Big data sources provide a significant opportunity for governments and development stakeholders to “sense” and identify in near real time, economic impacts of shocks on populations at high spatial and temporal resolutions. In this study, we assess the potential of transaction and location based measures obtained from automatic teller machine (ATM) terminals, belonging to a major private sector bank in Indonesia, to “sense” in near real time, the impacts of shocks across income groups.
Assessing the Use of Transaction and Location Based Insights Derived from Automatic Teller Machines (ATMs) as Near Real time “Sensing” Systems of Economic Shocks
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