Beyond Data: Handling Spatial and Analytical Contexts with Genetics-Based Machine Learning

Geographic information systems (GISs) are fairly good at handling three types of data: locational, attribute, and topological. Recent work holds promise for adding temporal data to this list as well (e.g., see Langran, 1992). Yet the unprecedentedly vast resources of geographically referenced data continue to outstrip our ability to derive meaningful information from such databases, despite dramatic improvements in computer processing power, algorithm efficiency, and parallel processing. In part this is because such research has emphasized improvements in processing efficiency rather than effectiveness. We humans are slow-minded compared with our silicon inventions; yet our analytical capabilities remain far more powerful, primarily because we have evolved elaborate cognitive infrastructures devoted to ensuring that we leverage our limited processing power by focusing our attention on the events and information most likely to be relevant. In GIS use, so far only human perception provides the requisite integration of spatial context, and human attention directs the determination of relevance and the selection of geographic features and related analyses. Understanding of spatial context and analytical purpose exists only in the minds of humans working with the GIS or viewing the displays and maps created by such operations. We still extract information from our geographic data systems primarily through long series of relatively tedious and complex spatial operations, performed—or at least explicitly preprogrammed—by a human, in order to derive each answer. Human integration of analytical purpose and spatial and attribute contexts is perhaps the most essential and yet the most invisible component of any geographic analysis, yet it is also perhaps the most fundamental missing link in any GIS. Only humans can glance at a map of a toxic waste dumps next to school yards, or oil spills upstream from fisheries, and recognize the potential threat of such proximity; human cartographers understand the importance of emphasizing either road or stream networks depending on the purpose of a map; humans understand that “near” operates at different scales for corner stores versus cities, or tropical jungle habitat versus open savannah. Given a GIS with the capability to deluge any inquiry with myriad layers of extraneous data, this natural human ability to filter data and manipulate only the meaningful elements is essential.