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Gwilym Eades 《Geoforum》2010,41(5):671-673
This short article reviews and critically appraises Kingsbury and Jones’ (2009)Geoforum article entitled “Walter Benjamin’s Dionysian Adventures on Google Earth”, and finds it lacking in several regards. First, the use of Nietzsche’s Dionysus/Apollo dichotomy is in fact a double binary, in which Apollonian logic is constructed as a binary within the dichotomy, while the Dionysian logic is ‘bacchanalian’. Second, Walter Benjamin as exemplar of Dionysian logic is shown to be problematic and more a distraction from the flawed Nietzschean logic than a useful construct in itself. Lastly, the educational potential of Google Earth is overlooked by Kingsbury and Jones, in their haste to see Google Earth through ‘Google goggles’, in which everything Google appears through Dionysian-tinted lenses. 相似文献
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以甘肃省为例,在基于Google Earth Engine (GEE)平台实现1995年、2000年、2005年、2010年、2015年和2020年土地变化监测的基础上,利用贝叶斯层次时空模型(BHM)分析土地利用程度的时空变化特征。结果表明：① 研究期间内甘肃省土地利用程度呈增长趋势,其中1995―2000年和2010―2015年增长速度较明显;② 土地利用程度空间格局“东高西低”,热点区域主要分布在陇中、陇东和陇南地区;③ 土地利用程度局部变化呈现明显区域差异,整体表现为“东弱西强”,局部变化热点区域主要分布在河西地区;④ 影响土地利用程度变化的主要因素是经济规模和产业结构,其中经济因素影响程度最高。 相似文献
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Google Earth作为功能十分强大的虚拟地球软件,已经广泛应用于多个行业。以Google Earth为平台,将通过GPS坐标集成化的地质信息转化为GE支持的KML格式,然后加载到Google Earth。充分利用Google Earth的海量数据管理能力和三维显示能力,提升地质调查工作的效率和成果的质量,更好地为地质调查工作服务。 相似文献
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New sources of data such as ‘big data’ and computational analytics have stimulated innovative pedestrian oriented research. Current studies, however, are still limited and subjective with regard to the use of Google Street View and other online sources for environment audits or pedestrian counts because of the manual information extraction and compilation, especially for large areas. This study aims to provide future research an alternative method to conduct large scale data collection more consistently and objectively on pedestrian counts and possibly for environment audits and stimulate discussion of the use of ‘big data’ and recent computational advances for planning and design. We explore and report information needed to automatically download and assemble Google Street View images, as well as other image parameters for a wide range of analysis and visualization, and explore extracting pedestrian count data based on these images using machine vision and learning technology. The reliability tests results based on pedestrian information collected from over 200 street segments in Buffalo, NY, Washington, D.C., and Boston, MA respectively suggested that the image detection method used in this study are capable of determining the presence of pedestrian with a reasonable level of accuracy. The limitation and potential improvement of the proposed method is also discussed. 相似文献
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As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are often the strongest indicators of human interaction with the environment, and understanding how urban areas develop through remotely sensed data allows for more sustainable practices. The Google Earth Engine (GEE) leverages cloud computing services to provide analysis capabilities on over 40 years of Landsat data. As a remote sensing platform, its ability to analyze global data rapidly lends itself to being an invaluable tool for studying the growth of urban areas. Here we present (i) An approach for the automated extraction of urban areas from Landsat imagery using GEE, validated using higher resolution images, (ii) a novel method of validation of the extracted urban extents using changes in the statistical performance of a high resolution population mapping method. Temporally distinct urban extractions were classified from the GEE catalog of Landsat 5 and 7 data over the Indonesian island of Java by using a Normalized Difference Spectral Vector (NDSV) method. Statistical evaluation of all of the tests was performed, and the value of population mapping methods in validating these urban extents was also examined. Results showed that the automated classification from GEE produced accurate urban extent maps, and that the integration of GEE-derived urban extents also improved the quality of the population mapping outputs. 相似文献
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Digging into Google Earth: An analysis of “Crisis in Darfur” 总被引：1，自引：0，他引：1
Google publicists have suggested the Crisis in Darfur is an example of the Google Earth software’s “success at tangibly impacting what is happening on the ground.” Yet whether or not Google Earth’s interface, along with a medley of other media representations of the conflict, have impacted events on the ground or led to coherent policies of humanitarian intervention remains open to debate. This article draws upon critical approaches from media studies—namely discourse analysis—to analyze several aspects of the Google Earth/USHMM Crisis in Darfur project. While this project was no doubt developed with the noble intention of generating international awareness about widespread violence that has recently occurred in the Darfur region, it is important to evaluate how representations of global conflicts are changing with uses of new information technologies and whether such representations can actually achieve their desired impacts or effects. The article begins with a discussion of the Crisis in Darfur project’s history, proceeds to analyze some of the press coverage of the project and then moves to a critique of the layer using four categories of analysis: (1) the shifting role of satellite image; (2) the temporality of the interface; (3) the practice of conflict branding; and (4) the practice of “information intervention.” Throughout the article, I explore how the presentation of Darfur-related materials through Google Earth reproduces problematic Western tropes of African tragedy and misses an opportunity to generate public literacy around satellite images. I also consider how humanitarianism is intertwined with digital and disaster capitalism, and suggest that this instance of “information intervention” makes patently clear that high visual capital alone cannot resolve global conflicts. 相似文献