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Crime Data Quality and Validation – A Necessity for Every Agency

Accurate Mapping is The Epicenter for Making Sense of Your Crime Data

Let’s talk about mapping. Very few mapping systems, whether you are using GIS or some other type of mapping system, are always spot on. The reasons for these inaccuracies vary widely. From inaccurate GIS mapping at the onset, to duplicate addresses in your city that are only separated by a North-South or East-West designation, or simply a user data-entry mistake. Previously, I couldn’t change these map points in my records management system, nor did I have admin over the county GIS system that would allow me to change the points. However, I can now change them with CommandCentral. Recently, PublicEngines released a new feature dubbed the Data Quality and Validation tool, or DQV for short. With just a few steps I am now able to take my map, with an average of 150 inaccuracies a month, and turn it into a completely accurate crime map, with no inaccuracies.

How My Data Accuracy Quest Began

When I began the intelligence unit at my agency in the greater Atlanta area, one of the things I noticed first off was how inaccurate our crime mapping system seemed to be. As I began looking into the problem, I found that instead of it being the result of a single error, it was actually the result of a myriad of errors. Among those, were inaccurate geocoding, areas of my city that had been annexed in but not yet geocoded, duplicate addresses within my city, and of course data entry mistakes.

Now as you can imagine, as I began to remedy this situation I felt a little like a dog chasing its tail — I was certainly moving but I wasn’t making any progress. During one staff meeting, it became even more apparent that I needed to do something about the mapping inaccuracies when we began looking at crimes broken down by zone specifics.  We looked for crimes that we knew had occurred in a certain zone so that we could speak about them as a command staff and form a tactical action plan. But when we began searching for them, we weren’t able to find the specific crimes. As I began searching within my RMS to locate these “lost” crimes, I found them all mapped outside of my city boundaries. Many of these “lost” crimes were plotted tens, hundreds, and even thousands of miles away from where they should have been.  We had a serious problem to say the least, and unfortunately, no solution.

Fast-forward to my time at PublicEngines. One of the key drivers in developing the DQV tool was the research that I conducted in proactively auditing our customer’s databases. I found very quickly that my agency, with 150 mis-maps a month, was by far not on its own. The vast majority of agencies have mapping problems that they are either not aware of, or lack the to ability to fix. This is why I am so excited to introduce to you the DQV tool. Not only will you be able to identify all occurrences mapped outside of your jurisdictional boundaries, but you will be able to correct those errors in just a few steps.

A New Solution to An Age-Old Problem

crime data, crime data qualtiyAs I’m sure you can attest, accurate data is paramount to conducting crime analysis that leads to actionable intelligence and crime reduction. The DQV tool in CommandCentral ensures that the most common data errors – mis-mapped and mis-classified crimes – are easily correctable so agency personnel can make resource decisions with confidence.

Here are a few highlights of its capabilities.

  • Built-in alert bar notifies CommandCentral Analytics administrators when incidents are geocoded outside of an agency’s jurisdiction.
  • Click-to-correct mismapped incidents – inaccurately mapped crimes can be corrected simply by clicking on the CommandCentral Map
  • Create rules so that all future data synched to CommandCentral is mapped accurately
  • Edit crime incident categorization
  • Maintain data fidelity – changes are only made in CommandCentral, not in your RMS

Identifying mis-mapped crimes is as easy as selecting an Out of Area button in the system’s administration section. The tool then generates a list of occurrences that were all mapped outside of your jurisdictional boundaries. You can select any single occurrence listed to see where it’s currently positioned on the map, and then override it. This process is easy: simply select where that occurrence should be mapped. You can change the pin for this specific incident only or for all incidents previously mis-mapped in the same manner — which is especially important when it is one of those addresses that are constantly in error.

Visualizing and analyzing crime data through crime mapping solutions has been an essential tool in every agency’s arsenal since the mid 1800’s with the advent of the pin-map. Today, online tools make the task easier than ever. But the question remains, is the data you’re viewing accurate? With the DQV tool you can now be sure of it.

Four Steps To Effectively Using Crime Data in Law Enforcement

It’s no secret that law enforcement agencies are consistently being asked to do more with fewer resources. Budget cuts have meant fewer feet on the street and ever increasing demands on agencies and officers alike. To meet these growing demands, many agencies are increasingly relying on technology to fill that gap.

There are four important steps to make sure that you’re using data to its fullest potential.

1. Collecting the data

The fact of the matter is there is a plethora of data available. Useful data may include department-specific information such as:

  • Recent bookings
  • Various types of crimes that have been committed
  • Calendar of when crimes occurred
  • Maps of where illegal activities took place
  • Written citations

However, according to Doug Wylie, Editor in Chief of PoliceOne, some cities take it a step further to include a much greater holistic view of the community a department serves to include everything from utilities and social services records to new building permits.

2 . Accurate Data

Recently, one big city police department announced it would no longer be releasing monthly crime reports because the Excel files they used to distribute the information were being corrupted. Someone had been changing the data the public viewed. This follows the accusations a couple of years ago that the New York Police Department had been falsifying data.

Audits by PublicEngines reveals that up to 25% of all law enforcement data housed in RMS or CAD systems is not accurately recorded.

However, there are ways to improve data accuracy. According to a recent report, some of the variables that agencies should consider include:

  • Data that is correctly captured. This is crucial because there are myriad of codes, statutes and other minor details that allow for human error. Information can be mislabeled or mapped incorrectly. Regular review and comparison can help catch errors and ensure greater accuracy.
  • Quality report writing that includes correct classifications, a built-in multiple-level review processes, and a system to all for reclassification, supplements and follow-up reports to be reviewed, approved and added.
  • Regular audits of reports to verify accuracy. This might also include periodic surveys of randomly selected citizens, who have reported criminal activity to verify your records accurately reflect the facts as they were reported.

3. Adequately Interpreted Data

Those agencies with analysts rely on these hard-working people to identify crime trends. But they’re stretched thin. The ability for officers to predict crimes not only relieves some of the pressure on analysts, it also helps reduce crime. Access to this information is the key factor.

But with sheer amount of data now being gathered, is there room to interpret it in a way that predicts even more crime?

Take some of the non-criminal data that agencies are gathering that was mentioned by PoliceOne’s Wylie. An officer knows that construction sites often experience the theft of materials, vandalism and graffiti. If he also knows from new building permits that construction is under way in several projects, redirecting himself to those areas can significantly reduce the potential for those crimes.

4.Getting data into hands of those that take action

As the above example illustrates, when officers on the street have access to data, they can act accordingly. However, that can prove a challenge.

Products like CommandCentral Predictive, work to eliminate those challenges. Since it’s cloud-based, it is available literally anywhere it is needed so long as an Internet connected device is available. Reports can even be sent directly to officers via email automatically.

Officers in the field are hardly desk jockeys, which is why allowing them to access the information while in the field via their mobile phone or tablet is so important. It can literally be the difference between a crime being prevented and a crime occurring.

Data is available – maybe even too much data is available – but there are ways to harness that information to help predict and prevent crime. Collecting that data from a wide variety of sources, ensuring its accuracy and interpreting its value are important first steps. However, utilizing technology – getting this information to officers wherever they may be – allows them to predict crime and make the streets safer for everyone.