PED-PHAM: An Automated and Scalable Spatial Tool That Predicts and Monetizes Health Impacts of the Built, Natural, and Social Environment

拟议的PEDESTRIAN公共卫生评估模型（PED-PHAM）使用经过验证的人工智能 (AI)检测方法，以获得行人环境特征（PEF），如人行道，人行横道， 照明，从数字图像。它将把这些PEFs添加到城市设计4健康现有的同行评审的国家- PHAM可以预测健康结果。PED-PHAM将更好地使公共部门的分析师和决策者 规划机构、咨询公司、土地开发商、医疗保健提供者、贷款机构和研究机构 和大数据实体，以说明已知的可修改和具有成本效益的设计特征的健康益处， 预测身体活动和BMI。城市设计4健康的和亚利桑那州州立大学的目标是：1：评估 度AI模型检测PEF，2：创建和优化块组级别AI导出的PEF指数，以及3： 评价PEF增强模型。创建PED-PHAM。缺乏身体活动是肥胖的主要危险因素， 心脏病中风和II型糖尿病大多数成年人不活跃。这种身体活动（PA）缺乏症 在过去的二十年里，美国人口没有发生有意义的变化。缺乏PA部分是由于敌对 步行环境、久坐不动的依赖汽车的生活方式以及不断扩展的城市环境。显著 建筑环境、PA、体重指数（BMI）、糖尿病 和整体心脏代谢健康。很少有同行评议的循证工具量化和预测物理 基于社区的交通投资、土地使用和社区设计的活动和健康影响 决策，没有捕获PEF。我们将计算两个NIH中2,173名参与者家庭位置的PEF- 资助R 01研究，创建新的客观身体活动和报告的BMI模型，并整合结果 N-PHAM工具。将采取以下步骤[1]使用经过培训和 经过验证的AI计算机视觉模型应用于谷歌街景全向图像，每隔15米 在巴尔的摩、凤凰城、圣地亚哥和圣地亚哥， 来自两项NIH资助研究的西雅图地区，[2]为每个检测到的PEF构建块组水平指标，[3] 通过将PEF指标转换为求和标准化分布来优化块组水平的PEF指标，[4] 在空间上加入新的PEFs，宏观步行能力，绿地和人口统计措施， 和自我报告的PA和BMI到一个合并的个人水平的数据库。[5]进行统计分析， 确定哪些PEF（单独和组合指数）和体重最能解释PA和BMI， 根据人口统计、步行能力和绿地进行调整，[6]在现有的N- PHAM平台创建PED-PHAM。第二阶段将扩大PED-PHAM在全国的应用， 商业化，并进一步考虑空气污染暴露，以创建优化的社区设计场所- 基于处方，以增加PA和减少慢性疾病的用户选择的地点在美国各地。

The proposed PEDestrian Public Health Assessment Model (PED-PHAM) uses proven Artificial Intelligence (AI) detection methods to derive pedestrian environment features (PEF), such as sidewalks, crosswalks, and lighting, from digital images. It will add these PEFs to Urban Design 4 Health's existing peer reviewed National- PHAM that predicts health outcomes. PED-PHAM will better enable analysts and decision makers at public planning agencies, consulting firms, land developers, health care providers, lending institutions, and research and big data entities to account for health benefits of modifiable and cost-effective design features known to predict physical activity and BMI. Urban Design 4 Health's and Arizona State University's aims are: 1: Evaluate Degree AI Models Detect PEFs, 2: Create and Optimize Block Group Level AI-derived PEF Indices, and 3: Evaluate PEF enhanced Models. Create PED-PHAM. Physical inactivity is a primary risk factor for obesity, heart disease, stroke, and Type II diabetes. Most adults are inactive. This physical activity (PA) deficiency has not changed meaningfully for the US population in the last two decades. Lack of PA is partially due to hostile pedestrian environments, sedentary car-dependent lifestyles, and sprawling urban environments. Significant relationships have been documented between the built environment, PA, body mass index (BMI), diabetes, and overall cardiometabolic health. Few peer-reviewed evidence-based tools quantify and predict physical activity and health impacts of community-based transportation investments, land use, and community design decisions, and none capture PEFs. We will calculate PEFs for 2,173 participant home locations in two NIH- funded R01 studies, create new objective physical activity and reported BMI models, and integrate the results into the N-PHAM tool. The following steps will be taken [1] Objectively detect PEF presence using trained and validated AI computer vision models applied to Google Street View omnidirectional imagery every 15 meters along roads in census block groups containing 2173 participants in the Baltimore, Phoenix, San Diego, and Seattle regions from two NIH funded studies, [2] Construct block group level metrics for each detected PEF, [3] Optimize block group level PEF indices by translating PEF metrics into summed standardized distributions, [4] Spatially join new PEFs, macro walkability, greenspace, and demographic measures with objectively assessed and self reported PA and BMI into a combined person-level database. [5] Conduct statistical analysis to determine which PEFs (individually and in combined indices) and weights best explain PA and BMI when adjusting for demographics, walkability, and greenspace, and [6] Add new statistical models to the existing N- PHAM platform creating PED-PHAM. Phase 2 will scale PED-PHAM for national application and commercialization and further account for air pollution exposure to create optimized community design place- based prescriptions to increase PA and reduce chronic diseases for user-selected locations across the US.