Although the widespread importance and use of geographicinformation is increasing (Sui, 2008), worldwide the act of mapping has been in decline (Dodge and Perkins, 2008). One example of the badly mapped world theory is how as recent as 2005 aid workers after the Nias (also referred to as the Sumatra) earthquake in Indonesia were forced to work from a 10” x 12” Dutch map from the 19 th century; the best available to them at the time (Thompson, 2009). Additionally, many cities around the world (until recently including Dublin) suffer the problem of not having a cheap and readily available digital map (Goodchild, 2007a). One suggested solution to this problem is to create volunteer generated maps driven by the easy accessibility of GPS trackers (Goodchild, 2007b). The GPS system allows rapid and accurate positioning of any location in the world within a high tolerance 2 , and thus information may be provided at a quality close to that generated by professionals (Nicholson et al., 2002). However, it is important to highlight how simply having easy access to technology is not enough to guarantee adoption and participation, people require motivation and education for this to occur (Rogers, 2003).
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).
Abstract Volunteered Geographical Information (VGI) has been extensively stud- ied in terms of its quality and completeness in the past. However, little attention is given to understanding what factors, beyond individuals’ expertise, contribute to the success of VGI. In this chapter we ask whether society and its characteristics such as socio-economic factors have an impact on what part of the physical world is being digitally mapped. This question is necessary, so to understand where crowd-sourced map information can be relied upon (and crucially where not), with direct implica- tions on the design of applications that rely on having complete and unbiased map knowledge. To answer the above questions, we study over 6 years of crowd-sourced contributions to OpenStreetMap (OSM) a successful example of the VGI paradigm. We measure the positional and thematic accuracy as well as completeness of this information and quantify the role of society on the state of this digital production. Finally we quantify the effect of social engagement as a method of intervention for improving users’ participation.
Lower population density positively correlates with fewer number of contributions, thus affecting data completeness or positional accuracy (Neis et al. 2013, Haklay 2010, Girres and Touya 2010, Mullen et al. 2015). However, more research needs to be done regarding this issue. Hence, a step further in this direction is to derive the socio-economic impacts on OSM data quality. As presented in section 5.2., there have been a number of studies and empirical research performed on the subject of OSM quality. Nevertheless, a solid framework for assessing OSM data is far from being established, let alone a framework of quality measurement for specific application domains. The limitation is that existing measures and indicators (described by ISO) are not inclusive enough to evaluate OSM data. This is mainly because the nature of OSM (and VGI in general) is fundamentally different to what geospatial experts have dealt with so far. Therefore, we argue that there are still research gaps when defining quality measures/indicators and proposing methods to calculate these measures/indicators. In addition, only few studies have been conducted to explore and analyze the differences in quality requirements for different application domains. Therefore, as a recommendation for future research in this topic, we suggest to develop a systematic framework that provides methods and measures to evaluate the fitness for purpose of each VGI type. This would need to not only focus on the analysis of data itself, but also explore the social factors which are the driving forces behind public contributions, and thus considerably affect the quality. For example, one could define a mathematical model based on OSM intrinsic data indicators (e.g., number of contributors, number of edits, etc.) to estimate the quality (e.g., completeness) of data without having reference data at hand. This would enrich and complete the new paradigm of intrinsic quality evaluation, which by far has received less attention from the research community, compared to the common extrinsic quality evaluation: i.e: comparison with reference data. The utilization of text and image-based VGI still mostly depends on the geotagged content. However, the sparse geotagged content of these two VGI types in most cases represent only a minority of the data. Therefore, generalization based on VGI is still limited and need further demographic studies.
Crowdsourcing Strategies. Next, we will focus on analyzing the crowdsourcing model that helps characterize, plan and design the central crowdsourced processing unit of a VGI system. Crowdsourcing is a process that involves task disaggregation and open participation. The process is driven by the use of the perceptual and cognitive abilities of a large distributed group of individuals who participate (especially online) in problem-solving and data management tasks. In addition, crowdsourcing makes it possible to acquire and share people’s local knowledge, improving and facilitating collaborative networks by working as an online community. Brabham [ 29 ] proposed a crowdsourcing typology consisting of four methods based on the problems that crowdsourcing is best suited to solve: (i) knowledge discovery and management, which consists of finding or collecting data and information in a common format, for example, reporting urban infrastructure problems; (ii) distributed human intelligence tasking, suitable for processing data and making analysis; (iii) broadcast search, ideal for solving problems with empirically provable solutions, such as scientific challenges; lastly, (iv) peer-vetted creative production, used to generate and select creative ideas. This classification points to certain differences in terms of the intellectual involvement required for solving the problem, being more demanding as we get closer to the latter method. This last point is examined in Haklay’s study on (geographic) Citizen Science as a VGI activity [ 30 ]. This work presents a typology based on four levels of participation, where the use of contributors’ cognitive abilities can be considered a main variable. The first level, identified as Crowdsourcing, makes minimal cognitive demands and focuses on citizens as sensors and volunteered computing. The second is called Distributed Intelligence and is associated with volunteered thinking, which involves citizens as basic interpreters and in which participants’ cognitive abilities are required in order to offer simple interpretations. The next level falls within the scope of community/civic science and requires increased cognitive engagement from participants, hence, it is described using the term “Participatory science” where people involved participate in the problem and in defining collection methods, as they would in a collaborative analysis among scientists. Finally, there is “Extreme Citizen Science”, which is a completely integrated participatory activity that consists of defining the problem, collecting and analyzing data with both professional and non-professional scientists in the role of experts, and which is also open to the possibility of doing community work without professional scientists. As we move up in this typology, projects require higher levels of participation and greater use of cognitive abilities, and their success depends on a strong and well-organized interdependent community.
There many motivation factors for the community-driven volunteer initiatives such as Landcare, Waterwatch, Coastcare, etc. The top three motivation factors are awareness and concern regarding environmental benefits, long standing love with the land and water, and social interactions/benefits. Coleman (2010) classified the motivation of contributors into two categories as constructive and damaging. Coleman (2009) et al. summarised the construction contributions into eight categories and analysed how many of these motivations apply within VGI application. They argued that pride of place, altruism, professional or personal interest, social rewards, and intellectual stimulation are the major one. When we compared the motivation factors for community-driven volunteer initiatives and VGI applications, there were similarities. So, there is an opportunity to utilise the networks and enthusiasm of community-driven volunteer activities for spatial information collection and management.
Despite initiatives like Crowd4City, one of the main challenges in the use of human sensors has been keeping their willingness to cooperate and consequently maintaining the LBSNs continuously. Several factors may affect the users’ motivation, such as the learning curve for correct operation of a LBSN and the time spent on such activities. Typically, only a few users are in charge of providing a significant volume of information. This issue is visible in terms of geographic location, where many areas around the world are mapped by only few users [Haklay and Weber 2008]. Therefore, it becomes necessary to find alternative methods of keeping the LBSNs up-to-date even when the volume of contributions from volunteers is below the expected number. In addressing this issue, we have considered applying GeographicInformation Retrieval (GIR) techniques to automatically produce VGI for LBSN improvement. We understand that produced spatiotemporal markers conforms to VGI policy as they are produced by people as the same as VGI is produced, and share features of spatial crowdsourced data. Thus, social media users non-intentionally will be in charge of volunteers in the automated production of VGI.
The Special Issue continues to show the importance of concerns over the quality of VGI, which is heterogeneous and has different kinds of spatial, temporal, and social bias. The trend towards automated methods, filtering systems, and stronger protocols, as shown in the papers in this Special Issue, may help to alleviate some of these quality concerns in the future. We are not yet in a position where VGI can replace proprietary and authoritative data sets, although OSM continues to be at the forefront of what is possible. We need more examples of such innovative and successful endeavors. Many of the application-oriented papers show the potential of VGI across multiple domains. The importance of VGI for disaster-related applications is a continuing theme that will definitely expand in the future since aiding humanitarian causes is a power incentive for participation. VGI for human behavioral analysis and land cover/land use mapping are only two growing application areas for VGI among many others. The focus on application-oriented research is also clearly going to continue in the future as novel applications of VGI are developed.
(e.g. Marseille’s News), thematic focus (e.g., natural hazards) or to news-related and ‘citizen journalism’ blogs. Aggregator users can use tools like TwitterFeed 1 to automatically re-publish the contents a RSS feeds into tweets, and thus very easily reproduce information contents on Twitter without human intervention. In this research’s sample, nearly 1 tweet out of 3 (31%) has been published by an aggregator. This finding is important to understand the apparent redundancy of every piece of information. It seems that aggregators act like a delay effect and propagate a redundant signal with limited added value. If for example a piece of information is published by a media agency, and then updated because it was erroneous, it is not guaranteed that the errata follow the same re-publication path via aggregators. The same applies to citizens that use the RT syntax when they ‘re-tweet’ information; in the sample 18.8% contained the “RT” code). This can create problems to set up quantitative quality filters on top of Twitter: the fact that information is tweeted numerous times may be not be interpreted as a proof of veracity, or other sense of ‘truth’. Such ‘echo effect’ poses a key methodological issue to VGI Sensing: when numerous VGI items are pure repetition of the same information, the cross-validation may become ineffective to assess its credibility (in a similar manner as someone giving more credit to a rumour if it is widely spread). Oppositely, VGI Sensing should be designed assess as credible information that relates the same facts independently from numerous sources.
The TRM methodology proposed in this study addresses the concern of data quality assessment and assurance measures advanced by civil servants. Developing countries are characterised by a lack of ground truth to establish the accuracy and reliability of contributed datasets, thus TRM can build trust and confidence of potential consumers to utilize the datasets (Bishr and Mantelas, 2008; Ali et al., 2014)An in-depth explanation of how the participatory system was designed will be presented in Section 5.1.2. Overall, the explanatory design strategy of the mixed methods approach provided a better understanding of the research problem that could not be achieved with either approach alone. For example, questionnaires were used to address the research problem and to record participants’ views about existing challenges in LAS, and suggestions on potential remedial factors. Structured interviews were then used to further explore information collected from the first data collection phase: key findings, outliers, and extreme cases from questionnaire responses were noted and pertinent questions created for the interview phase. The interviews in the preliminary stage were converted into numerical codes to be statistically analysed. Each sentence or phrase was assigned to a certain category. Additionally, text from each category was aggregated to allow a count of responses to create frequencies for better interpretation. The downfall of the coding technique is that it can result in the loss of depth of information from respondents (Light et al., 2009). Nonetheless, it provided valuable information about participants’ views and assessment of current systems, and desires for better inclusive LAS.
All aforementioned studies had a strong focus on geometrical accuracy and completeness. In the following years after these initial studies, different research projects shifted this focus to other geodata quality measures. The evaluation of attribute information revealed that the removal of topological errors in OSM for Great Britain was not keeping up with newly introduced data errors in the database . Canavosio-Zuzelski et al.  introduced a photogrammetric approach to assess and enhance the positional accuracy of the OSM street network data using stereo imagery and a vector adjustment model. In their method, they compared the road centerlines with referenced satellite imagery in the U.S. Based on several test areas, their proposed approach was able to improve the positional point accuracy and to recover the positional street displacement of OSM data. In a different study , a variety of methods were applied to evaluate positional and linear geometric accuracy and area shape similarity among datasets for integration purposes in different study areas for the UK and Iraq. The researchers concluded that the integration of OSM into the official dataset caused several issues from the geometrical matching perspective. Major differences can be accredited to the varying data collection procedures in OSM. In their test areas, some of the data was remotely mapped by contributors from different countries with little to no local knowledge. Hagenauer and Helbich  presented an algorithm that allowed the mining of land-use patterns from the OSM street network. This was the first approach that actively enhanced the existing or generated a new dataset based on the collected VGI data. Additionally, Helbich et al.  presented a spatial statistical method to compute the positional accuracy of road junctions by extracting and comparing these particular features in OSM to a proprietary dataset.
A human sensor web is an assembly of publicly available Web services which people with mobile phones (‘human sensors’) use to report and publicise issues and share information (Georgiadou et al. 2011). Water, electricity and urban transportation networks contain a multitude of electronic sensors recording parameters continuously or on demand, accessed remotely or physically. The human sensor can be considered as equivalent to this network, allowing operators to use them as collectors of quantitative and qualitative parameters. Human sensors enable their mobile devices (phones or tablet computers) to record these parameters, actively - when encountering an event or by allowing third parties to use their devices for sensor measurements, or passively - if the mobile device enters an active monitoring network. HSW is thus positioned between the open ended VGI and the more focused Participatory Sensing (Burke et al. 2006; Verplanke et al. 2010). In HSW, (see Case 2 below: HSW project, Zanzibar), the supplier of the content is “volunteering” to generate content with limited degrees of freedom; and with crowdsourcing techniques (participatory sensing), the task and purpose of collecting information are usually even more explicitly indicated to the user.
2.3.3 Factors driving contemporary LASs
The variables which drive contemporary land administration systems are themselves disparate and multi-faceted. Firstly, tenure may involve aspects such as occupancy, usufruct (official usage rights without ownership), informal rights, customary rights, and indigenous and nomadic rights, with varying levels of security depending on their level and application. Informal land rights differ from formal ones, but land tenure can migrate from one category to another, be upgraded over time and change towards better levels of security. For example, the informal settler may achieve an improved level of tenure if the government begins to formally recognize certain group rights (Quan and Payne, 2008). Secondly, the legal rights used in land administration may reflect a number of acceptable and practical systems such as tribal rights, or religious rights embedded in the Islamic system, for example, and customary rights. A third variable is accuracy, which reflects the different methods and techniques for collecting cadastral data employed by officials. This recognition of variability could be extended to public participation in collecting VGI, for example by field sketches, satellite imagery, or GPS observations. Volunteers may themselves exhibit useful variability. For example, young amateurs may have technical experience whilst elders possess useful historical information, while young people may prefer to use digital technologies (e.g. iPad or GPS) but older people may prefer to use sketch maps to present their land information to the system of public participation.
The findings of metric values showed that the differences between different spatial extents can be quite diverse. The first optimization method used was the quadtree gridded algorithm. While the optimal area result encapsulated a reasonable subset of the overall data matching the criteria needed for a skateability analysis, it could not overcome the inherent deficiency of standard grids being used. The optimal area found for the RinkWatch data contained several aggregated counts of readings that were much higher than the surrounding counts. This would bias the skateability analysis as a rink with 394 contributions would be much more accurate than the average rink with 73 contributions. While there could be uses for finding the most prolific contributors, such as an analysis to assess socio-economic factors that may explain strong participation rates, this was not the focus of this paper. Often, multi-scale approaches to an analysis may be constrained to a gridded approach, especially in cases of comparison to raster data, though there are efforts to combat this such as Galpern’s study of landscape gene flow at multiple spatial scales using landscape connectivity (2012).
One of the key issues identified through analysing current research relative to the human issues of neogeography is ‘how VGI maps and mashups are produced and used, and how do involved
stakeholders interact’ (Rouse, Bergeron & Harris 2007, Harding et al. 2009). This paper illustrates
how VGI is contributed and utilised by different individuals in terms of map choice, use of information, trust, influence, community, concerns, tensions, idealism and relationships.
1. I NTRODUCTION
Traditionally geographic data are captured by well trained specialists using state of the art technology. Land survey, photogrammety, remote sensing, sensor networks, are examples of methods used to capture data about social and environmental phenomena above, on, or under the Earth’s surface. Recent developments like Web 2.0 platforms, GPS enabled cell phones and sensor technology make capturing of geographic data no longer the exclusive domain of well trained professionals, but opens new possibilities for involvement of citizens (Craglia, 2008). Every human is able to capture geographicinformation about social and environmental phenomena, perhaps facilitated by simple aids as GPS and other means to take measurements of environmental variables. Internet provides the means to upload those observations and share it with other users (Goodchild, 2007b).
This section reviews the events and crowd mapping that occurred during the 2010/11 Australian Floods, the 2011 Christchurch earthquake in New Zealand and the 2011 tsunami in northern Japan. A common theme among the three cases is that the Ushahidi platform was utilized during each of the natural disasters. Ushahidi is a non‐ profit technology company that specializes in the development of free and open source software for information collection, visualization and interactive mapping (Ushahidi ,2011). Crowdmap is an on online interactive mapping service, based on the Ushahidi platform (Crowdmap, 2011). It offers the ability to collect information from cell phones, email and the web, aggregate that information into a single platform, and visualize it on a map and timeline. Ushahidi was originally created to coordinate information relating to riots that broke out after the disputed Kenyan election in 2007. Since then, the platform has been used extensively, ranging from spreading information during the Haitian earthquake in January 2010 to dealing with snow removal in New York City.
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.
Furthermore, the development of highly skilled amateur birders means that VGI dealing with bird counts may be more reliable than in other fields. In fact, “identification skills have become a standard by which many birders judge and accept others as rightful members of the birding social world…result[ing] in the emergence of performance standards characterized by exclusivity and elitism” (Scott, Cavin, Cronan and Kerina, 2005, p. 3). Indeed, there is a form of purism associated with counting birds, as in the case of James Vardaman’s Big Year in 1979, when he counted 699 birds by spending thousands of dollars on field guides, drawing the ire of dedicated amateur birders (Scott, Cavin, Cronan and Kerina, 2005, p. 3). Unlike scientists, who may rely on grants or limited field studies, “it is not uncommon for hardcore birders to drop everything they are doing and spend hundreds, if not thousands, of dollars for an opportunity to see [a rare] bird…those hardcore participants pursuing a Big Year spend hundreds of days birding, many of these away from home” (Scott, Cavin, Cronan and Kerina, 2005, p. 4). The passion and dedication of volunteers who provide geographic data for locating birds should not be underestimated.
The test dataset was derived from the Map Kibera Project (http:mapkibera.org), a res- ident led effort to map the neighborhoods and community amenities of the Kibera region of Nairobi, Kenya. The dataset consists of a shapefile of VGI, GPS points with an assigned toponym for one of fifteen neighborhoods. Additionally, the dataset contains a set of poly- gons for the neighborhood boundaries. The line dataset used to represent major physical barriers (and thus potential neighborhood boundaries) was derived from road, river, and railway data courtesy of OpenStreetMap, an open source repository of transportation data. The Map Kibera Project is a community and NGO effort to collect significant geographic features of Kibera. The group splits into teams to collect points of interest (e.g., schools, clinics) throughout the community using GPS receivers. Each point has the neighborhood that the point resides in as part of its metadata. The neighborhood assignment to indivi- dual points was determined through consensus with the residents involved in the project. We use the point locations and the metadata as the input to test our algorithm. The neigh- borhood boundaries were hand-drawn over a satellite image of Kibera by a group of resi- dents and volunteers. We use the resident drawn boundaries as the basis of comparison for our algorithm. The Kibera dataset was selected because the neighborhood boundaries were community derived rather than defined administratively or by a cartographic expert. Moreover, the VGI point data is well distributed and each labelled point falls into its re- spective neighborhood. Figure 1 shows the neighborhood boundaries overlaid with their respective neighborhood points.