The Kiva Lending Map

Inspiration

We were inspired to create this web app after exploring the KIVA website and finding it difficult to locate loans from specific countries. We wanted to make it easier for the lenders to engage with specific countries they were interested in funding.

What it does

We created a web app that displays an interactive map of KIVA loan lending. Loans will display within a popup window after users click on a country on the map. The popup displays limited information about the borrower. The user has the ability to utilize the 'learn more' feature to gain additional information about the borrower. With this information, the user then has the option to financially support the borrower on the KIVA website using the 'lend here' button.

How we built it

On the backend, we used Python with a flask server. We used the Google Maps API to display the map on the main page, while also using Arturs Smirnov's Github repository to enable clickable countries. After modifying the JavaScript code for clickable countries and creating a modal to use as the new popup window, we then used the country code displayed after clicking a country to query the KIVA GraphQL API. This then returns the loans from the country selected and displays the information we wanted. For styling we used CSS/HTML and bootstrap.

Challenges we ran into

The main challenge we had to overcome was working as a team within various time zone and scheduling constrictions. Initially, our team was composed of five members, but over time that number dwindled to three. This put pressure on the team to take on a heavier individual workload. We all graduated from Hackbright Academy together, but this was our first external coding project together. This experience further sharpened each of our skillset on simultaneously working on code, merging alterations and communicating as a unit.

Accomplishments that we're proud of

We are proud of the web app we crafted, and excited to work together to create this group project. Specifically, our ability to merge different schools of thought together into one cohesive idea.

What we learned

We learned how to use GraphQL and strengthened our JavaScript knowledge.

What's next for The Kiva Lending Map

To continue this project, we would like to add statistics to display the economics of the countries that KIVA loans to, to better show how the loans will affect the countries/people in a positive, calculated way.