Integrating complexity, GIS and soft computing for multi-scale spatial modeling and decision-making

人类活动对自然和自然环境的影响越来越大，导致了许多计划外的土地利用和土地覆盖变化。城市化、砍伐森林和工业发展等活动现在与城市蔓延密切相关，而森林火灾和虫害等自然灾害普遍降低了自然景观的质量。LULC变化过程很难预测，因为它们受到人类和生物物理系统变量之间许多复杂的非线性相互作用的影响。本研究的长期目标是促进对土地利用效率变化过程的结构和机制的科学认识，以改进空间预测和决策。复杂性科学和gisscience将作为理论基础，开发动态和现实的土地转化模型。主要目标是：(1)在地理信息系统（GIS）和Web-GIS框架内开发新的基于主体的建模（ABM）和CA方法；(2)处理和处理多时空尺度复杂地理空间数据集的软计算方法；(3)大型地理空间数据集鲁棒模拟的并行处理方法与算法；(4)标定和验证方法，为规划和管理环境下的空间决策提供可靠和有用的模型。将开发的方法和工具与利益相关者协作方法联系起来，将缩小研究层面的建模与有意义的现实世界应用之间的差距。该研究有助于扩展土地利用变化过程的科学知识，并通过创新的空间建模方法推进GIS和gisscience领域的动态能力。基于LULC模型的空间决策支持系统和工具的实施将进一步有助于协同环境规划和管理。林业、农业和城市土地利用领域的科学家、规划人员和决策者将受益于这项研究，因为它为做出有效决策提供了客观和科学的基础。

The increasing impact of human activities on the physical and natural environment has resulted in many unplanned land-use and land-cover (LULC) changes. Activities such as urbanization, deforestation and industrial development are now strongly associated with urban sprawl, while natural hazards such as forest fires and insect infestations have generally reduced the quality of the natural landscape. LULC change processes are difficult to predict as they are influenced by many complex and non-linear interactions between variables from human and biophysical systems. The long-term goal of this research program is to advance scientific understanding about the structure and mechanisms of LULC change processes for improving spatial prediction and decision-making. Complexity science and GIScience will be used as the theoretical foundations to develop dynamic and realistic land transformation models. The main objectives are to develop: (1) novel agent-based modeling (ABM) and CA approaches within geographic information systems (GIS) and Web-GIS frameworks; (2) soft computing approaches for handling and processing complex geospatial data sets at multiple spatial and temporal scales; (3) parallel processing methods and algorithms for robust geosimulations of large geospatial datasets; and (4) calibration and validation methods to generate reliable and useful models for spatial decision-making in planning and management contexts. Linking the developed approaches and tools to stakeholder collaboration methods will narrow the gap between modeling at a research level and meaningful real-world applications. This research contributes to expanding the scientific knowledge of LULC change processes and to advancing the field of GIS and GIScience to include dynamic capabilities using innovative spatial modeling approaches. The implementation of spatial decision support systems and tools based on the LULC models will further aid collaborative environmental planning and management. Scientists, planners and policy-makers in forestry, agriculture, and urban land-use will benefit from this research by having objective and scientific foundations on which to make effective decisions.