Crowd Sourcing Haiti

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Gaps in the Net:The Equity of Crowdsourced Reporting in Haiti

Grady Johnson

December 2010


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Introduction

The innovation of crowdsourced reporting has shown tremendous promise in

the field of humanitarian assistance. Through the use of new media technologies,

rescuers can harness the efforts of locals on the ground, humanitarian workers and

benevolent observers abroad to create timely, actionable information in a crisis

situation. Further, by tapping into the global network (including social media like

Facebook and Twitter), crowdsourcing platforms like Ushahidi can aggregate the

massive and often labyrinthine data flow into coherent and substantiated reports. It is

well know that in a crisis information is a precious resource and crucial to an effective

response.1

As the recent case of the 2010 Haitian earthquake shows, the potential for

this new technology is staggering.

But this approach raises certain questions. For instance, who constitutes the

crowd? Crowdsourcing may be open to everyone but access is not uniform. As

platforms like Ushahidi become central to coordinating humanitarian assistance, a

more thorough understanding of this technology is necessary. Does crowdsourced

mapping reflect the reality on the ground or merely the level of access to the network?

Beyond this, it is no secret that the media (and social media spheres as well) have a

tendency to focus on high profile cases, often to the detriment of others in similar

need. Might a crowdsourced approach to information gathering unintentionally

discriminate against those lesser-known cases? Crowdsourcings recent rise into the

limelight demands a more critical assessment of its potential costs.

A review of the literature to date reveals that no such assessment has been

made. As it stands today, crowdsourced reporting is largely treated as a panacea;

articles tend to range from uncritical acceptance to outright praise. Predominately

1Coyle, Meier. New Technologies, 9.


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qualitative in their approach, these authors appear to be caught up in the fervor of this

innovative and exciting technology.2

There is an overwhelming tendency to focus

solely on the benefits, with hardly a token discussion of the downsides of relying on

crowdsourced information. The result is a significant knowledge gap as to who

exactly constitutes the crowd and, more importantly, who is left out. Crowdsourced

crisis mapping may be effective, but this does not necessarily mean that it is fair.

This myopia in the literature may have real consequences for those individuals made

invisible by gaps in the net.

An analysis of the 2010 Haitian earthquake is an ideal starting point. It was

during this disaster that crowdsourced reporting really came into its own. Much of

the humanitarian response in the immediate aftermath was driven by this type of

reporting3; faced with an almost complete failure of domestic infrastructure and

emergency services, such reports were often the only information available.4

Crowdsourcing played a crucial role in coordinating relief and rescue efforts,

providing timely and critical information that could not have been garnered by other

means. The result: humanitarian agencies exhibited an unprecedented degree of

reliance on this innovative resource.5

The unique context of the Haitian earthquake

affords us an indispensable opportunity to critically assess crowdsource-driven

response mechanisms.

The fundamental question that needs to be asked regards the equity of

crowdsourced reporting. By relying so heavily on the reports of individuals, and the

online buzz of international observers, did we create a squeaky wheel problem?

Were certain at-risk groups or regions rendered invisible because they lacked

2Howe. Power of the Crowd, 9.

3Munro. Crowdsourcing Haiti, 1.

4 Munro. Crowdsourcing Haiti, 1.5

Munro. Crowdsourcing Haiti, 2.


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adequate access to the network? Did this lead to an unequal distribution of

humanitarian resources?

Hypothesis

Over-reliance on crowdsourced reporting drew undue attention to certain

regions, leaving other vulnerable areas underrepresented.

Methodology

This paper approaches these questions through an analysis of the

crowdsourced crisis-mapping platform Ushahidi. By closely examining damage

assessments and population distribution, and comparing this to the distribution and

concentration of reports by region, we can hope to determine whether the

crowdsourced reports represented an accurate reflection of the situation on the

ground. Discrepancies in need versus reporting may suggest that certain at-risk

groups or regions were underrepresented by this type of reporting.

Suitability as a Case Study

The 2010 Haitian earthquake is a particularly salient case study for two

reasons. First, it is the first time that crowdsourced reporting was widely used, and

this information was quickly adopted by several high-profile humanitarian agencies.6

Lack of intact emergency infrastructure meant that rescuers were practically forced to

rely on crowdsourced maps, perhaps to a higher degree than they would have under

different circumstances. Second, unlike political strife or other types of disasters, an

6Munro. Haiti Emergency Response, 10.


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earthquake is a highly localized event. A rudimentary assessment of need can be

discerned by relative proximity to the earthquakes epicentre.

Disclaimer

Before proceeding with the analysis, it must be clarified what this paper is and

what it is not. It is not a condemnation of crowdsourcing; on the contrary, innovative

platforms like Ushahidi are highly promising, and carry with them the potential to

dramatically enhance our ability to respond effectively to crises. This has already

been demonstrated in a variety of contexts, most notably in Haiti.7 There is no doubt

that crowdsourced reporting has saved lives and its validity as an integral part of

humanitarian response is not in question.

This paper is intended as a sobering reflection on the potential limitations and

drawbacks of relying on crowdsourced data. In particular, it is born out of concern

for those who may be left out of the loop. As crowdsourcing becomes part of the

landscape of humanitarian response, this is a discussion we need to have. It is this

authors sincere hope that this technology continues to be implemented and

recognized as the invaluable tool that it is and that through careful planning

crowdsourced reporting can become even more inclusive and equitable.

Analysis

On the 12th

of January 2010, at 16:53 local time, a 7.0 magnitude earthquake

struck southern Haiti.8

Emergency services, already overstretched, were quickly

disrupted and overwhelmed.9

This paucity of emergency responders was exacerbated


8 http://earthquake.usgs.gov/earthquakes/eqinthenews/2010/us2010rja6/#details9

Munro. Crowdsourcing Haiti, 1.


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by the near-total loss of public infrastructure, including the destruction of key

government buildings, such as the National Assembly,10

and the loss of the UN

missions headquarters in the capital.11

The final toll left over 230,000 dead, 300,000

injured, one million homeless and a country in ruin.12

Among the dead was the leader

of MINUSTAH, the UN mission for the stabilization of Haiti.13

The local government, already racked by political turmoil, weak institutions,

and lacking the confidence of many Haitians, was ill-equipped to deal with the

disaster.14

As rescuers reported, [t]he resulting scale of destruction - of

infrastructure, of government and other official organisations (sic) - also made it

much more difficult to respond.15 The chaos and lack of central organization left a

critical gap which crowdsourced crisis mapping was uniquely qualified to fill.

Within hours of the disaster, the architects of Ushahidi had set up a crisis-

mapping tool for Haiti.16

Reports from a number of sources, including NGOs, media

outlets and social media platforms were collated and geo-coded onto the web-based

map in near-real time. Within three days the site had received 33,000 unique

visitors.17

While the platform proved an effective tool for collaboration and

information-sharing, the crucial contribution of Ushahidi at this stage was acting as a

repository, a one-stop shop, for information pertaining to the unfolding crisis.

10http://www.thestar.com/news/world/haiti/article/754255--haiti-s-leaders-face-hell-

of-a-job11

Informal Briefing by Alain LeRoy, 12 January 201012

http://news.bbc.co.uk/2/hi/americas/8511997.stm13

Informal Briefing by Alain LeRoy, 12 January 201014

http://www.thestar.com/news/world/haiti/article/754255--haiti-s-leaders-face-hell-

of-a-job15

http://news.bbc.co.uk/2/hi/americas/8510900.stm16

http://www.washingtonpost.com/wp-

dyn/content/article/2010/01/15/AR2010011502650.html17 http://www.washingtonpost.com/wp-

dyn/content/article/2010/01/15/AR2010011502650.html


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Shortly thereafter, an SMS short code, 4636, was established for Haitians and

non-Haitians alike to submit reports via text message. In addition to aggregating time-

sensitive data, Ushahidi provided a crowdsourced translation service that was critical

to making information available to emergency responders, most of whom did not

speak the local language. Over 40,000 reports were received in the first six weeks,

and the average turn-around from a message arriving in Kreyol to it being translated,

categorized, geolocated and streamed back was 10 minutes.18

This impressive efficiency did not go unnoticed. Early on, FEMA...

identified [Ushahidi] as the most up-to-date crisis mapping service19 and its

information was employed by several agencies, including USAID, the World Food

Program and the UNDP20

. Not only did Ushahidi augment conventional emergency

services, in some cases the crowdsourced reports also filled critical knowledge gaps;

one salient example was when the World Food Program delivered food to an

informal camp of 2500 people, having yet to receive food or water, in... a location that

4636 had identified for them.21

As the case of Haiti clearly shows, crowdsourced reporting has an important

niche to fill. Platforms like Ushahidi can aggregate information in sheer volume and

with such speed that far exceeds more conventional methods of information gathering

in the midst of a crisis. Crowdsourcing has a unique value to humanitarian efforts

because it provides additional data at levels of granularity and timeliness that could

not be matched by other means.22




21 Munro. Haiti Emergency Response, 10.22

Zook et al. Volunteered Geographic Information, 12.


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Herein lies the rub. Without question, crowdsourcing does have an important

role, and as proponents are quick to remind us some information... [is] better than

none.23

But how much faith do we put in this information? Does crowdsourced

reporting give us a truly objective and accurate assessment of the situation on the

ground?

Regional Discrepancies

The case of Logne is telling. Located just west of the quakes epicentre, this

coastal town was nearly completely destroyed. Of its 181,000 inhabitants, nearly

10,000 were killed, and many thousands more were left homeless.24

A UN survey

team sent to assess the damage found that Logne was "the worst affected area with

80 to 90 per cent of buildings damaged."25

A BBC correspondent described the scene

as apocalyptic.26

In the gruesome aftermath nearly every home had collapsed27

and

government infrastructure was entirely absent.28

The people of Logne, many of

whom had fled to the fields, were left highly vulnerable.29

When reporters described the scene as even more dramatic than in the

capital30

, they did not exaggerate. But was this level of destruction reflected in the

crowdsourced maps?

23Okollah, Ushahidi, 65.

24http://online.wsj.com/article/SB10001424052748703569004575009493976627772.html

25http://www.abc.net.au/news/stories/2010/01/17/2794043.htm?section=justin

26http://news.bbc.co.uk/2/hi/americas/8463938.stm

27http://www.abc.net.au/news/stories/2010/01/17/2794190.htm

28http://www.abc.net.au/news/stories/2010/01/17/2794043.htm?section=justin

29 http://news.bbc.co.uk/2/hi/americas/8463938.stm30

http://news.bbc.co.uk/2/hi/americas/8463938.stm


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Figure 1: Reports by Region

Ushahidi Reports by Region

0

500

1000

1500

2000

2500

1 2 3 4 5

Weeks after Earthquake

Port-au-Princ

JacmelLogn

Port-au-Prince is far and away the most densely populated region of Haiti.31

While not located directly at the quakes epicentre, its proximity to the disaster and its

enormous population drew a great deal of attention to the troubled metropolis. As

shown in Figure 1, reporting from Port-au-Prince dwarfs that of Logne and Jacmel,

though both were closer or equidistant to the quakes epicentre and suffered

considerable damage.32

Naturally, by sheer virtue of its population, Port-au-Prince

warrants a significant number of reports in absolute terms. However, when

controlling for population, a troubling trend is illuminated.

Table 1 shows that both Logne and Jacmel consistently produce roughly

half the number of reports per thousand persons as Port-au-Prince.

31 http://www.ihsi.ht/produit_demo_soc.htm32



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Table 1: Reports Per Thousand Persons

Region Week 1 Week 2 Week 3 Week 4 Week 5

Port-au-Prince 0.357 0.814 0.884 0.966 1.023

Logne 0.186 0.481 0.535 0.615 0.650

Jacmel 0.218 0.321 0.351 0.390 0.412

Source: http://haiti.ushahidi.com/

Figure 2: Reports Per Thousand Persons

Reports by Population

0

0.2

0.4

0.6

0.8

1

1.2

1 2 3 4 5

Weeks after Earthquake

Port-au-Princ

Logn

Jacmel

Source: http://haiti.ushahidi.com/

As shown in Figure 2, when weighed by population, reporting in Port-au-

Prince far outstrips that of Logne and Jacmel. At first, a dearth in reporting outside

the capital could well be expected due to damage to communications infrastructure.


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This is of course an unavoidable consequence of natural disasters. However, initial

damage assessments show that Haitis cell phone towers, built to withstand

earthquakes, had largely survived intact.33

Digicel, the largest mobile company in

Haiti, reported that their cellular network, though congested, was still operational.34

Further, as Figure 2 illustrates, by the fifth week this disparity in reporting had

not only persisted, but in the case of Jacmel it had actually increased. To be fair, the

widening gap may be partially explained by migration, as displaced persons made

their way to the capital in the hopes of finding refuge. However, the consistency

with which Logne and Jacmel were underrepresented suggests that the focus on

Port-au-Prince may have occluded the plight of individuals in these two regions.

The glacial pace of aid reaching Logne only served to reinforce local

perceptions that the government did not see them as a priority.35

As rescuers were

preoccupied with the situation in the capital, it took several days for aid to reach the

beleaguered city.36 Despite the dire need of Lognes citizens, residents in Port-au-

Prince were clearly able to attract far more attention on a per capita basis. This may

help explain the inadequate resources devoted to the region.37

While this trend is disturbing, a comparison of the reports between urban and

rural areas poses serious questions as to the equity of crowdsourced reporting.

The Rural-Urban Divide

Discrepancies in reports per capita between rural and urban areas are even

more pronounced than those between cities. In contrast to the urban sprawl of Port-

33

http://www.internews.org/articles/2010/20100125_msnbc_haiti.shtm34

http://www.indiaprwire.com/pressrelease/telecommunications/2010011441347.htm35

http://online.wsj.com/article/SB10001424052748703569004575009493976627772.html36http://online.wsj.com/article/SB10001424052748703569004575009493976627772.html37

http://www.washingtonpost.com/wp-dyn/content/article/2010/01/18/AR2010011803833.html


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au-Prince, half of Haitis roughly ten million inhabitants live in the countryside38

, yet

reporting from most rural areas is scant or absent.39

Even four months after the

disaster, the crowdsourced map draws little attention outside urban areas. The

scattering and infrequency of reports from the countryside would seem to suggest that

rural Haitians were relatively unaffected by the disaster an unlikely prospect.

Before proceeding, it must be qualified that those structures most damaged by

the earthquake were larger, concrete buildings, most of which were in urban settings.

However, Ushahidi aggregated reports not just of collapsed structures or trapped

individuals but of myriad issues involving water, sanitation, shelter and medical

needs. Of course, the urban population was highly vulnerable, but the question

remains as to how much the discrepancies in reporting were driven by need and how

much by their increased access to the channels of reporting.

Figure 3: Reporting in Sud-Est District (January-April)

Rural vs. Urban Reporting in Sud-

Est District

0

0.5

1

1.5

2

2.5

3

Location

District Averag Jacme Re st of Distri

38 http://www.ihsi.ht/produit_demo_soc.htm39

http://haiti.ushahidi.com/


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As Figure 3 illustrates, there is an alarming disparity between rural and urban

reporting in the Sud-Est District. Jacmel, with its 40,000 inhabitants, comprises

roughly 7% of the districts population, yet accounts for 73% of the reporting. No

doubt, Jacmel sustained heavy damage40

and warranted significant attention, but

many areas of the Sud-Est district were closer to the epicentre and yet account for

only a handful of reports, if any.

Figure 4: Mobile Coverage in Haiti

Source: Digicel41

Perhaps the most illustrative depiction of the reporting discrepancies is

Digicels (the largest cellular service provider) mobile network map, shown in Figure

4. As seen here, there are large gaps in the network represented by the white spaces;

40 http://news.bbc.co.uk/2/hi/americas/8466385.stm41

http://www.digicelhaiti.com/en/coverage_roaming/coverage_map


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after all, mobile coverage is driven by demand, which is driven by socioeconomic and

market factors. Rural areas, with their higher incidence of poverty and lower

population density, are less likely to be covered by the cellular network.

What is eerie about the mobile coverage map is how closely it resembles the

crowdsourced crisis map. Areas with low coverage (or none at all) are conspicuously

devoid of reports.42

This seems hardly coincidental.

When discussing crowdsourcing initiatives, it is crucial to consider who

exactly is participating. Make no mistake, there are real constraints affecting

membership in the crowd. To begin with, one must have access to the network.

While many take such access for granted, in some contexts this connection is far from

a foregone conclusion. A further, and equally crucial, constraint is knowledge. One

must be aware that such a platform exists in order to participate.

In the case of Haiti, there were real obstacles determining who could submit

reports. Due to congestion of the network, text messages were the only effective way

to communicate and contribute reports43

yet only 37% of Haitians had cell phones.44

Further, Internet penetration was around 1%.45

Of course, this does not necessarily

suggest that only a third of Haitians had access; one could conceivably submit reports

on others behalf. But it does suggest that we should not assume that open access is

synonymous with equal access.

While it may not be intuitive, crowdsourced reporting implies a two-way

communication. Those in the crowd can only participate if they are first made aware

that such participation is possible. Thus, the crowd consists only of those who can be

42http://haiti.ushahidi.com/


44 https://www.cia.gov/library/publications/the-world-factbook/geos/ha.html45

https://www.cia.gov/library/publications/the-world-factbook/geos/ha.html


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reached initially by the organizers. Knowledge of Ushahidis existence was likely a

significant bottleneck constraining the number of reports.

Both access and knowledge were almost certainly restricted for Haitis rural

population. With the breakdown in public infrastructure, lack of mobile coverage and

low population density46

, communication both to and from the countryside would no

doubt have been difficult. As the data shows, some 500,000 Haitians in Sud-Est

appear to have been grossly underrepresented by the crowdsourced crisis mapping.

The consequences of this low incidence in reporting are uncertain, but this

discrepancy demands further investigation.

Conclusion

At first glance, the hypothesis that crowdsourced reporting drew undue

attention to certain areas seems to hold true. The concentration of reporting is

significantly higher in the capital and other urban centres while in smaller towns and

rural areas there is a significant dearth in reporting. This is alarming, given that by all

accounts their need is no less immediate. It would appear that there are other factors

affecting levels of reporting.

While this analysis is by no means definitive, it does suggest that more

research needs to be done regarding the equity of crowdsourced crisis mapping.

Platforms like Ushahidi are no doubt an important tool available to emergency

responders, but our optimism must be tempered with the knowledge that the number

of reports may be skewed by other factors, especially socioeconomic ones. The

crucial question that needs to be addressed is who is doing the reporting, and who is

left out.

46http://www.ihsi.ht/produit_demo_soc.htm


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For instance, do women have equal access to ICTs? Are the poorest groups,

or poorest regions, able to submit reports on a scale equal to the richest?

Perhaps an unforeseen advantage of the crowdsourced approach is to lay bare

the discrepancies in our disaster response, which may otherwise have gone

undocumented.

One possible solution to the squeaky wheel problem is to anticipate these

discrepancies by applying a weighting system, assigning higher priority or

significance to reports from regions where low participation is expected. Another

option is to expand on the efforts of organizations like Tlcoms Sans Frontires, who

set up satellite relay systems in a number of Haitian towns including Port-au-Prince

and Logne. Unfortunately, due to resource constraints, their activities were limited

to the major cities.47

Further, the knowledge bottleneck could be overcome by incorporating

crowdsourced mapping into a countrys emergency response plans prior to the

disaster. An awareness campaign promoting use of a crowdsourcing platform would

be a cost-effective approach to improving emergency preparedness. Increased

awareness, especially in rural areas, would enhance and equalize participation,

increasing both the effectiveness and reliability of crowdsourced reporting. Another

option would be to revamp local emergency services (after all, 911 is one of the oldest

forms of crowdsourcing48

) to better incorporate newer communications technologies

like SMS and social media.

It is high time for a more critical examination of crowdsourced reporting. As

the preceding analysis indicates, there are a number of potential pitfalls to over-

47

http://www.tsfi.org/en/action/emergencies/112-seisme-en-haiti-tsf-deploie-ses-

equipes48

https://irevolution.wordpress.com/2010/09/22/911-system/


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reliance on this kind of data. However, it should be acknowledged that these obstacles

are not insurmountable; crowdsourced reporting still retains enormous potential and

has a vital role to play in the future of humanitarian response. We must only be wary

of the assumption that the crowd is all-inclusive: it is not.


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Informal Briefing by Alain LeRoy, 12 January 2010 Informal Briefing by

Alain LeRoy, 12 January 2010 (available athttp://www.un.org/en/peacekeeping/missions/minustah/documents/nkolo_

haiti_statement_13012010.pdf)




http://online.wsj.com/article/SB10001424052748703569004575009493976627772.html

http://www.abc.net.au/news/stories/2010/01/17/2794043.htm?section=justi

n


http://www.abc.net.au/news/stories/2010/01/17/2794190.htm

http://www.ihsi.ht/produit_demo_soc.htm


http://haiti.ushahidi.com/

http://www.internews.org/articles/2010/20100125_msnbc_haiti.shtm

http://www.indiaprwire.com/pressrelease/telecommunications/201001144

1347.htm


http://www.digicelhaiti.com/en/coverage_roaming/coverage_map

https://www.cia.gov/library/publications/the-world-factbook/geos/ha.html

http://www.tsfi.org/en/action/emergencies/112-seisme-en-haiti-tsf-deploie-

ses-equipes
http://news.bbc.co.uk/2/hi/americas/8511997.stmhttp://news.bbc.co.uk/2/hi/americas/8511997.stmhttp://news.bbc.co.uk/2/hi/americas/8510900.stmhttp://news.bbc.co.uk/2/hi/americas/8510900.stmhttp://www.washingtonpost.com/wp-dyn/content/article/2010/01/15/AR2010011502650.htmlhttp://www.washingtonpost.com/wp-dyn/content/article/2010/01/15/AR2010011502650.htmlhttp://www.washingtonpost.com/wp-dyn/content/article/2010/01/15/AR2010011502650.htmlhttp://online.wsj.com/article/SB10001424052748703569004575009493976627772.htmlhttp://online.wsj.com/article/SB10001424052748703569004575009493976627772.htmlhttp://online.wsj.com/article/SB10001424052748703569004575009493976627772.htmlhttp://www.abc.net.au/news/stories/2010/01/17/2794043.htm?section=justinhttp://www.abc.net.au/news/stories/2010/01/17/2794043.htm?section=justinhttp://www.abc.net.au/news/stories/2010/01/17/2794043.htm?section=justinhttp://news.bbc.co.uk/2/hi/americas/8463938.stmhttp://news.bbc.co.uk/2/hi/americas/8463938.stmhttp://www.abc.net.au/news/stories/2010/01/17/2794190.htmhttp://www.abc.net.au/news/stories/2010/01/17/2794190.htmhttp://www.ihsi.ht/produit_demo_soc.htmhttp://www.ihsi.ht/produit_demo_soc.htmhttp://news.bbc.co.uk/2/hi/americas/8466385.stmhttp://news.bbc.co.uk/2/hi/americas/8466385.stmhttp://haiti.ushahidi.com/http://haiti.ushahidi.com/http://www.internews.org/articles/2010/20100125_msnbc_haiti.shtmhttp://www.internews.org/articles/2010/20100125_msnbc_haiti.shtmhttp://www.indiaprwire.com/pressrelease/telecommunications/2010011441347.htmhttp://www.indiaprwire.com/pressrelease/telecommunications/2010011441347.htmhttp://www.indiaprwire.com/pressrelease/telecommunications/2010011441347.htmhttp://www.washingtonpost.com/wp-dyn/content/article/2010/01/18/AR2010011803833.htmlhttp://www.washingtonpost.com/wp-dyn/content/article/2010/01/18/AR2010011803833.htmlhttp://www.washingtonpost.com/wp-dyn/content/article/2010/01/18/AR2010011803833.htmlhttp://www.digicelhaiti.com/en/coverage_roaming/coverage_maphttp://www.digicelhaiti.com/en/coverage_roaming/coverage_maphttps://www.cia.gov/library/publications/the-world-factbook/geos/ha.htmlhttps://www.cia.gov/library/publications/the-world-factbook/geos/ha.htmlhttp://www.tsfi.org/en/action/emergencies/112-seisme-en-haiti-tsf-deploie-ses-equipeshttp://www.tsfi.org/en/action/emergencies/112-seisme-en-haiti-tsf-deploie-ses-equipeshttp://www.tsfi.org/en/action/emergencies/112-seisme-en-haiti-tsf-deploie-ses-equipeshttp://www.tsfi.org/en/action/emergencies/112-seisme-en-haiti-tsf-deploie-ses-equipeshttp://www.tsfi.org/en/action/emergencies/112-seisme-en-haiti-tsf-deploie-ses-equipeshttps://www.cia.gov/library/publications/the-world-factbook/geos/ha.htmlhttp://www.digicelhaiti.com/en/coverage_roaming/coverage_maphttp://www.washingtonpost.com/wp-dyn/content/article/2010/01/18/AR2010011803833.htmlhttp://www.washingtonpost.com/wp-dyn/content/article/2010/01/18/AR2010011803833.htmlhttp://www.indiaprwire.com/pressrelease/telecommunications/2010011441347.htmhttp://www.indiaprwire.com/pressrelease/telecommunications/2010011441347.htmhttp://www.internews.org/articles/2010/20100125_msnbc_haiti.shtmhttp://haiti.ushahidi.com/http://news.bbc.co.uk/2/hi/americas/8466385.stmhttp://www.ihsi.ht/produit_demo_soc.htmhttp://www.abc.net.au/news/stories/2010/01/17/2794190.htmhttp://news.bbc.co.uk/2/hi/americas/8463938.stmhttp://www.abc.net.au/news/stories/2010/01/17/2794043.htm?section=justinhttp://www.abc.net.au/news/stories/2010/01/17/2794043.htm?section=justinhttp://online.wsj.com/article/SB10001424052748703569004575009493976627772.htmlhttp://online.wsj.com/article/SB10001424052748703569004575009493976627772.htmlhttp://www.washingtonpost.com/wp-dyn/content/article/2010/01/15/AR2010011502650.htmlhttp://www.washingtonpost.com/wp-dyn/content/article/2010/01/15/AR2010011502650.htmlhttp://news.bbc.co.uk/2/hi/americas/8510900.stmhttp://news.bbc.co.uk/2/hi/americas/8511997.stm

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