126
Integration of Physical, Biological, and Socioeconomic Infonnation
Product!). A small set of indicators may often be
more informative and defensible. Karr's index of
biotic integrity (1981) is a famous example based
in aquatic faunal diversity. It can be integrated with
other sorts of information, for example, land-use
data, to yield more integrative knowledge and assessments (Steedman, 1988).
9.6 Technical Tools
and Approaches
A wide range of tools is available for data acquisition, organization and analysis, understanding
and communication, and learning and evaluation.
In one way or another, all these are central to information integration; in fact, they may dominate
to the point that other kinds of tools are sometimes
forgotten. Many integration processes focus on acquiring and maintaining technical tools to the neglect of other more substantive issues.
There are many technical means of data acquisition, and each is a major area of research in its
own right. We mention just a few. Data loggers that
automatically record data on such variables as temperature or water level at predetermined intervals
can provide large amounts of information at a high
resolution. Remote sensing is increasingly used for
acquisition of vegetation, land use, and other spatial information. Costs are high, except compared
to field acquisition of the same data for large areas. Digitizing from existing maps can provide similar data for as far back as such maps go (which
can be a long way in some places, e.g., 80 years in
Ontario and 150 years in Italy). Digital photography is increasingly applicable in do-it-yourself remote sensing from low-altitude aircraft. Aerial photos have long provided a way of getting spatial
information at lower cost than remote sensing, at
least for smaller areas. Archival photos also have
historical applications, for example, in tracking glacier retreat. Global positioning systems (GPS) are
also an ever more available means of acquiring spatial data. Sample (1994) and Wilkie and Finn
(1996) provide good overviews and examples of
applications of most of these technologies; also see
Chapters 5 and 10.
The most basic tool for data organization and
analysis is an environmental database or information system (GUnther 1998). When such a system
is georeferenced in a geographical information system (GIS) and linked to spatial pattern analysis routines and includes historical data, it presents a powerful tool for integration and analysis (e.g., Kienast,
1993; Fotheringham and Rogerson, 1994; see also
Chapters 13 through 15). Data analysis and visualization can be supported by tools from spreadsheets
to scientific graphing and analysis packages to
high-end visualization tools. No tools that are readily available incorporate all functions, yet most
functions are necessary for a project of any size,
with both spatial and temporal information, bibliographies, and qualitative data. In this context, it
may be better to use a range of modestly priced
tools, rather than trying to buy the very expensive
top of the line product, which will still not be flexible enough (cf. Sharpe and Slocombe, 1995). Another option, increasingly common, may be networked, or distributed, environmental and
conservation information systems that allow pooling of resources at different resolutions, domains,
and extent (e.g., Davis, 1995).
Tools of growing importance for the process of
integration, and especially the presentation of integrated information, are hypertext documentation,
World Wide Web (WWW) sites, and interactive
simulations. These allow the user to follow links in
their own way, and in a nonlinear manner, depending on the questions of interest and the connections that make sense to them (cf., in a different context, Delany and Landow, 1994). While
fostering integration in the development process,
simulations also foster additional integration and
understanding when they are widely used and experimented with in later phases (Holling, 1978;
Hannon and Ruth, 1994). In addition to presenting
integrated information to end users, Web sites may
provide the means for teams to communicate, thus
fostering consultative, cooperative work that would
be harder with traditional paper documents and regular mail communication.
Finally, there are advanced tools for organizing
data and facilitating, if not automating, learning and
evaluation. Broadly, these embody efforts to integrate databases, GIS, and simulations in the first
instance; and then to extend them with various
kinds of expert, learning, and decision support
(knowledge-based) tools (see Chapters 11, 12, and
18). Integrated GIS-simulation systems allow the
extrapolation of existing spatial and temporal data,
which is key to understanding a system. Many of
these examples are driven by land and forest management problems for which predicting future spatial patterns is critical (e.g., Mackay et aI., 1994;
Miller, 1994; Sample, 1994). A related analytic tool
is object-oriented analysis, which could complement standard systems analysis as well as computerbased tool development (e.g., Saarenmaa et aI.,
1994).
Knowledge-based systems seek to embed our
knowledge of the world in a computer system that
Integration of Physical, Biological, and Socioeconomic Infonnation
Product!). A small set of indicators may often be
more informative and defensible. Karr's index of
biotic integrity (1981) is a famous example based
in aquatic faunal diversity. It can be integrated with
other sorts of information, for example, land-use
data, to yield more integrative knowledge and assessments (Steedman, 1988).
9.6 Technical Tools
and Approaches
A wide range of tools is available for data acquisition, organization and analysis, understanding
and communication, and learning and evaluation.
In one way or another, all these are central to information integration; in fact, they may dominate
to the point that other kinds of tools are sometimes
forgotten. Many integration processes focus on acquiring and maintaining technical tools to the neglect of other more substantive issues.
There are many technical means of data acquisition, and each is a major area of research in its
own right. We mention just a few. Data loggers that
automatically record data on such variables as temperature or water level at predetermined intervals
can provide large amounts of information at a high
resolution. Remote sensing is increasingly used for
acquisition of vegetation, land use, and other spatial information. Costs are high, except compared
to field acquisition of the same data for large areas. Digitizing from existing maps can provide similar data for as far back as such maps go (which
can be a long way in some places, e.g., 80 years in
Ontario and 150 years in Italy). Digital photography is increasingly applicable in do-it-yourself remote sensing from low-altitude aircraft. Aerial photos have long provided a way of getting spatial
information at lower cost than remote sensing, at
least for smaller areas. Archival photos also have
historical applications, for example, in tracking glacier retreat. Global positioning systems (GPS) are
also an ever more available means of acquiring spatial data. Sample (1994) and Wilkie and Finn
(1996) provide good overviews and examples of
applications of most of these technologies; also see
Chapters 5 and 10.
The most basic tool for data organization and
analysis is an environmental database or information system (GUnther 1998). When such a system
is georeferenced in a geographical information system (GIS) and linked to spatial pattern analysis routines and includes historical data, it presents a powerful tool for integration and analysis (e.g., Kienast,
1993; Fotheringham and Rogerson, 1994; see also
Chapters 13 through 15). Data analysis and visualization can be supported by tools from spreadsheets
to scientific graphing and analysis packages to
high-end visualization tools. No tools that are readily available incorporate all functions, yet most
functions are necessary for a project of any size,
with both spatial and temporal information, bibliographies, and qualitative data. In this context, it
may be better to use a range of modestly priced
tools, rather than trying to buy the very expensive
top of the line product, which will still not be flexible enough (cf. Sharpe and Slocombe, 1995). Another option, increasingly common, may be networked, or distributed, environmental and
conservation information systems that allow pooling of resources at different resolutions, domains,
and extent (e.g., Davis, 1995).
Tools of growing importance for the process of
integration, and especially the presentation of integrated information, are hypertext documentation,
World Wide Web (WWW) sites, and interactive
simulations. These allow the user to follow links in
their own way, and in a nonlinear manner, depending on the questions of interest and the connections that make sense to them (cf., in a different context, Delany and Landow, 1994). While
fostering integration in the development process,
simulations also foster additional integration and
understanding when they are widely used and experimented with in later phases (Holling, 1978;
Hannon and Ruth, 1994). In addition to presenting
integrated information to end users, Web sites may
provide the means for teams to communicate, thus
fostering consultative, cooperative work that would
be harder with traditional paper documents and regular mail communication.
Finally, there are advanced tools for organizing
data and facilitating, if not automating, learning and
evaluation. Broadly, these embody efforts to integrate databases, GIS, and simulations in the first
instance; and then to extend them with various
kinds of expert, learning, and decision support
(knowledge-based) tools (see Chapters 11, 12, and
18). Integrated GIS-simulation systems allow the
extrapolation of existing spatial and temporal data,
which is key to understanding a system. Many of
these examples are driven by land and forest management problems for which predicting future spatial patterns is critical (e.g., Mackay et aI., 1994;
Miller, 1994; Sample, 1994). A related analytic tool
is object-oriented analysis, which could complement standard systems analysis as well as computerbased tool development (e.g., Saarenmaa et aI.,
1994).
Knowledge-based systems seek to embed our
knowledge of the world in a computer system that