Journal of Geo-information Science >
Theme-Oriented Visual Analysis of Crime with Big Data
Received date: 2014-02-18
Request revised date: 2014-04-03
Online published: 2014-09-04
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Information visual analysis is one of the key technologies in big data. The advent of “big data” era promotes the development of visualization techniques and also brings changes to the traditional crime analysis. The crime visualization could offer assistance to crime analysis in practice. However, they are separated in application. The primary challenge that crime visualization faces is how to analyze data features’ heterogeneity, scale, timeliness and complexity. This problem can be resolved by applying visual analysis, which allows users to explore data of different types and dimensions, and to obtain more valuable information with high correlation through interactions. Public security data, in the “big data” era, is characterized by multi-source and heterogeneity, and multi-dimension and long temporal series. Based on the characteristics of the data and criminal analysis theory, this article mainly focuses on the visual content, the representing method and the interactive design of visual crime analysis combined with geo-visualization and information visualization technologies, such as Wordle, Story line, parallel coordinate and scatter plot matrices, etc. A series of topic-oriented visual analyses were proposed in this study, including visual analyses based on spatio-temporal trajectory data of serial crime, real-time criminal data, spatio-temporal data of criminal process, criminal time-series statistical data, descriptive crime texts, criminal multidimensional attribute data, and crime-related statistical data. Supports from criminal cases investigation, trend prediction, hotspot analysis and references of visualizing studies from other fields were also offered and discussed in this article.
Key words: Key Words: big data; visual analysis; crime analysis
LI Daichao , WU Sheng . Theme-Oriented Visual Analysis of Crime with Big Data[J]. Journal of Geo-information Science, 2014 , 16(5) : 735 -745 . DOI: 10.3724/SP.J.1047.2014.00735
Fig.1 Example of trajectories collision analysis between serial crimes and suspects图1 系列案件与犯罪嫌疑人时空轨迹碰撞分析例图 |
Fig.2 Example of trajectories collision analysis between different suspects图2 不同犯罪嫌疑人时空轨迹碰撞分析例图 |
Fig.3 Example of estimation of suspect’s location图3 系列案件犯罪嫌疑人落脚点估计例图 |
Fig.4 Example of real-time crime analysis图4 案事件实时态势分析例图 |
Fig.5 Example of serial crime analysis图5 系列案件发展过程分析例图 |
Fig.6 Example of case investigation图6 案件侦查分析例图 |
Fig.7 Example of criminal statistical indicators compared with the values from last year图7 犯罪数量指标与历史同期值对比分析例图 |
Fig.8 Example of criminal statistical indicators compared with other indicators图8 犯罪数量指标与其制约因素或警力绩效数量指标对比分析例图 |
Fig.9 Example of time pattern analysis of criminal statistical indicators图9 犯罪数量指标时间模式分析例图 |
Fig.10 Example of the highlighted view of a chosen date图10 选中某一天后的日历聚类图 |
Fig.11 Example of descripive criminal texts analysis图11 犯罪案情文本数据分析例图 |
Fig.12 Example of criminal multidimensional attribute data analysis图12 犯罪多维属性数据分析例图 |
Fig.13 Example of regional statistical indicators analysis图13 区域统计指标的分布情况对比的例图 |
Fig.14 Example of parallel coordinates with histograms embedded图14 嵌入直方图后的平行坐标图 |
Fig.15 Example of criminal spatial auto correlation analysis图15 犯罪空间自相关性分析例图 |
Fig.16 Example of criminal influence factor analysis图16 犯罪影响因素的分析例图 |
Fig.17 Example of the detailed view of clicking a certain scatter diagram图17 通过交互查看某一散点图大图 |
The authors have declared that no competing interests exist.
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