| dc.description.abstract | Parks have a significant impact on residents’ health and social activities. By using
smartphone mobility data tracking the activities of 28 parks in the College Station and
Bryan Metropolitan area of Texas, USA, I present the temporal and spatial patterns of park
usage within a two-year timeframe. I model the effects of the socio-economic, built
environment, climate, surrounding points of interest (POI), and spatial/accessibility factors
on park visitations through a machine learning model. The results show that climate
change and nearby POIs such as restaurants and gas stations are significant factors
enhancing park visitations while having hotels and apartment complexes are not. The study
also reveals how smartphone mobility data can be applied to case studies investigating
urban design/planning and understanding the social and adjacent points of interest
associated with urban greenspaces. It provides empirical evidence on park visitations as
well as what factors future planners, landscape architects, and park managers should
consider when deciding on park investment and planning decisions for mid-sized cities. | en_US |
