Accessing high-resolution, timely socioeconomic data (e.g, population, employment, consumption, and wealth) of human settlements is critical. However, reliable local-scale socioeconomic data remain scarce. In this project, we plan to harness the power of multiple sources of big data (e.g., satellite data, cell phone data), blockchain smart contracts, and machine learning algorithms to solve this problem.

Our previous research and showcase has revealed that big data can be a good predictor of socioeconomic attributes of locations; projects like OpenStreetMap have also demonstrated the value of open source + crowdsourcing in (global road network) data mapping. With the support of new big geo-data and AI, this project aims to mapping multidimensional socioeconomics on a global scale. We hope to use blockchain technology to allow all those who contribute data to share in the revenue generated from its application.

The final result is to output the coordinates/geofence of a location that can quickly return a series of structured information to the user (e.g. how many people are here, how many jobs, how much GDP is generated, what is the traffic situation, housing price, etc.).