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.
At the last day of INSPIRE conference, I’ve attended a session about apps and applications and the final plenary which focused on knowledge based economy and the role of inspire within it. Some notes from the talks including my interpretations and comments.
Dabbie Wilson from the Ordnance Survey highlighted the issues that the OS is facing in designing next generation products from an information architect point of view. She noted that the core large scale product, MasterMap has been around for 14 years and been provided in GML all the way through. She noted that now the client base in the UK is used to it and happy with (and when it was introduced, there was a short period of adjustment that I recall, but I assume that by now everything is routine). Lots of small scale products are becoming open and also provided as linked data. The user community is more savvy – they want the Ordnance Survey to push data to them, and access the data through existing or new services and not just given the datasets without further interaction. They want to see ease of access and use across multiple platforms. The OS is considering moving away from provision of data to online services as the main way for people to get access to the data. The OS is investing heavily in Mobile apps for leisure but also helping the commercial sector in developing apps that are based on OS data and tools. For example, OS locate app provide mechanisms to work worldwide so it’s not only UK. They also put effort to create APIs and SDKs – such as OS OnDemands – and also allowing local authorities to update their address data. There is also focus on cloud-based application – such as applications to support government activities during emergencies. The information architecture side moving from product to content. The OS will continue to maintain content that is product agnostic and running the internal systems for a long period of 10 to 20 years so they need to decouple outward facing services from the internal representation. The OS need to be flexible to respond to different needs – e.g. in file formats it will be GML, RDF and ontology but also CSV and GeoJSON. Managing the rules between the various formats is a challenging task. Different representations of the same thing is another challenge – for example 3D representation and 2D representation.
Didier Leibovici presented a work that is based on Cobweb project and discussing quality assurance to crowdsourcing data. In crowdsourcing there are issues with quality of both the authoritative and the crowdsourcing data. The COBWEB project is part of a set of 5 citizen observatories, exploring air quality, noise, water quality, water management, flooding and land cover, odour perception and nuisance and they can be seen at http://www.citizen-obs.eu. COBWEB is focusing on the infrastructure and management of the data. The pilot studies in COBWEB look at landuse/land cover, species and habitat observations and flooding. They are mixing sensors in the environment, then they get the data in different formats and the way to managed it is to validate the data, approve its quality and make sure that it’s compliant with needs. The project involve designing an app, then encouraging people to collect the data and there can be lack of connection to other sources of data. The issues that they are highlighting are quality/uncertainty, accuracy, trust and relevance. One of the core questions is ‘is crowd-sourcing data need to different to any other QA/QC?’ (my view: yes, but depending on the trade offs in terms of engagement and process) they see a role of crowdsourcing in NSDI, with real time data capture QA and post dataset collection QA (they do both) and there are also re-using and conflating data sources. QA is aimed to know what is collected – there are multiple ways to define the participants which mean different ways of involving people and this have implications to QA. They are suggesting a stakeholder quality model with principles such as vaueness, ambiguity, judgement, reliability, validity, and trust. There is a paper in AGILE 2014 about their framework. The framework suggests that the people who build the application need to develop the QA/QC process and do that with workflow authoring tool, which is supported with ontology and then running it as web processing service. Temporality of data need to be consider in the metadata, and how to update the metadata on data quality.
Patrick Bell considered the use of smartphone apps – in a project of the BGS and the EU JRC they review existing applications. The purpose of the survey to explore what national geological organisations can learn from the shared experience with development of smartphone apps – especially in the geological sector. Who is doing the development work and which partnerships are created? What barriers are perceived and what the role of INSPIRE directive within the development of these apps? They also try to understand who are the users? There are 33 geological survey organisations in the EU and they received responses from 16 of them. They found 23 different apps – from BGS – iGeology http://www.bgs.ac.uk/igeology/home.html and provide access to geological amps and give access to subsidence and radon risk with in-app payment. They have soil information in the MySoil app which allow people to get some data for free and there is also ability to add information and do citizen science. iGeology 3D is adding AR to display a view of the geological map locally. aFieldWork is a way to capture information in harsh environment of Greenland. GeoTreat is providing information of sites with special value that is relevant to tourists or geology enthusiasts. BRGM – i-infoTerre provide geological information to a range of users with emphasis on professional one, while i-infoNappe tell you about ground water level. The Italian organisation developed Maps4You with hiking route and combining geology with this information in Emilia-Romagna region. The Czech Geologcial survey provide data in ArcGIS online.
The apps deal with a wide range of topics, among them geohazards, coastline, fossils, shipwrecks … The apps mostly provide map data and 3D, data collection and tourism. Many organisation that are not developing anything stated no interest or a priority to do so, and also lack of skills. They see Android as the most important – all apps are free but then do in app purchase. The apps are updated on a yearly basis. about 50% develop the app in house and mostly work in partnerships in developing apps. Some focus on webapps that work on mobile platform, to cross platform frameworks but they are not as good as native apps, though the later are more difficult to develop and maintain. Many people use ESRI SDK and they use open licenses. Mostly there is lack of promotion of reusing the tools – most people serve data. Barriers – supporting multiple platform, software development skills, lack of reusable software and limited support to reuse across communities – heavy focus on data delivery, OGC and REST services are used to deliver data to an app. Most suggesting no direct link to INSPIRE by respondents but principles of INSPIRE are at the basis of these applications.
Timo Aarmio – presented the OSKARI platform to release open data to end users (http://www.oskari.org/). They offer role-based security layers with authenticates users and four levels of permissions – viewing, viewing on embedded maps, publishing and downloading. The development of Oskari started in 2011 and is used by 16 member organisations and the core team is running from National Land Survey of Finland. It is used in Arctic SDI, ELF and Finish Geoportal – and lots of embedded maps. The end-users features allow search of metadata, searching map layers by data providers or INSPIRE themes. they have drag and drop layers and customisation of features in WFS. Sharing is also possible with uploading shapefiles by users. They also have printing functionality which allow PNG or PDF and provide also embedded maps so you can create a map and then embed it in your web page. The data sources that they support are OGC web services – WMS, WMTS, WFS, CSW and also ArcGIS REST, data import for Shapefiles and KML, and JSON for thematic maps . Spatial analysis is provided with OGC Web Processing Service – providing basic analysis of 6 methods – buffer, aggregate, union, intersect, union of analysed layres and area and sector. They are planning to add thematic maps, more advanced spatial analysis methods, and improve mobile device support. 20-30 people work on Oskari with 6 people at the core of it.
The final session focused on knowledge based economy and the link to INSPIRE.
Andrew Trigg provide the perspective of HMLR on fueling the knowledge based economy with open data. The Land registry dealing with 24 million titles with 5 million property transaction a year. They provided open access to individual titles since 1990 and INSPIRE and the open data agenda are important to the transition that they went through in the last 10 years. Their mission is now include an explicit reference to the management and reuse of land and property data and this is important in terms of how the organisation defines itself. From the UK context there is shift to open data through initiatives such as INSPIRE, Open Government Partnership, the G8 Open Data Charter (open by default) and national implementation plans. For HMLR, there is the need to be INSPIRE Compliance, but in addition, they have to deal with public data group, the outcomes of the Shakespeare review and commitment to a national information infrastructure. As a result, HMLR now list 150 datasets but some are not open due to need to protect against fraud and other factors. INSPIRE was the first catalyst to indicate that HMLR need to change practices and allowed the people in the organisation to drive changes in the organisation, secure resources and invest in infrastructure for it. It was also important to highlight to the board of the organisation that data will become important. Also a driver to improving quality before releasing data. The parcel data is available for use without registration. They have 30,000 downloads of the index polygon of people that can potentially use it. They aim to release everything that they can by 2018.
The challenges that HMLR experienced include data identification, infrastructure, governance, data formats and others. But the most important to knowledge based economy are awareness, customer insight, benefit measurement and sustainable finance. HMLR invested effort in promoting the reuse of their data however, because there is no registration, their is not customer insight but no relationships are being developed with end users – voluntary registration process might be an opportunity to develop such relations. Evidence is growing that few people are using the data because they have low confidence in commitment of providing the data and guarantee stability in format and build applications on top of it, and that will require building trust. knowing who got the data is critical here, too. Finally, sustainable finance is a major thing – HMLR is not allowed to cross finance from other areas of activities so they have to charge for some of their data.
Henning Sten Hansen from Aalborg University talked about the role of education. The talk was somewhat critical of the corporatisation of higher education, but also accepting some of it’s aspects, so what follows might be misrepresenting his views though I think he tried to mostly raise questions. Henning started by noting that knowledge workers are defined by OECD as people who work autonomously and reflectively, use tools effectively and interactively, and work in heterogeneous groups well (so capable of communicating and sharing knowledge). The Danish government current paradigm is to move from ‘welfare society’ to the ‘competitive society’ so economic aspects of education are seen as important, as well as contribution to enterprise sector with expectations that students will learn to be creative and entrepreneurial. The government require more efficiency and performance from higher education and as a result reduce the autonomy of individual academics. There is also expectation of certain impacts from academic research and emphasis on STEM for economic growth, governance support from social science and the humanities need to contribute to creativity and social relationships. The comercialisation is highlighted and pushing patenting, research parks and commercial spin-offs. There is also a lot of corporate style behaviour in the university sector – sometime managed as firms and thought as consumer product. He see a problem that today that is strange focus and opinion that you can measure everything with numbers only. Also the ‘Google dream’ dream is invoked – assuming that anyone from any country can create global companies. However, researchers that need time to develop their ideas more deeply – such as Niels Bohr who didn’t published and secure funding – wouldn’t survive in the current system. But is there a link between education and success? LEGO founder didn’t have any formal education [though with this example as with Bill Gates and Steve Jobs, strangely their business is employing lots of PhDs - so a confusion between a person that start a business and the realisation of it]. He then moved from this general context to INSPIRE, Geoinformation plays a strong role in e-Governance and in the private sector with the increase importance in location based services. In this context, projects such as GI-N2K (Geographic Information Need to Know) are important. This is a pan European project to develop the body of knowledge that was formed in the US and adapting it to current need. They already identified major gaps between the supply side (what people are being taught) and the demand side – there are 4 areas that are cover in the supply side but the demand side want wider areas to be covered. They aim to develop a new BoK for Europe and facilitating knowledge exchange between institutions. He concluded that Higher education is prerequisite for the knowledge economy – without doubt but the link to innovation is unclear . Challenges – highly educated people crowd out the job market and they do routine work which are not matching their skills, there are unclear the relationship to entrepreneurship and innovation and the needed knowledge to implement ideas. What is the impact on control universities reducing innovation and education – and how to respond quickly to market demands in skills when there are differences in time scale.
Giacomo Martirano provided a perspective of a micro-enterprise (http://www.epsilon-italia.it/IT/) in southern Italy. They are involved in INSPIRE across different projects – GeoSmartCities, Smart-Islands and SmeSpire – so lots of R&D funding from the EU. They are also involved in providing GIS services in their very local environment. From a perspective of SME, he see barriers that are orgnaisational, technical and financial. They have seen many cases of misalignment of technical competencies of different organisations that mean that they can’t participate fully in projects. Also misalignment of technical ability of clients and suppliers, heterogeneity in client organisation culture that add challenges. Financial management of projects and payment to organisations create problems to SME to join in because of sensitivity to cash-flow. They experience cases were awarded contracts won offering a price which is sometime 40% below the reference one. There is a need to invest more and more time with less aware partners and clients. When moving to the next generation of INSPIRE – there is a need to engage with micro-SMEs in the discussion ‘don’t leave us alone’ as the market is unfair. There is also a risk that member states, once the push for implementation reduced and without the EU driver will not continue to invest. His suggestion is to progress and think of INSPIRE as a Serivce – SDI as a Service can allow SMEs to join in. There is a need for cooperation between small and big players in the market.
Andrea Halmos (public services unit, DG CONNECT) – covering e-government, she noted her realisation that INSPIRE is more than ‘just environmental information’. From DG CONNECT view, ICT enabled open government, and the aim of the digital agenda for Europe is to empowering citizen and businesses, strengthening the internal market, highlighting efficiency and effectiveness and recognised pre-conditions. One of the focus is the effort to put public services in digital format and providing them in cross border way. The principles are to try to be user centred, with transparency and cross border support – they have used life events for the design. There are specific activities in sharing identity details, procurement, patient prescriptions, business, and justice. They see these projects as the building blocks for new services that work in different areas. They are seeing challenges such financial crisis, but there is challenge of new technologies and social media as well as more opening data. So what is next to public administration? They need to deal with customer – open data, open process and open services – with importance to transparency, collaboration and participation (http://www.govloop.com/profiles/blogs/three-dimensions-of-open-government). The services are open to other to join in and allow third party to create different public services. We look at analogies of opening decision making processes and support collaboration with people – it might increase trust and accountability of government. The public service need to collaborative with third parties to create better or new services. ICT is only an enablers – you need to deal with human capital, organisational issue, cultural issues, processes and business models – it even question the role of government and what it need to do in the future. What is the governance issue – what is the public value that is created at the end? will government can be become a platform that others use to create value? They are focusing on Societal Challenge Comments on their framework proposals are welcomed – it’s available at http://ec.europa.eu/digital-agenda/en/news/vision-public-services
After these presentations, and when Alessandro Annoni (who was charring the panel) completed the first round of questions, I was bothered that in all these talks about knowledge-based economy only the government and the private sector were mentioned as actors, and even when discussing development of new services on top of the open data and services, the expectation is only for the private sector to act in it. I therefore asked about the role of the third-sector and civil-society within INSPIRE and the visions that the different speakers presented. I even provided the example of mySociety – mainly to demonstrate that third-sector organisations have a role to play.
To my astonishment, Henning, Giacomo, Andrea and Alessandro answered this question by first not treating much of civil-society as organisations but mostly as individual citizens, so a framing that allow commercial bodies, large and small, to act but citizens do not have a clear role in coming together and acting. Secondly, the four of them seen the role of citizens only as providers of data and information – such as the reporting in FixMyStreet. Moreover, each one repeated that despite the fact that this is low quality data it is useful in some ways. For example, Alessandro highlighted that OSM mapping in Africa is an example for a case where you accept it, because there is nothing else (really?!?) but in other places it should be used only when it is needed because of the quality issue – for example, in emergency situation when it is timely.
Apart from yet another repetition of dismissing citizen generated environmental information on the false argument of data quality (see Caren Cooper post on this issue), the views that presented in the talks helped me in crystallising some of the thoughts about the conference.
As one would expect, because the participants are civil servants, on stage and in presentations they follow the main line of the decision makers for which they work, and therefore you could hear the official line that is about efficiency, managing to do more with reduced budgets and investment, emphasising economic growth and very narrow definition of the economy that matters. Different views were expressed during breaks.
The level in which the citizens are not included in the picture was unsurprising under the mode of thinking that was express in the conference about the aims of information as ‘economic fuel’. While the tokenism of improving transparency, or even empowering citizens appeared on some slides and discussions, citizens are not explicitly included in a meaningful and significant way in the consideration of the services or in the visions of ‘government as platform’. They are reprieved as customers or service users. The lesson that were learned in environmental policy areas in the 1980s and 1990s, which are to provide an explicit role for civil society, NGOs and social-enterprises within the process of governance and decision making are missing. Maybe this is because for a thriving civil society, there is a need for active government investment (community centres need to built, someone need to be employed to run them), so it doesn’t match the goals of those who are using austerity as a political tool.
Connected to that is the fact that although, again at the tokenism level, INSPIRE is about environmental applications, the implementation now is all driven by narrow economic argument. As with citizenship issues, environmental aspects are marginalised at best, or ignored.
The comment about data quality and some responses to my talk remind me of Ed Parsons commentary from 2008 about the UK GIS community reaction to Web Mapping 2.0/Neogeography/GeoWeb. 6 years on from that , the people that are doing the most important geographic information infrastructure project that is currently going, and it is progressing well by the look of it, seem somewhat resistant to trends that are happening around them. Within the core area that INSPIRE is supposed to handle (environmental applications), citizen science has the longest history and it is already used extensively. VGI is no longer new, and crowdsourcing as a source of actionable information is now with a decade of history and more behind it. Yet, at least in the presentations and the talks, citizens and civil-society organisations have very little role unless they are controlled and marshaled.
Despite all this critique, I have to end with a positive note. It has been a while since I’ve been in a GIS conference that include the people that work in government and other large organisations, so I did found the conference very interesting to reconnect and learn about the nature of geographic information management at this scale. It was also good to see how individuals champion use of GeoWeb tools, or the degree in which people are doing user-centred design.
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.
5 December, 2012
Recently, I attended a meeting with people from a community that is concerned with vibration and noise caused by a railway near their homes. We have discussed the potential of using citizen science to measure the vibrations that pass the sensory threshold and that people classify as unpleasant, together with other perceptions and feeling about these incidents. This can form the evidence to a discussion with the responsible authorities to see what can be done.
As a citizen science activity, this is not dissimilar from the work carried out around Heathrow to measure the level of noise nuisance or air pollution monitoring that ExCiteS and Mapping for Change carried out in other communities.
In the meetings, the participants felt that they need to emphasise that they are not against the use of the railway or the development of new railway links. Like other groups that I have net in the past, they felt that it is important to emphasise that their concern is not only about their locality – in other words, this is not a case of ‘Not In My Back Yard’ (NIMBY) which is the most common dismissal of local concerns. The concern over NIMBY and citizen science is obvious one, and frequently come up in questions about the value and validity of data collected through this type of citizen science.
During my masters studies, I was introduced to Maarten Wolsink (1994) analysis of NIMBY as a compulsory reading in one of the courses. It is one of the papers that I keep referring to from time to time, especially when complaints about participatory work and NIMBY come up.
Inherently, what Wolsink is demonstrating is that the conceptualisation of the people who are involved in the process as selfish and focusing on only their own area is wrong. Through the engagement with environmental and community concerns, people will explore issues at wider scales and many time will argue for ‘Not in Anyone’s Back Yard’ or for a balance between the needs of infrastructure development and their own quality of life. Studies on environmental justice also demonstrated that what the people who are involved in such activities ask for are not narrow, but many times mix aspects of need for recognition, expectations of respect, arguments of justice, and participation in decision-making (Schlosberg 2007).
In other words, the citizen science and systematic data collection are a way for the community to bring to the table evidence that can enhance arguments beyond NIMBY, and while it might be part of the story it is not the whole story.
For me, these interpretations are part of the reason that such ‘activism’-based citizen science should receive the same attention and respect as any other data collection, most notably by the authorities.
Wolsink, M. (1994) Entanglement of Interests and Motives: Assumptions Behind the NIMBY-Theory on Facility Siting, Urban Studies, 31(6), pp. 851-866.
Scholsberg, D. (2007) Defining Environmental Justice: Theories, Movements, and Nature. Oxford University Press, 2007
In March 2008, I started comparing OpenStreetMap in England to the Ordnance Survey Meridian 2, as a way to evaluate the completeness of OpenStreetMap coverage. The rational behind the comparison is that Meridian 2 represents a generalised geographic dataset that is widely use in national scale spatial analysis. At the time that the study started, it was not clear that OpenStreetMap volunteers can create highly detailed maps as can be seen on the ‘Best of OpenStreetMap‘ site. Yet even today, Meridian 2 provides a minimum threshold for OpenStreetMap when the question of completeness is asked.
So far, I have carried out 6 evaluations, comparing the two datasets in March 2008, March 2009, October 2009, March 2010, September 2010 and March 2011. While the work on the statistical analysis and verification of the results continues, Oliver O’Brien helped me in taking the results of the analysis for Britain and turn them into an interactive online map that can help in exploring the progression of the coverage over the various time period.
Notice that the visualisation shows the total length of all road objects in OpenStreetMap, so does not discriminate between roads, footpaths and other types of objects. This is the most basic level of completeness evaluation and it is fairly coarse.
The application will allow you to browse the results and to zoom to a specific location, and as Oliver integrated the Ordnance Survey Street View layer, it will allow you to see what information is missing from OpenStreetMap.
Finally, note that for the periods before September 2010, the coverage is for England only.
Some details on the development of the map are available on Oliver’s blog.
How Many Volunteers Does It Take To Map An Area Well? The validity of Linus’ law to Volunteered Geographic Information
10 January, 2011
The paper “How Many Volunteers Does It Take To Map An Area Well? The validity of Linus’ law to “ has appeared in The Cartographic Journal. The proper citation for the paper is:
Haklay, M and Basiouka, S and Antoniou, V and Ather, A (2010) How Many Volunteers Does It Take To Map An Area Well? The validity of Linus’ law to Volunteered Geographic Information. The Cartographic Journal , 47 (4) , 315 – 322.
The abstract of the paper is as follows:
In the area of volunteered geographical information (VGI), the issue of spatial data quality is a clear challenge. The data that are contributed to VGI projects do not comply with standard spatial data quality assurance procedures, and the contributors operate without central coordination and strict data collection frameworks. However, similar to the area of open source software development, it is suggested that the data hold an intrinsic quality assurance measure through the analysis of the number of contributors who have worked on a given spatial unit. The assumption that as the number of contributors increases so does the quality is known as `Linus’ Law’ within the open source community. This paper describes three studies that were carried out to evaluate this hypothesis for VGI using the OpenStreetMap dataset, showing that this rule indeed applies in the case of positional accuracy.
To access the paper on the journal’s website, you can follow the link: 10.1179/000870410X12911304958827. However, if you don’t hold a subscription to the journal, a postprint of the paper is available at the UCL Discovery repository. If you would like to get hold of the printed version, email me.
29 November, 2010
The website GPS Business News published an interview with me in which I covered several aspects of OpenStreetMap and crowdsourced geographical information, including aspects of spatial data quality, patterns of data collection, inequality in coverage and the implications of these patterns to the wider area of Volunteered geographical Information.
The interview is available here .