Beyond Mapping
III
|
Map
Analysis book with companion CD-ROM for hands-on exercises and further reading |
An Experiential GIS — discusses a participatory GIS experience
An Understanding GIS — describes the translation of mapped data to
spatial information for decision-making
Dreams and Nightmares Are Born of Frustration — identifies concerns with cost/benefit analysis
of
GIS
Is Never Having to Say You Are Sorry — discusses
several human considerations in implementing GIS
Author’s Notes: The figures in this topic use MapCalcTM software.
An educational CD with online text, exercises and databases for
“hands-on” experience in these and other grid-based analysis procedures is
available for US$21.95 plus shipping and handling (www.farmgis.com/products/software/mapcalc/
).
<Click
here> right-click to download and print a printer-friendly version of
this topic (.pdf).
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An Experiential
(GeoWorld, ???, pg. ???)
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, 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...
"healthy a priori discussion
ensued." Most importantly, we
minimized
The
_______________________
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.
An Understanding
(GeoWorld, ???, pg. ???)
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,
Dreams and Nightmares are Born of Frustration
(GeoWorld, ???, pg. ???)
The dream is
that
Your first step
in this process is establishing "where you are coming from."
There, that's
easy. There is nothing to it. Just call in the accountants and they will
identify the numbers to plug into the Cost/Benefit equation. The reality is that even a strictly economic
perspective is not that easy. The
comfortable feeling of quantifying the evaluation process is quickly lost to
the pliable nature of the "yardsticks" used to measure the costs and
benefits.
The time-span
used in the analysis is critical. If it
is too short, the stream of benefits is artificially truncated. The high front-end costs, combined with the
confusion and frustration of implementing a new system, will far outweigh the
benefits. It's like a bare-knuckle
battle between Sylvester Stallone and a tiger cub. If it is delayed a few years, the outcome
will likely be different. If you had
used a two-week cost recovery period for word processing, would you have ever
dropped your pencil?
So what time
period should be used? That's a
judgement call-- your judgement call.
Like lying with statistics, you can choose the time period that insures
the answer you want. In general, a
longterm position favors the adoption of
Just as
important (and "mushy") is how you identify and quantify the
variables of the cost/benefit equation.
Four cost considerations quickly surface-- hardware/software, data base development/administration,
training and application models. The
hardware figures are the easiest to quantify through a litany of parameters
including MegaHertz, GigaBytes,
Although
relatively easy to quantify, these figures are fleeting and set you up for a
bad case of "buyer's remorse."
About the time you finally push through your procurement and take first
delivery, your system is out of date.
It's like that pocket calculator.
Within a couple of months, the same expenditure gets you five more keys
at half the price. The difficulty in
nailing down the hardware/software cost component isn't in the definitions, it
is keeping your footing in the quicksand of technology. Like shooting ducks, you had better have a
good lead on your target. For large,
bureaucratic organizations, it may be prudent to just set a budgetary figure
for the "best available technology" and postpone the specifications
to the moment of purchase. That may seem
preposterous, but it may be more realistic.
Data base
development, maintenance and management are not only larger expenses than
hardware and software, but it is even more tricky and slippery to
estimate. Rarely does a simple inventory
of your current map and file cabinets multiplied times an estimate of encoding
costs produce an acceptable cost figure.
The differences between the digital and paper map make it too tricky for
such a mechanical approach. It's prudent
to launch an Information Needs Assessment (INA) to determine data base
contents, structure, policy and costs (a later issue will focus on this
process).
Even if you do
get a good handle on the data base, you must develop, you're not out of the
woods yet. How you obtain these data is
slippery turf. Manual encoding, scanning
or purchasing are your basic options.
Not so long ago, in-house, manual encoding was your only option. More recently the scales have been tipping
toward scanning and purchasing, as a room full of digitizer folks is a major
cost and distraction from normal business activities. Also, many of the maps you might encode have
time-bombs ticking within them. For
example, if you encode (in-house or contract) a soils map, it will become
invalid once the Soil Conservation Service's "authoritative" version
is released. Its back to shooting ducks,
you had better get your data requirements in line and lead them, or you will
just be pumping pellets into the air.
The costs of
training your people to use
One reaction to
this reality is to form a
First, the
If costs of
training are identified at all, they are usually associated with vocational
instruction on system operations. But
The development
of application models is the other reason for failure of a centralized
approach. How the new technology leads
to new ways of doing things is the least understood cost (and benefit) of
The creative
assembly is entirely up to your people.
If you ignore or skimp on training and application model development,
you will incur opportunity costs at the minimum. More likely, you will generate a backlash of
confusion and apprehension that quickly outweighs the set benefits you
identify. A couple of strategically
placed anti-
A strict
economic perspective is the first step in scoping
(GeoWorld, ???, pg. ???)
Most
organizations begin their first step of what seems to be a thousand mile
journey to
The
organizational structure (both formal and informal) is an important concern, as
it is the direct expression of the "corporate character"— the most
basic element of any organization. If
extensive individual latitude and autonomy best describes the current
character,
However, if
Another concern
which may run amuck with the corporate character is the imposition of data
standards. In many organizations,
mapping standards are either non-existent, or merely address geographic
registration and data exchange formats.
But this is just the tip of the chilling iceberg of standards. The ability to export a map from one
A corporate
data base consists of three levels of maps based on their degree of
abstraction-- base, derived and interpreted.
Base maps are usually physical data we collect, such as roads, water and
ownership boundaries. They have minimal
abstraction, and as much as possible, represent a scale model with all of the
detail of a flatten model train set.
Definitions and procedures for mapping most these data are in place...
but not all.
Consider a map
of cover type. Is Forest/Non-Forest a
sufficient standard? Or should the
Forest class be further divided into Conifer and Deciduous? And the Conifer, in turn, subdivided into
Pine, Fir and Hemlock? What about age
and stocking classes? Should you
identify a lone pine tree in the middle of a meadow as a Conifer Stand? Two, three, four, five trees— what does it
take to form a forest stand? Ask a
forester, ecologist and recreation scientist and you'll get at least three
different responses. Or maybe four or
five different definitions depending on how different applications decipher the
landscape. You'll be sorry if you don't
tackle these questions before you implement
For example, a
wildfire had the audacity to burn across the boundary of two National
Forests. Maps of cover type were encoded
for both Forests, but they couldn't be edge-matched. One Forest had six classes of age and
stocking for Douglas Fir, the other had eight.
The
Vested
interests in the definitions of map categories goes beyond base data. Derived maps, such as slope, visual exposure
and proximity to roads, are physical things.
However, the data are too difficult to collect, so we use the computer
to calculate them. Even something as
simple as slope calculation has several algorithms, each with its pros and
cons. For something as complex as visual
exposure, there is a quagmire of assumptions, approaches and procedures. Which will you entrench in your system? Rest assured that the choice won't be by
consensus, nor the dissenting voices reserved.
Even more
volatile are the assumptions embedded in interpreted maps. These data are the most abstract, as they are
conceptual renderings of expert opinion.
Taunts of "my elk habitat model is better than yours"
reverberate through the halls whenever two wildlife ecologists are cornered in
the same room. It is naive to assume
that an elk model will edge-match across two forests, much less an entire
region. And certainly not across the
paradigm chasm of two experts.
So whose
derived and interpreted maps capture the standards in the corporate data
base? The question of standards runs a
lot deeper than just geographic registration and encoding effort. It involves organizational and individual
perceptions, reputations and vested interests.
You'll be sorry if your implementation plan ignores these elements. Sure, they will get sorted out later-- after
you and the
A
Figure 1. Institutional and Individual Threats and
responses.
Figure 1
outlines some of the threats and responses which need to be addressed. The outline is designed to stimulate
discussion in a workshop setting, but hopefully they will trip some thoughts in
your mind. As you look over the outline,
try some "free associations" with the points. Conjure up some of your own threats and
possible coping responses. It is a lot
of fun at the workshops and sparks a broader perspective on
_____________________
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