KEYWORDS: VOLUNTEEREDGEOGRAPHICINFORMATION; NEOGEOGRAPHY; HUMAN FACTORS; STAKEHOLDERS; USER-GENERATED CONTENT
VolunteeredGeographicInformation (VGI) has huge potential for influencing the use of geographicinformation systems. However, there is a wide range of individuals involved in this process, each with their own motivations for contributing and using volunteered data. This paper investigates the range of stakeholders involved with VGI, their relationships and the main tensions and issues involved. The research was based on a series of detailed interviews and theory-driven coding of data. From this, a Rich Picture (Monk, Howard 1998) was developed to graphically present and relate stakeholder relationship information. The findings have implications for how stakeholder groups may be described, and how VGI can lead to enhanced products and services.
& Palen 2009). When originally published, this paper aimed to provide a stimulus for further exploration of the role of LBSN sources such as Twitter for crisis management and to consider the valuable geospatial component they can contain, particularly for time-critical events. It seems it has been effective, since it contributed to a wide corpus of literature published since then (see Introduction for details). Twitter is notable in its design in relation to both time and space. Tweets are organised in timelines (i.e., series of tweets sorted and displayed in reverse chronological order) and the time each tweet has been published is available with a level of accuracy of 1 second. The spatial dimension of Twitter is more complex, where georeferencing takes several basic forms. Firstly details can be provided in relation to tweets indirectly or directly. In an indirect form, a user’s location is provided on their profile page but this location is expected to be the place were they live and not their location when a tweet is made. Notably, applications running on GPS-enabled smartphones allow users to automatically update this location field each time a tweet is posted, thus converting Twitter into a genuine LBSN. For example, a user living in San Francisco can tweet from his GPS-enabled smartphone and allow his Twitter app to disclose his precise location as metadata of his tweets (‘direct location’), this would be referred to as ‘geotweeting’) (Stone 2009),. Oppositely, he might tweet from a desktop computer on which the browser is configured to not disclose any location information (although the Twitter server could guess it from the IP of the client computer, this information is not disclosed to third parties). In this case time zone,, no location will be available for this particular tweet and only the reference to San Francisco in the user profile can be used to guess (‘indirect location’) from where it has been published (although the user can be travelling anywhere in the world at that particular time)., It has to be expected that Twitter features are used in an heterogeneous manner by users, depending on their smartphone’s settings, their privacy concerns and their technological literacy.
(3) Enhanced Collaboration. Many of the delegates felt that there was some- thing of an “Us-against-them” mentality in VGI. There are many different types of communities and actors involved and everyone could actually benefit from joint exploration of commonalities rather than differences. This includes Google Map Maker, OpenStreetMap, Wikipedia, Wikimapia, National Map- ping Agencies, etc. At the core of the work of all of these communities (be they commericial interests or crowdsourced projects) is an objective for better spatial data, more diversity in the features collected, greater participation, etc. There could be great benefits to mutual collaboration. The long-term future of the geospatial data industry could very well be built upon a plat- form which shares geospatial data collection through a mixture of traditional approaches (INSPIRE driven, National Mapping and Cadastral Agencies, commercial mapping and survey companies) and today’s “disruptive tech- nologies” (Hughes and Cosier 2001, Page 24) such as VGI and sensor-webs. (4) The value of passive crowdsourcing. One of the most powerful of the
While these two previous papers offer a high-level overview of a VGI project’s function and suggest methodologies to improve the processes of VGI quality assurance, the next three papers, in contrast, approach VGI quality by considering more low-level issues such as protocols, object classification, and attribution processes in order to improve overall data quality. In this context, Mooney et al.  provide a generic protocol for the creation of vector-based VGI content. The authors consider three different cases, namely, manual vectorization from maps and imagery, field survey, and bulk data import, and provide a protocol suitable to be implemented in any VGI project that tries to balance the need for rigorous data collection with the motivation of VGI project participants to follow the protocol as well as the freedom and flexibility that a volunteer-based project should provide to its participants. Through a step-by-step approach, the paper describes the challenges that various stakeholders of a VGI project should consider. Thus, the protocol could be of use for spatial data experts, the VGI project community/initiators, ICT experts, and users/contributors, as it provides best practices that should be followed at each stage of a VGI project.
However, several studies have shown that data quality of VolunteeredGeographicInformation varies largely depending on the area being investigated [7,8,9,10,11]. Therefore, quality control is a crucial part when dealing with VGI and applying it to projects. Due to the large size and complex nature of geo-spatial data visual exploration has been proven to be a suitable methodology to reveal patterns and anomalies and therefore quickly assess the quality of the data at a first glance [12,13].
2.1. The Challenges of Transport in Cities
Sustainable mobility was defined in the European Com- mission’s Thematic Strategy for the Urban Environment as being “a transport system which allows the basic ac- cess and development needs of individuals, companies and societies to be met safely and in a manner consis- tent with human and ecosystem health, and promotes equity within and between successive generations; is af- fordable, operated fairly and efficiently, offers choice of transport mode, and supports a competitive economy, as well as balanced regional development; limits emis- sions and waste within the planet’s ability to absorb them, uses renewable resources at or below their rates of generation, and, uses non-renewable resources at or below the rates of development of renewable substi- tutes while minimising the impact on the use of land and the generation of noise” (European Commission, 2004). Over the years this definition has raised many questions. As a vision or aspiration however it has stimulated a change in the thinking of policy makers and stakeholders. Goals like environmental protection and ideas like par- ticipatory democracy, which were foreign to the minds of transport planners not so long ago, are now establish- ing themselves on the transport policy agenda. Despite this, there is still a need for some guiding principles, if ‘sustainability’ is to become more than green rhetoric (Attard, 2006).
Watanabe et al.  proposed an automatic method of identifying geographic location in non- geotagged tweets. Such method is based on the clustering of messages according to the type of event, considering short time intervals, small geographic areas and geotagged tweets. Thus, geotagged tweets are used to allocate geotags in tweets which do not have the geographic tag yet. The authors did not consider the possibility of the geotagged tweets having a different geographic reference than the location discussed in the messages. In addition, users do not necessarily talk about their current locations. Therefore, there is a possibility of errors in the geographic precision and this must be considered. In a similar way, Jung  presented a method of analyzing sets of microtexts, aiming to identify contextual clusters of tweets. By establishing a contextual relation between the messages, a set of microtexts can be considered as a single document and make the process easier for the geoparsers. This task, however, can be very costly, depending on the volume of related tweets. In addition, there is also a possibility of errors in the geographic precision.
With the advent of Web 2.0 [8, 9] and the widespread availability of new technologies [6, 10], citizens are increasingly exposed to geographical information. Citi- zens also increasingly volunteer spatially explicit (geo- graphical) information that is of relevance or interest to them, often integrating this information with existing datasets, or mashups, utilizing it for their own gain [4, 11]. Boulos [12, 13] first introduced this concept of col- laboratively developed spatial information as the “Wikifi- cation of GIS by the masses”. Goodchild coined the term “volunteeredgeographicinformation” (VGI) to refer to spatial data that is contributed by ordinary citizens, irre- spective of their training in scientific methods . The notion of VGI grew out of recognition of the limitations of traditional methodologies for adequately mapping and assembling spatial information around the world that provided both good coverage and fine temporal resolu- tion [15–17]. As a framework, VGI encompasses citizen participation from a range of social classes and comput- ing practices with the express purpose of harnessing the collective intelligence [5, 18]; it builds on the notion that data can be shaped by social and political processes and an individual’s expertise, context, and spatial awareness [15, 19–21]. Local knowledge is crucial to an accurate geographic description of communities and social groups, involving the citizen in the process of data collection.
During the floods the Ushahidi crowd map was successfully deployed. Ushahidi is a non-profit technology company that specialises in developing free and open source software for information collection, visualisation and interactive mapping (Ushahidi, 2011). Crowdmap is an on online interactive mapping service, based on the Ushahidi platform (Crowdmap, 2011). It offered the ability to collect information from cell phones, email and the web, aggregate that information into a single platform, and visualise it on a map and timeline. Photos and videos were also able to be attached to these updates. This volunteered mapping information supplemented the informational already uploaded through Facebook, YouTube and Twitter and enabled people to connect and source updates and news on the flooding.
VolunteeredGeographicInformation and Remote Sensing The availability of spatial data on the web provided by crowdsourcing communities, as well as its scientic accept- ance has increased signicantly in recent years. The integration of VGI in data analysis provides promising opportunities due to the amount of information made available [Good- child 2007a, Kinley 2013, Sester et al. 2014]. Yet, it also poses new challenges in terms of heterogeneity of the data and quality assurance [Flanagin and Metzger 2008, Haklay 2010]. Therefore, the focus in recent research lies at discerning the quality of VGI and hence to identify its applicability to further projects by complementing or substituting commercial data [Arsanjani et al. 2015, Ather 2009, Ciepªuch et al. 2011, Fan et al. 2014, Haklay 2010, Hecht et al. 2013, Helbich et al. 2012, Kounadi 2009, Mooney et al. 2010, Neis and Zipf 2012, Zielstra and Zipf 2010]. Yet, the integration of non-authoritative data with traditional, well established data and methods is a novel approach [Kinley 2013, Schnebele and Cervone 2013]. Very little research has been conducted in terms of combining VGI with remote sensing techniques. Hence VGI has rarely been incorporated within classication algorithms used to generate thematic maps [Kinley 2013]. However, some recent studies take verication a step further and use VGI as an additional data source to rene or update existing information.
In addition to contributing to the crisis flood map reports, social networking also played a major role in keeping people informed during the January 2011 flood. The social networking service Twitter <www.twitter.com> allowed people to post and receive short text based updates about the flood in real time. Photos and videos could also be attached to these updates. Similarly, the website Facebook <www.facebook.com> allowed groups such as the Queensland Police Service to provide flood information updates to users who browsed their Facebook page. Finally, YouTube <www.youtube.com> provided a forum for people to connect and inform through the use of user‐generated and contributed videos. Photography and imagery of the floods across different regions were posted on sites such as Flickr which were linked to a location through the map. Individuals had the opportunity to add comments and additional information regarding the context of these images. The posting time was also time‐stamped by the system. These images provide an excellent historic and current record of the flood events and features in the imagery can easily be used to reference flood heights at a particular time.
Another approach to find the matching references (dataset) is to rely on a currently emerging development in the realm of the Semantic Web, namely linked data infrastructure. Linked Data Web is a technology to interlink data, information, concepts and facts on the WWW. The Possible matching data could be drawn from governmental and commercial agencies that publish some of their data through the Linked Data Web infrastructure. It has been argued that current developments in the Semantic Web could not fully support interlinked data from government and enterprise datasets due to a lack of research into the best practical approaches to support large scale adoption. Nonetheless, a few attempts have been made by major organizations, such as BBC media and the UK Government, including the Ordnance Survey of Great Britain (OSGB), to publish some of their data through the Linked Data Web infrastructure. Possible matching data could also draw from transforming VGI efforts such as OpenStreetMap dataset into an RDF data model to interlink
volunteer geographicinformation and citizen science: a case comparison in environmental application. Cartogr. Geogr. Inf. Sci. 44, 539–550.
Charmaz, K., 2011. A Constructivist Grounded Theory Analysis of Losing and Regaining a Valued Self, in: Wertz, F.J. (Ed.), Five Ways of Doing Qualitative Analysis : Phenomenological Psychology, Grounded Theory, Discourse Analysis, Narrative Research, and Intuitive Inquiry. Guilford Press, New York, pp. 165–204.
do not actually provide any input information to it. Of the remaining participants, 9% contribute relatively little, while the remaining 1% contribute most of the information. This phenomenon is known as the 90-9-1 rule [21-23]. This 1 % of contributors are, therefore, of paramount importance in crowdsourcing projects. One possible reason for this participation pattern is that only a few individual registered users tend to import a massive amount of created or edited data into the platform, while other contributors tend to create a few map features or digitize some features from aerial images. Drawing upon their work on Canada, Bégin et al.  note that more than 95% of the information was provided by just three users; similar research conducted on United Kingdom OSM dataset revealed that 84% of the total edits were made by only 12% of the total users . Neis and Zipf  analyzed the global OSM dataset to investigate OSM status and its users' behavior. Their results showed that only 38% of OSM users have edited a feature at least once, and only 5% of all OSM users are actively involved in updating and editing OSM information. They also noted that 30% of OSM users had made an edit on the day they entered the system. To ascertain VGI quality, Neis and Zipf devised a methodology based on user performance history, whereby users were divided into four categories based on the number of map features created. The first group is composed of Senior Mappers who have entered more than 1,000 features. The second group consists of Junior Mappers who entered 10 to 1000 features while the third group includes Nonrecurring Mapper, who have entered at least one or at most 10 features. The final fourth group is comprised of users who have not entered any feature. Their findings demonstrate that only 5% of users entered more than 1,000 features, while 62% of users did not enter any feature and primarily focused on using the OSM system. Therefore, it seems logical to infer that the behavioral history of this small group of content generators can help us to ascertain data quality issues. Overall, these findings indicate that user centric VGI quality assessment methods are typically utilized. Because most data are created by a small number of users, examining these users' history is relatively simple, enabling us to measure their data quality through accreditation.
The participation and engagement of grass-root level community groups and citizens for natural resource management has a long history. With recent developments in ICT tools and spatial technology, these groups are seeking a new opportunity to manage natural resource data. There are lot of spatial information collected/generated by landcare groups, land holders and other community groups at the grass-root level through their volunteer initiatives. State government organisations are also interested in gaining access to this spatial data/information and engaging these groups to collect spatial information under their mapping programs. The aim of this paper is to explore the possible utilisation of volunteeredgeographicinformation (VGI) for catchment management activities. This research paper discusses the importance of spatial information and spatial data infrastructure (SDI) for catchment management and the emergence of VGI. A conceptual framework has been developed to illustrate how these emerging spatial information applications and various community volunteer activities can contribute to a more inclusive spatial data infrastructure (SDI) development at local level. A survey of 56 regional NRM bodies in Australia was utilised to explore the current community-driven volunteer initiatives for NRM activities and the potential of utilisation of VGI initiatives for NRM decision making process. This research paper concludes that VGI activities have great potential to contribute to SDI development at the community level to achieve better natural resource management (NRM) outcomes.
Abstract: Urban neighborhoods are a unique form of geography in that their boundaries rely on a social definition rather than a well-defined physical or administrative boundary. Currently, geographic gazetteers capture little more than then the centroid of a neighbor- hood, limiting potential applications of the data. In this paper, we present µ-shapes, an algorithm that employs fuzzy-set theory to model neighborhood boundaries suitable for populating gazetteers using volunteeredgeographicinformation (VGI). The algorithm is evaluated using a reference dataset and VGI from the Map Kibera Project. A confusion matrix comparison between the reference dataset and µ-shape’s output demonstrated high sensitivity and accuracy. Analysis of variance indicated that the algorithm was able to dis- tinguish between boundary and interior blocks. This suggests that, given the existing state of GIS technology, the µ-shapes algorithm can enable neighborhood-related queries that incorporate spatial uncertainty, e.g., find all restaurants within the core of a neighborhood. Keywords: boundary delineation, urban neighborhoods, volunteeredgeographic informa- tion, spatial footprint, vague spatial region
which provide official or Formal Data (FD). Differences between the resultant datasets, in terms of measurement source, quality, coverage and purpose, are evident: for example, low cost GNSS receivers and the availability of GNSS signals make it possible for the public to acquire positional information about different locations using standard mobile phone handsets and upload it to local or personal databases; whilst access to virtual reference stations (VRS) and networks of high accuracy correction facilities used as standard by national mapping agencies give significantly different results even for basic point positioning. The mapping based on volunteered efforts, personal computers and the Internet  is different in many respects to traditional large-scale topographic mapping projects, long the hallmark of official governmental agencies.
The data contribution patterns of the active OSM community have changed over the past few years for different world regions. During the first few years of the project, most volunteers focused their data collection efforts on road network data. Nowadays, other real world features, such as buildings, land use or public transportation information are being added in many regions to provide more details to the users. When a volunteer creates an object in the OSM database, representing a real-world feature, she/he can use three different object types . Point information is represented by a Node object in OSM, whereas a Way object is utilized when mapping lines or polygons (latter, in the form of a closed line feature). If a number of Node and/or Way objects are related to each other, the Relation object can be utilized to map this particular information (e.g., turn restrictions of the street network or tram/bus lines for public transportation). Any modifications or contributions made to the OSM database by a single member are stored in a changeset, and its extent covers the entire area within which a contributor made her or his changes. Each object in OSM can be annotated by a variety of attribute information, also referred to as tags, which consist of a key-value pair. Any contributor is free to propose and discuss new tags to describe real-world features , resulting in a bottom-up tagging approach, indicating that there is no traditionally, enforced tagging limitation with which mappers have to comply. However, a large number of suggested key-value combinations that are widely used in the community are provided in a wiki  that helps to standardize certain objects in OSM. It also needs to be noted that a variety of map render-engines influences the creation of map “standards”, due to their specific rendering functions that only allow certain features with particular tags to be shown on the map.
The scale of reporting units and the degree of spatial precision for the pilot design allowed for 50 of the available public water taps on the island to be included as fixed point locations for citizens’ reports. At each of the 50 water points, a signboard was placed informing people about the project and asking them to report problems with water provision. SMS messages would be sent to a special phone number, requiring a specific code indicated on the signboard. Messages were relayed through a local internet provider and then uploaded on an online accessible map, which showed individual reports. The purpose of the map was to reflect points with more frequent reports and make them stand out. In terms of the captured information flow it was found however that the system does not capture functionality, but rather the need for public water services. Reports were predominantly made when the service had unexpectedly ceased and also during peak hours of domestic water needs. Although these seem obvious facts, the system did not record service failures that were not observed (e.g. night time service interruption). The system could therefore only properly be used to assess functionality of public water service if the active sensor network, which records water flows, would be included in the HSW.
6.3.1 Ownership data
Information about ownership is an important part of attribute data as it shows who the specific plot belongs to. It was difficult to obtain because many people were reluctant to give their names. The reason for this reluctance was primarily that information about the family’s name can reveal certain types of information that may lead to people being killed. This risk was particularly high during the sectarian war which arose in Iraq from 2003 onwards. In order to improve the collection of ownership data, the first step was to gain the trust and confidence of the communities about the purpose and possible benefits of applying the project in the community. Furthermore, the community was re-assured that personal data held by the researcher would be kept confidential, and that it was anonymised when reported. Consequently, although some of the volunteers were still not prepared to give ownership details, the majority were willing to do so. The VGI-collected ownership data were verified by crowd- source agreement, by presenting it in front of the community gatekeepers. These were community representatives who had already gained the trust of all community members, had been active in representing community needs, and usually had lived in the community for more than ten years. Other representative members of the community, including older residents with considerable levels of knowledge, who were able to attend a verification workshop after data collection, also contributed to this ‘crowd-sourcing’. Table 6.1 shows the results of the use of this crowd-sourcing technique to test the consistency of attribute data collected by VGI. A sample of ~20% of the total number of land parcels surveyed was used for this crowd-sourcing, as well as for further analysis.