DA-46 - Computational Geography
Computational Geography emerged in the 1980s in response to the reductionist limitations of early GIS software, which inhibited deep analyses of rich geographic data. Today, Computational Geography continues to integrate a wide range of domains to facilitate spatial analyses that require computational resources or ontological paradigms beyond that made available in traditional GIS software packages. These include novel approaches for the mass creation of geospatial data, large-scale database design for the effective storage and querying of spatial identifiers (i.e., distributed spatial databases), and methodologies which enable simulations and/or analysis in the context of large-scale, frequently near-real-time, spatially-explicit sources of information. The topics studied within Computational Geography directly enable many of the world’s largest public databases, including Google Maps and Open Street Map (OSM), as well as many modern analytic pipelines designed to study human behavior with the integration of large volumes of location information (e.g., mobile phone data) with other geospatial sources (e.g., satellite imagery).
PD-11 - Python for GIS
Figure 1. PySAL within QGIS Processing Toolbox: Hot-spot analysis of Homicide Rates in Southern US Counties.
Python is a popular language for geospatial programming and application development. This entry provides an overview of the different development modes that can be adopted for GIS programming with Python and discusses the history of Python adoption in the GIS community. The different layers of the geospatial development stack in Python are examined giving the reader an understanding of the breadth that Python offers to the GIS developer. Future developments and broader issues related to interoperability and programming ecosystems are identified.