should it be a active effort for inclusiveness? The fuzziness of the concept proved to be very useful as it led to many discussions about ‘what co-production means?’, as well as ‘what co-production does?’.
Two GIS education sessions were very good (see Patrick’s summery on the ExCiteS blog) and I found Nick Tate and Claire Jarvis discussion about the potential of virtual community of practice (CoP) for GIScience professionals especially interesting. An open question that was left at the end of the session was about the value of generic expertise (GIScience) or the way they are used in a specific area. In other words, do we need a CoP to share the way we use the tools and methods or is it about situated knowledge within a specific domain?
The Chair Early Career panel was, for me, the best session in the conference. Maria Escobar-Tello, Naomi Millner, Hilary Geoghegan and Saffron O’Neil discussed their experience in working with policy makers, participants, communities and universities. Maria explored the enjoyment of working at the speed of policy making in DEFRA, which also bring with it major challenges in formulating and doing research. Naomi discussed productive margins project which involved redesigning community engagement, and also noted what looks like very interesting reading: the e-book Problems of Participation: Reflections on Authority, Democracy, and the Struggle for Common Life. Hilary demonstrated how she has integrated her enthusiasm for enthusiasm into her work, while showing how knowledge is co-produced at the boundaries between amateurs and professionals, citizens and scientists. Hilary recommended another important resource – the review Towards co-production in research with communities (especially the diagram/table on page 9). Saffron completed the session with her work on climate change adaptation, and the co-production of knowledge with scientists and communities. Her research on community based climate change visualisation is noteworthy, and suggest ways of engaging people through photos that they take around their homes.
In another session which focused on mapping, the Connected Communities project appeared again, in the work of Chris Speed, Michelle Bastian & Alex Hale on participatory local food mapping in Liverpool and the lovely website that resulted from their project, Memories of Mr Seel’s Garden. It is interesting to see how methods travel across disciplines and to reflect what insights should be integrated in future work (while also resisting a feeling of ‘this is naive, you should have done this or that’!).
On the last day of the conference, the sessions on ‘the co-production of data based living‘ included lots to contemplate on. Rob Kitchin discussion and critique of smart-cities dashboards, highlighting that data is not-neutral, and that it is sometime used to decontextualised the city from its history and exclude non-quantified and sensed forms of knowledge (his new book ‘the data revolution’ is just out). Agnieszka Leszczynski continued to develop her exploration of the mediation qualities of techno-social-spatial interfaces leading to the experience of being at a place intermingled with the experience of the data that you consume and produce in it. Matt Wilson drawn parallel between the quantified self and the quantified city, suggesting the concept of ‘self-city-nation’ and the tensions between statements of collaboration and sharing within proprietary commercial systems that aim at extracting profit from these actions. Also interesting was Ewa Luger discussion of the meaning of ‘consent’ within the Internet of Things project ‘Hub of All Things‘ and the degree in which it is ignored by technology designers.
The highlight of the last day for me was the presentation by Rebecca Lave on ‘Critical Physical Geography‘. This is the idea that it is necessary to combine scientific understanding of hydrology and ecology with social theory. It is also useful in alerting geographers who are dealing with human geography to understand the physical conditions that influence life in specific places. This approach encourage people who are involved in research to ask questions about knowledge production, for example social justice aspects in access to models when corporations can have access to weather or flood models that are superior to what is available to the rest of society.
The co-production of knowledge isn’t entirely new and Wendy is quick to point out that themes like citizen science and participatory methods are well established within geography. “What we are now seeing is a sustained move towards the co-production of knowledge across our entire discipline.”
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.