Delineating life circles is an essential prerequisite for urban community life circle planning. Recent studies combined the environmental contexts with residents’ global positioning system (GPS) data to delineate the life circles. This method, however, is constrained by GPS data, and it can only be applied in the GPS surveyed communities. To address this limitation, this study developed a generalizable delineation method without the constraint of behavioral data. According to previous research, the community life circle consists of the walking-accessible range and internal structure. The core task to develop the generalizable method was to estimate the spatiotemporal behavioral demand for each plot of land to acquire the internal structure of the life circle, as the range can be delineated primarily based on environmental data. Therefore, behavioral demand estimation models were established through logistic regression and machine learning techniques, including decision trees and ensemble learning. The model with the lowest error rate was chosen as the final estimation model for each type of land. Finally, we used a community without GPS data as an example to demonstrate the effectiveness of the estimation models and delineation method. This article extends the existing literature by introducing spatiotemporal behavioral demand estimation models, which learn the relationships between environmental contexts, population composition and the existing delineated results based on GPS data to delineate the internal structure of the community life circle without employing behavioral data. Furthermore, the proposed method and delineation results also contributes to facilities adjustments and location selections in life circle planning, people-oriented transformation in urban planning, and activity space estimation of the population in evaluating and improving the urban policies.

中文翻译：

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基于时空行为需求的机器学习估计的城市社区生活圈的划分

划定生活圈是城市社区生活圈规划的必要前提。最近的研究将环境背景与居民的全球定位系统 (GPS) 数据相结合，以描绘生活圈。但这种方法受GPS数据的限制，只能应用于GPS调查的社区。为了解决这一限制，本研究开发了一种不受行为数据约束的可推广的描绘方法。根据以往的研究，社区生活圈由步行可达范围和内部结构组成。开发泛化方法的核心任务是估计每个地块的时空行为需求，以获取生命周期的内部结构，因为范围可以主要基于环境数据进行划定。所以，通过逻辑回归和机器学习技术，包括决策树和集成学习，建立了行为需求估计模型。选择错误率最低的模型作为每种土地类型的最终估计模型。最后，我们以一个没有 GPS 数据的社区为例，证明了估计模型和描绘方法的有效性。本文通过引入时空行为需求估计模型扩展现有文献，该模型学习环境背景、人口构成与基于GPS数据的现有圈定结果之间的关系，在不使用行为数据的情况下圈定社区生活圈的内部结构。此外，