Context This is a supplementary dataset to the [*Data Science for Good: Kiva Crowdfunding*](https://www.kaggle.com/kiva/data-science-for-good-kiva-crowdfunding) challenge. In the Kiva challenge, the **kiva_loans.csv** file contains a large record of loans with the borrower's locations. This dataset provides the latitude and longitude of these locations. The original dataset also includes another file **loan_themes_by_region.csv**, which provides some additional information on geographical locations of the loan themes offered. However, there are significantly more borrower locations than loan theme locations, and these two locations are not always the same. This dataset tries to solve this problem by directly obtaining the geocode of all borrower locations via Google Maps Geocoding API. Content There are four columns in the CSV. "Region" and "country" match the corresponding fields in **kiva_loans.csv**. "Latitude" and "longitude" are self-explanatory. Queries without valid results from the Google Maps API are indicated by latitude=-999, longitude=-999. The geocodes are **not** manually validated and should be used with caution. Bad query results may happen due to mistakes in the original dataset or Google Maps' autocorrection. Acknowledgements The building of this dataset uses the following API: https://developers.google.com/maps/documentation/geocoding/intro Inspiration This dataset can help participants in the Kiva challenge by allowing them to compare location proximity and visualise data on a world/regional map when analysing Kiva's loans. The original purpose of the dataset is for me to visualise loan type clustering results on a world map and find similarities in borrower needs between remote, disjoint regions, however I hope the community will find better, more creative uses for this tiny dataset.