Geospatial Data and Analysis

Chapter 4. Mapping Data, More Tools, and Analysis

“Often people transition straight from low-scale, antiquated, commercial desktop GIS tools to Big Data tools, overlooking a system that supports ad hoc agility while being able to do subsecond polygon intersection tests against hundreds of millions of polygons if properly indexed,” said Boris Shimanovsky of Factual. Meanwhile, the user experience of “medium-scale” tools continues to improve, which, aided by open source releases, is enabling the creation and sharing of standards that make web cartography translatable between tools and companies. “When the data is not big, such as in the several TB range, [it’s often appropriate] to prototype in PostGreSQL and PostGIS,” notes Shimanovsky. “It’s incredibly versatile and much faster and more scalable than most people realize.”

With the variety of map types and publishing formats currently accessible to mapmakers, choosing the appropriate tools and data for modest to moderate-sized projects can seem daunting. In this chapter, we’ll continue our focus on some medium-scale options for dealing with geospatial data using standard commercial and open source tools. At times, projects extend beyond the small-scale work we’ve seen in previous chapters but don’t quite justify a Big Data solution, so here we’ll be focusing on the platforms and data sources that best support medium-scale products.

This chapter will also include a shallow dive into some toolmakers and cartographers making maps ...