If you follow the discussion (search in Twitter for #geothink) you can see how it evolved and which issues were covered.
At one point, I have asked the question:
It is always intriguing and frustrating, at the same time, when a discussion on Twitter is taking its own life and many times move away from the context in which a topic was brought up originally. At the same time, this is the nature of the medium. Here are the answers that came up to this question:
You can see that the only legal expert around said that it’s a tough question, but of course, everyone else shared their (lay) view on the basis of moral judgement and their own worldview and not on legality, and that’s also valuable. The reason I brought the question was that during the discussion, we started exploring the duality in the digital technology area to ownership and responsibility – or rights and obligations. It seem that technology companies are very quick to emphasise ownership (expressed in strong intellectual property right arguments) without responsibility over the consequences of technology use (as expressed in EULAs and the general attitude towards the users). So the nub of the issue for me was about agency. Software does have agency on its own but that doesn’t mean that it absolved the human agents from responsibility over what it is doing (be it software developers or the companies).
In ethics discussions with engineering students, the cases of Ford Pinto or the Thiokol O-rings in the Discovery Shuttle disaster come up as useful examples to explore the responsibility of engineers towards their end users. Ethics exist for GIS – e.g. the code of ethics of URISA, or the material online about ethics for GIS professional and in Esri publication. Somehow, the growth of the geoweb took us backward. The degree to which awareness of ethics is internalised within a discourse of ‘move fast and break things‘, software / hardware development culture of perpetual beta, lack of duty of care, and a search for fast ‘exit’ (and therefore IBG-YBG) make me wonder about which mechanisms we need to put in place to ensure the reintroduction of strong ethical notions into the geoweb. As some of the responses to my question demonstrate, people will accept the changes in societal behaviour and view them as normal…
16 January, 2015
Thanks to invitations from UNIGIS and Edinburgh Earth Observatory / AGI Scotland, I had an opportunity to reflect on how Geographic Information Science (GIScience) can contribute to citizen science, and what citizen science can contribute to GIScience.
Despite the fact that it’s 8 years since the term Volunteers Geographic Information (VGI) was coined, I didn’t assume that all the audience is aware of how it came about or the range of sources of VGI. I also didn’t assume knowledge of citizen science, which is far less familiar term for a GIScience audience. Therefore, before going into a discussion about the relationship between the two areas, I opened with a short introduction to both, starting with VGI, and then moving to citizen science. After introduction to the two areas, I’m suggesting the relationships between them – there are types of citizen science that are overlapping VGI – biological recording and environmental observations, as well as community (or civic) science, while other types, such as volunteer thinking includes many projects that are non-geographical (think EyeWire or Galaxy Zoo).
However, I don’t just list a catalogue of VGI and citizen science activities. Personally, I found trends a useful way to make sense of what happen. I’ve learned that from the writing of Thomas Friedman, who used it in several of his books to help the reader understand where the changes that he covers came from. Trends are, of course, speculative, as it is very difficult to demonstrate causality or to be certain about the contribution of each trends to the end result. With these caveats in mind, there are several technological and societal trends that I used in the talk to explain how VGI (and the VGI element of citizen science) came from.
Of all these trends, I keep coming back to one technical and one societal that I see as critical. The removal of selective availability of GPS in May 2000 is my top technical change, as the cascading effect from it led to the deluge of good enough location data which is behind VGI and citizen science. On the societal side, it is the Flynn effect as a signifier of the educational shift in the past 50 years that explains how the ability to participate in scientific projects have increased.
In terms of the reciprocal contributions between the fields, I suggest the following:
GIScience can support citizen science by considering data quality assurance methods that are emerging in VGI, there are also plenty of Spatial Analysis methods that take into account heterogeneity and therefore useful for citizen science data. The areas of geovisualisation and human-computer interaction studies in GIS can assist in developing more effective and useful applications for citizen scientists and people who use their data. There is also plenty to do in considering semantics, ontologies, interoperability and standards. Finally, since critical GIScientists have been looking for a long time into the societal aspects of geographical technologies such as privacy, trust, inclusiveness, and empowerment, they have plenty to contribute to citizen science activities in how to do them in more participatory ways.
On the other hand, citizen science can contribute to GIScience, and especially VGI research, in several ways. First, citizen science can demonstrate longevity of VGI data sources with some projects going back hundreds of years. It provides challenging datasets in terms of their complexity, ontology, heterogeneity and size. It can bring questions about Scale and how to deal with large, medium and local activities, while merging them to a coherent dataset. It also provide opportunities for GIScientists to contribute to critical societal issues such as climate change adaptation or biodiversity loss. It provides some of the most interesting usability challenges such as tools for non-literate users, and finally, plenty of opportunities for interdisciplinary collaborations.
The slides from the talk are available below.
The British Ecological Society (BES) & Société Française d’Ecologie meeting organised their annual meetings to be a joint meeting, held in Lille 9-12 December. Over the past 5 years, my journey in citizen science gave me an opportunity to reconnect to ecology, a topic in which I was interested in during my high-school years. I was also working side by side with ecologists while learning ArcInfo in the early 1990s, since the GIS laboratory at the time started in the ecology department. Because there is so much citizen science activity in the area of ecological monitoring, it is not surprising to see that the BES already include a special interest group on citizen science.
Although I’m now a member of the BES, my notes from the meeting are of visitor to the annual festival of the discipline of ecology – and include much learning of what is of interest to this discipline. In many of the talks, I have been attempting to understand the specific disciplinary terminology and what topics people see as important. Some of the workshops and sessions that I found interesting include
The workshop on “Doing and funding effective public engagement” which was organised by Helen Featherstone, and Will Gosling.
Helen Featherstone from the University of Bath (public engagement unit) and she’s been working with the BES over 18 months to assist them in public engagement. Will Gosling – University of Amsterdam and involve in the BES, interested in science communication and outreach.
The workshop started by exploring ‘What is meant by public engagement?‘. doing a lecture at the university is seen by the audience is argued by some people as not enough engagement – need to be two ways interaction with people outside research. Who is coming to the lecture? There is the issue of Q&A at the end of the lecture that makes it more interactive. Another example is being interviewed on the local radio station, this is more outreach but less interactive, though that is depending on the form, for example phone-in. Going to a music festival to do demonstration of scientific issues – it’s considered more engaging, because it’s for audience that don’t go out of their way to do it. Engagement with art was suggested to be ‘completely waste of time’ although it is dependent on how it is shared and what is the aim. The generic questions of Public Engagement are “How direct? How many people are involved? How much science? Is there an opportunity for a dialogue (e.g. in Science Cafe the emphasis on the Q@&A)?.
The UK definition – “Public engagement describes the myriad of ways in which the activity and benefits of higher education and research can be shared with the public. Engagement is by definition a two-way process, involving interaction and listening, with the goal of generating mutual benefit.”. The workshop then explored why the public and researchers should be interested
For the public, the following points were suggested: curiosity, want to understand the natural world. If the public is asked to do things without understanding them, they will reject them, so understanding and explaining is an important part of acting. Interest in sharing lived experiences. The public want to know what their money was spent on. There is an issue of inclusiveness, and ensuring that everyone is involved. Also it was highlighted that because science is used in decision making, there is a need to provide the public with the ability to understand why thing are done to them. Generally, moving to researching with people and not ‘for’ or ‘about’.
For researchers: those who want to have impact on society, public engagement is a route for that. The public is funder and need to maintain interest in the topic. Ensuring that there is uptake of the results. The citizenship aspect of researchers engaging with society came up. Also the need for general advocacy for the investment in research. Harnessing interest and curiosity, getting information that is not possible to get otherwise (people’s backyards). Accountability to the public and how you spend their money. The issue between making paper open access and making it accessible came up. Improving communication skills as scientists. It takes you to different places and it can be fun and interesting. Reminding people why they do it in the first place.
The target audience for the activity in 2013 was adults music festival goers and professional researchers were involved and the activity was designed at this levels. The video was aimed at researchers, to encourage them to get involved in public engagement as part of BES activities, and therefore the view counts can be low – the issue is not just any number of views, but to consider who it is intended for. Through the festival, the BES had interactions with 5500 people, and some engaged quite seriously about the specific research. Some people in the event were very pleased that there was an opportunity to meet scientists in music festival. People were capable to bring in their own nature or ecology story. BES now committed to a person with a role of public engagement.
In public engagement there are questions about long term impact and how to evaluate it. Also the context of engagement came up – is it for something that is done as a family or as adults. There are differences between adults coming as themselves but when adults are with children, they become facilitators of the learning experience of the children.
The next session that I attended was ‘Long Term Monitoring of Agro-ecosystems’.
Vincent Bretagnolle – in the past 15 years, there is a stabilisation in the yield of wheat and other agricultural products and there are many other issues, so in agro-ecosystems, there is need to reduce anthropogenic pressure. Intensive agriculture in not environmental sustainable on ecosystems services and biodiversity. Need to reconcile biodiversity management, ecosystem services, and food production. Research need to be territorial in scale (landscape 450 sq km as an example). Social-ecological systems approach allows the analysis of processes at this scale. Implementing agri-environmental schemes as a way to increase support to flagship species. At the societal level, they promote several programme of citizen science to encourage people to think about complexity and uncertainty on the conditions around them. He sees it as part of social-ecological system approach. They carry out citizen science not for the purpose of collecting data, but to disseminate thinking about the environment and ecosystem services, aiming at groups of 20-40 people.
James Pearce-Higgins covered the BTOs breeding bird surveys (BBS) over 20 years – this is collaborative project with RSPB & BTO and suppoert from JNCC. The BBS replaces in 1994 the Common Birds Census which was more complex. The BBS monitor 100 bird species – 2854 volunteers in 3761 survey squares – 2013 . Stratified sampling programme to select the location of the squares. They ask people to walk two transects of 1km and each transect is divided to 5 sections and the data collected online. This allow them to convert counts to density. There is an issue of detectability which they have modelled, so for example the size is influencing the probability of detection. They check efficacy, quality and how to use the data. They provide lots of searchable information online. They analyse annual trends but there is a need to look at long, overall trend – there are very few species with very big increase, and species that are declining significantly. There is big decline in species that mgirate to africa and those that are in humid zones have special decline. There is also effect of habitats – decline in woodlands, farmland. There are strong spatial patterns – increase in species in richness as temprature increase. Generalist species are doing well, while specialists species are doing badly. There are impacts of land-use and climate change. Citizens science is the only way to get large scale monitoring of this sort of coverage which bring evidence on farmland and woodland decline, but there is need to focus on agricultural management. Volunteers reasons for participation closed the talk. A press release on BTO (my image is not clear, so I picked up citations from the press release of BTO):
I believe the data makes a very valuable contribution to the wider picture of the state of our wild bird populations, and there is no small satisfaction making the effort for this. Nick Tardivel
The achievement of reaching remote squares, after a seemingly vertical climb, with the reward of panoramic views of the Cheviots, singing Skylarks, ‘pipping’ pipits, calling Curlews, and Snipe drumming is all the incentive needed to keep going, year after year, to this beautiful part of Northumberland. Muriel Cadwallender
It’s great fun, it is always good to be out birding and recording birds; you never know what you will find – on one occasion I found the island’s first Lesser Whitethroat just after I finished the count. David Jardine
I appreciate the value of persisting with an average square of farmland, to enable the gathering of data to show just how badly our farmland birds are doing. Louise Bacon
The opportunity to take part in a bird survey in beautiful surroundings, being ‘up with the lark’ as the day wakens is great reward. So much so that I have taken on four more squares since! Andrew King
I am pleased to be a small part of an important conservation tool which makes doing the survey worthwhile. Heather Coats
2014 marks my 20th year of surveys. It is now part of my early summer; I love the excuse (and of course the prompt) to be up early and taking in the route and, like old friends, the birds I can expect to find along the way. Paul Copestake
Each visit I add a few more pieces to the ever changing pattern of bird trends both here on my patch and also in the wider countryside. Vic Fairbrother
Marc Botham covered UK Butterfly Monitoring Scheme – been going on since 1976, and standardised as ‘Pollard walks‘ – 26 weekly walks annually, citizen science based. Piloted in 1973-1976 and then started at full scale from 1976. The walks are from April to September. They also carry out series of other methods that are done year after year regularly – including larval web counts and egg counts. They have over 1000 sides, of which most are from the standard way. They are gradually covering the whole area of the UK although more in the south. That allow them to analyse long term trends and there are trends that are changing with different ecological cycles – crashes and peaks which are common in butterflies. The long term trend allow to notice major decline in ‘common’ widespread species, some of them by almost 80%. The use indicators that contribute to national policy. Butterfly are doing more badly than birds and bats on the long term scale. Agri-environmental schemes where there is site-specific advice have been shown effective and monitoring can demonstrate the improvement. They are now created a wider countryside butterfly survey and ask people to take randomised 1-km square that are selected for volunteers. 2 visits a year, and that add 1000 sides for the volunteers. There are, of course, similarities between this method and BTO BBS as it is based on it.
Ondine Filippi-Codaccioni presented in the poster session an interesting piece of analysis that compared the quality, in terms of providing information that represent species distribution, between very structured citizen science and opportunistic citizen science. The abstract of the poster (edited a bit) is ‘In most cases, citizen science is associated with with loosley defined and heterogeneous data collection protocols. Opportunistic data in our case are data collected by a large number of different observers whose spatial and temporal distribution is greatly heterogeneous, the effort is usually unknown, zeros are generally unreported, and finally positive count may be reported differently or even censured according to species. To analyse such data we propose a multivariate hierarchical model with latent spatial – spatiotemporal – fields for relative abundances of each considered species. Its specificity is to account for different types of observation and for observer characteristics in distribution or behaviour. First results show that it seems possible to correct several main biases, to model count positive-only data and to infer fairly well relative density maps in a multi-species context, using a Bayesian framework and INLA R-package tools. We analysed a case study data set of several thousand observations from the French Ligue pour la Protection des Oiseaux (LPO, Birdlife France) to show the feasibility of such approaches and we checked the inference quality and limits on smaller simulated examples.’ In short, the study shows that by having some well calibrated and trusted observations, the opportunistic data significantly improve the model and the ability to predict were species are.
In terms of terminology, it is interesting to note that in several presentation the source of data was recognised as ‘citizen science data’ as well as recognition of their work.
25 October, 2014
If you have been reading the literature on citizen science, you must have noticed that many papers that describe citizen science start with an historical narrative, something along the lines of:
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
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.
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…
12 July, 2014
The Vespucci initiative has been running for over a decade, bringing together participants from wide range of academic backgrounds and experiences to explore, in a ‘slow learning’ way, various aspects of geographic information science research. The Vespucci Summer Institutes are week long summer schools, most frequently held at Fiesole, a small town overlooking Florence. This year, the focus of the first summer institute was on crowdsourced geographic information and citizen science.
The workshop was supported by COST ENERGIC (a network that links researchers in the area of crowdsourced geographic information, funded by the EU research programme), the EU Joint Research Centre (JRC), Esri and our Extreme Citizen Science research group. The summer school included about 30 participants and facilitators that ranged from master students students that are about to start their PhD studies, to established professors who came to learn and share knowledge. This is a common feature of Vespucci Institute, and the funding from the COST network allowed more early career researchers to participate.
Apart from the pleasant surrounding, Vespucci Institutes are characterised by the relaxed, yet detailed discussions that can be carried over long lunches and coffee breaks, as well as team work in small groups on a task that each group present at the end of the week. Moreover, the programme is very flexible so changes and adaptation to the requests of the participants and responding to the general progression of the learning are part of the process.
This is the second time that I am participating in Vespucci Institutes as a facilitator, and in both cases it was clear that participants take the goals of the institute seriously, and make the most of the opportunities to learn about the topics that are explored, explore issues in depth with the facilitators, and work with their groups beyond the timetable.
The topics that were covered in the school were designed to provide an holistic overview of geographical crowdsourcing or citizen science projects, especially in the area where these two types of activities meet. This can be when a group of citizens want to collect and analyse data about local environmental concerns, or oceanographers want to work with divers to record water temperature, or when details that are emerging from social media are used to understand cultural differences in the understanding of border areas. These are all examples that were suggested by participants from projects that they are involved in. In addition, citizen participation in flood monitoring and water catchment management, sharing information about local food and exploring data quality of spatial information that can be used by wheelchair users also came up in the discussion. The crossover between the two areas provided a common ground for the participants to explore issues that are relevant to their research interests.
The holistic aspect that was mentioned before was a major goal for the school – so to consider the tools that are used to collect information, engaging and working with the participants, managing the data that is provided by the participants and ensuring that it is useful for other purposes. To start the process, after introducing the topics of citizen science and volunteered geographic information (VGI), the participants learned about data collection activities, including noise mapping, OpenStreetMap contribution, bird watching and balloon and kite mapping. As can be expected, the balloon mapping raised a lot of interest and excitement, and this exercise in local mapping was linked to OpenStreetMap later in the week.
The experience with data collection provided the context for discussions about data management and interoperability and design aspects of citizen science applications, as well as more detailed presentations from the participants about their work and research interests. With all these details, the participants were ready to work on their group task: to suggest a research proposal in the area of VGI or Citizen Science. Each group of 5 participants explored the issues that they agreed on – 2 groups focused on a citizen science projects, another 2 focused on data management and sustainability and finally another group explored the area of perception mapping and more social science oriented project.
Some of the most interesting discussions were initiated at the request of the participants, such as the exploration of ethical aspects of crowdsourcing and citizen science. This is possible because of the flexibility in the programme.
Now that the institute is over, it is time to build on the connections that started during the wonderful week in Fiesole, and see how the network of Vespucci alumni develop the ideas that emerged this week.