11.6 Infonnation Output
appropriate tools for statistical analysis of spatial
data (Goodchild, 1992a; Anselin, 1993). However,
several software packages for spatial statistical
analysis now exist and are being more widely used
(e.g., Anselin, 1999, and http://spacestat.coml).
The lack of integration of GIS and environmental models was a key topic of a series of international workshops and symposia (Goodchild et aI.,
1993; Goodchild et aI., 1996; http://bbq.ncgia.ucsb.
edulconf/santaje/papers). Some have argued that
GIS and applications software should be integrated
(Parks, 1993), but it is getting much easier and
more transparent to move data between software
packages; this offers an increasingly viable option
in spatial data modeling. The GIS may carry a
tremendous overhead in terms of data storage
(topology), and only a subset of the data may be
required for an analysis. Spatially explicit environmental modeling is advancing on both fronts. Models are developed outside the GIS and spatial data
are ported in and out with increasing ease (e.g., He
and Mladenoff, 1999). Modeling tools that are integrated into a GIS are also being developed (e.g.,
see a dynamic modeling language embedded within
a GIS, PCRaster, http://www.geog.uu.nl/pcraster).
11.5.1 Data Overlay and Map Algebra
The form of analysis that always has been well integrated into GIS software is map algebra or cartographic modeling-the arithmetic of combining
map layers that are co-registered and of identical
resolution (Burrough, 1986; Tomlin, 1990). Sometimes, viewing the spatial distribution of a variable
(looking at a map) or the co-occurrence of two variables (map overlay) can be a very powerful source
of information. Analogous to viewing a scatterplot
in exploratory statistical data analysis, the locations
of the intersection or union of several mapped variabIes can provide the information required for a
land suitability analysis, as was recognized early
on by McHarg (1969). Frequently, cartographic
overlay is considered to be synonymous with analysis in GIS. However, digital spatial data can also
be used in spatial extensions of statistical and
process models and in analyses of spatial pattern.
11.5.2 Other Fonns of Modeling in GIS
Of the multitude of potential types of spatial data
modeling, we will discuss one that is particularly
relevant to EA and mention several others that are
covered in other chapters or that represent research
challenges for the future. As discussed in Chapter
5, two important complementary uses of GIS that
157
have yet to be fully realized for EA are stratified,
unbiased, efficient selection of sites for field survey and predictive mapping as a method of extrapolating from those survey data. In addition to
the references cited in that chapter, explicit methods for survey design were well articulated by
Austin and Heyligers (1989, 1991), Austin and
Adomeit (1991), and Cocks and Baird (1991). Statistical or other forms of (static) predictive modeling of ecological patterns (species distributions,
soil properties, site potential) are a way to interpolate among expensive, sparse field survey data.
This approach relies on the relationships between
those ecological patterns and underlying environmental variables (climate, terrain, hydrology) that
may be more easily mapped (reviewed by Elston
and Buckland, 1993; Hunsaker et aI., 1993;
Franklin, 1995; Franklin et aI., 2000).
Spatially explicit simulation models of ecological processes, supported by GIS, are discussed in
Chapter 18. A current research issue in geographical information science, relevant to the integration
of GIS and these process models, is the temporal
data handling and modeling capabilities of GIS (for
examples see Newell et aI., 1992; Peuquet, 1994;
Kraak et aI., 1995; Egenhofer and Golledge, 1998).
11.6 Information Output
Discussed next are two issues related to spatial information output: data standards and visualizing
scientific information. Data and metadata quality
and standards are becoming increasingly important
in the management of all types of scientific data including spatial data. The National Institute of Standards and Technology (1992) has adopted Federal
Information Processing Standards for spatial data
(Spatial Data Transfer Standards-SDTS). The
Federal Geographic Data Committee (FGDC) "coordinates the development of the National Spatial
Data Infrastructure (NSDI). The NSDI encompasses policies, standards, and procedures for organizations to cooperatively produce and share geographic data. The 16 federal agencies that make
up the FGDC are developing the NSDI in cooperation with organizations from state, local and tribal
governments, the academic community, and the
private sector" (http://www.fgdc.gov/). The FGDC
recently approved a revised Content Standard for
Digital Geospatial Metadata (see the FGDC Web
page). By executive order, data sets released by
government agencies after 1994 must comply with
these data and metadata standards. Federal standards will soon include (International Standards
appropriate tools for statistical analysis of spatial
data (Goodchild, 1992a; Anselin, 1993). However,
several software packages for spatial statistical
analysis now exist and are being more widely used
(e.g., Anselin, 1999, and http://spacestat.coml).
The lack of integration of GIS and environmental models was a key topic of a series of international workshops and symposia (Goodchild et aI.,
1993; Goodchild et aI., 1996; http://bbq.ncgia.ucsb.
edulconf/santaje/papers). Some have argued that
GIS and applications software should be integrated
(Parks, 1993), but it is getting much easier and
more transparent to move data between software
packages; this offers an increasingly viable option
in spatial data modeling. The GIS may carry a
tremendous overhead in terms of data storage
(topology), and only a subset of the data may be
required for an analysis. Spatially explicit environmental modeling is advancing on both fronts. Models are developed outside the GIS and spatial data
are ported in and out with increasing ease (e.g., He
and Mladenoff, 1999). Modeling tools that are integrated into a GIS are also being developed (e.g.,
see a dynamic modeling language embedded within
a GIS, PCRaster, http://www.geog.uu.nl/pcraster).
11.5.1 Data Overlay and Map Algebra
The form of analysis that always has been well integrated into GIS software is map algebra or cartographic modeling-the arithmetic of combining
map layers that are co-registered and of identical
resolution (Burrough, 1986; Tomlin, 1990). Sometimes, viewing the spatial distribution of a variable
(looking at a map) or the co-occurrence of two variables (map overlay) can be a very powerful source
of information. Analogous to viewing a scatterplot
in exploratory statistical data analysis, the locations
of the intersection or union of several mapped variabIes can provide the information required for a
land suitability analysis, as was recognized early
on by McHarg (1969). Frequently, cartographic
overlay is considered to be synonymous with analysis in GIS. However, digital spatial data can also
be used in spatial extensions of statistical and
process models and in analyses of spatial pattern.
11.5.2 Other Fonns of Modeling in GIS
Of the multitude of potential types of spatial data
modeling, we will discuss one that is particularly
relevant to EA and mention several others that are
covered in other chapters or that represent research
challenges for the future. As discussed in Chapter
5, two important complementary uses of GIS that
157
have yet to be fully realized for EA are stratified,
unbiased, efficient selection of sites for field survey and predictive mapping as a method of extrapolating from those survey data. In addition to
the references cited in that chapter, explicit methods for survey design were well articulated by
Austin and Heyligers (1989, 1991), Austin and
Adomeit (1991), and Cocks and Baird (1991). Statistical or other forms of (static) predictive modeling of ecological patterns (species distributions,
soil properties, site potential) are a way to interpolate among expensive, sparse field survey data.
This approach relies on the relationships between
those ecological patterns and underlying environmental variables (climate, terrain, hydrology) that
may be more easily mapped (reviewed by Elston
and Buckland, 1993; Hunsaker et aI., 1993;
Franklin, 1995; Franklin et aI., 2000).
Spatially explicit simulation models of ecological processes, supported by GIS, are discussed in
Chapter 18. A current research issue in geographical information science, relevant to the integration
of GIS and these process models, is the temporal
data handling and modeling capabilities of GIS (for
examples see Newell et aI., 1992; Peuquet, 1994;
Kraak et aI., 1995; Egenhofer and Golledge, 1998).
11.6 Information Output
Discussed next are two issues related to spatial information output: data standards and visualizing
scientific information. Data and metadata quality
and standards are becoming increasingly important
in the management of all types of scientific data including spatial data. The National Institute of Standards and Technology (1992) has adopted Federal
Information Processing Standards for spatial data
(Spatial Data Transfer Standards-SDTS). The
Federal Geographic Data Committee (FGDC) "coordinates the development of the National Spatial
Data Infrastructure (NSDI). The NSDI encompasses policies, standards, and procedures for organizations to cooperatively produce and share geographic data. The 16 federal agencies that make
up the FGDC are developing the NSDI in cooperation with organizations from state, local and tribal
governments, the academic community, and the
private sector" (http://www.fgdc.gov/). The FGDC
recently approved a revised Content Standard for
Digital Geospatial Metadata (see the FGDC Web
page). By executive order, data sets released by
government agencies after 1994 must comply with
these data and metadata standards. Federal standards will soon include (International Standards