Master of Agribusiness / Department of Agricultural Economics / Nathan P. Hendricks / Public parks can provide considerable value to the population that visit them, for the community around them and the local economy. A well designed public park can attract growth in tourism, stimulate a habitat for wildlife, contribute to personal health and wellness, improve the aesthetics of an area and stimulate economic growth. Managing and operating a public park entails many complex issues such as designing an attractive green space, implementing and maintaining the park, attracting and managing visitors and obtaining financial support. Public parks need to identify factors that influence park visitation in order to more effectively manage park visitorship.. This thesis examines park visitation analyzing data of park users of The High Line in New York City to develop a model to more accurately predict visitation. The thesis focuses on the critical social and climatic variables that attract visitors to spend time in the High Line park. Understanding these factors will allow park management the ability to create a strategic plan for managing a public space that best serves its visitors and the community. More specifically, a strategic plan helps to determine who the visitors are and what activities they enjoy in the park. In conceptualizing a solution, High Line can put into practice what its visitors want to see offered in the park and which of its programming needs improvement to attract more visitors. Meeting the needs of park visitors will create a better experience for the customers and a better management strategy for operations. A multivariate regression analysis was used to establish the relationship between High Line visitation and the climatic and social variables. The climatic variables of daily average temperature and precipitation. The social variables of day of the week and season of the year were added to the structural model as dummies. A time trend variable characterized as time in years was added to the model to show any yearly change in visitation to the park. This method has been widely applied to a number of studies testing the relationship of climatic and social variables to park visitation (Micah, Scotter and Fenech 2016). The results of this regression analysis show that the social variables of day of the week and season and the climatic variables of average temperature and precipitation had a significant affect on park visitation. The model developed can be used to forecast park visitation, quantifying the many variables that influence park visitation.

Amusement parks are a huge business. Guest experiences determine the success or failure for an amusement park. This report suggests an approach to improve guest experience by managing guest flow. The guest happiness optimization problem is formulated into a visitor routing management model. The constraints for this model include attraction attributes and guest behavior. To build the attraction constraints, their information is first gathered from internet, field studies and surveys, and then input into simulation software. Constraints on guest behavior are set up with a literature study and a guest survey. A two phase heuristic is developed to solve this problem with constraints. Candidate routes are generated with a route construction algorithm in the first phase. Visitor distribution and selection on these candidate routes are determined in the second phase using a mixed integer programming solver. Visitor routes are then recommended to the park’s operator side, for them to distribute to guests visiting on their vacations. Data from Disney Epcot are collected and applied in the case study to implement the methodology in this report. Attraction operations capability is maintained at the current level with no additional cost for the project, while guest satisfaction is improved by ensuring the number and type of attractions they visit. In addition, average waiting time for visitors is reduced by at least 70% in the recommended operation strategy. / text

Atmospheric theming is the use of the sensory experience in connection to a theme. The purpose of this research is to examine the effects of atmospherics with theming and their effects on customer behavior in food and beverage operations of a theme park. The official research questions developed for this study include: Does the impact of the authenticity of atmospheric theming influence an effect on revisit intention? Does the type of theme (land's theme or venue's theme) influence the effect of visitor revisit intention? These questions guided the current research in previously non-evaluated fields of study. This study used the Mehrabian–Russel (M-R) model to create a new research model. In the current study, atmospheric theming was the stimuli, emotional value was the emotional response, and visitor revisit intentions was the behavioral intention. Restaurant image was added to the model to obtain a cognitive reason.The results from the multiple regression indicated that all hypotheses were accepted. Restaurant image had a positive influence on both authenticity variables, and both authenticity variables had a positive influence on emotional value. Finally, emotional value was found to have a positive influence on revisit intention. These results indicated that atmospheric theming influenced revisit intention through emotional value. Furthermore, the results indicated that when it comes to the different types of themes within a venue, as indicated in the second research question, the venue's theme has a stronger influence on the emotional value than the land's theme.