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Using Neural Networks to Predict Micro-Spatial Economic Growth

Author

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  • Arman Khachiyan
  • Anthony Thomas
  • Huye Zhou
  • Gordon H. Hanson
  • Alex Cloninger
  • Tajana Rosing
  • Amit Khandelwal

Abstract

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.

Suggested Citation

  • Arman Khachiyan & Anthony Thomas & Huye Zhou & Gordon H. Hanson & Alex Cloninger & Tajana Rosing & Amit Khandelwal, 2021. "Using Neural Networks to Predict Micro-Spatial Economic Growth," NBER Working Papers 29569, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:29569
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    Cited by:

    1. Chor, Davin & Li, Bingjing, 2024. "Illuminating the effects of the US-China tariff war on China’s economy," Journal of International Economics, Elsevier, vol. 150(C).
    2. GIBSON, John & ZHANG, Xiaoxuan & PARK, Albert & YI, Jiang & XI, Li, 2024. "Remotely measuring rural economic activity and poverty : Do we just need better sensors?," CEI Working Paper Series 2023-08, Center for Economic Institutions, Institute of Economic Research, Hitotsubashi University.

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    JEL classification:

    • R0 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General

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