An Analytical Framework for GIS Modeling

Joseph K. Berry¹ and Shitij Mehta²

¹Keck Scholar in Geosciences, Department of Geography, University of Denver, Denver, Colorado; jkberry@du.edu Website: www.innovativegis.com/basis/

²Former Graduate Teaching Assistant at the University of Denver; currently Product Engineer, Geoprocessing Team, Environmental Systems Research Institute (ESRI), Redlands, California

Abstract:

The U.S. Department of Labor has identified Geotechnology as one of three mega technologies for the 21^st century noting that it will forever change how we will conceptualize, utilize and visualize spatial information. Of the spatial triad comprising Geotechnology (GPS, GIS and RS), the spatial analysis and modeling capabilities of Geographic Information Systems provides the greatest untapped potential, but these analytical procedures are least understood. This paper develops a conceptual framework for understanding and relating various grid-based map analysis and modeling procedures, approaches and applications. Discussion topics include; 1) the nature of discrete versus continuous mapped data; 2) spatial analysis procedures for reclassifying and overlaying map layers; 3) establishing distance/connectivity and depicting neighborhoods; 4) spatial statistics procedures for surface modeling and spatial data mining; 5) procedures for communicating model logic/commands; and, 6) the impact of spatial reasoning/dialog on the future of Geotechnology.

Keywords: Geographic Information Systems, GIS modeling, grid-based map analysis, spatial analysis, spatial statistics, map algebra, map-ematics

This paper presents a conceptual framework used in organizing material presented in a graduate course on GIS Modeling presented at the University of Denver. For more information and materials see http://www.innovativegis.com/basis/Courses/GMcourse09/.

<Click here> for a printer- friendly version of this paper (.pdf)

Table of Contents
Section	Topic	Page
1.0	Introduction	2
2.0	Nature of Discrete versus Continuous Mapped Data	4
3.0	Spatial Analysis Procedures for Reclassifying Maps	6
4.0	Spatial Analysis Procedures for Overlaying Maps	8
5.0	Spatial Analysis Procedures for Establishing Proximity and Connectivity	10
6.0	Spatial Analysis Procedures for Depicting Neighborhoods	13
7.0	Spatial Statistics Procedures for Surface Modeling	15
8.0	Spatial Statistics Procedures for Spatial Data Mining	17
9.0	Procedures for Communicating Model Logic	18
10.0	Impact of Spatial Reasoning and Dialog on the Future of Geotechnology	20
	Further Reading and References	23

____________________________

1.0 Introduction

Historically information relating to the spatial characteristics of infrastructure, resources and activities has been difficult to incorporate into planning and management. Manual techniques of map analysis are both tedious and analytically limiting. The rapidly growing field of Geotechnology involving modern computer-based systems, on the other hand, holds promise in providing capabilities clearly needed for determining effective management actions.

Geotechnology refers to “any technological application that utilizes spatial location in visualizing, measuring, storing, retrieving, mapping and analyzing features or phenomena that occurs on, below or above the earth” (Berry, 2009). It is recognized by the U.S. Department of Labor as one of the “three mega-technologies for the 21^st Century,” along with Biotechnology and Nanotechnology (Gewin, 2004). As depicted in the left inset of figure 1 there are three primary mapping disciplines that enable Geotechnology— GPS (Global Positioning System) primarily used for location and navigation, RS (Remote Sensing) primarily used to measure and classify the earth’s cover, and GIS (Geographic Information Systems) primarily used for mapping and analysis of spatial information.