Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using maps to predict economic activity   We introduce a novel machine learning approach to leverage historical and
contemporary maps and systematically predict economic statistics. Our simple
algorithm extracts meaningful features from the maps based on their color
compositions for predictions. We apply our method to grid-level population
levels in Sub-Saharan Africa in the 1950s and South Korea in 1930, 1970, and
2015. Our results show that maps can reliably predict population density in the
mid-20th century Sub-Saharan Africa using 9,886 map grids (5km by 5 km).
Similarly, contemporary South Korean maps can generate robust predictions on
income, consumption, employment, population density, and electric consumption.
In addition, our method is capable of predicting historical South Korean
population growth over a century.

37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
19
The categories of this paper are: eess
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn