iscuss all facets of the 
comments on columns, 
  
/ Mobile} 
‚Interface j 
gs. comments 
(, JSON 
ometries, TA, 
"ments c 
Fusion | 
hon APí Tables 1 
i 
S 
Web interface, KML 
  
General public 
streams are represented 
ollectors. They gather 
mobile interfaces. The 
ie payload of the data 
ght of the lines denotes 
1aring: as discussed in 
re successful and gain 
tuned to the user's 
e, open means that the 
ups are targeted here: 
ier) who are not able or 
refer a pre-processed 
(volunteers in the third 
The built application 
through the following 
faces, GFT’s export 
ie developer API. The 
to not only input data, 
rmation. GFT's web- 
isualisations. The data 
s access to the data in 
ll-known Text, KML, 
N 
g together of a highly 
st technology and data 
d a certain disaster 
'edict beforehand how 
cipate and which tools 
jn for a crowdsourcing 
olatile nature. What is 
ystem, but a set of 
ell documented and can 
seamlessly be integrated on the fly to provide for the situation's 
needs. This will allow systems to be configured with respect to 
disaster management needs. 
The main strength of the built WebGIS compared to other web 
disaster management solutions is its ability to perform spatial 
analysis in the form of routing. The users are not only gathering 
data for others e.g. relief organizations, but also for themselves 
as they too can use the routing service. The quality of provided 
information is expected to rise once data gatherers experience 
first-hand how the provided data is used and how errors affect 
the routing solution. Data providers and gatherers' mindset 
might change from "contribute occasionally and forget" to 
"contribute continuously and guard quality". The implemented 
communication methods aim at creating a long lasting 
community . 
Parallels between upcoming crowdsourced disaster management 
initiatives and the open source communities are important to 
notice and foster: computer savvy users fiddle daily with the 
technology they later use for disaster management. As such, 
they have momentum and a running, hands-on experience with 
used technologies. Using open source is vital as it enables 
hackers to adapt the software to their needs on the fly. Open 
sourcing a project allows more people to get involved, which in 
turn results in a larger knowledge and contributing user base. 
6. FUTURE WORK 
The application's fitness for use has to be evaluated by 
deploying it in a real-world simulation. The implemented ideas 
and principles are based on theory and have been tested in a 
research environment. 
Trust is an important commodity in crowdsourced projects. 
Mechanisms for increasing trust and checking the 
trustworthiness of data sources should be researched and 
implemented. Several ways of trust generation have been 
brought forward, one of which is communication. For the 
Current application, an extra communication channel is for 
instance an application-wide chat module. Users will then be 
able to discuss all aspects of the application, not only the 
obstacles. Implementing a user management system further 
Strengthens the trust validation process by allowing the 
examination of the contributors' past actions. 
An in-depth study of obstacle input methods has to be 
performed. Drawing polygons may not be the most intuitive 
input method available. Research and field trials may be needed 
to assess the best input methods. 
Currently, an internet connection is required for the prototype 
to function. The availability of working wireless networks 
‘annot be taken for granted during disasters. Therefore a caching 
mechanism needs to be implemented that enables the prototype 
lo function in the absence of internet connectivity. 
Google Maps is chosen due to a lack of open source solutions at 
the time of the here presented application's inception. The 
situation has changed considerably. The OpenStreetM ap 
mapping success in Haiti suggests using OpenStreetMap data in 
combination with OpenLayers. 
7. REFERENCES 
Acuna, P., Diaz, P., & Aedo, I. (2010). Development of a design 
patterns catalog for web-based emergency management systems. In 
Proceedings of the 7th International ISCRAM Conference. 
ISCRAM 
Botterell, A., & Griss, M. (2011). Towards the next generation of 
emergency operation systems. In Proceedings of the 8th 
International ISCRAM Conference. ISCRAM. 
Douglas, D.H., & Peucker, T.K. (1973). Algorithms for the 
reduction of the number of points required to represent a digitized 
line or its caricature. Cartographica: The International Journal for 
Geographic Information and Geovisualization, 10(2), 112—122. 
Flanagin, A., & Metzger, M. (2008). The credibility of volunteered 
geographic information. GeoJournal, 72(3), 137-148. 
Francia, S. (2011). Soap vs. rest. 
http://spf13.com/post/soap-vs-rest 
Gonzalez, H., Halevy, A.Y., Jensen, C.S, Langen, A., Madhavan, 
J., Shapley, R., Shen, W., & Kidon, J.G. (2010b). Google fusion 
tables: web-centered data management and collaboration. In 
Proceedings of the 2010 international conference on Management 
of data, SIGMOD 710, (pp. 1061-1066). New York, NY, USA: 
ACM. 
Goodchild, M. (2007). Citizens as sensors: the world of volunteered 
geography. GeoJournal, 69(4), 211-221. 
Goodchild, M.F, & Glennon, J.A. (2010). Crowdsourcing 
geographic information for disaster response: a research frontier. 
International Journal of Digital Earth, 3(3), 231-241. 
Harvard Humanitarian Effort (2011). Disaster relief 2.0 report: 
The future of information sharing in humanitarian emergencies. 
Washington, DC and Berkshire, UK. 
http://bit.ly/pOW SW 3 
Heipke, C. (2010). Crowdsourcing geospatial data. ISPRS Journal 
of Photogrammetry and Remote Sensing Volume 65, Issue 6, 
November 2010, Pages 550—557 
Laituri, M., & Kodrich, K. (2008). On line disaster response 
community: People as sensors of high magnitude disasters using 
internet GIS. Sensors, 8(5), 3037-3055. 
Lukaszczyk, A. (2011). International experts blend space 
technologies and crowdsourcing to enhance disaster management 
tools. 
http://www .newswisc.com/articles/international- experts-blend- 
space-technologies-and-crowdsourcing-to-enhance-disaster- 
management-tools 
Nedkov S., S. Zlatanova (2011). Enabling obstalce avoidance for 
Google Maps’ navigation service. In: O. Altan, R. Backhous, P. 
Boccardo, D. Stevens, S. Zlatanova (Eds.); Proceedings Gi4DM 
2011, Antalya, 6 p. 
Pautasso, C., Zimmermann, O., & Leymann, F. (2008). Restful 
web services vs. "big"" web services: making the right architectural 
decision. In Proceeding of the 17th international conference on 
World Wide Web, WWW "'08, (pp. 805-814). New York, NY, 
USA: ACM. 
Pucher, A. (2009). Usability and implementation issues for 
cartographic ^ information systems in interdisciplinary 
environments. 
Neis, P., Singler, P., Zipf, A. (2010). Collaborative mapping and 
Emergency Routing for Disaster Logistics - Case studies from the 
Haiti earthquake and the UN portal for Afrika. In Proceedings of 
the Geoinformatics Forum Salzburg. 
Shirky, C. (2009). Here comes everybody : the power of 
organizing without organizations. Penguin. 
Tornqvist, E., Sigholm, J., & Nadjm-Tehrani, S. (2009). Hastily 
formed networks for disaster response: Technical heterogeneity 
and virtual pockets of local order. In Proceedings of the 6th 
International ISCRAM Conference. ISCRAM. 
451