30.6 Data Compilation and Analysis
layers have been assembled thus far. Spatial information is being processed according to specified
data standards in a common geographic information system data format, and metadata and accompanying documentation are being provided. All
data, except for threatened and endangered species
locations, are being distributed at appropriate resolution on the Internet by the Environmental Management Technical Center, USGS Biological Resource Division.
At the onset of this assessment, there was a
paucity of environmental, biological, and ecological data that were consistently available across the
entire northern Great Lakes region. Therefore, assessment team members set priorities on the types
of data that would be most useful in addressing the
issues. These data often had been constructed on a
county by county or state by state basis and thus
had to be further developed to provide a regionwide context for several issues. For example, maps
of early European-settlement-era vegetation from
General Land Office (GLO) survey records, depicting the extent and composition of forests in the
mid-1800s (Figure 30.2), had been compiled independently by different scientists in each state
(Marschner, 1974; Finley, 1976; Comer et aI.,
1995). The GLA map of historical vegetation
grouped forest types into generalized classes that
are commensurate with the Advanced Very High
Resolution Radiometer (A VHRR) and Forest Inventory and Analysis (FIA) data to allow meaningful comparisons with current forest conditions.
The metadata supporting this map are also significant in that they provide interested parties with information on the origin of the map and its strengths
and weaknesses.
The General Land Office surveyors also noted
natural disturbances of fire, wind, and flooding.
Maps reconstructed from these notes (Comer et aI.,
1995) provide a historical record of the location and
extent of different types of natural disturbance. This
information, in conjunction with information on
vegetation and the physical environment, is useful
in understanding landscape patterns, forest composition, and age-class distribution existing before
European settlement. The GLA is using General
Land Office notes, derived maps on vegetation and
disturbance, and information on surficial geology,
topography, hydrography, soils, and ecological
units (landtype associations and landtypes) to map
the northern Great Lakes region into areas of varying susceptibility to fire and windthrow. A comprehensive review of the literature is underway and
an annotated bibliography has been produced.
The following metadata, compliant with Federal
453
Geographic Data Committee (FGDC) Standards,
are being supplied for each map: (1) identification
information, (2) data quality information, (3) spatial data organization information, (4) spatial
reference information, (5) entity and attribute information, (6) distribution information, and (7)
metadata reference information. For Michigan's
early settlement vegetation (a portion of Figure
30.2), the metadata are ten single-spaced pages. A
brief excerpt, the abstract, exemplifies the role of
metadata:
1.2.1 Abstract: Vegetative cover maps derived from
General Land Office Survey notes (1816-1856), polygon interpretation included use of supplemental mapping
sources such as 1 : 24k topographic quads, soil surveys,
wetlands, etc. Boundary lines originally were drawn on
1 : 24k quad sheets, and digitized (by county) in Intergraph format. The GLA converted the county coverages
to ArclInfo format, edge-matched the county maps, converted the individual county coverages from three state
plane coordinate systems to a single Albers projection
and combined them into one coverage. This was then
subdivided into three smaller coverages (Upper, Northern Lower and Southern Lower Peninsula's).
(GLA created metadata for historic vegetation of Michigan)
Clearly, this type of detailed record will help
users better understand data and properly interpret
maps. The FGDC-compliant metadata were
thought to be too detailed for some users, and thus
a more user-friendly summary was linked to images of early-settlement vegetation maps on the
GLA Web site.
Social and economic data are plentiful in the
United States. State and federal agencies compile
these data at various spatial scales (e.g., the Bureau
of the Census, the Bureau of Economic Analysis,
and the Bureau of Labor Statistics). Our approach
to defining relevant data involved surveying 150+
managers, planners, and social scientists in the
northern Great Lakes region and across the United
States. They identified what types and scales of
data were needed to provide a social, cultural, and
economic context for planning. More than 300 suggestions were received; responses reflected data
and information needed to address questions on the
relationships between people and natural resources.
Most data identified were secondary data that can
be compiled from one or more existing information sources. Although this sounds rather formidable, it is just a small subset of potential data that
might be gathered.
The aforementioned issues did not constrain the
types of data identified. For example, one data request was for seasonal homes (Figure 30.1). This
figure shows the high concentration of seasonal
layers have been assembled thus far. Spatial information is being processed according to specified
data standards in a common geographic information system data format, and metadata and accompanying documentation are being provided. All
data, except for threatened and endangered species
locations, are being distributed at appropriate resolution on the Internet by the Environmental Management Technical Center, USGS Biological Resource Division.
At the onset of this assessment, there was a
paucity of environmental, biological, and ecological data that were consistently available across the
entire northern Great Lakes region. Therefore, assessment team members set priorities on the types
of data that would be most useful in addressing the
issues. These data often had been constructed on a
county by county or state by state basis and thus
had to be further developed to provide a regionwide context for several issues. For example, maps
of early European-settlement-era vegetation from
General Land Office (GLO) survey records, depicting the extent and composition of forests in the
mid-1800s (Figure 30.2), had been compiled independently by different scientists in each state
(Marschner, 1974; Finley, 1976; Comer et aI.,
1995). The GLA map of historical vegetation
grouped forest types into generalized classes that
are commensurate with the Advanced Very High
Resolution Radiometer (A VHRR) and Forest Inventory and Analysis (FIA) data to allow meaningful comparisons with current forest conditions.
The metadata supporting this map are also significant in that they provide interested parties with information on the origin of the map and its strengths
and weaknesses.
The General Land Office surveyors also noted
natural disturbances of fire, wind, and flooding.
Maps reconstructed from these notes (Comer et aI.,
1995) provide a historical record of the location and
extent of different types of natural disturbance. This
information, in conjunction with information on
vegetation and the physical environment, is useful
in understanding landscape patterns, forest composition, and age-class distribution existing before
European settlement. The GLA is using General
Land Office notes, derived maps on vegetation and
disturbance, and information on surficial geology,
topography, hydrography, soils, and ecological
units (landtype associations and landtypes) to map
the northern Great Lakes region into areas of varying susceptibility to fire and windthrow. A comprehensive review of the literature is underway and
an annotated bibliography has been produced.
The following metadata, compliant with Federal
453
Geographic Data Committee (FGDC) Standards,
are being supplied for each map: (1) identification
information, (2) data quality information, (3) spatial data organization information, (4) spatial
reference information, (5) entity and attribute information, (6) distribution information, and (7)
metadata reference information. For Michigan's
early settlement vegetation (a portion of Figure
30.2), the metadata are ten single-spaced pages. A
brief excerpt, the abstract, exemplifies the role of
metadata:
1.2.1 Abstract: Vegetative cover maps derived from
General Land Office Survey notes (1816-1856), polygon interpretation included use of supplemental mapping
sources such as 1 : 24k topographic quads, soil surveys,
wetlands, etc. Boundary lines originally were drawn on
1 : 24k quad sheets, and digitized (by county) in Intergraph format. The GLA converted the county coverages
to ArclInfo format, edge-matched the county maps, converted the individual county coverages from three state
plane coordinate systems to a single Albers projection
and combined them into one coverage. This was then
subdivided into three smaller coverages (Upper, Northern Lower and Southern Lower Peninsula's).
(GLA created metadata for historic vegetation of Michigan)
Clearly, this type of detailed record will help
users better understand data and properly interpret
maps. The FGDC-compliant metadata were
thought to be too detailed for some users, and thus
a more user-friendly summary was linked to images of early-settlement vegetation maps on the
GLA Web site.
Social and economic data are plentiful in the
United States. State and federal agencies compile
these data at various spatial scales (e.g., the Bureau
of the Census, the Bureau of Economic Analysis,
and the Bureau of Labor Statistics). Our approach
to defining relevant data involved surveying 150+
managers, planners, and social scientists in the
northern Great Lakes region and across the United
States. They identified what types and scales of
data were needed to provide a social, cultural, and
economic context for planning. More than 300 suggestions were received; responses reflected data
and information needed to address questions on the
relationships between people and natural resources.
Most data identified were secondary data that can
be compiled from one or more existing information sources. Although this sounds rather formidable, it is just a small subset of potential data that
might be gathered.
The aforementioned issues did not constrain the
types of data identified. For example, one data request was for seasonal homes (Figure 30.1). This
figure shows the high concentration of seasonal