Innovating in the midst of crisis: A case study of Ushahidi
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Abstract
Ushahidi is an open source collaborative mapping platform that has been developed
in an iterative form since 2008, with innovations and upgrades generated during its
intensive use in resourced constrained, time limited and dynamic crisis situations.
This article describes the origin and evolution of the platform as well as the
philosophical principles that underlie its design such as openness, participation and
democratization of information access. As a crowdsourced-mapping tool it allows
the synthesis of otherwise scattered information into concise situational maps built
from social media and SMS’s contributed by the general public. Three
representative examples of the use of Ushahidi are reviewed. In the first one it is
possible to observe how the crowdsourcing model is applied in different instances of
the deployment of the platform during the Haiti earthquake in 2010. Another
example shows how Ushahidi served as a social participation tool to help
communities get organized during the Russian wildfires. In the last example, the
crowdsourcing and open source paradigms are combined to design software to
improve the platform during the Christchurch (N.Z.) earthquake. Finally some
ideas about the importance of Ushahidi as an innovative social participation
technology are considered, especially in relation to the Kenyan and African IT
community.
Key Words: Crowdsourcing, crowdmapping, disaster information management,
social media, crisis management, innovation
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Introduction
Today, people are able to create and share digital content, which facilitates open
participation, collaboration and collective knowledge creation throughout the Internet.
Through these means, communities and groups can record, analyze, and discover a
variety of patterns that are important in their lives. By means of messages, blogs, micro-
blogs (tweets), pictures, videos, audio recordings, SMS’s, GPS’s and other ways of
conveying information, it is possible that communities and individuals can actively
participate in sensing, communicating and analyzing aspects of their lives in an on going
basis, acting less and less as passive consumers of information.
This new collective approach to knowledge creation has been termed
Crowdsourcing, which refers to the idea of outsourcing a specific task through the open
participation of a large group of individuals (Howe, 2006), mainly volunteers or
amateurs, that contribute to its accomplishment in many different ways. The advent of
crowdsourcing has changed the way some complex commercial, technical, health or
social activities are viewed nowadays. Functions that were originally performed by small
number of people, under very hierarchical organizational structures, are now being
transferred to open self-organizing communities that work collaboratively to tackle very
complex problems.
Until recently during political crisis, natural disasters or large-scale emergencies,
the flow of information was very predictable, going through regular channels, following
pre-established protocols to reach the centralized response efforts and to the traditional
media (Yates & Paquette, 2011). However, in latter years there has been a massive
change in how the population affected share their knowledge and impressions about the
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situation they’re facing. In this regard, people become sensors that generate a data stream
about the current crisis. These crowd-generated data can be used as a form of
Participatory Sensing (Goldman et al., 2009), where citizens and community groups
sense, detect and document what is happening in the particular crisis that they are facing.
The analysis of this information can reveal patterns across an entire region in terms of
types of events, locations, people involved and times of occurrences, which serves to
guide the response efforts.
For example, during the Haiti earthquake in January 12th of 2010, the
humanitarian field staff that first arrived there found themselves without reliable sources
of information about location and size of health facilities, demographics, roads and
besides that, the situation was changing constantly generating new dynamic data that had
to be processed in order to get a real picture of what was going on (Harvard Humanitarian
Initiative, 2011). In other words, the disaster encompassed a series of critical events that
were constantly changing the overall picture, the flow of information and the decision-
making processes. Moreover, by the time of the earthquake, approximately 85 % of
Haitian homes had access to mobile phones, and with the cell antennas quickly repaired,
a large number of SMS messages started to be sent to families, in the country and abroad,
to relief agencies, and to the media and also relayed to the world via Twitter, Facebook,
e-mail and other social media, thereby creating a large pool of data that required
appropriate processing to produce effective responses.
With the development and widespread use of social media, the availability of
powerful mobile phones armed with cameras and sensors, and the increased bandwidth
for data communications, the possibility that individuals record information about their
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situation and their surroundings during crisis situations has been increasingly present in a
number of critical events that have occurred in the period 2010-2011. As demonstrated in
Haiti, the affected citizens themselves, acting as sensors, convey an overwhelming
amount of data that, if properly processed, becomes a crowdsourced alternative to
information gathered through traditional channels. This new pattern has repeated lately in
earthquakes in Chile, New Zealand and Japan; floods in Pakistan and Australia; wild fires
in Russia; demonstrations in Egypt; civil war in Libya; election monitoring in several
countries and in many different critical instances. The question then is how to collect,
process, classify and display all this people generated data in a meaningful way. This is
where a small organization from Kenya comes out with an alternative for crowdsourced
mapping that has impacted the world and has changed the way crisis information is
managed.
Ushahidi Origins
Ory Okolloh is a young Kenyan lawyer who at the end of 2007 and the beginning
of 2008 was blogging intensively from South Africa (she had returned there due to threats
to her life) about the fraudulent elections that had taken place in Kenya and the resulting
consequences in terms of rumors, violence, riots, rapes, and the like. In view of the fierce
control by the government of the traditional media sources, Okolloh took the bold move
of asking people to post comments and send emails to her blog describing those events
that were not being reported elsewhere. The capabilities of the blog were quickly
overflowed by the number of reports and at that point she was prompted with the idea of
creating a website that collected reports, sent on-line on the site or either via SMS, and
then map them for easier visualization.
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On January 3rd of 2008, she shared her idea in the blog and asked the Kenyan IT
community to begin cooperating to build the site. Her request was simple: “…any techies
out there willing to do a mashup of where the violence and destruction is occurring using
Google Maps?” (Usahidi, 2009) That original idea wouldn't have gone anywhere if David
Kobia and Erik Hersman had not seen that post and gone ahead and start building the
application. Less than one week later, on January 9 th, the website was launched with the
cooperation of other African software developers. Volunteers did all the work during
those first two months: programming, data gathering, report checking (calling or emailing
reports, comparing with media information), maintenance, software upgrades and
promotion of the site. The created site gave citizens an alternative to traditional,
government censored media, because it was able to obtain reports as soon as the event
happened, covered a broader geography than traditional reporting and included a larger
number of reports from a varied source of informants.
Regarding that first implementation Okolloh states: “the idea behind
crowdsourcing is that with enough volume, a ‘truth’ emerges that diminishes any false
reports” (Okolloh, 2009). This has been a basic philosophy of Ushahidi, which means
“testimony” in Kiswahili since its inception. This emerging ‘truth’ comes from the
bottom up, generated by the accumulation of the testimonies of common people that are
the key witnesses of the particular situation, event or crisis, which is seen and perceived
almost in real-time through their SMS’s, tweets, Facebook messages, mobile camera
photos, Skype chat logs, and even voice recordings using a call-to-report feature that is
still under development.
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Due to the large and varied volume of data gathered, the ‘truth’ about the situation
is often buried and therefore needs to be mined in order to give meaning to the
information acquired. This characteristic of crowdsourced information must be kept in
mind when it is compared with other sources such as institutional surveys, which require
specialized personnel and are performed days or weeks after the event. It is the currency
of data, its density and availability, which give its power to crowdsourcing. Table I was
developed by Jackson, Rahemtulla & Morley (2010) to compare the paradigms of
crowdsourced and institutionally acquired data, showing the differences in nature, quality
and use that crowdsourced information. The table allows better understanding of the
importance of Ushahidi as a simple, near real-time, multichannel crisis data collection
and analysis platform. However, at the same time points to some of the possible
constraints that need to be taken care of by proper methodological design, or additional
processing steps in the form of machine or human intelligence, to resolve issues related to
high data volume, noisy or unreliable sources, lack of structure and protocols, and
incompleteness of information.
In its current form, Ushahidi is a collaborative mapping platform that enables
real-time aggregation of SMS’s, tweets, emails, photos, videos, comments and also voice
recordings, with location, time and date marks. After an initial categorization, reported
events or incidents are accumulated or clustered graphically on a map. The result is a
dynamic situational map updated through participatory sensing from the grass roots as
events unfold. In the aftermath of the crisis the resulting map becomes a searchable
repository or memory of an event, something that has extraordinary implications for
future evaluations, legal purposes or historical accounts.
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As a 2010 article in the New York Times put it, Ushahidi is an innovation that
comes from a world where entrepreneurship is born from hardship and survival and
innovators constantly improvise in order to do more with less (Giridharadas, 2010). The
political vulnerability and dangerous situation that prevailed at the time of Ushahidi
development and first deployment where such that the characteristics of simple, near real-
time, high density, high sampling frequency, unstructured and unconstrained data
acquisition were very important. As such, Ushahidi, a free open source platform (FOSS),
became part of a new kind of technologies that empower individuals, facilitate
communications, and foster mobilization, enabling citizens to provide humanitarian
response, to expose abuse, to protest, and to act as social auditors (Diamond, 2010).
It is undeniable that the crowdsourcing concept has been around for quite some
time, that is why Erik Hersman, one of the Ushahidi’s creators, is surprised that this
technology had not been attempted in the humanitarian field before. However, the
problem is that open access, a philosophy that permeates Ushahidi design, operates in
direct contrast to the underlying ideas in the humanitarian and crisis response
organizational world where knowledge silos seem to be prevalent (Yates & Paquette,
2011). In a critical tone, Hersman believes that aid organizations hold on to information
very tightly because it is a commodity that enables funding (De Waal, 2010).
Ushahidi disrupted the established informational paradigm by providing a
platform that allowed free, open and easy data entry by the general population and open
downloads of all the available information for free by whoever needed it. By eliminating
privileged access, it has provided an innovative first experience in the democratization of
crisis information access, the possibility of auditing a response effort, of discovering
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where aid is needed and how to distribute it. The receptivity of communities of
entrepreneurs in different countries that have implemented the platform has been
astonishing, which demonstrates that the old wineskin needed to be changed.
Due to the high demand for this crowdmapping tool, Ushahidi Inc, a non-profit
organization was established as a technology company specialized in developing free
open-source software for information collection, visualization and interactive mapping.
This assures the continuous improvement of the Ushahidi platform and the development
of new products such as Crowdmap, an “in the cloud” version of Ushahidi aimed at
smaller projects not expecting high load, and SwiftRiver, which is used as an intelligent
crowdsourced information filter that classifies messages from Twitter, email, RSS feeds,
and SMS using semantic analysis.
Ushahidi won the NetSquared 2008 Mashup Challenge that provided a seed
funding of US$ 25000. Additional funding started to pour in from Humanity United,
Cisco, Knight and MacArthur foundations, and the Open Society Institute. At the end of
2009 the organization secured a grant of 1.4 million US$ from the Omidyar Network for
the following two and a half years. The Omydiar Network was established in 2004 by
eBay founder Pierre Omidyar and his wife Pam, investing in innovative organizations
with projects that can foster economic and social change. This large grant together with
another from the Hivos Foundation, a Dutch non-governmental organization that
promotes projects that lead to fair, free and sustainable world, allowed Ushahidi to
establish a physical presence in Kenya, under the leadership of Erik Hersman. As such,
Ushahidi became a private non-profit company, totally independent of governmental
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organizations, something that is fundamental in the developing world where paternalism
and corruption destroy initiatives and entrepreneurship.
For the most part Ushahidi is a virtual organization whose staff is spread all over
the world. The Nairobi headquarters is seen as hub that connects the international tech
community with the growing Kenyan innovation community through the iHub
Community which they define as an “open innovation space for technologists, investors,
tech companies and hackers in Nairobi with a focus on young entrepreneurs, web and
mobile phone programmers, designers and researchers.”1 Recently, Technology Review,
an MIT publication, voted Ushahidi among the 50 most innovative companies of 20112.
Crowdsourcing crisis mapping
The old adage: “a picture is worth a thousand words” applies well to the field of
crisis mapping. Based on the idea that the use of visual information, rather than text or
numbers, is conducive to more powerful reasoning, understanding and learning, specially
in complex and stressful situations, geographic visualization allows an individual to see
complex relationships, understand better a phenomenon, and reduce the search time of
particular events (Dodge, McDerby and Turner, 2008). Geographic visualization helps to
discover unknowns and to obtain new insights that are not apparent by other means of
data representation. This is the basic idea of mapping in general, and especially when it is
necessary to extract meaning from complex and incomplete data in situations of crisis.
Basically, the idea of enhancing our perception of events through the help of
mapping is certainly not new. A classic and illustrative example that is often cited refers
1 http://ihub.co.ke/pages/home.php2 http://www.technologyreview.com/tr50/
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to the outbreak of cholera in London in 18543. John Snow, a physician and scientist, took
data collected by the government during an extended period of time, about where those
that died from cholera had lived and where they were at the time of death, and plotted it
over a city map. After careful and patient analysis it was discovered that most of the
deaths belonged to a neighborhood that drew their water from a contaminated supply,
which lead to its closure and the subsequent neutralization of the epidemic.
Until a few years ago, most maps and atlases were quite static and their
development and distribution was very slow and regarded as the function of specialized
individuals, researchers and officials. With the advent of the Internet, and specially Web
2.0 technologies, it became feasible for any layperson to make maps at affordable costs
and with the aid of powerful tools such as GPS technology and mapping software
(Goodchild and Glennon, 2010). The emerging field of volunteered geographic
information (VGI) or neogeography is based on the possibility of creating geographic
mashups that combine web-mapping services such as Google Maps with data provided by
non-expert individuals. These amateur geographers use their own acquisition tools in
order to create and document maps that serve very specific interests or that describe
unique events or circumstances (Haklay, Singleton and Parker, 2008). The word mashup
was originally coined to describe the mixing or blending of hip-hop musical tracks to
create a new one. In Web 2.0 terminology it now refers to websites that weave data from
different sources into new integrated applications without the need for intensive
programming tasks, something which has become specially appealing for the
development of the field of neogeography (Batty, et al., 2010).
3 http://en.wikipedia.org/wiki/John_Snow_(physician)
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This participatory way of web based mapping introduces a number of questions,
which are at the heart of crowdsourcing such as: What is the type of information
collected from volunteers? What are the characteristics of the input methods and how is
data structured? Is there any quality assessment of incoming data? What information
should be displayed or represented on the map? Can it be combined with institutional or
authoritative data? Who are the volunteers and how are they recruited? And, how the new
mashup service affects response in the event of crisis?
Two hours after the first quake hit Haiti on January 12th of 2010, Patrick Meier
who was Ushahidi’s Operations Manager and also headed The International Network of
Crisis Mappers4, and Kenyan Ushahidi’s lead developer David Kobia started to work on a
version of Ushahidi aimed at crowdmapping the crisis that was starting in Haiti. The
question that Meier posed was how to produce a “live map” of the crisis in Haiti, a map
that was recording the events, the incidents and the progress of the situation and that
could help the responders to act accordingly. It was a completely different set up as the
one that had been attempted in the post-election period in Kenya. Therefore, the
aforementioned questions did not have any clear answers, many things had to be learned
in real-time as the deployment was adapted to the situation. In the words of Erik
Hersman, one of the co-founders of Ushahidi, “it was like modifying the engine of a plane
at 30000 feet of altitude.” Thus innovation, creation and improvisation had to be
combined to adapt Ushahidi to the new conditions that were being faced in the response
to the earthquake. New challenges surfaced, human intelligence had to be combined with
new technology to improve the response time of the mapping system and the quality of
the information that it displayed.
4 http://www.crisismappers.net
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Once the information began to flow through the Ushahidi Haiti site, it became
clear that the initial small team did not have the capacity to handle the magnitude of the
data that was streaming through the site. More volunteers had to be recruited to continue
the monitoring and a number of students from the Fletcher School of Law and Diplomacy
at Tufts University were involved in the process. Starting from the people in need, it was
also necessary to access the social networks that existed within the Haitian society in
order to gather those volunteers that could feed the map with information. As the vast
majority of the messages would come in Haitian Creole, there was also a need to
crowdsource the translation efforts. Then the messages had to be geotagged, by finding
the GPS coordinates using Google Earth, classified according to pre-established
categories, confirmed and approved before plotting on Google Map or Open Street Map
and finally, report the event to those that were in the position to help. Just a few of the
tasks mentioned were fully automated, by far they were done by people located in
different countries connected through social networks on the Internet.
Besides Ushahidi, there were several other crowdsourced mapping efforts in place
at the time of the Haitian crisis, but the main difference in the model used by Usahahidi
was the possibility of aggregating SMS reports. However, another open source platform
known as FrontlineSMS was necessary for that purpose, which was set up by January
16th. Additionally, web-based submissions, email, monitored twitter messages that used
the #Haiti hashtag5, as well as the review of blogs, media and other websites were used as
data entries. The SMS functionality was called Mission4636 and was a joint effort
between Fletcher, FrontlineSMS, US State Department and Digicel a Caribbean cell
phone company. As Zook et al. (2010) points out, the ability of Ushahidi to collect on-the
5 Twitter terminology referring to a way of tagging messages that point to a particular subject or theme.
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ground knowledge via standard cell phones and then structure, categorize, map and share
it, was the main difference to other efforts that employed only the Internet as a way of
input and distribution.
Nevertheless, the availability of the 4636 number produced an input flow of 1000
to 2000 text messages per day (Heinzelman and Walters, 2010) which required both,
translation and geolocation. Machine translation engines for Haitian Creole was not
available at the time and resources to develop one within a very short time were limited
(Lewis, 2010; Lewis, Munro and Vogel, 2011). Only 12 million people, of whom 9
million live in Haiti, speak Haitian Creole in the world, therefore, linguistic resources and
knowledge about the language for the design of automatic translators were scarce. Due to
the pressing need to respond to the incoming messages in Haitian Creole, the Ushahidi
team was faced with the need of crowdsourcing translation also in near real-time (Meier,
2010). Volunteers from the Haitian diaspora were recruited as translators through an
Internet based dedicated interface organized by Brian Herbert of Ushahidi and Robert
Munro from Energy for Opportunity of Stanford University (Nelson, Sigal and
Zambrano, 2010). Munro was involved in researching the processing of large number of
text messages, and also was working in collaboration with FrontLineSMS (Biewald,
2010). Dozens of motivated ex-patriate Haitian volunteers participated in the translation,
categorization and geo-location of every message. Over 30000 text messages went
through Mission3636 during the first month after the earthquake (Harvard Humanitarian
Initiative, 2011), which speaks of the overwhelming amount of translation work that took
place.
Later on, to make the translation service more scalable it evolved through the use
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of CrowdFlower (Hester, Shaw & Biewald, 2010), a platform for managing tasks
outsourced to a distributed digital workforce on demand. The translation of each message
was a microtask offered to the pool of translators that had been recruited through social
media among Haitian Creole speakers around the world and to bilingual Haitian residents
who received an income for the translation work to alleviate their difficult situation as
survivors of the disaster. The vast majority of the workers that contributed through
CrowdFlower were located in the U.S.A. (89%) and the rest mostly in Canada, Haiti and
Switzerland (Hester, Shaw & Biewald, 2010). Figure 1 shows some of the reports
collected during a particular time window, in this case most of them are text messages
that had already been translated into English.
___________
Insert Figure 1 about here
____________
More Ushahidi’s deployments mean new challenges
As can be seen from the Haitian experience, Ushahidi can be counted among the
first participatory platforms that successfully combine collective human intelligence and
automatic methods to provide information during dynamic and time-constrained events
such as in crisis. As a matter of fact, in an evaluation of the Ushahidi Haitian deployment
the report states that: “(it) represents an impressive proof of concept for the applications
of crisis mapping and crowdsourcing to large scale catastrophes and a novel approach
to the rapidly evolving field of crisis informatics” (Morrow et al., 2011, p. 4).
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Nevertheless, as Ushahidi developers like to say, “Ushahidi the platform is a
piece of software, not a methodology” (Meier, 2010). As a platform it allows mapping
according to the interest of those implementing it. However, it is up to the users to
determine the methodology for data collection and the characteristics of the collected
data. As such, Ushahidi is not exclusively a platform for crowdsourcing, neither it is
restricted to crisis mapping alone.
This is somewhat better exemplified by looking at some of the deployments that
stand out from the reported installations done from 2009 until March of 2011 (George,
Gosier and Kaurin, 2011). Ushahidi products (Ushahidi, Crowdmapping and SwiftRiver)
have been deployed in many different scenarios ranging from social and political crisis,
natural disasters, observation of elections, tracking crime and civil unrest, promoting
human rights, documenting the impact of environmental disasters like oil-spills,
coordinating citizen response during wildfires, environmental monitoring, mapping the
disruptions in urban transportation systems, up to participatory epidemiology and
community health. Table II shows some selected Ushahidi implementations and some of
their main characteristics.
These deployments have been done under many different conditions and
methodologies affecting the quality and quantity of information required in a
crowdsourcing application such as Ushahidi. The lack of an adequate reporting structure,
such that data can be processed faster, can affect the quality of the implementation. This
makes the design phase of the deployment methodology a very important step. Also,
besides the general public that can access the platform in its different input modalities, it
is important to have trusted reporters in the field and somehow give more weight to this
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information in the processing stages. For example, in the evaluation of the Ushahidi
Haitian deployment there was some kind of “suspicion of the crowd”, fear about the
possibility of data manipulation, and questions about the representativeness and
exactitude of the data gathered (Morrow et al., 2011). Another critical part of data quality
assurance are the moderation, verification and analysis phases where a second step of
crowdsourcing is performed and which requires another group of volunteers typically for
translation of reports, geolocation, and the verification and analysis loop.
The quantity of the information also affects the deployment. On one hand,
underreporting can yield insufficient data for a meaningful analysis. Societies where
social activism is present will be more prone to use a crowdsourcing platform such as
Ushahidi, while in those where censorship and repression prevails, the public will be less
inclined. Underreporting can also happen when the primary sources of crowdsourced data
use a technology that is too complex or expensive for general use, this makes the use of
SMS’s a very important feature of this platform. Also voice messaging and more
rudimentary methods of reporting have been considered, allowing illiterate populations to
share their reports via short voicemail reports that can then be transcribed and then
mapped. The volume of work undertaken by the recruited volunteers during the aftermath
of Haiti earthquake demonstrates the immense potential of SMS enabled crowdsourcing
approaches to the management of information during crisis (Morrow et al., 2011).
Although many new things are learned with every new deployments of Ushahidi,
Erik Hersman (2011) thinks that HELP MAP in Russia and CHRISTCHURCH
RECOVERY MAP (CRM) in New Zealand are pretty much in tune with both the idea of
crowdsourcing from the general public to collect and process information from the
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bottom up, and with Ushahidi’s open source philosophy that allows continuous
innovation in the platform. HELP MAP was implemented during the Russian wildfires
that followed an unprecedented heat wave towards the end of July and beginning of 2010.
Its availability was publicized through newspapers, radio programs, Facebook, the
Russian social network Vkontakte, TV and in the portal of Yandex the main Russian ISP.
On the other hand, CRM was implemented to collect information for the general public
of what was happening near the disaster area via various media feeds in the aftermath of
the earthquake in February 22nd of 2011. The deployment was quite fast, just one hour
after the earthquake when still the response systems were not in place. It was started
using Crowdmap but later migrated to the Ushahidi platform (Leson, 2011).
HELP MAP is an excellent example of how technology can be a catalyst for
activism to go beyond computers and networks and move into practical action (Mora and
Flores, 2011). The deployment of Ushahidi during the Russian wildfires came about as a
response of bloggers, social networks and IT community in order to expand the number
of reporters and information beyond that of regular blogs and social networks. It was
basically implemented to aggregate the reports from those in need responding to the basic
question: “What is needed?” and the reports from those that were in the capacity of
providing help, by responding: “I wish to help”. Offers of help included transportation,
food, clothing, homes and many others and they were connected to those having specific
needs stated in the platform. The implementation of HELP MAP revealed the altruistic
potential of the Russian society, especially because of the timid or ineffective official
response. In this process, on-line communities and Internet users in general took a lot of
responsibilities in their shoulders in a critical moment where they felt that the Russian
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government was failing to provide the required coordination and the help needed to the
population (Asmolov, 2010).
For the crew implementing CRM the question was how to provide “information
that was relevant to people using information” (McNamara, 2011). There was interest in
knowing about local schools, water distribution and quality, availability of gasoline or
diesel in gas stations, open pharmacies, supermarket hours, location of free BBQs hosted
by neighbors, bus routes, location of free laundry services, sewage collection tanks,
recovery assistance centers, parks for children, availability of ATMs in the disaster area,
location of Wi-Fi hot spots, as well as official information about the disaster from the
government. Those interested in keeping fresh the aggregated information like banks,
stores, coffee shops, supermarkets, did the update of CRM when needed. Messages
contained in tweets, emails, SMSs and web form submissions were analyzed, and as in
other Ushahidi implementations they were categorized, geolocated, verified, and mapped.
Besides submitting the reports, users were also encouraged to find their location on a map
for more precise localization.
At the beginning, the information came from the organizations through some
social media, typically a twitter message that was read by volunteers, classified and
plotted. As the project advanced, organizations interested began to input information
directly into the map, which made the data of high quality and therefore important for the
community. In addition to this, third parties did mashups of Ushahidi collected data with
their own maps. The site for CRM was complemented with the Google’s Person Finder
application and information about other networking and community projects. During the
time that the Ushahidi crowdsourced map for Christchurch recovery was active, the site
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received 284829 page views, 127993 unique visitors and 1729 reports (George, Gosier
and Kaurin, 2011).
Many improvements were made to Ushahidi during the development of CRM,
which have been already incorporated in the deployments in Lybia and Japan. Changes
were introduced in the individual reports clustering algorithms to make them more
efficient. Also, the ability to see the full information of the individual reports over the
map with click-overs has also been added (See figure 2). The open source model was
essential for these changes in the platform. Basically what was occurring during the time
that CRM was being deployed and making it operational, is that its development was also
being crowdsourced, just as intensively as the crowdsourced map information that it
delivered. Volunteers participating in the development came from well-known and
respected technological institutions such as engineers from Google and the Apache
Foundation, but also students from local colleges and high schools were involved in the
process.
___________
Insert Figure 2 about here
____________
All of this is due to the philosophy behind the OSS paradigm in which volunteers
work at the technical level they are comfortable with, be it testing a feature as active
users, solving bugs found along the way or redesigning the user interface, creating new
algorithms or data management structures. One example of these improvements had to do
with the navigation speed on the displayed map. Users were taken a lot of time when they
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zoomed in and out in the broadband connection, even worse in the 3G links that people
where using in the city. The program was taken up to five seconds to recalculate and
display the report cluster when moving to a new zoom level and programmers worked
quickly to improve this feature (McNie, 2011). Another developer (Singh, 2011) created
a viewer to show each of the reports along with the trends over time. The viewer also
provides hotspot analysis of reports, which can be filtered down by categories if required.
These new features are especially useful for analysis of the repository of reports obtained
during the peak of the crisis to better understand the situation and improve disaster
preparedness and information management.
Tim McNamara (2011), one of the main participants in the project attributes the
success of CRM to the high network capital present in the New Zealand technological
community. This networked social capital fostered this collective software improvement
based on the OSS paradigm of collaboration by relying on social media in an intensive
way to generate new improvements, to innovate, to share ideas, problems, fears, creating
a positive and secure social environment on-line. Moreover, social networking extended
to the users of the technology and third parties, which allowed for an active interaction
and feedback about the end-user requirements. Although more technically oriented than
the HELP MAP experience, there are similar insights and questions that come out of both
events. One of them has to do with the long-term sustainability of the social capital
created during the realization of these projects.
Future of crowdsourcing crisis mapping
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Crowdsourcing crisis information has been a “big thing” since the Haiti
earthquake in 2010, all the way until May 2011. Besides Ushahidi, different other
products are starting to become available as the “wisdom of crowds” is brought forward
to play a fundamental role in crisis response. In this short period of time, several different
aspects of what could be called the crowdsourcing business model have been attempted
using the Ushahidi platform in a resource-constrained situation such as a crisis.
Massive gathering of social media that can fill the crisis maps with relevant
information; adoption of SMS interfaces for data collection in situations were
accessibility to the Internet is limited; use of volunteer force to crowdsource report
classification, verification and geolocation; use of methods to automatically manage
micro-payments for crowdsourced translation tasks; and the crowdsourcing of software
innovation and maintenance by means of the open source software paradigm employed
since the original software design, have been some of the experiences that have resulted
from an impressive number of deployments of the platform in just over a year. Figure 3
attempts to describe the different instances of Ushahidi and the complex interaction
between human and machine intelligence that is established for crowdsourced crisis
information management.
___________
Insert Figure 3 about here
____________
Most of the experiences expressed above are new in the field of crisis
management and they have generated a lot of enthusiasm and also some criticisms. Gao,
22
Barbier & Goolsby (2011) present concerns on the usefulness of crowdsourcing mapping
applications such as Ushahidi. According to them, one of the weaknesses is the lack of a
coordination instance within the platform to allow better collaboration between different
crisis responders. Also, integration of Usahidi with other platforms may require the
possibility of the resulting maps and data streams to be read or blended with other
sources of information available about the crisis being monitored (Hereema-Agostino et
al., 2011). Accuracy of geotagging has also been mentioned as a major drawback of the
system. However, new error reducing procedures have been experimented in some of
Ushahidi’s implementations. Shaw and Hester (2011) described the use of several
volunteers geotagging the same report and then using an algorithm based on the weight
the relative trust of volunteers to calculate the centroid of the points. Also, geotagging
accuracy depends upon the reporting method used whether it is manual entry of the
geographic description, which requires finding the coordinates using Google Maps or
OpenStreet Maps, or via 3G Internet. In addition to this, there are also concerns with
spurious, fraudulent and redundant reports (Gao, Barbier & Goolsby, 2011), and with the
lack of quality of the information, especially in SMS messages which are quite noisy
because of the extensive use of shorthand notations, lack of accents, punctuation and so
on, making things even more complicated when translation is needed (Lewis, 2010).
Research on crowdmapping for crisis situations has just begun after the large
number of deployments of the Ushahidi over the last two years. Experiments with new
social computing for increasing the trust factor of reports and improve validation,
development of textual analysis to tag, classify and cluster the large stream of data
coming from tweets, SMS’s, RSS feeds, web entries and so on, using real time social
23
media scanning, monitoring and curation are under way. Also, as in the case of the CRM
platform in Christchurch (See figure 2), new interfaces are being designed to allow better
representation of maps and the collected database of trusted reports, which would permit
the creation of specific reports for different agencies according to their scope in the
response effort. The application of artificial intelligence and modeling techniques in
conjunction with crowdsourced information is another frontier that would require a more
active participation of the research community in a long-term basis. In the relatively short
time in which Ushahidi has been developed, innovation has happened basically in the
midst of disasters, with time constrains, minimal resources and in the shadow of the more
traditional roles of relief agencies and geographic professionals.
According to Peter Drucker (1998) innovation occurs as a result of seven possible
sources, namely, unexpected occurrences, incongruities, process needs, industry and
market changes, demographic changes, changes in perception and the availability of new
knowledge. Many of these sources have been present in the innovative approaches taken
by the Ushahidi team and the community of users involved in the many deployments.
Crisis, in spite of the difficulties, time constraints and lack of resources usually present,
has been considered as a catalyst for creative solutions and innovation. Relief
organizations, responders, humanitarian aid NGO’s, communities and individuals learn
from each new situation and develop innovative solutions that improve their approach to
new disasters or critical events. However, as has been expressed before in this article,
there has been a huge change in how individuals, in the society at large, manage
information and how organizations have adapted to these new conditions. Demographic
changes account for a new global generational cohort of digital natives that are familiar
24
with new technology, especially social media and mobile devices of different kinds
(Balda & Mora, 2011). There are then many changes that are rocking the traditional
means of managing information during crisis, new ways of understanding the world, a
youth culture completely immersed in the digital realm, a culture of openness and
participation, a networked society where information flows freely, all of these factors are
driving technological innovations such as Ushahidi. Erik Hersman (De Waal, 2010) is
convinced that centralization of crisis information management is a concept that will soon
disappear or at least will be completely reengineered to provide access to collective
intelligence into the humanitarian sector.
Using Gartner terminology for innovation6, Ushahidi’s “trigger” or breakthrough
occurred during the Kenyan elections in 2008. Following the Haitian deployment, the
expectations for this new technology have been on the rise as the media has given to it a
lot of coverage. However, the number of implementations in real life situations that
followed under very heterogeneous circumstances, where different benefits and
challenges of this technology have been experimented, has created yet neither over-
enthusiasm nor disillusionment. For the most part, the field of crisis crowdmapping is
still at its infancy. As a one researcher has put it, perhaps “Crowdmap, Ushahidi’s hosted
cloud service, may do for Ushahidi what Blogger did for blogging” (Keay, 2010). That is,
by minimizing the technical barriers for implementing a crowdmapping project, the
technology would be quickly popularized and adopted. Some people may fear that the
technology will be used for applications completely out of the scope of crisis information
management, but Ushahidi Inc has repeatedly said that their products are not restricted to
disaster or crisis response alone.
6 http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp
25
The recent launching of Universities for Ushahidi (U4U) project together with the
United States Institute for Peace (USIP) will help to create a broader social network of
proactive, next-generation innovators in the field of crowdsourcing and crowdmapping in
particular. U4U will allow students from developing countries to learn how to use the
Ushahidi platform and related tools in their own countries; they will work together with
experts from USIP to identify applications of Ushahidi for their home countries. The
extension of the base of participants will certainly extend the life of this technology and
allow for a steady path into maturity.
Finally, it is important not to forget that Ushahidi was originated in an African
nation and therefore it has impacted technological development in countries that were out
of the radar screen in terms of innovation and new technologies. According to Erik
Hersman (2011), what Ushahidi has done to the African IT community is to “change the
belief structure, just as the Kenyan runners did in the Olympic games when they won
their first gold medal”. In other words, through Ushahidi, Africans have demonstrated
that they “can do” sophisticated software developments. It is interesting to read in this
regard the comments of Steven Livinsgston (2011) when describing his visits to iHub in
Nairobi and others IT development centers in Africa:
There is ownership and commitment and a palpable sense of ambition in these
places. There is a sense that, “We did this.” The fact that international analysts
and academics come to these groups to learn about their ongoing
accomplishments is itself a significant indicator of the depth of the changes at
hand. In the past, these international experts came to offer advice and lecture, not
26
learn about the latest innovation in the application of technology for positive
social change (P. 37-38).
Another mental stronghold that needs to be transformed is the dependence of
Africans from foreign charity, which destroys creativity, fosters laziness and corruption
and makes people parasites of the aid organizations. That is why David Kobia, who won
the prize as Humanitarian Innovator under the age of 35 from the Massachusetts Institute
of Technology, Technology Review, thinks that some Ushahidi’s projects must ultimately
generate revenue. For example, larger organizations might pay for Crowdmap's services
or license parts of the Ushahidi technology (Grenwald, 2010).
In the long term Ushahidi’s efforts will create a fairly large innovation ecosystem
in Kenya that could probably make Nairobi the Silicon Valley of Africa, some kind of
technology park for the development of advanced systems that originate from the real
needs of those that are left out by the traditional markets, and which are typically
forgotten by technology developers. Ushahidi is one the first steps towards a sustainable
African society by providing open access and democratization of information, fostering
social responsibility and the kind of change of paradigm that could truly make a
difference in their continent.
References
27
Asmolov G (2010). Online Cooperation as an alternative for government? Global Voices.
August 30th. http://globalvoicesonline.org/2010/08/30/russia-online-cooperation-as-an-
alternative-for-government. Last visit May 1st, 2011
Batty M, Hudson-Smith A, Milton R and Crooks A (2010). Map mashups, Web 2.0 and
the GIS revolution. Annals of GIS 16(1): 1-13
Balda J, Mora F (2011). Adapting leadership theory and practice for the networked
millennial generation. Accepted for publication in Journal of Leadership Studies.
Biegald L (20100. Crowdsourcing the Haiti Relief. The Crowdflower Blog. January 29th.
http://blog.crowdflower.com/2010/01/crowdsourcing-the-haiti-relief/. Last Visit April
29th, 2011.
De Waal M (2010). The technology that's seriously upsetting the aid sector, and the man
behind it. Daily Maverick. http://www.thedailymaverick.co.za/article/2010-06-22-the-
man-whos-seriously-upsetting-the-aid-sector. June 22nd. Last visit May 2nd, 2011.
Diamond L (2010) Liberation Technology. Journal of Democracy. 21(3): 69-83
Dodge M, McDerby M, Turner M (2008). The power of geographical visualizations. In
Dodge M, McDerby M, Turner M (eds) Geographic Visualization: Concepts, tools and
applications. New Jersey: John Wiley and Sons, 1-10.
Drucker P (1985). The Discipline of Innovation. Harvard Business Review. May-June,
67-72.
28
Gao H, Barbier G, Goolsby R (2011). Harnessing the crowdsourcing power of social
media for disaster relief. IEEE Intelligent Systems. 26 (3): 10-14.
Gartner (2011). Gartner Hype Cycle. Last visit September 6th, 2011.
http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp
George S, Gosier J, and Kaurin D (2011). Key deployment record- March 2011. Ushahidi
Inc., http://www.slideshare.net/Ushahidi/ushahidi-key-deployments-q1-2011/download.
Retrieved April 25th 2011.
Giridharadas A (2010). Africa’s Gift to Sillicon Valley: How to track a crisis. The New
York Times. Retrieved April 26th, 2011
http://www.nytimes.com/2010/03/14/weekinreview/14giridharadas.html?_r=1
Goldman J, Shilton K, Estrin D et al. (2009). Participatory Sensing: A citizen-powered
approach to illuminating the patterns that shape our world, UCLA Center for Embedded
Networking Sensing (CENS), White Paper, Los Angeles, May.
Goodchild M, and Glennon JA (2010). Crowdsourcing geographic information for
disaster response: a research frontier. International Journal of Digital Earth. 3 (3): 231-
241
Greenwald T (2010). David Kobia 32: Ushahidi, software that helps populations cope
with crisis. Technology Review, MIT. 2010 Young Innovators under 35 Award, last visit
May 3rd, 2011. http://www.technologyreview.com/TR35/Profile.aspx?TRID=947
29
Haklai M, Singleton A and Parker C (2008). Web Mapping 2.0: The neogeography of the
GeoWeb. Geography Compass, 2 (6): 2011-2039.
Harvard Humanitarian Initiative (2011). Disaster Relief 2.0: The Future of Information
Sharing in Humanitarian Emergencies. Washington, D.C. and Berkshire, UK: UN
Foundation & Vodafone Foundation Technology Partnership.
Heerema-Agostino S, Lekkerkerk H-J, Pepping R (2011). Crisis mapping 2.0: Publishing
and finding crisis data on the web. Utrecht University, Utretch (Netherlands). Retrieved
September 10th, 2011.
http://www.findinggeo.com/Ushahidi/ushahidi/media/uploads/Gima6-crowdsourcing-
gr1-Final%20paper.pdf
Heinzelman J and Walters C (2010). Crowdsourcing Crisis Information in Disaster
Affected Haiti. Washington: United States Institute of Peace. Special Report 252.
http://www.usip.org/publications/ October. Retrieved April 28th, 2011.
Hersman E (2011). Phone interview. April 18th.
Hester V, Shaw A and Biewald L (2010). Scalable crisis relief: Crowdsourced SMS
translation and categorization with Mission 4636. ACM DEV’ 10, December 17-18th,
London (UK).
Howe J (2008). Crowdsourcing: Why the power of the crowd is driving the future of
business. New York: Crown Business.
30
Jackson MJ, Rahemtulla HA, and Morley J (2010). The synergistic use of authenticated
and crowdsourced data for emergency response. 2nd International Workshop on
Validation of geo-information products for crisis management. Ispra-Italy, October 11th-
13th, 91-98.
Keay A (2010). Ushahidi and Crowdmap: micro-streaming as time binding media. 3PM
Journal of Digital Research and Publishing. 1 (2):116-125
Leson H (2011). How the Eq.org.nz site came about to help with the Christchurch
earthquake. Crisis Commons. http://crisiscommons.org/2011/02/24/how-the-eq-org-nz-
site-came-about-to-help-with-the-christchurch-earthquake. Last visit May 1st, 2011.
Livingston S (2011). African Infosystems: a pathway to security and stability. Africa
Center for Strategic Studies Research Paper No. 2. Washington: National Defense
University Press. March.
Lewis W D (2010). Haitian Creole: How to build and ship an MT engine from scratch in
4 days, 17 hours & 30 minutes. Proceedings of the 14th Annual conference of the
European Association for Machine Translation, May, Saint-Raphaël (France): 27-28.
Lewis W D, Munro R, and Vogel S (2011). Crisis MT: developing a cookbook for MT in
crisis situations. Proceedings of the 6th Workshop on Statistical Machine
Translation, Edinburgh (Scotland, UK), July 30th -31st , 501-511.
McNamara T (2011). The power of Ushahidi. NZCS Newsline. March 18th. Last visit May
2nd, 2011. http://www.nzcs.org.nz/newsletter/article/94
31
McNie N (2011). Fixing website performance issues III: whack-a-mole. Nigel McNie
Blog, March 28th, 2011. Last visited May 2nd, 2011. http://nigel.mcnie.name/blog/fixing-
website-performance-issues-whack-a-mole
Meier P (2010a). Ushahidi and the unprecedented role of SMS in disaster response.
IRevolution blog. February 20th. http://irevolution.net/2010/02/20/sms-disaster-response.
Retrieved April 29th, 2011.
Meier P (2010b). Think you know what Ushahidi is? Think again. IRevolution blog. June
16th. http://irevolution.net/2010/06/16/think-again/. Retrieved April 29th, 2011.
Mora F and Flores R (2011). Social interaction and crowd engagement in emergent
leadership around the world. Accepted for presentation at International Leadership
Association Annual Conference, London, October 26th to 29th.
Morrow N, Mock N, Papendieck A and Kocmich N (2011). Independent evaluation of
the Ushahidi Haiti Project. Development Information Systems International (DISI).
Retrieved April 30th, 2011.
https://sites.google.com/site/haitiushahidieval/news/finalreportindependentevaluationofth
eushahidihaitiproject
Nelson A, Sigal I and Zambrano D (2010). Media, information systems and communities:
Lessons from Haiti. CDAC (Communicating with Disaster Affected Communities) and
Knight Foundation. Retrieved on April 28th, 2011. http://www.pbs.org/mediashift/Haiti
%20Report%20English%2001.10.11-4.pdf.
32
Okolloh O (2009). Ushahidi or “testimony”: Web 2.0 tools for crowdsourcing disaster
response. Participatory learning and action 59: 65-70.
http://pubs.iied.org/pdfs/14563IIED.pdf, retrieved on May 1st, 2011
Shaw A and Hester V (2011). Human Computing as a horizon of SM4D: Crowdsourced
crisis response and beyond. CSCW 2011, Social Media for Development Workshop,
March 19-23, Hangzhou, China. Retrieved September 10th, 2011.
https://sites.google.com/site/sm4dev/file-cabinet
Singh J (2011). Ushahidi trends for the Christchurch earthquake. Geogeek New Zealand.
March 16th, 2011. http:// geo.geek.nz/esri-new-zealand/ushahidi-trends-for-the-
christchurch-earthquake. Last visit May 2nd, 2011.
Ushahidi (2009). Building Ushahidi. retrieved April 27th, 2011
http://wiki.ushahidi.com/doku.php?id=building_ushahidi
Yates D and Paquette S (2011). Emergency knowledge management and social media
technologies: A case study of the 2010 Haitian earthquake. International Journal of
Information Management. 31 (1): 6-13
Zook M, Graham M, Shelton T and Gorman S (2010). Volunteered Geographic
Information and Crowdsourcing disaster relief: A case study of the Haitian earthquake.
World Medical & Health Policy. 2 (2): Article 2. www.psocommons.org/wmhp.
Retrieved April 28th, 2011.
33
Table I. Data collection paradigms during crisis
Crowdsourcing Institutional or authoritative data
Simple means for data collection using
standard social media communication
channels.
Complex protocol driven methods for data
collection.
Near real-time data collection and
streaming which allows trend plotting and
analysis.
Historic or snapshot data reflecting a
particular time window.
Un-calibrated data with high density and
high sampling frequency acquired from
free-lance volunteers.
Quality assured expensive data generated
by experts on the field.
Unstructured data with user generated tags
and categorization.
Structured data following pre-defined
ontologies and taxonomies.
Unconstrained capture of data from
different locations, through different means
and channels.
Controlled methodology, policies and
rights for data gathering.
Non-systematic and incomplete coverage. Systematic and comprehensive coverage.
34
Table II. Selected Key Ushahidi Deployments
Name Location Date Objective Brief description
Ushahidi Haiti Port au
Prince,
Haiti
January
2010
Disaster
response
Provision of up to date
situational information in
the very early period of
response with good
geographic precision
Ushahidi Chile Central
Chile
February
2010
Disaster
response
Initial support of
emergency responders
which shifted to long-term
reconstruction efforts
Louisiana
Bucket Brigade
Louisiana,
USA
April
2010
Environmental
advocacy
A transparent,
participatory, localized
source of information
about human and
ecological impacts of the
oil spill for Gulf Coast
residents
HELP MAP Moscow,
Russia
August
2010
Disaster
response
To serve as a bridge
between those in need and
those needing help during
the Russian wildfires.
TUBESTRIKE London,
UK
September
2010
Transportation
information
BBC London plotted text
reports, tweets and audio
updates from listeners and
viewers about their
problems with transport
during the Tube strikes
Christchurch Christchurc 2011 Disaster Provision of up to date
35
recovery map h, New
Zealand
response situational information
with improved protocols
for information
submission and better
geographic precision
Libya Crisis
Map
Libya 2011 Tracking of
civil unrest
The map reflects social
media, mainstream news
and situation reports
crowdsourced from a
network of informers
36
37
Figure 1. Screen Shot of Ushahidi-Haiti typical reports.
38
Fig. 2 Example of Christchurch Recovery Map (CRM) after the activation period was over. Reports are still available for investigative
purposes.
39
Figure 3. Block diagram of Ushahidi and interactions between human intelligence and machine intelligence and data sources.
40