No Arabic abstract
We examine crime patterns in Santa Monica, California before and after passage of Proposition 47, a 2014 initiative that reclassified some non-violent felonies to misdemeanors. We also study how the 2016 opening of four new light rail stations, and how more community-based policing starting in late 2018, impacted crime. A series of statistical analyses are performed on reclassified (larceny, fraud, possession of narcotics, forgery, receiving/possessing stolen property) and non-reclassified crimes by probing publicly available databases from 2006 to 2019. We compare data before and after passage of Proposition 47, city-wide and within eight neighborhoods. Similar analyses are conducted within a 450 meter radius of the new transit stations. Reports of monthly reclassified crimes increased city-wide by approximately 15% after enactment of Proposition 47, with a significant drop observed in late 2018. Downtown exhibited the largest overall surge. The reported incidence of larceny intensified throughout the city. Two new train stations, including Downtown, reported significant crime increases in their vicinity after service began. While the number of reported reclassified crimes increased after passage of Proposition 47, those not affected by the new law decreased or stayed constant, suggesting that Proposition 47 strongly impacted crime in Santa Monica. Reported crimes decreased in late 2018 concurrent with the adoption of new policing measures that enhanced outreach and patrolling. These findings may be relevant to law enforcement and policy-makers. Follow-up studies needed to confirm long-term trends may be affected by the COVID-19 pandemic that drastically changed societal conditions.
Risk-limiting post-election audits limit the chance of certifying an electoral outcome if the outcome is not what a full hand count would show. Building on previous work, we report on pilot risk-limiting audits in four elections during 2008 in three California counties: one during the February 2008 Primary Election in Marin County and three during the November 2008 General Elections in Marin, Santa Cruz and Yolo Counties. We explain what makes an audit risk-limiting and how existing and proposed laws fall short. We discuss the differences among our four pilot audits. We identify challenges to practical, efficient risk-limiting audits and conclude that current approaches are too complex to be used routinely on a large scale. One important logistical bottleneck is the difficulty of exporting data from commercial election management systems in a format amenable to audit calculations. Finally, we propose a bare-bones risk-limiting audit that is less efficient than these pilot audits, but avoids many practical problems.
The $beta^-$ decay of $^{47}$K to $^{47}$Ca is an appropriate mechanism for benchmarking interactions spanning the $sd$ and $pf$ shells, but current knowledge of the $beta^-$-decay scheme is limited. We have performed a high-resolution, high-efficiency study of the $beta^-$-decay of $^{47}$K with the GRIFFIN spectrometer at TRIUMF-ISAC. The study revealed 48 new transitions, a more precise value for the $^{47}$K half-life (17.38(3)~s), and new spin and parity assignments for eight excited states. Levels placed for the first time here raise the highest state observed in $beta^-$ decay to within 568(3) keV of the $Q$-value and confirm the previously measured large $beta^-$-decay branching ratios to the low-lying states. Previously unobserved $beta^-$-feeding to 3/2$^+$ states between 4.5 and 6.1~MeV excitation energy was identified with a total $beta^-$-feeding intensity of 1.29(2)%. The sum of the $B(GT)$ values for these states indicates that the $1s_{1/2}$ proton hole strength near this excitation energy is comparable to the previously known $1s_{1/2}$ proton and neutron hole strengths near 2.6 MeV.
In estimating the causal effect of a continuous exposure or treatment, it is important to control for all confounding factors. However, most existing methods require parametric specification for how control variables influence the outcome or generalized propensity score, and inference on treatment effects is usually sensitive to this choice. Additionally, it is often the goal to estimate how the treatment effect varies across observed units. To address this gap, we propose a semiparametric model using Bayesian tree ensembles for estimating the causal effect of a continuous treatment of exposure which (i) does not require a priori parametric specification of the influence of control variables, and (ii) allows for identification of effect modification by pre-specified moderators. The main parametric assumption we make is that the effect of the exposure on the outcome is linear, with the steepness of this relationship determined by a nonparametric function of the moderators, and we provide heuristics to diagnose the validity of this assumption. We apply our methods to revisit a 2001 study of how abortion rates affect incidence of crime.
We conduct the largest ever investigation into the relationship between meteorological conditions and the sentiment of human expressions. To do this, we employ over three and a half billion social media posts from tens of millions of individuals from both Facebook and Twitter between 2009 and 2016. We find that cold temperatures, hot temperatures, precipitation, narrower daily temperature ranges, humidity, and cloud cover are all associated with worsened expressions of sentiment, even when excluding weather-related posts. We compare the magnitude of our estimates with the effect sizes associated with notable historical events occurring within our data.
Crime prediction plays an impactful role in enhancing public security and sustainable development of urban. With recent advances in data collection and integration technologies, a large amount of urban data with rich crime-related information and fine-grained spatio-temporal logs has been recorded. Such helpful information can boost our understandings about the temporal evolution and spatial factors of urban crimes and can enhance accurate crime prediction. In this paper, we perform crime prediction exploiting the cross-type and spatio-temporal correlations of urban crimes. In particular, we verify the existence of correlations among different types of crime from temporal and spatial perspectives, and propose a coherent framework to mathematically model these correlations for crime prediction. The extensive experimental results on real-world data validate the effectiveness of the proposed framework. Further experiments have been conducted to understand the importance of different correlations in crime prediction.