| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w29569 |
| 来源ID | Working Paper 29569 |
| Using Neural Networks to Predict Micro-Spatial Economic Growth | |
| Arman Khachiyan; Anthony Thomas; Huye Zhou; Gordon H. Hanson; Alex Cloninger; Tajana Rosing; Amit Khandelwal | |
| 发表日期 | 2021-12-20 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | 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. |
| 主题 | Regional and Urban Economics |
| URL | https://www.nber.org/papers/w29569 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/587243 |
| 推荐引用方式 GB/T 7714 | Arman Khachiyan,Anthony Thomas,Huye Zhou,et al. Using Neural Networks to Predict Micro-Spatial Economic Growth. 2021. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w29569.pdf（886KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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