Thunderhead Explorer

Insight (or GeoInsight in our case) is lost in the deluge of data that we are acquiring today from everyday sensors that are machine or humanly generated. This project is a set of heuristic Spark based implementations to reveal signals from the movement of ships in and out of the Port of Miami. The idea is to extract small clean data (PicoData) from the overlap of a massive amount of data (MacroData). The aggregation of "clean" PicoData derived from MacroData trust into the forefront patterns of life. For example, given the following display of AIS broadcasts: We can mutate the data to reveal the "clean" influx of ships into the harbor at high tide: Like usual, you can download all the source code from here .

Happy 2018 all. It has been a while since my last post. Thank you for your patience dear reader. Like usual, the perpetual resolutions for every year in addition to blogging more are to eat well, often exercise and climb Ventoux . Onward. I genuinely believe that 2018 will be the year of the ubiquity of Geo-AI. It will be the year when Machine Learning and Spatial Awareness will blossom inside and mostly outside the GIS community. We at Esri have had Machine Learning based tools in our "shed" for a long time. Every time an ArcGIS user performs a graphically weighted regression , trains a random trees classifier or detects an emerging hot spot , that user is using a form of Machine Learning without knowing it! So one of my "missions" for 2018, it to make this knowledge more explicit to our users and non-traditional GIS users. Also, start to implement new forms of Machine Learning. Machine Learning (ML), a branch of Artificial Intelligence (AI), is a disruptive force...