Monthly Archives: April 2009

JRC: Geo-Spatial Analysis for Global Security

The European Commission’s Joint Research Center (JRC) is doing some phenomenal work on Geo-Spatial Information Analysis for Global Security and Stability. I’ve had several meetings with JRC colleagues over the years and have always been very impressed with their projects.

The group is not very well known outside Europe so the purpose of this blog post is to highlight some of the Center’s projects.

  • Enumeration of Refugee Camps: The project developed an operational methodology to estimate refugee populations using very high resolution (VHR) satellite imagery. “The methodology relies on a combination of machine-assisted procedures, photo-interpretation and statistical sampling.”


  • Benchmarking Hand Held Equipment for Field Data Collection: This project tested new devices for the collection for geo-referenced information. “The assessment of the instruments considered their technical characteristics, like the availability of necessary instruments or functionalities, technical features, hardware specifics, software compatibility and interfaces.”


  • GEOCREW – Study on Geodata and Crisis Early Warning: This project analyzed the use of geo-spatial technology in the decision-making process of institutions dealing with international crises. The project also aimed to show best practice in the use of geo-spatial technologies in the decision-making process.
  • Support to Peacekeeping Operations in the Sudan: Maps are generally not available or often are out of date for most of the conflict areas in which peacekeping personnel is deployed,  This UNDPKO Darfur mapping initiative aimed to create an alliance of partners that addressed this gap and shared the results.


  • Temporary Settlement Analysis by Remote Sensing: The project analyzes different types of refugee and IDP settlements to identify single structures inside refugee settlements. “The objective of the project is to establish the first comprehensive catalog of image interpretation keys, based on last-generation satellite data and related to the analysis of transitional settlements.”

JRC colleagues often publish papers on their work and I highly recommend having a look at this book when it comes out in June 2009:


Patrick Philippe Meier

Video Introduction to Crisis Mapping

I’ve given many presentations on crisis mapping over the past two years but these were never filmed. So I decided to create this video presentation with narration in order to share my findings more widely and hopefully get a lot of feedback in the process. The presentation is not meant to be exhaustive although the video does run to about 30 minutes.

The topics covered in this presentation include:

  • Crisis Map Sourcing – information collection;
  • Mobile Crisis Mapping – mobile technology;
  • Crisis Mapping Visualization – data visualization;
  • Crisis Mapping Analysis – spatial analysis.

The presentation references several blog posts of mine in addition to several operational projects to illustrate the main concepts behind crisis mapping. The individual blog posts featured in the presentation are listed below:

This research is the product of a 2-year grant provided by Humanity United  (HU) to the Harvard Humanitarian Initiative’s (HHI) Program on Crisis Mapping and Early Warning, where I am a doctoral fellow.

I look forward to any questions/suggestions you may have on the video primer!

Patrick Philippe Meier

Folksomaps: Gold Standard for Community Mapping

There were a number of mapping-related papers, posters and demo’s at ICTD2009. One paper in particular caught my intention given the topic’s direct relevance to my ongoing consulting work with the UN’s Threat and Risk Mapping Analysis (TRMA) project in the Sudan and the upcoming ecosystem project in Liberia with Ushahidi and Humanity United.


Entitled “Folksomaps – Towards Community Intelligent Maps for Developing Regions,” the paper outlines a community-driven approach for creating maps by drawing on “Web 2.0 principles” and “Semantic Web technologies” but without having to rely entirely on a web-based interface. Indeed, Folksomaps “makes use of web and voice applications to provide access to its services.”

I particularly value the authors’ aim to “provide map-based services that represent user’s intuitive way of finding locations and directions in developing regions.” This is an approach that definitely resonates with me. Indeed, it is our responsibility to adapt and customize our community-based mapping tools to meet the needs, habits and symbology of the end user; not the other way around.

I highly recommend this paper (or summary below) to anyone doing work in the crisis mapping field. In fact, I consider it required reading. The paper is co-authored by Arun Kumar, Dipanjan Chakraborty, Himanshu Chauhan, Sheetal Agarwal and Nitendra Rajput of IBM India Research Lab in New Delhi.


Vast rural areas of developing countries do not have detailed maps or mapping tools. Rural populations are generally semi-literate, low-income and non-tech savvy. They are hardly like to have access to neogeography platforms like Google Earth. Moreover, the lack of electricity access and Internet connection also complicates the situation.

We also know that cities, towns and villages in developing countries “typically do not have well structured naming of streets, roads and houses,” which means “key landmarks become very important in specifying locations and directions.”

Drawing on these insights, the authors seek to tap the collective efforts of local communities to populate, maintain and access content for their own benefit—an approach I have described as crowdfeeding.

Surveys of Tech and Non-Tech Users

The study is centered on end-user needs, which is rather refreshing. The authors carried out a series of surveys to be better understand the profiles of end-users, e.g., tech and non-tech users.

The first survey sought to identify answers to the following questions:

  • How do people find out points of interest?
  • How do much people rely on maps versus people on the streets?
  • How do people provide local information to other people?
  • Whether people are interested in consuming and feeding information for a community-driven map system?

The results are listed in the table below:


Non-tech savvy users did not use maps to find information about locations and only 36% of these users required precise information. In addition, 75% of non-tech respondents preferred the choice of a phone-based interface, which really drives home the need for what I have coined “Mobile Crisis Mapping” or MCM.

Tech-users also rely primarily on others (as opposed to maps) for location related information. The authors associate this result with the lack of signboards in countries like India. “Many a times, the maps do not contain fine-grained information in the first place.”

Most tech-users responded that a phone-based location and direction finding system in addition to a web-based interface. Almost 80% expressed interest in “contributing to the service by uploading content either over the phone or though a web-based portal.”

The second survey sought to identify how tech and non-tech users express directions and local information. For example:

  • How do you give directions to people on the road or to friends?
  • How do you describe proximity of a landmark to another one?
  • How do you describe distance? Kilometers or using time-to-travel?

The results are listed in the table below:


The majority of non-tech savvy participants said they make use of landmarks when giving directions. “They use names of big roads […] and use ‘near to’, ‘adjacent to’, ‘opposite to’ relations with respect to visible and popular landmarks […].” Almost 40% of responders said they use time only to describe the distance between any two locations.

Tech-savvy participants almost always use both time and kilometers as a measure to represent distance. Only 10% or so of participants used kilometers only to represent distance.

The Technology

The following characteristics highlight the design choices that differentiate Folksomaps from established notions of map systems:

  • Relies on user generated content rather than data populated by professionals;
  • Strives for spatial integrity in the logical sense and does not consider spatial integrity in the physical sense as essential (which is a defining feature of social maps);
  • Does not consider visual representation as essential, which is important considering the fact that a large segment of users in developing countries do not have access to Internet (hence my own emphasis on mobile crisis mapping);
  • Is non-static and intelligent in the sense that it infers new information from what is entered by the users.
  • User input is not verified by the system and it is possible that pieces of incorrect information in the knowledgebase may be present at different points of time. Folksomaps adopts the Wiki model and allows all users to add, edit and remove content freely while keeping maps up-to-date.

Conceptual Design

Folksomaps uses “landmark” as the basic unit in the mapping knowledgebase model while “location” represents more coarse-grained geographical areas such as a village, city or country. The model then seeks to capture a few key logical characteristics of locations such as direction, distance, proximity and reachability and layer.

The latter constitutes the granularity of the geographic area that a location represents. “The notion of direction and distance from a location is interpreted with respect to the layer that the location represents. In other words, direction and distance could be viewed as binary operator over locations of the same level. For instance, ‘is towards left of ’ would be appropriate if the location pair being considered is <Libya, Egypt>,” but not if the pair is <Nairobi, India>.

The knowledgebase makes use of two modules, the Web Ontology Language (OWL) and a graph database, to represent and store the above concepts. The Semantic Web language OWL is used to model the categorical characteristics of a landmark (e.g., direction, proximity, etc), and thence infer new relationships not explicitly specified by users of the system. In other words, OWL provides an ontology of locations.

The graph database is used represent distance (numerical relationships) between landmarks. “The locations are represented by nodes and the edges between two nodes of the graph are labeled with the distance between the corresponding locations.” Given the insights gained from user surveys, precise distances and directions are not integral components of community-based maps.

The two modules are used to generate answers to queries submitted by users.

User Interaction

The authors rightly recognize that the user interface design is critical to the success of community-based mapping projects. To be sure, users of may be illiterate, or semi-illiterate and not very tech-savvy. Furthermore, users will tend to query the map system when they need it most, e.g., “when they are stuck on the road looking for directions […] and would be pressed for time.” This very much holds true for crisis mapping as well.

Users can perform three main tasks with the system: “find place”, “trace path” and “add info.” In addition, some or all users may be granted the right to edit or remove entries from the knowledgebase. The Folksomaps system can also be bootstrapped from existing databases to populate instances of location types. “Two such sources of data in the absence of a full-fledged Geographical Information System (GIS) come from the Telecom Industry and the Postal Department.”


How the users interface with the system to carry out these tasks will depend on how tech-savvy or literate they are and what type of access they have to information and communication technologies.

Folksomaps thus provides three types of interface: web-based, voice-based and SMS-based. Each interface allows the user to query and update the database. The web-based interface was developed using Java Server Pages (JSP) while the voice-based interface uses JSPs and VoiceXML.


I am particularly interested in the voice-based interface. The authors point to previous studies that suggest a voice-based interaction works well with users who are illiterate or semi-illiterate and who cannot afford to have high-end devices but can use ordinary low-end phones.


I will share this with the Ushahidi development team with the hopes that they will consider adding a voice-based interface for the platform later this year. To be sure, could be very interesting to integrate Freedom Fone’s work in this area.

Insights from User Studies

The authors conducted user studies to verify the benefit and acceptability of Folksomaps. Tech-savvy used the web-based interface while non-tech savvy participants used the voice-based interface. The results are shown in the two tables below.


Several important insights surfaced from the results of the user studies. For example, an important insight gained from the non-tech user feedback was “the sense of security that they would get with such a system. […] Even though asking for travel directions from strangers on the street is an option, it exposes the enquirer to criminal elements […].”

Another insight gain was the fact that many non-tech savvy participants were willing to pay for the call even a small premium over normal charges as they saw value to having this information available to them at all times.” That said, the majority of participants “preferred the advertisement model where an advertisement played in the beginning of the call pays for the entire call.”

Interestingly, almost all participants preferred the voice-based interface over SMS even though the former led to a number of speech recognition errors. The reason being that “many people are either not comfortable using SMS or not comfortable using a mobile phone itself.”

There were also interesting insights on the issue of accuracy from the perspective of non-tech savvy participants. Most participants asked for full accuracy and only a handful were tolerant of minor mistakes. “In fact, one of the main reasons for preferring a voice call over asking people for directions was to avoid wrong directions.”

This need for high accuracy is driven by the fact that most people use public transportation, walk or use a bicycle to reach their destination, which means the cost of incorrect information is large compared to someone who owns a car.

This is an important insight since the authors had first assumed that tolerance for incorrect information was higher. They also learned that meta information is as important to non-tech savvy users as the landmarks themselves. For instance, low-income participants were more interested in knowing the modes of available transportation, timetables and bus route numbers than the road route from a source to a destination.


In terms of insights from tech-savvy participants, they did not ask for fine-grained directions all the time. “They were fight with getting high level directions involving major landmarks.” In addition, the need for accuracy was not as strong as for the non-tech savvy respondents and they preferred the content from the queries sent to them via SMS so they could store it for future access, “pointing out that it is easy to forget the directions if you just hear it.”

Some tech-savvy participants also suggested that the directions provided by Folksomaps should “take into consideration the amount of knowledge the subject already has about the area, i.e., it should be personalized based upon user profile. Other participants mentioned that “frequent changes in road plans due to constructions should be captured by such a system—thus making it more usable than just getting directions.”


In sum, the user interface of Folksomaps needs to be “rich and adaptive to the information needs of the user […].” To be sure, given user preference towards “voice-based interface over SMS, designing an efficient user-friendly voice-based user interface […].” In addition, “dynamic and real-time information augmented with traditional services like finding directions and locations would certainly add value to Folksomaps.” Furthermore, the authors recognize that Folksomaps can “certainly benefit from user interface designs,” and “multi-model front ends.”

Finally, the user surveys suggest “the community is very receptive towards the concept of a community-driven map,” so it is important that the TRMA project in the Sudan and the ecosystem Liberia project build on the insights and lessons learned provided in this study.

Patrick Philippe Meier

Improving Quality of Data Collected by Mobile Phones

The ICTD2009 conference in Doha, Qatar, had some excellent tech demo’s. I had the opportunity to interview Kuang Chen, a PhD student with UC Berkeley’s computer science department about his work on improving data quality using dynamic forms and machine learning.

I’m particularly interested in this area of research since ensuring data quality continues to be a real challenge in the fields of conflict early warning and crisis mapping. So I always look for alternative and creative approaches that address this challenge. I include below the abstract for Kuang’s project (which includes 5 other team members) and a short 2-minute interview.


“Organizations in developing regions want to efficiently collect digital data, but standard data gathering practices from the developed world are often inappropriate. Traditional techniques for form design and data quality are expensive and labour-intensive. We propose a new data-driven approach to form design, execution (filling) and quality assurance. We demonstrate USHER, an end-to-end system that automatically generates data entry forms that enforce and maintain data quality constraints during execution. The system features a probabilistic engine that drives form-user interactions to encourage correct answers.”

In my previous post on data quality evaluation, I pointed to a study that suggests mobile-based data entry has significantly higher error rates. The study shows that a voice call to a human operator results in superior data quality—no doubt due to the human operator double-checking the respondent’s input verbally.  USHER’s ability to dynamically adjust the user interface (form layout and data entry widgets) is one approach to provide some context-specific data-driven user feedback that is currently lacking in mobile forms, as an automated proxy of a human data entry person on the other end of the line.


This is my first video so many thanks to Erik Hersman for his tips on video editing! And many thanks to Kuang for the interview.

Patrick Philippe Meier

Evaluating Accuracy of Data Collection on Mobile Phones

The importance of data validation is unquestioned but few empirical studies seek to assess the possible errors incurred during mobile data collection. Authors Somani Patnaik, Emma Brunskill and William Thies thus carried out what is possibly the first quantitative evaluation  (PDF) of data entry accuracy on mobile phones in resource-constrained environments. They just presented their findings at ICTD 2009.

Mobile devices have become an increasingly important tool for information collection. Hence, for example, my interest in pushing forward the idea of Mobile Crisis Mapping (MCM). While studies on data accuracy exist for personal digital assistants (PDAs), there are very few that focus on mobile phones. This new study thus evaluates three user interfaces for information collection: 1) Electronic forms; 2) SMS and 3) voice.

The results of the study indicate the following associated error rates:

  • Electronic forms = 4.2%
  • SMS = 4.5%
  • Voice = 0.45%

For compartive purposes and context, note that error rates using PDAs have generally been less than 2%. These figures represent the fraction of questions that were answered incorrectly. However, since “each patient interaction consisted of eleven questions, the probability of error somewhere in a patient report is much higher. For both electronic forms and SMS, 10 out of 26 reports (38%) contained an error; for voice, only 1 out of 20 reports (5%) contained an error (which was due to operator transcription).

I do hope that the results of this study prompt many others to carry out similar investigations.  I think we need a lot more studies like this one but with a larger survey sample (N) and across multiple sectors (this study drew on just 13 healthworkers).

The UN Threat and Risk Mapping Analysis (TRMA) project I’m working on in the Sudan right now will be doing a study on data collection accuracy using mobile phones when they roll out their program later this month. The idea is to introduce mobile phones in a number of localities and not in neighboring ones. The team will then compare the data quality of both samples.

I look forward to sharing the results.

Patrick Philippe Meier

ICT for Development Highlights


For a moment there, during the 8-hour drive from Kassala back to Khartoum, I thought Doha was going to be a miss. My passport was still being processed by the Sudanese Ministry of Foreign Affairs and my flight to Doha was leaving in a matter of hours. I began resigning myself to the likelihood that I would miss ICT4D 2009. But thanks to the incredible team at IOM, not only did I get my passport back, but I got a one-year, mulitple re-entry visa as well.

I had almost convinced myself that missing ICT4D would ok. How wrong I would have been. When the quality of poster presentations and demo’s at a conference rival the panels and presentation, you know that you’re in for a treat. As the title of this posts suggest, I’m just going to point out a few highlights here and there.


  • Onno Purbo gave a great presentation on wokbolic, a  cost saving wi-fi receiver  antenna made in Indonesia using a wok. The wokbolic has as 4km range, costs $5-$10/month. Great hack.


  • Kentaro Toyama with Microsoft Research India (MSR India) made the point that all development is paternalistic and that we should stop fretting about this since development will by definition be paternalistic. I’m not convinced. Partnership is possible without paternalism.
  • Ken Banks noted the work of QuestionBox, which I found very interesting. I’d be interested to know how they remain sustainable, a point made by another colleague of mine at DigiActive.
  • Other interesting comments by various panelists included (and I paraphrase): “Contact books and status are more important than having an email address”; “Many people still think of mobile phones as devices one holds to the ear… How do we show that phones can also be used to view and edit content?”

Demo’s & Posters

I wish I could write more about the demo’s and posters below but these short notes and few pictures will have to do for now.


  • Analyzing Statistical Relationships between Global Indicators through Visualization:


  • Numeric Paper Forms for NGOs:


  • Uses of Mobile Phones in Post-Conflict Liberia:


  • Improving Data Quality with Dynamic Forms


  • Open Source Data Collection Tools:


Patrick Philippe Meier

Crisis Mapping and Agent Based Models

The idea of combining crisis mapping and agent based modeling has been of great interest to me ever since I took my first seminar on complex systems back in 2006. There are few studies out there that ground agent based models (ABM) on conflict dynamics within a real-world geographical space. One of those few, entitled “Global Pattern Formation and Ethnic/Cultural Violence,” appeared in the journal Science in 2007.

Note that I take issue with a number of assumptions that underlie this study as well as the methodology used. That said, the study is a good illustration of how crisis mapping and ABM can be combined.


The authors suggest that global patterns of violence arise due to “the structure of boundaries between groups rather than the groups themselves.” In other words, the spatial boundaries between different populations create a propensity for conflict, “so that spatial heterogeneity itself is predictive of local violence.”

The authors argue that this pattern is “consistent with the natural dynamics of “type separation,” a specific pattern formation also observed in physical and chemical phase separation. The unit of analysis in this study’s ABM, however, is the local ethnic “patch size,” which represents the smallest unit of ethnic members that act collectively as one.

The Model

A simple model of type separation assumes that individuals (or ethnic units) prefer to move to areas where more individuals of the same time reside. Playing the ABM yields progressively larger patches or “islands” of each ethnic group over time. The relationship between patch size and time follows a power law distribution, “a universal behavior that does not depend on many of the details of the model […].”

In other words, the model depicts scale invariant behavior, which implies that “a number of individual agents of the model can be aggregated into a single agent if time is rescaled correspondingly without changing the behavior at the larger scales.”

To model violent conflict, the authors assume that both highly mixed regions and well-segregated groups do not engage in violence. The rationale regarding the former being that in highly mixed regions, “groups of the same type are not large enough to develop strong collective identities, or to identify public spaces as associated with one or another group. When groups are much bigger, “they typically form self-sufficient entities that enjoy local sovereignty.”

To this end, the authors argue that partial separation with poorly defined boundaries fosters conflict when groups are of a size that allows them to impose cultural norms on public spaces, “but where there are still intermittent violations of these rules due to the overlap of cultural domains.” In other words, conflict is a function of population distribution and not of the “specific mechanism by which the population achieves this structure, which may include internally or externally directed migrations.”

The model is therefore founded on the principle that the conditions under which violent conflict becomes likely can be determined by census.

The Analysis

The authors used 1991 census data of the former Yugoslavia and the Indian census data from 2001 and converted the data into map form (see figure below), which they used in an ABM simulation. “Mathematically, the expected violence was determined by detecting patches consisting of islands or peninsulas of one type surrounded by populations of other types.”


A wavelet filter that has a positive center and a negative surround (also called a Mexican hat filter) was used to detect and correlate the islands/peninsulas. scienceabm1

The red overlays depicted in Figure D above represents the maximum correlation over population types. The diameter of the positive region of the wavelet, i.e., “the size of the local population patches that are likely to experience violence,” is the main predictor of the model.


To test the predictive power of their model, the authors compared the locations of red overlays with actual incidents of violence as reported in books, newspapers and online sources (the yellow dots in the crisis map below).


Their statistical results indicate that the Yugoslavia crisis map model has a correlation of 0.89 with reports. Moreover, “the predicted results are highly robust to parameter variation [patch size], with essentially equivalent agreement obtained for filter diameters ranging from 18 to 60 km […].”

The statistical results for the India crisis map model indicate a correlation of 0.98. The range of the patch size overlapped that of the former Yugoslavia but is shifted to larger values, up to 100km. This suggests that “regions of width less than 10km or greater than 100km may provide sufficient mixing or isolation to reduce the chance of violence.”


While the authors recognize the importance of social and institutional drivers of violence, they argue that, “influencing the spatial structure might address the conditions that promote violence described [in this study].” In sum, they suggest that, “peaceful coexistence need not require complete integration.”

What do you think?

Patrick Philippe Meier