9.3 Information Integration Foundations
mation is growing steadily, and much of it is increasingly complex. Consider, for example, environmental history information or ecological health
assessments. The challenge here is relating this information not interdisciplinarily, but transdisciplinarily, as Erich Jantsch put it 25 years ago (Jantsch,
1971) and the ecological economists have more recently promoted (Costanza et al., 1991). Different domains and disciplines produce disparate kinds of
knowledge. Most importantly, knowledge can be
quantitative versus qualitative, historical versus ahistorical, specific-disaggregated versus general-aggregated, and certain or uncertain; to different degrees
experimental, observational, theoretical, and/or experiential in origin.
Differences in format are often apparent between
domains and disciplines. The most fundamental
difference may be the division between quantitative and qualitative information. Also noteworthy
are the presence or absence of spatial or temporal
dimensions, issues regarding data volume, and issues regarding standards of certainty, reproducibility and statistical confidence. More specific differences may include technical and administrative
definitions of variables collected, times and places
collected, and formats for storing digital and paper
time series and maps. Challenges to reconciliation
vary accordingly. On the one hand, links must be
made between qualitative and quantitative information, starting by accepting both as useful and
valid in their own ways. More simply, approaches
may require coordination and establishment of
technical processes for format translation.
Differences also may stem from sources that embody fundamentally different assumptions and
methods. The most common example is scientific
knowledge and local, indigenous, or traditional
knowledge (Inglis, 1993). Scientific knowledge is
typically observational, quantitative, and experimentally testable, whereas traditional knowledge is
typically historical, verbal, and mostly qUalitative.
Dealing with differences in source, we confront the
basic issues around assumptions of cause and effect, repeatability, predictability, and quantifiability under which science has historically operated.
These enter the fray in efforts as simple as integrating physical and biological knowledge, never
mind social science. The fundamental challenges
121
to reconciling source differences are as much cultural as technical and process tools at least as important as technical ones.
Thus the primary challenge is finding ways to
integrate information in different formats, from different sources, so that information about different
domains can be brought together in ways that a
range of experts and decision makers find useful
and acceptable-without making insupportable assumptions or fostering unjustifiable conclusions.
This can entail a wide range of approaches: changing assumptions on the part of the analysts, developing information through participatory processes,
coordinating processes for technical standardization, and combining conceptual and technical tools.
All these are addressed next.
9.3 Information Integration
Foundations
Two problems lie at the heart of the challenge to
integrate information: integration across domains
and disciplines and integration across space and
time. Any substantial ecological assessment or
ecosystem management exercise must achieve
these forms of integration. Before addressing these
kinds of integration more specifically, several prerequisites should be considered: defining integration products, understanding units and scales, and
accepting both quantitative and qualitative information.
The type of product to emerge from information
integration exercise depends on earlier definition
of goals and activities (Table 9.3). Consider products of two sorts. One is a set of final products displaying information in particular ways or answering particular questions, for example, a simple atlas
juxtaposing different kinds of information (Thomas
et a!., 1991) or a major technical report or environmental impact statement (Forest Ecosystem
Management Association Team, 1993). Another
type of product is an information system or set with
which different users can work to create their own
products and answer their own questions, whether
through new analysis and map generation or simply through the ability to navigate nonlinearly
TABLE 9.3. Examples of information integration products categorized in terms of their customizability
and linearity.
Linear
Nonlinear
Fixed products
Atlases, reports
Hypertext documents, Web sites
Customizable products
Databases
GIS system, decision support systems
mation is growing steadily, and much of it is increasingly complex. Consider, for example, environmental history information or ecological health
assessments. The challenge here is relating this information not interdisciplinarily, but transdisciplinarily, as Erich Jantsch put it 25 years ago (Jantsch,
1971) and the ecological economists have more recently promoted (Costanza et al., 1991). Different domains and disciplines produce disparate kinds of
knowledge. Most importantly, knowledge can be
quantitative versus qualitative, historical versus ahistorical, specific-disaggregated versus general-aggregated, and certain or uncertain; to different degrees
experimental, observational, theoretical, and/or experiential in origin.
Differences in format are often apparent between
domains and disciplines. The most fundamental
difference may be the division between quantitative and qualitative information. Also noteworthy
are the presence or absence of spatial or temporal
dimensions, issues regarding data volume, and issues regarding standards of certainty, reproducibility and statistical confidence. More specific differences may include technical and administrative
definitions of variables collected, times and places
collected, and formats for storing digital and paper
time series and maps. Challenges to reconciliation
vary accordingly. On the one hand, links must be
made between qualitative and quantitative information, starting by accepting both as useful and
valid in their own ways. More simply, approaches
may require coordination and establishment of
technical processes for format translation.
Differences also may stem from sources that embody fundamentally different assumptions and
methods. The most common example is scientific
knowledge and local, indigenous, or traditional
knowledge (Inglis, 1993). Scientific knowledge is
typically observational, quantitative, and experimentally testable, whereas traditional knowledge is
typically historical, verbal, and mostly qUalitative.
Dealing with differences in source, we confront the
basic issues around assumptions of cause and effect, repeatability, predictability, and quantifiability under which science has historically operated.
These enter the fray in efforts as simple as integrating physical and biological knowledge, never
mind social science. The fundamental challenges
121
to reconciling source differences are as much cultural as technical and process tools at least as important as technical ones.
Thus the primary challenge is finding ways to
integrate information in different formats, from different sources, so that information about different
domains can be brought together in ways that a
range of experts and decision makers find useful
and acceptable-without making insupportable assumptions or fostering unjustifiable conclusions.
This can entail a wide range of approaches: changing assumptions on the part of the analysts, developing information through participatory processes,
coordinating processes for technical standardization, and combining conceptual and technical tools.
All these are addressed next.
9.3 Information Integration
Foundations
Two problems lie at the heart of the challenge to
integrate information: integration across domains
and disciplines and integration across space and
time. Any substantial ecological assessment or
ecosystem management exercise must achieve
these forms of integration. Before addressing these
kinds of integration more specifically, several prerequisites should be considered: defining integration products, understanding units and scales, and
accepting both quantitative and qualitative information.
The type of product to emerge from information
integration exercise depends on earlier definition
of goals and activities (Table 9.3). Consider products of two sorts. One is a set of final products displaying information in particular ways or answering particular questions, for example, a simple atlas
juxtaposing different kinds of information (Thomas
et a!., 1991) or a major technical report or environmental impact statement (Forest Ecosystem
Management Association Team, 1993). Another
type of product is an information system or set with
which different users can work to create their own
products and answer their own questions, whether
through new analysis and map generation or simply through the ability to navigate nonlinearly
TABLE 9.3. Examples of information integration products categorized in terms of their customizability
and linearity.
Linear
Nonlinear
Fixed products
Atlases, reports
Hypertext documents, Web sites
Customizable products
Databases
GIS system, decision support systems