Visits to public points of interest can impact community health measures

UNIVERSITY PARK, Pa. — It’s not so much where people live, but where they frequently spend their time that can provide useful information for predicting community health measures. A team led by geographers in the Penn State College of Earth and Mineral Sciences found that adding place visitation data — geographical data points collected from millions of anonymous cell phone users with GPS-enabled devices — to a population health model increased the model’s predictive performance by an average of 7.5%. This improvement in predicting public health factors, including the rates of depression and binge drinking, may assist public policymakers and health planners, researchers said.

They published their results in Computational Urban Science.

“In daily life, people visit many different places, such as restaurants, parks, gyms and bars,” said Zhenlong Li, associate professor of geography, director of the Geoinformation and Big Data Research Lab and corresponding author of the paper. “Our results show that these place visitation patterns can provide useful additional information for estimating community health measures, complementing traditional demographic and social variables.”

Traditionally, community health outcomes are determined based on static demographic and social statistics collected by the U.S. Census and State Departments of Health, such as race, age, socioeconomic status, access to healthcare and education levels. After creating a model containing just these measures, the researchers added aggregated cell phone data from the year 2019, which tracked visits to approximately seven million public points of interest, such as parks, restaurants, gyms, casinos, convenience stores, primary healthcare facilities and religious centers. The team then analyzed the data for the entire continental U.S., sorted into approximately 12,800 rural and 56,600 urban census tracts. The addition of place visitation data not only improved the model’s predictive capabilities but also uncovered information about how the places people visit may shape community health.

“Our hypothesis was that the daily activity patterns at the neighborhood level would better predict neighborhood health, and by analyzing place visitation data on a supercomputer in the lab, we were able to verify that hypothesis,” said Temitope Akinboyewa, doctoral student in geography and first author of the paper. “In particular, the model showed the biggest predictive gains for place visitations related to binge drinking, depression, routine medical checkups, obesity and asthma.”

The researchers highlighted the role of place visitation data in increasing the predictive capabilities of two community health measures: depression and binge drinking. Place visitation data increased the predictive capability of binge drinking by 38.8% in urban areas, while the data increased the predictive capability of depression by 48.9% in rural areas. That means researchers could more closely estimate the prevalence of binge drinking based on where people in urban areas visit, as well as for predicting depression based on where people in rural areas visit.

Researchers showed, for example, that in both urban and rural areas, frequent visits to places where alcohol is sold were associated with a higher prevalence of both binge drinking and depression in a community. In urban areas, excluding visits to those drinking places, visits to bowling centers and amusement parks were associated with a higher prevalence of binge drinking, while visits to religious organizations, limited-service restaurants and malls were associated with a lower prevalence of binge drinking in urban communities.

In rural areas, excluding visits to drinking places, visits to standalone casinos and convenience stores were associated with a higher prevalence of binge drinking in rural communities, while visits to general stores, limited-service restaurants and gas stations were associated with a lower prevalence of binge drinking.

In urban areas, again excluding drinking places, visits to entertainment- and commercial-service-related destinations, such as standalone casinos, jewelry stores and general merchandise stores, were associated with a higher prevalence of depression in a community, whereas visits to caterers, art dealers and child daycare services were associated with a lower prevalence of depression.

In rural areas, visits to limited-service restaurants and casinos were associated with a higher prevalence of depression, while visits to convenience stores, hotels and motels, and child daycare services were associated with a lower prevalence of depression.

“Using place visitation data can enhance prediction in models where traditional demographic and social measures do not fully capture community health patterns,” Li said, explaining that this present study was not a causation analysis but rather identified an association or pattern in how the places people visit may have an impact on a population’s health. “We could use the same type of data to make predictions on the percentage of the population with obesity, for example. Data from devices that record movement, such as smart wearable devices, could also be used to capture activity patterns and support predictions related to health and human mobility.”

In addition to Li and Akinboyewa, the co-authors of the paper are Huan Ning and M. Naser Lessani, Penn State doctoral students in geography; and Xiaoming Li and Shan Qiao, both from the University of South Carolina.

This work was supported by the National Institutes of Health’s National Institute on Minority Health and Health Disparities under award number R21MD018666. This content is solely the responsibility of the authors and does not necessarily represent the views of the funder.

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