Contextualizing the global relevance of local land change observations

8th International Symposium of the Digital Earth

地球科学;计算机科学

Magliocca, N.R.^1 ; Ellis, E.C.^1 ; Oates, T.^2 ; Schmill, M.^2

Department of Geography and Environmental Systems, University of Maryland, Baltimore County, Baltimore, MD, United States^1

Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, United States^2

关键词: Data limitations;    Geo visualizations;    Global patterns;    Global synthesis;    Sampling efforts;    Scientific workflows;    Socio-economics;    Statistical inference;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/18/1/012107/pdf
DOI  :  10.1088/1755-1315/18/1/012107

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

To understand global changes in the Earth system, scientists must generalize globally from observations made locally and regionally. In land change science (LCS), local field-based observations are costly and time consuming, and generally obtained by researchers working at disparate local and regional case-study sites chosen for different reasons. As a result, global synthesis efforts in LCS tend to be based on non-statistical inferences subject to geographic biases stemming from data limitations and fragmentation. Thus, a fundamental challenge is the production of generalized knowledge that links evidence of the causes and consequences of local land change to global patterns and vice versa. The GLOBE system was designed to meet this challenge. GLOBE aims to transform global change science by enabling new scientific workflows based on statistically robust, globally relevant integration of local and regional observations using an online social-computational and geovisualization system. Consistent with the goals of Digital Earth, GLOBE has the capability to assess the global relevance of local case-study findings within the context of over 50 global biophysical, land-use, climate, and socio-economic datasets. We demonstrate the implementation of one such assessment-a representativeness analysis-with a recently published meta-study of changes in swidden agriculture in tropical forests. The analysis provides a standardized indicator to judge the global representativeness of the trends reported in the meta-study, and a geovisualization is presented that highlights areas for which sampling efforts can be reduced and those in need of further study. GLOBE will enable researchers and institutions to rapidly share, compare, and synthesize local and regional studies within the global context, as well as contributing to the larger goal of creating a Digital Earth.