Memphis Cuts Crime With Predictive Analytics Reports Reflect

Recent press reports from Information Week reflects that Tennessee city attributes 31% drop in crime rate to knowing when and where to put cops on the street.

What started as an experimental predictive crime-prevention initiative in 2005 is now a proven program that’s about to get a big boost with new data and new forms of analysis.

IBM announced on Wednesday that the Memphis Police Department (MPD) is enhancing a predictive-analytics-based “Blue CRUSH” (Criminal Reduction Utilizing Statistical History) program by adding public call data to the information it analyzes to thwart crime before it occurs.

Mark Armstrong, president of WhamTech, discusses how the company helps customers such as the government integrate their disparate data sources. Microsoft Windows Server Group Product Manager, Manlio Vecchiet answers questions about Hyper V, network access protection and the adoption to date of Windows Server 2008. We caught up with CEI’s CEO D. Raja to talk about the state of the custom development market, what CEI’s role in it is and also about the entrepreunerial nature of the Pittsburgh PA market.
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Launched five years ago by the University of Memphis Department of Criminology and Criminal Justice, the Blue CRUSH program started with analysis of historical data on gun crimes committed in North Memphis. Using predictive analytics software from SPSS (which was acquired last year by IBM), the University modeled patterns of crime and shared the results with area law enforcement agencies.

“Of course we knew the areas that had a lot of gun-related crime, but [Blue CRUSH] analyses helped us see patterns of exactly when and where the incidents were occurring,” said John Williams, Crime Analyst Unit Manager at MPD. With better insight, the department was able to send patrols to the right places at the right times, increasing arrest while also curbing incidents, Williams said.

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Early successes with Blue CRUSH led MPD to partner with the University as well as county and federal law enforcement agencies in 2006, pooling data and sharing insights on crime patterns. Analyses gradually expanded citywide, and the system now works in tandem with an MPD Real Time Crime Center (RTCC) monitoring and analysis hub opened in 2008.

Each week, the last four weeks’ worth of crime data is joined to spatial geographic information system data. Incidents are then plotted on digital map of the city within the RTCC that is updated and monitored around the clock. Blue CRUSH analyses of 28-day and seven-day statistics pinpoint crime hot spots, with details down to the day of the week and times of the day that are most active.

Blue CRUSH is credited as a primary driver of a 31% reduction in serious crime in Memphis since 2006, but Williams said MPD hopes to do better. Predictive analyses are currently based strictly on crime reports submitted by police officers. Not included are the more than two million emergency and non-emergency calls received from the public each year. MPD is now adding these call records to the Blue CRUSH analysis.

“There are some parts of town where we get reports of shots fired, gang members hanging out, or drug activity, but when we get there, the subjects are gone and there are no arrests,” Williams explained.

Call-volume and call-pattern analyses will not only uncover hot spots that don’t show up in arrest reports, it will help the department predict where it can put resources and change its approach to be more effective.

“If we can deploy some of our unmarked cars and undercover officers in these areas, we’re going to make arrests,” Williams said.

InformationWeek has published an in-depth report on energy-efficient government data centers. Download the report here (registration required).

Predictive Analytics Gains More Market Acceptance

In this three minute interview, Phil Ice, Director Research and Development Public talks about how Predictive Analytics is gaining more acceptance at the university level.

Dr. John F. Elder heads a data mining consulting team with offices in Charlottesville, Virginia and Washington DC. Founded in 1995, Elder Research, Inc. focuses on scientific and commercial applications of pattern discovery and optimization, including stock selection, image recognition, text mining, biometrics, drug efficacy, credit scoring, cross-selling, investment timing, and fraud detection.

Dr. Elder has authored innovative data mining tools, is active on Statistics, Engineering, and Finance conferences and boards, is a frequent keynote conference speaker, and is General Chair of the 2009 Knowledge Discovery and Data Mining conference in Paris. John’s courses on data analysis techniques – taught at dozens of universities, companies, and government labs – are noted for their clarity and effectiveness. Dr. Elder was honored to serve for 5 years on a panel appointed by the President to guide technology for National Security.

Check out the three minute interview on the Predictive Analyticswebsite.

Transcript:

Bill Cullifer: I am on the phone with Dr. John Elder, CEO and Founder of Elder Research out of Virginia. Good afternoon. Thanks for agreeing to this interview.

John Elder: Thanks for having me.

Bill Cullifer: You bet. You recently provided a presentation at the conference entitled the High ROI of Data Mining for Innovative Organizations. Can you summarize that session for the subscribers of this podcast?

John Elder: Sure Bill. I just gave a talk about nine of the kind of more interesting success stories we have had at Elder Research over the last decade or so and we will get them at a higher level. So, it handled the way data mining really helps and I have broken it into three parts, approving and emphasizing good, detecting and eliminating bad, streamlining and automating are… I gave examples of how each of those had worked [Voice Cross Over]

Bill Cullifer: Can you cite an example?

John Elder: Sure. One kind of interesting one was Anheuser-Busch[Phonetic] pictures of beer on the shelf, they like to correlate how the positioning of their product related to [Inaudible], they also want to know if they are missing any stock [Inaudible] so, that’s a relatively laborious process, to take a picture and turn it into some [Inaudible] interpretation of what exact product and what exact orientation is there. So, of course… so we did a pilot process, automated identification of what[Phonetic] product was there. Recognition is a very hard [Inaudible] is one that’s well lived, well under[Phonetic] good condition. Products are trying to make themselves be recognized a whole lot easier than say identifying somebody who just canceled on a [Inaudible] might be for instance but we were able to get 90% accuracy which was our goal instantaneously essentially meaning that they would spend one tenth as much time editing it as they would doing it by previous metric.

Bill Cullifer: Time is money. Right?

John Elder: Exactly. So, that would save them millions of dollars there. So, that was an example of automating and speeding up. There were a couple of examples of return by eliminating, but that there… the examples [Inaudible]. We had projects we did for the IRS and projects that we did for a large [Inaudible] electronics firm that saved tens of millions of dollars, actually the numbers were astoundingly large compared to the [Inaudible].

Bill Cullifer: So, if I… and the subsribers of this podcast were considering entering into the predictive analytics realm, any high level recommendations on first step?

John Elder: Yeah, the… one of the extreme[Phonetic] things that came out as I was putting this talk together were you know what were some characteristics that were common to some of these successful projects and all of them were technically successful, but not all of them were completed and that was interesting to see what were the differences there. For instance, on the Anheuser-Busch[Phonetic] one, they never put it in production because the day that we were to sign the development contract was 09/11; that was definitely a whole lot of a hell of a tragic event and there were some other times when a project didn’t see the way to appreciate it[Phonetic], but most of the nine examples I gave complete business successes saw their way into implementation and one of the key characteristics that we needed was a champion who would take the business risk inside the organization to see it all the way through and what you really want[Phonetic] there is nothing harder than starting something brand new because all those who talk about the old way of doing it will hold it and those who talk about the new way only lukewarmly support it and can still do [Inaudible]. So, we found that the innovative organization part of the title was really [Inaudible] organization that allowed it’s… that rewarded success for its people, not just punished because that is the environment in which a project will thrive and the return can be [Voice Cross Over]

Bill Cullifer: Sounds like excellent advice and we certainly appreciate your time.

John Elder: Thanks for having me.

Today’s topic is Predictive Analytics and this is the second in a series of audio podcast interviews on this topic. Dr. Eric Siegel is a former computer science professor at Columbia University and the President of Prediction Impact.

Q:  How do companies benefit?

A:  Well, the business case for a predictive analytics initiative is clear because having a predictive score for each customer is so clearly actionable.  For example, knowing which customers are worth targeting for an offer is about as actionable as business intelligence can get.

To be more specific, let’s say we generate scores to predict the risk each customer has of canceling or defecting from a subscription-base service. Targeted customer retention has impact on such businesses — such as ISPs, magazine subscriptions, online dating, you-name-it.

Retention offers – say a discount – are usually expensive.  You can’t extend the offer to all subscribers.  The only way to target a retention campaign precisely where it’s needed is with predictive scores that earmark which customers are most likely to leave.  When you follow these predictions, you don’t expend the cost of the offer on customers that don’t need it. So, the campaign ROI is much better, growth rates improve and bottom-line profit increases.

So, in general with predictive analytics, each customer’s predictive score informs actions to be taken with that customer.  You just can’t get more actionable than that.

To listen to the entire interview visit: http://predictiveanalytics.org/?p=7