利用核密度估计描述肥胖环境的空间肥胖风险评分：开发和参数变化。

BACKGROUND: Overweight and obesity are severe public health problems worldwide. Obesity can lead to chronic diseases such as type 2 diabetes mellitus. Environmental factors may affect lifestyle aspects and are therefore expected to influence people's weight status. To assess environmental risks, several methods have been tested using geographic information systems. Freely available data from online geocoding services such as OpenStreetMap (OSM) can be used to determine the spatial distribution of these obesogenic factors. The aim of our study was to develop and test a spatial obesity risk score (SORS) based on data from OSM and using kernel density estimation (KDE). METHODS: Obesity-related factors were downloaded from OSM for two municipalities in Bavaria, Germany. We visualized obesogenic and protective risk factors on maps and tested the spatial heterogeneity via Ripley's K function. Subsequently, we developed the SORS based on positive and negative KDE surfaces. Risk score values were estimated at 50 random spatial data points. We examined the bandwidth, edge correction, weighting, interpolation method, and numbers of grid points. To account for uncertainty, a spatial bootstrap (1000 samples) was integrated, which was used to evaluate the parameter selection via the ANOVA F statistic. RESULTS: We found significantly clustered patterns of the obesogenic and protective environmental factors according to Ripley's K function. Separate density maps enabled ex ante visualization of the positive and negative density layers. Furthermore, visual inspection of the final risk score values made it possible to identify overall high- and low-risk areas within our two study areas. Parameter choice for the bandwidth and the edge correction had the highest impact on the SORS results. DISCUSSION: The SORS made it possible to visualize risk patterns across our study areas. Our score and parameter testing approach has been proven to be geographically scalable and can be applied to other geographic areas and in other contexts. Parameter choice played a major role in the score results and therefore needs careful consideration in future applications.

背景：超重和肥胖是全球严重的公共卫生问题。肥胖可导致 2 型糖尿病等慢性疾病。环境因素可能会影响生活方式方面，因此预计会影响人们的体重状况。为了评估环境风险，已经使用地理信息系统测试了几种方法。可以使用在线地理编码服务（如 OpenStreetMap，OSM）提供的免费数据来确定这些致肥胖因素的空间分布。我们的研究目的是开发并测试基于 OSM 数据和核密度估计（KDE）的空间肥胖风险评分（SORS）。

方法：从德国巴伐利亚的两个直辖市下载与肥胖相关的因素。我们将致肥胖和保护因素在地图上可视化，并通过 Ripley 的 K 函数测试空间异质性。随后，我们基于正和负 KDE 曲面开发了 SORS。在 50 个随机空间数据点估计风险评分值。我们检查了带宽、边缘校正、权重、插值方法和网格点数。为了考虑不确定性，我们整合了空间自举（1000 个样本），并通过 ANOVA F 统计量来评估参数选择。

结果：我们根据 Ripley 的 K 函数发现了致肥胖和保护环境因素的显著聚类模式。单独的密度图使我们能够预先查看正密度层和负密度层。此外，对最终风险评分值的直观检查使我们能够在我们的两个研究区域内识别出整体高风险和低风险区域。带宽和边缘校正的参数选择对 SORS 结果的影响最大。

讨论：SORS 使我们能够跨研究区域可视化风险模式。我们的评分和参数测试方法已被证明在地理上具有可扩展性，可应用于其他地理区域和其他背景。参数选择在评分结果中起着重要作用，因此在未来的应用中需要仔细考虑。