ABSTRACT

Advances in Land System Science (LSS) rely on the evidence generated by different types of research activities, including place-based case studies, landscape/land-system mapping and synthesis research. However, these activities are usually conducted in parallel, with a lack of integration often leading to important knowledge gaps and limitations. In this article, we provide tools for the application of geographic similarity analysis (GSA), a collection of spatially-explicit methods assessing the degree of similarity between geographic locations, and thereby help to address these limitations. We identify opportunities for employing GSA to support: 1) selecting geographically representative sets of case studies; 2) integrating empirical evidence generated at different scales and levels of abstraction; and 3) facilitating context-sensitive knowledge transfer. The resulting toolbox provides approaches for facilitating researchers to get an enhanced understanding of multi-scale land change processes, as well as supporting land governance in scaling up the knowledge and solutions generated by LSS research.

摘要

土地系统科学 (LSS) 的进步依赖于不同类型研究活动产生的证据，包括基于地点的案例研究、景观/土地系统制图和综合研究。然而，这些活动通常是并行进行的，缺乏整合往往会导致严重的知识差距和限制。在本文中，我们提供了地理相似性分析（GSA）的应用工具，这是评估地理位置之间相似程度的空间显式方法的集合，从而有助于解决这些限制。我们确定雇用 GSA 的机会来支持：1）选择具有地理代表性的案例研究组；2）整合不同规模和抽象层次产生的经验证据；3) 促进情境相关的知识转移。由此产生的工具箱提供了多种方法，帮助研究人员更好地了解多尺度土地变化过程，并支持土地治理扩大 LSS 研究产生的知识和解决方案。