We start by identifying where nature exists.

For NatureScore this involves an aggregation of 30 different data sources to locate both green space (urban parks, public open spaces, street trees, landscaped plant life) and blue space (rivers, lakes, canals, waterfalls, and even some human-made water features).

These data sources include computer vision processes for analyzing aerial and satellite imagery, pollution indices, human modifications, and vegetation databases.

The geolocated nature elements are then weighted by a machine learning process to develop a 0 – 100 score representing the highest associated health impacts of nature around a location. A high NatureScore has a strong positive association with increased longevity, and reductions in negative outcomes like cancer, diabetes, and cardiovascular disease.

The NatureScore map identifies not just where nature exists, but where quality nature that improves health outcomes exists (and more importantly, where it is deficient).

NatureScore and TreePlotter are then used to visualize the data at multiple scales in the community, from citywide to census block.

NatureScore is currently available for purchase for any location in the United States, and expansions for all of North America and Europe are underway.

As the underlying data sources are updated, so is NatureScore, that way the scores always represent the most topical information available.