7 February, 2015
The Open University, with support from Nominet Trust and UTC Sheffield have launched the nQuire-it.org website, which seem to have a great potential for running citizen science activities. The nQuire platform allows participants to create science inquiry ‘missions’. It is accompanied by an Android app called Sense-it that exposed all the sensors that are integrated in a smartphone and let you see what they are doing and the values that they are showing.
The process of setting up a project on the nQuire-it site is fairly quick and you can figure it out in few clicks. Then, joining the project that you’ve created on the phone is also fairly simple, and the integration with Google, Facebook and Twitter accounts mean that linking the profiles is quick. Then you can get few friends to start using it, and the Sense-it app let you collect the data and then share it with other participants in the project on the nQuire website. Then participants can comment on the data, ask questions about how it was produced and up or down vote it. All these make nQuire a very suitable place for experimentation with sensors in smartphones and prototyping citizen science activities. It also provides an option for recording geographic location, and it good to see that it’s disabled by default, so the project designer need to actively switch it on.
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.
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.
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-reviewed papers that mention OpenStreetMap. Since then, OpenStreetMap has received plenty of academic attention. More ‘conservative’ search engines such as ScienceDirect or Scopus find 286 and 236 peer reviewed papers (respectively) that mention the project. The ACM digital library finds 461 papers in the areas that are relevant to computing and electronics, while Microsoft Academic Research finds 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 of 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 of 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, with 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 think about it, the more uncomfortable I am with this equivalence. I feel that the recent line from Neis & Zielstra (2014) 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 paper have emerged.
First, 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.
Third, 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, many of these papers are locked behind paywalls, but thatis another issue… ]
In short, there is a significant body of knowledge regarding the nature of the project, the implications of what it produces, and ways to understand the information that emerges from it. Clearly, we now know that OSM produces good data and are ware of the patterns of contribution. What is also clear is that many of these patterns are specific to OSM. Because of the importance of OSM to so many application areas (including illustrative maps in string theory!) these insights are very important. Some of these insights are expected to also be present in other VGI projects (hence my suggestions for assertions about VGI) but this needs 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.