Mapping has always been data driven, and the more data we gather, the more important practical visualization of that data becomes.
Bob DuCharme, a technical writer with CCRi, writes about how open source GeoMesa can help users with managing large spatio-temporal datasets. GeoMesa can stored petabytes of GIS data and serve up tens of millions of points in seconds.
The Oregon Department of Transportation (ODOT) has set a precedent for public agencies across the country with the purchase of a Strava dataset in order to inform their research. In the fall of 2013, the ODOT bought a one-year license of a dataset that includes over 400,000 individual bicycle trips […]
Strava, a GPS running and biking application, recently released a detailed global heat map of popular routes across the globe. The map contains over 160 million activities with 375,000,000,000 data points.
Analysis of Big Data in a geographic context has empowered organizations and businesses faced with huge amount of data and diverse technologies. The integration of maps with multiple layers of information tells the full story behind the data. Trends and patterns are revealed. Queries are answered and new questions are also addressed. At the same time, predictive modeling on massive datasets help drive accurate decision-making, profitability and effective resource management.
Mike Sanderson, the Director of Strategy at 1Spatial, provides a perspective piece on the rise of big spatial data and the importance of being able to base management decisions on correct real-world data.