从环境特征推断儿童体力活动的空间分布
Inferring the Spatial Distribution of Physical Activity in Children Population from Characteristics of the Environment
摘要：
肥胖影响儿童和青少年人口的比例不断上升，导致生活质量下降和合并症风险增加。虽然肥胖的主要原因是已知的，但致胖行为表现为个体与生活环境复杂相互作用的结果。因此，解决儿童肥胖问题仍然是公共卫生当局面临的一个挑战性问题。BigO项目（bigoprogram.eu）依靠大规模的行为和环境数据汇集来创建支持政策制定和干预设计的工具。在这项工作中，我们提出了一种新的分析方法来模拟预期的人口行为作为当地环境的函数。我们实验评估这种方法在预测预期的身体活动水平在小的地理区域，使用城市环境特征。从156名儿童和青少年收集的数据的实验验证了所提出的方法的潜力。具体来说，我们训练模型，预测一个地区的身体活动水平，达到81%的留一准确率。此外，我们利用模型预测来自动可视化感兴趣领域的预期人口行为的热图，从中我们得出有用的见解。总体而言，预测模型和自动热图是获得对人群行为空间分布的直接感知的有前途的工具，公共卫生当局可能会使用。
英文摘要：
Obesity affects a rising percentage of the children and adolescent population, contributing to decreased quality of life and increased risk for comorbidities. Although the major causes of obesity are known, the obesogenic behaviors manifest as a result of complex interactions of the individual with the living environment. For this reason, addressing childhood obesity remains a challenging problem for public health authorities. The BigO project (https://bigoprogram.eu) relies on large-scale behavioral and environmental data collection to create tools that support policy making and intervention design. In this work, we propose a novel analysis approach for modeling the expected population behavior as a function of the local environment. We experimentally evaluate this approach in predicting the expected physical activity level in small geographic regions using urban environment characteristics. Experiments on data collected from 156 children and adolescents verify the potential of the proposed approach. Specifically, we train models that predict the physical activity level in a region, achieving 81% leave-one-out accuracy. In addition, we exploit the model predictions to automatically visualize heatmaps of the expected population behavior in areas of interest, from which we draw useful insights. Overall, the predictive models and the automatic heatmaps are promising tools in gaining direct perception for the spatial distribution of the population's behavior, with potential uses by public health authorities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
