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

这个项目将研究人们日常生活节奏和克里思的变化如何与移动的交通和通信基础设施相互依赖地形成。人类和工程系统的动态之间的弹性往往受到人类地理图案中的小皱纹的挑战，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，由于冬季天气导致的工作日延迟开始可能会影响高峰通勤的节奏，延迟全市范围内交付系统的物流，或产生通信活动的突发。虽然这些可能会在特定的地方和时间形成与正常情况相比的小的局部变化，但它们可以转移，扩散和适应，对通勤，劳动力市场，物流和城市管理等更广泛的现象产生不可预见的后果和严重影响。如果我们要为这些动态进行规划、缓解和管理，那么了解这些动态如何在日益互联的系统中产生、形成和传播，以及对其进行测量和建模至关重要。建立这种理解需要跨学科的方法，桥梁工程，信息学和计算，以及社会行为科学：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、建设、维护和管理城市系统时将面临的问题，这些城市系统越来越多地与城市系统交织在一起，依赖于城市系统，并适应我们不断变化和不断发展的活动模式。类似地，将正确的数据、指标和模型提供给不同的城市管理者、工程师和广大公众，使他们能够有效地理解相互依赖性，这对于形成能够更好地应对这些挑战的系统至关重要。研究这些联系的一个起点是探索人类地理学的传统数据来源，但也要开发可扩展的系统，这些系统可以使用来自位置感知技术的新形成的数据，这些技术可以在混乱的背景和复杂的日常城市生活中快速生成整个人口的快照。对这些数据的新分析可以产生动态演变的相互依赖的地图集和普查，从中可以提取和解决行为的主题，如土地使用，活动，流动性和社会性。这些图案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，从而更好地构建和描述活动、运动、访问和信息中的相互依赖性。为了协助将这项研究转化为公共领域，该项目将正式确定几项产出：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产出，华盛顿特区冬季天气情景的试点示范，将充分探索人文地理与移动的运输和通信基础设施之间相互依赖的情景，以及一套用于联合模型系统的代码库。通过应用于地理学、信息学和工程学中的实质性问题，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.