NSF Convergence Accelerator Track J: Artificial-Intelligence-Based Decision Support for Equitable Food and Nutrition Security in the Houston Area

由于获得营养食品的机会有限，对健康食品选择的知识不足，流离失所者社区继续受到健康问题的困扰。COVID-19疫情加剧了这些问题。解决粮食不安全问题的最关键步骤之一是提高对所需资源的认识，以及在服务不足的社区采购、分配、获取和利用粮食资源方面面临的挑战。在需求方面，利益攸关方必须查明粮食无保障家庭的营养需求、文化偏好、粮食准备设备和供应。如果一个人不知道什么是特定的食物，也不知道如何准备它，它将被浪费，食品慈善生态系统的努力将失败。在供应方面，利益相关者必须简化物流，改善沟通和协调，以优化供应链（上游采购和下游分销），最大限度地减少效率低下，并协调各种食品慈善机构的努力。通过社区驱动的方法，该项目将公民合作者与大学研究人员聚集在一起，以大休斯顿地区的数据驱动智能技术为基础，开发和建立一个面向当地的食品慈善生态系统。我们的社会技术方法解决了四个总体研究问题：（1）我们如何重新定义，建模和预测食物沙漠？(2)如何减少食物浪费？(3)我们如何影响营养扫盲？(4)我们如何衡量获得文化上适当的营养食品对健康的影响？这些问题推动了以下目标：（i）评估、建模和预测难以获得负担得起的营养食品的地理区域; ㈡分析各社区，以找出在编制文化上适当的食谱方面尚未利用的优势，并将这一知识传授给志同道合的社区成员，㈢为食品储藏室建立基础设施，以提供其库存的文化上适当的食品和食谱/食品编制说明，（iv）创建基于区块链的食品捐赠市场，（v）开发多种语言的教育材料，并与社区卫生工作者合作传播，（vi）衡量获得文化上合适的营养食品的健康影响。我们的多学科团队汇集了计算机科学，城市规划，社区参与，营养教育/社区健康生活方式计划开发，供应链、衡量和评价以及一系列“实地”从业者合作伙伴。我们的团队专注于将研究转化为行动。决策工具将传播给其他食品生态系统成员，以解决文化意识食品分配的各个方面。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估。

Disadvantaged communities continue to be afflicted with health issues due to limited access to nutritious food and inadequate knowledge about healthy food choices. The COVID-19 pandemic has exacerbated these issues. One of the most critical steps in addressing food insecurity is to improve understanding of the resources required and the challenges faced in procurement, distribution, access, and utilization of food resources in underserved communities. On the demand side, stakeholders must identify the nutritional needs, cultural preferences, and food preparation equipment and supplies of food-insecure households. If an individual does not know what a particular food is nor how to prepare it, it will go to waste, and the efforts of the food charity ecosystem will fail. On the supply side, stakeholders must streamline logistics and improve communication and coordination to optimize the supply chain (upstream procurement and downstream distribution) to minimize inefficiencies and coordinate the efforts of various food charities. Through a community-driven approach, this project brings together civic collaborators with university researchers to develop and build a locally-oriented food charity ecosystem based on data-driven smart technologies in the Greater Houston region. Our socio-technical approach addresses four overall research questions: (1) How can we redefine, model, and predict food deserts? (2) How can we minimize food waste? (3) How can we impact nutrition literacy? (4) How can we measure the health effects of having access to culturally appropriate nutritious food?These questions drive the following objectives: (i) Assess, model, and predict geographic areas with limited access to affordable and nutritious food; (ii) Analyze communities to find untapped strengths in preparing culturally appropriate recipes and transfer this knowledge to like-minded community members, (iii) Build infrastructure for food pantries to provide culturally appropriate food they have in stock and recipes/food preparation instructions, (iv) Create a blockchain-based marketplace for food donations, (v) Develop educational material in multiple languages and partner with Community Health Workers to disseminate it, and (vi) Measure the health effects of having access to culturally appropriate nutritious food.Our multidisciplinary team brings together researchers in computer science, urban planning, community engagement, nutrition education/community healthy lifestyle program development, supply chain, measurement and evaluation, and a diverse set of “on the ground” practitioner partners. Our team is focused on turning research into action. The decision-making tools will be disseminated to other food ecosystem members to address all aspects of culturally aware food distribution.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.