Impact of Population Density on Urban Stress in Schools

To what extent does population density impact urban stress in the Robinson Secondary School District? Date: January 4, 2022 Word Count: 2,492

Table of Contents Page Fieldwork Question and Geographical Context 3 Methodology 6 Data Presentation and Analysis 8 Conclusion 13 Evaluation 14 Works Cited 15 2

Fieldwork Question and Geographic Context Fieldwork Question: To what extent does population density impact urban stress in the Robinson Secondary School District? Hypothesis 1: As density increases, traffic levels will increase Hypothesis 2: As density increases, noise levels will increase Hypothesis 3: As density increases, residents will experience higher levels of stress Hypothesis 4: As population density increases, the commute time increases. Link With Syllabus: Urban Stress and its impact on urban residents is studied as part of an optional themes unit on Urban Environments (Option G). In addition, population density is studied as part of the Core Theme of Changing Population. Geographic Context: The research area is Fairfax County, Virginia, which is where the students live and where they collected data. The students are a part of one of 26 public high schools in Fairfax County, Virginia. It’s a mix of high, medium, and low density suburbs and is an area with a lot of land, residents, homes, restaurants, parks, etc. This is the main area of where the majority of the students lived, which is why we decided to carry out our data in this location. It’s a part of the Atlantic religion with mild climate and four seasons, with a population of 1,139,720 and 406 per square mile. It’s one of the most populated county in the state of Virginia, however, it’s not severely crowded and has a lot of open space for people to walk and drive, making it a reason why we decided for students to collect data in this area, where it’s easier and more convenient for them to gather information. Compared to the US median household income of $64,994, Fairfax County is higher by having its median household income be $127,866, making Fairfax County an expensive area with the median property value being $576,200, and the homeownership rate being 68.4%. The reason why we decided to collect data in a socioeconomic area is because we can measure how most people in a given household can afford a car and use less public transportation. This causes the collection of our data to be more densely populated with more cars and traffic congestion. As the majority of the population can afford a car and use less public transportation, it can cause more cars to pass through neighborhoods and other central areas where students live. 3

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Figure 1: Map of East Coast Figure 2: Map of Virginia Figure 3: Map of Fairfax County 4 D ul le s Ai rp or t ~4 8 k m ~42k m

Figure 4: Map of Robinson Research Area Methodology 5 Robins on Second ary School Burke, a city 30 min from Washington DC 1 inch=2.5 miles

1. Traffic Study: I collected data by finding an intersection near my home and for 10 minutes, I counted the number of vehicles and added them into the four categories: Cars, Commercial Vehicles, Bus/Public Transportation, Pedestrians/Bicycles. I counted all vehicles in both directions and recorded the number of pedestrians who walked past for each minute. I made observations and clearly recorded the count based on my intersection point. 2. Noise Survey: I determined the noise level at the intersection near my home by timing a 1- minute vehicular count at my location and counting the number of cars that pass by in one direction on my side of the street. I recorded the number of cars that drive past for each minute on my data sheet and observed the crossings and sidewalk conditions at the stop. I calculated the noise level by using the iphone app, Decibel X. Figure 5: Decibel X image 3. Neighbor Survey: I visited 5 neighboring households and asked the neighbors questions regarding their regular commute, their main stress levels while living in Fairfax County, and what type of transportation they use in order for them to commute, and then recorded their response. Figure 6: Neighborhood Survey 6

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Point-systematic sampling was used for data collection and 250 students are collecting data and sharing it using a Google Form. The collected data was grouped by zones in different places around the Robinson Secondary school district and was all averaged out from the total amount. This sampling method is justified as it allows the data to be collected from each of the nine zones (Figure 4: Map of Robinson Research Area). We calculated the residential density per zone (km) and compared data (traffic, noise, stress level, etc) per zone relative to density per zone. We used Rs to figure this out and give a correlation coefficient. By using the data of 250 people, it will create statistically significant results and can control for anomalies and outliers. Figure 7: Spearman's Rank formula Data Presentation and Analysis Hypothesis 1: As population density increases, traffic levels will increase. 7 Other factors that give people stress People’s stress level living in Fairfax county

This hypothesis explains that if there is a higher population density, then there are higher traffic levels due to more people living closer together. In this data, the average number of cars, commercial vehicles, public transportation, pedestrians and cyclists are totaled together by each zone. When there are more people in an area, there is a higher likelihood of foot traffic and vehicles. In this graph, it’s measuring each zone and its density level on the average traffic level of buses that students recorded. The significance of this shows that as a zone becomes more densely populated, the traffic levels will increase as there is a positive, linear trend line when going through each zone. When the students recorded data in the less densely populated zones of H and G, there were less buses that made the zones have less traffic congestion. However, when other students recorded data in a more populated area of zone D, there were more buses and traffic congestion. After ranking each zone in order of most to least average number of buses recorded, the Rs correlation coefficient is calculated to 0.714. This value is above 0 and far from -1, indicating that there is a strong, positive, and statistically significant correlation between traffic levels and population density. This is because as the number of people in an area increases, the more public transportation is needed for people to get to places, which can cause more traffic congestion and levels to increase as well. This is a significant result of accepting my hypothesis that as population density increases, traffic levels will increase. Figure 8: Bar Graph of Hypothesis 1 Hypothesis 2: As population density increases, noise levels will increase. 8

The hypothesis explains that if there is a higher population density, then there are higher noise levels due to more people living together in a given environment. In this data, the students recorded the average noise level in their area using the app Decibel X. When there are more people in a zone, there is a higher likelihood of increased noise levels due to more people communicating with one another, more traffic noises from the street, etc. In this graph, it’s measuring each zone and its density level on the average noise levels that the students recorded. The significance of this map helps us see as when a zone becomes more densely populated, the noise levels will increase as there is a slight positive correlation trendline when going through each zone. When the students recorded in the less densely populated zones of H and G, the noise levels were not as prominent as there were less people in the given zone. Also, when other students recorded in higher densely populated zones of F and D, the noise levels decreased as there were more people in the given zone. After ranking each zone in order of most to least average levels of noise, the Rs correlation coefficient is calculated to 0.452. This value is above 0 and a little far from -1, indicating that there is a moderate, positive, and not statistically significant correlation coefficient between noise levels and population density. This is because you cannot tell the difference between the differing decibels in each zone. It’s much more difficult to tell the difference when each zone has different noise levels from day to night. Even though there’s some data that supports this hypothesis, the ranking and data does not accurately match the given statement. This is a significant result of not accepting my hypothesis that as population density increases, noise levels will increase as well. Figure 9: Bar Graph of Hypothesis 2 Hypothesis 3: As population density increases, residents will experience higher levels of stress. 9

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The hypothesis explains that if there is a higher population density, then there are higher levels of stress due to people living together in the same environment. In this data, the students recorded the average levels of stress in the given zones. In those areas, the students surveyed residents on their stress levels, while living in the Robinson Secondary school district. When there are more people in an area, there is a higher likelihood of increased stress levels due to urban stresses. In this graph, it’s measuring each zone and its density level on the average stress level that the students recorded. This significance of this map helps us see as when a zone becomes more densely populated, the stress levels decrease as there is a negative correlation trendline when going through each zone. When students recorded in the less densely populated zones of H and G, the stress levels were more prominent and higher when there were less people living in a given area. However, when students are recording in a higher densely populated zone of D, the stress levels are not as prominent and decreased as there are more people in the given zone. After ranking each zone in order of most to least average levels of stress, the Rs correlation coefficient is calculated to -0.714. This value is below 0 and very close to -1, indicating that there is a very weak, negative, and not statistically significant correlation coefficient between stress levels and population density. This is because as the number of people in a given area increases, the less stress it creates for people because they’re not isolated and are close to many places. This is a result of not accepting my hypothesis that as population density increases, stress levels will increase as well. Figure 10: Bar Graph of Hypothesis 3 Hypothesis 4: As population density increases, the commute time increases. 10

The hypothesis explains that if there is a higher population density, then there are higher levels of commute time due to people living in the same area. In this data, the students recorded the average levels of commute time in the given zones. In those areas, the students surveyed residents on their commute time from to and from work while living in the Robinson Secondary school district. When there are more people in a given zone, there is a higher likelihood of commute time due to more traffic and congestion caused by people leaving and coming back. In these pie charts, it’s measuring each zone and its density level on various urban stresses that the students surveyed the residents on. While going through each chart, I noticed that traffic was the biggest urban stressor for residents that live in each zone. This is because when living in a highly populated area, there are more people trying to get to places at the same time as you, causing more traffic. When students recorded in the less densely populated areas of zones H and G, the traffic levels were very prominent, compared to other zones in the district. As we see the higher densely populated zones of F and D, traffic levels are still prominent but not as prominent as zones H and G. This is because when people live in a less populated area, commute time tends to increase since they’re more isolated than those who live in a more populated area. This is a result of not accepting my hypothesis that as population density increases, commute time increases. Figure(s) 11: Pie charts of all zones 11

Conclusion Hypothesis 1: as density increases, traffic levels will increase. 12

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The results of this hypothesis show that the Rs correlation coefficient is to be calculated to 0.714, a very positive and strong correlation. This supports the hypothesis of how higher densities will increase traffic levels on the average number of buses that students recorded. Hypothesis 2: as density increases, noise levels will increase. The results of this hypothesis show that the Rs correlation coefficient is calculated to 0.452, a moderate and positive correlation. This refutes the hypothesis of how higher densities will increase noise levels. Hypothesis 3: as density increases, residents will experience higher levels of stress. The results of this hypothesis show that the Rs correlation coefficient is calculated -0.714, a very negative and weak correlation. This refutes the hypothesis of how higher densities will increase stress levels. Hypothesis 4: As population density increases, the commute time increases. The results of the hypothesis show that throughout the given pie charts that were based on the data that the students recorded, it does not support the given hypothesis of how higher densities will increase commute time. Fieldwork Question: To what extent does population density impact urban stress in the Robinson Secondary School District? Population density impacts urban stress in the Robinson Secondary School district by having traffic increase as the population becomes higher. Throughout the data that was recorded, as the students began to measure the levels of traffic, they soon realized that it began to increase as the population became higher. This is due to more people going to the same places and using vehicles to get to those places, which causes more traffic. Noise levels do not increase as the population becomes higher because people can live in woody areas with less population. Stress levels don’t increase as the population becomes bigger because people in isolated areas have more time to destress and the commute also decreases because they have less people that go to the same places as them. Evaluation One area of weakness in this study was that the average and max noise levels recorded by the 13

students were taken during a random minute in the middle of the day, which gave no significance of the actual noise stress in the given zone. Also, since the noise levels were recorded through Decibel X, the data could be skewed by how you placed the phone while recording the noise, the student collecting the data could be talking, etc. A suggestion for this would be to have the noise data be recorded for a long period of time instead of just one minute. Instead of one minute, a recommended 20 minutes would provide more accuracy of the decibels recorded. This would give more opportunities to capture the decibels of something to create more of a maximum noise, which would create more accurate data that’s less skewed. Also, the directions weren’t clear enough for how to collect the noise data, so to avoid this error the procedure should include putting the phone down before hitting record and making sure that the student is not doing anything else that could affect the noise levels. One area of strength in this study was that every zone has data that has a lot of stress factors that have been accounted for (traffic, noise, stress levels, etc). The zoning of the districts makes it so there is no zone too small and that there is more than enough data to analyze, which allows the many factors to be measured accordingly and accurately. Work Cited “East Coast of the United States.” Wikipedia , Wikimedia Foundation, 23 Nov. 2018, 14

en.wikipedia.org/wiki/East_Coast_of_the_United_States. “Fairfax County House & Property Search: VA Real Estate Map of Homes for Sale.” FrontDoorHomes.com , www.frontdoorhomes.com/fairfax-county-property-search/ . “Fairfax County, VA.” Data USA , https://datausa.io/profile/geo/fairfax-county-va . Ferguson, Joe. “Fairfax County, Virginia.” Fairfax County, Virginia: History and Information , https://www.ereferencedesk.com/resources/counties/virginia/fairfax.htm Gupta, Aryan. “Spearman's Rank Correlation: The Definitive Guide to Understand: Simplilearn.” Simplilearn.com , Simplilearn, 15 Nov. 2022, https://www.simplilearn.com/tutorials/statistics- tutorial/spearmans-rank-correlation . “Robinson Zone Map .” Rbss.maps.arcgis.com , rbss.maps.arcgis.com/home/webmap/viewer.html? webmap=637a3949e2b142d6a0a4e79af18d004e+ . “VA Map - Virginia Maps.” State Maps - United States Flag , www.state-maps.org/va-map.htm 15

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