Mobile Device Tracking Research Articles

Time in nature is associated with higher levels of positive mood: Evidence from the 2023 NatureDose™ student survey

Growing evidence shows that exposure to nature can improve mood. However, this evidence is mostly based on short-term or subjectively reported exposures. Machine learning and GPS mobile device tracking offer fine-grained assessments of minute-by-minute nature exposure over long periods. Few studies on nature exposure and mood have leveraged these technologies. We conducted one of the first longer-term investigations of daily nature exposure and mood with a mobile app as part of the NatureDose™ Student Study (NDSS). The NatureDose™ app uses GPS and a phone's sensors to evaluate whether a person is inside or outside and checks levels of nature surrounding the person with the NatureScore™ measure. This measure is based on more than 30 remotely sensed datasets, geo-located health records, and machine learning models. College students' nature exposure (N = 154 initial recruitment) was monitored for eight weeks (March 7 to May 1, 2022). Their mood states were calculated weekly using the Positive and Negative Affect Schedule (PANAS). Both exposure and outcome measures were continuous values. After three weeks, randomly selected groups of students were asked to either maintain or increase their nature exposures. However, students asked to increase their nature exposures maintained them. Therefore, evaluating cause-and-effect relationships through an experimental design was not possible. Instead, we evaluated observational associations between average nature exposure and mood levels across the study period using mixed-effect linear regression models adjusting for gender and allergies. Among the 97 participants with available data, we observed a positive association between nature exposure and positive mood (β = 0.10 [0.04–0.17], p = .003) but not negative mood (β = 0.01 [-0.07-0.09], p = .76). These findings were supported by sensitivity analyses with ANOVA models between tertiles on average NatureDose™ levels. We also found that the average nature exposure was 71 min per day, most of which (66%) was earned within 10 km of the center of the university campus. The study supports the utility of using NatureDose™ as an app-based tracking tool for objective nature exposure measurement. The study also reinforces previous findings on the associations between nature exposure and positive mood states. Greening university campuses and surrounding locations where college students live and spend leisure time could support college students' mental health.

DuctiLoc: Energy-Efficient Location Sampling With Configurable Accuracy

Mobile device tracking technologies based on various positioning systems have made location data collection ubiquitous. The frequency at which location samples are recorded varies across applications, yet it is usually pre-defined and fixed, resulting in redundant information, and draining the battery of mobile devices. In this paper, we first answer the question “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">at what frequency should individual human movements be sampled so that they can be reconstructed with minimum loss of information?</i> ”. Our analysis unveils a novel linear scaling law of the localization error with respect to the sampling interval. We then present DuctiLoc, a location sampling mechanism that utilises the law above to profile users and adapt the position tracking frequency to their mobility. DuctiLoc is energy efficient, as it does not rely on power-hungry sensors or expensive computations; moreover, it provides a handy knob to control energy usage, by configuring the target positioning accuracy. Controlling the trade-off between accuracy and sampling rate of human movement is useful in a number of contexts, including mobile computing and cellular networks. Real-world experiments with an Android implementation show that DuctiLoc can effectively adjust the sampling frequency to individual mobility habits and target accuracy level, reducing the energy consumption by 60% to 98% with respect to a baseline periodic sampling.

CARES and crime

PurposeThe US signed the Coronavirus Aid, Relief, and Economic Security (CARES) Act in March 2020 to alleviate the harsh economic effects of the pandemic and related shutdowns. A substantial part of the bill expanded and increased unemployment insurance payments, where a growing area of research estimates strong anti-poverty effects. The authors examine the effect of these policies on crime.Design/methodology/approachThe authors use new event study and difference-in-differences techniques to estimate the effect of increasing unemployment insurance payments on property crime and violent crime. Then, the authors estimate the effect of expanded unemployment qualification programs on crime. The authors use a rich set of controls including unemployment, contemporaneous policies and mobile device tracking data to estimate the degree to which people stayed at home.FindingsThey find that increasing unemployment insurance payments decreased crime by 20%, driven by a 24% decrease in property crime. The authors also find suggestive evidence that expanding unemployment qualifications decreases crime.Practical implicationsThe authors find a new and substantial benefit of expanded unemployment insurance beyond their antipoverty effects.Originality/valueTo the authors' knowledge, this is the first study that directly examines the impact of the CARES Act on crime.

COVID-19 and Domestic Violence: Economics or Isolation?

Recent studies estimate that the COVID-19 pandemic significantly increases reports of domestic violence in several countries. Using mobile device tracking data, city-level unemployment data, and new data on labor market conditions caused by the coronavirus pandemic, we isolate the effects of unemployment and staying at home on incidents of domestic violence. We find that unemployment decreases domestic violence after controlling for the degree to which people stay at home. We also provide evidence that staying at home increases domestic violence. However, we find that the effects of unemployment and staying at home are concentrated right after an initial shock from mid-March to mid-June 2020. Finally, we find that some labor market conditions linked to COVID-19, such as being prevented from looking for work due to the pandemic, decrease domestic violence, and these labor market effects are often gendered.

Policy and weather influences on mobility during the early US COVID-19 pandemic

As the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to proliferate across the globe, it is a struggle to predict and prevent its spread. The successes of mobility interventions demonstrate how policies can help limit the person-to-person interactions that are essential to infection. With significant community spread, experts predict this virus will continue to be a threat until safe and effective vaccines have been developed and widely deployed. We aim to understand mobility changes during the first major quarantine period in the United States, measured via mobile device tracking, by assessing how people changed their behavior in response to policies and to weather. Here, we show that consistent national messaging was associated with consistent national behavioral change, regardless of local policy. Furthermore, although human behavior did vary with outdoor air temperature, these variations were not associated with variations in a proxy for the rate of encounters between people. The independence of encounters and temperatures suggests that weather-related behavioral changes will, in many cases, be of limited relevance for SARS-CoV-2 transmission dynamics. Both of these results are encouraging for the potential of clear national messaging to help contain any future pandemics, and possibly to help contain COVID-19.

The Effect of Sheltering in Place on Police Reports of Domestic Violence in the US

This study analyzes the effect of sheltering in place in response to COVID-19 on domestic violence incidents in the US using novel daily mobile device tracking data, the timing of shelter-in-place orders, and dispatch and crime data from twenty-eight police departments in eighteen US states. Findings show that reports of domestic violence rise after local shelter-in-place orders are enacted and that domestic violence increases with measures of sheltering in place, as indicated by mobile device tracking data. This result is consistent with an exposure reduction theory of domestic violence and, in the context of the COVID-19 pandemic, underscores the importance of providing potential victims of violence with spaces that are safe from abusers and risk of infection. When applied to the entire US, it is estimated that sheltering in place increased domestic violence by approximately 6 percent, or more than 24,000 cases, from March 16 to April 30, 2020. HIGHLIGHTS Having to confine at home with an abuser can increase violence. Mobile device tracking data was used to measure the percentage of people who stayed at home all day. US police reports of domestic violence rose with shelter-in-place orders and fell when efforts relaxed. The real effect may be larger due to pandemic-related reporting issues. Economic and behavioral interventions for potential victims will not fully counteract the effects of exposure.

COVID-19, staying at home, and domestic violence.

We analyze how staying at home during the COVID-19 pandemic affects the rates of domestic violence in the U.S. Using police dispatch and crime data from 36 police and sheriff’s departments and mobile device tracking data, we find that reported incidents of domestic violence increase as more people stay at home. Specifically, we estimate that staying at home due to COVID-19 increased domestic violence by over 5% on average from March 13 to May 24, 2020. This is consistent with a theory of exposure reduction, where victims and abusers stuck at home are more likely to fight.

The Effect of Social Distancing on Police Reports of Domestic Violence

We analyze the effect of social distancing related to COVID-19 on domestic violence incidents in the U.S. using novel daily mobile device tracking data, the timing of stay-at-home orders, and dispatch and crime data from twenty-eight police departments. We find that reported incidents of domestic violence increase after local stay-at-home orders are enacted and that domestic violence increases with mobile device tracking measures of social distancing. Our result is consistent with an exposure reduction theory of domestic violence. When applied to the entire U.S., we estimate that social distancing increased domestic violence by approximately 6 percent, or more than 24,000 cases, from March 16 to April 30, 2020.

Preliminary study of wireless balloon network using adaptive position tracking technology for post disaster event

Limited resources in post-disaster areas, one of which is a communication where coordination needed for aid distribution in disaster areas. Wireless balloon technology as a solution for use in post-disaster areas. Bandwidth limitations and high delay in communication systems on wireless balloons create limitations in aid coordination, especially mobile device tracking on BPBD volunteers or officers. This research develops an effective communication system at the wireless balloon to track personal device officers in disaster areas that use cellular devices. This mobile device tracking system utilizes a wireless balloon using a publish-subscribe system on their mobile devices, namely volunteers as publishers and those responsible for disasters or communities as subscribers. To overcome the limitations of communication resources on cellular devices and wireless balloons using the Adaptive method on publish-subscribe called UM-Disaster. The results of this study, the UM-Disaster system for multi-cell tracking has an average efficiency of 40-63% for bandwidth and processor use on mobile devices at 51-70%.

Mobile Devices and Location Privacy: Where Do We Go from Here?

The eruption of concern over mobile device tracking has led to important public debate over location privacy. These cases demonstrate a lack of transparency in mobile systems, with users in the dark about how companies collect and use their location information.