Assignment 1

GEOG 410: Spatial Analysis
Assignment 1
Dylan Molnar
April 4, 2016

Question 4:
Descriptive Statistics:
Mean= 32.16
mean(Spots)
Median= 20
median(Spots)
Mode = 10
mode(Spots)
Standard Deviation= 36.77
sd(Spots)
Skewness= 4.93
skewness(Spots)
Kurtosis= 35.32
Coefficient of Variation= -0.9953821
Spatial Descriptive Statistics:
Mean Center= (44.04943,-123.0825)
newx = sum(Latitude)/159
newx
[1] 44.04455
newy = sum(Longitude)/159
newy
[1] -123.0735

Weighted Mean Center= (44.04764, -123.0787)
dataset = cbind(dataset,fX=Spots*Latitude)
weightedX = sum(dataset$fX)/sum(Spots)
weightedX
[1] 44.04464
dataset = cbind(dataset,fY=Spots*Longitude)
weightedY = sum(dataset$fY)/sum(Spots)
weightedY
[1]-123.0731

Standard Distance= calculates the average distance from the mean center. I was unable to figure this out using R. 🙁
Relative Distance=

Question 5:
Histogram of the number of parking spots at each bike rack.

Map showing the distributing of the bike racks around campus. Additionally, the mean center and weighted mean central.

Question 6:
Introduction:
Biking is an effective mode of transportation that has minimal impact on the environment and offers an opportunity to increase physical activity. There are numerous health benefits to increased bike use as transportation and potentially economic benefits to increased bike use (Bopp, 2013). Physical activity is important for personal health and has been proven to reduce several chronic diseases, all potentially causing fatalities (Branca, 1999). Increased physical activity has been shown to help with blood pressure, atherogenic lipoprotein profile, blood clotting/fibrinolysis, insulin-mediated glucose uptake, bone and muscle strength, autonomic nervous system regulation (Branca, 1999). Biking is an effective way of increasing the amount of physical activity, while also promoting a more eco friendly lifestyle.

Having data on the location and number of bike racks at the University can be useful in understanding where the highest concentrations of bike traffic is and where the ideal location of a bike repair station should be located.

The objective of the study is to find the best location for a bicycle repair station based on the location of bike racks and the amount of spots they contain. Due to the relatively large amount of bike traffic on campus this will be particularly useful and it will help encourage more bike traffic, promoting a healthier and greener campus.

Methods:
Study Site- I chose to use to University of Oregon because there is currently location data on the number of bike racks, but there is not the data on the number of spots at each rack. Having this data will provide useful in using spatial statistics to find and recommend a location for a bike repair station.

My data collection method was using a GPS and recording the location of bike racks and the number of spots at each rack. The class divided the campus area up and recorded the data for the sections they were responsible for. Upon collecting the data, it was uploaded into a spreadsheet that contained the necessary information for the analysis.

The analytical methods used were first to create descriptive statistics of the data and then to create descriptive spatial statistics. The descriptive statistics include, mean, median, mode, standard deviation, skewness, kurtosis, and the coefficient of variation. The descriptive spatial statistics include the mean center, weighted mean center, standard distance, and relative distance. These analytical methods proved useful in dissecting the large dataset and understanding how the data was distributed.

Results:

The histogram provides a visualization of the frequency of the distribution of bike parking spots. It is interesting that the most of the racks have less than 50 parking spots. The distribution of spots is skewed left.
The map shows a distribution across campus with various clusters. The clusters seem to be more densely concentrated near the middle of campus. The weighted mean center seems to show the most representative location for bike racks.

Discussion:

This study provided some interesting results. A bike repair facility that is located slightly north of the weighted mean center would be the ideal location for a bike repair station. The weighted mean center is a central location in campus and would be relatively easy to access from nearly every bike rack. The weighted mean center is a better location than the mean center because it takes into account not only the number of bike racks, but also the number of parking spots, which provides a more accurate location of the highest density of bike racks.

This study does a good job at showing the distribution of bike racks across campus. It aims to additionally show the amount of spots at the racks. I learned that the highest density of bike racks are located near the center of campus. When the number of spots at each bike rack are taken into account, the weighted mean center moves slightly to the northeast, where the larger bike racks are likely located.

In a future study it would be interesting to see the number of spots shown on the map with either proportional symbols or graduated colors. This would show spatially where the largest bike racks are located. Additionally, I think it would be beneficial to have place this bike rack data on a basemap. This would give more context to the bike rack data.

References:

Bopp, M., Hastmann, T. J., & Norton, A. N. (2013). Active Commuting among K-12 Educators: A Study Examining Walking and Biking to Work. Journal of Environmental and Public Health, 2013, 1-8. Retrieved April 4, 2016.

Branca, F. (1999). Physical activity, diet and skeletal health. Public Health Nutrition PHN, 2(3a). Retrieved April 3, 2016.