Tag Archives: management

The Value of Timely Information During Disasters (Measured in Hours)

In the 2005 World Disaster Report (PDF), the International Federation of the Red Cross states unequivocally that access to information during disasters is equally important as access to food, water, shelter and medication. Of all these commodities, however, crisis information is the most perishable. In other words, the “sell-by” or “use-by” date of information for decision-making during crisis is very short. Put simply: information rots fast, especially in the field (assuming that information even exists in the first place). But how fast exactly as measured in hours and days?

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Enter this handy graph by FEMA, which is based on a large survey of emergency management professionals across the US. As you’ll note, there is a very clear cut-off at 72 hours post-disaster by which time the value of information for decision making purposes has depreciated by 60% to 85%. Even at 48 hours, information has lost 35% to 65% of its initial tactical value. Disaster responders don’t have the luxury of waiting around for actionable information to inform their decisions during the first 24-72 hours after a disaster. So obviously they’ll take those decisions whether or not timely data is available to guide said decisions.

In a way, the graph also serves as a “historical caricature” of the availability of crisis information over the past 25 years:

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During the early 1990s, when the web and mobile phones were still in their infancy, it often took weeks to collect detailed information on disaster damage and needs following major disasters. Towards the end of the 2000’s, thanks to the rapid growth in smartphones, social media and the increasing availability of satellite imagery plus improvements in humanitarian information management systems, the time it took to collect crisis information was shortened. One could say we crossed the 72-hour time barrier on January 12, 2010 when a devastating earthquake struck Haiti. Five years later, the Nepal earthquake in April 2015 may have seen a number of formal responders crossing the 48-hour threshold.

While these observations are at best the broad brushstrokes of a caricature, the continued need for timely information is very real, especially for tactical decision making in the field. This is why we need to shift further left in the FEMA graph. Of course, information that is older than 48 hours is still useful, particularly for decision-makers at headquarters who do not need to make tactical decisions.

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In fact, the real win would be to generate and access actionable information within the first 12- to 24-hour mark. By the end of the 24-hours, the value of information has “only” depreciated by 10% to 35%. So how do we get to the top left corner of the graph? How do we get to “Win”?

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By integrating new and existing sensors and combining these with automated analysis solutions. New sensors: like Planet Lab’s growing constellation of micro-satellites, which will eventually image the entire planet once every 24 hours at around 3-meter resolution. And new automated analysis solutions: powered by crowdsourcing and artificial intelligence (AI), and in particular deep learning techniques to process the Big Data generated by these “neo-sensors” in near real-time, including multimedia posted to social media sites and the Web in general.

And the need for baseline data is no less important for comparative analysis and change detection purposes. As a colleague of mine recently noted, the value of baseline information before a major disaster is at an all time high but then itself depreciates as well post-disaster.

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Of course, access to real-time information does not make a humanitarian organization a real-time response organization. There are always delays regard-less of how timely (or not) the information is (assuming it is even available). But the real first responders are the local communities. So the real win here would be to make make this real-time analysis directly available to local partners in disaster prone countries. They often have more of an immediate incentive to generate and consume timely, tactical information. I described this information flow as “crowdfeeding” years ago.

In sum, the democratization of crisis information is key (keeping in mind data-protection protocols). But said democratization isn’t enough. The know-how and technologies to generate and analyze crisis information during the first 12-24 hours must also be democratized. The local capacity to respond quickly and effectively must exist; otherwise timely, tactical information will just rot away.


I’d be very interested to hear from human rights practitioners to get their thoughts on how/when the above crisis information framework does, and does not, apply when applied to human rights monitoring.

A 10 Year Vision: Future Trends in Geospatial Information Management

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The United Nations Committee of Experts on Global Geospatial Information Management (UN-GGIM) recently published their second edition of Future Trends in Geospatial Information Management. I blogged about the first edition here. Below are some of the excerpts I found interesting or noteworthy. The report itself is a 50-page document (PDF 7.1Mb).

  • The integration of smart technologies and efficient governance models will increase and the mantra of ‘doing more for less’ is more relevant than ever before.
  • There is an increasing tendency to bring together data from multiple sources: official statistics, geospatial information, satellite data, big data and crowdsourced data among them.
  • New data sources and new data collection technologies must be carefully applied to avoid a bias that favors countries that are wealthier and with established data infrastructures. The use of innovative tools might also favor those who have greater means to access technology, thus widening the gap between the ‘data poor’ and the ‘data rich’.
  • The paradigm of geospatial information is changing; no longer is it used just for mapping and visualization, but also for integrating with other data sources, data analytics, modeling and policy-making.
  • Our ability to create data is still, on the whole, ahead of our ability to solve complex problems by using the data.  The need to address this problem will rely on the development of both Big Data technologies and techniques (that is technologies that enable the analysis of vast quantities of information within usable and practical timeframes) and artificial intelligence (AI) or machine learning technologies that will enable the data to be processed more efficiently.
  • In the future we may expect society to make increasing use of autonomous machines and robots, thanks to a combination of aging population, 
rapid technological advancement in unmanned autonomous systems and AI, and the pure volume of data being beyond a human’s ability to process it.
  • Developments in AI are beginning to transform the way machines interact with the world. Up to now machines have mainly carried out well-defined tasks such as robotic assembly, or data analysis using pre-defined criteria, but we are moving into an age where machine learning will allow machines to interact with their environment in more flexible and adaptive ways. This is a trend we expect to 
see major growth in over the next 5 to 10 years as the technologies–and understanding of the technologies–become more widely recognized.
  • Processes based on these principles, and the learning of geospatial concepts (locational accuracy, precision, proximity etc.), can be expected to improve the interpretation of aerial and satellite imagery, by improving the accuracy with which geospatial features can be identified.
  • Tools may run persistently on continuous streams of data, alerting interested parties to new discoveries and events.  Another branch of AI that has long been of interest has been the expert system, in which the knowledge and experience of human experts 
is taught to a machine.
  • The principle of collecting data once only at the highest resolution needed, and generalizing ‘on the fly’ as required, can become reality.  Developments of augmented and virtual reality will allow humans to interact with data in new ways.
  • The future of data will not be the conflation of multiple data sources into a single new dataset, rather there will be a growth in the number of datasets that are connected and provide models to be used across the world.
  • Efforts should be devoted to integrating involuntary sensors– mobile phones, RFID sensors and so
on–which aside from their primary purpose may produce information regarding previously difficult to collect information. This leads to more real-time information being generated.
  • Many developing nations have leapfrogged in areas such as mobile communications, but the lack of core processing power may inhibit some from taking advantage of the opportunities afforded by these technologies.
  • Disaggregating data at high levels down to small area geographies. This will increase the need to evaluate and adopt alternative statistical modeling techniques to ensure that statistics can be produced at the right geographic level, whilst still maintaining the quality to allow them to be reported against.
  • The information generated through use of social media and the use of everyday devices will further reveal patterns and the prediction of behaviour. This is not a new trend, but as the use of social media 
for providing real-time information and expanded functionality increases it offers new opportunities for location based services.
  • There seems to have been
 a breakthrough from 2D to 3D information, and
 this is becoming more prevalent.

 Software already exists to process this information, and to incorporate the time information to create 4D products and services. It 
is recognized that a growth area over the next five to ten years will be the use of 4D information in a wide variety of industries.
  • 
 The temporal element is crucial to a number of applications such as emergency service response, for simulations and analytics, and the tracking of moving objects. 
 4D is particularly relevant in the context of real-time information; this has been linked to virtual reality technologies.
  • Greater coverage, quality and resolution has been achieved by the availability of both low-cost and affordable satellite systems, and unmanned aerial vehicles (UAVs). This has increased both the speed of collection and acquisition in remote areas, but also reduced the cost barriers of entry.
  • UAVs can provide real-time information to decision-makers on the ground providing, for example, information for disaster manage-ment. They are
 an invaluable tool when additional information 
is needed to improve vital decision making capabilities and such use of UAVs will increase.
  • The licensing of data in an increasingly online world is proving to be very challenging. There is a growth in organisations adopting simple machine-readable licences, but these have not resolved the issues to data. Emerging technologies such as web services and the growth of big data solutions drawn from multiple sources will continue to create challenges for the licensing of data.
  • A wider issue is the training and education of a broader community of developers and users of location-enabled content. At the same time there is a need for more automated approaches to ensuring the non-geospatial professional community get the right data at the right time. 
Investment in formal training in the use of geospatial data and its implementation is still indispensable.
  • Both ‘open’ and ‘closed’ VGI 
data play an important and necessary part of the wider data ecosystem.

What is Big (Crisis) Data?

What does Big Data mean in the context of disaster response? Big (Crisis) Data refers to the relatively large volumevelocity and variety of digital information that may improve sense making and situational awareness during disasters. This is often referred to the 3 V’s of Big Data.

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Volume refers to the amount of data (20 million tweets were posted during Hurricane Sandy) while Velocity refers to the speed at which that data is generated (over 2,000 tweets per second were generated following the Japan Earthquake & Tsunami). Variety refers to the variety of data generated, e.g., Numerical (GPS coordinates), Textual (SMS), Audio (phone calls), Photographic (satellite Imagery) and Video-graphic (YouTube). Sources of Big Crisis Data thus include both public and private sources such images posted as social media (Instagram) on the one hand, and emails or phone calls (Call Record Data) on the other. Big Crisis Data also relates to both raw data (the text of individual Facebook updates) as well as meta-data (the time and place those updates were posted, for example).

Ultimately, Big Data describe datasets that are too large to be effectively and quickly computed on your average desktop or laptop. In other words, Big Data is relative to the computing power—the filters—at your finger tips (along with the skills necessary to apply that computing power). Put differently, Big Data is “Big” because of filter failure. If we had more powerful filters, said “Big” Data would be easier to manage. As mentioned in previous blog posts, these filters can be created using Human Computing (crowdsourcing, microtasking) and/or Machine Computing (natural language processing, machine learning, etc.).

BigData1

Take the above graph, for example. The horizontal axis represents time while the vertical one represents volume of information. On a good day, i.e., when there are no major disasters, the Digital Operations Center of the American Red Cross monitors and manually reads about 5,000 tweets. This “steady state” volume and velocity of data is represented by the green area. The dotted line just above denotes an organization’s (or individual’s) capacity to manage a given volume, velocity and variety of data. When disaster strikes, that capacity is stretched and often overwhelmed. More than 3 million tweets were posted during the first 48 hours after the Category 5 Tornado devastated Moore, Oklahoma, for example. What happens next is depicted in the graph below.

BigData 2

Humanitarian and emergency management organizations often lack the internal surge capacity to manage the rapid increase in data generated during disasters. This Big Crisis Data is represented by the red area. But the dotted line can be raised. One way to do so is by building better filters (using Human and/or Machine Computing). Real world examples of Human and Machine Computing used for disaster response are highlighted here and here respectively.

BigData 3

A second way to shift the dotted line is with enlightened leadership. An example is the Filipino Government’s actions during the recent Typhoon. More on policy here. Both strategies (advanced computing & strategic policies) are necessary to raise that dotted line in a consistent manner.

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See also:

  • Big Data for Disaster Response: A List of Wrong Assumptions [Link]

Social Media for Emergency Management: Question of Supply and Demand

I’m always amazed by folks who dismiss the value of social media for emergency management based on the perception that said content is useless for disaster response. In that case, libraries are also useless (bar the few books you’re looking for, but those rarely represent more than 1% of all the books available in a major library). Does that mean libraries are useless? Of course not. Is social media useless for disaster response? Of course not. Even if only 0.001% of the 20+ million tweets posted during Hurricane Sandy were useful, and only half of these were accurate, this would still mean over 1,000 real-time and informative tweets, or some 15,000 words—i.e., the equivalent of a 25-page, single-space document exclusively composed of fully relevant, actionable & timely disaster information.

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Empirical studies clearly prove that social media reports can be informative for disaster response. Numerous case studies have also described how social media has saved lives during crises. That said, if emergency responders do not actively or explicitly create demand for relevant and high quality social media content during crises, then why should supply follow? If the 911 emergency number (999 in the UK) were never advertised, then would anyone call? If 911 were simply a voicemail inbox with no instructions, would callers know what type of actionable information to relay after the beep?

While the majority of emergency management centers do not create the demand for crowdsourced crisis information, members of the public are increasingly demanding that said responders monitor social media for “emergency posts”. But most responders fear that opening up social media as a crisis communication channel with the public will result in an unmanageable flood of requests, The London Fire Brigade seems to think otherwise, however. So lets carefully unpack the fear of information flooding.

First of all, New York City’s 911 operators receive over 10 million calls every year that are accidental, false or hoaxes. Does this mean we should abolish the 911 system? Of course not. Now, assuming that 10% of these calls takes an operator 10 seconds to manage, this represents close to 3,000 hours or 115 days worth of “wasted work”. But this filtering is absolutely critical and requires human intervention. In contrast, “emergency posts” published on social media can be automatically filtered and triaged thanks to Big Data Analytics and Social Computing, which could save time operators time. The Digital Operations Center at the American Red Cross is currently exploring this automated filtering approach. Moreover, just as it is illegal to report false emergency information to 911, there’s no reason why the same laws could not apply to social media when these communication channels are used for emergency purposes.

Second, if individuals prefer to share disaster related information and/or needs via social media, this means they are less likely to call in as well. In other words, double reporting is unlikely to occur and could also be discouraged and/or penalized. In other words, the volume of emergency reports from “the crowd” need not increase substantially after all. Those who use the phone to report an emergency today may in the future opt for social media instead. The only significant change here is the ease of reporting for the person in need. Again, the question is one of supply and demand. Even if relevant emergency posts were to increase without a comparable fall in calls, this would simply reveal that the current voice-based system creates a barrier to reporting that discriminates against certain users in need.

Third, not all emergency calls/posts require immediate response by a paid professional with 10+ years of experience. In other words, the various types of needs can be triaged and responded to accordingly. As part of their police training or internships, new cadets could be tasked to respond to less serious needs, leaving the more seasoned professionals to focus on the more difficult situations. While this approach certainly has some limitations in the context of 911, these same limitations are far less pronounced for disaster response efforts in which most needs are met locally by the affected communities themselves anyway. In fact, the Filipino government actively promotes the use of social media reporting and crisis hashtags to crowdsource disaster response.

In sum, if disaster responders and emergency management processionals are not content with the quality of crisis reporting found on social media, then they should do something about it by implementing the appropriate policies to create the demand for higher quality and more structured reporting. The first emergency telephone service was launched in London some 80 years ago in response to a devastating fire. At the time, the idea of using a phone to report emergencies was controversial. Today, the London Fire Brigade is paving the way forward by introducing Twitter as a reporting channel. This move may seem controversial to some today, but give it a few years and people will look back and ask what took us so long to adopt new social media channels for crisis reporting.

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Predicting the Future of Global Geospatial Information Management

The United Nations Committee of Experts on Global Information Management (GGIM) recently organized a meeting of thought-leaders and visionaries in the geo-spatial world to identify the future of this space over the next 5-10 years. These experts came up with some 80+ individual predictions. I’ve included some of the more interesting ones below.

  • The use of Unmanned Aerial Vehicles (UAVs) as a tool for rapid geospatial data collection will increase.
  • 3D and even 4D geospatial information, incorporating time as the fourth dimension, will increase.
  • Technology will move faster than legal and governance structures.
  • The link between geospatial information and social media, plus other actor networks, will become more and more important.
  • Real-time info will enable more dynamic modeling & response to disasters.
  • Free and open source software will continue to grow as viable alternatives both in terms of software, and potentially in analysis and processing.
  • Geospatial computation will increasingly be non-human consumable in nature, with an increase in fully-automated decision systems.
  • Businesses and Governments will increasingly invest in tools and resources to manage Big Data. The technologies required for this will enable greater use of raw data feeds from sensors and other sources of data.
  • In ten years time it is likely that all smart phones will be able to film 360 degree 3D video at incredibly high resolution by today’s standards & wirelessly stream it in real time.
  • There will be a need for geospatial use governance in order to discern the real world from the virtual/modelled world in a 3D geospatial environ-ment.
  • Free and open access to data will become the norm and geospatial information will increasingly be seen as an essential public good.
  • Funding models to ensure full data coverage even in non-profitable areas will continue to be a challenge.
  • Rapid growth will lead to confusion and lack of clarity over data ownership, distribution rights, liabilities and other aspects.
  • In ten years, there will be a clear dividing line between winning and losing nations, dependent upon whether the appropriate legal and policy frameworks have been developed that enable a location-enabled society to flourish.
  • Some governments will use geospatial technology as a means to monitor or restrict the movements and personal interactions of their citizens. Individuals in these countries may be unwilling to use LBS or applications that require location for fear of this information being shared with authorities.
  • The deployment of sensors and the broader use of geospatial data within society will force public policy and law to move into a direction to protect the interests and rights of the people.
  • Spatial literacy will not be about learning GIS in schools but will be more centered on increasing spatial awareness and an understanding of the value of understanding place as context.
  • The role of National Mapping Agencies as an authoritative supplier of high quality data and of arbitrator of other geospatial data sources will continue to be crucial.
  • Monopolies held by National Mapping Agencies in some areas of specialized spatial data will be eroded completely.
  • More activities carried out by National Mapping Agencies will be outsourced and crowdsourced.
  • Crowdsourced data will push National Mapping Agencies towards niche markets.
  • National Mapping Agencies will be required to find new business models to provide simplified licenses and meet the demands for more free data from mapping agencies.
  • The integration of crowdsourced data with government data will increase over the next 5 to 10 years.
  • Crowdsourced content will decrease cost, improve accuracy and increase availability of rich geospatial information.
  •  There will be increased combining of imagery with crowdsourced data to create datasets that could not have been created affordably on their own.
  • Progress will be made on bridging the gap between authoritative data and crowdsourced data, moving towards true collaboration.
  • There will be an accelerated take-up of Volunteer Geographic Information over the next five years.
  • Within five years the level of detail on transport systems within OpenStreetMap will exceed virtually all other data sources & will be respected/used by major organisations & governments across the globe.
  • Community-based mapping will continue to grow.
  • There is unlikely to be a market for datasets like those currently sold to power navigation and location-based services solutions in 5 years, as they will have been superseded by crowdsourced datasets from OpenStreetMaps or other comparable initiatives.

Which trends have the experts missed? Do you think they’re completely off on any of the above? The full set of predictions on the future of global geospatial information management is available here as a PDF.

Mobile Technologies for Conflict Management

“Mobile Technologies for Conflict Management: Online Dispute Resolution, Governance, Participation” is the title of a new book edited by Marta Poblet. I recently met Marta in Vienna, Austria during the UN Expert Meeting on Croudsource Mapping organized by UN SPIDER. I’m excited that her book has just launched. The chapters are is divided into 3 sections: Disruptive Applications of Mobile Technologies; Towards a Mobile ODR; and Mobile Technologies: New Challenges for Governance, Privacy and Security.

The book includes chapters by several colleagues of mine like Mike Best on “Mobile Phones in Conflict Stressed Environments”, Ken Banks on “Appropriate Mobile Technologies,” Oscar Salazar and Jorge Soto on “How to Crowdsource Election Monitoring in 30 Days,” Jacok Korenblum and Bieta Andemariam on “How Souktel Uses SMS Technology to Empower and Aid in Conflict-Affected Communities,” and Emily Jacobi on “Burma: A Modern Anomaly.”

My colleagues Jessica Heinzelman, Rachel Brown and myself also contributed one of the chapters. I include the introduction below.

I had long wanted to collaborate on a peer-reviewed chapter in which I could combine my earlier study of conflict resolution theory with my experience in conflict early warning and crisis mapping. See also this earlier blog post on “Crowdsourcing for Peace Mapping.”  I’ve been a big fan of Will Ury’s approach ever since coming across his work while at Columbia University back in 2003. Little did I know then that I’d be co-authoring this book chapter with two new stellar colleagues. Rachel has taken much of this thinking and applied it to the real world in her phenomenal project called Sisi ni Amni, or “We Are Peace.” You can follow them on Twitter. Jessica now serves on their Advisory Board.