Researchers in China studied how the types of food stores and restaurants in neighborhoods affect whether people gain weight. They surveyed 405 neighborhoods and found something surprising: having lots of healthy food options like supermarkets and fresh markets didn’t always help people stay at a healthy weight. Instead, the biggest factor was whether people felt they could easily access good food. The study identified three types of neighborhoods based on their food environments and found that in Chinese cities, the real problem isn’t a lack of food stores—it’s having the wrong mix of stores and unhealthy eating habits.

The Quick Take

  • What they studied: How the types and locations of food stores in neighborhoods influence whether people become overweight or obese
  • Who participated: 405 neighborhoods in Tianjin, China, a major city with both wealthy and less wealthy areas
  • Key finding: The study found four main neighborhood factors that affect obesity risk. The strongest factor was whether people felt they could easily get healthy food nearby. Surprisingly, having many supermarkets and fresh food markets actually increased obesity risk in some cases, especially when looking at larger areas around homes.
  • What it means for you: Your neighborhood’s food environment shapes your eating choices. If you live in an area where healthy food feels hard to reach, you’re more likely to gain weight. City planners might need to focus less on adding more stores and more on making sure people feel they can actually access healthy options. This finding applies mainly to Chinese cities and may differ in other countries.

The Research Details

Researchers used two types of information to understand neighborhood food environments: objective data (actual store locations from maps and databases) and subjective data (what people felt and believed about food access in their area). They surveyed 405 neighborhoods in Tianjin, China in 2023, collecting information about food stores, restaurants, and residents’ perceptions of food availability.

They then used a statistical method called principal component analysis to simplify all their measurements into key patterns. This helped them identify which factors most strongly connected to obesity. Finally, they used a clustering algorithm (a computer method that groups similar things together) to sort neighborhoods into three distinct types based on their food environment characteristics.

This approach was novel because most previous studies looked at either the actual food environment or what people perceived—but not both together. By combining these two perspectives, the researchers got a more complete picture of how neighborhoods influence weight.

Understanding neighborhood food environments is important because where you live affects what you eat, and what you eat affects your weight. Many countries, including China, are dealing with rising obesity rates. By identifying which neighborhood characteristics matter most, city planners and health officials can make better decisions about where to place stores, restaurants, and community programs. This study provides evidence specifically from a developing country, which is important because most previous research focused on wealthy Western countries.

This study has several strengths: it surveyed a large number of neighborhoods (405), used both real-world data and people’s actual experiences, and was conducted in a real city rather than a controlled setting. However, because it’s a cross-sectional study (a snapshot in time), it shows connections between neighborhoods and obesity but can’t prove that the neighborhood caused the weight gain. The study was conducted in one Chinese city, so results may not apply everywhere. Some of the statistical findings were close to the borderline of statistical significance, meaning we should be somewhat cautious about those particular results.

What the Results Show

The researchers identified four main neighborhood factors that influence obesity risk. The strongest factor was how easy people felt it was to access healthy food in their community—when people felt good access was available, obesity risk went down by about 32%. The second factor was the variety and availability of food stores within a 500-1000 meter radius (about a 5-10 minute walk)—having more options in this range slightly increased obesity risk. The third factor was unhealthy eating behaviors in the neighborhood, and the fourth was the mix of food stores within 500 meters of homes.

The study also sorted neighborhoods into three types: ‘Objective Deprived’ neighborhoods (6% of areas) had few food stores overall; ‘Objective Overloaded’ neighborhoods (23% of areas) had many food stores but mixed quality; and ‘Objective Overloaded-Dietary Behavior Integrated’ neighborhoods (71% of areas) had many stores combined with unhealthy eating patterns. Most neighborhoods fell into the third category.

Surprisingly, the study found that having many supermarkets and fresh food markets—things that usually help prevent obesity in Western countries—actually increased obesity risk in some Tianjin neighborhoods. This effect was stronger when looking at larger areas around homes, suggesting that the scale or size of the area matters.

The research revealed that ‘food deserts’ (areas with almost no food stores) are rare in Tianjin, unlike in some Western cities. Instead, the problem is having too many stores with unhealthy options or having stores that people don’t feel they can easily access. The study also showed that people’s perceptions of food access were just as important as the actual locations of stores—if people didn’t feel they could reach healthy food, they were less likely to eat well, even if stores were technically nearby.

Previous research, mostly from wealthy Western countries, suggested that having supermarkets and fresh food markets nearby helps prevent obesity. This study challenges that finding in the Chinese context, suggesting that the relationship between food stores and obesity is more complex and may differ between countries. The study also adds to a growing body of research showing that both objective factors (what’s actually there) and subjective factors (what people perceive) matter for health outcomes.

The study was conducted in only one Chinese city, so results may not apply to rural areas or other cities. Because it’s a snapshot study rather than following people over time, we can’t be certain that the neighborhood caused obesity—it’s possible that people with certain eating habits chose to live in certain neighborhoods. Some of the statistical findings were borderline significant, meaning we should be cautious about those results. The study also relied on survey data for some information, which can be affected by how people remember or report things. Finally, the study didn’t account for individual factors like income, education, or family history, which also influence weight.

The Bottom Line

If you live in an urban area in China or a similar developing country: (1) Look for neighborhoods where residents report easy access to healthy food, as this appears protective against obesity; (2) Don’t assume that having many supermarkets nearby automatically means you’ll eat healthier—focus on your actual eating habits; (3) Consider walking or biking to food stores within 500 meters if possible, as very distant options may be less likely to be used. For city planners: focus on making sure residents feel they can access healthy food, not just on adding more stores. These recommendations have moderate confidence because the study is cross-sectional and conducted in one city.

This research is most relevant to people living in Chinese cities and other developing countries with similar urban food environments. City planners, public health officials, and community organizations should pay attention to these findings. People living in areas with many food stores should be especially mindful of their eating choices, since having stores nearby doesn’t automatically lead to healthy eating. This research is less directly applicable to people in Western countries, where food environments work differently.

Changes in eating habits based on neighborhood food access typically take weeks to months to show up as weight changes. If you improve your access to healthy food or your perception of that access, you might notice changes in your eating within 2-4 weeks, but weight changes usually take 8-12 weeks to become noticeable.

Want to Apply This Research?

  • Track your weekly visits to different types of food stores (supermarkets, fresh markets, convenience stores, restaurants) and rate how easy you felt it was to find healthy options at each location on a scale of 1-10. Also log what you purchased and ate.
  • Use the app to identify healthy food stores within a 500-meter (5-10 minute) walk from your home or workplace. Set a weekly goal to visit at least one of these stores and purchase one new healthy food item. Rate your perception of food accessibility in your neighborhood weekly to track whether you’re becoming more aware of available options.
  • Over 3 months, track: (1) frequency of visits to different store types, (2) your perceived ease of accessing healthy food (weekly rating), (3) types of foods purchased, and (4) weight changes. Look for patterns between your perception of food access and your actual food choices. This helps you understand whether your neighborhood’s food environment is truly limiting your options or whether improving your perception and habits could help.

This research describes associations between neighborhood food environments and obesity in Chinese cities but does not prove cause-and-effect relationships. Individual weight is influenced by many factors including genetics, overall diet, physical activity, sleep, stress, and medical conditions—not just neighborhood food stores. Before making major changes to your diet or lifestyle based on this research, consult with a healthcare provider or registered dietitian, especially if you have existing health conditions. This study’s findings may not apply to your specific location or situation. The research is current as of 2025 but represents a single study in one city and should be considered alongside other evidence.