III: Medium: Collaborative Research: Integrating Behavioral, Geometrical and Graphical Modeling to Simulate and Visualize Urban Areas

III：媒介：协作研究：集成行为、几何和图形建模来模拟和可视化城市地区

• 批准号: 0964412
• 负责人: Paul Waddell
• 金额: $ 45万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0964412&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; Integrating

In this project, the PI and his team will develop a new simulation framework to interactively model and visualize socio-economic and geometric characteristics of urban areas. The framework will consist of a synergistic collaboration of three different areas: behavioral urban modeling, probabilistic graphical modeling, and visualization and computer graphics. In machine learning and statistics, the area of probabilistic graphical modeling offers a flexible framework to build, estimate and simulate from models of substantial complexity and scale, with partially observed data. By accounting for uncertainty and interdependencies, including aspects of dynamic equilibrium that arise in modeling the complex spatio-temporal dynamics of urban areas, the PI argues there is significant potential for breakthroughs in modeling large-scale urban systems. Similarly, by integrating behavioral and geometrical dimensions of urban areas, he expects to exploit the power of behavioral simulations more effectively by filling in geometric details that behavioral models are not well suited to manage, and at the same time provide a powerful framework to generate 2D and 3D geometric representations of urban areas that are behaviorally and geometrically consistent. The PI will take advantage of massive datasets available for urban areas, including parcel and building inventories, business establishment inventories, census data, household surveys, and GIS data on physical and political features, and will fuse these data into a coherent and consistent database to support his modeling objectives. This data fusion will address imputation of missing data, accounting for complex spatial and relational connections among the data sources. The PI will evaluate the accuracy and usability of his system through several deployments in diverse contexts. The PI has elicited engagement from the Urban Land Institute, the European Research Council, and the Council for Scientific and Industrial Research. Several organizations in the San Francisco Bay Area in California and the Puget Sound region in Washington will serve as testbeds for the research. Finally, the PI will collaborate with other NSF-funded research projects, such as the Drought Research Initiative Network, in order to investigate correlations between urban development and water/drought. Broader Impacts: The results of this multidisciplinary project will have a transformative effect on the area of urban simulation, in that they will enable non-professionals as well as the general public to better understand urban phenomena. City planners, researchers, students, and citizens will be able to efficiently simulate urban processes not previously possible, and to visualize the effects of adopting different urban policies on urban livability and sustainability outcomes, and to address local and global concerns regarding equity, infrastructure, and economic development. The framework will provide interactive desktop and web-based interfaces for configuring urban scenario inputs to a simulation that may reach petabytes in data size, and to visualize the simulation results using 2D aerial views, 3D city walkthroughs, and choroplethic maps and tables of indicators portraying the simulated area. Thus, the work will also advance the fields of visualization and computer graphics, through development of new techniques for large-scale urban modeling and rendering. The PI will develop an open-source system to make the results of this research widely available.

在这个项目中，PI 和他的团队将开发一个新的模拟框架，以交互方式对城市地区的社会经济和几何特征进行建模和可视化。 该框架将包括三个不同领域的协同合作：行为城市建模、概率图形建模以及可视化和计算机图形学。 在机器学习和统计学中，概率图形建模领域提供了一个灵活的框架，可以利用部分观察到的数据来构建、估计和模拟具有相当复杂性和规模的模型。 通过考虑不确定性和相互依赖性，包括在对城市地区复杂的时空动态进行建模时出现的动态平衡方面，PI 认为在大规模城市系统建模方面存在巨大的突破潜力。 同样，通过整合城市地区的行为和几何维度，他希望通过填充行为模型不太适合管理的几何细节来更有效地利用行为模拟的力量，同时提供一个强大的框架来生成2D行为和几何上一致的城市地区的 3D 几何表示。 PI 将利用城市地区可用的大量数据集，包括地块和建筑清单、商业机构清单、人口普查数据、家庭调查以及有关自然和政治特征的 GIS 数据，并将这些数据融合到一个连贯一致的数据库中，以支持他的建模目标。 这种数据融合将解决缺失数据的插补问题，解释数据源之间复杂的空间和关系连接。 PI 将通过在不同环境中进行多次部署来评估其系统的准确性和可用性。 该 PI 得到了城市土地研究所、欧洲研究委员会以及科学与工业研究委员会的参与。 加利福尼亚州旧金山湾区和华盛顿普吉特海湾地区的几个组织将作为该研究的测试平台。最后，PI 将与其他 NSF 资助的研究项目（例如干旱研究计划网络）合作，以调查城市发展与水/干旱之间的相关性。更广泛的影响：这个多学科项目的结果将对城市模拟领域产生变革性影响，因为它们将使非专业人士和公众更好地理解城市现象。 城市规划者、研究人员、学生和公民将能够有效地模拟以前不可能的城市进程，并可视化采取不同城市政策对城市宜居性和可持续性成果的影响，并解决当地和全球有关公平、基础设施、和经济发展。 该框架将提供交互式桌面和基于网络的界面，用于将城市场景输入配置到数据大小可能达到 PB 级的模拟，并使用 2D 鸟瞰图、3D 城市演练以及分区分布图和指标表来可视化模拟结果模拟区域。 因此，这项工作还将通过开发大规模城市建模和渲染的新技术来推进可视化和计算机图形学领域的发展。 PI 将开发一个开源系统，以使这项研究的结果广泛可用。