(November 1, 2017) Austin, TX – Case Closed Software™, a leading provider of investigation case management software to law enforcement agencies, today announced that a large North Carolina Sheriff’s Office has signed a multi-year contract for their best-in-class software.
The Tar Hill State county, serving several hundred thousand residents, selected Case Closed Software after a nearly year-long search for sophisticated software that can help them work investigations more efficiently with a goal to close criminal cases more quickly.
According to Douglas Wood, President of Case Closed Software, the county selected his company’s offering due to the flexibility and overall feature set it offers.
“We’re thrilled to add this Sheriff’s Office to our delighted customer base”, said Mr. Wood. “One of the reasons we won the business is the fantastic references provided by our existing clientele, which include Police Departments, Sheriff’s Offices, State Investigation Bureaus, District Attorneys and more.”
Case Closed Software has begun implementation of the software and expects the County to be fully installed and trained by November 30, 2017.
Case Closed Software, who recently announced a strategic relationship with analytics software provider Visallo, develops and markets investigation management software, sophisticated investigation analytics, and advanced criminal intelligence software for law enforcement.
“The big data policing revolution has arrived. The singular insight of this innovation is that data-driven predictive technologies can identify and forecast risk for the future. Risk identification is also the goal of this book — to forecast the potential problems of big data policing as it reshapes law enforcement.”
In the meantime, Case Closed Software™ reminds you that, as the only true alternative to Palantir®, we specialize in big data investigation analytics combined with the industry’s most robust investigative case management solution.
We are “Palantir® without the price tag and data-lock”.
The article examines the intersection of two emerging developments: the increase in surveillance and the massive exploration of “big data.” Drawing on observations and interviews conducted within the Los Angeles Police Department, Sarah offers an empirical account of how the adoption of big data analytics does—and does not—transform police surveillance practices.
She argues that the adoption of big data analytics facilitates may amplify previous surveillance practices, and outlines the following findings:
Discretionary assessments of risk are supplemented and quantified using risk scores.
Data tends to be used for predictive, rather than reactive or explanatory, purposes. (Here, Crime Tech Weekly would want to differentiate between predictive analytics and investigation analytics)
The proliferation of automatic alert systems makes it possible to systematically surveil an unprecedentedly large number of people.
The threshold for inclusion in law enforcement databases (gang databases, criminal intelligence data, etc) is lower, now including individuals who have not had direct police contact. (Here again, Crime Tech Weekly would point out that adherence to criminal intelligence best practices vastly reduces this likelihood)
Previously separate data systems are merged, facilitating the spread of surveillance into a wide range of institutions.
Based on these findings, Sarah develops a theoretical model of big data surveillance that can be applied to institutional domains beyond the criminal justice system. Finally, she highlights the social consequences of big data surveillance for law and social inequality.
The full PDF report can be downloaded via Sage Publishing by clicking here. Or, if you have general comments or questions and do not wish to download the full version, please feel free to contact us through the form below. Crime Tech Weekly will be happy to weigh in.
The investigative units of law enforcement agencies all around the world face many of the same challenges. Chief among those challenges is the fact that budgets are tightening, while resources are becoming more and more scarce. Even in the face of reduced funding, investigators are asked to deliver higher levels of service in their quest to solve and deter crime.
The key to doing ‘more’ with ‘less’ in law enforcement is really no different than in any other industry. That is, deploying resources in the most effective manner possible for the maximum value possible. The trick is using what the agency already knows to determine what the future may hold. What that all boils down to is Big Data Investigation Analytics.
Data in Law Enforcement comes in many forms, and is typically stored in disparate silos. Arrest records, calls for service, criminal intelligence, field reports, human resource data, telephone records, case management files, and so on. Together, the data from those systems can represent a virtual goldmine of investigative information if used correctly.
‘Correctly’ is the operative term, of course. The ability to turn these large data stores into actionable investigation intelligence requires more than a simple data warehouse or data mining tool, and for the most part police departments recognize this need. They understand that their data holds the key to understanding the hidden connections between people, places, and things – the lynchpin of any successful investigation.
For years, law enforcement agencies and commercial organizations have built data mining tools and data warehouses. Unfortunately, these analytical techniques are no longer sufficient in an age of rapidly growing data. Moreover, much of the data that investigators need to access is unstructured text – word processing documents, narratives, search warrants, witness statements, email text, and more. By applying big data text analytics, investigators can begin to extract actionable insights from both structured and unstructured data.
Big Data Investigation Analytics – such as those provided by Virginia based Visallo and California’s Palantir Technologies – are two examples that provide a powerful indexing architecture allowing investigators to find non-textual data, including multimedia files such as 911 calls, interrogation videos, and images. This architecture helps investigators find things that they simply could not find otherwise.
Finally, world-class investigation analytics provide a flexible graph visualization tool, as well. This user interface allows investigators to organize data through a variety of layout options, find hidden and non-obvious relationships between entities, and perform a variety of what-if scenarios.
When paired with the robust investigative case management and criminal intelligence systems available from Crime Tech Solutions, big data investigation analytics build a foundation upon which investigators can solve more crimes, more quickly.
Without advanced investigation analytics, agencies often find themselves looking for a needle in a haystack. In fact, too often the needle is broken into several pieces spread across multiple haystacks. To simplify these tasks, investigative agencies must deploy the correct analytical technologies which have become a key element of doing more with less in the global investigation market. For more information on how Crime Tech Solutions and Visallo are changing the law enforcement analytics landscape, please contact us below!
September 16, 2016 – (Leander, TX) Crime Tech Solutions, a fast-growing provider of low cost / high performance crime fighting software and analytics is delighted to announce the addition of Jamie May as senior analyst and strategic advisor to the company.
“To me, Crime Tech Solutions represents a truly innovative company that understands how to develop and market very good technology at prices that most agencies can actually afford”, said Ms. May. “I’m looking forward to being part of the continued growth here.”
In her role with the company, Ms. May will interact with customers and prospects to help align the company’s solution strategy with market and user requirements.
Law enforcement agencies everywhere are tasked with reducing and investigating crime with fewer and fewer resources at their disposal. “To protect and serve” is the highest responsibilities one can sign up for, particularly in light of recent well-publicized criticisms of police by activists in every city.
That responsibility weighs even heavier in a world with no shortage of criminals and terrorists. There’s never enough money in the budget to adequately deal with all of the issues that face an individual agency on a daily basis. Never enough feet on the street, as they say.
New Tools for Age-Old Problems
Perhaps that’s why agencies everywhere are moving to fight crime with an evolving 21st century weapon – law enforcement software including investigative case management, link and social network analysis, and, importantly, crime analytics with geospatial and temporal mapping.
Crime analytics and investigation software have proven themselves to be valuable tools in thwarting criminal activity by helping to better define resource allocation, target investigations more accurately, and enhancing public safety,
According to some reports, law enforcement budgets have been reduced by over 80% since the early 2000s. Still, agencies are asked to do more and more, with less and less.
Analytics in Policing
Analytics in law enforcement play a key role in helping law enforcement agencies better forecast what types of crimes are most likely to occur in a certain area within a certain window of time. While no predictive analytics solution offers the clarity of a crystal ball, they can be effective in affecting crime reduction and public safety.
Predictive analysis, in essence, is taking data from disparate sources, analyzing them and then using the results to anticipate, prevent and respond more effectively to future crime. Those disparate data sources typically include historical crime data from records management systems, calls for service/dispatch information, tip lines, confidential informant information, and specialized criminal intelligence data.
The Five W’s of Predictive Analytics
Within this disparate data lie the 5 W’s of information that can be used by crime analysis software to build predictions. Those key pieces include:
Arrest records – who committed crimes
Geospatial data – where crimes have occurred
Temporal data – when crimes have occurred
Statistical data – what crimes have occurred
Investigation data – why (and how) the crimes occurred
Using the 5 W’s, agencies are able to gain insight and make predictions about likely future criminal behavior. For example, if a certain type of crime (what) tends to occur in ‘this’ area (where) at ‘this’ time (when), and by ‘this’ type of individual (who) for ‘this’ reason (why)… it would be wise to deploy resources in that area at that time in order to prevent the incidents from ever occurring. This, of course, is a dramatic over-simplification of the types of analytics that make up predictive policing, but illustrates the general concept well.
Although criminals will always try to be one step ahead of the law, agencies deploying predictive analytics are able to maximize the effectiveness of its staff and other resources, increasing public safety, and keeping bad guys off the street.
A recent report questions how some police departments are using data to forecast future crimes.
The report examined how departments are utilizing predictive policing, a computer software that uses data to forecast where crime may happen, who may commit it and who could be potential victims.
Logan Koepke, an analyst who coauthored the report, said the handful of departments that use the software don’t appear to give patrol officers much guidance on what to do with the information provided.
“One problem we’ve seen is there is not a lot of direction from police departments or from vendors about what officers in the field should do once they look at a prediction,” Koepke said.
Upturn, an analysis organization out of Washington, D.C., that works with a variety of groups, published the report “Stuck in a Pattern” last month. “Predictive policing,” the report states, is a marketing term popularized by vendors who sell the software.
The group’s research showed areas that use the software often don’t engage the public in discussion about predictive policing and questioned whether or not departments who use it are measuring the impact the data-driven tool has on policing and crime rates.
How police departments nationwide utilize crime statistics and the software available to patrol officers monitoring incidents has evolved a lot in the past couple decades, Sgt. Tracy Barton with the St. Joseph Police Department said. Barton started as a patrol officer 20 years ago. He said having software like police have today would have been useful.
“I was in District 7, which is the Midtown area. It would have been really cool to know where crime is occurring in District 7 and that I could look that up on a computer and have it at my fingertips where I could see it on an up-to-date basis,” Barton said.
In the past, crime analysts used physical maps and mathematical algorithms to do what software does instantly today. Barton, the St. Joseph Police Department’s crime analyst, uses software that takes into account crimes reported to police to show crime hot spots in the city. A map of those hot spots is available online. Officers, he said, have access to a more in-depth version of that hot spots map, which includes extra details about the area and the crimes occurring.
The hot spots approach St. Joseph police use is most helpful in preventing crime when a series of crimes occur or when there is a crime pattern in a given area, he said. St. Joseph’s small size, Barton added, poses an extra challenge.
The St. Joseph Police Department has had its current software for just a few years. It doesn’t fit the definition of the predictive policing software Upturn outlines in the recent report. The software used locally depends on crime reports police receive. Predictive policing also looks at what types of business are located in an area, if repeat offenders live nearby and other information to predict where crimes may occur. Typically, according to the recent report, predictive policing takes one of two approaches, focusing either on place-based data or person-based data to make predictions.
Kevin Bryant, sociology and criminology professor at Benedictine College, said when people hear predictive policing described, they often think of the movie “Minority Report.” The software, he said, is nothing like in the movies.
“Predictive policing now has kind of morphed into better proactive methods that are based on prediction to some extent in forecasting risk,” Bryant said of how data-driven policing has changed over time. “But what we’ve learned through evaluation studies is that it’s really more important what the police do when they’re in a crime hot spot.”
Bryant worked with the Shawnee, Kansas, Police Department on research that looked at what he calls “smart policing.” In his research and in other work he has read, Bryant said it’s important for police to have a high visibility in crime hot spots, for officers to make connections with the public and for them to avoid staying in crime hot spots for extended periods of time.
When police are in a hot spot for too long, public surveys show area residents feel like they are being picked on rather than protected, he said. Predictive policing takes into account businesses in a given area. Bryant said some types of facilities are at a higher risk of victimization and others can attract crime to an area.
“Knowing where bars, taverns, restaurants, gas stations, convenience stores are, we can actually use their locations as a means of forecasting where crime might emerge at a later date,” Bryant said. “Part of predictive policing is predicting who the risky offenders are and that can be controversial.”
Upturn’s report also explored an ongoing debate among criminologists on the impact of using crime reports when determining patrol.
“Criminologists have long emphasized that crime reports and other statistics gathered by the police are not an accurate record of the crime that happens in a community,” the report states.
Police statistics, Koepke said, reflect officer enforcement efforts, not just crime.