Imagine a nation where problems encountered by locals could be collected in one location and then resolved by locals utilizing local resources and data. A community where each resident takes responsibility for local problems and finds solutions.

As Abhijit Naskar said “A nation can run without government, but it can’t run without the accountability of its citizens”

Reapbenefit is one such organization that strives to be a platform that inspires youth to become action-based citizen champions called Solve Ninjas. These Solve Ninjas are then spread in every community across India, to tackle local civic and environmental problems using Local Data, Local Campaigns, and Local Solutions. 

Since the worldwide covid pandemic began, ReapBenefit has been using Glific for the past two years to carry out its WhatsApp program. Checkout these blogs to see how they have been using Glific and their use case

• A citizen-centric chatbot to drive small, problem-solving actions for a BIG change
• Reap Benefit: Glific + Google Sheets makes a great chatbot to help Covid affected families

Usecase

Reapbenefit has been using Glific as a chatbot for citizen engagement where any local issue can be reported by their beneficiaries by sending details of the issue through WhatsApp messages to the chatbot.

In addition to reporting the issue through the chatbot, Reapbenefit also uses Glific to send advisory messages to beneficiaries on how to resolve the issue or to whom a beneficiary should reach out for the issue they reported.

Over the period of time as more people start interacting with chatbot and start reporting issues more frequently, Reapbenefit wants to keep track of issues reported which they can then send to government officials and also it will help in visualizing the number of issues and the distribution of issues across the region.

Visualizing data

Visualizing data can be broken down into three-step process:

• Collecting data
• Storing and cleaning up data
• Using data for visualizing

Collecting data

Reapbenefit uses Glific to gather information on the local problems that people are experiencing, with WhatsApp serving as the platform for communication between the Bot and the recipients. With the help of flows in Glific information like location, image, city, state, district, and detail of the issues can be gathered through multiple messages.

Storing and cleaning up data

In Glific all the data like messages, contacts, flows, and details of contact are synced to NGO’s BigQuery instance across multiple tables. Thus the collected information through multiple messages about the local issue can be combined in a single table using simple queries.

Using data for visualizing

For visualizing data we experimented with R and leaflet js to create a dashboard in Rstudio. We used bigrquery package to fetch data from the table in BigQuery and populate the data in the dashboard. 

R provides many in-built functions like clustering, heatmap, custom icons, and filtering along with a wide range of packages to perform data analysis, represent data, and build visualizations.

As a result, since data can be visualized, it is easy to recognize patterns, trends, and exceptions in data using the R mapping dashboard.

We will continue to experiment with Rstudio over the coming weeks as we look for better ways to visualize data and measure how chatbot is being used by citizens to solve local issues. We learn something new every day so on and upwards towards greater impact and wider reach