Issue link: https://read.dmtmag.com/i/109821
Recast Map-Analysis Operations for General Consumption BEYONDMAPPING S everal recent ���Beyond Mapping��� columns suggested that there���s a ���fundamental mathematical structure underlying grid-based map analysis and modeling that aligns with traditional nonspatial quantitative data analysis��� (see ���Author���s Note 1,��� page 11). This conceptual framework provides a common foothold for understanding, communicating and teaching basic concepts, procedures and considerations in spatial reasoning and analysis resonating with GIS and non-GIS communities���a SpatialSTEM schema���that can be applied to any grid-based map-analysis BY JOSEPH BERRY system (see ���Author���s Note 2���). Getting in the Game For example, the top portion of Figure 1 identi���es the 22 map-analysis ���toolsets,��� containing more than 170 individual ���tools,��� in the Spatial Analyst module (ArcGIS by Esri). Organizing the classes of operations involves a mixture of the following: obtained for processing, while my earlier groupings of ���Reclassify, Overlay, Distance, Neighbors and Statistical��� re���ect the characteristics of the mapped data generated by the processing. However, all three of these GIS-based schemas are foreign and confusing to the majority of potential map-analysis users (all STEM disciplines), because they don���t align with traditional quantitative dataanalysis experiences. This conceptual disconnect keeps GIS on the sidelines of the much larger quantitative-analysis communities, and reinforces the idea that GIS is a ���technical tool��� (mapping and geoquery) and not a full-���edged ���analytical tool��� (spatial analysis and statistics). Better Understood The bottom portion of Figure 1 identi���es the two broad categories of traditional data analysis���mathematics and statistics���broken into seven major groupings that resonate with non-GIS communities. All of Spatial Analysts��� 117 analytical operations (the other 53 are ���reporting/housekeeping���) can be reorganized into the commonly recognized quantitative-analysis categories. Figures 2 and 3 show my initial attempts at the reorganization (see ���Author���s Note 3���). The bottom line is that the SpatialSTEM framework recasts map-analysis concepts and procedures into a more generally understood organization. Within this general schema, map analysis is recognized as a set of natural extensions to familiar nonspatial math/stat operations. The following are several examples: (Conditional, Map Algebra, Math General, Math Bitwise, Math Logical, Math Trigonometric, Multivariate, Reclass) procedures (Distance, Interpolation, Surface) (Density, Local, Neighborhood, Overlay, Zonal) (Groundwater, Hydrology, Solar Radiation) Joseph Berry is a principal in Berry & Associates, consultants in GIS technology. He can be reached via e-mail at jkberry@du.edu. 10 Generalization, Raster Creation) In large part, such toolset structuring is the result of the module���s development over time, responding to ���business case��� demands by clients instead of a comprehensive conceptual organization. In contrast, Tomlin���s ���Local, Focal, Zonal and Global��� ��� Figure 1. Grid-based map-analysis operations in any GIS, such as Spatial Analyst, can be reorganized into commonly understood classes characterize the analytical classes of traditional quantitative data analysis. operations on how the input data are G E O W O R L D / F E B R U A R Y 2 O 1 3