A recent article from UC Berkeley’s Blum Center considers the lessons learned from the trial run of Next drop, an application intended to help residents of Bangalore, India optimize their time dedicated towards collecting water. NextDrop was designed to alert households in Bangalore when they should expect to receive water, based off of real-time data input by valvemen – public works employees tasked with the physical regulation of water flow. Due to the unpredictability of water deliverance to homes and businesses, it is reported that most households lose up to a week of time per year simply waiting for water.
However, valvemen were found to have only input data 70% of the time, while the alerts that were produced were only accurate 37% of the time. ERG faculty Isha Ray and PhD candidate Chris Hyun, along with ERG affiliate Allison Post of the Department of Political Science, conducted two studies analyzing why NextDrop’s performance was poorer than expected. The first study, led by political science PhD candidate Tanu Kumar, developed a nodal framework to describe the key points in the communication flow from the valvemen to households where alert viability could break down. The other study, led by Hyun, analyzed the responsibilities and program compliance of the valvemen, and came to the primary confusion that they were often too burdened with other tasks to report accurately report water flow timing. Post and Ray were co-authors on both studies.
The original article from the Blum Center can be read here.