Modeling geospatial processes: Integrating artificial intelligence with complexity, networks and geographic information sciences

快速城市化及其对自然资源的相关需求给宝贵的农地和林地造成了越来越大的压力，并带来了很高的损失风险。研究人与自然耦合系统(CHAN)之间的界面、相互作用和反馈是一项多学科的努力，需要扩展思维，将社会科学和自然科学中常见的传统研究方法结合起来。土地利用/土地覆盖(LULC)变化过程是一个典型的CHANS过程，是一个复杂的系统，植根于区域层面的人类与环境的相互作用，并在多个空间尺度上产生影响。为了将复杂性科学理论与地理信息科学相结合，需要创新的地理空间建模和地理模拟方法。这些新办法将提供有效的方法来：预测可能出现的情况；评估不同管理和政策战略的影响；并帮助减轻土地利用、土地利用的变化和土地利用的变化的后果。拟议的研究计划的具体目标是：(1)开发先进的一代人工智能地理模拟方法，用于表示过程中各种行为者的行为，并预测土地利用/土地利用变化的空间动力学和模式；(2)利用网络科学概念增强开发的模型，使其能够在更大的空间范围内使用；(3)设计模型测试、校准和验证程序，以评估和比较开发的模型对各种土地利用/土地利用/土地利用变化问题的有效性。地理自动机，特别是基于地理空间代理的建模，将与人工智能前沿技术以及新兴的网络科学学科相结合，所有这些都在地理信息系统和科学框架内。空间建模方法将使用地理信息系统和遥感数据集，并在主要在加拿大、不列颠哥伦比亚省和温哥华大都市区的城市大都市区和森林复盖区的空间决策和土地利用规划的背景下实施。拟议的研究计划将提高理解和评估土地利用/土地利用/土地利用变化变化过程的潜在结果的能力。它还将创建地理空间建模方法和工具，以改进地方和国家两级的城市和自然资源规划、决策和政策建设。所产生的知识将可转移到其他背景下。**

Rapid urbanization and the associated need for natural resources create increasing pressures on and impose a high risk of loss for valuable agricultural and forested lands. Examining the interfaces, mutual interactions and feedbacks between coupled human and natural system (CHANS) is a multidisciplinary effort and require an extension in thinking that combines the traditional research methods common in the social and natural sciences. The land-use and land-cover (LULC) change process is a typical CHANS, characterized as a complex system, and rooted in local-level human interactions with the environment that have consequences at multiple spatial scales. Innovative geospatial modeling and geosimulation approaches are needed to integrate the theory of complexity science with geographic information science. These new approaches will provide effective ways to: forecast possible scenarios; evaluate the effects of different management and policy strategies; and aid in mitigating the consequences of LULC change. The proposed research program has the specific objectives to: (1) develop an advanced generation of artificial intelligence geosimulation approaches for representing the behavior of various actors in the process and forecasting the spatial dynamics and pattern of LULC changes; (2) enhance the developed models with network science concepts to enable their use in larger spatial extents; (3) design model testing, calibration and validation procedures to evaluate and compare the effectiveness of the developed models for various LULC problems. Geographic automata, particularly geospatial agent-based modeling, will be integrated with techniques at the forefront of artificial intelligence as well as with the emerging discipline of network science all within geographic information systems (GIS) and science frameworks. The spatial modeling approaches will use GIS and remote sensing datasets and implemented in the context of spatial decision-making and land-use planning for the urban metropolitan areas and forest covers primarily in Canada, British Columbia and the Metro Vancouver Region. The proposed research program will enhance the capability to understand and assess the potential outcomes of the LULC change process. It will also create geospatial modeling methodologies and tools to improve urban and natural resources planning, decision-making and policy-building at local and national levels. The knowledge generated would be transferable to other contexts.**