Big data and spatial have lived at opposite ends of the sector’s toolset for far too long, and have only recently begun to stop glaring at each other across the room.
Tyler Bell, product executive specializing in IoT and spatial tech
Thus far, our review of geospatial data tooling and approaches to visualization has focused on tools for small- to medium-scale data. The geospatial development landscape can be subdivided according to these systems of scale, helping us to compartmentalize best-fit approaches to the data and the audience suited to each project. We’ve seen how Internet of Things and mobile technologies promise to push data constraints beyond medium-scale capacities, and how layered networks of IoT sensors make available a richness of data coverage that was nearly unimaginable until recent years. As automated systems, robots, self-driving cars, and drones interact in our world, they provide feedback to our mapped environments and engage more sophisticated control systems that leverage spatial analytics.
In its 2015 Mobility Report, Sony Ericsson predicted a growth goal of 50 billion connected devices distributed globally; their subsequent report suggests that 500 billion might be more accurate after 2020. We are fast approaching an era of location-centered products that necessitates Big Data solutions. In this chapter, we’ll focus on anecdotes that support these growing capacity requirements, and discuss how tools ...