What Brings More People to Parks? Open Space Environment and Visitation in Tokyo
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AbstractIn dense urban settings, efficient use of land is a crucial issue. Especially, urban parks take a large amount of space in high land value areas. Creating an attractive park environment for current and future visitors has the potential to increase the benefits provided by parks to surrounding communities as well as individual park visitors (Dramstad, Tveit, Fjellstad, & Fry, 2006; Roberts, McEachan, Margary, Conner, & Kellar, 2016; Whyte, 1980). This study provides models to predict the number of annual visitors to parks, and identifies the associations between park visitations and environmental factors in parks with a specific focus on medium sized neighborhood parks. The main research questions that this study addresses include the followings. Are the proportions of specific land covers (tree, water, and building) associated with visitation volumes after removing the effects from park land size and demographic variables? If so, what are the characteristics and magnitudes of the associations?
This study uses neighborhood parks in Tokyo, Japan as its samples. Among more than 5,000 official urban parks in the 23 central wards of Tokyo, 185 medium-sized parks between 1ha and 10ha were selected. The annual visitation to parks was estimated through cell-phone GPS records. This GPS processing method effectively estimates visitation to medium sized parks that oftentimes are impossible to acquire. Through this application, this study exemplifies how newly emerging industry scale data can contribute to understanding of everyday activities of people that can aid more efficient planning and design (Calabrese, Diao, Di Lorenzo, Ferreira, & Ratti, 2013; Girardin, Vaccari, Gerber, Biderman, & Ratti, 2009).
This study investigates the research questions through both OLS and multi-level models. First, a baseline model with three most probable factors - park size, surrounding population, and building footprint - were carefully established. The associations between land cover variables and visitation volumes were estimated on top of the baseline model. To enable a more robust conclusion, several sensitivity tests were conducted.
The final baseline model found to have a very high fit (R2=0.8628), and the estimation remained robust when multi-level modeling was introduced. When several models were tested, the quadratic relationship between visitation and tree cover percentage appeared clearly and consistently. Although less clear than tree cover percentage, there was a positive association between visitation and water cover percentage in an acceptable significance. Also, the magnitude and certainty of the association between visitation and tree cover percentage remained almost same after water cover was added to the model.
This study found there is an approximate optimal value of tree cover, which is 45%. When tree cover percentage increases from 10% to 30%, the number of visitors is likely to go up by 21,696 people yearly and 59 people daily. When tree cover percentage increases from 60% to 80%, the number of visitors is likely to decrease by 22,124 people yearly and 61 people daily. As for water cover, 10% increase in water cover percentage is likely to be related with 14,168 more visitors per year and 39 more visitors per day. The magnitudes of these associations are considerable, even when compared to the baseline factors. For example, 1ha increase in park land area is associated with an increase of 15,527 visitors yearly.
These results, although not strictly causal, suggest that design or environmental quality variables in parks are important in attracting more people to parks. Careful design of parks with appropriate amount of diverse environmental elements may be more effective than creating large parks in terms of benefitting individuals and community. Furthermore, considering the high land values of large cities such as Tokyo, these findings can help the government with cost-effective park planning.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:37925656