Play benefits childhood development and well-being, and is a key factor in sustainable city design. Though previous studies have examined the effects of various urban features on how much children play and where they play, such studies rely on quantitative measurements of play such as the precise location of play and the duration of play time, while people's subjective feelings regarding the playability of their environment are overlooked. In this study, we capture people's perception of place playability by employing Amazon Mechanical Turk (MTurk) to classify street view images. A deep learning model trained on the labelled data is then used to evaluate neighborhood playability for three U.S. cities: Boston, Seattle, and San Francisco. Finally, multivariate and geographically weighted regression models are used to explore how various urban features are associated with playability. We find that higher traffic speeds and crime rates are negatively associated with playability, while higher scores for perception of beauty are positively associated with playability. Interestingly, a place that is perceived as lively may not be playable. Our research provides helpful insights for urban planning focused on sustainable city growth and development, as well as for research focused on creating nourishing environments for child development.

玩福利儿童发展和福祉，而且是可持续城市设计的关键因素。虽然以前的研究已经检查了各种城市功能的影响有多少孩子玩，在那里玩，这样的研究依靠发挥定量测量，如玩的精确位置和上场时间的持续时间，而人的主观感受有关的可玩性他们的环境中被忽视。在这项研究中，我们采用亚马逊的Mechanical Turk（MTurk）分类街景影像，留住人的地方可玩性的看法。然后接受了有关标记数据深学习模型来评估三个美国城市附近的可玩性：波士顿，西雅图和旧金山。最后，多元和地理加权回归模型来探索城市功能如何将各种与可玩性有关。我们发现，较高的流量速度和犯罪率存在负相关的可玩性，而对美的感知更高的分数存在正相关的可玩性。有趣的是，被认为是热闹的地方可能无法播放。我们的研究提供了城市规划的有益见解集中在城市可持续的增长和发展，以及为研究主要集中在儿童发展创造滋补环境。这被认为是热闹的地方可能无法播放。我们的研究提供了城市规划的有益见解集中在城市可持续的增长和发展，以及为研究主要集中在儿童发展创造滋补环境。这被认为是热闹的地方可能无法播放。我们的研究提供了城市规划的有益见解集中在城市可持续的增长和发展，以及为研究主要集中在儿童发展创造滋补环境。