Using multi-source data to understand the factors affecting mini-park visitation in Yancheng

Abstract

Mini-parks are becoming a popular form of outdoor recreational space in densely populated areas, largely because their small size makes site selection easier than for ordinary parks. However, existing studies on mini-parks are limited because most of them rely on data collected through traditional surveys, which are severely restricted by space and time. In this study, we utilised Tencent user density data – a type of space–time synchronous data with high spatial resolution – to trace mini-park visitation in the main city of Yancheng, China, and we integrated data about land use, points of interest, transportation, demographics and housing prices to measure the parks’ surrounding features. We investigated how factors relating to the parks’ spatial and internal attributes, surrounding physical features and surrounding socio-economic features affected the number of park visits during the week versus during the weekend by establishing a series of multiple linear regression models. The results showed that higher resident population, more surrounding public toilets and larger open site promoted mini-park visits while distance to the city centre, surrounding large parks and main roads discouraged mini-park visits. This study also found that the effects of weekend visitation factors were more complex than those of weekday visitation factors. These findings can help urban green space planners and decision makers to efficiently allocate mini-parks to areas where they will be most effective.

What Affects the Use Flexibility of Pocket Parks? Evidence from Nanjing, China

“…One to two team members were responsible for data collection in each area, ensuring that the use conditions of each sample were recorded through photos in each target period during each investigation day. As existing studies have demonstrated that the factors affecting park use on weekdays and weekends differ [33,45], we collected the usage data of all the samples over 10 days, including five weekdays and five weekends.…”

Exploring Factors Affecting Urban Park Use from a Geospatial Perspective: A Big Data Study in Fuzhou, China

“…Recent studies have employed RTUD to map urban population distribution at the building scale in the study of land use [32], urban structure [33], and population migration [30]. However, few studies have applied these data to the measurement of park use at the city level [20] even though they have been shown to do a far better job than other data in revealing how many people use parks of any size (except mini ones) [34].…”

“…Regarding research perspectives and methodologies, previous research has mainly used traditional regression models [12,20], such as multiple linear regression, to explore the factors affecting park use but has often ignored the multicollinearity among influencing factors and the spatial relationship between driving factors [35]. This has led these studies to miss important details and insufficiently reflect the actual phenomena that reveal the correlations between park use and its influencing factors [20,23,34]. Moreover, recent studies have shown that the relationship between park use and its driving factors is not linear but rather shows significant spatial heterogeneity [14,15,17,20].…”

User-generated content may increase urban park use: Evidence from multisource social media data

“…The proximity, often measured by metric or topological distance, was also used to assess park accessibility. Besides, the social and physical features of the surrounding area have varying effects on how and how much citizens use them, including population density (Donahue et al, 2018; Li et al, 2020), socioeconomic status (Gu et al, 2020; Wang et al, 2021), development density, available facilities and services, and land-use mix (Donahue et al, 2018; Li et al, 2020; Lyu and Zhang, 2019; Zhang and Zhou, 2018; Zhou et al, 2021).…”