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Using Neural Networks to Predict Micro-Spatial Economic Growth / Arman Khachiyan, Anthony Thomas, Huye Zhou, Gordon H. Hanson, Alex Cloninger, Tajana Rosing, Amit Khandelwal.
- Format:
- Book
- Author/Creator:
- Khachiyan, Arman.
- Series:
- Working Paper Series (National Bureau of Economic Research) no. w29569.
- NBER working paper series no. w29569
- Language:
- English
- Physical Description:
- 1 online resource: illustrations (black and white);
- Place of Publication:
- Cambridge, Mass. National Bureau of Economic Research 2021.
- Summary:
- 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 1.2km and 2.4km (where the average US county has dimension of 55.6km), our model predictions achieve R2 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.
- Notes:
- Print version record
- December 2021.
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