A human mobility dataset collected via LBSLab

Location-Based Services (LBS) have been prosperous owing to technological advancements of smart devices. Analyzing location-based user-generated data is a helpful way to understand human mobility patterns, further fueling applications such as recommender systems and urban computing. This dataset documents user activities of location-based services through LBSLab, a smartphone-based system implemented as a mini-program in the WeChat app. The dataset contains activity data of multiple types including logins, profile viewing, weather checking, and check-ins with location information (latitude and longitude), POI and mood indicated, collected from 467 users over a period of 11 days. We also present some temporal and spatial data analysis and believe the reuse of the data will allow researchers to better understand user behaviors of LBS, human mobility, and also temporal and spatial characteristics of people's moods.

Estimating dynamic population served by wastewater treatment plants using location-based services data

Wastewater-based epidemiology is a useful approach to estimate population-level exposure to a wide range of substances (e.g., drugs, chemicals, biological agents) by wastewater analysis. An important uncertainty in population normalized loads generated is related to the size and variability of the actual population served by wastewater treatment plants (WWTPs). Here, we built a population model using location-based services (LBS) data to estimate dynamic consumption of illicit drugs. First, the LBS data from Tencent Location Big Data and resident population were used to train a linear population model for estimating population (r² = 0.92). Then, the spatiotemporal accuracy of the population model was validated. In terms of temporal accuracy, we compared the model-based population with the time-aligned ammonia nitrogen (NH₄-N) population within the WWTP of SEG, showing a mean squared error of < 10%. In terms of spatial accuracy, we estimated the model-based population of 42 WWTPs in Dalian and compared it with the NH₄-N and design population, indicating good consistency overall (5% less than NH₄-N and 4% less than design). Furthermore, methamphetamine consumption and prevalence based on the model were calculated with an average of 111 mg/day/1000 inhabitants and 0.24%, respectively, and dynamically displayed on a visualization system for real-time monitoring. Our study provided a dynamic and accurate population for estimating the population-level use of illicit drugs, much improving the temporal and spatial trend analysis of drug use. Furthermore, accurate information on drug use could be used to assess population health risks in a community.

A smartphone application for dispatch of lay responders to out-of-hospital cardiac arrests

BACKGROUND

Dispatch of lay volunteers trained in cardiopulmonary resuscitation (CPR) and equipped with automated external defibrillators (AEDs) may improve survival in cases of out-of-hospital cardiac arrest (OHCA). The aim of this study was to investigate the functionality and performance of a smartphone application for locating and alerting nearby trained laymen/women in cases of OHCA.

METHODS

A system using a smartphone application activated by Emergency Dispatch Centres was used to locate and alert laymen/women to nearby suspected OHCAs. Lay responders were instructed either to perform CPR or collect a nearby AED. An online survey was carried out among the responders.

RESULTS

From February to August 2016, the system was activated in 685 cases of suspected OHCA. Among these, 224 cases were Emergency Medical Services (EMSs)-treated OHCAs (33%). EMS-witnessed cases (n = 11) and cases with missing survey data (n = 15) were excluded. In the remaining 198 OHCAs, lay responders arrived at the scene in 116 cases (58%), and prior to EMSs in 51 cases (26%). An AED was attached in 17 cases (9%) and 4 (2%) were defibrillated. Lay responders performed CPR in 54 cases (27%). Median distance to the OHCA was 560 m (IQR 332-860 m), and 1280 m (IQR 748-1776 m) via AED pick-up. The survey-answering rate was 82%.

CONCLUSION

A smartphone application can be used to alert CPR-trained lay volunteers to OHCAs for CPR. Further improvements are needed to shorten the time to defibrillation before EMS arrival.