Replication Data for: Learning from Intermittent Water Supply Schedules: Visualizing Equality, Equity and Hydraulic Capacity in Bengaluru and Delhi, India

Intermittent Schedule Vizualization
David Meyer
2022-10-24

The repository contains the data, code, and figures associated with the
paper Meyer et al. 2023. Over a billion people get water from water supply networks
that regularly interrupt their service. Unfortunately, the intermittent
operations induce inequalities within the network. In our work, we
created the tools needed to use Water Supply Schedules to quantify and
compare the inequality of water supply schedules between and within
cities. We apply these tools to the largest and most complicated
intermittent water systems ever described in peer-reviewed papers: Delhi
and Bangalore; they supply water to 25 million people according to 3278
schedules.

Uniquely, we propose to use publicly posted water supply schedules to
estimate service quality and service equality within intermittent
systems at a novel scale: the supply schedule scale. While we
demonstrate and visualize the use of supply schedules in Delhi and
Bangalore, the implications of our work are much broader: we showcase a
new scale at which intermittent systems could and should be researched
and regulated and we provide the methods required to do so.

Research into intermittent water supplies is often limited by data
availability. Against this trend, we openly share our digitized versions
of each city’s water supply schedules and the code required to process
them in this repository. In doing so, we hope to enable other
researchers to explore and model the operations of intermittent systems
in ways that reflect the complexity and inequalities of intermittent
supply.

In this repository, you’ll find the:

1. original schedule data from both cities
2. manually transcribed schedule data
3. processed schedules in long and wide formats
4. code to process the transcribed schedules
5. visual summaries of the schedules
6. code to generate these visual summaries

We extended our analysis by intersecting schedule census data using GIS.
We include:

7. Census data
8. Our intersected data
9. Code to generate visual summaries of the equity data

Relevant files are contained in Data, Code, and Figure subfolders.