|
Epilog |
Spatial Reasoning
book |
Don’t Forget the Human Factor:
an Experiential GIS — describes an early experience (1980) in
the application of
Developing an Understanding GIS
— describes
the translation of mapped data to spatial information for decision-making
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Don’t Forget the Human Factor: An Experiential GIS
(GeoWorld, July 1996)
It is often said that "experience is what you get when you don't
get what you want." The
corollary to this universal truth is "learn
from other's mistakes, so you won't have to make them all yourself." As
Given this line of reasoning, let me
describe an early experience in the application of
Where we went wrong was an attempt to
address a "real world" problem.
The town had recently completed its Comprehensive Plan of Development
and Conservation as a requirement of the Coastal Wetlands Act. It was the result of several years’ effort
among citizen groups and town officials.
The plan consisted of twenty-one policy statements, such as
"protect inland wetlands ...from contamination and other
modifications," "preserve farmlands," and "encourage
development near or within existing developed areas."
Since all twenty-one of the statements
had a spatial component, it seemed natural to map the conceptual model embodied
in the plan. Using a three-tier ranking
scheme of suitable, less suitable and unsuitable, each policy statement was interpreted into a map of
suitability for development. For
example, the policy to "preserve farmland" used the town's land use
map to identify farmland and then assign the areas as less suitable. Similarly, the policy statement to
"protect inland wetlands" caused these areas on the sensitive soil
map to be designated as unsuitable. In
contrast, the areas near or within existing development indicated on the land
use map were identified as suitable for development. Following the plan's organization, the
statements were grouped into four submodels of Water
and Sewage, Growth, Preservation, and Natural Land Use, and then combined into
one overall suitability map.
Near the end of the term, enthusiasm was
high and success seemed imminent. That
was until we hosted a town meeting at the local high school to present the
results. Students served refreshments
and proudly stood by their computer-generated maps draping the
walls. As fledgling
So what went wrong? We had done our homework. We had developed an accurate database. We had conscientiously translated their
policy statements into maps and integrated them as implied by their
plan. We thought we had done it all...
and we had from a
Being a slow learner and somewhat bent
on self-flagellation, I decided to extend the project the following year. First, the students refined both the database
and the model, then determined the most limiting
policy goals by systematically relaxing criteria in successive runs
(sensitivity analysis). Armed with this
insight, we solicited the help of the three town commissions instrumental in
the plan's development; the Economic Development Commission, the Planning and
Zoning Commission and the Conservation Commission. At working meetings, policy-rating questions
were posed to each group and their hierarchical orderings of the policy
statements where used for subsequent model runs.
The results were three maps of overall
suitability, expressing alternative interpretations of the plan. For example, the Conservation Commission's
interpretation of "protect inland wetlands" was emphatic. Since it's damp about everywhere, 83% of the
town was deemed unsuitable for development.
The Economic Commission, on the other hand, believed sound engineering
protects wetlands, thereby lowering the wetland policy's rating, which resulted
in only 21% being unsuitable. By simply
subtracting the two maps, the locations of agreement and contention were easily
identified. The comparison map and the
three alternative interpretations by the commissions were published in the
local paper... "a healthy a priori discussion ensued."
Most importantly, we minimized
The
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For more on this "watershed experience,” see
Assessing Spatial Impacts of Land Use Plans, by Berry and Berry, 1988, in
Journal of Environmental Management, 27:1-9; and Analysis of Spatial
Ramifications of the Comprehensive Plan of a Small Town, Berry, et. al., 1981, in the proceedings of the 41st Symposium, American
Congress of Surveying and Mapping.
Developing
an Understanding GIS
(GeoWorld, August 1996)
Effective
First,
let's split hairs on some important words borrowed from the philosophers-- data, information, knowledge, and wisdom. You often hear them interchangeably, but they
are distinct from one another in some subtle and not-so-subtle ways.
The
first is data, the "factoids" of our Information Age. Data
are bits of information, typically but not exclusively, in a numeric form, such
as cardinal numbers, percentages, statistics, etc. It is exceedingly obvious that data are
increasing at an incredible rate.
Coupled with the barrage of data, is a requirement for the literate
citizen of the future to have a firm understanding of averages, percentages,
and to a certain extent, statistics.
More and more, these types of data dominate the media and are the
primary means used to characterize public opinion, report trends and persuade
specific actions.
The
second term, information, is closely related to data. The difference is that we tend to view
information as more word-based and/or graphic than numeric. Information
is data with explanation. Most of what
is taught in school is information.
Because it includes all that is chronicled, the amount of information
available to the average citizen substantially increases each day. The power of technology to link us to
information is phenomenal. As proof,
simply "surf" the exploding number of "home pages" on the
Internet.
The philosophers'
third category is knowledge, which
can be viewed as information within a context.
Data and information that are used to explain a phenomenon become
knowledge. It probably does not double
at fast rates, but that really has more to do with the learner and processing
techniques than with what is available.
In other words, knowledge is data and information once we can process
and apply it.
The
last category, wisdom, is what
certainly does not double at a rapid rate.
It is the application of all three previous categories, and some
intangible additions. Wisdom is rare and
timeless, and is important because it is rare and timeless. We seldom encounter new wisdom in the popular
media, nor do we expect deluge of newly derived wisdom to spring forth from our
computer monitors each time we log on.
Knowledge
and wisdom, like gold, must be aggressively processed from tons of near
worthless overburden. Simply increasing
data and information does not assure the increasing amounts of the knowledge
and wisdom we need to solve pressing problems.
Increasing the processing "thruput"
by efficiency gains and new approaches might.
OK, how
does this philosophical diatribe relate to
Understanding
sits at the juncture between information and knowledge. Understanding
involves the honest dialog among various interpretations of data and
information in an attempt to reach common knowledge and wisdom. Note that understanding is not a
"thing," but a process. It's
how concrete facts are translated into the slippery slope of beliefs. It involves the clash of values, tempered by
judgment based on the exchange of experience.
Technology, and in particular
Our
earliest encounters with
Tomorrow's
This
step needs to fully engage the end-user in
I hope
we consider the importance of knowledge and wisdom in the Information Age, and
eagerly grasp the opportunity
Like
the automobile and indoor plumbing,
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