Tag Archives: Google Earth

Google’s New Earth Engine for Satellite Imagery Analysis: Applications to Humanitarian Crises

So that’s what they’ve been up to. Google is developing a new computational platform for global-scale analysis of satellite imagery to monitor deforestation. But this is just “the first of many Earth Engine applications that will help scientists, policymakers, and the general public to better monitor and understand the Earth’s ecosystems.”

How about the Earth’s social systems? Humanitarian crises? Armed conflicts? This has been one of the main drivers of the Program on Crisis Mapping and Early Warning (CM&EW) which I co-direct at the Harvard Humanitarian Initiative (HHI) with Dr. Jennifer Leaning. Indeed, we had a meeting with the Google Earth team earlier this year to discuss the development of a computational platform to analyze satellite imagery of humanitarian crises for the purposes of early detection and early response.

In particular, we were interested in determining whether certain spatial patterns could be identified and if so whether we could develop a taxonomy of different spatial patterns of humanitarian crises; something like a library of “crisis finger prints.” As we noted to Google in writing following the conversations,

It is our view that the work of interpretation will be powerfully enhanced by the development of valid patterns relating to issues of importance in specific sets of circumstances that can be reproducibly recognized in satellite imagery. To be sure, the geo-spatial analysis of humanitarian crisis can serve as an important control mechanism for Google’s efforts in extending the functionality of Google Earth and Google’s analytical expertise.

This is something that a consortium of organizations including HHI can get engaged in. Population movement and settlement, shelter options and conditions, environmental threats, access to food and water, are discernible from various elements and resolution levels of satellite imagery.  But much more could be apprehended from these images were patterns assembled and then tested against other information sources and empirical field assessments. For an excellent presentation on this, see my colleague Jennifer Leaning’s excellent Keynote address at ICCM 2009:

The military uses of satellite imagery are far more developed than the humanitarian capacities because the interpretive link between what can be seen in the image and what is actually happening on the ground has been made, in great iterative detail, over a period of many years, encompassing a wide span of geographies and technological deployments. We need to develop a process to explore and validate what can be understood from satellite imagery about key humanitarian concerns by augmenting standard satellite analytics with time-specific and informed assessments of what was concurrently taking place in the location being photographed.

The potential for such applications has just begun to surface in humanitarian circles.  The Darfur Google initiative has demonstrated the force of vivid images of destruction tethered to actual locations of villages across the span of Darfur.  Little further detail is available from the actual images, however, and much of the associated information depicted by clicking on the image is static derived from other sources, somewhat laboriously acquired.  The full power of what might be gleaned simply from the satellite image remains to be explored.

Because systematic and empirical analysis of what a series of satellite images might reveal about humanitarian issues has not yet been undertaken, any effort to draw inferences from current images does not lead far.  The recent coverage of the war in Sri Lanka included satellite photos of the same contested terrain in the northeast, for two time frames, a month apart.  The attempt to determine what had transpired in that interim, relating to population movement, shelter de-construction and reconstruction, and land bombardment, was a matter of conjecture.

Bridging this gap from image to insight will not only be a matter of technological enhancement of satellite imaging. It will require interrogating the satellite images through the filter of questions and concerns that are relevant to humanitarian action and then infusing other kinds of information, gathered through a range of methods, to create visual metrics for understanding what the images project.

There is a lot of exciting work to be done in this space and I do hope that Google will seek to partner with humanitarian organizations and applied research institutes to develop an Earth Engine for Humanitarian Crises. While the technological and analytical breakthroughs are path breaking, let us remember that they can be even more breathtaking by applying them to save lives in humanitarian crises.

Patrick Philippe Meier

Evolving a Global System of Info Webs

I’ve already blogged about what an ecosystem approach to conflict early warning and response entails. But I have done so with a country focus rather than thinking globally. This blog post applies a global perspective to the ecosystem approach given the proliferation of new platforms with global scalability.

Perhaps the most apt analogy here is one of food webs where the food happens to be information. Organisms in a food web are grouped into primary producers, primary consumers and secondary consumers. Primary producers such as grass harvest an energy source such as sunlight that they turn into biomass. Herbivores are primary consumers of this biomass while carnivores are secondary consumers of herbivores. There is thus a clear relationship known as a food chain.

This is an excellent video visualizing food web dynamics produced by researchers affiliated with the Santa Fe Institute (SFI):

Our information web (or Info Web) is also composed of multiple producers and consumers of information each interlinked by communication technology in increasingly connected ways. Indeed, primary producers, primary consumers and secondary consumers also crawl and dynamically populate the Info Web. But the shock of the information revolution is altering the food chains in our ecosystem. Primary consumers of information can now be primary producers, for example.

At the smallest unit of analysis, individuals are the most primary producers of information. The mainstream media, social media, natural language parsing tools, crowdsourcing platforms, etc, arguably comprise the primary consumers of that information. Secondary consumers are larger organisms such as the global Emergency Information Service (EIS) and the Global Impact and Vulnerability Alert System (GIVAS).

These newly forming platforms are at different stages of evolution. EIS and GIVAS are relatively embryonic while the Global Disaster Alert and Coordination Systems (GDACS) and Google Earth are far more evolved. A relatively new organism in the Info Web is the UAV as exemplified by ITHACA. The BrightEarth Humanitarian Sensor Web (SensorWeb) is further along the information chain while Ushahidi’s Crisis Mapping platform and the Swift River driver are more mature but have not yet deployed as a global instance.

InSTEDD’s GeoChat, Riff and Mesh4X solutions have already iterated through a number of generations. So have ReliefWeb and the Humanitarian Information Unit (HIU). There are of course additional organisms in this ecosystem, but the above list should suffice to demonstrate my point.

What if we connected these various organisms to catalyze a super organism? A Global System of Systems (GSS)? Would the whole—a global system of systems for crisis mapping and early warning—be greater than the sum of its parts? Before we can answer this question in any reasonable way, we need to know the characteristics of each organism in the ecosystem. These organisms represent the threads that may be woven into the GSS, a global web of crisis mapping and early warning systems.

Global System of Systems

Emergency Information Service (EIS) is slated to be a unified communications solution linking citizens, journalists, governments and non-governmental organizations in a seamless flow of timely, accurate and credible information—even when local communication infrastructures are rendered inoperable. This feature will be made possible by utilizing SMS as the communications backbone of the system.

In the event of a crisis, the EIS team would sift, collate, make sense of and verify the myriad of streams of information generated by a large humanitarian intervention. The team would gather information from governments, local media, the military, UN agencies and local NGOs to develop reporting that will be tailored to the specific needs of the affected population and translated into local languages. EIS would work closely with local media to disseminate messages of critical, life saving information.

Global Impact and Vulnerability Alert System (GIVAS) is being designed to closely monitor vulnerabilities and accelerate communication between the time a global crisis hits and when information reaches decision makers through official channels. The system is mandated to provide the international community with early, real-time evidence of how a global crisis is affecting the lives of the poorest and to provide decision-makers with real time information to ensure that decisions take the needs of the most vulnerable into account.

BrightEarth Humanitarian Sensor Web (SensorWeb) is specifically designed for UN field-based agencies to improve real time situational awareness. The dynamic mapping platform enables humanitarians to easily and quickly map infrastructure relevant for humanitarian response such as airstrips, bridges, refugee camps, IDP camps, etc. The SensorWeb is also used to map events of interest such as cholera outbreaks. The platform leverages mobile technology as well as social networking features to encourage collaborative analytics.

Ushahidi integrates web, mobile and dynamic mapping technology to crowdsource crisis information. The platform uses FrontlineSMS and can be deployed quickly as a crisis unfolds. Users can visualize events of interest on a dynamic map that also includes an animation feature to visualize the reported data over time and space.

Swift River is under development but designed to validate crowdsourced information in real time by combining machine learning for predictive tagging with human crowdsourcing for filtering purposes. The purpsose of the platform is to create veracity scores to denote the probability of an event being true when reported across several media such as Twitter, Online news, SMS, Flickr, etc.

GeoChat and Mesh4X could serve as the nodes connecting the above platforms in dynamic ways. Riff could be made interoperable with Swift River.

Can such a global Info Web be catalyzed? The question hinges on several factors the most important of which are probably awareness and impact. The more these individual organisms know about each other, the better picture they will have of the potential synergies between their efforts and then find incentives to collaborate. This is one of the main reasons I am co-organizing the first International Conference on Crisis Mapping (ICCM 2009) next week.

Patrick Philippe Meier

Crisis Mapping and Health Geographics

Crisis Mapping is by definition a cross-disciplinary field. Crises can be financial, ecological, humanitarian, etc., but these crises all happen in time and space, and necessarily interact with social networks. We may thus want to learn how different fields such as health, environment, biology, etc., visualize and analyze large complex sets of data to detect and amplify or dampen specific patterns.

We can’t all become specialists in each others’ areas of expertise but we can learn from each other, especially if we share a common language. Like the field of complexity science, Crisis Mapping can provide a common but malleable language, taxonomy and conceptual framework to facilitate the exchange of insights driven by innovative thinking in diverse fields.

This explains why I was excited to come across the International Journal of Health Geographics a few days ago. The Journal is an online and open-access resource. This means new ideas can be shared openly, which is conducive to innovation, just like arXiv.

Two of the Journal’s latest articles caught my interest:

1) An Agent-Based Approach for Modeling Dynamics of Contagious Disease Spread

This study developed a spatially explicit epidemiological model of infectious disease to better understand how contagious diseases spatially diffuse through a network of human contacts. To do this, the authors developed an agent-based model (ABM) that integrates geographic information systems (GIS) to simulate the spatial diffusion. (See my previous post on ABM and crisis mapping).

What is very neat about the authors’ approach is that they chose to draw on georeferenced land use data and census data. In other words, they combined the fomalistic rules of ABM with empirical GIS data. This means that the model can actually be tested and different scenarios can be played out by adding or changing some of the parameters. Could we use this model for conflict contageon?

2) Combining Google Earth and GIS Mapping Technologies in a Dengue Surveillance System

This study overlayed georeferenced epidemiological data on a town in Nicaragua with satellite imagery from Google Earth to enable dengue control specialists to prioritize specific neighborhoods for targetted interventions. The authors used ArcGIS to “accurately identify areas with high indices of mosquito infestation and interpret the spatial relationship of these areas with potential larval development sites such as garbage piles and large pools of standing water.”

It’s worth noting that the above Google Earth imagery was not particularly high resolution but the authors were still able to make full use of the imagery.

This approach to mapping for decision-support is particularly relevant for resource-limited settings since. As the authors note, the surveillance project “utilizes readily available technologies that do not rely on Internet access for daily use and can easily be implemented in many developing countries for very little cost.”

While the team had a free copy of ArcGIS thanks to the Global Fund, they plan to consider free and low-cost alternatives such as SaTScan, MapServer and Quantum GIS in the future. (See this post for additional alternatives like GeoCommons). I hope the authors also know about Walking Papers. I’ll email them just in case. Here’s to cross-disciplinary collaboration!

Patrick Philippe Meier


Field Guide to Humanitarian Mapping

MapAction just released an excellent mapping guide for the humanitarian community. Authored principally by Naomi Morris, the guide comprises four chapters that outline a range of mapping methods suitable for humanitarian field word.

The first chapter serves as an introduction to humanitarian mapping. Chapter two explains how to make the best use of GPS for data collection. Note that the latest version of Google Earth (v5.0) includes GPS connectivity. The third and fourth chapters provide a user-friendly, hands-on tutorial on how to use Google Earth and MapWindow for humanitarian mapping.

The purpose of this post is to quickly summarize some of the points I found most interesting in the Guide and to offer some suggestions for further research. I do not summarize the tutorials but I do comment on Google Earth and MapWindow might be improved for humanitarian mapping. The end of this post includes a list of recommended links.

Introduction

John Holmes, the UN Emergency Relief Coordinator and Under-Secretary-General for Humanitarian Affairs argues that “information is very directly about saving lives. If we take the wrong decisions, make the wrong choices about where we put our money and our effort because our knowledge is poor, we are condemning some of the most deserving to death or destitution.”

I completely agree with this priority-emphasis on information. The purpose of crisis mapping and particularly mobile crisis mapping is for at-risk communities to improve their situational awareness during humanitarian crises. The hope is that relevant and timely information will enable communities to make more informed—and thus better— decisions on how to get out of harm’s way. Recall the purpose of people-centered early warning as defined by the UNISDR:

To empower individuals and communities threatened by hazards to act in sufficient time and in an appropriate manner so as to reduce the possibility of personal injury, loss of life, damage to property and the environment, and loss of livelihoods.

Naomi also cites a Senior Officer from the IFRC who explains the need to map vulnerability and develop baselines prior to a disaster context. “The data for these baselines would include scientific hazard data and the outputs from qualitative assessments at community level.”

This point is worth expanding on. I’ve been meaning to write a blog post specifically on crisis mapping baselines for monitoring and impact evaluation. I hope to do so shortly. In the meantime, the importance of baselines vis-à-vis crisis mapping is a pressing area for further research.

Community Mapping

I really appreciate Naomi’s point that humanitarian mapping does not require sophisticated, proprietary software. As she note, “there has been a steady growth in the number of ‘conventional’ desktop GIS packages available under free or open-source licenses.”

Moreover, maps can also be “created using other tools including a pad of graph paper and a pencil, or even an Excel spreadsheet.” Indeed, we should always “consider whether ‘low/no tech’ methods [can meet our] needs before investing time in computer-based methods.”

To this end, Naomi includes a section in her introduction on community-level mapping techniques.

Community-level mapping is a powerful method for disaster risk mitigation and preparedness.  It is driven by input from the beneficiary participants; this benefits the plan output with a broader overview of the area, while allowing the community to be involved. Local people can, using simple maps that they have created, quickly see and analyse important patterns in the risks they face.

Again, Naomi emphasizes the fact that computer-based tools are not essential for crisis mapping at the community level. Instead, we can “compile sketches, data from assessments and notes into representations of the region [we] are looking at using tools like pen and paper.”

To be sure, “in a situation with no time or resources, a map can be enough to help to identify the most at-risk areas of a settlement, and to mark the location of valuable services […].”

Conclusion

I highly recommend following the applied  Google Earth and MapWindow tutorials in the Guide. They are written in a very accessible way that make it easy to follow or use as a teaching tool, so many thanks to Naomi for putting this together.

I would have liked to see more on crisis mapping analysis in the Guide but the fact of the matter is that Google Earth and MapWindow provide little in the way of simple features for applied geostatistics. So this is not a criticism of the report or the author.

Links

Patrick Philippe Meier

A Brief History of Crisis Mapping (Updated)

Introduction

One of the donors I’m in contact with about the proposed crisis mapping conference wisely recommended I add a big-picture background to crisis mapping. This blog post is my first pass at providing a brief history of the field. In a way, this is a combined summary of several other posts I have written on this blog over the past 12 months plus my latest thoughts on crisis mapping.

Evidently, this account of history is very much influenced by my own experience so I may have unintentionally missed a few relevant crisis mapping projects. Note that by crisis  I refer specifically to armed conflict and human rights violations. As usual, I welcome any feedback and comments you may have so I can improve my blog posts.

From GIS to Neogeography: 2003-2005

The field of dynamic crisis mapping is new and rapidly changing. The three core drivers of this change are the increasingly available and accessible of (1) open-source, dynamic mapping tools; (2) mobile data collection technologies; and lastly (3) the development of new methodologies.

Some experts at the cutting-edge of this change call the results “Neogeography,” which is essentially about “people using and creating their own maps, on their own terms and by combining elements of an existing toolset.” The revolution in applications for user-generated content and mobile technology provides the basis for widely distributed information collection and crowdsourcing—a term coined by Wired less than three years ago. The unprecedented rise in citizen journalism is stark evidence of this revolution. New methodologies for conflict trends analysis increasingly take spatial and/or inter-annual dynamics into account and thereby reveal conflict patterns that otherwise remain hidden when using traditional methodologies.

Until recently, traditional mapping tools were expensive and highly technical geographic information systems (GIS), proprietary software that required extensive training to produce static maps.

In terms of information collection, trained experts traditionally collected conflict and human rights data and documented these using hard-copy survey forms, which typically became proprietary once completed. Scholars began coding conflict event-data but data sharing was the exception rather than the rule.

With respect to methodologies, the quantitative study of conflict trends was virtually devoid of techniques that took spatial dynamics into account because conflict data at the time was largely macro-level data constrained by the “country-year straightjacket.”

That is, conflict data was limited to the country-level and rarely updated more than once a year, which explains why methodologies did not seek to analyze sub-national and inter-annual variations for patterns of conflict and human rights abuses. In addition, scholars in the political sciences were more interested in identifying when conflict as likely to occur as opposed to where. For a more in-depth discussion of this issue, please see my paper from 2006  “On Scale and Complexity in Conflict Analysis” (PDF).

Neogeography is Born: 2005

The pivotal year for dynamic crisis mapping was 2005. This is the year that Google rolled out Google Earth. The application marks an important milestone in Neogeography because the free, user-friendly platform drastically reduced the cost of dynamic and interactive mapping—cost in terms of both availability and accessibility. Microsoft has since launched Virual Earth to compete with Google Earth and other  potential contenders.

Interest in dynamic crisis mapping did exist prior to the availability of Google Earth. This is evidenced by the dynamic mapping initiatives I took at Swisspeace in 2003. I proposed that the organization use GIS tools to visualize, animate and analyze the geo-referenced conflict event-data collected by local Swisspeace field monitors in conflict-ridden countries—a project called FAST. In a 2003 proposal, I defined dynamic crisis maps as follows:

FAST Maps are interactive geographic information systems that enable users of leading agencies to depict a multitude of complex interdependent indicators on a user-friendly and accessible two-dimensional map. […] Users have the option of selecting among a host of single and composite events and event types to investigate linkages [between events]. Events and event types can be superimposed and visualized through time using FAST Map’s animation feature. This enables users to go beyond studying a static picture of linkages to a more realistic dynamic visualization.

I just managed to dig up old documents from 2003 and found the interface I had designed for FAST Maps using the template at the time for Swisspeace’s website.

fast-map1

fast-map2

However, GIS software was (and still is) prohibitively expensive and highly technical. To this end, Swisspeace was not compelled to make the necessary investments in 2004 to develop the first crisis mapping platform for producing dynamic crisis maps using geo-referenced conflict data. In hindsight, this was the right decision since Google Earth was rolled out the following year.

Enter PRIO and GROW-net: 2006-2007

With the arrival of Google Earth, a variety of dynamic crisis maps quickly emerged. In fact, one if not the first application of Google Earth for crisis mapping was carried out in 2006 by Jen Ziemke and I. We independently used Google Earth and newly available data from the Peace Research Institute, Oslo (PRIO) to visualize conflict data over time and space. (Note that both Jen and I were researchers at PRIO between 2006-2007).

Jen used Google Earth to explain the dynamics and spatio-temporal variation in violence during the Angolan war. To do this, she first coded nearly 10,000 battle and massacre events as reported in the Portuguese press that took place over a 40 year period.

Meanwhile, I produced additional dynamic crisis maps of the conflict in the Democratic Republic of the Congo (DRC) for PRIO and of the Colombian civil war for the Conflict Analysis Resource Center (CARC) in Bogota. At the time, researchers in Oslo and Bogota used proprietary GIS software to produce static maps (PDF) of their newly geo-referenced conflict data. PRIO eventually used Google Earth but only to publicize the novelty of their new geo-referenced historical conflict datasets.

Since then, PRIO has continued to play an important role in analyzing the spatial dynamics of armed conflict by applying new quantitative methodologies. Together with universities in Europe, the Institute formed the Geographic Representations of War-net (GROW-net) in 2006, with the goal of “uncovering the causal mechanisms that generate civil violence within relevant historical and geographical and historical configurations.” In 2007, the Swiss Federal Institute of Technology in Zurich (ETH), a member of GROW-net, produced dynamic crisis maps using Google Earth for a project called WarViews.

Crisis Mapping Evolves: 2007-2008

More recently, Automated Crisis Mapping (ACM), real-time and automated information collection mechanisms using natural language processing (NLP) have been developed for the automated and dynamic mapping of disaster and health-related events. Examples of such platforms include the Global Disaster Alert and Crisis System (GDACS), CrisisWire, Havaria and HealthMap. Similar platforms have been developed for  automated mapping of other news events, such as Global Incident Map, BuzzTracker, Development Seed’s Managing the News, and the Joint Research Center’s European Media Monitor.

Equally recent is the development of Mobile Crisis Mapping (MCM), mobile crowdsourcing platforms designed for the dynamic mapping of conflict and human rights data as exemplified by Ushahidi (with FrontLineSMS) and the Humanitarian Sensor Web (SensorWeb).

Another important development around this time is the practice of participatory GIS preceded by the recognition that social maps and conflict maps can empower local communities and be used for conflict resolution. Like maps of natural disasters and environmental degradation, these can be developed and discussed at the community level to engage conversation and joint decision-making. This is a critical component since one of the goals of crisis mapping is to empower individuals to take better decisions.

HHI’s Crisis Mapping Project: 2007-2009

The Harvard Humanitarian Initiative (HHI) is currently playing a pivotal role in crafting the new field of dynamic crisis mapping. Coordinated by Jennifer Leaning and myself, HHI is completing a two-year applied research project on Crisis Mapping and Early Warning. This project comprised a critical and comprehensive evaluation of the field and the documentation of lessons learned, best practices as well as alternative and innovative approaches to crisis mapping and early warning.

HHI also acts as an incubator for new projects and  supported the conceptual development of new crisis mapping platforms like Ushahidi and the SensorWeb. In addition, HHI produced the first comparative and dynamic crisis map of Kenya by drawing on reports from the mainstream media, citizen journalists and Ushahidi to analyze spatial and temporal patterns of conflict events and communication flows during a crisis.

HHI’s Sets a Research Agenda: 2009

HHI has articulated an action-oriented research agenda for the future of crisis mapping based on the findings from the two-year crisis mapping project. This research agenda can be categorized into the following three areas, which were coined by HHI:

  1. Crisis Map Sourcing
  2. Mobile Crisis Mapping
  3. Crisis Mapping Analytics

1) Crisis Map Sourcing (CMS) seeks to further research on the challenge of visualizing disparate sets of data ranging from structural and dynamic data to automated and mobile crisis mapping data. The challenge of CMS is to develop appropriate methods and best practices for mashing data from Automated Crisis Mapping (ACM) tools and Mobile Crisis Mapping platforms (see below) to add value to Crisis Mapping Analytics (also below).

2) The purpose of setting an applied-research agenda for Mobile Crisis Mapping, or MCM, is to recognize that the future of distributed information collection and crowdsourcing will be increasingly driven by mobile technologies and new information ecosystems. This presents the crisis mapping community with a host of pressing challenges ranging from data validation and manipulation to data security.

These hurdles need to be addressed directly by the crisis mapping community so that new and creative solutions can be applied earlier rather than later. If the persistent problem of data quality is not adequately resolved, then policy makers may question the reliability of crisis mapping for conflict prevention, rapid response and the documentation of human rights violations. Worse still, inaccurate data may put lives at risk.

3) Crisis Mapping Analytics (CMA) is the third critical area of research set by HHI. CMA is becoming increasingly important given the unprecedented volume of geo-referenced data that is rapidly becoming available. Existing academic platforms like WarViews and operational MCM platforms like Ushahidi do not include features that allow practitioners, scholars and the public to query the data and to visually analyze and identify the underlying spatial dynamics of the conflict and human rights data. This is largely true of Automated Crisis Mapping (ACM) tools as well.

In other words, new and informative metrics are need to be developed to identify patterns in human rights abuses and violent conflict both retrospectively and in real-time. In addition, existing techniques from spatial econometrics need to be rendered more accessible to non-statisticians and built into existing dynamic crisis mapping platforms.

Conclusion

Jen Ziemke and I thus conclude that the most pressing need in the field of crisis mapping is to bridge the gap between scholars and practitioners who self-identify as crisis mappers. This is the most pressing issue because bridging that divide will enable the field of crisis mapping to effectively and efficiently move forward by pursuing the three research agendas set out by the Harvard Humanitarian Initiative (HHI).

We think this is key to moving the crisis-mapping field into more mainstream humanitarian and human rights work—i.e., operational response. But doing so first requires that leading crisis mapping scholars and practitioners proactively bridge the existing gap. This is the core goal of the crisis mapping conference that we propose to organize.

Patrick Philippe Meier

NeoGeography and Crisis Mapping Analytics

WarViews is Neogeography

Colleagues at the Swiss Federal Institute of Technology Zurich (ETH) are starting to publish their research on the WarViews project. I first wrote about this project in 2007 as part of an HHI deliverable on Crisis Mapping for Humanity United. What appeals to me about WarViews is the initiative’s total “Neogeography” approach.

WarView

picture-21

What is Neogeography? Surprisingly, WarViews‘s first formal publication (Weidmann and Kuse, 2009) does not use the term but my CrisisMappers colleague Andrew Turner wrote the defining chapter on Neogeography for O’Reilly back in 2006:

Neogeography means ‘new geography’ and consists of a set of techniques and tools that fall outside the realm of traditional GIS, Geographic Information Systems. Where historically a professional cartographer might use ArcGIS, talk of Mercator versus Mollweide projections, and resolve land area disputes, a neogeographer uses a mapping API like Google Maps, talks about GPX versus KML, and geotags his photos to make a map of his summer vacation.

Essentially, Neogeography is about people using and creating their own maps, on their own terms and by combining elements of an existing toolset. Neogeography is about sharing location information with friends and visitors, helping shape context, and conveying understanding through knowledge of place.

Compare this language with Wiedmann and Kuse, 2009:

[The] use of geographic data requires specialized software and substantial training and therefore involves high entry costs for researchers and practitioners. [The] War Views project [aims] to create an easy-to-use front end for the exploration of GIS data on conflict. It takes advantage of the recent proliferation of Internet-based geographic software and makes geographic data on conflict available for these tools.

With WarViews, geographic data on conflict can be accessed, browsed, and time-animated in a few mouse clicks, using only standard software. As a result, a wider audience can take advantage of the valuable data contained in these databases […].

The team in Zurich used the free GIS server software GeoServer, which reads “vector data in various formats, including the shapefile format used for many conflict-related GIS data sets.” This way, WarViews allows users to visualize data both statically and dynamically using Google Earth.

Evidently, the WarViews project is not groundbreaking compared to many of the applied mapping projects carried out by the CrisisMappers Group. (Colleagues and I in Boston created a Google Earth layer of DRC and Colombia conflict data well before WarViews came online).

That said the academic initiative at the University of Zurich is an important step forward for neogeography and an exciting development for political scientists interested in studying the geographic dimensions of conflict data.

Geographic Data

Geo-tagged conflict data is becoming more widely available. My Alma Matter, the Peace Research Institute in Oslo (PRIO), has made an important contribution with the Armed Conflict Location and Event Dataset (ACLED). This dataset includes geo-tagged conflict data for 12 countries between 1946 to present time.

In addition to ACLED, Wiedman and Kus (2009) also reference two additional geo-tagged datasets. The first is the Political Instability Task Force’s Worldwide Atrocities Dataset (PITF), which comprises a comprehensive collection of violent events against noncombatants. The second is the Peacekeeping Operations Locations and Event Dataset (Doroussen 2007, PDF), which provides geo-tagged data on interventions in civil wars. This dataset is not yet public.

Weidmann and Kuse (2009) do not mention Ushahidi, a Mobile Crisis Mapping (CMC) platform nor do the authors reference HHI’s Google Earth Crisis Map of Kenya’s Post-Election violence (2008). Both initiatives provide unique geo-tagged peace and conflict data. Ushahidi has since been deployed in the DRC, Zimbabwe and Gaza.

Unlike the academic databases referenced above, the Ushahidi data is crowdsourced and geo-tagged in quasi-real time. Given Ushahidi’s rapid deployment to other conflict zones, we can expect a lot more geo-tagged information in 2009. The question is, will we know how to analyze this data to detect patterns?

Crisis Mapping Analytics (CMA)

The WarViews project is “not designed for sophisticated analyses of geographic data […].” This is perhaps ironic given that academics across multiple disciplines have developed a plethora of computational methods and models to analyze geographic data over time and space. These techniques necessarily require advanced expertise in spatial econometric analysis and statistics.

The full potential of neography will only be realized when we have more accessible ways to analyze the data visualized on platforms like Google Earth. Neogeography brought dynamic mapping to many more users, but spatial econometric analysis has no popular equivalent.

This is why I introduced the term Crisis Mapping Analytics (CMA) back in August 2008 and why I blogged about the need to develop the new field of CMA here and here. The Harvard Humanitarian Initiative (HHI) is now spearheading the development of CMA metrics given the pressing need for more accessible albeit rigorous methods to identify patterns in crisis mapping for the purposes of early warning. Watching data played on Google Earth over and over will only take us so far, especially as new volumes of disparate datasets become available in 2009.

HHI is still in conversation with a prospective donor to establish the new field of CMA so I’m unable to outline the metrics we developed here but hope the donor will provide HHI with some funding so we can partner and collaborate with other groups to formalize the field of CMA.

Crisis Mapping Conference

In the meantime, my colleague Jen Ziemke and I are exploring the possibility of organizing a 2-3 day conference on crisis mapping for Fall 2009. The purpose of the conference is to shape the future of crisis mapping by bridging the gap that exists between academics and practitioners working on crisis mapping.

In our opinion, developing the new field of Crisis Mapping Analytics (CMA) will require the close and collegial collaboration between academic institutes like PRIO and operational projects like Ushahidi.

Jen and I are therefore starting formal conversations with donors in order to make this conference happen. Stay tuned for updates in March. In the meantime, if you’d like to provide logistical support or help co-sponsor this unique conference, please email us.

Patrick Philippe Meier

GIS Technology for Genocide Prevention

Matthew Levinger at USIP kindly shared a copy of his forthcoming publication on “Geographic Information Systems Technology as a Tool for Genocide Prevention.” The article will be published as part of the special issue of Space and Polity on “Geography and Genocide.”

The article considers the uses of virtual globes such as Google Earth for “stimulating more effective responses to emerging threats of genocide and mass atrocities.”

Matt draws on two case studies that utilize commercial satellite imagery to document the genocide in Darfur: the U.S. Holocaust Memorial Museum’s (USHMM) Crisis in Darfur project and  and Amnesty International  (AI) USA’s Eyes on Darfur initiative. (See also my previous post USHMM’s and AI’s initiative here and here).

Matt concludes that “GIS-based early warning systems may have the greatest value not for public advocacy movements but rather for policy practitioners charged with designing and implementing responses to emerging threats.  Such technology also has the potential to help endangered populations in conflict zones to organize timely and effective defensive action against threats of atrocities.”

John Prendergast, a senior African analyst at the International Crisis Group (ICG), predicted that the USHMM‘s project with Google Earth  would “bring a spotlight to a very dark corner of the earth, a torch that will indirectly help protect the victims.  It is David versus Goliath, and Google Earth just gave David a stone for his slingshot.”

I’m far from convinced. First of all, the USHMM‘s Google Earth layer is not updated so the information depicted is of no operational value.  Second, the Museum has only produced a Google Earth layer for every corner of the Earth. Third of all, drawing a correlation between virtual globes and the supposed “Global Panopticon” effect is difficult to prove.

In Discipline and Punish: The Birth of the Prison, Michel Foucault reflects on the role of surveillance as an instrument of power.  He cites the example of Jeremy Bentham’s “Panopticon,” an architectural model for a prison enabling a single guard, located in a central tower, to watch all of the inmates in their cells.  The “major effect of the Panopticon,” writes Foucault, is “to induce in the inmate a state of conscious and permanent visibility that assures the automatic functioning of power.”

According to Foucault, the Panopticon renders power both “visible and unverifiable”:

Visible: the inmate will constantly have before his eyes the tall outline of the central tower from which he is being spied upon.

Unverifiable: the inmate must never know whether he is being looked at at any one moment; but he must be sure that he may always be so.

Does high-resolution satellite imagery coupled with virtual globes lead to a reversal of Bentham’s Panopticon effect? That is, does this new medium enable the many to watch (and control) the few?

As Matt correctly notes vis-a-vis Jeremy Bentham’s Panopticon, “the use of surveillance was always coupled to the threat of punishment for deviant acts.” So while AI‘s advocacy efforts and those of the Museum‘s are important for keeping the issues in the public discourse, they are hardly acts of punishment.

Google Earth may very well have given David a stone for his slingshot; problem is, David doesn’t have a slingshot and his hands are most likely tied.

Patrick Philippe Meier