Understanding the spatiotemporal dynamics of urban population is crucial for addressing a wide range of urban planning and management issues. Aggregated geospatial big data have been widely used to quantitatively estimate population distribution at fine spatial scales over a given time period. However, it is still a challenge to estimate population density at a fine temporal resolution over a large geographical space, mainly due to the temporal asynchrony of population movement and the challenges to acquiring a complete individual movement record. In this article, we propose a method to estimate hourly population density by examining the time-series individual trajectories, which were reconstructed from call detail records using BP neural networks. We first used BP neural networks to predict the positions of mobile phone users at an hourly interval and then estimated the hourly population density using log-linear regression at the cell tower level. The estimated population density is linearly correlated with population census data at the sub-district level. Trajectory clustering results show five distinct diurnal dynamic patterns of population movement in the study area, revealing spatially explicit characteristics of the diurnal commuting flows, though the driving forces of the flows need further investigation.

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ObjectivesTo investigate and describe the epidemiological characteristics of suicide in the elderly in Jiading, Shanghai, for the period 2003–2013.DesignRetrospective, observational, epidemiological study using routinely collected data.SettingJiading District, Shanghai.MethodsSuicide data were retrieved from the Shanghai Vital Registry database for the period 2003–2013. Crude and age-standardised mortality rates were calculated for various groups according to sex and age. Joinpoint regression was performed to estimate the percentage change (PC) and annual percentage change (APC) for suicide mortality.ResultA total of 956 deaths due to suicide occurred among people aged ≥65 years during the study period, accounting for 76.7% (956/1247) of all suicide decedents. Among the 956 people with suicide deaths, 88.7% (848/956) had a history of a psychiatric condition. The age-standardised mortality rates for suicide without and with a psychotic history in people aged ≥65 years were much higher than those for people aged <65 years in both genders. Suicide mortality in the elderly showed a declining trend, with a PC of −51.5% for men and −47.5% for women. The APC was −29.1 in 2003–2005, 4.6 in 2005–2008 and −9.7 in 2008–2013 for aged men, and −12.2 in 2003–2006 and −5.2 in 2006–2013 for aged women, respectively. Women living in Jiading had a higher risk of suicide death than men, especially among the elderly. The mortality rate for suicide increased with age in the elderly, and was more marked for those with a psychiatric history than for those without.ConclusionsSuicide mortality declined in Jiading during the study period 2003–2013 overall, but remained high in the elderly, especially those with a psychiatric history.

In response to the increasing concerns and challenges for most frequently left-turn crashes at intersections, partial proportional odds models, for which some of the beta coefficients vary across variables, are proposed to examine and understand the influence of contributory factors (i.e. human attributes, traffic flow features, roadway geometrics, and environmental factors, etc.) on injury severity involved in left-turn crashes, using the selected 317 crash data over latest 6 years from Xian city. The results show that partial proportional odds model has better performance than general ordered logit or probit probability approach. Specifically, the aged and younger drivers are more prone to cause left-turn crashes, and the increasing effect of trucks involvement, impact points of both vehicles, environmental factors, safety belt usage, alcohol and/or drugs are also significantly associated with higher injury severities, which was underestimated or underreported in previous researches.