Thermal Environment of Urban Schoolyards: Current and Future Design with Respect to Children’s Thermal Comfort

A direct observation tool to measure interactions between shade, nature, and children's physical activity: SOPLAY-SN

BACKGROUND

Most physical activity (PA) during school occurs at recess; however, recess PA may be influenced by children's thermal comfort and interaction with nature, neither of which have concurrently been measured reliably in previous studies. This study tests the reliability of SOPLAY-SN, an adaption of the validated System for Observing Play and Leisure Activity in Youth (SOPLAY) to measure Shade and Nature (SN) alongside PA, and associations between children's PA and interaction with shade and nature during recess to highlight the utility of the tool.

METHODS

Interactions with shade and nature were measured using systematic direct observation at two playgrounds (primary-grade = ages 5-8, upper-grade = ages 9-12) during recess at an elementary school in Phoenix, Arizona (USA). Pairs conducted observations over four warm days (primary = 29-34 °C, upper-grade = 32-36 °C) in May 2021 (N = 179 scans). Intraclass correlation coefficients (ICC) were used to calculate inter-rater reliability. Mean counts, frequencies, and Kendall rank correlation coefficient tests were used to assess relations between PA level and interactions with shade and nature.

RESULTS

Reliability was good for sedentary behavior (ICC = 0.98); light PA (LPA; ICC = 0.80) and moderate-to-vigorous PA (MVPA; ICC = 0.94); shade interaction (ICC = 0.95); and nature interaction (ICC = 0.80) and average agreement was good (86% overall PA, 88% shade, 90% nature). Most (60%) primary-grade children were observed in the shade, with 64% under a covered play structure where children were mainly (47%) sedentary. Of the 11% of primary-grade students observed interacting with nature, 90% occurred in a grass field with trees. Among upper-grade children, 23% were observed in the shade with 53% in grass fields where 48% of play was light. Few (7%) upper-grade children were observed interacting with nature, with most instances (76%) in a grass field with trees. Among primary-grade children, shade was correlated with sedentary behavior (τ_b = 0.63, p < .05); LPA (τ_b = 0.39, p < .05); MVPA (τ_b = 0.56, p < .05); and nature interactions with sedentary behavior (τ_b = 0.16, p < .05). Among upper-grade children, shade was correlated with sedentary behavior (τ_b = 0.27, p < .05) and LPA (τ_b = 0.21, p < .05).

CONCLUSIONS

SOPLAY-SN is a reliable tool for measuring children's interaction with shade and nature and participation in PA. Understanding how shade and nature impact movement during recess can inform playground design for children's health and well-being.

Landscape and vegetation traits of urban green space can predict local surface temperature

Societal and technological advances have triggered demands to improve urban environmental quality. Urban green space (UGS) can provide effective cooling service and thermal comfort to alleviate warming impacts. We investigated the relative influence of a comprehensive spectrum of UGS landscape and vegetation factors on surface temperature in arid Urumqi city in northwest China. Built-up area range was extracted from Luojia 1-01 (LJ1-01) satellite data, and within this range, the landscape metric information and vegetation index information of UGS were obtained based on PlanetScope data, and a total of 439 sampling grids (1 km × 1 km) were generated. The urban surface temperature of built-up areas was extracted from Landsat8-TIRS images. The 12 landscape metrics and 14 vegetation indexes were assigned as independent variables, and surface temperature the dependent variable. Support Vector Machine (SVM), Gradient Boost Regression Tree (GBRT) and Random Forest (RF) were enlisted to establish numerical models to predict surface temperature. The results showed that: (1) It was feasible to predict local surface temperature using a combination of landscape metrics and vegetation indexes. Among the three models, RF demonstrated the best accuracy. (2) Collectively, all the factors play a role in the surface-temperature prediction. The most influential factor was Difference Vegetation Index (DVI), followed by Green Normalized Difference Vegetation Index (GNDVI), Class Area (CA) and AREA. This study developed remote sensing techniques to extract a basket of UGS factors to predict the surface temperature at local urban sites. The methods could be applied to other cities to evaluate the cooling impacts of green infrastructures. The findings could provide a scientific basis for ecological spatial planning of UGS to optimize cooling benefits in the arid region.