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Effects of Smart City Infrastructure on Millennials

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Adopting smart city tactics is important because it allows cities to develop sustainable communities through efficient policy initiatives. This study exemplifies how data analytics enables planners within smart cities to gain a better understanding of their population, and can make

Adopting smart city tactics is important because it allows cities to develop sustainable communities through efficient policy initiatives. This study exemplifies how data analytics enables planners within smart cities to gain a better understanding of their population, and can make more informed choices based on these consumer choices. As a rising share of the millennial generation enters the workforce, cities across the world are developing policy initiatives in the hopes of attracting these highly educated individuals. Due to this generation's strength in driving regional economic vitality directly and indirectly, it is in the best interests of city planners to understand the preferences of millennials so this information can be used to improve the attractiveness of communities for this high-purchasing power, productive segment of the population. Past research has revealed a tendency within this demographic to make location decisions based on the degree of ‘livability’ in an area. This degree represents a holistic approach at defining quality of life through the interconnectedness of both the built and social environments in cities.

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.

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Date Created
2015-05

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Spatial-Temporal Analysis of Barrett Freshmen 2007-2012: Source Area Analysis and Poisson Regression

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In order to help enhance admissions and recruiting efforts, this longitudinal study analyzed the geographic distribution of matriculated Barrett freshmen from 2007-2012 and sought to explore hot and cold spot locations of Barrett enrollment numbers using geographic information science (GIS)

In order to help enhance admissions and recruiting efforts, this longitudinal study analyzed the geographic distribution of matriculated Barrett freshmen from 2007-2012 and sought to explore hot and cold spot locations of Barrett enrollment numbers using geographic information science (GIS) methods. One strategy involved   weighted mean center and standard distance analyses for each year of data for non-resident (out-of-state) freshmen home zip codes. Another strategy, a Poisson regression model, revealed recruitment "hot and cold spots" across the U.S. to project the expected counts of Barrett freshmen by zip code. This projected count served as a comparison for the actual admissions data, where zip codes with over and under predictions represented cold and hot spots, respectively. The mean center analysis revealed a westward shift from 2007 to 2012 with similar distance dispersions. The Poisson model projected zero-student zip codes with 99.2% accuracy and non-zero zip codes with 73.8% accuracy. Norwalk, CA (90650) and New York, NY (10021) represented the top out-of-state cold spot zip codes, while the model indicated that Chandler, AZ (85249) and Queen Creek, AZ (85242) had the most in-state potential for recruitment. The model indicated that more students have come from Albuquerque, NM (87122) and Aurora, CO (80015) than anticipated, while Phoenix, AZ (85048) and Tempe, AZ (85284) represent in-state locations with higher correlations between the variables included, especially regarding distance decay, and the than expected numbers of freshmen. The regression also indicated the existence of strong likelihood of attracting Barrett students.

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2013-05