Filtering by
- Resource Type: Text
Ultimate Frisbee or "Ultimate," is a fast growing field sport that is being played competitively at universities across the country. Many mid-tier college teams have the goal of winning as many games as possible, however they also need to grow their program by training and retaining new players. The purpose of this project was to create a prototype statistical tool that maximizes a player line-up's probability of scoring the next point, while having as equal playing time across all experienced and novice players as possible. Game, player, and team data was collected for 25 different games played over the course of 4 tournaments during Fall 2017 and early Spring 2018 using the UltiAnalytics iPad application. "Amount of Top 1/3 Players" was the measure of equal playing time, and "Line Efficiency" and "Line Interaction" represented a line's probability of scoring. After running a logistic regression, Line Efficiency was found to be the more accurate predictor of scoring outcome than Line Interaction. An "Equal PT Measure vs. Line Efficiency" graph was then created and the plot showed what the optimal lines were depending on what the user's preferences were at that point in time. Possible next steps include testing the model and refining it as needed.
Through an investigation of agriculture and cuisine and its consequential influence on culture, education, and design, the following project intends to reconceptualize the learning environment in order facilitate place-based practices. Challenging our cognitive dissonant relationship with food, the design proposal establishes a food identity through an imposition of urban agriculture and culinary design onto the school environment. Working in conjunction with the New American University’s mission, the design serves as a didactic medium between food, education, and architecture in designing the way we eat.
Due to the importance of millennials to cities around the globe, this study uses 2010 ZIP code area data and the Phoenix metropolitan area as a case study to test the relationships between thirteen parameters of livability and the presence of millennials after controlling for other correlates of millennial preference.
The results of a multiple regression model indicated a positive linear association between livability parameters within smart cities and the presence of millennials. Therefore, the selected parameters of livability within smart cities are significant measures in influencing location decisions made by millennials. Urban planners can consequently increase the likelihood in which millennials will choose to live in a given area by improving livability across the parameters exemplified in this study. This mutually beneficial relationship provides added support to the notion that planners should develop solutions to improve livability within smart cities.
In light of climate change and urban sustainability concerns, researchers have been studying how residential landscape vegetation affect household water consumption and heat mitigation. Previous studies have analyzed the correlations among residential landscape practices, household water consumption, and urban heating at aggregate spatial scales to understand complex landscape decision tradeoffs in an urban environment. This research builds upon those studies by using parcel-level variables to explore the implications of vegetation quantity and height on water consumption and summertime surface temperatures in a set of single-family residential homes in Tempe, Arizona. QuickBird and LiDAR vegetation imagery (0.600646m/pixel), MASTER temperature data (approximately 7m/pixel), and household water billing data were analyzed. Findings provide new insights into the distinct variable, vegetation height, thereby contributing to past landscape studies at the parcel-level. We hypothesized that vegetation of different heights significantly impact water demand and summer daytime and nighttime surface temperatures among residential homes. More specifically, we investigated two hypotheses: 1) vegetation greater than 1.5 m in height will decrease daytime surface temperature more than grass coverage, and 2) grass cover will increase household water consumption more than other vegetation classes, particularly vegetation height. Bivariate and stepwise linear regressions were run to determine the predictive capacity of vegetation on surface temperature and on water consumption. Trees of 1.5m-10m height and trees of 5m-10m height lowered daytime surface temperatures. Nighttime surface temperatures were increased by trees of 5m-10m height and decreased by grass. Houses that experienced higher daytime surface temperatures consumed less water than houses with lower daytime surface temperatures, but water consumption was not directly related to vegetation cover or height. Implications of this study support the practical application of tree canopy (vegetation of 5m-10m height) to mitigate extreme surface temperatures. The trade-offs between water and vegetation classes are not yet clear because vegetation classes cannot singularly predict household water consumption.