Predicting cell phone adoption metrics using satellite imagery
- URL: http://arxiv.org/abs/2006.07311v5
- Date: Tue, 8 Jun 2021 20:52:14 GMT
- Title: Predicting cell phone adoption metrics using satellite imagery
- Authors: Edward J. Oughton and Jatin Mathur
- Abstract summary: Approximately half of the global population does not have access to the internet, even though digital connectivity can reduce poverty by revolutionizing economic development opportunities.
Mobile Network Operators and governments struggle to effectively determine if infrastructure investments are viable, especially in greenfield areas where demand is unknown.
We present a machine learning method that uses publicly available satellite imagery to predict telecoms demand metrics, including cell phone adoption and spending on mobile services, and apply the method to Malawi and Ethiopia.
- Score: 1.2691047660244335
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Approximately half of the global population does not have access to the
internet, even though digital connectivity can reduce poverty by
revolutionizing economic development opportunities. Due to a lack of data,
Mobile Network Operators and governments struggle to effectively determine if
infrastructure investments are viable, especially in greenfield areas where
demand is unknown. This leads to a lack of investment in network
infrastructure, resulting in a phenomenon commonly referred to as the `digital
divide`. In this paper we present a machine learning method that uses publicly
available satellite imagery to predict telecoms demand metrics, including cell
phone adoption and spending on mobile services, and apply the method to Malawi
and Ethiopia. Our predictive machine learning approach consistently outperforms
baseline models which use population density or nightlight luminosity, with an
improvement in data variance prediction of at least 40%. The method is a
starting point for developing more sophisticated predictive models of
infrastructure demand using machine learning and publicly available satellite
imagery. The evidence produced can help to better inform infrastructure
investment and policy decisions.
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