利用夜光遥感图像和推特数据来研究停电情况

C. Hultquist, Mark B. Simpson, G. Cervone, Qunying Huang
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引用次数: 25

摘要

飓风桑迪在美国人口最多的地区之一登陆,影响了近800万人。由于现有数据和对人口稠密地区的巨大影响,该事件为研究停电提供了一个独特的机会。飓风登陆“之前”和“之后”的卫星夜光图像被用来量化停电造成的灯光变暗。通过关键字过滤的地理位置tweet在事件期间以高时间和空间分辨率提供有关人类活动的有价值信息。对卫星数据的亮度变化和与权力相关的推文密度的分析指出了一种空间关系,可以识别出人类存在的严重受影响地区。通过文本分析对推文进行分类,可以进一步缩小信息搜索范围,找到最相关、最可靠的内容。Twitter数据与卫星图像融合,以街道级别的分辨率识别停电信息,这是单独使用卫星图像无法实现的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using nightlight remote sensing imagery and Twitter data to study power outages
Hurricane Sandy made landfall in one of the most populated areas of the United States, and affected almost 8 million people. The event provides a unique opportunity to study power outages because of the data available and the large impact to a densely populated area. Satellite nightlight imagery of "before" and "after" the landfall of the hurricane is used to quantify the light dimming caused by power outages. Geolocated tweets filtered by keywords provide valuable information on human activity at a high temporal and spatial resolution during the event. Analysis of brightness change in the satellite data and the density of power related tweets points to a spatial relationship that identifies severely impacted areas with human presence. Classification of tweets through text analysis serves to further narrow the information search to find the most relevant and reliable content. Twitter data fused with satellite imagery identifies power outage information at a street-level resolution that is not achievable with satellite imagery alone.
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