Accurately predicting visitation as a strategic tool for management of a public park

Abstract

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.