Data and Code for: Using Neural Networks to Predict Micro-Spatial Economic Growth
收藏ICPSR2025-01-01 更新2026-04-16 收录
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https://www.openicpsr.org/openicpsr/project/158002/version/V2/view
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资源简介：
We apply deep learning to daytime satellite imagery to predict changes in income and population at high spatial resolution in US data. For grid cells with lateral dimensions of 1.2sq-km and 2.4sq-km (where the average US county has dimension of 55.6km), our model predictions achieve R-sq values of 0.85 to 0.91 in levels, which far exceed the accuracy of existing models, and 0.32 to 0.46 in decadal changes, which have no counterpart in the literature and are 3-4 times larger than for commonly used nighttime lights. Our network has wide application for analyzing localized shocks.
提供机构：
Columbia University; UC San Diego; Harvard University
创建时间：
2025-01-01
