Infusing Grid-Based Map Analysis Into Geo-Business Decisions


Joseph K. Berry1 and David Wright2

Berry & Associates // Spatial Information Systems

2000 South College Avenue, Suite 300, Fort Collins, Colorado, USA 80525

Phone: 970-215-0825, Email:



Business applications of desktop mapping have skyrocketed.  The ability to geo-query databases and visualize the results in a variety of thematic map forms has developed an appreciation of “where” as well as “what” in business solutions.  However, the infusion of grid-based map analysis in geo-business has not been as widespread.  While vector-based approaches are ideal for spatial database management, grid-based analysis supports mathematical/statistical approaches for quantifying spatial relationships within and among maps.  This paper describes the similarities and differences between the two approaches, identifies considerations in their use and presents several examples of surface modeling, spatial data mining and analysis techniques. 


Note: this paper broadly outlines the major groups of grid-based operators.  Readers are encouraged to use the online links in the References section to extend the discussions presented in this paper and obtain educational software for hands-on experience.




Vector-based desktop mapping applications are rapidly becoming part of the modern business environment.  The close link between these systems and traditional spreadsheet and database management programs has fueled the adoption.  In many ways, a “database is just picture waiting to happen.”  The direct link between the attributes described in a database record and its spatial characterization is conceptually easy.  Geo-query by clicking on a map to pop-up the attribute record or searching a database then plotting the selected records is an extremely useful extension of traditional database technology.  Couple decreasing desktop mapping system costs and complexity with increasing data availability and Internet access makes the adoption of spatial database technology a “no-brainer.”  Maps in their traditional form of point, lines and polygons identifying discrete spatial object align with manual mapping concepts and experiences learned as early as girl and boy scouts.   


Grid-based maps, on the other hand, represent a different paradigm of geographic space.  Whereas traditional vector maps emphasize “precise placement of physical features,” grid maps seek to “statistically characterize continuous space in both real and cognitive terms.”  The tools for mapping of database attributes are extended to analysis of spatial relationships.  This paper describes some of the basic concepts, considerations and procedures in grid-based data handling and analysis operations as they apply to geo-business applications.  Three fundamental capabilities are discussed—surface modeling, spatial data mining and map analysis.


Surface Modeling


Surface modeling involves the translation of discrete point data into a continuous surface that represents the geographic distribution of data.  Traditional non-spatial statistics involves an analogous process when a numerical distribution (e.g., standard normal curve) is used to generalize the central tendency of a data set.  The derived mean (average) and standard deviation reflects the typical response and provides a measure of how typical it is.  This characterization seeks to explain data variation in terms of the numerical distribution of measurements without any reference to their spatial distribution. 

In fact, an underlying assumption in most statistical analyses is that the data is randomly distributed in space.  If the data exhibits spatial autocorrelation many of the analysis techniques are less valid.


Spatial statistics, on the other hand, utilizes geographic patterns in the data to further explain the variance. There are numerous techniques for characterizing the spatial distribution inherent in a data set but they can be characterized by three basic approaches:

·        Point Density mapping that aggregates the number of points within a specified distance (number per acre),

·        Spatial Interpolation that weight-averages measurements within a localized area (e.g., kriging), and 

·        Map Generalization that fits a functional form to the entire data set (e.g., polynomial surface fitting). 



 Figure 1-1.  Point density map aggregating the number of customers within a quarter of a mile.


For example, consider Figure 1-1 showing a point density map derived from customer addresses.  The project area is divided into an analysis frame of 250-foot grid cells (100c x 100r = 10,000 cells).  The number of customers for each grid space is determined street addresses in a desktop mapping system (“spikes” in the 3D map on the left).  A neighborhood summary operation is used to pass a “roving window” over the project area calculating the total customers within a half-mile of each map location.  The result is a continuous map surface indicating the relative density of customers—peaks where there is a lot of customers and valleys where there aren’t many.


In essence, the map surface quantifies what your eye sees in the spiked map—some areas with lots of customers and others with very few.  Spatial interpolation also moves a roving window about point data but utilizes more sophisticated summary techniques, such as Inverse Distance, Kriging and Minimum Curvature.  The result in either case, are map surfaces that respond to the spatial distribution in the data.


An underlying assumption of surface modeling is that that the variable under study forms a gradient in geographic space (termed “isopleth” data).  The derived surface is an approximation of that gradient.  A further assumption is that the data exhibits spatial autocorrelation—“nearby things are more alike than distant things.”  While some maps containing discrete objects do not have these qualities, many business decision variables, such as sales and demographics, express themselves as spatially auto-correlated gradients.  In these instances, surface modeling is a viable approach to characterizing the geographic distribution of point-sampled data.


Spatial Data Mining


Spatial data mining seeks to uncover relationships within and among mapped data.  A companion paper presented at this conference, entitled “Quantitative Methods for Analyzing Map Similarity and Zoning” describes some of the techniques—coincidence summary, proximal alignment, statistical tests, percent difference, surface configuration, level-slicing, map similarity, and clustering—used in comparing maps and assessing similarities in data patterns. 


Another group of spatial data mining techniques focuses on developing predictive models.  For example, the customer density map described in the previous section might be strongly related to mapped data of demographics.  If that is the case, a mathematical (or “map-ematical”) prediction equation can be derived.  Simple linear regression, often used in research, can be applied to a stack of grid maps—they are just an organized set of numbers awaiting analysis.  In essence, the technique goes to a grid location and notes the density of customers (dependent variable) and the demographic information, (independent variables) and quantifies the data pattern.  As the process is repeated for 10,000 cells a predictable pattern between the density values and the demographic values often emerges.  If the relationship is strong, the regression equation can be used to predict a map of expected customer levels for another city slated for a new office.


Figure 1-2.  Spatial data mining can be used to derived predictive models of the relationships among mapped data.


For example, an early use of predictive modeling was in extending a test market project for a phone company.  The customer’s address was used to geo-code sales of a new product that enabled two numbers with distinctly different rings to be assigned to a single phone—one for the kids and one for the parents.  Like pushpins on a map, the pattern of sales throughout the city emerged with some areas doing very well, while in other areas sales were few and far between. 


The demographic data for the city was analyzed to calculate a prediction equation between product sales and census block data.  The prediction equation derived from the test market sales in one city was applied to another city by evaluating exiting demographics to “solve the equation” for a predicted sales map.  In turn the predicted map was combined with a wire-exchange map to identify switching facilities that required upgrading before release of the product in the new city.


A couple of considerations are important in predictive modeling.  First, the mapped data needs to form spatially auto-correlated gradients as previously mentioned.  Secondly, traditional multivariate techniques assume that the data values are not categorical or binary (such as male/female), as the regression technique needs a continuum of values (such as income levels) to work properly.  However, there are other more advanced predictive techniques (such as CART technology) that can utilize nominal data types. 


Spatial data mining approaches have been used for years in automated classification of remote sensing data.  In these instances, spectral values are analyzed for a stack of grid layers.  Geo-business spatial data mining applications simply relate grid layers that characterize other information.  In addition, geo-business applications focus more on predictive statistics than descriptive classification. 


Cutting-edge research in spatial data mining is pushing the envelop from descriptive and predictive statistics to prescriptive modeling that seeks to spatially optimize management action.  An example is the generation of a prescription map in precision agriculture that changes a fertilization program throughout a field based on the current distribution of nutrients and yield prediction.  Variable-rate technology actually alters the blend of nutrients “on-the-fly” as a GPS-equipped spray rig moves across the field.  Future decision support systems for business will likely implement prescriptive modeling based on predictive/descriptive statistics derived from mapped data.  These systems will generate spatially responsive guidance—“do this over here but that over there”—that fully incorporates the geographic distribution inherent in mapped data.


Map Analysis


Whereas spatial data mining responds to “numerical” relationships in mapped data, map analysis investigates the “contextual” relationships.  Tools such as slope/aspect, buffers, effective proximity, optimal path, visual exposure and shape analysis, fall into this class of spatial operators.  Rather than statistical analysis of mapped data, these techniques examine geographic patterns, vicinity characteristics and connectivity among features.   


A example of this group of operations builds on two specific map analysis capabilities—effective proximity and accumulation surface analysis.  The following discussion focuses on the application of these tools to competition analysis between two stores. 


Figure 1-3.  Travel-time surfaces show increasing distance from a store considering the relative speed along different road types.


The left side of figure 1-3 shows the travel-time surface from Kent’s Emporium.  It is calculated by starting at the store then moving out along the road network like waves propagating through a canal system.  As the wave front moves, it adds the time to cross each successive road segment to the accumulated time up to that point. 


The result is the estimated travel-time to every location in the city.  The surface starts at 0 and extends to 24.4 minutes away.  Note that it is shaped like a bowl with the bottom at the store’s location.  In the 2D display, travel-time appears as a series of rings—increasing distance zones.  The critical points to conceptualize are 1) that the surface is analogous to a football stadium (continually increasing) and 2) that every road location is assigned a distance value (minutes away).  


The right side of figure 1-3 shows the travel-time surface for another store, Colossal Mart, with its origin in the northeast portion of the city.  The perspective in both 3D displays is consistent and Kent’s surface appears to “grow” away from you while Colossal’s surface seems to grow toward you.


Figure 1-4.  Two travel-time surfaces can be combined to identify the relative advantage of each store.


Simply subtracting the two surfaces derives the relative travel-time advantage for the stores (figure 1-4).  Keep in mind that the surfaces actually contain geo-registered values and a new value (difference) is computed for each map location.  The inset on the left side of the figure shows a computed Colossal Mart advantage of 6.1 minutes (22.5 – 16.4= 6.1) for the location in the extreme northeast corner of the city.


Locations that are the same travel distance from both stores result in zero difference and are displayed as black.  The green tones on the difference map identify positive values where Kent’s travel-time is larger than its competitor’s—advantage to Colossal Mart.  Negative values (red tones) indicate the opposite—advantage to Kent’s Emporium.  The yellow tone indicates the “combat zone” where potential customers are about the same distance from either store—advantage to no one.


Figure 1-5.  A transformed display of the difference map shows travel-time advantage as peaks (red) and locations with minimal advantage as an intervening valley (yellow).


Figure 1-5 displays the same information in a bit more intuitive fashion.  The combat zone is shown as a yellow valley dividing the city into two marketing regions—peaks of strong travel-time advantage.  Targeted marketing efforts, such as leaflets, advertising inserts and telemarketing might best be focused on the combat zone.  Indifference towards travel-time means that the combat zone residents might be more receptive to store incentives.


At a minimum the travel-time advantage map enables retailers to visualize the lay of the competitive landscape.  However the information is in quantitative form and can be readily integrated with other customer data.   Knowing the relative travel-time advantage (or disadvantage) of every street address in a city can be a valuable piece of the marketing puzzle.  Like age, gender, education, and income, relative travel-time advantage is part of the soup that determines where one shops. 


There are numerous other map analysis operations in the grid-based “toolbox”—too many to enumerate and fully discuss in this paper.  The travel-time and competition analysis examples merely illustrate a couple of geo-business applications capitalizing on the new tools.  Motivated readers are encouraged to use the online links in the References section to extend the discussion.




Vector-based systems for mapping and spatial database management are gaining a solid foothold in business.  Their thematic mapping and geo-query capabilities align with and extend traditional data processing approaches.  Grid-based systems, on the other hand, involve new data structures, storage/retrieval procedures and entirely new analysis paradigms.  Its surface modeling, spatial data mining and map analysis approaches better align with statistics and mathematics—an important but often secondary side of information systems technology.   These procedures, however, support new ways of conceptualizing, expressing and modeling business systems.  The explicit consideration of numerical and contextual relationships within and among mapped data can provide new insight and solution approaches to complex problems.  Consideration of “where,” as well as “what,” is a natural extension to business decision-making.  The computer systems, data and access for infusing grid-based analysis into geo-business decisions are at hand—what remains is infusing the techniques into the business mindset. 




The online book, Map Analysis (available at is a compilation of popular “Beyond Mapping” columns published in GEOWorld magazine from 1996 through 2001.  It contains seventeen chapters discussing various aspects of grid-based analysis including surface modeling (Topics 2, 3 and 8), spatial data mining (Topics 7,10 and 16) and map analysis (Topics 5,6,14 and 17).  Motivated readers are encouraged to review these sections to extend the discussions in this paper.


The MapCalc Learner-Academic system (see, select Productsà MapCalc) is designed for students and teachers who want “hands-on” experience with the concepts, procedures and considerations of grid-based analysis.  A companion paper presented at this conference, entitled “Infusing Grid-Based Map Analysis Into Introductory GIS Courses” describes the educational software and materials for classroom and self-learning.  The MapCalc Learner version for students is US$ 21.95; MapCalc Academic for instructors is US$ 495 plus shipping and handling.



1Joseph K. Berry is a leading consultant and educator in the application of Geographic Information Systems (GIS) technology.  He is the president of BASIS, consultants and software developers in GIS and the author of the “Beyond Mapping” column for GEOWorld magazine.  He has written over two hundred papers on the theory and application of map analysis, and is the author of the popular books Beyond Mapping and Spatial Reasoning.  Since 1976, he has presented workshops on GIS to thousands of individuals from a wide variety of disciplines.  Dr. Berry conducted basic research and taught courses in GIS for twelve years at Yale University's Graduate School of Forestry and Environmental Studies, and is currently a Special Faculty member at Colorado State University and the W. M. Keck Visiting Scholar in Geography at the University of Denver.  He holds a B.S. degree in forestry, an M.B.A. in business management and a Ph.D. emphasizing remote sensing and land use planning.


2 David Wright is a Research and Development Manager at Red Hen Systems, Fort Collins, Colorado, Email