With the advent of “social sensing” in the Big Data era, location-based social media (LBSM) data are increasingly used to explore anthropogenic activities and their impacts on the environment. This study converts a typical kind of LBSM data, geo-tagged tweets, into raster images at the 500 m spatial resolution and compares them with the new generation nighttime lights (NTL) image products, the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) monthly image composites. The results show that the monthly tweet images are significantly correlated with the VIIRS-DNB images at the pixel level. The tweet images have nearly the same ability on estimating electric power consumption and better performance on assessing personal incomes and population than the NTL images. Tweeted areas (i.e. the pixels with at least one posted tweet) are closer to satellite-derived built-up/urban areas than lit areas in NTL imagery, making tweet images an alternative to delimit extents of human activities. Moreover, the monthly tweet images do not show apparent seasonal changes, and the values of tweet images are more stable across different months than VIIRS-DNB monthly image composites. This study explores the potential of LBSM data at relatively fine spatiotemporal resolutions to estimate or map socioeconomic factors as an alternative to NTL images in the United States.

随着大数据时代“社会感知”的到来，基于位置的社交媒体（LBSM）数据越来越多地用于探索人为活动及其对环境的影响。这项研究将具有地理标记的推文的一种典型的LBSM数据转换为空间分辨率为500 m的光栅图像，并将其与新一代夜间照明（NTL）图像产品（可见红外成像辐射计套件（VIIRS）Day /夜间乐队（DNB）每月图像合成。结果表明，在像素级别，每月的推文图像与VIIRS-DNB图像显着相关。与NTL图像相比，tweet图像在估计电能消耗方面具有几乎相同的功能，并且在评估个人收入和人口方面具有更好的性能。鸣叫区域（即 （至少有一个发布了推文的像素）比NTL图像中的照明区域更靠近卫星衍生的建筑物/城市区域，从而使推文图像成为界定人类活动范围的替代方法。此外，每月的推文图像没有显示明显的季节性变化，并且在不同月份中的推文图像的值比VIIRS-DNB每月的图像复合图像更稳定。这项研究探索了相对精细的时空分辨率下的LBSM数据来估计或绘制社会经济因素的潜力，以替代美国的NTL图像。并且在不同月份中，tweet图像的值比VIIRS-DNB月度图像复合图像更稳定。这项研究探索了相对精细的时空分辨率下的LBSM数据来估计或绘制社会经济因素的潜力，以替代美国的NTL图像。并且在不同月份中，tweet图像的值比VIIRS-DNB月度图像复合图像更稳定。这项研究探索了相对较好的时空分辨率下的LBSM数据来估计或绘制社会经济因素的潜力，以替代美国的NTL图像。