Optimization of well placement to eliminate water poverty

The dashboard provides important information, including functionality
of each existing pump through different colors of the card, allowing
rapid identification of pump maintenance problems.

The card too
shows over-man levels to represent the people who are not at ease
access to water. This is important because some locations with access to
a natural water source may not have as critical a need for a new well.

The visualization also shows a crucial metric called “pump preparation level.” This is a total number of different political values ​​that indicate whether the community is prepared to receive a well, including support from each village manager and population stability based on survey data. Water for good users can drill further into this metric to see text fields with notes from volunteers. Eg. Could volunteers notice places with populations that were on the run from recent military attacks. This lets the Water for Good team know that there is probably not a large population in this place, so investing in placing a well there is not ideal.

The SAS team suggested where new pumps should be based on these factors and clearly marked the exact locations on the visualization. This, combined with a simple dashboard that highlights the total cost and total number of new receivers for each pump, allows the Water for Good team to act quickly and strategically. And it can share the data with others who need it.

“Now we can more easily share the level of water needs in these communities with the local government and financiers as well,” Allen said.

Collection of population data

While the first phase of the project helped Water for Good with reporting and optimization needs, the second phase aimed to help the organization collect more accurate population data in CAR.

As there is limited census data available for CAR, Water for Good has had to manually collect population data over the years, which can be tedious and imprecise. For this next project, a new volunteer team from SAS used satellite data from public online sources to supplement the manually collected census data from the organization.

Using a data set that combined both Water for Good and satellite data, the team identified locations of overlap between the two data sources. When the organization mapped it out with SAS Visual Analytics, Water for Good could easily see the overall difference in population estimates between the two data sources.

Population data is critical to choosing the ideal water solution for each area – whether it is a larger-stock solar tank system or a foot or hand pump for smaller populations.

“This is a level of complex analysis that we wouldn’t have been able to do on our own,” Allen said. “SAS gave us a better sense of the reliability of this data when planning for projects in the future.”

As a result, Madina Fanette and millions of others like her can now have water for their businesses, their families and their communities.

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