Big Geospatial Data Processing Made Easy: A Working Guide to GeoSpark

In the past decade, the volume of available geospatial data increased tremendously. Such data includes but not limited to: weather maps, socio-economic data, and geo-tagged social media. Moreover, the unprecedented popularity of GPS-equipped mobile devices and Internet of Things (IoT) sensors has led to continuously generating large-scale location information combined with the status of surrounding environments. For example, several cities have started installing sensors across the road intersections to monitor the environment, traffic and air quality. Making sense of the rich geospatial properties hidden in the data may greatly transform our society. This includes many subjects undergoing intense study: (1) Climate analysis: that includes climate change analysis (N. R. C. Committee on the Science of Climate Change 2001), study of deforestation (Zeng et al. 1996), population migration (Chen et al. 1999), and variation in sea levels (Woodworth et al. 2011), (2) Urban planning: assisting government in city/regional planning, road network design, and transportation/traffic engineering, (3) Commerce and advertisement (Dhar and Varshney 2011): e.g., point-of-interest (POI) recommendation services. These data-intensive spatial analytics applications highly rely on the underlying database management systems (DBMSs) to efficiently manipulate, retrieve and manage data.