BE/CNH: Urban Landscape Patterns: Complex Dynamics and Emergent Properties

美国的城市发展正在深刻地改变景观格局和生物多样性，同时也受到这些变化的影响。 然而，人们对人类主导的景观中模式和过程之间的相互作用知之甚少。 城市景观动态的一个最不为人所知的方面是人类和生物物理过程的局部相互作用产生大都市地区景观格局的方式。 研究这些相互作用和由此产生的城市景观格局之间的关系，对于规划和管理城市增长，最大限度地减少对生态系统的生态影响，同时维持经济和社会上可行的城市社区至关重要。 本研究项目将探讨城市景观作为新兴的现象，从人类代理人，真实的房地产市场，建成的基础设施和生物物理因素，如土地覆盖，地貌和自然干扰制度的当地相互作用的结果，发展城市景观动力学理论。 该研究将采用复杂系统，补丁动力学，层次理论和基于代理的建模方法来研究耦合的人类-自然动力学和经验测试这种方法在两个不同的生物区（西雅图和凤凰城）。 这些模型将被开发并用于测试关于城市景观的新兴特性的假设，并加强对城市景观中跨尺度人类生态相互作用的基本理解。 城市景观表现出复杂自组织系统的一些基本特征。 它们是高度异质性的，空间嵌套，层次结构。 城市的空间结构可以被描述为一个累积和聚合的模式，结果从许多地方的决定，涉及大量的自适应代理人之间的相互作用，并与生物物理因素。 这些行为最终会导致不同的大都市模式。 本研究将解决四个问题：（1）动态景观系统如何演变，以产生新兴的模式，在城市景观是显而易见的？ (2)什么样的非线性、阈值、不连续性和路径依赖性解释了城市景观的不同轨迹？ (3)新兴城市景观格局如何影响生物多样性和生态系统功能？ (4)规划如何整合这些知识来发展可持续的城市景观模式？ 该模型的实施将基于一个动态概率关系模型（DPRM），其中参数和空间规则的经验估计从两个纵向的土地覆盖和土地利用数据集为西雅图和凤凰城metropolitan areas.The项目将有重大的理论和实践影响。 它将更好地了解复杂的人类生态动态，导致发展模式，如蔓延，这是美国最紧迫和最有争议的问题之一。 该项目还将有助于推进生物复杂性科学。 这项研究的结果将对社会科学和自然科学产生影响，特别是对城市化地区的发展模式、土地利用变化、生态恢复力和公共政策的研究。 该项目还将采用新的计算技术，这些技术对研究人类动力学，生态学和人工智能的广泛学科具有重要意义。 研究结果还将通过提供模拟工具来评估城市发展和生态保护替代战略的生态影响和反馈，从而有助于城市地区的规划和管理。 该项目得到了2005财政年度环境中生物复杂性特别竞赛的支持，该竞赛侧重于自然和人类系统耦合的动力学。

Urban development in the United States is profoundly changing landscape patterns and biodiversity and is simultaneously affected by these changes. Little is known about the interactions between patterns and processes in human dominated landscapes, however. One of the least understood aspects of urban landscape dynamics is the way in which local interactions of humans and biophysical processes generate the landscape patterns of metropolitan regions. Studying the relationships between these interactions and the resulting urban landscape patterns is critical to plan and manage urban growth in ways that minimize the ecological impacts on ecosystems while sustaining economically and socially viable urban communities. This research project will examine urban landscapes as emergent phenomena that result from local interactions of human agents, real estate markets, built infrastructure, and biophysical factors like land cover, geomorphology, and natural disturbance regimes to develop a theory of urban landscape dynamics. The study will employ complex systems, patch dynamics, hierarchical theory, and an agent-based modeling approach to study coupled human-natural dynamics and empirically test this approach in two different bioregions (Seattle and Phoenix). The models will be developed and used to test hypotheses regarding emergent properties of urban landscapes and to enhance basic understanding of humans-ecological interactions in urban landscapes across scales. Urban landscapes exhibit some fundamental features of complex self-organizing systems. They are highly heterogeneous, spatially nested, and hierarchically structured. The urban spatial structure can be described as a cumulative and aggregate pattern that results from numerous local decisions involving a large number of adaptive agents interacting among themselves and with biophysical factors. These behaviors eventually can lead to different metropolitan patterns. This research will address four questions: (1) How do dynamic landscape systems evolve to generate emergent patterns that are evident in urban landscapes? (2) What nonlinearities, thresholds, discontinuities, and path dependencies explain divergent trajectories of urban landscapes? (3) How do emergent urban landscape patterns influence biodiversity and ecosystem functioning? (4) How can planning integrate this knowledge to develop sustainable urban landscape patterns? The model implementation will be based on a dynamic probabilistic relational model (DPRM) in which parameters and spatial rules are estimated empirically from two longitudinal land-cover and land-use data sets developed for the Seattle and Phoenix metropolitan areas.The project will have significant theoretical and practical impacts. It will develop a better understanding of complex human-ecological dynamics leading to development patterns such as sprawl, one of the most pressing and controversial problem in the United States. The project will also contribute to advancing biocomplexity science. The findings of this research will have an impact on both the social and natural sciences particularly the study of development patterns, land-use change, ecological resilience, and public policy in urbanizing regions. This project will also employ new computational techniques that are of importance to a broad range of disciplines studying human dynamics, ecology, and artificial intelligence. The findings will also aid planning and management of urban regions by providing simulation tools to assess the ecological impacts and feedback of alternative strategies for urban development and ecological conservation. This project is supported by an award resulting from the FY 2005 special competition in Biocomplexity in the Environment focusing on the Dynamics of Coupled Natural and Human Systems.