Making machine intelligence less scary for criminal analysts: reflections on designing a visual comparative case analysis tool

Jentner, Wolfgang; Sacha, Dominik; Stoffel, Florian; Ellis, Geoffrey; Zhang, Leishi; Keim, Daniel A.

doi:10.1007/s00371-018-1483-0

Cited by 15 publications

(11 citation statements)

References 31 publications

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“…Currently, visualization assisted clustering techniques only allow users to control the results by changing technique parameters. 53–56 Enabling users to guide the input feature configuration renders a much more flexible control, since users can explicitly steer the semantics of the input data and the similarity relationships (e.g. images are similar due to the color vs images are similar due to the presence of objects).…”

Section: Resultsmentioning

confidence: 99%

Visual feature fusion and its application to support unsupervised clustering tasks

Hilasaca

Paulovich

2019

Information Visualization

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On visual analytics applications, the concept of putting the user on the loop refers to the ability to replace heuristics by user knowledge on machine learning and data mining tasks. On supervised tasks, the user engagement occurs via the manipulation of the training data. However, on unsupervised tasks, the user involvement is limited to changes in the algorithm parametrization or the input data representation, also known as features. Depending on the application domain, different types of features can be extracted from the raw data. Therefore, the result of unsupervised algorithms heavily depends on the type of employed feature. Since there is no perfect feature extractor, combining different features have been explored in a process called feature fusion. The feature fusion is straightforward when the machine learning or data mining task has a cost function. However, when such a function does not exist, user support for combination needs to be provided otherwise the process is impractical. In this paper, we present a novel feature fusion approach that uses small data samples to allows users not only to effortless control the combination of different feature sets but also to interpret the attained results. The effectiveness of our approach is confirmed by a comprehensive set of qualitative and quantitative tests, opening up different possibilities of user-guided analytical scenarios not covered yet. The ability of our approach to providing real-time feedback for the feature fusion is exploited on the context of unsupervised clustering techniques, where the composed groups reflect the semantics of the feature combination.

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Section: Resultsmentioning

confidence: 99%

Visual feature fusion and its application to support unsupervised clustering tasks

Hilasaca

Paulovich

2019

Information Visualization

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“…In the research field of visual analytics, this is also known as the human being, the ultimate decision-maker. In a criminal investigation, interactions with a tool relevant for decision-making must be tracked and presented at court in combination with the findings [ 38 ]. We envision our approach and tool as an alternative view for 4D scenes.…”

Section: Discussionmentioning

confidence: 99%

“…Sacha et al introduced a tool for the interactive analysis of spatio-temporal metadata of crime reports using abstract data visualizations such as correlation matrices and scatterplots [ 37 ]. Similarly, Jentner et al analyzed crime reports, but focused on the analysis of patterns to provide insights on a large bulk of data and to find clusters of similar crimes [ 38 ].…”

Section: Related Workmentioning

confidence: 99%

Toward Mass Video Data Analysis: Interactive and Immersive 4D Scene Reconstruction

Kraus

Pollok

Miller

et al. 2020

Sensors

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The technical progress in the last decades makes photo and video recording devices omnipresent. This change has a significant impact, among others, on police work. It is no longer unusual that a myriad of digital data accumulates after a criminal act, which must be reviewed by criminal investigators to collect evidence or solve the crime. This paper presents the VICTORIA Interactive 4D Scene Reconstruction and Analysis Framework (“ISRA-4D” 1.0), an approach for the visual consolidation of heterogeneous video and image data in a 3D reconstruction of the corresponding environment. First, by reconstructing the environment in which the materials were created, a shared spatial context of all available materials is established. Second, all footage is spatially and temporally registered within this 3D reconstruction. Third, a visualization of the hereby created 4D reconstruction (3D scene + time) is provided, which can be analyzed interactively. Additional information on video and image content is also extracted and displayed and can be analyzed with supporting visualizations. The presented approach facilitates the process of filtering, annotating, analyzing, and getting an overview of large amounts of multimedia material. The framework is evaluated using four case studies which demonstrate its broad applicability. Furthermore, the framework allows the user to immerse themselves in the analysis by entering the scenario in virtual reality. This feature is qualitatively evaluated by means of interviews of criminal investigators and outlines potential benefits such as improved spatial understanding and the initiation of new fields of application.

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“…Some studies can serve this purpose, since their approach include one or several characteristics. In this regard, these studies include: Investigations with methodology that include georeferencing and spatial and statistical analysis to find critical points such as [5,[24][25][26][27][28] or; visualizations with predictive usefulness, such as [29][30][31][32][33]; simulations of criminal behavior aimed at analyzing collateral effects and resources allocations [34][35][36]; systems that use algorithms for trajectory analysis to identify crimes from different sources of information [37,38]; mechanisms for calculating severity of incidents determined by distance, time and type of transition, plotted on heatmaps with dynamic transition, to extract information on street crimes [39,40]; systems for merging, associating and clustering data attributes, as well as for associating with other nodes to extract patterns from evolving criminal networks, based on observation and analysis of temporary data [41][42][43]; and finally, visualizations that use comparisons to find similarities in criminal patterns through associative searches with knowledge graphs [44] and multivariate reduction plots for comparative analysis of cases [45].…”

Section: Work Related To Criminal Activity Visualization Methodsmentioning

confidence: 99%

A Novel Method of Spatiotemporal Dynamic Geo-Visualization of Criminal Data, Applied to Command and Control Centers for Public Safety

Salcedo-Gonzalez

Suarez-Paez

Esteve

et al. 2020

IJGI

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This article shows a novel geo-visualization method of dynamic spatiotemporal data that allows mobility and concentration of criminal activity to be study. The method was developed using, only and significantly, real data of Santiago de Cali (Colombia), collected by the Colombian National Police (PONAL). This method constitutes a tool that allows criminal influx to be analyzed by concentration, zone, time slot and date. In addition to the field experience of police commanders, it allows patterns of criminal activity to be detected, thereby enabling a better distribution and management of police resources allocated to crime deterrence, prevention and control. Additionally, it may be applied to the concepts of safe city and smart city of the PONAL within the architecture of Command and Control System (C2S) of Command and Control Centers for Public Safety. Furthermore, it contributes to a better situational awareness and improves the future projection, agility, efficiency and decision-making processes of police officers, which are all essential for fulfillment of police missions against crime. Finally, this was developed using an open source software, it can be adapted to any other city, be used with real-time data and be implemented, if necessary, with the geographic software of any other C2S.

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Making machine intelligence less scary for criminal analysts: reflections on designing a visual comparative case analysis tool

Cited by 15 publications

References 31 publications

Visual feature fusion and its application to support unsupervised clustering tasks

Visual feature fusion and its application to support unsupervised clustering tasks

Toward Mass Video Data Analysis: Interactive and Immersive 4D Scene Reconstruction

A Novel Method of Spatiotemporal Dynamic Geo-Visualization of Criminal Data, Applied to Command and Control Centers for Public Safety

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