IEEE Geoscience and Remote Sensing Magazine - September 2016 - 45

complex tasks requiring additional responsibility. Where
possible, tasks should also be fun, and participation should
be made as easy as possible, minimizing technical, logistical, legal, and intellectual barriers.
Motivation is also clearly linked to maintaining participation long term to ensure the sustainability of the project
should this be desired, e.g., some tasks may be completed
after a finite period of time. Sustainability refers to retaining a community as well as the services or apps that have
been developed as part of a project. In terms of retaining a
community, methods that have been shown to work include
giving rapid feedback to participants and providing regular
communication about their contributions. Volunteers like
the idea of knowing that their work is important and that
their contributions can help scientists make better and more
comprehensive analyses. They also like to see their contributions, e.g., if you upload a new track to OSM, you will see the
change reflected very quickly. Rewarding citizen scientists
is another effective way to encourage and support participation, e.g., by providing participants with certificates of recognition, providing access to the data, providing different levels
of progression or reputation ranking (e.g., used by eBay and
TripAdvisor), gamefication to introduce an element of competition between participants, different kinds of prizes, and
inclusion in scientific publications.
In the Geo-Wiki project, different prizes, such as Amazon vouchers and electronics, have been used as incentives
as well as coauthorship on papers [32], [45]. In the Earthwatchers and guardians applications, there were no extrinsic
motives, but the users could alert local authorities when they
detected illegal deforestation, giving them the feeling of empowerment and the effectiveness of their contributions. In
the iSPEX project, it was noted that people who were intrinsically motivated were the most valuable. They committed to
the project by buying the iSPEX add on for a couple of euros.
These participants keep on measuring because they like contributing to science, and, in the end, the data may help to
improve air quality. They also contribute because it is fun.
For this reason, iSPEX units will not be free anymore. There is
also instant qualitative feedback on the measurements, and
the measurement appears on a live map with all the others,
creating a sense of community.
THE DIGITAL DIVIDE AND OTHER INEQUALITIES
Individuals are motivated by different drivers that differ
across communities and demographic groups. Some communities are excluded, and identifying the barriers to participation is important for finding solutions to widening
participation. For example, certain age groups have lower
rates of household Internet penetration and mobile phone
usage, so online CS and crowdsourcing activities will not
reach those age groups as effectively as others. Technology
is also a potential barrier due to the cost as well as the ability to use it effectively, which is also partly determined by
socioeconomics. Certain ethnic groups have been underrepresented within CS projects in the United States, and
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there has been an overrepresentation by more affluent
groups [8]. Language barriers may also be an issue because
many CS projects and websites are in English, which can
make it difficult to manage a large-scale project that is
meant to be geographically distributed. In the future, as
technology and the Internet of Things become even more
prevalent, the barriers to participation may be significantly
lowered; however, other inequalities may still persist and
should be considered.
DATA ISSUES: PROTOCOLS, INTEROPERABILITY/
STANDARDS, LEGAL ISSUES, AND DATA PRIVACY
Many CS projects have established data collection protocols, particularly those in the fields of ecology, biology, and
nature conservation. However, this is less true of VGI and
projects, such as OSM, where contributors typically operate
without central coordination or strict data collection frameworks. Moreover, the GPS in mobile phones may not be accurate enough to meet the minimum data specifications of
some NMCAs. Despite some of these known limitations, if
crowdsourced data are to be used alongside authoritative
sources of data, they must meet certain quality standards.
Data collection protocols or templates are one way to help
ensure that these standards are met. Project design will inevitably involve tradeoffs between achieving scientific goals,
e.g., gathering comprehensive high-quality data according to
rigorous scientific protocols, and the ease of data collection.
If the data collection is too complex or too time consuming,
volunteers often lose their desire to participate. Therefore,
understanding and adapting the program to the skills, expectations, and interests of the volunteers is critical. Training
is another way to help ensure more accurate data collection;
e.g., Jackson et al. [46] found that the accuracy of data points
collected in OSM improved when a collaboration was set up
between OSM and the U.S. Geological Survey, which provided feedback to volunteers on quality. There are issues related to social media platforms being used as the data, which
may often need to be scraped, cannot be archived, curated,
or made available for reuse. This represents a barrier to the
reproduction of scientific results, which makes it difficult to
evaluate research that uses data from these sources.
Data interoperability and the use of standards are other issues that become relevant when crowdsourced data
are shared between distributed systems. Unlike authoritative data, crowdsourced geospatial data do not normally have metadata that conform to specific standards. Kalantari et al. [47] have recently proposed a new metadata
standard for VGI called Geospatial Metadata 2.0. The Infrastructure for Spatial Information in the European Community directive may provide some useful guidance, particularly for CS and crowdsourcing initiatives in the EU.
The long-term preservation and curation of the data are
also issues related to the sustainability and require a data
management plan.
Finally, addressing legal issues and data privacy are key
challenges for CS and crowdsourcing, which are complicated
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Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - September 2016