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

地理空间过程建模：将人工智能与复杂性、网络和地理信息科学相结合

• 批准号: RGPIN-2017-03939
• 负责人: Dragicevic, Suzana
• 金额: $ 4.23万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=754781
• 关键词: Modeling; geospatial; processes; Integrating; artificial

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.

快速的城市化和对自然资源的相关需求给宝贵的农业和林地带来了越来越大的压力，并带来了很高的损失风险。研究人类与自然耦合系统（CHANS）之间的界面、相互影响和反馈是一项多学科的工作，需要结合社会科学和自然科学中常见的传统研究方法进行思维扩展。土地利用和土地覆盖（LULC）变化过程是一个典型的CHANS，其特征是一个复杂的系统，植根于地方层面的人类与环境的相互作用，在多个空间尺度上产生后果。需要创新的地理空间建​​模和地理模拟方法来将复杂性科学理论与地理信息科学相结合。这些新方法将为以下方面提供有效的方法： 预测可能的情况；评估不同管理和政策策略的效果；并帮助减轻土地利用和土地利用变化变化的后果。拟议的研究计划的具体目标是：（1）开发新一代人工智能地理模拟方法，用于表示过程中各种参与者的行为并预测土地利用和土地利用变化的空间动态和模式； （2）利用网络科学概念增强已开发的模型，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，以评估和比较所开发模型对各种 LULC 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，将与人工智能最前沿的技术以及地理信息系统（GIS）和科学框架内的新兴网络科学学科相集成。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大温哥华地区的城市大都市区和森林覆盖的空间决策和土地使用规划的背景下实施。拟议的研究计划将增强理解和评估土地利用和土地利用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，以改善地方和国家层面的城市和自然资源规划、决策和政策制定。生成的知识可以转移到其他环境。