Illicit drug trafficking via social media sites such as Instagram have become a severe problem, thus drawing a great deal of attention from law enforcement and public health agencies. How to identify illicit drug dealers from social media data has remained a technical challenge for the following reasons. On the one hand, the available data are limited because of privacy concerns with crawling social media sites; on the other hand, the diversity of drug dealing patterns makes it difficult to reliably distinguish drug dealers from common drug users. Unlike existing methods that focus on posting-based detection, we propose to tackle the problem of illicit drug dealer identification by constructing a large-scale multimodal dataset named Identifying Drug Dealers on Instagram (IDDIG). Nearly 4,000 user accounts, of which more than 1,400 are drug dealers, have been collected from Instagram with multiple data sources including post comments, post images, homepage bio, and homepage images. We then design a quadruple-based multimodal fusion method to combine the multiple data sources associated with each user account for drug dealer identification. Experimental results on the constructed IDDIG dataset demonstrate the effectiveness of the proposed method in identifying drug dealers (almost 95% accuracy). Moreover, we have developed a hashtag-based community detection technique for discovering evolving patterns, especially those related to geography and drug types.

The smart city has become a popular topic of investigation. How to focus large amounts of distributed information resources to efficiently cope with public emergencies and provide support for personalized decision-making is a vitally important issue in the construction of smart cities. In this paper, an event-driven focusing service (EDFS) method that uses cyber-physical infrastructures for emergency response in smart cities is proposed. The method consists of a focusing service model at the top level, an informational representation of the model and a focusing service process to operate the service model in emergency response. The focusing service method follows an event-driven mechanism that allows the focusing service process to be triggered by public emergencies sensed by wireless sensor networks (WSNs) and mobile crowd sensing, and it integrates the requirements of different societal entities with regard to response to emergencies and information resources, thereby providing comprehensive and personalized support for decision-making. Furthermore, an EDFS prototype system is designed and implemented based on the proposed method. An experiment using a real-world scenario—the gas leakage in August 2014 in Taiyuan, China—is presented demonstrating the feasibility of the proposed method for assisting various societal entities in coping with and efficiently responding to public emergencies.

Hydraulic fracturing or fracking has led to a rapid growth of oil and gas production in the United States, but the impact of fracking on public health is an important but underresearched topic. We designed a methodology to study spatiotemporal correlations between the risk of fracking and stroke mortality. An annualized loss expectancy (ALE) model is applied to quantify the risk of fracking. The geographically and temporally weighted regression (GTWR) model is used to analyze spatiotemporal correlations of stroke mortality, fracking ALE, and nine other socioeconomic- and health-related factors. The analysis shows that fracking ALE is moderately correlated with stroke mortality at ages over 65 in most states of fracking, in addition to cardiovascular disease and drug overdose being positively correlated with stroke mortality. Furthermore, the correlations between fracking ALE and stroke mortality in men appear to be higher than in women near the Marcellus Shale, including Ohio, Pennsylvania, West Virginia, and Virginia, while stroke mortality among women is concentrated in the Great Plains, including Montana, Wyoming, New Mexico, and Oklahoma. Lastly, within two kilometers of the fracking mining activity, the level of benzene in the air was found to be significantly correlated with the fracking activity in Colorado.

Immediate risk assessment and analysis are crucial in managing urban hazard events (UHEs). However, it is a challenge to develop an immediate risk assessment process (RAP) that can integrate distributed sensors and data to determine the uncertain model parameters of facilities, environments, and populations. To solve this problem, this paper proposes a RAP modeling method within a unified spatio-temporal framework and forms a 10-tuple process information description structure based on a Meta-Object Facility (MOF). A RAP is designed as an abstract RAP chain that collects urban information resources and performs immediate risk assessments. In addition, we propose a prototype system known as Risk Assessment Process Management (RAPM) to achieve the functions of RAP modeling, management, execution and visualization. An urban gas leakage event is simulated as an example in which individual risk and social risk are used to illustrate the applicability of the RAP modeling method based on the 10-tuple metadata framework. The experimental results show that the proposed RAP immediately assesses risk by the aggregation of urban sensors, data, and model resources. Moreover, an extension mechanism is introduced in the spatio-temporal RAP modeling method to assess risk and to provide decision-making support for different UHEs.