Non-Stationary Model for Crime Rate Inference Using Modern Urban Data.

Crime is one of the most important social problems in the country, affecting public safety, children development, and adult socioeconomic status. Understanding what factors cause higher crime rate is critical for policy makers in their efforts to reduce crime and increase citizens' life quality. We tackle a fundamental problem in our paper: crime rate inference at the neighborhood level. Traditional approaches have used demographics and geographical influences to estimate crime rates in a region. With the fast development of positioning technology and prevalence of mobile devices, a large amount of modern urban data have been collected and such big data can provide new perspectives for understanding crime. In this paper, we use large-scale Point-Of-Interest data and taxi flow data in the city of Chicago, IL in the USA. We observe significantly improved performance in crime rate inference compared to using traditional features. Such an improvement is consistent over multiple years. We also show that these new features are significant in the feature importance analysis. The correlations between crime and various observed features are not constant over the whole city. In order to address this geospatial non-stationary property, we further employ the geographically weighted regression on top of negative binomial model (GWNBR). Experiments have shown that GWNBR outperforms the negative binomial model.

Orderliness predicts academic performance: behavioural analysis on campus lifestyle.

Quantitative understanding of relationships between students' behavioural patterns and academic performances is a significant step towards personalized education. In contrast to previous studies that were mainly based on questionnaire surveys, recent literature suggests that unobtrusive digital data bring us unprecedented opportunities to study students' lifestyles in the campus. In this paper, we collect behavioural records from undergraduate students' (N = 18 960) smart cards and propose two high-level behavioural characters, orderliness and diligence. The former is a novel entropy-based metric that measures the regularity of campus daily life, which is estimated here based on temporal records of taking showers and having meals. Empirical analyses on such large-scale unobtrusive behavioural data demonstrate that academic performance (GPA) is significantly correlated with orderliness. Furthermore, we show that orderliness is an important feature to predict academic performance, which improves the prediction accuracy even in the presence of students' diligence. Based on these analyses, education administrators could quantitatively understand the major factors leading to excellent or poor performance, detect undesirable abnormal behaviours in time and thus implement effective interventions to better guide students' campus lives at an early stage when necessary.