Table 1 - uploaded by Jagath Gunatilake
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Source publication
The manual crime recording and investigation systems in police stations all around the world are generating piles of crime documents which make storage and retrieval of reliable crime information extremely difficult as well as inefficient. Furthermore, investigators of central authorities have to manually search through these documents and communic...
Contexts in source publication
Context 1
... 76 2014 grave crimes were classified under 21 categories and in 2015 another 5 new crime 77 categories were introduced by making it 26 categories of grave crime types. Table 1 shows the 78 26 crime types and their corresponding Penal codes/ Sections. 117 Literature shows several systems implemented to achieve the task of crime data integration 118 and investigation. ...
Citations
... Sri Lanka Police developed the Crime Investigation Decision Support System (CIDSS, Chamikara et al., 2015), a web based intelligent crime analysis system. Various processes were incorporated into the system, for instance a crime clock and periodic pattern visualizer (temporal aspects of crimes), crime map and hotspot visualizer (based on geographic information system), and crime comparator and modus operandi analysis (fuzzy matching of links, Chamikara et al., 2016). ...
A review was undertaken of the developments made with integrating forensic evidence into the analytical process to support police investigations. Evidence such as DNA, fingerprints, fibers, accelerants, tyre marks, and so forth, can support to differing degrees the various working theories or hypotheses about the nature of the alleged crime, the persons of interest and the modus operandi. Investigators however, either forensic or detective, bring various biases to evidence capture and analysis, biases which are better understood in the intelligence community. Structured analytical techniques have a long history in intelligence analysis, for example analysis of competing hypotheses, which serves several purposes: information sharing, clarity of communication, and to highlight the common forms of bias brought to bear in an investigation. We illustrate the representation of links based on traces and intelligence, and how these can be stored in databases permitting better “reasoning” with evidence. We also present some recommendations for integration of forensic intelligence into the investigative analytic process and review information systems in this area.
This article is categorized under:
• Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction
• Application Areas > Society and Culture
• Fundamental Concepts of Data and Knowledge > Knowledge Representation
... As such, the causes of this higher statics in Chicago City still not known entirely. Also, there is no united consensus on the cause of these violent crimes (Chamikara et al., 2015). These facts of Chicago crime rate make it an excellent example to explore what we proposed in our study, as shown in Figure 1. ...
... As such, the causes of this higher statics in Chicago City still not known entirely. Also, there is no united consensus on the cause of these violent crimes (Chamikara et al., 2015). These facts of Chicago crime rate make it an excellent example to explore what we proposed in our study, as shown in Figure 1. ...
This study aims to: 1) to explore the benefits of adding a spatial GIS layer of analysis to other existing visualisation techniques; 2) to identify and evaluate the patterns in selected crime data by analysing Chicago's open dataset; 3) provide a better understanding of patterns and prediction of crime trends within the selected geographical location. We conclude that Chicago seems to be on course to have both the lowest violent crime rate since 1972, and the lowest murder frequency since 1967. Chicago has witnessed a vigorous drop in most crimes types over the last few years in compares to the previous crime index data. Also, Chicago crime naturally upsurges during summer months and declines during winter months. Our study results align with previous several decades of studies and analysis of Chicago crimes, in which the same communities of highest crime rates still experience the mainstream of crime.
Keywords: spatial distribution, geographic information system, GIS, crime analysis, visualisation tools, GIS techniques, data visualisation, crime mapping
This article examines the impact of new AI‐related technologies in data mining and big data on important research questions in crime analytics. Because the field is so broad, the review focuses on a selection of the most important topics. Challenges for information management, and in turn law and society, include: AI‐powered predictive policing; big data for legal and adversarial decisions; bias using big data and analytics in profiling and predicting criminality; forecasting crime risk and crime rates; and, regulating AI systems.
This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining
Fundamental Concepts of Data and Knowledge > Big Data Mining
Technologies > Artificial Intelligence
Application Areas > Data Mining Software Tools
It is a well-known fact that some criminals follow perpetual methods of operations
known as modi operandi. Modus operandi is a commonly used term to describe
the habits in committing crimes. These modi operandi are used in relating criminals
to crimes for which the suspects have not yet been recognized. This paper presents
the design, implementation and evaluation of a new method to find connections
between crimes and criminals using modi operandi. The method involves generating a
feature matrix for a particular criminal based on the flow of events of his/her previous
convictions. Then, based on the feature matrix, two representative modi operandi
are generated: complete modus operandi and dynamic modus operandi. These two
representative modi operandi are compared with the flow of events of the crime at hand,
in order to generate two other outputs: completeness probability (CP) and deviation
probability (DP). CP and DP are used as inputs to a fuzzy inference system to generate a
score which is used in providing a measurement for the similarity between the suspect
and the crime at hand. The method was evaluated using actual crime data and ten
other open data sets. In addition, comparison with nine other classification algorithms
showed that the proposed method performs competitively with other related methods
proving that the performance of the new method is at an acceptable level.
