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 datasetsHowever, 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.

Edward Betts. CC-By-SA 2.0 via Wikimedia Commons

Second, there are studies about OSM data: quality, evolution of objects and other aspects from researchers such as Peter Mooney, Pascal Neis, Alex Zipf  and many others.

Thirdly, there are studies that also look at the interactions between the contribution and the data – for example, in trying to infer trustworthiness.

Fourth, there are studies that look at the wider societal aspects of OpenStreetMap, with people like Martin Dodge, Chris Perkins, and Jo Gerlach contributing in interesting discussions.

Finally, there are studies of the social practices in OpenStreetMap as a project, with the work of Yu-Wei Lin, Nama Budhathoki, Manuela Schmidt and others.

[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.

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!

OSM Interface, 2006 (source: Nick Black)

OSM Interface, 2006 (source: Nick Black)

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.

TripAdvisor FlorenceThen, 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.

Today marks the publication of the report ‘crowdsourced geographic information in government‘. ReportThe 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.

You can download the full report from UCL Discovery repository

Or download a high resolution copy for printing and find much more information about the project on the Crowdsourcing and government website 

About a month ago, Francois Grey put out a suggestion that we should replace the term ‘bottom-up’  science with upscience  – do read his blog-post for a fuller explanation. I have met Francois in New York in April, when he discussed with me the ideas behind the concept, and why it is worth trying to use it.

At the end of May I had my opportunity to use the term and see how well it might work. I was invited to give a talk as part of the series Trusting the crowd: solving big problems with everyday solutions‘ at Oxford Martin School. The two previous talks in the series, about citizen science in the 19th Century and about crowdsourced journalism, set a high bar (and both are worth watching). My talk was originally titled ‘Beyond the screen: the power and beauty of ‘bottom-up’ citizen science projects’ so for the talk itself I have used ‘Beyond the screen: the power and beauty of ‘up-science’ projects‘ and it seem to go fine.

For me, the advantage of using up-science (or upscience) is in the avoidance of putting the people who are active in this form of science in the immediate disadvantage of defining themselves as ‘bottom’. For a very similar reason, I dislike the term ‘counter-mapping‘ as it puts those that are active in it in confrontational position, and therefore it can act as an additional marginalisation force. For few people, who are in favour of fights, this might make them more ‘fired up’, but for others, that might be a reason to avoid the process. Self-marginalisation is not a great position to start a struggle from.

In addition, I like the ability of upscience to be the term that catches the range of practices that Francois includes in the term, from DIY science, community based projects, civic science etc.

The content of the talk included a brief overview of the spectrum of citizen science, some of the typologies that help to make sense of them, and finally a focus on the type of practices that are part of up-science. Finally, some of the challenges and current solutions to them are covered. Below you can find a video of the talk and the discussion that followed it (which I found interesting and relevant to the discussion above).

If any of the references that I have noted in the talk is of interest, you can find them in the slide set below, which is the one that I used for the talk.

 

 

More or Less‘ is a good programme on BBC Radio 4. Regularly exploring the numbers and the evidence behind news stories and other important things, and checking if they stand out. However, the piece that was broadcast  this week about Golf courses and housing in the UK provides a nice demonstration of when not to use crowdsourced information. The issue that was discussed was how much actual space golf courses occupy, when compared to space that is used for housing. All was well, until they announced in the piece the use of clever software (read GIS) with a statistical superhero to do the analysis. Interestingly, the data that was used for the analysis was OpenStreetMap – and because the news item was about Surrey, they started doing the analysis with it.

For the analysis to be correct, you need to assume that all the building polygons in OpenStreetMap and all the Golf courses have been identified and mapped. My own guess that in Surrey, this could be the case – especially with all the wonderful work of James Rutter catalysed. However, assuming that this is the case for the rest of the country is, well, a bit fancy. I wouldn’t dare to state that OpenStreetMap is complete to such a level, without lots of quality testing which I haven’t seen. There is only the road length analysis of ITO World! and other bits of analysis, but we don’t know how complete OSM is.

While I like OpenStreetMap very much, it is utterly unsuitable for any sort of statistical analysis that works at the building level and then summing up to the country levelbecause of the heterogeneity of the data . For that sort of thing, you have to use a consistent dataset, or at least one that attempts to be consistent, and that data comes from the Ordnance Survey.

As with other statistical affairs, the core case that is made about the assertion as a whole in the rest of the clip is relevant here. First, we should question the unit of analysis (is it right to compare the footprint of a house to the area of Golf courses? Probably not) and what is to be gained by adding up individual building’s footprints to the level of the UK while ignoring roads, gardens, and all the rest of the built environment. Just because it is possible to add up every building’s footprint, doesn’t mean that you should. Second, this analysis is the sort of example of ‘Big Data’ fallacy which goes analyse first, then question (if at all) what the relationship between the data and reality.

Some ideas take long time to mature into a form that you are finally happy to share them. This is an example for such thing.

I got interested in the area of Philosophy of Technology during my PhD studies, and continue to explore it since. During this journey, I found a lot of inspiration and links to Andrew Feenberg’s work, for example, in my paper about neogeography and the delusion of democratisation. The links are mostly due to Feenberg’s attention to ‘hacking’ or appropriating technical systems to functions and activities that they are outside what the designers or producers of them thought.

In addition to Feenberg, I became interested in the work of Albert Borgmann and because he explicitly analysed GIS, dedicating a whole chapter to it in Holding on to RealityIn particular, I was intrigues by his formulation to The Device Paradigm and the notion of Focal Things and Practices which are linked to information systems in Holding on to Reality where three forms of information are presented – Natural Information, Cultural Information and Technological Information. It took me some time to see that these 5 concepts are linked, with technological information being a demonstration of the trouble with the device paradigm, while natural and cultural information being part of focal things and practices (more on these concepts below).

I first used Borgmann’s analysis as part of ‘Conversations Across the Divide‘ session in 2005, which focused on Complexity and Emergence. In a joint contribution with David O’Sullivan about ‘complexity science and Geography: understanding the limits of narratives’, I’ve used Borgmann’s classification of information. Later on, we’ve tried to turn it into a paper, but in the end David wrote a much better analysis of complexity and geography, while the attempt to focus mostly on the information concepts was not fruitful.

The next opportunity to revisit Borgmann came in 2011, for an AAG pre-conference workshop on VGI where I explored the links between The Device Paradigm, Focal Practices and VGI. By 2013, when I was invited to the ‘Thinking and Doing Digital Mapping‘ workshop that was organise by ‘Charting the Digital‘ project. I was able to articulate the link between all the five elements of Borgmann’s approach in my position paper. This week, I was able to come back to the topic in a seminar in the Department of Geography at the University of Leicester. Finally, I feel that I can link them in a coherent way.

So what is it all about?

Within the areas of VGI and Citizen Science, there is a tension between the different goals or the projects and identification of practices in terms of what they mean for the participants – are we using people as ‘platform for sensors’ or are we dealing with fuller engagement? The use of Borgmann’s ideas can help in understanding the difference. He argues that modern technologies tend to adopt the myopic ‘Device Paradigm’ in which specific interpretation of efficiency, productivity and a reductionist view of human actions are taking precedence over ‘Focal Things and Practices’ that bring people together in a way meaningful to human life. In Holding On to Reality (1999), he differentiates three types of information: natural, cultural and technological.  Natural information is defined as information about reality: for example, scientific information on the movement of the earth or the functioning of a cell.  This is information that was created in order to understand the functioning of reality.  Cultural information is information that is being used to shape reality, such as engineering design plans.  Technological information is information as reality and leads to decreased human engagement with fundamental aspects of reality.  Significantly, these categories do not relate to the common usage of the words ‘natural’, ‘cultural and ‘technological’ rather to describe the changing relationship between information and reality at different stages of socio-technical development.

When we explore general geographical information, we can see that some of it is technological information, for example SatNav and the way that communicate to the people who us them, or virtual globes that try to claim to be a representation of reality with ‘current clouds’ and all. The paper map, on the other hand, provide a conduit to the experience of hiking and walking through the landscape, and is part of cultural information.

Things are especially interesting with VGI and Citizen Science. In them, information and practices need to be analysed in a more nuanced way. In some cases, the practices can become focal to the participants – for example in iSpot where the experience of identifying a species in the field is also link to the experiences of the amateurs and experts who discuss the classification. It’s an activity that brings people together. On the other hand, in crowdsourcing projects that grab information from SatNav devices, there is a demonstration of The Device Paradigm, with the potential of reducing of meaningful holiday journey to ‘getting from A to B at the shortest time’. The slides below go through the ideas and then explore the implications on GIS, VGI and Citizen Science.

Now for the next stage – turning this into a paper…

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)

And

‘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.

Distribution of House Sparrow

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.

Participation Inequality 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.

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