BACKGROUND The increasing prevalence of obesity is a major public health problem in many countries. Built environment factors are known to be associated with obesity, which is an important risk factor for type 2 diabetes. Online geocoding services could be used to identify regions with a high concentration of obesogenic factors. The aim of our study was to examine the feasibility of integrating information from online geocoding services for the assessment of obesogenic environments. METHODS We identified environmental factors associated with obesity from the literature and translated these factors into variables from the online geocoding services Google Maps and OpenStreetMap (OSM). We tested whether spatial data points can be downloaded from these services and processed and visualized on maps. True- and false-positive values, false-negative values, sensitivities and positive predictive values of the processed data were determined using search engines and in-field inspections within four pilot areas in Bavaria, Germany. RESULTS Several environmental factors could be identified from the literature that were either positively or negatively correlated with weight outcomes in previous studies. The diversity of query variables was higher in OSM compared with Google Maps. In each pilot area, query results from Google showed a higher absolute number of true-positive hits and of false-positive hits, but a lower number of false-negative hits during the validation process. The positive predictive value of database hits was higher in OSM and ranged between 81 and 100% compared with a range of 63-89% for Google Maps. In contrast, sensitivities were higher in Google Maps (between 59 and 98%) than in OSM (between 20 and 64%). CONCLUSIONS It was possible to operationalize obesogenic factors identified from the literature with data and variables available from geocoding services. The validity of Google Maps and OSM was reasonable. The assessment of environmental obesogenic factors via geocoding services could potentially be applied in diabetes surveillance.

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使用在线地理编码服务中的数据评估环境致病因素：可行性研究。

背景技术肥胖症的流行是许多国家的主要公共卫生问题。已知环境因素与肥胖有关，肥胖是2型糖尿病的重要危险因素。在线地理编码服务可用于识别致癌因素高度集中的地区。我们研究的目的是检验整合在线地理编码服务中的信息以评估致肥胖环境的可行性。方法我们从文献中确定了与肥胖相关的环境因素，并将这些因素转化为在线地理编码服务Google Maps和OpenStreetMap（OSM）的变量。我们测试了是否可以从这些服务下载空间数据点，并在地图上对其进行处理和可视化。正负值，使用搜索引擎和德国巴伐利亚州四个试点地区的现场检查确定了处理后数据的假阴性值，敏感性和阳性预测值。结果从文献中可以确定一些环境因素，这些因素与先前研究中的体重结果呈正相关或负相关。与Google Maps相比，OSM中查询变量的多样性更高。在每个试点区域中，Google的查询结果显示，在验证过程中，真假匹配和假假匹配的绝对值均较高，但假阴性匹配的数量则较少。在OSM中，数据库命中的阳性预测值较高，介于81％和100％之间，而Google Maps为63-89％。相反，Google Maps（在59％到98％之间）的敏感度高于OSM（在20％到64％之间）的敏感度。结论可以使用可从地理编码服务获得的数据和变量来操作从文献中确定的致肥胖因素。Google Maps和OSM的有效性是合理的。通过地理编码服务评估环境致病因素可能会应用于糖尿病监测。