Collaborative Research: RIPS Type 1: Human Geography Motifs to Evaluate Infrastructure Resilience

这个项目将研究人们日常节奏和节奏中的变化主题是如何与移动交通和通信基础设施相互依存地形成的。人类动态和工程系统之间的弹性经常受到人文地理主题中的小皱纹的挑战，这些皱纹可能会改变人口和基础设施的时间和地理位置。例如，由于冬季天气而推迟上班时间，可能会影响高峰通勤节奏，延误全市递送系统的物流，或者导致通信活动突然爆发。虽然这些可能是在特定地点和时间从正常情况下的微小局部变化形成的，但它们可能会转移、扩散和适应，带来意想不到的后果，并对通勤、劳动力市场、物流和城市管理等更广泛的现象产生严重影响。如果我们要规划、缓解和管理这些动态，了解这些动态是如何在日益互联的系统中产生、形成和传播的，以及对它们进行测量和建模是至关重要的。建立这种理解需要一种跨学科的方法，将工程学、信息学、计算机和社会行为科学联系起来：这是一个多管齐下的挑战，表明下一代学生和工程师在设计、构建、维护和管理城市系统时将面临的问题，这些城市系统日益与我们不断变化和不断演变的活动模式交织在一起、依赖和适应。同样，向不同的城市管理人员、工程师和广大公众群体提供正确的数据、指标和模型，使他们能够有效地了解相互依存关系，对于形成能够更好地应对此类挑战的系统至关重要。研究这些联系的一个起点是探索传统的人文地理数据来源，但也要开发可扩展的系统，这些系统可以使用位置感知技术产生的新数据，在杂乱的背景和日常城市生活的复杂性中生成整个人口的快速快照。对这些数据的新颖分析可以产生动态演变的相互依赖的地图集和普查，从中可以提取和解析行为的主题，如土地使用、活动、流动性和社会性。这些主题可以为计算机模型提供信息，这些模型旨在探索人、地点、流程和基础设施之间的假设动态，从而更好地框架和描述活动、移动、访问和信息方面的相互依赖。为了协助将这项研究转化为公共领域，该项目将使几项成果正规化：一套可通过社区门户网站访问的可重复使用的数据和模型输出；华盛顿特区冬季天气情景的试点演示，它将充分探索人文地理与移动交通和通信基础设施之间相互依存的情景；以及一套供盟军模型系统使用的代码库。通过应用于与地理、信息学和工程学相关的实质性问题，这些成果将使其他社区能够将这些方法应用于其城市、数据和基础设施。

This project will examine how shifting motifs in the everyday rhythms and tempo of people form, interdependently, with mobile transport and communications infrastructure. The resilience between dynamics of human and engineered systems is often challenged by small wrinkles in the motifs of human geography that may shift the timing and geography of populations and infrastructure off-normal. For example, delayed starts to the workday because of winter weather can bump peak commuting off-rhythm, delay the logistics of citywide delivery systems, or produce bursts in communications activity. While these may form as small local shifts from normal in particular places and times, they can transfer, diffuse, and adapt with unforeseen consequences and serious impacts on broader phenomena as diverse as commuting, the labor market, logistics, and urban management. Understanding how these dynamics arise, form, and spread through increasingly connected systems, as well as measuring and modeling them is critical if we are to plan for them, mitigate them, and manage them. Building this understanding requires an interdisciplinary approach that bridges engineering, informatics and computing, and the socio-behavioral sciences: a multipronged challenge that is indicative of the problems that a next-generation of students and engineers will face in designing, constructing, maintaining, and managing urban systems that are increasingly intertwined with, dependent upon, and adapting to the shifting and ever-evolving patterns of our activities. Similarly, getting the right data, metrics, and models to diverse groups of urban managers, engineers, and the public-at-large in ways that can usefully inform their understanding of interdependency will be critical in fashioning systems that can better weather such challenges. A starting point in investigating these connections is to explore conventional sources of data on human geography, but to also develop extensible systems that can use newly-forming data from location-aware technologies that produce rapid snapshots of whole populations in the messy context and complexity of everyday urban life. Novel analyses on these data can produce dynamically-evolving atlases and censuses of interdependency, from which motifs of behavior can be extracted and resolved, as land-use, activity, mobility, and sociality. These motifs can inform computer models designed to explore what-if dynamics between people, place, process, and infrastructure, that better frame and describe interdependency in activity, movement, access, and information. To assist in translating this research into the public domain, the project will formalize several outputs: a set of reusable data and model outputs accessible via a community Web portal, a pilot demonstration for winter weather scenarios in Washington DC that will fully explore scenarios of interdependency between human geography and mobile transport and communications infrastructure, and a set of code libraries for use in allied model systems. Through application to substantive issues of relevance in geography, informatics, and engineering, these outputs will enable other communities to apply and adapt these methods to their cities, data, and infrastructure.