International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B4, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
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Figure 5. OSM Map of Port-au-Prince as of 20 April 2012 
2. SHARING INFORMATION 
2.1 Setting the foundation 
When a disaster occurs one of the priority is to establish a 
communication network. Humanitarian organizations know that 
effective coordination is based on how efficiently the 
information is shared. While in many places on the globe the 
internet connection is still an issue, many investments have 
being done in information and communication technologies 
(ICTs). 
In Haiti the internet connection wasn’t a problem except in the 
very early days after the quake. Field staff was able to 
communicate with their headquarters and receive data coming 
from different sources. In the last few years the problem is 
leaning from sharing basic information (e.g. in paper maps, 
using radio equipment) to receive a huge amount of data and not 
being able to manage it. 
That’s what happened in Haiti. Volunteer and technical 
communities like OpenStreetMap, Ushahidi, Sahana and Crisis 
Mappers created open platforms able to disseminate a 
continuous flow of data the emergency system was not prepared 
to receive due to the lack of adequate resources. Although a 
conservative and consolidated approach is widely adopted and 
recommended during a crisis response, the contribution from 
crowdsourcing will be more and more integrated into the 
system. 
Crowd source mapping is a relative new field that has been 
evolving in the last two years of experience in Haiti, Libya, the 
Sudan, Somalia, Syria, the United States and the UK. Patrick 
Meier identifies the three core drivers of the crowd source 
mapping evolution as open-source mapping tools, mobile data 
technologies and the development of new methodologies 
(Meier, 2009). This is the innovative aspect: the tools are free 
and open-source and do not require much in the way of prior 
training. As the these elements rapidly evolve so does the crowd 
source mapping. It’s a complex phenomenon, rapidly adapting 
to different context and tools and how it could evolve in the 
next future and be integrated in the emergency system is beyond 
the scope of this article. What will be described are some 
patterns that can be identified in order to facilitate the 
dissemination of data before and during an emergency. 
  
2.2 Interoperability 
Collaborative systems rely on Spatial Data Infrastructures (SDI) 
that because of size, cost and number of interactions are 
expensive to develop and require skilled users. This is the 
reason why SDIs are often government (such as the US National 
Spatial Data Infrastructure) or big organizations related (such as 
the projects for a European SDI based on the INSPIRE initiative 
and the United Nations Spatial Data Infrastructure UNSDI). 
The traditional approach in implemeting a SDI seems not to be 
sustainable by smaller organizations, academia or individuals 
that could or wished to contribute with their own data or 
analysis. This is particularly emphasized during an emergency 
response. It’s not only a matter of cost but also of policies as 
barriers to participation, lack of incentives for contribution, high 
level of expertise required. 
A different model of Spatial Data Infrastructure could be based 
on lessons learnt from the web communities and tools and 
features designed on Web 2.0 principles. Common patterns can 
be identified as following: 
- make it extremely simple to share data; 
- provide user statistics; 
- easily add comments, ratings, tags; 
- allow collaborative filtering; 
- provide rankings of best ‘views’ and data sets 
contributed - such as highest rated, most viewed, most 
shared; 
- allow connectivity between several instances to 
augment the collaborative potential of government 
GIS programs. 
Nevertheless it must be pointed out that the basics for efficiently 
disseminate spatial data rely on proper organization of the data 
into repositories prepared in a suitable format for being shared. 
The most successful integration of tools and data between 
organizations, institutions or even individuals, happens when 
the most open standards are adopted. Many GIS officers and 
volunteer mappers use Open Geospatial Consortium standards 
like WMS (Web Mapping Service) and WFS (Web Feature 
Service). The OGC standards (http://www.opengeospatial.org/) 
are widely adopted both from Open Source and both from 
commercial platforms (such as ESRI products). These service 
interfaces are considered as the standard for interoperability 
between diverse sources, enabling overlay of rendered images 
and access to the raw data for further analysis and modelling. 
2.3 Architecture frameworks 
According to crowd source mapping, it is assumed that 
scientists as individuals or as part of an organization want to 
share their spatial information as open data. Open data can be 
core and thematic datasets, spatial analysis or even volunteered 
observation by citizens carrying sensors such as phones or 
driving vehicles equipped with GPS. Some examples of 
effective data sharing platforms are described as follows. 
OpenStreetMap is based on the model of Wikipedia with the 
aim to create a free and editable map of the world without 
restrictions on use and re-distribution (both commercial and 
non-commercial). In terms of architecture OpenStreetMap has a 
centralized approach. It is a hub where users log in and upload 
the spatial data they want to share or download the data they 
need. In addition the most common GIS tools such as ESRI 
ArcGIS 10 and QGIS have begun to support reading and 
writing to OpenStreetMap. 
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