25 October, 2014
As Silvertown (2009) noted, until the late 19th century, science was mainly developed by people who had additional sources of employment that allowed them to spend time on data collection and analysis. Famously, Charles Darwin joined the Beagle voyage, not as a professional naturalist but as a companion to Captain FitzRoy[*]. Thus, in that era, almost all science was citizen science albeit mostly by affluent gentlemen and gentlewomen scientists[**]. While the first professional scientist is likely to be Robert Hooke, who was paid to work on scientific studies in the 17th century, the major growth in the professionalisation of scientists was mostly in the latter part of the 19th and throughout the 20th century.
Even with the rise of the professional scientist, the role of volunteers has not disappeared, especially in areas such as archaeology, where it is common for enthusiasts to join excavations, or in natural science and ecology, where they collect and send samples and observations to national repositories. These activities include the Christmas Bird Watch that has been ongoing since 1900 and the British Trust for Ornithology Survey, which has collected over 31 million records since its establishment in 1932 (Silvertown 2009). Astronomy is another area in which amateurs and volunteers have been on a par with professionals when observation of the night sky and the identification of galaxies, comets and asteroids are considered (BBC 2006). Finally, meteorological observations have also relied on volunteers since the early start of systematic measurements of temperature, precipitation or extreme weather events (WMO 2001). (Haklay 2013 emphasis added)
The general messages of this historical narrative are: first, citizen science is a legitimate part of scientific practice as it was always there, we just ignored it for 50+ years; second, that some citizen science is exactly as it was - continuous participation in ecological monitoring or astronomical observations, only that now we use smartphones or the Met Office WOW website and not pen, paper and postcards.
The second aspect of this argument is one that I was wondering about as I was writing a version of the historical narrative for a new report. This was done within a discussion on how the educational and technological transitions over the past century reshaped citizen science. I have argued that the demographic and educational transition in many parts of the world, and especially the rapid growth in the percentage and absolute numbers of people with higher education degrees who are potential participants is highly significant in explaining the popularity of citizen science. To demonstrate that this is a large scale and consistent change, I used the evidence of Flynn effect, which is the rapid increase in IQ test scores across the world during the 20th century.
However, while looking at the issue recently, I came across Jim Flynn TED talk ‘Why our IQ levels are higher than our grandparents‘ (below). At 3:55, he raise a very interesting point, which also appears in his 2007 ‘What is Intelligence?‘ on pages 24-26. Inherently, Flynn argues that the use of cognitive skills have changed dramatically over the last century, from thinking that put connections to concrete relationship with everyday life as the main way of understanding the world, to one that emphasise scientific categories and abstractions. He use an example of a study from the early 20th Century, in which participants where asked about commonalities between fish and birds. He highlights that it was not the case that in the ‘pre-scientific’ worldview people didn’t know that both are animals, but more the case that this categorisation was not helpful to deal with concrete problems and therefore not common sense. Today, with scientific world view, categorisation such as ‘these are animals’ come first.
This point of view have implications to the way we interpret and understand the historical narrative. If correct, than the people who participate in William Whewell tide measurement work (see Caren Cooper blogpost about it), cannot be expected to think about contribution to science, but could systematically observed concrete events in their area. While Whewell view of participants as ‘subordinate labourers’ is still elitist and class based, it is somewhat understandable. Moreover, when talking about projects that can show continuity over the 20th Century – such as Christmas Bird Count or phenology projects – we have to consider the option that an the worldview of the person that done that in 1910 was ‘how many birds there are in my area?’ while in 2010 the framing is ‘in order to understand the impact of climate change, we need to watch out for bird migration patterns’. Maybe we can explore in historical material to check for this change in framing? I hope that projects such as Constructing Scientific Communities which looks at citizen science in the 19th and 21th century will shed light on such differences.
[*] Later I found that this is not such a simple fact – see van Wyhe 2013 “My appointment received the sanction of the Admiralty”: Why Charles Darwin really was the naturalist on HMS Beagle
[**] And we shouldn’t forget that this was to the exclusion of people such as Mary Anning
Call for papers: OpenStreetMap Studies: Research Perspectives on a Decade of OSM
Association of American Geographers Annual Meeting
April 21-25, 2015
Alan McConchie, University of British Columbia
Muki Haklay, University College London
Since its founding in 2004, OpenStreetMap has grown into one of the pre-eminent open collaborative geographic knowledge projects online, growth that has been tracked closely by the emerging research domains of Volunteered Geographic Information (VGI) and the Geospatial Web. Due to OSM’s size (now boasting well over 1 million users), and its relative accessibility (open source code, public mailing lists, freely-downloadable data), OSM has been the preferred case study for many VGI researchers. This is in contrast to arguably more successful VGI projects, such as Google Map Maker, Waze, Facebook places and others, which are closed and researchers cannot access their data easily. Recently, however, there has been growing awareness that OSM and VGI are too often conflated, and that OSM should not be taken to stand in for all VGI. To this end, Muki Haklay suggested that the breadth and complexity of research into OSM may warrant a potential subfield “OpenStreetMap Studies”
Taking the 10th birthday of OSM as a starting point, this session will survey the state of geographical research on OpenStreetMap. This session seeks research demonstrating a variety of approaches, with particular interest in papers that investigate (1) how OSM has changed over the last 10 years, (2) how OSM research has also evolved over that time and how it compares to other crowdsourced systems, and (3) how OSM research differs from VGI research.
Possible paper topics include, but are not limited to:
- Comparisons between OSM and other open knowledge initiatives such as Wikipedia or other VGI projects like Google Map Maker.
- Studies of data quality and completeness in OSM data, and consideration if these studies are possible in closed systems such as Google Map Maker or only possible with OSM.
- OSM and its role in crisis mapping and disaster response and the role of other crowdsourced systems.
- Novel applications of OSM data used in other fields, such as software algorithms, computer vision, traffic modeling, etc.
- Social histories and social geographies of OSM and its community of contributors, and comparison to other, open or closed VGI projects.
- Feminist and critical approaches to the societal impacts of OSM, the epistemological assumptions of its data structures, and the demographics of its community.
- Political economic approaches to OSM, and open source software and open geodata more generally.
The session is supported by the European COST Energic (COST Action IC1203) network: European Network Exploring Research into Geospatial Information Crowdsourcing.
Please email abstracts of 250 words or less to Alan (firstname.lastname@example.org) and Muki (email@example.com) before October 31st, 2014. All accepted papers will need to register for the AAG conference at AAG.org.
19 September, 2014
The Association of American Geographers is coordinating an effort to create an International Encyclopedia of Geography. Plans started in 2010, with an aim to see the 15 volumes project published in 2015 or 2016. Interestingly, this shows that publishers and scholars are still seeing the value in creating subject-specific encyclopedias. On the other hand, the weird decision by Wikipedians that Geographic Information Science doesn’t exist outside GIS, show that geographers need a place to define their practice by themselves. You can find more information about the AAG International Encyclopedia project in an interview with Doug Richardson from 2012.
As part of this effort, I was asked to write an entry on ‘Volunteered Geographic Information, Quality Assurance‘ as a short piece of about 3000 words. To do this, I have looked around for mechanisms that are used in VGI and in Citizen Science. This are covered in OpenStreetMap studies and similar work in GIScience, and in the area of citizen science, there are reviews such as the one by Andrea Wiggins and colleagues of mechanisms to ensure data quality in citizen science projects, which clearly demonstrated that projects are using multiple methods to ensure data quality.
Below you’ll find an abridged version of the entry (but still long). The citation for this entry will be:
Haklay, M., Forthcoming. Volunteered geographic information, quality assurance. in D. Richardson, N. Castree, M. Goodchild, W. Liu, A. Kobayashi, & R. Marston (Eds.) The International Encyclopedia of Geography: People, the Earth, Environment, and Technology. Hoboken, NJ: Wiley/AAG
In the entry, I have identified 6 types of mechanisms that are used to ensure quality assurance when the data has a geographical component, either VGI or citizen science. If I have missed a type of quality assurance mechanism, please let me know!
Here is the entry:
Volunteered geographic information, quality assurance
Volunteered Geographic Information (VGI) originate outside the realm of professional data collection by scientists, surveyors and geographers. Quality assurance of such information is important for people who want to use it, as they need to identify if it is fit-for-purpose. Goodchild and Li (2012) identified three approaches for VGI quality assurance , ‘crowdsourcing‘ and that rely on the number of people that edited the information, ‘social’ approach that is based on gatekeepers and moderators, and ‘geographic’ approach which uses broader geographic knowledge to verify that the information fit into existing understanding of the natural world. In addition to the approaches that Goodchild and li identified, there are also ‘domain’ approach that relate to the understanding of the knowledge domain of the information, ‘instrumental observation’ that rely on technology, and ‘process oriented’ approach that brings VGI closer to industrialised procedures. First we need to understand the nature of VGI and the source of concern with quality assurance.
While the term volunteered geographic information (VGI) is relatively new (Goodchild 2007), the activities that this term described are not. Another relatively recent term, citizen science (Bonney 1996), which describes the participation of volunteers in collecting, analysing and sharing scientific information, provide the historical context. While the term is relatively new, the collection of accurate information by non-professional participants turn out to be an integral part of scientific activity since the 17th century and likely before (Bonney et al 2013). Therefore, when approaching the question of quality assurance of VGI, it is critical to see it within the wider context of scientific data collection and not to fall to the trap of novelty, and to consider that it is without precedent.
Yet, this integration need to take into account the insights that emerged within geographic information science (GIScience) research over the past decades. Within GIScience, it is the body of research on spatial data quality that provide the framing for VGI quality assurance. Van Oort’s (2006) comprehensive synthesis of various quality standards identifies the following elements of spatial data quality discussions:
- Lineage – description of the history of the dataset,
- Positional accuracy – how well the coordinate value of an object in the database relates to the reality on the ground.
- Attribute accuracy – as objects in a geographical database are represented not only by their geometrical shape but also by additional attributes.
- Logical consistency – the internal consistency of the dataset,
- Completeness – how many objects are expected to be found in the database but are missing as well as an assessment of excess data that should not be included.
- Usage, purpose and constraints – this is a fitness-for-purpose declaration that should help potential users in deciding how the data should be used.
- Temporal quality – this is a measure of the validity of changes in the database in relation to real-world changes and also the rate of updates.
While some of these quality elements might seem independent of a specific application, in reality they can be only be evaluated within a specific context of use. For example, when carrying out analysis of street-lighting in a specific part of town, the question of completeness become specific about the recording of all street-light objects within the bounds of the area of interest and if the data set includes does not include these features or if it is complete for another part of the settlement is irrelevant for the task at hand. The scrutiny of information quality within a specific application to ensure that it is good enough for the needs is termed ‘fitness for purpose’. As we shall see, fit-for-purpose is a central issue with respect to VGI.
To understand the reason that geographers are concerned with quality assurance of VGI, we need to recall the historical development of geographic information, and especially the historical context of geographic information systems (GIS) and GIScience development since the 1960s. For most of the 20th century, geographic information production became professionalised and institutionalised. The creation, organisation and distribution of geographic information was done by official bodies such as national mapping agencies or national geological bodies who were funded by the state. As a results, the production of geographic information became and industrial scientific process in which the aim is to produce a standardised product – commonly a map. Due to financial, skills and process limitations, products were engineered carefully so they can be used for multiple purposes. Thus, a topographic map can be used for navigation but also for urban planning and for many other purposes. Because the products were standardised, detailed specifications could be drawn, against which the quality elements can be tested and quality assurance procedures could be developed. This was the backdrop to the development of GIS, and to the conceptualisation of spatial data quality.
The practices of centralised, scientific and industrialised geographic information production lend themselves to quality assurance procedures that are deployed through organisational or professional structures, and explains the perceived challenges with VGI. Centralised practices also supported employing people with focus on quality assurance, such as going to the field with a map and testing that it complies with the specification that were used to create it. In contrast, most of the collection of VGI is done outside organisational frameworks. The people who contribute the data are not employees and seemingly cannot be put into training programmes, asked to follow quality assurance procedures, or expected to use standardised equipment that can be calibrated. The lack of coordination and top-down forms of production raise questions about ensuring the quality of the information that emerges from VGI.
To consider quality assurance within VGI require to understand some underlying principles that are common to VGI practices and differentiate it from organised and industrialised geographic information creation. For example, some VGI is collected under conditions of scarcity or abundance in terms of data sources, number of observations or the amount of data that is being used. As noted, the conceptualisation of geographic data collection before the emergence of VGI was one of scarcity where data is expensive and complex to collect. In contrast, many applications of VGI the situation is one of abundance. For example, in applications that are based on micro-volunteering, where the participant invest very little time in a fairly simple task, it is possible to give the same mapping task to several participants and statistically compare their independent outcomes as a way to ensure the quality of the data. Another form of considering abundance as a framework is in the development of software for data collection. While in previous eras, there will be inherently one application that was used for data capture and editing, in VGI there is a need to consider of multiple applications as different designs and workflows can appeal and be suitable for different groups of participants.
Another underlying principle of VGI is that since the people who collect the information are not remunerated or in contractual relationships with the organisation that coordinates data collection, a more complex relationships between the two sides are required, with consideration of incentives, motivations to contribute and the tools that will be used for data collection. Overall, VGI systems need to be understood as socio-technical systems in which the social aspect is as important as the technical part.
In addition, VGI is inherently heterogeneous. In large scale data collection activities such as the census of population, there is a clear attempt to capture all the information about the population over relatively short time and in every part of the country. In contrast, because of its distributed nature, VGI will vary across space and time, with some areas and times receiving more attention than others. An interesting example has been shown in temporal scales, where some citizen science activities exhibit ‘weekend bias’ as these are the days when volunteers are free to collect more information.
Because of the difference in the organisational settings of VGI, a different approaches to quality assurance is required, although as noted, in general such approaches have been used in many citizen science projects. Over the years, several approaches emerged and these include ‘crowdsourcing ‘, ‘social’, ‘geographic’, ‘domain’, ‘instrumental observation’ and ‘process oriented’. We now turn to describe each of these approaches.
The ‘crowdsourcing’ approach is building on the principle of abundance. Since there are is a large number of contributors, quality assurance can emerge from repeated verification by multiple participants. Even in projects where the participants actively collect data in uncoordinated way, such as the OpenStreetMap project, it has been shown that with enough participants actively collecting data in a given area, the quality of the data can be as good as authoritative sources. The limitation of this approach is when local knowledge or verification on the ground (‘ground truth’) is required. In such situations, the ‘crowdsourcing’ approach will work well in central, highly populated or popular sites where there are many visitors and therefore the probability that several of them will be involved in data collection rise. Even so, it is possible to encourage participants to record less popular places through a range of suitable incentives.
The ‘social’ approach is also building on the principle of abundance in terms of the number of participants, but with a more detailed understanding of their knowledge, skills and experience. In this approach, some participants are asked to monitor and verify the information that was collected by less experienced participants. The social method is well established in citizen science programmes such as bird watching, where some participants who are more experienced in identifying bird species help to verify observations by other participants. To deploy the social approach, there is a need for a structured organisations in which some members are recognised as more experienced, and are given the appropriate tools to check and approve information.
The ‘geographic’ approach uses known geographical knowledge to evaluate the validity of the information that is received by volunteers. For example, by using existing knowledge about the distribution of streams from a river, it is possible to assess if mapping that was contributed by volunteers of a new river is comprehensive or not. A variation of this approach is the use of recorded information, even if it is out-of-date, to verify the information by comparing how much of the information that is already known also appear in a VGI source. Geographic knowledge can be potentially encoded in software algorithms.
The ‘domain’ approach is an extension of the geographic one, and in addition to geographical knowledge uses a specific knowledge that is relevant to the domain in which information is collected. For example, in many citizen science projects that involved collecting biological observations, there will be some body of information about species distribution both spatially and temporally. Therefore, a new observation can be tested against this knowledge, again algorithmically, and help in ensuring that new observations are accurate.
The ‘instrumental observation’ approach remove some of the subjective aspects of data collection by a human that might made an error, and rely instead on the availability of equipment that the person is using. Because of the increased in availability of accurate-enough equipment, such as the various sensors that are integrated in smartphones, many people keep in their pockets mobile computers with ability to collect location, direction, imagery and sound. For example, images files that are captured in smartphones include in the file the GPS coordinates and time-stamp, which for a vast majority of people are beyond their ability to manipulate. Thus, the automatic instrumental recording of information provide evidence for the quality and accuracy of the information.
Finally, the ‘process oriented’ approach bring VGI closer to traditional industrial processes. Under this approach, the participants go through some training before collecting information, and the process of data collection or analysis is highly structured to ensure that the resulting information is of suitable quality. This can include provision of standardised equipment, online training or instruction sheets and a structured data recording process. For example, volunteers who participate in the US Community Collaborative Rain, Hail & Snow network (CoCoRaHS) receive standardised rain gauge, instructions on how to install it and an online resources to learn about data collection and reporting.
Importantly, these approach are not used in isolation and in any given project it is likely to see a combination of them in operation. Thus, an element of training and guidance to users can appear in a downloadable application that is distributed widely, and therefore the method that will be used in such a project will be a combination of the process oriented with the crowdsourcing approach. Another example is the OpenStreetMap project, which in the general do not follow limited guidance to volunteers in terms of information that they collect or the location in which they collect it. Yet, a subset of the information that is collected in OpenStreetMap database about wheelchair access is done through the highly structured process of the WheelMap application in which the participant is require to select one of four possible settings that indicate accessibility. Another subset of the information that is recorded for humanitarian efforts is following the social model in which the tasks are divided between volunteers using the Humanitarian OpenStreetMap Team (H.O.T) task manager, and the data that is collected is verified by more experienced participants.
The final, and critical point for quality assurance of VGI that was noted above is fitness-for-purpose. In some VGI activities the information has a direct and clear application, in which case it is possible to define specifications for the quality assurance element that were listed above. However, one of the core aspects that was noted above is the heterogeneity of the information that is collected by volunteers. Therefore, before using VGI for a specific application there is a need to check for its fitness for this specific use. While this is true for all geographic information, and even so called ‘authoritative’ data sources can suffer from hidden biases (e.g. luck of update of information in rural areas), the situation with VGI is that variability can change dramatically over short distances – so while the centre of a city will be mapped by many people, a deprived suburb near the centre will not be mapped and updated. There are also limitations that are caused by the instruments in use – for example, the GPS positional accuracy of the smartphones in use. Such aspects should also be taken into account, ensuring that the quality assurance is also fit-for-purpose.
References and Further Readings
Bonney, Rick. 1996. Citizen Science – a lab tradition, Living Bird, Autumn 1996.
Bonney, Rick, Shirk, Jennifer, Phillips, Tina B. 2013. Citizen Science, Encyclopaedia of science education. Berlin: Springer-Verlag.
Goodchild, Michael F. 2007. Citizens as sensors: the world of volunteered geography. GeoJournal, 69(4), 211–221.
Goodchild, Michael F., and Li, Linna. 2012, Assuring the quality of volunteered geographic information. Spatial Statistics, 1 110-120
Haklay, Mordechai. 2010. How Good is volunteered geographical information? a comparative study of OpenStreetMap and ordnance survey datasets. Environment and Planning B: Planning and Design, 37(4), 682–703.
Sui, Daniel, Elwood, Sarah and Goodchild, Michael F. (eds), 2013. Crowdsourcing Geographic Knowledge, Berlin:Springer-Verlag.
Van Oort, Pepjin .A.J. 2006. Spatial data quality: from description to application, PhD Thesis, Wageningen: Wageningen Universiteit, p. 125.
10 September, 2014
At the end of June, I noticed a tweet about new words in Oxford English Dictionary (OED):
I like dictionary definitions, as they help to clarify things, and OED is famous for the careful editing and finding how a term is used before adding it. Being in the OED is significant for Citizen Science, as it is a recognised “proper” term. At the same time, the way that OED define citizen science, and their careful work on finding out when it was first used can help in noticing some aspects. This is how.
Here is the definition, in all its glory:
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.
1989 Technol. Rev. Jan. 12/4 Audubon involves 225 society members from all 50 states in a ‘citizen science’ program… Volunteers collect rain samples, test their acidity levels, and report the results to Audubon headquarters.
2002 M. B. Mulder & P. Coppolillo Conservation xi. 295/1 Citizen science has the potential to strengthen conservation practice in the developing world.
2012 M. Nielsen Reinventing Discov. vii. 151 Citizen science can be a powerful way both to collect and also to analyze enormous data sets.
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.
1912 Manch. Guardian 11 Sept. 4/2 Trafford, thus serenely established, should..have returned to his researches with a new confidence and content and become a noble citizen-scientist.
1936 Headmaster Speaks 65 Could not Science..turn out a race of citizen scientists who do not make an absolute religion of the acquisition of new scientific knowledge however useless or harmful it may be?
1949 Collier’s 16 July 74/3 By 1930 most citizen-scientists had perfected a technique which brought gin to its peak of flavor and high-octane potency five minutes after the ingredients had been well mixed.
1979 New Scientist 11 Oct. 105/2 The ‘citizen-scientist’, the amateur investigator who in the past contributed substantially to the development of science through part-time dabbling.
2013 G. R. Hubbell Sci. Astrophotogr. xiii. 233 A citizen scientist in the astronomical field has a unique opportunity because astronomy is a wholly observational science.
Dictionaries are more interesting than they might seem. Here are 3 observations on this new definition:
First, the core definition of ‘citizen science’ is interestingly inclusive, so a community based air quality monitoring to volunteers bird surveys and running climate model on your computer at home are all included. This makes the definition useful across projects and types of activities.
Second, the ‘citizen scientist’ is capturing two meanings. The first meaning is noteworthy, as it is the one falls well within Alan Irwin’s way of describing citizen science, or in Jack Stilgoe’s pamphlet that describe citizen scientists. Notice that this meaning is not the common one to describe who is a citizen scientists, but arguably, scientists that are active in citizen science usually become such citizen scientists (sorry for the headache!).
Third, it’s always fun to track down the citations that OED use, as they are trying to find the first use of phrase. So let’s look at the late 20th century citations for ‘citizen science’ and ‘citizen scientist’ (the one from the early 20th century are less representative of current science in my view).
The first use of ‘citizen science’ in the meaning that we’re now using is traced to an article in MIT Technology Review from January 1989. The article ‘Lab for the Environment’ tell the story of community based laboratories to explore environmental hazards, laboratory work by Greenpeace, and Audubon recruitment of volunteers in a ‘citizen science’ programme. The part that describes citizen science is provided below (click here to get to the magazine itself). Therefore, groups such as the Public Laboratory for Open Technology and Science are linked directly to this use of citizen science.
Just as interesting is the use of ‘citizen scientist’. It was used 10 years earlier, in an article in New Scientist that discussed enthusiasts who are researching Unidentified Flying Objects (UFO) and identified ‘ufology’ as a field of study for these people. While the article is clearly mocking the ufologists as unscientific, it does mention, more or less in passing, the place of citizen-scientists, which is “all but eliminated” by the late 1970s (click here to see the original magazine). This resonate with many of the narrative about how citizen science disappeared in the 20th century and is reappearing now.
If you would like to use these original references to citizen science and citizen scientists, here the proper reference (I’ll surely look out for an opportunity to do so!)
Kerson, R., 1989, Lab for the Environment, MIT Technology Review, 92(1), 11-12
Oberg, J., 1979, The Failure of the ‘Science’ of Ufology, New Scientist, 84(1176), 102-105
Thanks to Rick Bonney who asked some questions about the definition that led to this post!
6 September, 2014
When you look at the discussions that are emerging around the term ‘Citizen Science‘, you can often find discussion about the ‘Citizen‘ part of the term. What about the ‘Science‘ part? This is something that once you start being involved in Citizen Science you are forced to contemplate. As Francois Grey like to note ‘Science is too important to be left out to scientists‘ and we need to find a way to make it more inclusive as a process and practice. Sometime, Citizen Science challenges ‘established’ science and protocols. This can be about small things – such as noticing that diffusion tubes are installed at 2.5m (while the area of real concern is 1-1.5m), or bigger things, such as noticing that a lot of noise measurement is about what is possible to measure (sound) and avoiding what is difficult (noise). Even more challenging is the integration of local, lay and traditional knowledge within the citizen science framework with scientific knowledge. In short, there is value in considering what we mean by ‘science’.
For me, the challenge that evolved was ‘how can we have a definition of science that recognises that it’s a powerful form of knowledge, while allowing other forms of knowledge to work with it?‘. After experimenting with different ideas in the past year, I ended with the following, directly paraphrasing from the famous quote* from Winston Churchill about democracy as the least worst form of government. So the current, work in progress, definition that I’m using is the following:
“Science is the least worst method to accumulate human knowledge about the natural world (and it need to work, in a respectful way, with other forms of knowledge)”
What I am trying to do with this definition is first to recognise that knowledge is produced collaboratively and, ideally, in a democratic process. For that, the original form of the phrase is useful. Second, I wanted to note that science is not infallible but meandering, getting into blind alleys and all the rest, which the ‘least worst’ is capturing better than ‘the best’. Third, it is allowing the recognition that it is a very effective and powerful form of human knowledge.
Does it work? Is it suitable?
* I always like to find the correct source, and if you look at the Hansard, you’ll see that Churchill was more forthright and said: “Many forms of Government have been tried, and will be tried in this world of sin and woe. No one pretends that democracy is perfect or all-wise. Indeed, it has been said that democracy is the worst form of Government except all those other forms that have been tried from time to time;”. Now that I know that, it’s tempting to try and replace democracy with science and government with knowledge…
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.”
14 August, 2014
As far as I can tell, Nelson et al. 2006 ‘Towards development of a high quality public domain global roads database‘ and Taylor & Caquard 2006 Cybercartography: Maps and Mapping in the Information Era are the first peer review papers that mention OpenStreetMap. Since then, OpenStreetMap received plenty of academic attention. More ‘conservative’ search engines such as ScienceDirect or Scopus find 286 and 236 peer review papers that mention the project (respectively). The ACM digital library finds 461 papers in the areas that are relevant to computing and electronics, while Microsoft Academic Research find only 112. Google Scholar lists over 9000 (!). Even with the most conservative version from Microsoft, we can see an impact on fields ranging from social science to engineering and physics. So lots to be proud about as a major contribution to knowledge beyond producing maps.
Michael Goodchild, in his 2007 paper that started the research into Volunteered Geographic Information (VGI), mentioned OpenStreetMap (OSM), and since then there is a lot of conflation between OSM and VGI. In some recent papers you can find statements such as ‘OpenstreetMap is considered as one of the most successful and popular VGI projects‘ or ‘the most prominent VGI project OpenStreetMap‘ so at some level, the boundary between the two is being blurred. I’m part of the problem – for example, in the title of my 2010 paper ‘How good is volunteered geographical information? A comparative study of OpenStreetMap and Ordnance Survey datasets‘. However, the more I was thinking about it, the more I am uncomfortable with this equivalence. I would think that the recent line from Neis & Zielstra (2013) is more accurate: ‘One of the most utilized, analyzed and cited VGI-platforms, with an increasing popularity over the past few years, is OpenStreetMap (OSM)‘. I’ll explain why.
Let’s look at the whole area of OpenStreetMap studies. Over the past decade, several types of research papers emerged.
There is a whole set of research projects that use OSM data because it’s easy to use and free to access (in computer vision or even string theory). These studies are not part of ‘OSM studies’ or VGI, as for them, this is just data to be used.
Thirdly, there are studies that also look at the interactions between the contribution and the data – for example, in trying to infer trustworthiness.
[Unfortunately, due to academic practices and publication outlets, a lot of these papers are locked behind paywalls, but this is another issue... ]
In short, this is a significant body of knowledge about the nature of the project, the implications of what it produces, and ways to understand the information that emerge from it. Clearly, we now know that OSM produce good data and know about the patterns of contribution. What is also clear that the many of these patterns are specific to OSM. Because of the importance of OSM to so many applications areas (including illustrative maps in string theory!) these insights are very important. Some of them are expected to be also present in other VGI projects (hence my suggestions for assertions about VGI) but this need to be done carefully, only when there is evidence from other projects that this is the case. In short, we should avoid conflating VGI and OSM.