How Google Street View data can help improve public health

Big data and artificial intelligence are transforming the way we think about health, from detecting diseases and discovering patterns to predicting outcomes and speeding response times.

In a new study that analyzed two million Google Street View images of the streets of New York City, a team of researchers from New York University evaluated the usefulness of this digital data in informing public health decision-making . Their findings, published in the Proceedings of the National Academy of Sciencesshow how relying on streetscape images alone can lead to inaccuracies and misplaced interventions, but combining this with other knowledge increases its potential.

“There is a lot of excitement around leveraging new data sources to gain a holistic view of health, including deploying machine learning and data science methods to gain new insights,” said Rumi Chunara, associate professor of biostatistics at the NYU School of Global Public Health. associate professor of computer science at NYU Tandon School of Engineering, and senior author of the study.

“Our study highlights the potential of digital data sources such as street images in improving public health research, while also pointing out the limitations of data and the complex dynamics between the environment, individual behavior and health outcomes,” said Miao Zhang, a researcher. Ph.D. student at the NYU Tandon School of Engineering and the first author of the study.

A street-level vision on health

In recent years, researchers have begun using streetscape imagery to connect an area’s environment and infrastructure to outcomes such as mental health, infectious diseases or obesity – a task that would be difficult to measure by hand.

“We know that a city’s built environment can shape our health, whether it’s the availability of sidewalks and green spaces for walking, or grocery stores with healthy food,” says Chunara. “Some studies show that sidewalk availability correlates with lower obesity rates, but is that the whole story?”

“Our motivation for this study was to delve deeper into these associations to see if there are potential factors causing them,” says Zhang.

Chunara, Zhang and their colleagues analyzed more than two million Google Street View images of every street in New York City, using artificial intelligence to assess the availability of sidewalks and crosswalks in the images. They then compared this information with localized data on obesity, diabetes and physical activity from the Centers for Disease Control and Prevention to see if the built environment predicted health outcomes.

The researchers found that neighborhoods with more crosswalks had lower rates of obesity and diabetes. However, no significant association was found between sidewalks and health outcomes, contrary to previous research.

“This may be because many of New York City’s sidewalks are in places that people don’t use — along a highway, on a bridge, or in a tunnel — so the density of the sidewalks may not as accurately reflect the walkability of the neighborhood like crosswalks,” said Zhang.

They also highlighted issues with the accuracy of the AI-generated labels for the streetscape images, warning that these may not match ‘ground truth’ and would not be a reliable benchmark on their own. When comparing existing data on sidewalk availability in New York City with the labeled streetscape images, they found that many were incorrectly labeled with sidewalks or no sidewalks, which may have been due to cars or shadows obscuring them in photos.

If you build it, will they come?

Although crosswalks were linked to lower rates of obesity and diabetes, the researchers applied a public health lens to determine what might explain this association. Their analyzes of the CDC data found that physical activity – and not just crosswalks, as measured in street scenes – was responsible for the decline in obesity and diabetes.

In one test, they found that increasing physical activity could result in a four times greater reduction in obesity and a 17 times greater reduction in diabetes than could be achieved by installing more crosswalks.

“We saw that physical activity delivers the benefits of crosswalks, so it is important to take such mechanisms into account, especially if they act at different levels, such as the built environment versus individuals,” says Zhang.

Based on their findings, the researchers conclude that public health decision-making should not rely solely on new data sources, but should also take domain knowledge into account. When analyzing streetscape images, incorporating computer science knowledge (for example, how image processing techniques can improve accuracy or how to correct biases in algorithms) and public health knowledge (what drives the associations between the built environment and health outcomes) is crucial interest. Combining this expertise with big data can inform how programs are designed and implemented to improve public health.

In this case, adding more sidewalks and crosswalks would be less effective at improving health outcomes than the same increase in physical activity, for example through local community exercise classes.

“While growing amounts of digital data can be useful in informing decision-making, our results show that simply using associations from new data sources may not lead to the most useful interventions or the best allocation of resources,” Chunara said. “A more nuanced approach using big data in combination with expertise is needed to make optimal use of this new data.”

Salman Rahman and Vishwali Mhasawade of NYU Tandon were also authors of the study.