Tag Archives: Visualization

Is Crime Mapping the Future of Crisis Mapping?

My new fascination is crime mapping.

The field of crisis mapping may still in its infancy, but crime mapping, relatively speaking, is a mature science. I have no doubt that many of the best practices, methods and software platforms developed for crime mapping are applicable to crisis mapping. This is why I plan to spend the next few months trying to get up to speed on crime mapping. If you’re interested in learning more about crime mapping, here’s how I’m getting up to speed.

First, I’m following the CrimeReports blog and Twitter feed.

Second, I got in touch with Professor Timothy Hart who is co-editor of the peer-reviewed journal Crime Mapping for some guidance. He suggested that a good place to start is with the primary criminology theory, from which many of the ideas found in the field of crime mapping grew.

To this end, Tim kindly recommended the following book:

In terms of the applied side of crime mapping, Tim recommended this book to gain a better understanding of theory in practice:

Third, I’ve registered to attend the 10th Crime Mapping Research Conference being held in New Orleans this August. And to think that I’m just co-organizing the first International Conference on Crisis Mapping, ICCM 2009. Yes, we’re 10 years behind. Just have a look at a sample of the presentations lined up:

  • The Spatial Dependency of Crime Dispersion.
  • A Time Geographic Approach to Crime Mapping.
  • Space-time Hotspots and their Prediction Accuracy.
  • Using Cluster Analysis to Identify Gang Mobility Patterns.
  • Defining Hotspots: Adding an Explanatory Power to Hotspot Mapping.
  • Application of Spatial Scan Statistic Methods to Crime Hot Spot Analysis.
  • Applying Key Spatial Theories to Understand Maps and Preventing Crime.
  • Using a Spatial Video to Capture Dynamically Changing Crime Geographies.

Fourth, I’m keeping track of news articles that refer to crime mapping, like the Wall Street Journal’s recent piece entitled “New Program Put Crime Stats on the Map.” According to the article,

Police say they use the sites to help change citizens’ behavior toward crime and encourage dialogue with communities so that more people might offer tips or leads. Some of the sites have crime-report blogs that examine activity in different locales. They also allow residents to offer tips and report crimes under way.

Is crime mapping the future of crisis mapping? Regardless of the answer, we have a lot to learn from our colleagues in the field of crime mapping as I plan to demonstrate in future blog posts. In the meantime, I hope that donors in the humanitarian and human rights communities realize that tremendous potential of crisis mapping given the value of added of maps for crime analysis.

Patrick Philippe Meier

GeoTime: Crisis Mapping Analysis in 3D

I just came across GeoTime, a very neat GIS platform for network analyis in time and space. GeoTime is developed by a company called Oculus and does a great job in presenting an appealing 3D visual interface for the temporal analysis of geo-referenced data. The platform integrates timeline comparisons, chart statistics and network analysis tools to support decision making. GeoTime also includes plug-ins for Excel and ArcGIS.


GeoTime includes a number of important functionalities including:

  • Movement trails: to display the history and behavior as paths in space-time;
  • Animation: to play back sequences and see how events unfold. Both the direction and speed of the animation can be adjusted.
  • Pattern recognition: to automatically identify key behaviors.
  • Annotate and Sketch: to add notes directly in the scene and save views as reports.
  • Fast Maps: to automatically adjust level of detail.
  • Interactive Chain and Network Analysis: to identify related events.


Below is an excerpt of a video demo of GeoTime which is well worth watching to get a sense of how these functionalities come into play:

The demo above uses hurricane data to highlight GeoTime’s integrated functionalities. But the application can be used to analyze a wide range of data such as crime incidents to identify patterns in space and time. A demo for that is avaiable here.


My interest in GeoTime stems from it’s potential application to analyzing conflict datasets. Problem is, the platform will set you back a cool $3,925. They do have a university rate of $1,675 but what’s missing is a rate for humanitarian NGOs or even a limited trial version.

Patrick Philippe Meier

How to Lie with Maps

I just finished reading Mark Monmonier‘s enjoyable book on “How to Lie with Maps” and thought I’d share some tidbits. Mark is the distinguished Professor of Geography at the Maxwell School of Syracuse University in New York.

In writing this book, Mark wanted to “make readers aware that maps, like speeches and paintings, are authored collections of information and also are also subject to distortions arising from ignorance, greed, ideological blindness, or malice.” Note that this second edition was published in 1996.



Mark uses some terms that made me chuckle at times. Take “cartographic priesthood,” for example, or “cartographic license.” Other terms of note include “cartopropaganda,” “cartographic disinformation and censorship,” and “cartographic security.”


  • “The map is the perfect symbol of the state.”
  • “Maps can even make nuclear war appear survivable.”
  • “A legend might make a bad map useful, but it can’t make it efficient.”
  • “Maps are like milk: their information is perishable, and it is wise to check the date.”
  • “People trust maps, and intriguing maps attract the eye as well as connote authority.”
  • “Circles bring to the map a geometric purity easily mistaken for accuracy and authority.”
  • “Like guns and crosses, maps can be good or bad, depending on who’s holding them, who they’re aimed at, how they’re used, and why.”
  • “No other group has exploited the map as an intellectual weapon so blatantly, so intensely, so persistently, and with such variety [as the Nazis].”
  • “That maps drawn up by diplomats and generals became a political reality lends an unintended irony to the aphorism that the pen is mightier than the sword.”


“Even tiny maps on postage stamps can broadcast political propaganda. Useful both on domestic mail to keep aspirations alive and on international mail to suggest national unity and determination, postage stamps afford a small but numerous means for asserting territorial claims.”

“In 1668, Louis XIV of France commissioned three-dimensional scale models of eastern border towns, so that his generals in Paris and Versailles could plan realistic maneuvers. […] As late as World War II, the French government guarded them as military secrets with the highest security classification.” See picture.


“Government maps have for centuries been ideological statements rather than fully objective scientific representations of geographic reality. […] Governments practice two kinds of cartographic censorship—a censorship of secrecy to serve military defense and a censorship of  silence to enforce social and political values” (citing historian Brian Harley).

“Few maps symbols are as forceful and suggestive as the arrow. A bold, solid line might make the map viewer infer a well-defined, generally accepted border separating nations with homogeneous populations, but an arrow or a set of arrows can dramatize an attack across the border, exaggerate a concentration of troops, and perhaps even justify a ‘pre-emptive strike’.”

“Faulty map reading almost led to an international incident in 1988, when the Manila press reported the Malaysian annexation of the Turtle Islands.” The faulty map was “later traced to the erroneous reading of an American navigation chart by a Philippines naval officer who mistook a line representing the recommended deepwater route for ships passing the Turtle Islands for the boundary of Malaysia’s newly declared exclusive economic zone.”


“As display systems become more flexible, and more like video games, users must be wary that maps, however realistic, are merely representations, vulnerable to bias in both what they show and what they ignore.”

“Skepticism is especially warranted when a dynamic map supporting a simulation model pretends to describe the future.”

“Although electronic cartography may make complex simulations easier to understand, no one should trust blindly a map that acts like a crystal ball.”

Patrick Philippe Meier

Part 7: A Taxonomy for Visual Analytics

This is Part 7 of 7 of the highlights from the National Visualization Analysis Center. (NVAC). Unlike previous parts, this one focuses on a May 2009 article. Please see this post for an introduction to the study and access to the other 6 parts.

Jim Thomas, the co-author of “Illuminating the Path: A Research and Development Agenda for Visual Analytics,” and Director of the National Visualization Analysis Center (NVAC) recently called for the development of a taxonomy for visual analytics. Jim explains the importance of visual analytics as follows:

“Visual analytics are valuable because the tool helps to detect the expected, and discover the unexpected. Visual analytics combines the art of human intuition and the science of mathematical deduction to perceive patterns and derive knowledge and insight from them. With our success in developing and delivering new technologies, we are paving the way for fundamentally new tools to deal with the huge digital libraries of the future, whether for terrorist threat detection or new interactions with potentially life-saving drugs.”

In the latest edition of VAC Views, Jim expresses NVAC’s interest in helping to “define the study of visual analytics by providing an order and arrangement of topics—the taxa that are at the heart of studying visual analytics. The reason for such a “definition” is to more clearly describe the scope and intent of impact for the field of visual analytics.”

Jim and colleagues propose the following higher-order classifications:

  • Domain/Applications
  • Analytic Methods/Goals
  • Science and Technology
  • Data Types/Structures.

In his article in VAC Views, Jim requests feedback and suggestions for improving the more detailed taxonomy that he provides in the graphic below. The latter was not produced in very high resolution in VAC Views and does not reproduce well here, so I summarize below whilst giving feedback.


1. Domain/Applications

While Security (and Health) are included in the draft NVAC proposal as domains / applications, what is missing is Humanitarian Crises, Conflict Prevention and Disaster Management.

Perhaps “domain/applications” should not be combined since “applications” tends to be a subset of associated “domains” which poses some confusion. For example, law enforcement is a domain and crime mapping analysis could be considered as an application of visual analytics.

2. Analytic Methods/Goals

Predictive, Surveillance, Watch/Warn/Alert, Relationship Mapping, Rare Event Identification are included. There are a host of other methods not referred to here such as cluster detection, a core focus of spatial analysis. See the methods table in my previous blog post for examples of spatial cluster detection.

Again I find that combining both “analytic methods” and “goals” makes the classification somewhat confusing.

3. Scientific and Technology

This classification includes the following entries (each of which are elaborated on individually later):

  • Analytic reasoning and human processes
  • Interactive visualization
  • Data representations and theory of knowledge
  • Theory of communications
  • Systems and evaluations.

4. Data Types/Structures

This includes Text, Image, Video, Graph Structures, Models/Simulations, Geospatial Coordinates, time, etc.

Returning now to the sub-classifications under “Science and Technology”:

Analytic reasoning and human processes

This sub-classification, for example, includes the following items:

  • Modes of inference
  • Knowledge creation
  • Modeling
  • Hypothesis refinement
  • Human processes (e.g., perception, decision-making).

Interactive visualization

This is comprised of:

  • The Science of Visualization
  • The Science of Interaction.

The former includes icons, positioning, motion, abstraction, etc, while the latter includes language of discourse, design and art, user-tailored interaction and simulation interaction.

Data representations and theory of knowledge

This includes (but is not limited to):

  • Data Sourcing
  • Scale and Complexity
  • Aggregation
  • Ontology
  • Predictions Representations.

Theory of communications

This sub-classification includes for example the following:

  • Story Creation
  • Theme Flow/Dynamics
  • Reasoning representation.

Systems and evaluations

This last sub-classification comprises:

  • Application Programming Interface
  • Lightweight Standards
  • Privacy.

Patrick Philippe Meier

Part 6: Mobile Technologies and Collaborative Analytics

This is Part 6 of 7 of the highlights from “Illuminating the Path: The Research and Development Agenda for Visual Analytics.” Please see this post for an introduction to the study and access to the other 6 parts.

Mobile Technologies

The National Visual Analytics Center (NVAC) study recognizes that “mobile technologies will play a role in visual analytics, especially to users at the front line of homeland security.” To this end, researchers must “devise new methods to best employ these technologies and provide a means to allow data to scale between high-resolution displays in command and control centers to field-deployable displays.”

Collaborative Analytics

While collaborative platforms from wiki’s to Google docs allow many individuals to work collaboratively, these functionalities rarely feature in crisis mapping platforms. And yet, humanitarian crises (just like homeland security challenges) are so complex that they cannot be addressed by individuals working in silos.

On the contrary, crisis analysis, civilian protection and humanitarian response efforts are “sufficiently large scale and important that they must be addressed through the coordinated action of multiple groups of people, often with different backgrounds working in disparate locations with differing information.”

In other words, “the issue of human scalability plays a critical role, as systems must support the communications needs of these groups of people working together across space and time, in high-stress and time-sensitive environments, to make critical decisions.”

Patrick Philippe Meier

Updated: Humanitarian Situation Risk Index (HSRI)

The Humanitarian Situation Risk Index (HSRI) is a tool created by UN OCHA in Colombia. The objective of HSRI is to determine the probability that a humanitarian situation occurs in each of the country’s municipalities in relation to the ongoing complex emergency. HSRI’s overall purpose is to serve as a “complementary analytical tool in decision-making allowing for humanitarian assistance prioritization in different regions as needed.”

UPDATE: I actually got in touch with the HSRI group back in February 2009 to let them know about Ushahidi and they have since “been running some beta-testing on Ushahidi, and may as of next week start up a pilot effort to organize a large number of actors in northeastern Colombia to feed data into [their] on-line information system.” In addition, they “plan to move from a logit model calculating probability of a displacement situation for each of the 1,120 Colombian municipalities, to cluster analysis, and have been running the identical model on data [they] have for confined communities.”


HSRI uses statistical tools (principal component analysis and the Logit model) to estimate the risk indexes. The indexes range from 0 to 1, where 0 is no risk and 1 is maximum risk. The team behind the project clearly state that the tool does not indicate the current situation in each municipality given that the data is not collected in real-time. Nor does the tool quantify the precise number of persons at risk.

The data used to estimate the Humanitarian Situation Risk Index “mostly comes from official sources, due to the fact that the vast majority of data collected and processed are from State entities, and in the remaining cases the data is from non-governmental or multilateral institutions.” The following table depicts the data collected.


I’d be interested to know whether the project will move towards doing any temporal analysis of the data over time. This would enable trends analysis which could more directly inform decision-making than a static map representing static data. One other thought might be to complement this “baseline” type data with event-data by using mobile phones and a “bounded crowdsourcing” approach a la Ushahidi.

Patrick Philippe Meier

Part 5: Data Visualization and Interactive Interface Design

This is Part 5 of 7 of the highlights from “Illuminating the Path: The Research and Development Agenda for Visual Analytics.” Please see this post for an introduction to the study and access to the other 6 parts.

Data Visualization

The visualization of information “amplifies human cognitive capabilities in six basic ways” by:

  • Increasing cognitive resources, such as by using a visual resource to expand human working memory;
  • Reducing search, such as by representing a large amount of data in a small place;
  • Enhancing the recognition of patterns, such as when information is organized in space by its time relationships;
  • Supporting the easy perceptual inference of relationships that are otherwise more difficult to induce;
  • Enabling perceptual monitoring of a large number of potential events;
  • Providing a manipulable medium that, unlike static diagrams, enables the exploration of a space of parameter values.

The table below provides additional information on how visualization amplifies cognition:


Clearly, “these capabilities of information visualization, combined with computational data analysis, can be applied to analytic reasoning to support the sense-making process.” The National Visualization and Analysis Center (NVAC) thus recommends developing “visually based methods to support the entire analytic reasoning process, including the analysis of data as well as structured reasoning techniques such as the construction of arguments, convergent-divergent investigation, and evaluation of alternatives.”

Since “well-crafted visual representations can play a critical role in making information clear […], the visual representations and interactions we develop must readily support users of varying backgrounds and expertise.” To be sure, “visual representations and interactions must be developed with the full range of users in mind, from the experienced user to the novice working under intense pressure […].”

As NVACs notes, “visual representations are the equivalent of power tools for analytical reasoning.” But just like real power tools, they can cause harm if used carelessly. Indeed, it is important to note that “poorly designed visualizations may lead to an incorrect decision and great harm. A famous example is the poor visualization of the O-ring data produced before the disastrous launch of the Challenger space shuttle […].”

Effective Depictions

This is why we need some basic principles for developing effective depictions, such as the following:

  • Appropriateness Principle: the visual representation should provide neither more or less information than that needed for the task at hand. Additional information may be distracting and makes the task more difficult.
  • Naturalness Principle: experiential cognition is most effective when the properties of the visual representation most closely match the information being represented. This principle supports the idea that new visual metaphors are only useful for representing information when they match the user’s cognitive model of the information. Purely artificial visual metaphors can actually hinder understanding.
  • Matching Principle: representations of information are mst effective when they match the task to be performed by the user. Effective visual representations should present affordances suggestive of the appropriate action.
  • Congruence Principle: the structure and content of a visualization should correspond to the structure and content of the desired mental representation.
  • Apprehension Principle: the structure and content of a visualization should be readily and accurately perceived and comprehended.

Further research is needed to understand “how best to combine time and space in visual representation. “For example, in the flow map, spatial information is primary” in that it defines the coordinate system, but “why is this the case, and are there visual representations where time is foregrounded that could also be used to support analytical tasks?”

In sum, we must deepen our understanding of temporal reasoning and “create task-appropriate methods for integrating spatial and temporal dimensions of data into visual representations.”

Interactive Interface Design

It is important in the visual analytics process that researchers focus on visual representations of data and interaction design in equal measure. “We need to develop a ‘science of interaction’ rooted in a deep understanding of the different forms of interaction and their respective benefits.”

For example, one promising approach for simplifying interactions is to use 3D graphical user interfaces. Another is to move beyond single modality (or human sense) interaction techniques.

Indeed, recent research suggests that “multi-modal interfaces can overcome problems that any one modality may have. For example, voice and deictic (e.g., pointing) gestures can complement each other and make it easier for the user to accomplish certain tasks.” In fact, studies suggest that “users prefer combined voice and gestural communication over either modality alone when attempting graphics manipulation.”

Patrick Philippe Meier

Part 4: Automated Analysis and Uncertainty Visualized

This is Part 4 of 7 of the highlights from “Illuminating the Path: The Research and Development Agenda for Visual Analytics.” Please see this post for an introduction to the study and access to the other 6 parts.

As data flooding increases, the human eye may have difficulty focusing on patterns. To this end, VA systems should have “semi-automated analytic engines and user-driven interfaces.” Indeed, “an ideal environment for analysis would have a seamless integration of computational and visual techniques.”

For example, “the visual overview may be based on some preliminary data transformations […]. Interactive focusing, selecting, and filtering could be used to isolate data associated with a hypothesis, which could then be passed to an analysis engine with informed parameter settings. Results could be superimposed on the original information to show the difference between the raw data and the computed model, with errors highlighted visually.”

Yet current mathematical techniques “for representing pattern and structure, as well as visualizing correlations, time patterns, metadata relationships, and networks of linked information,” do not work well “for more complex reasoning tasks—particularly temporal reasoning and combined time and space reasoning […], much work remains to be done.” Furthermore, “existing techniques also fail when faced with the massive scale, rapidly changing data, and variety of information types we expect for visual analytics tasks.”

Furthermore, “the complexity of this problem will require algorithmic advances to address the establishment and maintenance of uncertainty measures at varying levels of data abstraction.” There is presently “no accepted methodology to represent potentially erroneous information, such as varying precision, error, conflicting evidence, or incomplete information.”

To this end, “interactive visualization methods are needed that allow users to see what is missing, what is known, what is unknown, and what is conjectured, so that they may infer possible alternative explanations.”

In sum, “uncertainty must be displayed if it is to be reasoned with and incorporated into the visual analytics process. In existing visualizations, much of the information is displayed as if it were true.”

Patrick Philippe Meier

Part 3: Data Tetris and Information Synthesis

This is Part 3 of 7 of the highlights from “Illuminating the Path: The Research and Development Agenda for Visual Analytics.” Please see this post for an introduction to the study and access to the other 6 parts.

Visual Analytics (VA) tools need to integrate and visualize different data types. But the integration of this data needs to be “based on their meaning rather than the original data type” in order to “facilitate knowledge discovery through information synthesis.” However, “many existing visual analytics systems are data-type-centric. That is, they focus on a particular type of data […].”

We know that different types of data are regularly required to conduct solid anlaysis, so developing a data synthesis capability is particularly important. This means ability to “bring data of different types together in a single environment […] to concentrate on the meaning of the data rather than on the form in which it was originally packaged.”

To be sure, information synthesis needs to “extend beyond the current data-type modes of analysis to permit the analyst to consider dynamic information of all types in seamless environment.” So we need to “eliminate the artificial constraints imposed by data type so that we can aid the analyst in reaching deeper analytical insight.”

To this end, we need breakthroughs in “automatic or semi-automatic approaches for identifying [and coding] content of imagery and video data.” A semi-automatic approach could draw on crowdsourcing, much like Ushahidi‘s Swift River.

In other words, we need to develop visual analytical tools that do not force the analyst to “perceptually and cognitively integrate multiple elements. […] Systems that force a user to view sequence after sequence of information are time-consuming and error-prone.” New techniques are also needed to do away with the separation of ‘what I want and the act of doing it.'”

Patrick Philippe Meier

Armed Conflict and Location Event Dataset (ACLED)

I joined the Peace Research Institute, Oslo (PRIO) as a researcher in 2006 to do some data development work on a conflict dataset and to work with Norways’ former Secretary of State on assessing the impact of armed conflict on women’s health for the Ministry of Foreign Affairs (MFA).

I quickly became interested in a related PRIO project that had recently begun called the “Armed Conflict and Location Event Dataset, or ACLED. Having worked with conflict event-datasets as part of operational conflict early warning systems in the Horn, I immediately took interest in the project.

While I have referred to ACLED in a number of previous blog posts, two of my main criticisms (until recently) were (1) the lack of data on recent conflicts; and (2) the lack of an interactive interface for geospatial analysis, or at least more compelling visualization platform.

Introducing SpatialKey

Independently, I came across UniveralMind back November of last year when Andrew Turner at GeoCommons made a reference to the group’s work in his presentation at an Ushahidi meeting. I featured one of the group’s products, SpatialKey, in my recent video primer on crisis mapping.

As it turns out, ACLED is now using SpatialKey to visualize and analyze some of it’s data. So the team has definitely come a long way from using ArcGIS and Google Earth, which is great. The screenshot below, for example, depicts the ACLED data on Kenya’s post-election violence using SpatialKey.


If the Kenya data is not drawn from the Ushahidi then this could be an exciting research opportunity to compare both datasets using visual analysis and applied geo-statistics. I write “if” because PRIO somewhat surprisingly has not made the Kenya data available. They are usually very transparent so I will follow up with them and hope to get the data. Anyone interested in co-authoring this study?

Academics Get up To Speed

It’s great to see ACLED developing conflict data for more recent conflicts. Data on Chad, Sudan and the Central African Republic (CAR) is also depicted using SpatialKey but again the underlying spreadsheet data does not appear to be available regrettably. If the data were public, then the UN’s Threat and Risk Mapping Analysis (TRMA) project may very well have much to gain from using the data operationally.


Data Hugging Disorder

I’ll close with just one—perhaps unwarranted—concern since I still haven’t heard back from ACLED about accessing their data. As academics become increasingly interested in applying geospatial analysis to recent or even current conflicts by developing their own datasets (a very positive move for sure), will these academics however keep their data to themselves until they’ve published an article in a peer-reviewed journal, which can often take up to a year or more to publish?

To this end I share the concern that my colleague Ed Jezierski from InSTEDD articulated in his excellent blog post yesterday: “Academic projects that collect data with preference towards information that will help to publish a paper rather than the information that will be the most actionable or help community health the most.” Worst still, however, would be academics collecting data very relevant to the humanitarian or human rights community and not sharing that data until their academic papers are officially published.

I don’t think there needs to be competition between scholars and like-minded practitioners. There are increasingly more scholar-practitioners who recognize that they can contributed their research and skills to the benefit of the humanitarian and human rights communities. At the same time, the currency of academia remains the number of peer-reviewed publications. But humanitarian practitioners can simply sign an agreement such that anyone using the data for humanitarian purposes cannot publish any analysis of said data in a peer-reviewed forum.


Patrick Philippe Meier