Tag Archives: Crime

Does Digital Crime Mapping Work? Insights on Engagement, Empowerment & Transparency

In 2008, police forces across the United Kingdom (UK) launched an online crime mapping tool “to help improve the credibility and confidence that the public had in police-recorded crime levels, address perceptions of crime, promote community engagement and empowerment, and support greater public service transparency and accountability.” How effective has this large scale digital mapping effort been? “There continues to be a lack of evidence that publishing crime statistics using crime mapping actually supports improvements in community engagement and empowerment.” This blog post evaluates the project’s impact by summarizing the findings from a recent peer-reviewed study entitled: “Engagement, Empowerment and Transparency: Publishing Crime Statistics using Online Crime Mapping.” Insights from this study have important implications for crisis mapping projects.

The rationale for publishing up-to-date crime statistics online was to address the “reassurance gap” which “relates to the counterintuitive relationship between fear of crime and the reality of crime.” While crime in the UK has decreased steadily over the past 15 years, there was no corresponding in the public’s fear of crime during this period. Studies subsequently found a relationship between a person’s confidence in the criminal justice system and the level to which a person felt informed about crime and justice issue. “Hence, barriers to accurate information were one of the main reasons why the reassurance gap, and lack of confidence in the police, was believed to exist.”

A related study  found that people’s opinions on crime levels were “heavily influenced by media depictions, demographic qualities, and personal ex- perience.” Meanwhile, “the countervailing source of information—nationally reported crime statistics—was not being heard. Simply put, the message that crime levels were falling was not getting through to the populace over the cacophony of competing information.” Hence the move to publish crime statistics online using a crime map.

Studies have long inferred that “publically disseminating crime information engages the public and empowers them to get involved in their communities. This has been a key principle in the adoption of community policing, where the public are considered just as much a part of community safety as the police themselves. Increasing public access to crime information is seen as integral to this whole agenda.” In addition, digital crime mapping was “seen as a ‘key mechanism for encouraging the public to take greater responsibility for holding the local police to account for their performance.’ In other words, it is believed that publishing information on crime at a local level facilitates greater public scrutiny of how well the police are doing at suppressing local crime and serves as a basis for dialogue between the public and their local police.”

While these are all great reasons to launch a nationwide crime mapping initiative online, “the evidence base that should form the foundations of the policy to pub- lish crime statistics using crime maps is distinctly absent.” When the project launched, “no police force had conducted a survey (robust or anecdotal) that had measured the impact that publishing crime statistics had on improving the credibility of these data or the way in which the information was being used to inform, reassure, and engage with the public.” In fact, “the only research-derived knowledge available on the impact of crime maps on public perceptions of crime was generated in the USA, on a small and conveniently selected sample.”

Moreover, many practitioners had “concerns with the geocoding accuracy of some crime data and how this would be represented on the national crime mapping site (i.e. many records cannot be geographically referenced to the exact location where the crime took place) […] which could make the interpretation of street-level data misleading and confusing to the public.” The authors thus argue that “an areal visualization method such as kernel density estimation that is commonly used in police forces for visualizing the geographic distribution of crime would have been more appropriate.”

The media was particularly critical of the confusion provoked by the map and the negative media attention “may have done long-term damage to the reputation of crime statistics. Whilst these inaccuracies are few in number, they have been high profile and risk undermining the legitimacy and confidence in the accuracy of all the crime statistics published on the site. Indeed, these concerns were highlighted further in October 2011 when the crime statistics for the month of August appeared to show no resemblance to the riots […].”

Furthermore, “contemporary research has stressed that information provision needs to be relevant to the recipients, and should emphasize police responsive-ness to local issues to chime with the public’s priorities.” Yet the UK’s online crime map does not “taylor sub-neighborhood reassurance messages alongside the publishing of crime statistics (e.g., saying how little crime there is in your neighborhood). General messages of reassurance and crime prevention often fail to resonate. This also under-scores the need for tailored information that is actively passed on to local communities at times of heightened crime risk, which local residents can then use to minimize their own immediate risk of victimization and improve local public safety.”

In addition, the presentation of “crime statistics on the national website is very passive, offering little that will draw people back and keep them interested on crime trends and policing in their area.” For example, the project did not require users to register their email addresses and home post (zip) codes when using the map. This meant the police had no way to inform interested audiences with locally relevant crime information such as “specific and tailored crime prevention advice regarding a known local crime issue (e.g. a spate of burglaries), directly promoting messages of reassurance and used as a means to publicize police activity.” I would personally argue for the use of automated alerts and messages of reassurance via geo-fencing. (The LA Crime Map provides automated alerts, for example). I would also recommend social networking tools such as Facebook and Twitter to the map.

In conclusion, the authors question the “assumption that all police-recorded crime data are fit for purpose for mapping at street level.” They recommend using the Management of Police Information (MOPI) protocol, which states that “information must fulfill a necessary purpose for it to be recorded and retained by the police.” MOPI would “help to qualify what should and what should not be published, and the mechanism by which it is published.” Instead of mapping everything and anything, the authors advocate for the provision of “better quality information that the public can actually do something with to minimize their risk of victimization, or use as a basis for dialogue with their local policing teams.” In sum, “the purpose of publishing the crime statistics must not lose sight of the important potential it can contribute to improving the dialogue and involvement of local communities in improving community safety, and must avoid becoming an exercise in promoting political transparency when the data it offers provides little that encourages the public to react.”

For more on crime mapping, see:

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

Crime Mapping Analytics

There are important parallels between crime prevention and conflict prevention.  About half-a-year ago I wrote a blog post on what crisis mapping might learn from crime mapping. My colleague Joe Bock from Notre Dame recently pointed me to an excellent example of crime mapping analytics.

The Philadelphia Police Department (PPD) has a Crime Analysis and Mapping Unit  (CAMU) that integrates Geographic Information System (GIS) to improve crime analysis. The Unit was set up in 1997 and the GIS data includes a staggering 2.5 million new events per year. The data is coded from emergency distress calls and police reports and overlaid with other data such as bars and liquor stores, nightclubs, locations of surveillance cameras, etc.

For this blog post, I draw on the following two sources: (1) Theodore (2009). “Predictive Modeling Becomes a Crime-Fighting Asset,” Law Officer Journal, 5(2), February 2009; and (2) Avencia (2006). “Crime Spike Detector: Using Advanced GeoStatistics to Develop a Crime Early Warning System,” (Avencia White Paper, January 2006).


Police track criminal events or ‘incidents’ which are “the basic informational currency of policing—crime prevention cannot take place if there is no knowledge of the location of crime.” Pin maps were traditionally used to represent this data.


GIS platforms now make new types of analysis possible beyond simply “eyeballing” patterns depicted by push pins. “Hot spot” (or “heat map”) analysis is one popular example in which the density of events is color coded to indicate high or low densities.

Hotspot analysis, however, in itself, does not tell people much they did not already know. Crime occurs in greater amounts in downtown areas and areas where there are more people. This is common sense. Police organize their operations around these facts already.

The City of Philadelphia recognized that traditional hot spot analysis was of limited value and therefore partnered with Avencia to develop and deploy a crime early warning system known as the Crime Spike Detector.

Crime Spike Detector

The Crime Spike Detector is an excellent example of a crime analysis analytics tool that serves as an early warning system for spikes in crime.

The Crime Spike Detector applies geographic statistical tools to discover  abrupt changes in the geographic clusters of crime in the police incident database. The system isolates these aberrations into a cluster, or ‘crime spike’. When such a cluster is identified, a detailed report is automatically e-mailed to the district command staff responsible for the affected area, allowing them to examine the cluster and take action based on the new information.

The Spike Detector provides a more rapid and highly focused evaluation of current conditions in a police district than was previously possible. The system also looks at clusters that span district boundaries and alerts command staff on both sides of these arbitrary administrative lines, resulting in more effective deployment decisions.


More specfically, the spike detector analyzes changes in crime density over time and highlights where the change is statistically significant.

[The tool] does this in automated fashion by examining, on a nightly basis, millions of police incident records, identifying aberrations, and e-mailing appropriate police personnel. The results are viewed on a map, so exactly where these crime spikes are taking place are immediately understandable. The map supports ‘drill-through’ capabilities to show detailed graphs, tables, and actual incident reports of crime at that location.

Spike Detection Methodology

The Spike Detector compares the density of individual crime events over both space and time. To be sure, information is more actionable if it is geographically specified for a given time period regarding a specific type of crime. For example, a significant increase in drug related incidents in a specific neighborhood for a given day is more concrete and actable than simply observing a general increase in crime in Philadelphia.

The Spike Detector interface allows the user to specify three main parameters: (1) the type of crime under investigation; (2) the spatial and, (3) the temporal resolutions to analyze this incident type.

Obviously, doing this in just one way produces very limited information. So the Spike Detector enables end users to perform its operations on a number of different ways of breaking up time, space and crime type. Each one of these is referred to as a user defined search pattern.

To describe what a search pattern looks like, we first need to understand how the three parameters can be specified.

Space. The Spike Detector divides the city into circles of a given radius. As depicted below, the center points of these circles from a grid. Once the distance between these center points is specified, the radius of the circle is set such that the area of the circles completely covers the map. Thus a pattern contains a definition of the distance between the center points of circles.


Time. The temporal parameter is specified such that a recent period of criminal incidents can be compared to a previous period. By contrasting the densities in each circle across different time periods, any significant changes in density can be identified. Typically, the most recent month is compared to the previous year. This search pattern is know as bloc style comparison. A second search pattern is periodic, which “enables search patterns based on crime types that vary on a seasonal basis.”

Incident. Each crime is is assigned a Uniform Crime Reporting code. Taking all three parameters together, a search pattern might look like the following

“Robberies no Gun, 1800, 30, Block, 365”

This means the user is looking for robberies committed without a gun, with distance between cicle center points of 1,800 feet, over the past 30 days of crime data compared to the previous year’s worth of crime.

Determining Search Patterns

A good search pattern is determined by a combination of three factors: (1) crime type density; (2) short-term versus long-term patterns; and (3) trial and error. Crime type is typically the first and easiest parameter of the search pattern to be specified. Defining the spatial and temporal resolutions requires more thought.

The goal in dividing up time and space is to have enough incidents such that comparing a recent time period to a comparison time period is meaningful. If the time or space divisions are too small, ‘spikes’ are discovered which represent a single incident or few incidents.

The rule of thumb is to have an average of at least 4-6 crimes each in each circle area. More frequent crimes will permit smaller circle areas and shorter time periods, which highlights spikes more precisely in time and space.

Users are typically interested in shorter and most recent time periods as this is most useful to law enforcement while “though the longer time frames might be of interest to other user communities studying social change or criminology.” In any event,

Patterns need to be tested in practice to see if they are generating useful information. To facilitate this, several patterns can be set up looking at the same crime type with different time and space parameters. After some time, the most useful pattern will become apparent and the other patterns can be dispensed with.

Running Search Patterns

The spike detection algorithm uses simple statistical analysis to determine whether the  probability that the number of recent crimes as compared to the comparison period crimes in a given circle area is possible due to chance alone. The user specifies the confidence level or sensitivity of the analysis. The number is generally set at 0.5% probability.

Each pattern results in a probability (or p-value) lattice assigned to every circle center point. The spike detector uses this lattice to construct the maps, graphs and reports that the spike detector presents to the user. A “Hypergeometic Distribution” is used to determine the p-values:


Where, for example:

N – total number of incidents in all Philadelphia for both the previous 365 days and the current 30 days.

G – total number of incidents in all Philadelphia for just the past 30 days.

n – number of incidents in just this circle for both the previous 365 days and the past 30 days.

x – number of incidents in just this circle for the past 30 days.

After the probability lattice is generated, the application displays spikes in order of severity and whether they have increased or decreased as compared to the previous day.


One important element of crisis mapping which is often overlooked is the relevance to monitoring and evaluation. With the Spike Detector, the Police Department “can assess the impact and effectiveness of anticrime strategies.” This will be the subject of a blog post in the near future.

For now, I conclude with the following comment from the Philadelphia Police Department:

GIS is changing the way we operate. All police personnel, from the police commissioner down to the officer in the patrol car, can use maps as part of their daily work. Our online mapping applications needed to be fast and user-friendly because police officers don’t have time to become computer experts. I think we’ve delivered on this goal, and it’s transforming what we do and how we serve the community.

Clearly, crime mapping analytics has a lot offer those of us interested in crisis mapping of violent conflict in places like the DRC and Zimbabwe. What we need is a Neogeography version of the Spike Detector.

Patrick Philippe Meier