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Socio-Spatial Intelligence: social media and spatial cognition for territorial behavioral analysis

机译:社会空间情报:社交媒体和空间认知,用于领土行为分析

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Investigative analysts gather data from different sources, especially from social media (SM), in order to shed light on cognitive factors that may explain criminal spatial behavior. A former research shows how tweets can be used to estimate private points of interest. Authors' aim was to demonstrate, as they extend the analysis to a wider statistical base, how social maps and Web applications could be used in investigative analysis and spatial cognition research. A total of 100 Twitter accounts with approximately 250 tweets each were submitted to common geographical techniques (measures such as Convex-Hull, Mean-Center, Median-Center, Standard-Deviation-Ellipse) in order to test the hypothesis that user areas of activity are predictable. Predictions were tested through a set of specific information: clear reference to areas of activity and clear reference about user's residence. Simple algorithms and procedures demonstrated that they could be used to predict where SM users live, giving positive results in about 4/5 cases and giving indications about their home location. In fact, all home positions were found in the Convex-Hull and most of them in the Standard-Deviation-Ellipse. Furthermore, in up to 80 % of cases, houses were found within a buffer zone of 1.500 m with Median-Center as centrum (70 % using Median-Center as centrum) with a minimum effectiveness threshold of 12-13 tweets. SM may help in studying people mobility and their cognition of space and, moreover, where they live, or their traveling behavior. The processing of geographical data in conjunction with the SM analysis may facilitate the construction of models describing specific behavior of people. The use of geographical information system tools and SM analysis represents an effective approach in order to acquire spatial and territorial information, referred to social relationship. The results may be used successfully in the understanding of social dynamics and for the prevention of criminal behavior.
机译:调查分析人员从不同来源收集数据,尤其是从社交媒体(SM)收集数据,以阐明可能解释犯罪空间行为的认知因素。先前的研究表明,如何使用推文来估计私人兴趣点。作者的目的是演示如何将分析扩展到更广泛的统计基础,以及如何将社会地图和Web应用程序用于调查分析和空间认知研究。为了检验用户活动区域的假设,总共向100个Twitter帐户提交了约250条推文,每个帐户都使用了通用地理技术(例如,凸包,均值中心,中位数中心,标准偏差椭圆)。是可以预见的。通过一组特定的信息对预测进行了测试:明确提及活动区域以及明确提及用户居住地。简单的算法和过程表明,它们可用于预测SM用户的住所,在大约4/5的情况下给出肯定的结果,并给出有关其家庭位置的指示。实际上,所有原始位置都在凸包中找到,并且大多数在标准偏差椭圆中找到。此外,在多达80%的情况下,发现房屋位于1.500 m的缓冲区内,中间居中为中心(70%使用中间居中为中心),最小有效阈值为12-13条鸣叫。 SM可能有助于研究人们的流动性及其对空间的认知,此外还可以研究人们的居住地或出行行为。结合SM分析对地理数据进行处理可能有助于构建描述人们特定行为的模型。地理信息系统工具和SM分析的使用代表了一种有效的方法,可以获取称为社会关系的空间和领土信息。该结果可成功用于理解社会动态和预防犯罪行为。

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