change is essentially a human problem. It results from myriad human actions occurring
in local places. At the same time, people experience and respond to global environmental
changes in localities. Consequently, there has been a proliferation of research
centers and sites dedicated to studying the local implications of the human dimensions
of global environmental change (HDGEC).
The local HDGEC
data that are and will be generated by these centers, however, tend to be place-specific
and are difficult to replicate and generalize. This weakness in the data stems
directly from a poorly developed scientific infrastructure. There are no common
protocols for collecting, reporting, analyzing, storing, and sharing the data.
There are no data standards, which are necessary if the data are to be a lasting
resource. Moreover, if HDGEC scientists are to be able to take advantage of the
technological advances that are being made in intelligent data retrieval and analysis
and in remote-collaboration, they must coordinate research efforts to make the
data amenable to these techniques.
research proposes to develop infrastructure for studying the long-term implications
of HDGEC at small regional and local scales. To meet this goal, the 10-year project
will promote infrastructure development by implementing four components. First,
it will develop protocols for observing, collecting, reporting, storing, and sharing
data. Second, it will build an intelligent networking environment for data management,
Web-based access, and GeoCollaboratory that will help match data with research
problems and will facilitate collaboration among scientists at remote sites. Third,
it will test proof of concept by applying the protocols and intelligent networking
environment to local HDGEC research problems. Fourth, it will build networks by
linking the US regional and local HDGEC research sites through this infrastructure.
To develop protocols, HDGEC research will be carried our at four human-environment
regional observatories (HEROs). The HEROs located in the Southwest-Mexico border
region, the High Plains of Kansas, central Pennsylvania, and central Massachusetts
represent a diverse set of natural and human environments. The University of Arizona,
Kansas State University, Pennsylvania State University, and Clark University scientists
will approach infrastructural development by tackling broad human-environment
problems, but in the local context. The four HEROs will address three core research
themes: land-use change, greenhouse-gas emissions, and climate impacts.
In order to meet
these goals, the Geographic Visualization Science Technology and Applications
Center (GeoVISTA) at Penn State will work with the National Mapping Division of
the United States Geological Survey to develop the HERO intelligent networking
environment (HEROINE). For HEROINE to be successful, the geospatial technology
and methods developed must meet two goals. First, the technology and methods must
facilitate context- and task-sensitive retrieval of data from the very large and
complex data warehouses that will develop as a product of the research. Second,
they must support collaboration among scientists at different HEROs as they work
together on common problems. HEROINE, thus, will include two complementary components:
Data management and distributed Web-access linking HERO scientists with data.
Planned HEROINE data management and web-access capabilities include: (1) consistent
and comprehensive metadata that are grounded in National Spatial Data Infrastructure
(NSDI) protocols and that provide the key to practical data access by scientists;
(2) implementation of state of the art technology for managing geospatial data
and metadata in distributed geospatial databases; and (3) development of innovative
methods for intelligent Web-based geospatial data retrieval.
Linking HERO scientists with each other. Planned HEROINE GeoCollaboratory capabilities
include: (1) tools to facilitate collaboration in developing and refining protocols
and standards; (2) methods to support collaborative query specification that are
integrated with data retrieval tools; (3) collaborative geovisualization methods
that facilitate joint exploration of locational aspects of both quantitative and
qualitative data; and (4) integration of geovisualization methods with geocomputational
methods (such as spatial statistics, classification, data mining, or process models)
in an environment that supports joint work by scientists with these integrated