27. Reading and writing spatial data for the non-spatial programmer

Audience level:
March 11th 8:10 a.m. – 8:15 a.m.


Location has become mainstream in society and computing. Developers are being tasked with working with spatial data of varying formats. Lucky for Python developers, there are many packages and libraries that can help us make sense of and utilize spatial data, and this poster will explain some of those options.


Location has become ubiquitous in today’s society and is integral in everything from web applications, to smartphone apps, to automotive navigation systems. Spatial data, often derived from Geographic Information Systems (GIS), drives these applications at their core. More and more, non-spatial developers and programmers with little or no knowledge of spatial data formats are being tasked with working with and consuming spatial data in their applications. Spatial data exists in a wide variety of formats which often adds to the confusion and complexity. Fortunately, Python is tightly integrated, accepted, and used within the GIS community, and has been for some time. Python packages and other libraries that are accessible through Python exist to both read and write many common (and some not so common) spatial data formats. With the help of these packages and libraries, Python developers can easily manipulate, read, and write data formats such as ESRI shapefile, raster datasets, KML, and LiDAR.