Global environmental 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.

Consequently, this 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.

GeoCollaboratory 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 methods.