Activity in high forested areas




Within my hometown I had noticed that the students who had a forest, near their house tended to be more active, and enjoyed being outside more than the students who lived in the city or in a large subdivision. As a child, I was always outside playing in the woods in my backyard. I feel that growing up playing in the woods has led me to be more active than some of my peers who grew up in city/urban environments. This has led me to hypothesizing that living with easy access to forests will cause the surrounding population to be more active due to increased access to exercise activity.  
Lewis County, Washington has an area of 6,309,842,400 feet^2. Of this land area, 51.96% is evergreen forest, 7.59% mixed forests, and 17.86 % is considered shrubs. Overall, this is considered a highly forested area when compared to other counties across America. King County, Washington is a similar size to Lewis County and is made up of 41.79% evergreen forest, 13.42% mixed forest, and 10.75% shrubs. I am going to see if these two forested areas are going to significantly lower the percentage of obesity and inactivity within a given county within the US. 
I chose 3 other counties at random throughout the US that showed low percentages of landmass covered by forest. Figure A compares the counties based on exercise data from the population of residents. 
             Figure A
County
Percent of landmass covered by forests(all types)
Percent of Persons with access to exercise opportunities
Percent of obese persons(20 years and over)
Percent of physically inactive persons(20 years and over)
Lewis County, WA
59.55%
60%
33.1%
22.4%
King County, WA
55.21%
98.1%
22.3%
14.7%
Perkins County, SD
0.52%
3.8%
34.1%
32.5%
Weld County, CO
0.71%
79.6%
25.5%
17.6%
Desha County, AR
0.97%
33.9
38.8
34.9
After collecting data on the locations, I was surprised to see no distinct or outstanding correlations between living in areas with forests and lowered obesity and inactivity rates. This suggests that there are other factors affecting activity and obesity rates other than land coverage of forests. Lewis County and King County both had large amounts of forest covering the land, yet their obesity and inactivity rates were not similar. The same was observed between the other counties with low percentage of land covered in forests. Because there was no clear difference between highly forested areas and lower forested areas when looking at their obesity and inactivity rates, it cannot be said that there is a correlation between having forests covering the land, and higher activity of the population. 
A reason for this could be that even though locations had high levels of forests in the area, the access to them may be limited due to wildlife protection, and a lack of public parks within the forests.  
When collecting the data, I had looked at forests and their connection with a population's rate of inactive and obese people. After looking at figure A, it seemed that obesity and inactive person rates were more closely correlated with a population’s access to exercise opportunities. It makes sense for these things to be correlated because when there are more opportunities for people to be active, they are more likely to take advantage of them. To examine whether this was true, I looked at 6 additional random counties in the US (figure B), 3 counties with low access to exercise opportunities, and 3 with high access to exercise opportunities.

        Figure B


The data that was gathered from the counties shows 3 counties with relatively low levels of access to exercise, along with 3 counties that have relatively high levels of access. The counties which had high access to exercise opportunities showed lower levels of obesity and inactivity, however I am surprised that there is not a larger decrease in obesity compared to the communities with very low levels of access to exercise opportunities. That may suggest that even though there is abundant access to these exercise opportunities, there is still a fair amount of people that either choose not to participate, or there are other barriers preventing people to participate.


Works Cited:

US Health Data, 2016.Map.Social Explorer.Social Explorer, n.d. Web. Jan 29, 2020 
(based on data from U.S. Census Bureau)

US Environmental Summaries 2011.Map.Social Explorer, n.d. Web. Jan 29, 2020. 
(based on data from U.S. Census Bureau)

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