Tag Archives: Doug Wood

Overland Park senior crime analyst Jamie May joins Crime Tech Solutions

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.

jamiemayMs. May has spent over 17 years as a crime and intelligence analyst for Overland Park Police Department in Kansas, and is a recognized expert in crime analysis, mapping, and criminal intelligence. She has sat on critical crime analysis committees including the International Association of Crime Analysts’ Ethics Committee (IACA) and is a past Vice President / Secretary at Mid American Regional Crime Analyst Network (MARCAN).

“Jamie brings an incredible amount of user experience and innovation to the company”, said Kevin Konczal, Crime Tech Solutions’ VP of Sales. “She’s been active in this community for years, and co-authored the ground-breaking guide, GIS in Law Enforcement: Implementation Issues and Case Studies.”

“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.

black versionCrime Tech Solutions, who earlier this year acquired TN based Case Closed Software, delivers unique value to customers with comprehensive investigative case management software, sophisticated link analysis tools, criminal intelligence management software, and crime mapping technology that includes some of the industry’s best analytics and reporting capabilities.

 

 

 

 

Crime Hot Spots and Risk Terrain Modeling

abmpegasus-intelligence-led-policing
A typical ‘hot spot’ in crime analytics

By Tyler Wood, Operations Manager at Crime Tech Solutions.

One of the many functions crime analysis performs is the identification of “hot spots”, or geographical areas that seem to be hubs for criminal activity. Identifying these hot spots through best practices in geospatial crime mapping allows law enforcement to focus their efforts in areas that need them most. The trouble that law enforcement and crime analysts have encountered is displacement – the fact that once a hot spot is “cleared”, crime seems to pop up again in a different location. The good news is that the displacement is never 100%, so policing hot spots is important – it’s just not a magic bullet.

To solve this problem, a team at Rutgers University’s School of Criminal Justice set out to develop new methodologies that would result in peaceful outcomes that are built to last instead of merely temporary.

The difference between the old approach and the new approach is stark. Where police and analysts used to focus solely on geographical concentration of crimes, Risk Terrain Modeling examines the factors that contribute to such dense concentrations to begin with. Rutgers team have identified several characteristics of any given geographical location which may attract or generate crime. Their technology takes these characteristics, which include socioeconomic data, physical layout, types of local businesses, etc… and uses them to calculate the likelihood crime occurring in the area. This allows law enforcement to be proactive in the prevention of crime in these areas.

CrimeMapLite
Advanced crime analytics show statistically significant risk factors.

The technique seems to be highly effective. After a trial run in New Haven, CT, police were able to identify sixteen “statistically significant risk factors that underlie violent crime occurrences.” A high percentage of violent crime in New Haven during the test period occurred in locations already identified by the concept of risk terrain modeling. Though the technology is still new, it is clearly showing impressive results already.

Shutting down hot spots is important policing, and risk terrain modeling technology allows analysts and law enforcement officers to be even more proactive in their prevention of crime.

The author, Tyler Wood, is head of operations at Austin, TX based Crime Tech Solutions – an innovator in crime analytics and law enforcement crime-fighting software. The clear price/performance leader for crime fighting software, the company’s offerings include sophisticated Case Closed™ investigative case management and major case management, GangBuster™ gang intelligence software, powerful link analysis software, evidence management, mobile applications for law enforcement, comprehensive crime analytics with mapping and predictive policing, and 28 CFR Part 23 compliant criminal intelligence database management systems.)

Link Analysis and Crime – An examination.

Posted by Tyler Wood, Operations Manager at Crime Tech Solutions

Pic003The topic of fraud is widely discussed, and the focus of thousands upon thousands of articles. Television shows such as Crime, Inc and American Greed have become popular due, in part, to our fascination with the topic of fraud.

The organizations that are affected by fraud are also fascinated… but for entirely different reasons. Some estimates suggest that the US economy loses 11 trillion dollars each year due to one form of fraud or another. It’s little wonder, therefore, that the companies most frequently defrauded have been heavily investing in anti-fraud technologies at an increasing rate over the past decade or more.

The biggest problem with fraud, of course, is that it is always evolving in a very Darwinian fashion. Like a living, breathing entity, fraud schemes change over time in order to survive. As the targets of fraud schemes put new policies, procedures and/or systems to deter the activities, the schemes modify and find new ways to survive.

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So, since the nature of criminal activity is such that they constantly change, how do investigators find a fool proof methodology to ensure they are 100% safe from them? The answer, of course, is that they can’t. They never will; at least not until we live in a world such as the one depicted in the 2002 film Minority Report, starring Tom Cruise. In that movie, criminals are arrested prior to committing a crime based upon the predictions of psychics called ‘Precogs’. Corporations and individual targets of fraud can only wish.

Nope, there are no Precogs running around locking up would-be practitioners of fraud that would protect banks, insurance companies, Medicaid and Medicare programs, victims of Ponzi schemes, victims of identity theft, and countless others. Instead, organizations rely upon skilled knowledge workers using purpose-built crime and fraud analytics technology that can detect anomalies in patterns, suspicious transactions, hotspot mapping, networks of fraudsters, and other sophisticated data analytics tools.

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Crime and fraud analytics

Any discussion of analytics and investigation software must touch upon the topic of ‘big data’. No longer just a buzz word, big data literally fuels the insights gathered by organizations in every area of business. Naturally, then, organizations who have been traditionally targeted by fraudsters have increasingly invested in crime technology such as investigation software and analytics in order to exploit the phenomenon.

gotbigdata.pngOf course, big data in and by itself does nothing. It just sits there. Nobody has ever yelled “Help! We’ve been defrauded! Call the big data!” Big data is only useful when it can be transformed into ‘smart data’. In other words, understanding the big picture of costly fraudulent activities is not akin to understanding the specifics of ‘who’ is defrauding you, and ‘how’ they are doing it.

Those questions can best be answered through the powerful data mining and link analysis software tools offered by Austin, TX based Crime Tech Solutions in partnership with Sterling, VA based Visallo. Effective link analysis complements big data analytics platforms, helping to expose previously undetected fraud, and the entities (people or organizations) committing it.

Link Analysis – Transforming big data into smart data

By definition, link analysis is a data analysis technique that examines relationships among people, places, and things. As a visual tool, link analysis provides users a powerful method to quickly understand and ‘see’ what is happening. Because of this, it is widely used by financial institutions such as banks and insurance companies to uncover criminal networks, improve fraud investigations, detect insider fraud, and expose money laundering schemes. Similarly, government agencies use link analysis to investigate fraud, enhance screening processes, uncover terrorist networks and investigate criminal activities.

At Crime Tech Solutions, we liken the question of how to detect and deter fraud to ‘How do you eat an elephant?’ The answer, of course, is one bite at a time. If big data is the elephant, comprehensive link analysis software is part of the one ‘bite’ at a time. Or should we say ‘byte’.

(NOTE: Crime Tech Solutions is an Austin, TX based provider of investigation software and analytics for commercial and law enforcement groups. We proudly support the Association of Certified Fraud Examiners (ACFE), International Association of Chiefs of Police (IACP), Association of Law Enforcement Intelligence Units (LEIU) and International Association of Crime Analysts (IACA). Our offerings include sophisticated link analysis software, an industry-leading investigation case management solution, and criminal intelligence database management systems.)

What is Crime Analysis?

Posted by Tyler Wood Crime Tech Solutions, your source for investigation software.

The information provided on this page comes primarily from Boba, R. (2008: Pages 3 through 6) Crime Analysis with Crime Mapping. For a full discussion of the crime analysis discipline, refer to the book which can be obtained through www.sagepub.com.

Over the past 20 years, many scholars have developed definitions of crime analysis. Although definitions of crime analysis differ in specifics, they share several common components: all agree that crime analysis supports the mission of the police agency, utilizes systematic methods and information, and provides information to a range of audiences. Thus, the following definition of crime analysis is used as the foundation of this initiative:

Crime analysis is the systematic study of crime and disorder problems as well as other police–related issues—including sociodemographic, spatial, and temporal factors—to assist the police in criminal apprehension, crime and disorder reduction, crime prevention, and evaluation.

Clarification of each aspect of this definition helps to demonstrate the various elements of crime analysis. Generally, to study means to inquire into, investigate, examine closely, and/or scrutinize information. Crime analysis, then, is the focused and systematic examination of crime and disorder problems as well as other police-related issues. Crime analysis is not haphazard or anecdotal; rather, it involves the application of social science data collection procedures, analytical methods, and statistical techniques.

More specifically, crime analysis employs both qualitative and quantitative data and methods. Crime analysts use qualitative data and methods when they examine non-numerical data for the purpose of discovering underlying meanings and patterns of relationships. The qualitative methods specific to crime analysis include field research (such as observing characteristics of locations) and content analysis (such as examining police report narratives). Crime analysts use quantitative data and methods when they conduct statistical analysis of numerical or categorical data. Although much of the work in crime analysis is quantitative, crime analysts use simple statistical methods, such as frequencies, percentages, means, and rates. Typical crime analysis tools include link analysis and crime mapping software.

The central focus of crime analysis is the study of crime (e.g., rape, robbery, and burglary); disorder problems (e.g., noise complaints, burglar alarms, and suspicious activity); and information related to the nature of incidents, offenders, and victims or targets of crime (targets refer to inanimate objects, such as buildings or property). Crime analysts also study other police-related operational issues, such as staffing needs and areas of police service. Even though this discipline is called crime analysis, it actually includes much more than just the examination of crime incidents.

Although many different characteristics of crime and disorder are relevant in crime analysis, the three most important kinds of information that crime analysts use are sociodemographic, spatial, and temporal. Sociodemographic information consists of the personal characteristics of individuals and groups, such as sex, race, income, age, and education. On an individual level, crime analysts use sociodemographic information to search for and identify crime suspects. On a broader level, they use such information to determine the characteristics of groups and how they relate to crime. For example, analysts may use sociodemographic information to answer the question, “Is there a white, male suspect, 30 to 35 years of age, with brown hair and brown eyes, to link to a particular robbery?” or “Can demographic characteristics explain why the people in one group are victimized more often than people in another group in a particular area?”

The spatial nature of crime and other police-related issues is central to understanding the nature of a problem. In recent years, improvements in computer technology and the availability of electronic data have facilitated a larger role for spatial analysis in crime analysis. Visual displays of crime locations (maps) and their relationship to other events and geographic features are essential to understanding the nature of crime and disorder. Recent developments in criminological theory have encouraged crime analysts to focus on geographic patterns of crime, by examining situations in which victims and offenders come together in time and space.

Finally, the temporal nature of crime, disorder, and other police-related issues is a major component of crime analysis. Crime analysts conduct several levels of temporal analysis, including (a) examination of long-term patterns in crime trends over several years, the seasonal nature of crime, and patterns by month; (b) examination of mid-length patterns, such as patterns by day of week and time of day; and (c) examination of short-term patterns, such as patterns by day of the week, time of day, or time between incidents within a particular crime series.

The final part of the crime analysis definition—”to assist the police in criminal apprehension, crime and disorder reduction, crime prevention, and evaluation” generally summarizes the purpose and goals of crime analysis. The primary purpose of crime analysis is to support (i.e., “assist”) the operations of a police department. Without police, crime analysis would not exist as it is defined here.

The first goal of crime analysis is to assist in criminal apprehension, given that this is a fundamental goal of the police. For instance, a detective may be investigating a robbery incident in which the perpetrator used a particular modus operandi (i.e., method of the crime). A crime analyst might assist the detective by searching a database of previous robberies for similar cases.

Another fundamental police goal is to prevent crime through methods other than apprehension. Thus, the second goal of crime analysis is to help identify and analyze crime and disorder problems as well as to develop crime prevention responses for those problems. For example, members of a police department may wish to conduct a residential burglary prevention campaign and would like to target their resources in areas with the largest residential burglary problem. A crime analyst can assist by conducting an analysis of residential burglary to examine how, when, and where the burglaries occurred along with which items were stolen. The analyst can then use this information to develop crime prevention suggestions, (such as closing and locking garage doors) for specific areas.

Many of the problems that police deal with or are asked to solve are not criminal in nature; rather, they are related to quality of life and disorder. Some examples include false burglar alarms, loud noise complaints, traffic control, and neighbor disputes. The third goal of crime analysis stems from the police objective to reduce crime and disorder. Crime analysts can assist police with these efforts by researching and analyzing problems such as suspicious activity, noise complaints, code violations, and trespass warnings. This research can provide officers with information they can use to address these issues before they become more serious criminal problems.

The final goal of crime analysis is to help with the evaluation of police efforts by determining the level of success of programs and initiatives implemented to control and prevent crime and disorder and measuring how effectively police organizations are run. In recent years, local police agencies have become increasingly interested in determining the effectiveness of their crime control and prevention programs and initiatives. For example, an evaluation might be conducted to determine the effectiveness of a two-month burglary surveillance or of a crime prevention program that has sought to implement crime prevention through environmental design (CPTED) principles within several apartment communities. Crime analysts also assist police departments in evaluating internal organizational procedures, such as resource allocation (i.e., how officers are assigned to patrol areas), realignment of geographic boundaries, the forecasting of staffing needs, and the development of performance measures. Police agencies keep such procedures under constant scrutiny in order to ensure that the agencies are running effectively.

In summary, the primary objective of crime analysis is to assist the police in reducing and preventing crime and disorder. Present cutting edge policing strategies, such as hotspots policing, problem-oriented policing, disorder policing, intelligence-led policing, and CompStat management strategies, are centered on directing crime prevention and crime reduction responses based on crime analysis results. Although crime analysis is recognized today as important by both the policing and the academic communities, it is a young discipline and is still being developed. Consequently, it is necessary to provide new and experienced crime analysts with training and assistance that improves their skills and provides them examples of best practices from around the country and the world. http://crimetechsolutions.com

 

Professor urges increased use of technology in fighting crime

risk_terrain_modeling_resizedPosted by Crime Tech Solutions

This article originally appeared HERE in Jamaica Observer. It’s an interesting read…

A University of the West Indies (UWI) professor is calling for the increased use of technology by developing countries, including Jamaica, to assist in the fight against crime.

Professor Evan Duggan, who is Dean of the Faculty of Social Sciences, said there have been “amazing advancements” in information and communications technologies (ICT), over the past six decades, which offer great potential for improving security strategies.

The academic, who was addressing a recent National Security Policy Seminar at UWI’s Regional Headquarters, located on the Mona campus, pointed to Kenya as a developing country that has employed the use of inexpensive technology in its crime fighting initiatives.

“Potential applications and innovations have been implemented through the use of powerful but not very expensive technologies that have allowed law enforcers to make enormous leaps in criminal intelligence, crime analysis, emergency response and policing,” he said.

He pointed to the use of a variety of mobile apps for crime prevention and reporting, web facilities, and citizen portals for the reporting of criminal activity.

Professor Duggan said that in order for Jamaica to realise the full benefit of technology in crime fighting, national security stakeholders need to engage local application developers.

“I would enjoin our stakeholders to engage the extremely creative Jamaican application developers, who now produce high quality apps for a variety of mobile and other platforms. I recommend interventions to assist in helping these groups to cohere into a unified force that is more than capable of supplying the applications we need,” he urged.

The UWI Professor pointed to the Mona Geoinformatic Institute as one entity that has been assisting in fighting crime, through analyses of crime data as well as three dimensional (3D) reconstruction of crime scenes; and mapping jurisdictional boundaries for police posts and divisions, as well as the movement of major gangs across the country.

In the meantime, Professor Duggan called for “purposeful activism” in the fight against crime and lawlessness which, he said, are “serious deterrents to economic development and national growth prospects” and could derail the national vision of developed country status by 2030.

“In the current global landscape where security challenges are proliferating across borders and have taken on multifaceted physiognomies, all hands on deck are vital,” he stressed.

“We need to …consolidate pockets of research excellence in this area …to provide the kinds of insight that will lead to more fruitful and productive collaborative engagements that are required to help us better understand the security challenges and threats from crime in order to better inform our national security architecture and direction,” he added.

What is Link / Social Network Analysis?

Posted by Crime Tech SolutionsPic003

Computer-based link analysis is a set of techniques for exploring associations among large numbers of objects of different types. These methods have proven crucial in assisting human investigators in comprehending complex webs of evidence and drawing conclusions that are not apparent from any single piece of information. These methods are equally useful for creating variables that can be combined with structured data sources to improve automated decision-making processes. Typically, linkage data is modeled as a graph, with nodes representing entities of interest and links representing relationships or transactions. Links and nodes may have attributes specific to the domain. For example, link attributes might indicate the certainty or strength of a relationship, the dollar value of a transaction, or the probability of an infection.

Some linkage data, such as telephone call detail records, may be simple but voluminous, with uniform node and link types and a great deal of regularity. Other data, such as law enforcement data, may be extremely rich and varied, though sparse, with elements possessing many attributes and confidence values that may change over time.

Various techniques are appropriate for distinct problems. For example, heuristic, localized methods might be appropriate for matching known patterns to a network of financial transactions in a criminal investigation. Efficient global search strategies, on the other hand, might be best for finding centrality or severability in a telephone network.

Link analysis can be broken down into two components—link generation, and utilization of the resulting linkage graph.

Link Generation

Link generation is the process of computing the links, link attributes and node attributes. There are several different ways to define links. The different approaches yield very different linkage graphs. A key aspect in defining a link analysis is deciding which representation to use.

Explicit Links

A link may be created between the nodes corresponding to each pair of entities in a transaction. For example, with a call detail record, a link is created between the originating telephone number and the destination telephone number. This is referred to as an explicit link.

Aggregate Links

A single link may be created from multiple transactions. For example, a single link could represent all telephone calls between two parties, and a link attribute might be the number of calls represented. Thus, several explicit links may be collapsed into a single aggregate link.

Inferred Relationships

Links may also be created between pairs of nodes based on inferred strengths of relationships between them. These are sometimes referred to as soft links, association links, or co-occurrence links. Classes of algorithms for these computations include association rules, Bayesian belief networks and context vectors. For example, a link may be created between any pair of nodes whose context vectors lie within a certain radius of one another. Typically, one attribute of such a link is the strength of the relationship it represents. Time is a key feature that offers an opportunity to uncover linkages that might be missed by more typical data analysis approaches. For example, suppose a temporal analysis of wire transfer records indicates that a transfer from account A to person X at one bank is temporally proximate to a transfer from account B to person Y at another bank. This yields an inferred link between accounts A and B. If other aspects of the accounts or transactions are also suspicious, they may be flagged for additional scrutiny for possible money laundering activity.

A specific instance of inferred relationships is identifying two nodes that may actually correspond to the same physical entity, such as a person or an account. Link analysis includes mechanisms for collapsing these to a single node. Typically, the analyst creates rules or selects parameters specifying in which instances to merge nodes in this fashion.

Utilization

Once a linkage graph, including the link and node attributes, has been defined, it can be browsed, searched or used to create variables as inputs to a decision system.

Visualization

In visualizing linking graphs, each node is represented as an icon, and each link is represented as a line or an arrow between two nodes. The node and link attributes may be displayed next to the items or accessed via mouse actions. Different icon types represent different entity types. Similarly, link attributes determine the link representation (line strength, line color, arrowhead, etc.).

Standard graphs include spoke and wheel, peacock, group, hierarchy and mesh. An analytic component of the visualization is the automatic positioning of the nodes on the screen, i.e., the projection of the graph onto a plane. Different algorithms position the nodes based on the strength of the links between nodes or to agglomerate the nodes into groups of the same kind. Once displayed, the user typically has the ability to move nodes, modify node and link attributes, zoom in, collapse, highlight, hide or delete portions of the graph.

Variable Creation

Link analysis can append new fields to existing records or create entirely new data sets for subsequent modeling stages in a decision system. For example, a new variable for a customer might be the total number of email addresses and credit card numbers linked to that customer.

Search

Link analysis query mechanisms include retrieving nodes and links matching specified criteria, such as node and link attributes, as well as search by example to find more nodes that are similar to the specified example node.

A more complex task is similarity search, also called clustering. Here, the objective is to find groups of similar nodes. These may actually be multiple instances of the same physical entity, such as a single individual using multiple accounts in a similar fashion.

Network Analysis

Network analysis is the search for parts of the linkage graph that play particular roles. It is used to build more robust communication networks and to combat organized crime. This exploration revolves around questions such as:

  • Which nodes are key or central to the network?
  • Which links can be severed or strengthened to most effectively impede or enhance the operation of the network?
  • Can the existence of undetected links or nodes be inferred from the known data?
  • Are there similarities in the structure of subparts of the network that can indicate an underlying relationship (e.g., modus operandi)?
  • What are the relevant sub-networks within a much larger network?
  • What data model and level of aggregation best reveal certain types of links and sub-networks?
  • What types of structured groups of entities occur in the data set?

Applications

Link analysis tools such as those provided by Crime Tech Solutions are increasingly used in law enforcement investigations, detecting terrorist threats, fraud detection, detecting money laundering, telecommunications network analysis, classifying web pages, analyzing transportation routes, pharmaceuticals research, epidemiology, detecting nuclear proliferation and a host of other specialized applications. For example, in the case of money laundering, the entities might include people, bank accounts and businesses, and the transactions might include wire transfers, checks and cash deposits. Exploring relationships among these different objects helps expose networks of activity, both legal and illegal.

What is Geospatial Crime Mapping?

Geospatial4Posted by Crime Tech Solutions with information gathered from Wikipedia.

Here’s a fact: Any understanding of where and why crimes occur can help prevent future crimes.

Mapping crime can help law enforcement protect citizens more effectively. Simple maps that display the locations where crimes or concentrations of crimes have occurred can be used to help direct patrols to places they are most needed. Policymakers can use more complex maps to observe trends in criminal activity; such maps can prove invaluable in solving criminal cases. For example, detectives can use maps to better understand the hunting patterns of serial criminals and to hypothesize where these offenders might live.

Products like CrimeMap Pro™ from Crime Tech Solutions are used by analysts in law enforcement agencies to map, visualize, and analyze crime incident patterns. It is a key component of crime analysis and the CompStat policing strategy. Mapping crime, using Geographic Information Systems (GIS), allows crime analysts to identify crime hot spots, along with other trends and patterns.CrimeMapLite

Using GIS, crime analysts can overlay other datasets such as census demographics, locations of pawn shops, schools, etc., to better understand the underlying causes of crime and help law enforcement administrators to devise strategies to deal with the problem. GIS is also useful for law enforcement operations, such as allocating police officers and dispatching to emergencies.

Crime analysts use crime mapping and analysis to help law enforcement management (e.g. the police chief) to make better decisions, target resources, and formulate strategies, as well as for tactical analysis (e.g. crime forecasting, geographic profiling). New York City does this through the CompStat approach, though that way of thinking deals more with the short term. There are other, related approaches with terms including Information-led policing, Intelligence-led policing, Problem-oriented policing, and Community policing. In some law enforcement agencies, crime analysts work in civilian positions, while in other agencies, crime analysts are sworn officers.

From a research and policy perspective, crime mapping is used to understand patterns of incarceration and recidivism, help target resources and programs, evaluate crime prevention or crime reduction programs (e.g. Project Safe Neighborhoods, Weed & Seed and as proposed in Fixing Broken Windows), and further understanding of causes of crime.

The boom of internet technologies, particularly web-based geographic information system (GIS) technologies, is opening new opportunities for use of crime mapping to support crime prevention. Research indicates that the functions provided in web-based crime mapping are less than in most traditional crime mapping software. In conclusion, existing works of web-based crime mapping focus on supporting community policing rather than analytical functions such as pattern analysis and prediction.