Content uploaded by Joao Porto De Albuquerque
Author content
All content in this area was uploaded by Joao Porto De Albuquerque on Nov 06, 2016
Content may be subject to copyright.
Improving the Involvement of Digital Volunteers in
Disaster Management
Roberto dos Santos Rocha1,2, Adam Widera2, Roelof P. van den Berg2,
João Porto de Albuquerque3,1, and Bernd Helingrath2
1 ICMC, University of São Paulo, São Carlos, Brazil
rsrocha@usp.br
2 ERCIS, University of Münster, Münster, Germany
{adam.widera, roelof.vandenberg,
bernd.hellingrath}@ercis.uni-muenster.de
3 CIM, University of Warwick, Coventry, United Kingdom
j.porto@warwick.ac.uk
Abstract. Volunteered geographic information (VGI) has been seen as useful
information in times of disasters. Several authors have shown that VGI is useful
for coping with preparedness and response phases of disaster management. How-
ever, because it is still a young technology, the use of VGI remains uncertain,
due to its lack of strong reliability and validity. It is our assumption that to im-
prove reliability and validity the promotion of citizen engagement (CE) is
needed. CE is not new topic, but in the digital humanitarian context, it involves
important factors that are not yet considered by disaster managers, such as com-
munication processes, motivation of volunteers, different media for production
of information, etc. To fill this gap, we identified a set of preliminary factors
which should be considered to promote the involvement of volunteers in disaster
management. These factors were derived from critical review of CE literature
and from an analysis of lessons learned from an experiment on interaction with
citizens carried out in context of the EU-project “DRIVER – Driving Innovation
in Crisis Management for European Resilience”.
Keywords: Citizen Engagement · Volunteered Geographic Information · Moti-
vation · Crowd Sensing · Disaster Management
1 Introduction
Citizen Engagement (CE) refers to actions designed to identify and address issues of
public concern [1]. Community participation can augment officials’ abilities to govern
in a crisis, improve application of communally held resources in a disaster or epidemic,
and mitigate community wide losses [2].
Participatory community approaches in research and governance are not new [3, 4].
However, Web 2.0 platforms, mobile internet, and social networking access through
smartphones have made a significant difference by encouraging the social responsibil-
ity and active engagement of citizens [3]. These technologies enable the public to con-
tribute and participate on an unprecedented scale and have led to many diverse initia-
tives using information from citizens [3, 4]. Examples include, among others, partici-
pation of the public in event reporting, environmental monitoring, and providing infor-
mation on natural disasters. This phenomenon is called volunteered geographic infor-
mation (VGI).
VGI is the harnessing of tools to create, assemble, and disseminate geographic data
provided voluntarily by individuals [5]. VGI has been increasingly recognized by re-
searchers as an important resource to support disaster management [23, 24, 25]. The
production of geographic information is predominantly made through social media (e.g.
Twitter
1
), crowd sensing (e.g. citizens equipped with smartphones can report about lo-
cal conditions using dedicated applications) and online mapping tools (e.g. Open-
StreetMap
2
, Wikimapia
3
and Google Map Maker
4
) [5, 6, 7].
Whilst those platforms can be potentially used to provide useful information for
dealing with disaster management, there are still many challenges to be addressed, for
instance: (i) how can people be encouraged to provide valuable information; (ii) how
can information from volunteers be validated; and (iii) how can this information be
integrated with other sources of data [6, 8].
Many governments and agencies recognize the opportunities and challenges posed
by informal volunteers, and many have developed strategies and resources for engaging
and managing them. However, organizational culture, risks and liabilities impose sig-
nificant barriers to greater involvement of informal volunteers in emergency and disas-
ter management [2].
Different VGI categories – social media, crowd sensing, and collaborative mapping
activities – require different strategies for promoting citizen engagement. It is our as-
sumption that knowledge of the VGI categories is relevant for disaster managers to
recruit and motivate users to utilize VGI-systems.
Additionally, works related to VGI in disaster management focus on production by
volunteers and the use of this information by disaster managers. They disregard the fact
that the production and consumption of VGI should be seen within a communication
process, i.e., the communication among the stakeholders should be multidirectional.
To help fill this gap, we present in this paper a preliminary set of key factors to help
promote the involvement of volunteers in the disaster management domain. These fac-
tors were derived from review of CE literature and from an analysis of lessons learned
from a simulation exercise carried out in context of the EU-project
“DRIVER – Driving Innovation in Crisis Management for European Resilience”. The
DRIVER project was launched in May 2014. This project, gathers the expertise of 37
organizations, and will jointly develop solutions for improved crisis management. Rep-
resentatives from the security & defense industry, research and academia, SMEs, end-
1
https://twitter.com/
2
https://www.openstreetmap.org
3
http://wikimapia.org/
4
https://www.google.com.br/mapmaker
users and several European institutions, from 13 EU member states and 2 associated
countries participate in this innovative venture.
With this work we aim to answer the following research question:
RQ. What factors should be considered by disaster managers to improve the
involvement of digital volunteers?
The remainder of this paper is organized as follows. First, in order to set a ground
on the different VGI approaches an overview is presented in Section 2. In Section 3 we
present a review on the motivation and engagement of digital volunteers. In Section 4
we present factors to improve citizen engagement, based on lessons learned from an
experiment on interaction with citizens carried out in context of the EU-project. Finally,
in Section 5, we conclude with final remarks and give potential directions for future
works.
2 Volunteered Geographic Information
Volunteered Geographic Information (VGI) [5], also called Crowdsourcing Geo-
graphic Information (CGI) [6], is spatially related information which is volunteered,
produced, and made available by common citizens using appropriate tools, such as in-
teractive mobile devices and Web portals [5, 6]. In the last few years, VGI has emerged
as an alternative spatial data source on the Web and has been increasingly recognized
by researchers as an important resource to support disaster risk management [9].
According to Senaratne et al. [12], the effective utilization of VGI is strongly asso-
ciated with data quality, which varies depending primarily on, (i) the way data is col-
lected on different VGI platforms (VGI source), (ii) the type of VGI, and (iii) the con-
text of usage. VGI is not uniform and the understanding of different components and
perspectives is key to develop the strategies necessary to promote the engagement of
volunteers.
2.1 VGI Source
In general, VGI in the context of disaster risk management can be collected through
different collaborative sources [6]: (i) social media; (ii) crowd sensing; and (iii) collab-
orative mapping activities.
The first category of geo-information (i) involves the use of existing social media
platforms to exchange information in an unstructured way. These platforms enable cit-
izens to share self-produced content within a network of contacts or for the general
public [6]. Common social media platforms include Twitter, Facebook
5
, Flickr
6
, and
YouTube
7
.
The second category of geo-information (ii) relies on citizens on the Web or
equipped with smartphones to act as sensors and share observations [6, 10]. The term
5
https://www.facebook.com/
6
https://www.flickr.com/
7
https://www.youtube.com/
‘crowd sensing’ is used to describe approaches that make use of specific software ap-
plications to provide more precise structured data [6]. Ushahidi-based platforms and
mobile applications are the most commonly used in this category for data collection.
GDACSmobile, for instance, is a tool that facilitates the self-organization of volunteers
and improves the situational awareness of citizens by sharing an easy-to-understand
overview of the state of affairs. At the same time, GDACSmobile also provides a feed-
back mechanism to the crisis manager/control center [11].
The third category of geo-information (iii) consists of a specific type of information
and collaboration platform: the collaborative editing of geographic features to fulfill
internet-based interactive maps. Well-known platforms like OpenStreetMap (OSM),
Wikimapia and Google Map-Maker fall into this category [6].
Collaborative mapping activities are essential for disaster management, because they
collect a very specific type of data – namely, georeferenced data about features like
streets and roads, buildings etc. – and structures this information in the form of a map
[23]. The OpenStreetMap (OSM) project has great potential in disaster scenarios, which
was shown when a large number of volunteers provided their support in mapping events
after the 2015 Haiti earthquake [26, 27] and the 2015 Nepal earthquake [23]. Collabo-
rative mapping in OSM has emerged as a key mechanism through which individuals
can provide information about affected areas, thus making a tangible difference to aid
agencies and relief work without actually being physically present on-site [23].
2.2 VGI Types
Senaratne et al. [12], categorize the main types of VGI as (i) text-based VGI, (ii)
image-based VGI, and (iii) map-based VGI.
Text-based VGI is generally produced implicitly on portals, such as Twitter or vari-
ous blogs, where people contribute geographic information in the form of text by using
smartphones, tablets etc. [12]. Twitter, for example is used as an information foraging
source [12, 13]
Image-based VGI is generally produced implicitly within portals such as Flickr, In-
stagram, etc., where contributors take pictures of a particular geographic object or sur-
rounding with cameras, smart phones, or any hand held device, and attach a geospatial
reference to it [12].
Map-based VGI covers all sources that include geometries as points, lines, and pol-
ygons, which are the basic elements to design a map. Among others, OSM, Wikimapia,
Google Map Maker, and Map Insight are examples of map-based VGI projects [12].
Table 1 presents the relationship between sources and the different types of VGI.
Table 1. A summary of the source and types of VGI
Source
Type
Text
Image
Map
Social Media
X
X
Crowd Sensing
X
X
Collaborative Mapping
X
2.3 Typology of VGI
Craglia et al. [3] introduced the concept of typology in VGI. According to these
authors, there are two modes through which individuals or communities contribute such
information: first, the way the information was made available and second, the way
geographic information forms a part of it.
Each of these two dimensions can be ‘explicit’ or ‘implicit’, with explicit denoting
that the dimension is of primary concern to the piece of VGI, while implicit denotes
that the dimension was not originally an integral part, and is of secondary concern [3].
Thus the topology of VGI proposed by Craglia et al. [3] is a matrix of four types of
VGI as shown in Table 2.
Geographic location is essential in disaster analysis [14]. Thus, only the explicit ge-
ographic category – explicitly- or implicitly-volunteered information – of the VGI ty-
pology is used in this paper.
Table 2. Typology of VGI [14] (adapted from Craglia et al. [3])
Geographic Information
Explicit
Implicit
Explicitly Volunteered
“True” VGI, e.g., Open-
StreetMap
Volunteered (geo)spatial
Information, such as Wik-
ipedia articles about non-ge-
ographic topics containing
place names
Implicitly Volunteered
Citizen-generated geo-
graphic content (CGGC),
e.g., Tweets referring to
the properties of an iden-
tifiable place
User-Generated
(geo)Spatial Content
(UGSC), such as Tweets
only mentioning a place
in the context of another
(non-geographic) topic
Nevertheless, the typology of VGI proposed does not take in account the fact that
different VGI types have different translation needs, which this may imply excess noise
(e.g., many useless messages before a useful message to be found). For example, a
picture of a flooded area is more effective (i.e., it has less translation needs) for disaster
managers than a short message (tweet) describing the same flooded area. Considering
this aspect, we propose a new typology of VGI, which considers different levels of
uncertainty – noise and translation needs. As can be seen, the explicit VGI sources –
crowd sensing, and collaborative mapping activities – have fewer translation needs than
social media data.
Fig. 1. Typology of VGI considering different levels of uncertainty - noise and translation needs
3 Motivation and Engagement of Digital Volunteers
A Digital volunteer or a digital humanitarian is an individual that applies and lever-
ages their technical skills in collecting, processing and managing data in support of
response efforts for disasters [30]. In most cases, he/she is not physically present at the
place where the disaster has occurred. The Digital Humanitarian Network
(DHNetwork) grew out of this ecosystem of emerging technical volunteer involvement
based throughout the globe [29]. Since the 2010 Haiti earthquake, these communities
have provided support to formal humanitarian operations [27], and more recently have
provided a crucial compliment to operational organizations and governments active in
the field [29].
However, it is still a challenge to keep digital volunteers motivated and engaged for
longer periods, especially considering that they do not have strong connections to
events due to their digital presence instead of physical presence in affected areas. This
requires different ways to motivate and engage them to provide high quality contribu-
tions in future crisis/disaster situations.
To deal with this critical issue, we propose in this section a first attempt to under-
stand how motivation of digital volunteers can be understood using, for instance, the
Valence, Instrumentality, Expectancy (VIE) approach [17].
In general, volunteers are motivated by many incentives. Examples include, ideol-
ogy, personal satisfaction, community, and humanitarian values. Particularly, in the
context of a digital humanitarian, there is another factor that should be considered: the
desire to apply and improve technical knowledge. Considering that many of these vol-
unteers come from open source communities [28], why is it so important to understand
the incentives of citizens in order for them share their observations from the field? Con-
sidering the 90-9-1-Rule [31], there is a so called “participation inequality”. According
to this rule, in a collaborative online environment, 90% of the participants of a commu-
nity only view content, 9% of the participants edit content, and only 1% of the partici-
pants actively create new content [31].
In addition to such factors, it is important to understand two specific questions: (i)
why does a citizen report an observation from the field, and (ii) does the information
reported supports the decision making of disaster managers.
The first question is related to behavioral aspects, i.e., what are the incentives of
citizens in order for them to want to share their observations from the field? Literature
commonly understands incentives as instruments influencing the behavior of members
of an organization or community in order to adapt to the organization wide system of
objectives [18]. By creating incentives, certain desired modes of behavior from indi-
viduals are promoted, enabling specific situational conditions which in turn result in
the activation of individual motives. In this case, a motive denotes a time-invariant psy-
chological disposition, i.e. an isolated, not yet activated incitement for the behavior.
Capelo et al. [28] highlight some important incentives:
─ Encourage volunteers by giving feedback, recognition, appreciation and grati-
tude;
─ Cultivate a sense of ownership and accountability. Team members have to know
that they matter, and that they are making a difference in the humanitarian oper-
ation;
─ Generate a feeling of inclusivity based on a system of collaboration, partnership
and sharing with multiple stakeholders;
─ Provide training and capacity-building opportunities for volunteers.
The second question is related to the contribution of effort input. For instance, the
reporting of a flooded road is higher, if the reporter (or citizen) is affected or not. Ac-
cording to the VIE approach [17], the effort input of an individual is high when it ex-
pects that the contribution will yield results, which are first important to the organiza-
tion or community, secondly, due to the expected instrumentality, show close relation-
ships to individually aspired results from extrinsic incentives, and thirdly exhibit va-
lences as high as possible.
4 Lessons Learned to Improve the Involvement of Volunteers
In this section, we summarize the lessons learned to improve the involvement of
volunteers in the production of high-quality relevant information for disaster manage-
ment. The elements presented here were derived from the VGI and citizen engagement
literature reviews, as well as the analysis of lessons learned from a field experiment
carried out in the EU-funded demonstration project DRIVER.
The selection process of the literature was based on the experience of the authors of
this paper. Regarding the VGI literature, four main works were selected as an input to
identify the main characteristics that may affect the digital volunteers’ engagement:
Albuquerque et al. [6], Craglia et al. [3], Klonner et al. [14], Senaratne et al. [12]. Re-
garding motivation and engagement of volunteers, we selected Nielsen [31], Lawler
[17], and Capelo et al. [28] to understand the main aspects that should be considered in
the digital volunteers’ engagement.
The field experiment was based on a storyline designed by practitioners. They de-
fined a fictitious disaster event based on past experience, which resulted in a more re-
alistic and relevant scenario compared to a more tool-friendly situation designed by the
tool providers. The experiments conducted in the DRIVER “Interaction with Citizens”
campaign concentrate on the following functions (a more detailed description of this
experiment can be founded in Havlik et al. [33], Middelhoff et al. [32] and van den
Berg et al. [22]):
─ Provision of context-aware and timely information tailored to the specific needs
of different societal groups over various channels, in order to improve their un-
derstanding of the crisis situation and to minimize adverse impacts.
─ Context-aware (micro-)tasking of non-affiliated volunteers to perform real and
virtual tasks.
─ Efficient gathering of situational information about an incident from volunteers.
─ Efficient usage of received information from volunteers to improve the situational
awareness of crisis managers and consequently their handling of the crisis.
In the following subsections, we will summarize the findings into two categories: (i)
characteristics of VGI, and (ii) communication processes.
4.1 Characteristics of VGI
The characteristics of VGI – origin, type and typology – shown in Section 2 have an
impact on citizen engagement.
In regard to the origin of VGI, crowd sensing and collaborative mapping activities
have the potential to promote citizen engagement, due the ways in which the infor-
mation is created.
Thus, we propose a typology of engagement based on five levels of involvement
from volunteers in scientific work [20] and a typology of participation proposed by
Pretty and Hine [21]. At the first level, citizens provide resources, while having only a
minimal cognitive engagement. This level is called ‘basic’. In second level, ‘distributed
intelligence’ relies on the cognitive ability of the participants. After some training, the
participants collect data or engage in minor interpretation activities. At this level, qual-
ity evaluation by the volunteers is crucial. The third level represents ‘participatory en-
gagement’, where users take part actively in the problem definition and data collection.
On the last level, ‘self-mobilization’, non-professionals collaborate with professionals,
and together, decide on a problem they want to focus on and the methods for data col-
lection. This allows for both the consideration of interests and motivation of the volun-
teers. On this level, volunteers are not only experts, but also have the role of facilitators
[20, 14].
Table 3. Typology of engagement
Typology of VGI
Levels of Engagement
Source
Explicitly
Implicitly
I
II
III
IV
Social
Media
X
X
Crowd
Sensing
X
X
X
X
X
Collaborative
Mapping
X
X
X
X
X
Legend: I-Basic. II-Distributed intelligence. III-Participatory engagement.
IV-Self-mobilization
As shown in Table 3, social media presents a ‘basic’ level of engagement. This is be-
cause of the nature of its contribution, i.e., social media is provided implicitly. All other
VGI sources, provided explicitly, require different strategies for citizen engagement,
since many have volunteers with different levels of knowledge and motivation.
4.2 Communication Process
The elements in the communication process determine the quality of communica-
tion. A problem in any one of these elements can reduce communication effectiveness
[15]. For instance, different perceptions of the message, language barriers, interrup-
tions, emotions, and attitudes can all reduce communication effectiveness. Therefore, a
feedback mechanism should be considered to promote the involvement of volunteers.
The existing literature on VGI focuses on the production of geographic information,
and the use of this information by disaster managers. It disregards the fact that the pro-
duction of VGI should be seen within a more effective communication process, i.e., the
communication among the stakeholders should have to include a mechanism for con-
tinuous feedback.
In the context of the DRIVER Project, tools were proposed that should address both
objectives for the benefit of the community, and for individual members of the popula-
tion according to the VIE approach. In a recent field exercise, the software tool
GDACSmobile was used to communicate observations to crisis mangers (community-
objective). The personal objectives of members of the population were addressed by
sharing reports with the community as an information layer on a map of the environ-
ment around the user. In this way, the users could assure themselves of their safety in
the situation and strengthen their situational awareness using map representation.
Consequently, by acknowledging the perceptions of community members, a com-
mon language visualizing interactions becomes a basic requirement for an appropriate
crisis communication environment. One way to establish a common language is to use
VGI systems in combination with commonly used information categories and accord-
ing pictograms [22]. In the meantime, many different VGI tools are available, having
different pros and cons regarding particular tasks. However, as mentioned above, the
main challenge here is less a technical problem, but rather an organizational one. Ac-
cording to the discussion on incentives, we identify a trustful and open solution as most
appropriate. However, it must be able to visualize benefits for the community, i.e. an
easy- and fast-to-understand situation overview including a connection to responding
authorities. In order to do so, the information should be structured and visualized re-
specting the communities’ attributes (like age distribution, language, technical affinity,
etc.).
5 Conclusion
Further research is still necessary for engaging volunteers in the production of high-
quality relevant information for disaster management. For instance, how can we ensure
that local communities are involved at a meaningful level in different phases of a dis-
aster? This could be achieved, for instance, by initiating a community group or by
providing training to volunteers to produce high-quality VGI. Moreover, how to im-
prove collaboration between formal humanitarian organizations and volunteer technical
communities (VTCs) should also be explored in future works.
Additionally, the different VGI categories require different strategies for promoting
citizen engagement, given that the knowledge of the VGI categories is relevant for dis-
aster managers to recruit and motivate users to utilize VGI-systems. Therefore, one of
the expected outcomes will be the development of a new framework for promoting
engagement of digital volunteers in the disaster management context.
Acknowledgments
The research that led to this work was funded by the European Community's Seventh
Framework Programme: Marie Curie Actions/Initial Training Networks under grant
agreement n° 317382 and FP7/2007-2013 under grant agreement n° 607798. João Porto
de Albuquerque is grateful for the financial support from the CAPES Pró-Alertas (grant
nº 88887.091744/2014-01 and nº 88887.091743/2014-01). The authors would like to
thank Robin Mays and Lívia Degrossi for their helpful comments and suggestions, and
the DRIVER “Interaction with Citizens” experiment team that has been working to-
gether for several months in order to prepare, conduct and finally assess the field exer-
cise in The Hague.
References
1. American Psychological Association, http://www.apa.org/education/undergrad/civic-en-
gagement.aspx
2. Schoch-Spana, M., Franco, C. Nuzzo, J. B., Usenza, C.: Community Engagement: Leader-
ship Tool for Catastrophic Health Events Biosecur. Bioterror. 5(1), 8-25 (2007)
3. Craglia, M, Ostermann, F., Spinsanti L.: Digital Earth from vision to practice: making sense
of citizen-generated content. Int. J. Digital Earth. 5(5), 398-416 (2012)
4. Weiner, D. and Harris, T.M.: Participatory geographic information systems. In: Wilson, J.P.
and Fotheringham, A.S. (eds.) The handbook of geographic information science, 466 -480.
Blackwell Publishing, Malden (2008)
5. Goodchild, M.F.: Citizens as sensors: the world of volunteered geography. GeoJournal,
69(4), 211-221 (2007)
6. Albuquerque, J.P., Eckle, M., Herfort, B, Zipf, A.: Crowdsourcing geographic information
for disaster risk management and improving urban resilience: an overview of the lessons
learned. In: Capineri, C. et al. (eds) European Handbook of Crowdsourced Geographic In-
formation, Ubiquity Press. (2016)
7. Haworth, B.: Emergency management perspectives on volunteered geographic information:
Opportunities, challenges, and change. Comput Environ Urban Syst. 57, 189-198 (2016)
8. Horita, F.E., Albuquerque, J.P., Degrossi, L.C., Mendiondo, E.M., Ueyama, J.: Develop-
ment of a spatial decision support system for flood risk management in Brazil that combines
volunteered geographic information with wireless sensor networks. Computers & Geosci-
ences, 80, 84-94 (2015)
9. Poser, K., Dransch, D: Volunteered geographic information for disaster management with
application to rapid flood damage estimation. Geomatica, 64, 89–98 (2010)
10. Ma, H., Zhao, D., and Yuan, P.: Opportunities in mobile crowd sensing. IEEE Communica-
tions Magazine. 52(8), 29–35 (2014)
11. Link, D., Widera, A., Hellingrath, B., Limbu, M.K.: GDACSmobile - An IT Tool Supporting
Assessments for Humanitarian Logistics. In: The International Emergency Management So-
ciety 2015 Annual Conference (2015)
12. Senaratne, H. Mobasheri, A., Ali, A.L., Capineri, C., Haklay, M.: A review of volunteered
geographic information quality assessment methods. Int. J. Geogr. Inf. Sci. 1-29 (2016)
13. MacEachren, A.M., Jaiswal, A., Robinson, A.C., Pezanowski, S., Savelyev, A., Mitra, P.,
Zhang, X., Blanford, J.: Senseplace2: geotwitter analytics support for situational awareness.
In: IEEE conference on Visual Analytics Science and Technology, Providence, RI. Pisca-
taway, NJ: IEEE, 181–190 (2011)
14. Klonner, C., Marx, S., Uson, T., Albuquerque, J.P., Höfle, B.: Volunteered Geographic In-
formation in Natural Hazard Analysis: A Systematic Literature Review of Current Ap-
proaches with a Focus on Preparedness and Mitigation. ISPRS Int. J. Geo-Inf. 5,
103, 1-20 (2016)
15. Keyton, J.: Communication and organizational culture: A key to understanding work expe-
rience. Thousand Oaks, CA: Sage (2011)
16. Ulmer, R.R.: Effective Crisis Management through Established Stakeholder Relationships:
Malden Mills as a Case Study Manag. Commun. Q. 14, 590-615 (2011)
17. Lawler, E.E.: Pay and organization development. Reading, Mass (1981)
18. Grob, H.L., Buddendick, C., Albrecht, N., Widera, A.: Towards A Holistic Approach of E-
learning Incentive Management. In: International Conference E-Activity and Leading Tech-
nologies, 280–94 (2007)
19. Horita, F.E.A, Degrossi, L.C., Assis, L.F.F.G, Zipf, A., Albuquerque, J.P.: The use of Vol-
unteered Geographic Information and Crowdsourcing in Disaster Management: A System-
atic Literature Review. In: 19th Americas Conference on Information Systems (2013)
20. Haklay, M.: Citizen Science and Volunteered Geographic Information: Overview and Ty-
pology of Participation. Crowdsourcing Geographic Knowledge, 105-122 (2013)
21. Pretty, J., Hine, R.: Participatory appraisal for community assessment. Centre for Environ-
ment and Society, University of Essex (1999)
22. van der Berg, R., Widera, A., Lechtenberg, S., Middelhoff, M. Hellingrath, B.: Pictograms
and Assessment Categories as Crisis Communication Language: Lessons from a field exer-
cise with GDACSmobile. In: The 3rd International Conference on Information Communi-
cation Technologies for Disaster Management (submitted).
23. Poiani, T.H., Rocha, R.S., Degrossi, L.C., Albuquerque, J.P.: Potential of Collaborative
Mapping for Disaster Relief: A Case Study of OpenStreetMap in the Nepal Earthquake
2015. In 49th Hawaii International Conference on System Sciences (HICSS) (2016)
24. Horita, F.E.A, Albuquerque, J.P.: An approach to support decision-making in disaster man-
agement based on volunteer geographic information (VGI) and spatial decision support sys-
tems (SDSS). In: 10th International Conference on Information Systems for Crisis Response
and Management, Baden-Baden, Germany (2013).
25. Erskine, M., Gregg, D.: Utilizing Volunteered Geographic Information to Develop a
RealTime Disaster Mapping Tool: A Prototype and Research Framework. In CONFIRM
2012 (2012)
26. Eckle, M. and Albuquerque, J.P.: Quality Assessment of Remote Mapping in Open-
StreetMap for Disaster Management Purposes. In Proceedings of the ISCRAM 2015 Con-
ference - Kristiansand, May 24-27 (2015).
27. Zook, M., Graham, M., Shelton, T. and Gorman, S.: Volunteered Geographic Information
and Crowdsourcing Disaster Relief: A Case Study of the Haitian Earthquake, World Medical
& Health Policy, vol. 2, pp. 7–33 (2010).
28. Capelo, L., Chang, N. and Verity, A., “Guidance for collaborating with volunteer and tech-
nical communities”.
29. Humanitarian Response, https://www.humanitarianresponse.info/en/applications/tools/cat-
egory/digital-volunteers
30. Liu, S. “Crisis Crowdsourcing Framework: Designing Strategic Configurations of
Crowdsourcing for the Emergency Management Domain,” Computer Supported Coopera-
tive Work (23:4), pp. 389–443 (2014).
31. Jakob Nielsen, The 90-9-1 Rule for Participation Inequality in Social Media and Online
Communities. https://www.nngroup.com/articles/participation-inequality/ (2006).
32. Middelhoff, M., Widera, A., van den Berg, R., Hellingrath, B., Auferbauer, D., Havlik, D.,
Pielorz, J.: Crowdsourcing and Crowdtasking in Crisis Management Lessons Learned from
a Field Experiment Simulating a Flooding in City of The Hague. In: The 3rd International
Conference on Information Communication Technologies for Disaster Management (sub-
mitted).
