A Framework for Visual Representation of Crime Information

Shah, Samiullah; Khalique, Vijdan; Saddar, Salahuddin; Mahoto, Naeem Ahmed

doi:10.17485/ijst/2017/v10i40/120079

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…On the contrary, the scientific research literature also reports several proposed methods to detect meaningful information from variety of data, such as text from video [40], clickstream data [37] relational database of criminal activities [38].…”

Section: The Building Blocks Of Proposed Frameworkmentioning

confidence: 99%

Bringing Shape to Textual Data – A Feasible Demonstration

Shaikh

Mahoto

Unar

2019

Mehran Univ. res. j. eng. technol.

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The Internet has revolutionized the communication paradigm. This has led towards immense amount of unstructured data (i.e. textual data), which is a major source to get useful knowledge about people in several application domains. TM (Text Mining) extracts high quality information to discover knowledge by drawing patterns and relationships in textual data. This field has taken great attention of the research community. As a result, several attempts have been made to propose, introduce and refine techniques applied for uncovering knowledge from text data. This study aims at: (1) presenting existing TM techniques in the scientific literature, (2) reporting challenges/issues and gaps that still need attention, and (3) proposing a framework to bring shape to textual data. A prototype has been developed to demonstrate the effectiveness and potential worth of proposed approach to display how unstructured data (i.e. news articles in this study) has been brought to a shape representing interesting knowledge. The proposed framework implements basic NLP (Natural Language Processing) functions in combination of AYLIEN API (Application Programming Interface) functions. The results reveal the fact that how events, celebrities and popular news-items have been covered in the electronic media, and it also represents subjectivity of topical news events. The news coverage trends highlight the significance of daily news events, which may assist in getting insight about the media groups.

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Section: The Building Blocks Of Proposed Frameworkmentioning

confidence: 99%

Bringing Shape to Textual Data – A Feasible Demonstration

Shaikh

Mahoto

Unar

2019

Mehran Univ. res. j. eng. technol.

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“…Police officers are in general responsible for maintaining law and order situation. Crime analysis is the activity of aggregating all the crime reports to generate a single comprehensive report of all crimes, which helps in decision-making and controlling crimes [4]. Visualizing this data in an immersive environment can help in better understanding the trends and predictive modelling of the gathered information.…”

Section: Introductionmentioning

confidence: 99%

Data visualization of crime data using immersive virtual reality

Aishwarya¹,

Sharad²

2022

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Visualization explores quantitative content of data with human intuition and plays an integral part in the data mining process. When the data is big there are different analysis methods and approaches used to find inherent patterns and relationships. However, sometimes there is a need to incorporate human in loop approach to find new patterns and relationships. Immersive virtual reality (VR) offers a human centric approach to visualize data by discovering new relationships that an existing algorithm cannot provide. This paper demonstrates the data visualization tool to visualize Baltimore crime data in immersive environment and non-immersive environment. This paper focuses on VR visualization tool design, development, and usability assessment. A pilot user study was conducted for the VR visualization tool based on the system usability scale. The study results show that the crime data visualization tool is relatively easy to use, and that the application can be considered as a storyteller with removing the noise from the data and highlighting the usefulness of the information.

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“…Furthermore, the trending topics are statistically represented as knowledge to end-users. The information visualization has been considered an emerging field for the several application domains; for instance, structural information by injecting parameters of location has been represented in visual formats [7]. The paper is organized as follows: Related studies are discussed in section 2; the working principle of extracting news coverage patterns is reported in section 3; the outcomes of the study are described in the discussion section 4; finally, section 5 presents conclusions.…”

Section: Introductionmentioning

confidence: 99%

Estimating News Coverage Patterns using Latent Dirichlet Allocation (LDA)

Mahoto

2018

SJET

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The growing rate of unstructured textual data has made an open challenge for the knowledge discovery, which aims extracting desired information from large collection of data. This study presents a system to derive news coverage patterns with the help of probabilistic model – Latent Dirichlet Allocation. Pattern is an arrangement of words within collected data that more likely appear together in certain context. The news coverage patterns have been computed as number function of news articles comprising of such patterns. A prototype, as a proof, has been developed to estimate the news coverage patterns for a newspaper – The Dawn. Analyzing the news coverage patterns from different aspects has been carried out using multidimensional data model. Further, the extracted news coverage patterns are illustrated by visual graphs to yield in-depth understanding of the topics, which have been covered in the news. The results also assist in identification of schema related to newspaper and journalists’ articles.

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A Framework for Visual Representation of Crime Information

Cited by 4 publications

References 10 publications

Bringing Shape to Textual Data – A Feasible Demonstration

Bringing Shape to Textual Data – A Feasible Demonstration

Data visualization of crime data using immersive virtual reality

Estimating News Coverage Patterns using Latent Dirichlet Allocation (LDA)

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