Could social media reflect acquisitive crime patterns in London?

Embraced within the framework of crime opportunities integrated with Social Disorganization theory and Broken Windows theory,this paper intends to explore the patterns of four types of acquisitive crimes,using social media data,i.e.,Twitter,Foursquare and cross-sectional data acquired through text analysis technique.With Greater London as the study area,models like negative binominal regression(NBR)and geographically weighted regression(GWR)are performed to illustrate the aggregated relationships between acquisitive crimes and crime opportunities at London-wide and sub-regional MSOAs levels respectively.The results work towards to hypotheses that:the tweets sentiment could reflect property-related crime rates positively in light of Broken Windows Theory;more tweets with negative sentiment may incur increases in acquisitive crimes.It contributed to existing studies in(1)providing empirical evidence for integrating these three theories;(2)complementing current research on local discrepancies of acquisitive crimes by utilising both GWR and NBR models;(3)challenging the traditional stereotypes about racial disparities with the finding that ethnic heterogeneity and instrumental crimes have counterintuitive association,especially taking education factor into consideration;(4)implicating some localised acquisitive crime prevention strategies to policy makers in light of the reality that the relationship between local variations and different crime types may vary by place.

Visualizing ordered bivariate data on node-link diagrams

Node-link visual representation is a widely used tool that allows decision-makers to see details about a network through the appropriate choice of visual metaphor.However,existing visualization methods are not always effective and efficient in representing bivariate graph-based data.This study proposes a novel node-link visual model–visual entropy(Vizent)graph–to effectively represent both primary and secondary values,such as uncertainty,on the edges simultaneously.We performed two user studies to demonstrate the efficiency and effectiveness of our approach in the context of static nodelink diagrams.In the first experiment,we evaluated the performance of the Vizent design to determine if it performed equally well or better than existing alternatives in terms of response time and accuracy.Three static visual encodings that use two visual cues were selected from the literature for comparison:Width-Lightness,Saturation-Transparency,and Numerical values.We compared the Vizent design to the selected visual encodings on various graphs ranging in complexity from 5 to 25 edges for three different tasks.The participants achieved higher accuracy of their responses using Vizent and Numerical values;however,both Width-Lightness and Saturation-Transparency did not show equal performance for all tasks.Our results suggest that increasing graph size has no impact on Vizent in terms of response time and accuracy.The performance of the Vizent graph was then compared to the Numerical values visualization.The Wilcoxon signed-rank test revealed that mean response time in seconds was significantly less when the Vizent graphs were presented,while no significant difference in accuracy was found.The results from the experiments are encouraging and we believe justify using the Vizent graph as a good alternative to traditional methods for representing bivariate data in the context of node-link diagrams.