158
Geographic Information Science and Ecological Assessment
Organization (ISO) standards so that data sets can
be shared globally.
Ideas from the field of scientific visualization
have implications for the ways that information
output resulting from spatial data analysis with a
GIS are displayed, perceived, and interpreted. Buttenfield (1996, p. 463) states that "GIS technology
relies heavily on user visualization skills .... Poorly
designed displays convey false ideas, and they bias
analysis and interpretation. Many GIS users are not
trained in graphical design, nor should they be."
Butterfield suggests elements of map design and
visualization methods that could help correct these
problems for both static and animated map display
(see also Hearnshaw and Unwin, 1994; MacEchren
and Fraser Taylor, 1994).
11.7 Conclusion
It is difficult to envision an ecological assessment
taking place without the support of a GIS. Consequently, several issues from geographical information science must be addressed in order for the geographical analyses that support an EA to be valid
and defensible.
1. Those who are conducting GIS analysis in support of EA are most effective when they are
trained in the fundamentals of both geographical information science and ecosystem management-and have the methodological and technical skills required to "drive" a GIS.
2. Although the availability of large spatial data
sets to support ecosystem analysis has increased,
this availability should never be assumed nor the
cost of developing them underestimated. The
appropriateness of the scale, precision, classification scheme, and other characteristics of existing digital maps to the task at hand should always be carefully considered.
3. Geographical analyses in support of EA can be
very powerful tools, even when based on simple cartographic overlay modeling; however, the
results of these analyses can be spurious or misleading when the effect of data uncertainty on
model output is not calculated or simulated.
4. Two fruitful areas of further research and development are (a) closer integration of GIS and
the ecological and resource management models used in EA and (b) developing better decision support and visualization tools that help decision makers to see the uncertainty in both the
input data and the model outcome.
11.8 References
Allen, T. H. F.; Starr, T. B. 1982. Hierarchy: perspectives for ecological complexity. Chicago: University of
Chicago Press.
Anselin, L. 1993. Discrete spatial autoregressive models. In: Goodchild, M. P.; Parks, B. 0.; Steyaert,
L. T., eds. Environmental modeling with GIS. New
York: Oxford University Press: 454-469.
Anselin, L. 1999. Interactive techniques and exploratory
spatial data analysis. In: Longley, P.; Goodchild,
M. P.; Maguire, D.; Rhind, D., eds. Geographical information systems: principles, techniques, management and applications. New York: John Wiley &
Sons: 253-266.
Austin, M. P.; Adomeit, E. M. 1991. Sampling strategies
costed by simulation. In: Margules, C. R.; Austin,
M. P., eds. Nature conservation: cost effective biological surveys and data analysis. East Melbourne,
Australia: Commonwealth Scientific and Industrial
Research Organization, 167-175.
Austin, M. F.; Heyligers, P. C. 1989. Vegetation survey
design for conservation: gradsect sampling of forests
in North-eastern New South Wales. Bioi. Conserv.
50:13-32.
Austin, M. F.; Heyligers, F. C. 1991. New approaches to
vegetation sample survey design: gradsect sampling.
In: Margules, C. R; Austin, M. P., eds. Nature conservation: cost effective biological surveys and data
analysis. East Melbourne, Australia: Commonwealth
Scientific and Industrial Research Organization:
31-36.
Brewster, A. E. 1996. Utilizing geographic technologies
to analyze the nesting habitat preferences of the Belding's Savannah sparrow. Master of Arts thesis. San
Diego, CA: Department of Geography, San Diego
State University.
Burrough, P. A. 1986. Principles of geographical information systems for land resources assessment. Oxford,
UK: Clarendon Press.
Burrough, P. A.; McDonnell, R 1998. Principles of geographical information systems (spatial information
systems and geostatistics), New York: Oxford University Press.
Buttenfield, B. P. 1996. Scientific visualization for environmental modeling: interactive and proactive
graphics. In: Goodchild, M. F.; Steyaert, L. T.; Parks,
B. 0., eds. GIS and environmental modeling: progress
and research issues. Fort Collins, CO: GIS World
Books: 463-467.
Clarke, K. C. 1995. Analytical and computer cartography, 2nd ed. Upper Saddle River, NJ: Prentice Hall.
Clarke, K. C. 1999. Getting started with geographic information systems, 2nd ed. Upper Saddle River, NJ:
Prentice Hall.
Cocks, K. D.; Baird, L. A. 1991. The role of geographic
information systems in the collection, extrapolation
and use of survey data. In: Margules, C. R; Austin,
M. P., eds. Nature conservation: cost effective biological surveys and data analysis. East Melbourne,
Geographic Information Science and Ecological Assessment
Organization (ISO) standards so that data sets can
be shared globally.
Ideas from the field of scientific visualization
have implications for the ways that information
output resulting from spatial data analysis with a
GIS are displayed, perceived, and interpreted. Buttenfield (1996, p. 463) states that "GIS technology
relies heavily on user visualization skills .... Poorly
designed displays convey false ideas, and they bias
analysis and interpretation. Many GIS users are not
trained in graphical design, nor should they be."
Butterfield suggests elements of map design and
visualization methods that could help correct these
problems for both static and animated map display
(see also Hearnshaw and Unwin, 1994; MacEchren
and Fraser Taylor, 1994).
11.7 Conclusion
It is difficult to envision an ecological assessment
taking place without the support of a GIS. Consequently, several issues from geographical information science must be addressed in order for the geographical analyses that support an EA to be valid
and defensible.
1. Those who are conducting GIS analysis in support of EA are most effective when they are
trained in the fundamentals of both geographical information science and ecosystem management-and have the methodological and technical skills required to "drive" a GIS.
2. Although the availability of large spatial data
sets to support ecosystem analysis has increased,
this availability should never be assumed nor the
cost of developing them underestimated. The
appropriateness of the scale, precision, classification scheme, and other characteristics of existing digital maps to the task at hand should always be carefully considered.
3. Geographical analyses in support of EA can be
very powerful tools, even when based on simple cartographic overlay modeling; however, the
results of these analyses can be spurious or misleading when the effect of data uncertainty on
model output is not calculated or simulated.
4. Two fruitful areas of further research and development are (a) closer integration of GIS and
the ecological and resource management models used in EA and (b) developing better decision support and visualization tools that help decision makers to see the uncertainty in both the
input data and the model outcome.
11.8 References
Allen, T. H. F.; Starr, T. B. 1982. Hierarchy: perspectives for ecological complexity. Chicago: University of
Chicago Press.
Anselin, L. 1993. Discrete spatial autoregressive models. In: Goodchild, M. P.; Parks, B. 0.; Steyaert,
L. T., eds. Environmental modeling with GIS. New
York: Oxford University Press: 454-469.
Anselin, L. 1999. Interactive techniques and exploratory
spatial data analysis. In: Longley, P.; Goodchild,
M. P.; Maguire, D.; Rhind, D., eds. Geographical information systems: principles, techniques, management and applications. New York: John Wiley &
Sons: 253-266.
Austin, M. P.; Adomeit, E. M. 1991. Sampling strategies
costed by simulation. In: Margules, C. R.; Austin,
M. P., eds. Nature conservation: cost effective biological surveys and data analysis. East Melbourne,
Australia: Commonwealth Scientific and Industrial
Research Organization, 167-175.
Austin, M. F.; Heyligers, P. C. 1989. Vegetation survey
design for conservation: gradsect sampling of forests
in North-eastern New South Wales. Bioi. Conserv.
50:13-32.
Austin, M. F.; Heyligers, F. C. 1991. New approaches to
vegetation sample survey design: gradsect sampling.
In: Margules, C. R; Austin, M. P., eds. Nature conservation: cost effective biological surveys and data
analysis. East Melbourne, Australia: Commonwealth
Scientific and Industrial Research Organization:
31-36.
Brewster, A. E. 1996. Utilizing geographic technologies
to analyze the nesting habitat preferences of the Belding's Savannah sparrow. Master of Arts thesis. San
Diego, CA: Department of Geography, San Diego
State University.
Burrough, P. A. 1986. Principles of geographical information systems for land resources assessment. Oxford,
UK: Clarendon Press.
Burrough, P. A.; McDonnell, R 1998. Principles of geographical information systems (spatial information
systems and geostatistics), New York: Oxford University Press.
Buttenfield, B. P. 1996. Scientific visualization for environmental modeling: interactive and proactive
graphics. In: Goodchild, M. F.; Steyaert, L. T.; Parks,
B. 0., eds. GIS and environmental modeling: progress
and research issues. Fort Collins, CO: GIS World
Books: 463-467.
Clarke, K. C. 1995. Analytical and computer cartography, 2nd ed. Upper Saddle River, NJ: Prentice Hall.
Clarke, K. C. 1999. Getting started with geographic information systems, 2nd ed. Upper Saddle River, NJ:
Prentice Hall.
Cocks, K. D.; Baird, L. A. 1991. The role of geographic
information systems in the collection, extrapolation
and use of survey data. In: Margules, C. R; Austin,
M. P., eds. Nature conservation: cost effective biological surveys and data analysis. East Melbourne,