Date of Award
Background: In El Paso, areas of the city do not have access to parks within walking distance. The rates of people suffering from obesity and the comorbidities that come with obesity are rising annually. Physical activity has been shown to increase lifespan and provide positive health outcomes for the individual. Increasing physical activity in adults by using the parks can decrease their risk for heart disease and diabetes. Objectives: The overall aim of the procedure is to determine a relationship between park density and distance from residence to the nearest park, adult population density, and the obesity rate density within the limits of the city of El Paso. Therefore, exploring the relationship between the three layers to make inferences to the rates of obesity in El Paso, the population density, and the parks is the project’s aim. This project is only relevant to the sample and not to the city of El Paso as a whole. Hypothesis: The general hypothesis is that for those who live more than ½ mile we can look for a relationship between rates of obesity and the greater distance they must travel to get to the park. The researcher expects that the areas of El Paso that have long distances to parks will have higher rates of obesity. The spatial data collected will show the distance from the parks to the areas with high rates of obesity. The spatial analysis used to measure this will be queries, measurements, and hypothesis testing. Method: The project used an existing data set from the Department of State Health Services (DSHS) that includes zip code, gender, age, height, weight, and BMI of 380 citizens in El Paso. The data set was not explicitly collected to determine the obesity status in relation to parks thus there is a lack of clustered data in the data set. Using ArcPro, the map layers included the following layers: a layer representing the obesity dataset, a layer to represent the parks, and a zip code data layer. The layers were created to determine a relationship between the distance to parks and obesity among adults in El Paso. Results: Of the 380 samples, 21 outliers were excluded due to living in areas not in the parks vicinity. Of the remaining 359 samples, 251 lived within 0.50 miles to a park, while 108 lived more than 0.50 miles. Of the sample, 289 were female, and 91 were male. The BMIs varied from 15.8 to 55.2 The sample ages ranged from 18 to 85 years. The average distance to the park was 0.374 miles. After the map layers were created, an analysis was done. The first analysis was to determine the number of samples in each zip code inside the predetermined 0.50-mile radius to the nearest park. To determine this, the buffer and intersect tools were utilized. The distance tool was utilized to determine the space from each sample to their nearest park. The average BMI for those who lived within 0.50 miles of a park was 29.6029 (n=251) while those who lived outside of the 0.50-mile radius had an average BMI of 29.2591 (n=108). Based on the BMI guidelines, this would put the people who live inside and outside the radius in the overweight category. Spatial analysis was used to make inferences with the data upon layer completion. The spatial analysis methods used to measure the data set were the buffer, intersect, and near tools. Using the Global Moran’s I-test it was determined to be a clustered pattern. The clustered pattern indicates that a positive spatial correlation exists among the map layer and the samples have similar values. Using the above information, we can infer that spatial correlation exists. A T-test was performed showing statistically significant differences (p=1). Based on the guidelines, this would classify the people who live inside and outside the radius in the overweight category. Spatial analysis was used to make inferences with the data upon layer completion. The spatial analysis methods used to measure the data set were the buffer, intersect, and near tools. Using the Global Moran’s I-test, it was determined to be a clustered pattern. The clustered pattern indicates that a positive spatial correlation exists among the map layer, and the samples have similar BMI, age, gender, and distances to the parks. In Global Moran's I statistic, the null hypothesis would be that the attribute being analyzed is randomly distributed among the features among the area you are studying. The Global Moran’s I result we received were clustered, with a p-value that is statistically significant and a positive z-score, therefore we can reject the null hypothesis. The high and low values in the dataset were more spatially clustered than those that are random. Using the above information, we can infer that spatial correlation exists. Conclusion: Although the BMI of the 359 samples from the obesity dataset varied in size, they are remarkably close to the researcher's expectations. The BMI average of both groups statistically non-significant. However, the group outside the 0.50-mile radius was less than half of the group inside the radius, making it challenging to get a clear picture of whether being further from a park has a relationship to having a higher BMI. The information obtained from the map layers and samples showed that future studies need to focus more on obtaining information specific to the parks and the local areas. Recommendation: More research needs to be done on park distance and resident information to understand the relationship between park density and rates.
Recieved from ProQuest
Payan, Shawna, "Exploring The Relationship Between The Distance Of Parks And Rates Of Adult Obesity In El Paso, Texas" (2021). Open Access Theses & Dissertations. 3318.