Using aggregated mobile phone location data to compare the realised foodscapes of different socio-economic groups

The foodscape (the built food environment) is considered one of the driving factors of the higher burden of obesity and chronic disease observed in low socio-economic status (SES) groups. Traditional data collection methods struggle to accurately capture actual access and exposure to the foodscape (realised foodscape). We assess the use of anonymised mobile phone location data (location data) in foodscape studies by applying them to a case study in Perth, Western Australia to test the hypothesis that lower SES groups have poorer realised foodscapes than high SES groups. Kernel density estimation was used to calculate realised foodscapes of different SES groups and home foodscape typologies, which were compared to home foodscapes of the different groups. The location data enabled us to measure realised foodscapes of multiple groups over an extended period and at the city scale. Low SES groups had poor availability of food outlets, including unhealthy outlets, in their home and realised foodscapes and may be more susceptible to a poor home foodscape because of low mobility.

The role of alcohol outlet visits derived from mobile phone location data in enhancing domestic violence prediction at the neighborhood level

Domestic violence (DV) is a serious public health issue, with 1 in 3 women and 1 in 4 men experiencing some form of partner-related violence every year. Existing research has shown a strong association between alcohol use and DV at the individual level. Accordingly, alcohol use could also be a predictor for DV at the neighborhood level, helping identify the neighborhoods where DV is more likely to happen. However, it is difficult and costly to collect data that can represent neighborhood-level alcohol use especially for a large geographic area. In this study, we propose to derive information about the alcohol outlet visits of the residents of different neighborhoods from anonymized mobile phone location data, and investigate whether the derived visits can help better predict DV at the neighborhood level. We use mobile phone data from the company SafeGraph, which is freely available to researchers and which contains information about how people visit various points-of-interest including alcohol outlets. In such data, a visit to an alcohol outlet is identified based on the GPS point location of the mobile phone and the building footprint (a polygon) of the alcohol outlet. We present our method for deriving neighborhood-level alcohol outlet visits, and experiment with four different statistical and machine learning models to investigate the role of the derived visits in enhancing DV prediction based on an empirical dataset about DV in Chicago. Our results reveal the effectiveness of the derived alcohol outlets visits in helping identify neighborhoods that are more likely to suffer from DV, and can inform policies related to DV intervention and alcohol outlet licensing.

Nation-wide human mobility prediction based on graph neural networks

Nowadays, the anticipation of human mobility flow has important applications in many domains ranging from urban planning to epidemiology. Because of the high predictability of human movements, numerous successful solutions to perform such forecasting have been proposed. However, most focus on predicting human displacements on an intra-urban spatial scale. This study proposes a predictor for nation-wide mobility that allows anticipating inter-urban displacements at larger spatial granularity. For this goal, a Graph Neural Network (GNN) was used to consider the latent relationships among large geographical regions. The solution has been evaluated with an open dataset including trips throughout the country of Spain and the current weather conditions. The results indicate a high accuracy in predicting the number of trips for multiple time horizons, and more important, they show that our proposal only needs a single model for processing all the mobility areas in the dataset, whereas other techniques require a different model for each area under study.

Coupling mobile phone data with machine learning: How misclassification errors in ambient PM2.5 exposure estimates are produced?

BACKGROUND

Most studies relying on time-activity diary or traditional air pollution modelling approach are insufficient to suggest the impacts of ignoring individual mobility and air pollution variations on misclassification errors in exposure estimates. Moreover, very few studies have examined whether such impacts differ across socioeconomic groups.

OBJECTIVES

We aim to examine how ignoring individual mobility and PM2.5 variations produces misclassification errors in ambient PM2.5 exposure estimates.

METHODS

We developed a geo-informed backward propagation neural network model to estimate hourly PM2.5 concentrations in terms of remote sensing and geospatial big data. Combining the estimated PM2.5 concentrations and individual trajectories derived from 755,468 mobile phone users on a weekday in Shenzhen, China, we estimated four types of individual total PM2.5 exposures during weekdays at multi-temporal scales. The estimate ignoring individual mobility, PM2.5 variations or both was compared with the hypothetical error-free estimate using paired sample t-test. We then quantified the exposure misclassification error using Pearson correlation analysis. Moreover, we examined whether the misclassification error differs across different socioeconomic groups. Taking findings of ignoring individual mobility as an example, we further investigated whether such findings are robust to the different selections of time.

RESULTS

We found that the estimate ignoring PM2.5 variations, individual mobility or both was statistically different from the hypothetical error-free estimate. Ignoring both factors produced the largest exposure misclassification error. The misclassification error was larger in the estimate ignoring PM2.5 variations than that ignoring individual mobility. People with high economic status suffered from a larger exposure misclassification error. The findings were robust to the different selections of time.

CONCLUSIONS

Ignoring individual mobility, PM2.5 variations or both leads to misclassification errors in ambient PM2.5 exposure estimates. A larger misclassification error occurs in the estimate neglecting PM2.5 variations than that ignoring individual mobility, which is seldom reported before.

Who are more exposed to PM2.5 pollution: A mobile phone data approach

BACKGROUND

Few studies have examined exposure disparity to ambient air pollution outside North America and Europe. Moreover, very few studies have investigated exposure disparity in terms of individual-level data or at multi-temporal scales.

OBJECTIVES

This work aims to examine the associations between individual- and neighbourhood-level economic statuses and individual exposure to PM2.5 across multi-temporal scales.

METHODS

The study population included 742,220 mobile phone users on a weekday in Shenzhen, China. A geo-informed backward propagation neural network model was developed to estimate hourly PM2.5 concentrations by the use of remote sensing and geospatial big data, which were then combined with individual trajectories to estimate individual total exposure during weekdays at multi-temporal scales. Coupling the estimated PM2.5 exposure with housing price, we examined the associations between individual- and neighbourhood-level economic statuses and individual exposure using linear regression and two-level hierarchical linear models. Furthermore, we performed five sensitivity analyses to test the robustness of the two-level effects.

RESULTS

We found positive associations between individual- and neighbourhood-level economic statuses and individual PM2.5 exposure at a daytime, daily, weekly, monthly, seasonal or annual scale. Findings on the effects of the two-level economic statuses were generally robust in the five sensitivity analyses. In particular, despite the insignificant effects observed in three of newly selected time periods in the sensitivity analysis, individual- and neighbourhood-level economic statuses were still positively associated with individual total exposure during each of other newly selected periods (including three other seasons).

CONCLUSIONS

There are statistically positive associations of individual PM2.5 exposure with individual- and neighbourhood-level economic statuses. That is, people living in areas with higher residential property prices are more exposed to PM2.5 pollution. Findings emphasize the need for public health intervention and urban planning initiatives targeting socio-economic disparity in ambient air pollution exposure, thus alleviating health disparities across socioeconomic groups.

How do people in different places experience different levels of air pollution? Using worldwide Chinese as a lens

Air pollution, being especially severe in the fast-growing developing world, continues to post a threat to public health. Yet, few studies are capable of quantifying well how different groups of people in different places experience different levels of air pollution at the global scale. In this paper, we use worldwide Chinese as a lens to quantify the spatiotemporal variations and geographic differences in PM_2.5 exposures using unprecedented mobile phone big data and air pollution records. The results show that Chinese in South and East Asia suffer relatively serious PM_2.5 exposures, where the Chinese in China have the highest PM_2.5 exposures (52.8 μg/m³/year), which is fourfold higher than the exposures in the United States (10.7 μg/m³/year). Overall, the Chinese in Asian cities (35.5 μg/m³/year) experienced the most serious PM_2.5 exposures when compared with the Chinese in the cities of other continents. These results, partly presented as a spatiotemporally explicit map of PM_2.5 exposures for worldwide Chinese, help researchers and governments to consider how to address the effects of air pollution on public health with respect to different population groups and geographic locations.