Developing spatially explicit methods to enhance geovisual analytics and decision support

当面临重要决策时，我们会尝试将决策建立在一个理性的框架上，该框架通常包括多个决策标准。近20年前，多准则决策分析(MCDA)首次被引入地理信息系统(GIS)领域。基于地理信息系统的MCDA目前在各种应用领域进行，包括环境、交通和城市/区域规划、废物管理以及水文学、农业和林业。 矛盾的是，在基于地理信息系统的MCDA中，除了评价结果在地图上的可视化之外，几乎没有利用位置特征。例如，既没有被普遍接受的空间明确决策标准框架，也没有将空间差异纳入标准权重或决策规则的系统方法。MCDA计算通常基于非空间标准，不包括空间上明确的参数或决策者的偏好。 拟议的研究旨在缩小这一概念差距，并展示空间显式方法在选定的应用中用于多准则决策的实用性。我的团队将把地理可视化和MCDA工具与空间统计相结合，并制定以地图为中心的综合方法，指导用户在社区服务、规划和环境评估等领域执行有效的决策任务。我们还将研究决策准则、准则标准化和准则权重的空间差异对基于GIS的MCDA结果的影响，并开发一个时空MCDA框架。 这项研究为地球信息学的知识体系做出了贡献。它有可能使基于地理信息系统的MCDA更有效，并解决空间决策支持中可能阻碍其更广泛接受的概念性问题。最终，这项研究计划将改善政府、非营利组织和企业的战略决策，在这些决策中，决策具有地理空间成分。

When faced with important decisions, we try to base our decision-making on a rational framework which often includes multiple decision criteria. Multi-criteria decision analysis (MCDA) was first introduced to the field of Geographic Information Systems (GIS) nearly 20 years ago. GIS-based MCDA is now conducted in various fields of application, including environmental, transportation, and urban/regional planning, in waste management, as well as in hydrology, agriculture, and forestry. Paradoxically, there has been little use of the locational characteristics in GIS-based MCDA besides the visualization of evaluation results on maps. For example, there is neither a generally accepted framework for spatially explicit decision criteria nor a systematic approach to incorporating spatial variations in criterion weights or decision rules. MCDA calculations are typically based on non-spatial criteria and do not include spatially explicit parameters or decision-maker preferences. The proposed research aims at closing this conceptual gap and demonstrating the utility of spatially explicit methods for multi-criteria decision-making in selected applications. My team will integrate geovisualization and MCDA tools with spatial statistics and develop a comprehensive map-centred methodology that guides users through valid sequences of decision-making tasks in fields such as community service planning and environmental assessments. We will also study the effects of spatial variations in decision criteria, criterion standardization, and criterion weighting on GIS-based MCDA results, and develop a framework for spatio-temporal MCDA. This research contributes to the body of knowledge in Geomatics. It has the potential to make GIS-based MCDA more effective and addresses conceptual issues in spatial decision support that may have hampered its broader acceptance. Ultimately, this research program will improve strategic decision-making by government, non-profit organizations, and businesses, where decisions have a geospatial component.