GIS Out of the Box

Current and Future Directions in Geographic Information Systems


by Joseph K. Berry, W.M. Keck Visiting Scholar


based on the presentation "GeoBusiness Out of the Box, Business Geographics Conference '99, Chicago, Illinois, October 5, 1999

Fall Colloquium Series, Department of Geography

University of Denver — Denver, Colorado — October 14, 1999



[#1 …Title]   Traditionally, talks on GIS technology began with a definition of the acronym… "Guessing Is Simpler," "Gee It's Stupid," and "Guaranteed Income Stream" made sense to most early users and software developers, while the formal definition of "Geographic Information Systems" seemed somewhat diffuse. 

            However, in contemplating what to include in this address, the idea of “where is GIS?” kept cropping up.  Not so many years ago the answer to that question was simply, “down the hall and to the right, …I think?”  In three short decades, GIS has evolved from a mapping system to a spatial database technology, and more recently, to modeling complex spatial relationships.  However, with the popularity of GIS, the readings of its current trends and probable futures are as diverse as its growing community of users. 


[#2 Outline of Topics]   In an attempt to avoid a “laundry list” of challenges facing our maturing technology, I have narrowed the list to just three topics we ought to discuss…       

·        first, a brief reflection on the historical setting over three decades and the legacy left by the pioneers;

·        secondly, a series of contemporary applications that demonstrate the common threads among GIS procedures and applications,

·        and finally, some thoughts on trends that provide new ways of linking mapped data, processing and spatial reasoning.


[#3 …Setting and Evolution Highlighted] 


Information has always been the cornerstone of effective decisions.  Spatial information is particularly complex as it requires two descriptors— where is what.  For hundreds of years the link between the two descriptors has been the traditional, manually drafted map.  Its historical use was for navigation through unfamiliar terrain and seas, with emphasis on accurate location of physical features. 

            More recently, analysis of mapped data has become an important part of decision-making.  This new perspective marks a turning point in the use of maps—from one emphasizing physical description of geographic space …to one of interpreting mapped data …and finally, to spatially characterizing and communicating management actions.  The movement from "where is what" to "so what and why" has set the stage for entirely new concepts in planning and management of geographic space.


The First Decade… COMPUTER MAPPING. 

The early 1970's saw computer mapping automate the cartographic process.  The pioneering work during this period established many of the underlying concepts and procedures of modern GIS technology.  An obvious advantage of computer mapping is the ability to change a portion of a map and quickly redraft the entire area.  A less obvious advantage is the radical change in the format of mapped data—from an analog image of inked lines on paper, to thousands of numbers stored on a disk.  However, the most lasting implication of computer mapping is the realization "that it comes …with some assembly required." 



The early 1980's exploited the change in the format of mapped data.  Spatial database management systems were developed that linked computer mapping techniques with traditional database capabilities.  Thematic mapping and geo-query capabilities enabled users to quickly retrieve information and generate map products— "…sort of a database, with a picture waiting to happen." 

            Prior to spatial database management, procedures involved file cabinets of information that were linked to maps on the wall through "shoe leather."  One would simply wear a path between the map and files whenever spatial and descriptive data were needed.  With today's technology the link is a lot easier.


[#4 …RealEstate1]  For example, a new-age real estate agent can search the local multiple listing for suitable houses, then electronically “post” them to a map of the city. 


[#5  …RealEstate2]  A few more mouse-clicks and a prospective buyer a thousand miles away can take a video tour of the homes "within three-quarters of a mile from the hospital where he will work."  And by viewing a GPS-linked video, take a drive around the neighborhood.


[#6  …RealEstate3]  A quick geo-query of the spatially-linked database, locates neighboring shopping centers, churches, schools and parks.


[#7  …RealEstate4]  The city’s zoning map, land use plan, proposed developments and aerial imagery can be superimposed for a glimpse of future impacts.  Demographic summaries by census tracts can be generated and financial information for “comparables” can be plotted and cross-linked for a better understanding of market dynamics.  Armed with this information narrowing the housing choices, a prospective buyer can “hit the ground running” right off the plane—the revolution of the digital map and spatial database management is here, and increasingly, everywhere.


[#8  Linking Maps and Data]  The electronic link between mapping and data management certainly has expedited this process and saved considerable shoe leather… but come to think of it, it hasn’t fundamentally changed the process.  GIS software’s mapping and data management components are a result of a technological evolution, whereas its modeling component is a revolution in our perception of geographic space, spatial relationships and users of maps. 


The Third Decade… GIS MODELING

[#9  Software Life Cycles]   Like technology itself, the software life cycle begins as an idea for a super-sonic tool, then takes on a somewhat different shape as implementation reality sets in…but keep in mind, spatial technology is more than just software—it's thinking with maps.  In our search to automate mapping, we stumbled onto an entirely new way of doings, and things to do.


[#10  …Investigating Spatial Relationships]  In today's world, maps are numbers first, pictures later and this new perspective of spatial data is destined to change our paradigm of map analysis, as much as it changes our procedures.  While the early systems concentrated on automating traditional mapping and data management practices, more recent applications focus on understanding complex map relationships within a decision-context.  These “map-ematical” extensions involve a comprehensive modeling theory that is rooted in the digital nature of GIS maps and has all of the rights, privileges and responsibilities of traditional mathematics. 

            For example, consider the emerging field of Precision Farming.  With mud up to axles and 400 acres left to plow, precision in farming can seem worlds away. Yet site-specific management makes sense to a rapidly growing number of farmers.  Mapping and analyzing field variability for better economic and ecological decisions puts production agriculture at the cutting edge of GIS applications—both down to earth and downright ambitious.


[#11  SStat1_Descrete]  Traditionally, fertilization programs were determined by averaging soil samples taken throughout a field.  Today, soil samples are collected with GPS coordinates then spatially interpolated for maps of nutrient variations.  This process can be conceptualized…


[#12  SStat2_Animation]  …as first "guessing" that all of the non-sampled locations are identical to the closest sample point (click on the hyperlink to the SStat slide set).  The next series of steps involves passing a "smoothing filter" over the data… once, twice, three, four times.  Now that looks like what the point data was trying to tell us—more phosphorous in the NE portion of the field, not much in the NW. 

            The “smoothing” process is similar to slapping a big chunk of modeler’s clay over the data spikes, then taking a knife and cutting away the excess to leave a continuous surface that encapsulates the peaks and valleys implied in the original field samples—a map of the variation in phosphorous throughout the field.


            But what if we keep smoothing the data?  … 9 times, 19, 29, 39, 49, 69, 99 times!  What do you think would happen if you smoothed it 9,999 times?  (last slide in the animated series) Yep, it would be a horizontal plane aligning with the arithmetic average (…press Esc to return, then advance to slide #16). 


[#13  SStat3_Continuous]  Note that the whole-field average (identified as the red band) is hardly anywhere.  Most of the field is either well-above or well-below the average.  A fertilization application based on the assumption that the "average" amount of phosphorous is everywhere, would be adding even more in the NE where it's not needed and probably not enough in the NW where it' deficient—bad for the environment and bad the pocketbook. 


[#14  …PF Process]   As a combine moves through a field it checks the GPS location and yield flow every second and writes this information to a data file that is used to generate a map of yield variation every few feet throughout a field.  This map is combined with soil, terrain and other mapped data to derive a “Prescription Map” that is used to automatically adjust fertilization levels every few feet as a spray rig moves in the field.  The result is to constantly adjust the fertilization prescription to the unique combination of conditions occurring in the field. 


            Site-specific management recognizes the variability within a field and is about doing the right thing, in the right way, at the right place and time.  Its environmental and economic benefits are radically changing mankind’s oldest profession.  Farmers at the cutting edge of GIS …what'll they think of next?  How about the unlikely processing partner of a market forecaster? 


 [#15 …Spatial Data Mining]  The precision farming approach is not restricted to the back roads, it promises revolutionary changes in most geographical-based analysis.  Maps of an item of interest, be it corn yield, animal activity, or product sales, are encoded along with related “driving” variables, then analyzed to derive a “map-ematical” relationship that is used to predict the item at another place or time.  Like traditional statistics, the approach is independent of the application and exploits the dependency among variables— in spatial data mining, the geographic dependency is the focus and the results predict where responses will be high or low.


[#16 …Account Value Distribution] For example, consider a spatial data mining application investigating a bank’s home equity loan accounts.  Normally, this analysis would be based on descriptive information about each customer with minimal direct consideration of where they lived.  The map in this slide is a plot of a density surface identifying the geographic distribution of account values.  It is analogous to a map on the wall with a bunch of push-pins colored by the amount of the loan.  The warmer tones indicate areas of higher average values that translate into fertile locations for home equity loans.  Like the corn yield map, this map layer establishes the spatial patterns of interest.


 [#17 …Propensity Density]  The patterns on the loan activity map are related to other mapped data, such as demographics, economics, housing, and lifestyle.  The applications might be different, sales and social data being related instead of crop yield and dirt, but the data mining process is basically the same.  Applying the relationships, in this case, generates a “propensity density surface” that identifies pockets of potential equity loan customers throughout a city as shown in this slide. 

            The information can be critical in market forecasting and in locating areas where you should be doing well, but aren’t.  Targeted marketing and competition analysis are obvious offshoots of this type of GIS modeling.


[#18 …Data/Map Views]  The link between "maps as data" and "maps as images" provides an entirely new view of spatial relationships.  Once again, consider the farmer's phosphorous map as depicted in the upper inset of this slide.  The histogram of the data in the center forms a statistician's traditional view.  When linked in a GIS, one can "click" on an interval in the histogram and the locations with those data values will be highlighted in the 2-D and 3-D maps. 

            The lower inset takes this capability a bit further by linking a "scatterplot" to a couple of views of another farmer's field.  The Y-axis depicts the distribution of phosphorous in the topsoil while the X-axis shows the distribution in the subsoil.  Each dot in the scatterplot identifies the "joint condition" for the locations outlined in red on the map surfaces in the lower-left corner.  If you lasso a group of dots in the scatterplot, their geographic locations are identified.  Similarly, lassoing an area on the map causes the corresponding dots in the scatterplot to be highlighted.  The linkage allows us to simultaneously visualize the relationships between the geographic and data distributions.

            This graphical link can be extended to spatial statistics.  For example, traditional statistics can be used to derive a regression equation for predicting subsoil levels of phosphorous based on the topsoil levels (as reported in the red annotations).


 [#19 …Prediction Maps]  Non-spatial statistics evaluates the predictions without consideration of geographic patterns and reports R-squared values as overall assessment of how well a prediction model is performing.  The lower inset in the slide shows the results of using the prediction model in different parts of the farmer's field.  The paired maps on the left depict the actual and predicted phosphorous levels for an interior portion of the field.  The relatively flat "difference surface" on the bottom indicates that the predictions are fairly good. 

            However, the lumpy-bumpy difference surface for the paired maps on the right show that the model isn't anywhere near as good a predictor outside the partitioned area.  In fact, it suggests that the big ridge of over estimation along the western portion should be analyzed separately—some spatial guidance that isn't possible without GIS's link between the geographic and data distributions.

            The recognition that GIS maps are numbers first and pictures later, extends our perspective from qualitative to quantitative map analysis and should titillate the researchers among us.  Now let’s turn our attention to the flip-side of spatial statistics that focuses on numerical relationships of mapped data … to that of spatial analysis that characterizes the spatial context and arrangement of map features.  A good example of this type of GIS processing is landscape structure analysis.


 [#20 …Mosaic and Patch Indices]   Recall from Forestry 101 that the basic unit in landscape analysis is the forest parcel—sort of like the individual pieces of the jigsaw puzzle comprising the vegetation mosaic we see when gazing from a ridge top. 

            A wide variety of structural metrics tracking the shape, pattern and arrangement of the puzzle pieces are becoming available through GIS.  The most basic metrics are the area and perimeter of each forest polygon.  Edge contrast extends the description of the perimeter by weighting the boundary segments by the nature of the abutting patches.  For example, a portion an aspen stand’s boundary adjoining mixed hardwoods has less contrast than a portion adjoining conifers or open water.  In a sense, edge contrast describes the “ecological porosity” of the individual landscape units.

            Another extended metric is the shape index that is a normalized ratio of the perimeter to the area.  As the perimeter increases for a given area, an increasingly irregular boundary is indicated.  These and numerous other metrics are used to characterize the shape, pattern and arrangement "puzzle-pieces" comprising our forests.


 [#21 …Querying Results]  Procedures for calculating landscape metrics have been available for years. What has been lacking is its operational GIS expression.  Now that extensive databases have been compiled, the direct link to landscape analysis capabilities is coming online.  For example, this slide shows a simple spatial model that first selects all of the aspen stands (shown in gold), then identifies those that are small (shown in green with areas less than 15 hectares) and finally those that are “irregular” (shown in red where the shape index is > 2.0).  Subsequent steps might be a thematic map, tabular listing, and graph summarizing the edge contrast of the small, irregular aspen stands. 


 [#22 …Nearby Neighbor Statistics]  A further analysis might focus on the fragmentation of these threatened stands by calculating the nearest neighbor distances for each patch and summarizing the results for the entire vegetation class.  For example, the relative isolation of white birch stands in this area could be determined.  The process begins by identifying the polygons of interest…


 [#23 …NN Proximity Map] …then calculating the proximity from every location in the study area to the nearest white birch polygon.  The increasing proximity values emanating from each parcel are analogous to the ripples surrounding a rock thrown into a pond— splash, one-away, two-away and so on.  The pinks and purples in this slide identify locations that are far from the nearest birch polygon.


 [#24 …NN Proximity Ridges]  This slide shows the same information but is represented as a 3-D surface with increasing distance rising from the birch polygons like a series of abutting football stadiums.  The ridges shown in red identify interesting locations that are equidistant between two birch stands. 

            All locations within the ridge-lines are closer to one of the stands and form its “area of influence.”  A wealth of information about the relative isolation of each polygon is contained in the proximity map and its ridges.  For example, the lowest point along the ridge surrounding a polygon determines the distance to its nearest neighbor… sort of an ecological expression of "competition analysis" routinely used in retail siting models—just a shift in application perspective.


 [#25 …Assigning NN Statistics]  The table in this slide identifies several additional indices that summarize a polygon’s surrounding neighbors.  The nearest neighbor to one in the center is just over a thousand meters away while the farthest is over six thousand.  These extremes identify the best and worst case scenarios for a venturesome creature striking out to another habitat-island, while the average distance of a little over three thousand three meters away represents the typical wandering required.  

            While the forester's landscape view is comprised of vegetation polygons, keep in mind that an urban planner's view of a cityscape or a chemist's view of an electron microscope slide—a micro-scape? —has a similar set of "puzzle-pieces" forming important patterns and arrangements that determine the connectivity of the system. 

Spatial analysis of landscape elements provides useful information about animal habitat.  It can also provide information about shopper habitat, such as a superstore.  Consider another "non-traditional" perspective of geographic space—a floor plan of a superstore—and another off-the-wall new user of spatial technology—a retail store manager.


 [#26 …Floor Plan]  This is an interesting geographic space… the floor plan of a retail super store.


 [#27 …Item Nodes]  The fixtures and shelving spaces are encoded to form map features similar to the buildings and addresses in a city.


 [#28 …Analysis Grid]  These data are gridded at a 1-foot resolution to form a continuous analysis space.


 [#29 …Barriers to Movement]  The result is a map of barriers to shopper movement and the locations the shoppers want to visit.


 [#30 …Shopper Path 1]  The items in a shopper’s basket identify where he or she has been and spatial analysis is used to identify the plausible path used to collect the items.




 [#31 …Shopper Path #2]  Additional paths are derived for other shopping carts that pass through the checkout.


  [#32 …Analyzing Shopper Movement Patterns] The paths for shopper movement surfaces for user-specified time steps hundreds of carts throughout a day are aggregated into accumulated.  The brighter tones in these maps show higher shopper movement. 

            Note the high levels of sales for impulse items in both the AM and PM periods which is understandable… but the consistently high level for items in the Card and Candy section is not.  At first we thought there must be a problem with analysis procedure; then we suspected the data.  Actually, it all made sense when the client revealed that the data for the pilot project was for a 24-hour period before Valentine’s Day.


 [#33 …Analyzing Coincidence]  Coincidence analysis between shopper movement and sales activity can be investigated as well.  The orange locations on this map identify the counter-intuitive condition where sales are high, but shopper traffic is low… a retailer’s dream.  The opposite condition of areas with high traffic, but low sales, on the other hand, provides the retailer with a map of in-store marketing problems such as shown in bright blue on the large map on the right—these are candidate areas for changing the product mix on the end-cap shelves. 


[#34  …Shopper Movement--Animation]   The GIS model can be extended by "animation" of the maps of Shopper Movement and Sales to show how patterns change throughout the day (…click on the hyperlink to activate).  When the side-by-side displays are animated, the warmer colors of higher activity appear to roll in and out like wisps of fog under the Golden Gate Bridge.  The similarities and miss-matches in the ebb and flow of movement and sales provide a dramatic view of the spatial/temporal relationships contained in the traditionally non-spatial records of cash registers receipts. (…stop the mpeg movie, then advance to slide #20).


[#35  Video Mapping System]

That brings us to another "beyond mapping" application—the linking of multimedia and GIS.  GPS signals can be "stamped" to one of the audio channels whenever a handy-cam is used.  When the tape is played back to the computer, it's automatically geo-referenced to a base map.  This allows users to click on a map and retrieve the streaming footage or a captured still image for any location.   (…hyperlink to HTMLs)

            [#] For example, an ultralite—you know a hang glider with an engine—was used for a “bumblebee” flight over Lorry State Park near Fort Collins, Colorado.  Clicking anywhere along the flight path (shown as the blue line) brings up the aerial footage beginning at that location.  Users can “drop a pin” at any point and capture a still image for that location (…click on a couple of blue dots). 

            [#] Field plots can be augmented with images, as well as traditional inventory data and summary statistics (…click on a couple of red dots).  In this vein, field data collection is extended to field experience collection that tempers abstract maps and dense tables with glimpses of reality (…return from hyperlink).


[#36  VF General Scene]  GIS's "paper map" legacy is extended through a rich set of geo-query and display tools that facilitate data handling.  Video multimedia links the GIS to reality.  However, effective decision-making requires more than just data access and graphical presentation of current conditions.

            GIS not only describes “what is,” but can help us visualize and communicate “what could be.”  This slide is a computer-generated scene with texture mapping and rendering replacing familiar map colors and symbols with realistic tree-objects that are “poured” onto a terrain surface.  The result is a virtual reality of a forest database that resonates with viewers. 


 [#37 …Steps in 3D Rendering]  There are several basic steps in generating a rendered scene.  A light-shaded terrain surface is generated and the polygon containers linked to the forest inventory are identified.  Based on the vegetation data, appropriate textures are chosen for the forest floor, open spaces and landscape features.  This step is like laying a carpet within each polygon container.

            Next the tree objects are added. The vegetation factors of tree-type, age and density are combined with the viewing factors to determine how the bit maps of the tree-objects are resized and positioned.  The final map combines the surface texturing with the tree objects.  Atmospheric conditions, such as haze, add a final touch.  Seasonal effects, such as a winter-scene or fall coloration, simply assign a new set of texture maps and tree objects.


[#38 …Forested Scene]  An important advantage of a virtual forest is the ability to simulate management alternatives and get a good picture of various effects.  For example, consider this computerized landscape derived from an ArcInfo vegetation map.  Inventory data of tree type, age, composition and stocking for each forest parcel is used to place the trees, grass, and other features in the scene.  But what would the scene look like if a clear-cut were introduced?


[#39 …Clear-cut Scenario A]  The user should be able to query a simulation as easily as they geo-query a static database.  In this example, the user simply identified the type of harvest and the forest parcels involved to generate the simulated rendering.  Or different harvest boundaries can be simulated…


[#40 …Clear-cut Scenario B] …to investigate the visual impacts of other possible bad haircuts.  To be effective in decision-making, the interaction with a GIS must be immediate and comfortable for the decision-makers.  If there is a time-lag for GIS wizards to concoct their magic, the interactive dialog with mapped data is lost.

 [#41 …What's Ahead]  Our historical roots focused on automating the cartographic process and refining the digital map.  These efforts evolved into spatial database management systems providing a host of useful thematic mapping and geo-query tools.  Our current focus is on extending these capabilities to larger databases accessed over the Internet and broadening applications in both their general use and ability to model complex spatial relationships.  So what's ahead?  On the technical front, without question, it's object-oriented databases and programming.


 [#42 …Object-Oriented dB1]  Historically, maps have been abstractions of reality aligning with disciplinary perspectives.  They often are described as separate map layers conjuring up thoughts of laying transparent sheets on a light-table and viewing the coincidence within a stack of maps.  But spatial reality is that most things geographical are interrelated, often fugitive and subject to interpretation—rarely independent perspectives complied into disjoint renderings by varied disciplines.  The figure on the right suggests a complex spatial reality of spatially linked occurrences.


 [#43 …Object-Oriented dB2]  That's the underlying assumption of object-oriented databases.  3-D product design software pioneered this perspective.  You have probably seen an engineering equivalent in a host of TV commercials, such as the Chrysler one that "peels-away" the body and interior components to just the car's drive train, then reconstructs it.  This is accomplished by a database with all of the components inter-linked and the rigidly enforced associations can be traversed and displayed by simple queries. The linking of the "design" parts are analogous to the linking of "map parts" in an object-oriented GIS database that tracks all common features, coincident lines and spatial dependencies. 


 [#44 …Programming Objects1]  Programming objects promise a similar revolution in software design and use.  Most current GIS programs have evolved into large and complex systems that can do just about anything, but getting them to solve your problem was often problematic.  As general Halftrack notes "…there’s only one problem having all this sophisticated equipment; we don’t have anyone sophisticated enough to use it.”  Like object-oriented databases, GIS systems are being broken into pieces (termed controls) and standardized for interoperability (termed wrapping).


 [#45 …Programming Objects2]  What this means is that software developers are "exposing" the individual operations within a GIS to application developers.  The result is tailored software that strips away all of the unnecessary routines and picks-and-chooses the ones from a host of GIS packages and other systems that best fits the problem—sort of like choosing off the al cart menu for just want you want.  Also, the approach makes the assembly of these "boutique packages" much easier by adhering to common computer programming standards rather than developing proprietary scripting environments.  At the bottom-line are packages that are laser-focused on the applications of specific groups of users, not cumbersome, all-purpose toolboxes with hundreds of commands and a shelf full of manuals.  As you stroll the vender area, ask about their objectives in "object-oriented" databases and programming.


 [#46 …Mickey & Minney]  But the future of GIS lies in its acceptance and creative use as much as it lies in its technological advancements.  Indisputably, GIS technology has grown up and moved from the laboratories of the pioneers to large software houses and applications in almost every business activity.  As a result, it is facing a utilitarian user who lacks the sentimental attachment and patience of earlier GIS zealots.  The excitement of “developing technology for technology's self” has given way to its practical use.  It has been sold as a toolbox, and users are clambering for it to be as easy to use as a hammer.  A friendly, graphical user interface composed of icons, scroll lists, buttons, and bows makes interacting with a GIS much easier. 

            But has it enhanced the understanding of complex applications?  The rise in the mechanical ease of accessing a GIS might actually result in the “dumbing-down” its use, and ultimately stimulate inappropriate use of "big button" solutions. 

            Remember, GIS used to be “down the hall and to the right” in a room populated with technical specialists.  Now that it’s on everyone’s desk, we need a mechanism that helps users understand a GIS application, as well as its operational expedients.  What is needed is… a “humane” GIS enabling users to interact with a GIS application, as easily as they interact with the color pallets of its display.  Key to this cognitive view is the emerging concept of a “dynamic map pedigree linking GIS code to a flowchart of the processing. 


 [#47 …GIS Modeling Framework]  A GIS model involves a series of processing steps that converts mapped data into information, and in some cases into actual decision alternatives.  Throughout the processing, assumptions are made and interpretations of the conditions are implemented.  The link between a model’s logic and its specific expression as a command macro forms a chasm between users and GIS specialists.  A dynamic flowchart of the processing might help bridge this gap.


 [#48 …Model Structure]  For example, a Campground Suitability Model can be flowcharted as shown in this slide.  Its criteria are identified as rows, while the level of abstraction from base maps, to derived maps, to interpreted maps are represented as columns.

            The top row expresses a concern for siting the campground on gentle slopes.  It begins with an Elevation map and uses the slope command to derive a map of relative steepness.  In turn, this map is interpreted to identify the good slopes that are gentle. 

            In a similar manner, concerns to be near roads, near water, have good views of water and westerly oriented are evaluated.  Note that the model criteria form submodels that have a common logical flow— base, derived, then interpreted data.

 [#49 …Processing Levels]  In this example, weighted-averaging of the criteria is used to combine the five factors.  This is something akin to a professor using different weights for five exams when assigning a grade for the semester.  Locations on each of the criteria maps are first graded, then combined for an overall campground suitability map. 

            The important point is that while the left side of the flowchart primarily involves the GIS specialist, the right side involves end-user knowledge and sensitivities.  As processing moves from left to right, the physical characteristics and conditions (Data) are translated by assumptions and values (Judgement).  Within a decision context, a variety of assumptions and values are simulated so the decision-makers can visualize the sensitivity and relative merits of a series of possible perspectives. 

            In most applications, this interactive dialog with the logic of a model is extremely limited.  To the users, it requires a pilgrimage “down the hall and to the right” to the GIS alter for each iteration of possible alternatives.  To the specialists it perpetrates frustration with the “endless waffling of the policy-wonks” who never seem to make up their minds.  In many organizations, the cultural clash has lead to simply wallpapering the conference room with a bunch of colorful base maps, and very little GISing.


 [#50 …Linking Logic]  An alternative is to interactively link the flowchart of a model’s logic to its processing code.  In this environment the specialist and end-user collaborate on building the structure of the model, then the specialist implements it.  In doing so, global variables are used at decision points and linked to the flowchart’s boxes (representing maps) and lines (representing processing steps).  Once assembled, users can click on any part of the model’s logic and assess the assumptions at that step.  On their own, they can modify the “calibration and weighting factors” to run different scenarios.


 [#51 …Stamping Logic]  The different scenarios can be compared to visualize the effects of various alternatives.  The change in model results as different assumptions are investigated provides an entirely new perspective on the sensitivities of potential decisions. 

            The “pedigree” of each modeled map is contained in its flowchart and parameterization.  These files are stored with the metadata for each map and can be accessed through a button in the map’s legend.  In a sense, a dynamic flowchart is like a geo-query, except the logic of a spatial model is queried.  A better analogy might be a “spatial spreadsheet” where users can induce and visualize the effects of management alternatives— it’s main difference is that the “bottom line” is a map.  In this example, candidate locations for a campground, but with minimal modifications the model could help identify locations for "mega-buck estates" or "fractious factory outlet mall."


 [#52  …Where We Are] 


In just three decades GIS has evolved from its historical roles of computer mapping and spatial database management to GIS modeling and new forms of interacting with mapped data.  The next decade of GIS will take us from the map room to the boardroom, and even to the public hearing, where it is used as an active ingredient in conceptualizing alternatives.  Within this context GIS isn't used to provide colorful map answers and conference room wallpaper, but is used in participatory decision-making.  Within this context, GIS is used as a means to respond to a series of "what if" scenarios in which any single map isn't important.  It is how maps change as different perspectives are tried that becomes the information for a decision. 

            The new paradigm actively involves decision-makers in the analysis process instead of just choosing from a set of alternatives, or tacit decisions, produced by detached analysis in the GIS shop down the hall and to the right.

            The trek from the map room to the boardroom and the kitchen table isn’t so much increased number crunching and friendly user interfaces for geo-query, as it is communication of ideas and possibilities.  It isn’t just archiving more data and developing faster mappers simply to increase the deluge of colorful map products.  It isn’t just sharing data, but expanding on how that data is assimilated and transformed into useful information, and ultimately viable management alternatives.  As exciting as the past three decades in GIS has been, our future will be even more exhilarating as we move beyond mapping to spatial reasoning—the common thread in GIS. 


 [#53 …Last Slide] Thank you for the opportunity to speak this afternoon and the opportunity to work with you throughout the academic year.