更准确但意义不大?为什么质量指标不能揭示土地利用地图中的社会技术实践?

Andreas Christian Braun
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引用次数: 0

摘要

遥感技术在现代地理学和环境科学中发挥着重要作用。与此同时,它的认识论基础往往很薄弱。遥感结果大多被视为严格客观、与环境无关的人工制品。这极大地忽视了产生这些结果的人类实践。因此,遥感数据被不加批判地纳入(环境)政策决策过程,而不了解它们究竟是如何产生的。最近的研究对此提出了批评。在之前的一项研究中,我发现土地利用结果的准确性可以通过分类汇总来提高,而结果的地理或环境意义却受到了影响。我将这一现象称为 "更准确、更无意义(MALM)"效应,并证明无论分类的技术水平如何,这种效应都是存在的。在本研究中,我讨论了在多大程度上可以通过选择适当的质量指标来纠正 MALM 效应。我的研究表明,在可以想象的最大范围内,质量指标不会也无法揭示社会技术实践的影响,而这些社会技术实践是土地利用地图的物质载体。因此,质量指标无法将研究人员的实践和价值观的影响客观化。因此,它们无法解决 MALM 问题。相反,我的研究表明,将地理知识明确纳入质量指标可以最大程度地解决 MALM 效应。这更加证实了我的观点,即需要更加重视考虑遥感信息背后的价值和实践。我在广泛的背景下讨论了这些结果,并论证了基于批判性(自然)地理学和科学技术研究的批判性遥感对于更好地将这些结果纳入决策至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
More accurate less meaningful? Why quality indicators do not unveil the socio-technical practices inscribed into land use maps
Remote sensing plays an important role for modern geography and environmental science. At the same time, it often stands on a weak epistemological foundation. Remote sensing results are mostly treated as strictly objective, context-independent artifacts. This vastly ignores the human practices that led to these results. Thus, remote sensing data are uncritically incorporated into (environmental) policy decision-making processes without understanding exactly how they were generated. Recent research has been critical of this. In a previous study, I showed that the accuracy of land use results can be increased by class aggregation, while the geographic or environmental meaning of the results suffers. I called this provocatively the “more accurate, less meaningful (MALM)” effect and showed that it exists regardless of the technical level of classification. In this study, I discuss the extent to which MALM can be remedied by choosing an appropriate quality indicator. I show that, to the largest extent conceivable, the quality indicator does not and cannot unveil the effects of socio-technical practices, which are materially inscribed into land use maps. Hence, quality indicators are unable to objectivize the effects of practices and values by the researchers. Consequently, they do not solve the MALM problem. On the contrary, I show that the explicit inclusion of geographic knowledge in quality addresses the MALM effect to the largest extent possible. This reinforces my claim that more attention needs to be paid to considering the values and practices behind remote sensing information. I discuss the results in a broad context and argue that and why critical remote sensing based on critical (physical) geography and science-and-technology studies is vital to better incorporate such results into policymaking.
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