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.
9 August, 2014
Today, OpenStreetMap celebrates 10 years of operation as counted from the date of registration. I’ve heard about the project when it was in early stages, mostly because I knew Steve Coast when I was studying for my Ph.D. at UCL. As a result, I was also able to secured the first ever research grant that focused on OpenStreetMap (and hence Volunteered Geographic Information – VGI) from the Royal Geographical Society in 2005. A lot can be said about being in the right place at the right time!
Having followed the project during this decade, there is much to reflect on – such as thinking about open research questions, things that the academic literature failed to notice about OSM or the things that we do know about OSM and VGI because of the openness of the project. However, as I was preparing the talk for the INSPIRE conference, I was starting to think about the start dates of OSM (2004), TomTom Map Share (2007), Waze (2008), Google Map Maker (2008). While there are conceptual and operational differences between these projects, in terms of ‘knowledge-based peer production systems’ they are fairly similar: all rely on large number of contributors, all use both large group of contributors who contribute little, and a much smaller group of committed contributors who do the more complex work, and all are about mapping. Yet, OSM started 3 years before these other crowdsourced mapping projects, and all of them have more contributors than OSM.
Since OSM is described as ‘Wikipedia of maps‘, the analogy that I was starting to think of was that it’s a bit like a parallel history, in which in 2001, as Wikipedia starts, Encarta and Britannica look at the upstart and set up their own crowdsourcing operations so within 3 years they are up and running. By 2011, Wikipedia continues as a copyright free encyclopedia with sizable community, but Encarta and Britannica have more contributors and more visibility.
Knowing OSM closely, I felt that this is not a fair analogy. While there are some organisational and contribution practices that can be used to claim that ‘it’s the fault of the licence’ or ‘it’s because of the project’s culture’ and therefore justify this, not flattering, analogy to OSM, I sensed that there is something else that should be used to explain what is going on.
Then, during my holiday in Italy, I was enjoying the offline TripAdvisor app for Florence, using OSM for navigation (in contrast to Google Maps which are used in the online app) and an answer emerged. Within OSM community, from the start, there was some tension between the ‘map’ and ‘database’ view of the project. Is it about collecting the data so beautiful maps or is it about building a database that can be used for many applications?
Saying that OSM is about the map mean that the analogy is correct, as it is very similar to Wikipedia – you want to share knowledge, you put it online with a system that allow you to display it quickly with tools that support easy editing the information sharing. If, on the other hand, OSM is about a database, then OSM is about something that is used at the back-end of other applications, a lot like DBMS or Operating System. Although there are tools that help you to do things easily and quickly and check the information that you’ve entered (e.g. displaying the information as a map), the main goal is the building of the back-end.
Maybe a better analogy is to think of OSM as ‘Linux of maps’, which mean that it is an infrastructure project which is expected to have a lot of visibility among the professionals who need it (system managers in the case of Linux, GIS/Geoweb developers for OSM), with a strong community that support and contribute to it. The same way that some tech-savvy people know about Linux, but most people don’t, I suspect that TripAdvisor offline users don’t notice that they use OSM, they are just happy to have a map.
The problem with the Linux analogy is that OSM is more than software – it is indeed a database of information about geography from all over the world (and therefore the Wikipedia analogy has its place). Therefore, it is somewhere in between. In a way, it provide a demonstration for the common claim in GIS circles that ‘spatial is special‘. Geographical information is infrastructure in the same way that operating systems or DBMS are, but in this case it’s not enough to create an empty shell that can be filled-in for the specific instance, but there is a need for a significant amount of base information before you are able to start building your own application with additional information. This is also the philosophical difference that make the licensing issues more complex!
In short, both Linux or Wikipedia analogies are inadequate to capture what OSM is. It has been illuminating and fascinating to follow the project over its first decade, and may it continue successfully for more decades to come.
30 June, 2014
Today marks the publication of the report ‘crowdsourced geographic information in government‘. The report is the result of a collaboration that started in the autumn of last year, when the World Bank Global Facility for Disaster Reduction and Recovery(GFDRR) requested to carry out a study of the way crowdsourced geographic information is used by governments. The identification of barriers and success factors were especially needed, since GFDRR invest in projects across the world that use crowdsourced geographic information to help in disaster preparedness, through activities such as the Open Data for Resilience Initiative. By providing an overview of factors that can help those that implement such projects, either in governments or in the World Bank, we can increase the chances of successful implementations. To develop the ideas of the project, Robert Soden (GFDRR) and I run a short workshop during State of the Map 2013 in Birmingham, which helped in shaping the details of project plan as well as some preliminary information gathering. The project team included myself, Vyron Antoniou, Sofia Basiouka, and Robert Soden (GFDRR). Later on, Peter Mooney (NUIM) and Jamal Jokar (Heidelberg) volunteered to help us – demonstrating the value in research networks such as COST ENERGIC which linked us.
The general methodology that we decided to use is the identification of case studies from across the world, at different scales of government (national, regional, local) and domains (emergency, environmental monitoring, education). We expected that with a large group of case studies, it will be possible to analyse common patterns and hopefully reach conclusions that can assist future projects. In addition, this will also be able to identify common barriers and challenges.
We have paid special attention to information flows between the public and the government, looking at cases where the government absorbed information that provided by the public, and also cases where two-way communication happened.
Originally, we were aiming to ‘crowdsource’ the collection of the case studies. We identified the information that is needed for the analysis by using few case studies that we knew about, and constructing the way in which they will be represented in the final report. After constructing these ‘seed’ case study, we aimed to open the questionnaire to other people who will submit case studies. Unfortunately, the development of a case study proved to be too much effort, and we received only a small number of submissions through the website. However, throughout the study we continued to look out for cases and get all the information so we can compile them. By the end of April 2014 we have identified about 35 cases, but found clear and useful information only for 29 (which are all described in the report). The cases range from basic mapping to citizen science. The analysis workshop was especially interesting, as it was carried out over a long Skype call, with members of the team in Germany, Greece, UK, Ireland and US (Colorado) while working together using Google Docs collaborative editing functionality. This approach proved successful and allowed us to complete the report.
18 June, 2014
The INSPIRE 2014 conference marks the middle of the implementation process of the INSPIRE directive (Infrastructure for Spatial Information in the European Community). The directive is aimed at establishing a pan-European Spatial Data Infrastructure (SDI), and that mean lots of blueprints, pipes, machine rooms and protocols for enabling the sharing of geographic information. In GIS jargon, blueprints translate to metadata which is a standardise way to describe a GIS dataset; pipes and machine rooms translate to data portals and servers, and the protocols translate to web services that use known standards (here you’ll have a real acronym soup of WMS, WCS, WFS and OGC). It is all aimed to allow people across Europe to share data in an efficient way so data can be found and used. In principle, at least!
This is the stuff of governmental organisations that are producing the data (national mapping agencies, government offices, statistical offices etc.) and the whole INSPIRE language and aims are targeted at the producers of the information, encouraging them to publish information about their data and share it with others. A domain of well established bureaucracies (in the positive sense of the word) and organisations that are following internal procedure in producing, quality checking and distributing their information products. At first sight, seem like the opposite world of ‘upscience‘ where sometime there are only ad-hoc structures and activities.
That is why providing a talk in the plenary session that was dedicated to Governance and Information, and aimed to “assess how INSPIRE is contributing to a more effective and participated environmental policy in Europe, and how it provides connectivity with other policies affecting our environment, society, and the economy” was of concern. So where are the meeting points of INSPIRE and citizen science?
One option, is to try a top-down approach and force those who collect data to provide it in INSPIRE compliant way. Of course this is destined to fail. So the next option is to force the intermediaries to do the translation – and projects such as COBWEB is doing that, although it remain to be seen what compromises will be needed. Finally, there is an option to adapt and change procedures such as INSPIRE to reflect the change in the way the world works.
To prepare the talk, I teamed with Dr Claire Ellul, who specialises in metadata (among many other things) and knows about INSPIRE more than me.
The talk started with my previous work about the three eras of environmental information, noticing the move from data by experts, and for experts (1969-1992) to by experts & the public, for experts & the public (2012 on)
As the diagrams show, a major challenges of INSPIRE is that it is a regulation that was created on the basis of the “first era” and “second era” and it inherently assumes stable institutional practices in creating and disseminating and sharing environmental information.
Alas, the world has changed – and one particular moment of change is August 2004 when OpenStreetMap started, so by the time INSPIRE came into force, crowdsourced geographic information and citizen science became legitimate part of the landscape. These data sources are coming from a completely different paradigm of production and management, and now, with 10 years of experience in OSM and growing understanding of citizen science data, we can notice the differences in production, organisation and practices. For example, while being very viable source of geographic information, OSM still doesn’t have an office and ‘someone to call’.
Furthermore, data quality methods also require different framing for these data. We have metadata standards and quality standards that are assuming the second era, but we need to find ways to integrate into sharing frameworks like INSPIRE the messy, noisy but also rich and important data from citizen science and crowdsourcing.
Claire provided a case study that analyses the challenges in the area of metadata in particular. The case looks at different noise mapping sources and how the can be understood. Her analysis demonstrates how the ‘producer centric’ focus of INSPIRE is challenging when trying to create systems that record and use metadata for crowdsourced information. The case study is based on our own experiences over the past 6 years and in different projects, so there is information that is explicit in the map, some in a documentation – but some that is only hidden (e.g. calibration and quality of smart phone apps).
We conclude with the message that the INSPIRE community need to start noticing these sources of data and consider how they can be integrated in the overall infrastructure.
The slides from the talk are provided below.
Following the two previous assertions, namely that:
‘you can be supported by a huge crowd for a very short time, or by few for a long time, but you can’t have a huge crowd all of the time (unless data collection is passive)’ (original post here)
‘All information sources are heterogeneous, but some are more honest about it than others’ (original post here)
The third assertion is about pattern of participation. It is one that I’ve mentioned before and in some way it is a corollary of the two assertions above.
‘When looking at crowdsourced information, always keep participation inequality in mind’
Because crowdsourced information, either Volunteered Geographic Information or Citizen Science, is created through a socio-technical process, all too often it is easy to forget the social side – especially when you are looking at the information without the metadata of who collected it and when. So when working with OpenStreetMap data, or viewing the distribution of bird species in eBird (below), even though the data source is expected to be heterogeneous, each observation is treated as similar to other observation and assumed to be produced in a similar way.
Yet, data is not only heterogeneous in terms of consistency and coverage, it is also highly heterogeneous in terms of contribution. One of the most persistence findings from studies of various systems – for example in Wikipedia , OpenStreetMap and even in volunteer computing is that there is a very distinctive heterogeneity in contribution. The phenomena was term ‘Participation Inequality‘ by Jakob Nielsn in 2006 and it is summarised succinctly in the diagram below (from Visual Liberation blog) – very small number of contributors add most of the content, while most of the people that are involved in using the information will not contribute at all. Even when examining only those that actually contribute, in some project over 70% contribute only once, with a tiny minority contributing most of the information.
Therefore, when looking at sources of information that were created through such process, it is critical to remember the nature of contribution. This has far reaching implications on quality as it is dependent on the expertise of the heavy contributors, on their spatial and temporal engagement, and even on their social interaction and practices (e.g. abrasive behaviour towards other participants).
Because of these factors, it is critical to remember the impact and implications of participation inequality on the analysis of the information. There will be some analysis to which it will have less impact and some where it will have major one. In either cases, it need to be taken into account.
Following the last post, which focused on an assertion about crowdsourced geographic information and citizen science I continue with another observation. As was noted in the previous post, these can be treated as ‘laws’ as they seem to emerge as common patterns from multiple projects in different areas of activity – from citizen science to crowdsourced geographic information. The first assertion was about the relationship between the number of volunteers who can participate in an activity and the amount of time and effort that they are expect to contribute.
This time, I look at one aspect of data quality, which is about consistency and coverage. Here the following assertion applies:
‘All information sources are heterogeneous, but some are more honest about it than others’
What I mean by that is the on-going argument about authoritative and crowdsourced information sources (Flanagin and Metzger 2008 frequently come up in this context), which was also at the root of the Wikipedia vs. Britannica debate, and the mistrust in citizen science observations and the constant questioning if they can do ‘real research’.
There are many aspects for these concerns, so the assertion deals with the aspects of comprehensiveness and consistency which are used as a reason to dismiss crowdsourced information when comparing them to authoritative data. However, at a closer look we can see that all these information sources are fundamentally heterogeneous. Despite of all the effort to define precisely standards for data collection in authoritative data, heterogeneity creeps in because of budget and time limitations, decisions about what is worthy to collect and how, and the clash between reality and the specifications. Here are two examples:
Take one of the Ordnance Survey Open Data sources – the map present themselves as consistent and covering the whole country in an orderly way. However, dig in to the details for the mapping, and you discover that the Ordnance Survey uses different standards for mapping urban, rural and remote areas. Yet, the derived products that are generalised and manipulated in various ways, such as Meridian or Vector Map District, do not provide a clear indication which parts originated from which scale – so the heterogeneity of the source disappeared in the final product.
The census is also heterogeneous, and it is a good case of specifications vs. reality. Not everyone fill in the forms and even with the best effort of enumerators it is impossible to collect all the data, and therefore statistical analysis and manipulation of the results are required to produce a well reasoned assessment of the population. This is expected, even though it is not always understood.
Therefore, even the best information sources that we accept as authoritative are heterogeneous, but as I’ve stated, they just not completely honest about it. The ONS doesn’t release the full original set of data before all the manipulations, nor completely disclose all the assumptions that went into reaching the final value. The Ordnance Survey doesn’t tag every line with metadata about the date of collection and scale.
Somewhat counter-intuitively, exactly because crowdsourced information is expected to be inconsistent, we approach it as such and ask questions about its fitness for use. So in that way it is more honest about the inherent heterogeneity.
Importantly, the assertion should not be taken to be dismissive of authoritative sources, or ignoring that the heterogeneity within crowdsources information sources is likely to be much higher than in authoritative ones. Of course all the investment in making things consistent and the effort to get universal coverage is indeed worth it, and it will be foolish and counterproductive to consider that such sources of information can be replaced as is suggest for the census or that it’s not worth investing in the Ordnance Survey to update the authoritative data sets.
Moreover, when commercial interests meet crowdsourced geographic information or citizen science, the ‘honesty’ disappear. For example, even though we know that Google Map Maker is now used in many part
s of the world (see the figure), even in cases when access to vector data is provided by Google, you cannot find out about who contribute, when and where. It is also presented as an authoritative source of information.
Despite the risk of misinterpretation, the assertion can be useful as a reminder that the differences between authoritative and crowdsourced information are not as big as it may seem.
Looking across the range of crowdsourced geographic information activities, some regular patterns are emerging and it might be useful to start notice them as a way to think about what is possible or not possible to do in this area. Since I don’t like the concept of ‘laws’ – as in Tobler’s first law of geography which is stated as ‘Everything is related to everything else, but near things are more related than distant things.’ – I would call them assertions. There is also something nice about using the word ‘assertion’ in the context of crowdsourced geographic information, as it echos Mike Goodchild’s differentiation between asserted and authoritative information. So not laws, just assertions or even observations.
The first one, is rephrasing a famous quote:
‘you can be supported by a huge crowd for a very short time, or by few for a long time, but you can’t have a huge crowd all of the time (unless data collection is passive)’
So the Christmas Bird Count can have tens of thousands of participants for a short time, while the number of people who operate weather observation stations will be much smaller. Same thing is true for OpenStreetMap – for crisis mapping, which is a short term task, you can get many contributors but for the regular updating of an area under usual conditions, there will be only few.
The exception for the assertion is the case for passive data collection, where information is collected automatically through the logging of information from a sensor – for example the recording of GPS track to improve navigation information.
20 October, 2012
The Spatial Data Infrastructure Magazine (SDIMag.com) is a relatively new e-zine dedicated to the development of spatial data infrastructures around the world. Roger Longhorn, the editor of the magazine, conducted an email interview with me, which is now published.
In the interview, we are covering the problematic terminology used to describe a wider range of activities; the need to consider social and technical aspects as well as goals of the participants; and, of course, the role of the information that is produced through crowdsourcing, citizen science, VGI with spatial data infrastructures.
The previous post focused on citizen science as participatory science. This post is discussing the meaning of this differentiation. It is the final part of the chapter that will appear in the book:
The typology of participation can be used across the range of citizen science activities, and one project should not be classified only in one category. For example, in volunteer computing projects most of the participants will be at the bottom level, while participants that become committed to the project might move to the second level and assist other volunteers when they encounter technical problems. Highly committed participants might move to a higher level and communicate with the scientist who coordinates the project to discuss the results of the analysis and suggest new research directions.
This typology exposes how citizen science integrates and challenges the way in which science discovers and produces knowledge. Questions about the way in which knowledge is produced and truths are discovered are part of the epistemology of science. As noted above, throughout the 20th century, as science became more specialised, it also became professionalised. While certain people were employed as scientists in government, industry and research institutes, the rest of the population – even if they graduated from a top university with top marks in a scientific discipline – were not regarded as scientists or as participants in the scientific endeavour unless they were employed professionally to do so. In rare cases, and following the tradition of ‘gentlemen/women scientists’, wealthy individuals could participate in this work by becoming an ‘honorary fellow’ or affiliated to a research institute that, inherently, brought them into the fold. This separation of ‘scientists’ and ‘public’ was justified by the need to access specialist equipment, knowledge and other privileges such as a well-stocked library. It might be the case that the need to maintain this separation is a third reason that practising scientists shy away from explicitly mentioning the contribution of citizen scientists to their work in addition to those identified by Silvertown (2009).
However, similarly to other knowledge professionals who operate in the public sphere, such as medical experts or journalists, scientists need to adjust to a new environment that is fostered by the Web. Recent changes in communication technologies, combined with the increased availability of open access information and the factors that were noted above, mean that processes of knowledge production and dissemination are opening up in many areas of social and cultural activities (Shirky 2008). Therefore, some of the elitist aspects of scientific practice are being challenged by citizen science, such as the notion that only dedicated, full-time researchers can produce scientific knowledge. For example, surely it should be professional scientists who can solve complex scientific problems such as long-standing protein-structure prediction of viruses. Yet, this exact problem was recently solved through a collaboration of scientists working with amateurs who were playing the computer game Foldit (Khatib et al. 2011). Another aspect of the elitist view of science can be witnessed in interaction between scientists and the public, where the assumption is of unidirectional ‘transfer of knowledge’ from the expert to lay people. Of course, as in the other areas mentioned above, it is a grave mistake to argue that experts are unnecessary and can be replaced by amateurs, as Keen (2007) eloquently argued. Nor is it suggested that, because of citizen science, the need for professionalised science will diminish, as, in citizen science projects, the participants accept the difference in knowledge and expertise of the scientists who are involved in these projects. At the same time, the scientists need to develop respect towards those who help them beyond the realisation that they provide free labour, which was noted above.
Given this tension, the participation hierarchy can be seen to be moving from a ‘business as usual’ scientific epistemology at the bottom, to a more egalitarian approach to scientific knowledge production at the top. The bottom level, where the participants are contributing resources without cognitive engagement, keeps the hierarchical division of scientists and the public. The public is volunteering its time or resources to help scientists while the scientists explain the work that is to be done but without expectation that any participant will contribute intellectually to the project. Arguably, even at this level, the scientists will be challenged by questions and suggestions from the participants and, if they do not respond to them in a sensitive manner, they will risk alienating participants. Intermediaries such as the IBM World Community Grid, where a dedicated team is in touch with scientists who want to run projects and a community of volunteered computing providers, are cases of ‘outsourcing’ the community management and thus allowing, to an extent, the maintenance of the separation of scientists and the public.
As we move up the ladder to a higher level of participation, the need for direct engagement between the scientist and the public increases. At the highest level, the participants are assumed to be on equal footing with the scientists in terms of scientific knowledge production. This requires a different epistemological understanding of the process, in which it is accepted that the production of scientific insights is open to any participant while maintaining scientific standards and practices such as systematic observations or rigorous statistical analysis to verify that the results are significant. The belief that, given suitable tools, many lay people are capable of such endeavours is challenging to some scientists who view their skills as unique. As the case of the computer game that helped in the discovery of new protein formations (Khatib et al. 2011) demonstrated, such collaboration can be fruitful even in cutting-edge areas of science. However, it can be expected that the more mundane and applied areas of science will lend themselves more easily to the fuller sense of collaborative science in which participants and scientists identify problems and develop solutions together. This is because the level of knowledge required in cutting-edge areas of science is so demanding.
Another aspect in which the ‘extreme’ level challenges scientific culture is that it requires scientists to become citizen scientists in the sense that Irwin (1995), Wilsdon, Wynne and Stilgoe (2005) and Stilgoe (2009) advocated (Notice Stilgoe’s title: Citizen Scientists). In this interpretation of the phrase, the emphasis is not on the citizen as a scientist, but on the scientist as a citizen. It requires the scientists to engage with the social and ethical aspects of their work at a very deep level. Stilgoe (2009, p.7) suggested that, in some cases, it will not be possible to draw the line between the professional scientific activities, the responsibilities towards society and a fuller consideration of how a scientific project integrates with wider ethical and societal concerns. However, as all these authors noted, this way of conceptualising and practising science is not widely accepted in the current culture of science.
Therefore, we can conclude that this form of participatory and collaborative science will be challenging in many areas of science. This will not be because of technical or intellectual difficulties, but mostly because of the cultural aspects. This might end up being the most important outcome of citizen science as a whole, as it might eventually catalyse the education of scientists to engage more fully with society.