通过带偏差校正的众包测量分析互联网质量的差异与时间演进
Analyzing Disparity and Temporal Progression of Internet Quality through Crowdsourced Measurements with Bias-Correction
AI总结：
本文利用近百万条Ookla Speedtest记录和人口普查数据，提出区域抽样偏差校正方法，揭示互联网速度与收入、年龄、族裔等因素的关系及速度随时间的增长。
摘要：
众包速度测试量是从最终用户视角研究互联网性能的重要工具。然而，尽管单个测量具有准确性，但由于这些数据点固有的抽样偏差，对其进行简单聚合存在问题。在本研究中，我们使用包含近100万条独立Ookla Speedtest测量记录的数据集，将每个数据点与2019年人口普查的人口统计数据相关联，并开发新方法以提出一种新颖分析，用于量化区域抽样偏差以及互联网性能与人口统计特征之间的关系。基于同质性统计检验，我们发现众包Ookla Speedtest数据点在不同人口普查区块组之间存在显著抽样偏差。我们引入两种方法，依据各个人口普查区块组的人口数量来校正区域偏差。尽管抽样偏差导致一个城市互联网速度的整体累积分布函数在原始样本估计与偏差校正估计之间出现小幅差异，但与跨区域抽样异质性的规模相比，这一差异要小得多。此外，我们表明抽样偏差与若干人口统计变量密切相关，例如收入、教育水平、年龄和族裔分布。通过回归分析，我们发现收入较高、人口较年轻且西班牙裔居民占比较低的地区往往测得更快的互联网速度，同时社会经济属性与族裔构成之间存在显著共线性。最后，基于状态空间模型的线性和非线性分析，我们发现平均互联网速度随时间提高，不过区域抽样偏差可能导致对互联网速度时间增长的小幅高估。
英文摘要：
Crowdsourced speedtest measurements are an important tool for studying internet performance from the end user perspective. Nevertheless, despite the accuracy of individual measurements, simplistic aggregation of these data points is problematic due to their intrinsic sampling bias. In this work, we utilize a dataset of nearly 1 million individual Ookla Speedtest measurements, correlate each datapoint with 2019 Census demographic data, and develop new methods to present a novel analysis to quantify regional sampling bias and the relationship of internet performance to demographic profile. We find that the crowdsourced Ookla Speedtest data points contain significant sampling bias across different census block groups based on a statistical test of homogeneity. We introduce two methods to correct the regional bias by the population of each census block group. Whereas the sampling bias leads to a small discrepancy in the overall cumulative distribution function of internet speed in a city between estimation from original samples and bias-corrected estimation, the discrepancy is much smaller compared to the size of the sampling heterogeneity across regions. Further, we show that the sampling bias is strongly associated with a few demographic variables, such as income, education level, age, and ethnic distribution. Through regression analysis, we find that regions with higher income, younger populations, and lower representation of Hispanic residents tend to measure faster internet speeds along with substantial collinearity amongst socioeconomic attributes and ethnic composition. Finally, we find that average internet speed increases over time based on both linear and nonlinear analysis from state space models, though the regional sampling bias may result in a small overestimation of the temporal increase of internet speed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
