ReGNL：使用夜光灯快速预测破坏性事件期间的GDP
ReGNL: Rapid Prediction of GDP during Disruptive Events using Nightlights
摘要：
政策制定者经常根据国内生产总值、失业率、工业产出等参数做出决策，获得甚至估计这些信息的主要方法是资源密集型和耗时的。为了做出及时和充分知情的决策，必须能够为这些参数提出可以快速有效采样的代理，特别是在COVID-19大流行等破坏性事件期间。最近，人们非常关注将遥感数据用于此目的。与调查相比，收集数据的成本更低，而且可以实时的获得。在这项工作中，我们提出了区域GDP夜光（ReGNL），这是一个基于神经网络的模型，它是在历史夜光和GDP数据的自定义数据集以及一个地方的地理坐标上训练的，并在给定其他参数的情况下估计该地方的GDP。以美国50个州为例，我们发现ReGNL是破坏性不可知的，能够预测正常年份（2019年）和破坏性事件年份（2020年）的GDP。ReGNL在预测方面优于时间序列ARIMA方法，即使在大流行期间也是如此。根据我们的研究结果，我们提出了建立基础设施以收集和提供粒度数据的案例，特别是在资源贫乏的地区，以便在破坏性事件期间利用这些数据制定政策。
英文摘要：
Policy makers often make decisions based on parameters such as GDP, unemployment rate, industrial output, etc. The primary methods to obtain or even estimate such information are resource intensive and time consuming. In order to make timely and well-informed decisions, it is imperative to be able to come up with proxies for these parameters which can be sampled quickly and efficiently, especially during disruptive events, like the COVID-19 pandemic. Recently, there has been a lot of focus on using remote sensing data for this purpose. The data has become cheaper to collect compared to surveys, and can be available in real time. In this work, we present Regional GDP NightLight (ReGNL), a neural network based model which is trained on a custom dataset of historical nightlights and GDP data along with the geographical coordinates of a place, and estimates the GDP of the place, given the other parameters. Taking the case of 50 US states, we find that ReGNL is disruption-agnostic and is able to predict the GDP for both normal years (2019) and for years with a disruptive event (2020). ReGNL outperforms timeseries ARIMA methods for prediction, even during the pandemic. Following from our findings, we make a case for building infrastructures to collect and make available granular data, especially in resource-poor geographies, so that these can be leveraged for policy making during disruptive events.
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。
