LPRC - CrimeScience – The Weekly Review – Episode 195

Episode Date: November 14, 2024

This week our host discusses the latest in LPRC news, research, visitors, and events! In this episode, our host discusses the most recent lab visitor, an update on LPRC's SaferPlaces Initiatives, the LPRC Resources available to our members, the continued growth of the LPRC labs, and so much more. Listen in to stay updated on hot topics in the industry and more!

Hi everyone and welcome to Crime Science. In this podcast, we explore the science of crime and the practical application of this science for loss prevention and asset protection practitioners as well as other professionals. Welcome everybody to another episode of Crime Science podcast from the LPRC. Today is the latest in our weekly update series and we are preparing to meet with ESRI. We're going to meet with ESRI regarding even working more closely with their team of experts, in our opinion the world's best mapping geospatial aim and platform software capability in the world. And we have been working pretty closely with Esri now

for about three years. And as we've talked about for on this podcast and elsewhere, the first thing we do, if we're going to work in a community, whether it's a block or entire city or region, is set up an interactive dashboard and populate the dashboard with layers of data, and that can include what's already there in the software package as a default. And that's

going to be every roadway and building and every feature, terrain feature and otherwise. But also it's going to be satellite imagery as well as an array, probably six or eight different versions of dark and light backgrounds and things like that. And then you can start to layer in demographic information. You can layer in really different sorts of blocks and areas and zones and lines, obviously city and county and state demarcation lines for one example.

You can also put things like police patrol zones. You could put districts or regions and so on for retail chains, depending on the scale of the map that you're looking at. And so what we do, of course, and we've talked a lot about this as a layer in data from partners and the partners include the retailers where their stores are.

And then we have those with different color distinguishes. We also can put in their data as far as what their reported incidents might be by type, time, and so on. All those data could be put in. And then with law enforcement agencies, we also get the same thing, calls for service by type and time and location. It could be a violent type crime, a fraud or violence, or reporting something, a whole host of different types of crime that could be reported or calls for help and assistance.

Also, if they make an arrest, where that arrest might've been made, and then what the offender's information might be that's publicly available, including their residence that they listed at the time of the arrest or during the booking process. We can also put in there certain information from the law enforcement agency partners, RMS,

or reporting management systems, RMS data. To handle some of the data that are not necessarily publicly available. Our team again have all gone through the criminal justice information system or CGIS training all to level two so that we can observe and handle very sensitive information. We just can't make changes or make additions to the data. So that allows us to create very detailed maps about where law enforcement officers, police officers or deputy sheriffs are called to that

for a call for service and why and where and when and how and things like that. Also though with the RMS data, the reporting data and the arrest and arrestee information, now we can look and tie together offenders, where they reside, where they're offending, where they're being arrested, who they're arrested with, other places they're associated with to understand

what's the exposure that a given store or other location is exposed to or a close cluster of stores, since that's what we're working on. What are they exposed to out there? Who are they exposed to? What are these people been up to? Who are they associated with?

How prolific of a defender are they? And so on. And the software automatically create hotspots saying, hey, there's a inordinate compared to everything else randomly assigned. If you looked at a bunch of grid squares, these locations have a much denser saturation of that type of call for service,

arrestee or type of crime and things like that. So we can break it out by violent theft and fraud. And then we also, of course, have put in data sets from the code enforcement people to get an idea where dangerous or abandoned buildings, other signs of disorder that might be reported and being corrected or just permanently a problem. We can put in areas where there might be homeless encampments, we have travel routes on foot and by vehicle and so on to again provide a much richer,

denser and much more realistic idea about what all is going on in that area immediately around the store and much more realistic idea about what all is being, is going on in that area, immediately around the store or co-located near the store and so forth. So it's this denseness, this richness, the variety and the ability to only portray what we want to by placing time and type and do it situationally

if we're working on a shoplifting gangs or robberies or burglaries or other concentrated crimes, then this is all very helpful to do. So with ESRI, we'll be planning a whole lot more we're gonna be doing. There are a lot of crime prevention analytics that are out there looking at what types of expected crimes there might be and what the actual observed crimes are.

That's really one of the main ways that we analyze crime in place is what was expected versus what was observed. And the grant, again, now trying to explain why crime is concentrated in certain places and with certain people and certain times in those combinations of all the above and by type of crime. So we'll be planning the long-term more dashboards in addition to Port St. Lucie and Gainesville, Atlanta, Albuquerque, Detroit, Portland, now putting together some more for Scottsdale, Arizona, Memphis, Tennessee, to name two more, and then parts of New York City.

So we're excited to work with this group from ESRI to employ more and more sophisticated analytical techniques. We have CEMSEE already. CEMSEE was created by researchers at Rutgers University. They're a strong partner with LPRC. We of course have CAP index data. We believe we're strong partners. We are with CAP index, typically with grant and some of the new team there. So all that type of information is in there as well in Esri. So stay tuned.

We'll get back to you, give you some more information on what all is going on. So just remember that mapping, that visualization of problems and solution sets are vital to what's going on and being able to portray, even in planting or embedding imagery. If we click on a store, if we click on a solution that we put out there or whatever, you'll see an image of it.

So with no further ado, I'm going to check out because we've got a whole lot of meetings and visits going on and we will be back shortly. So stay safe and stay in touch. Thanks for listening to the Crime Science Podcast presented by the Loss Prevention Research Council. If you enjoyed today's episode, you can find more Crime Science episodes and valuable information at lpresearch.org. The content provided in the Crime Science Podcast is for informational purposes only and is not a substitute for legal, financial, or other advice. Views expressed by guests of the Crime Science Podcast are for informational purposes only and is not a substitute for legal, financial, or other advice. Views expressed by guests of the Crime

Science Podcast are those of the authors and do not reflect the opinions or positions of the Loss Prevention Research Council.