Representing Complex Geographic Phenomena in GIS

Conventionally, spatial data models have been designed according to object- or field-based conceptualizations of reality. Conceptualization of complex geographic phenomena that have both object- and field-like properties, such as wildfire and precipitation, has not yet been incorporated into GIS data models. To this end, a new conceptual framework is proposed in this research for organizing data about such complex geographic phenomena in a GIS as a hierarchy of events, processes, and states. In this framework, discrete objects are used to show how events and processes progress in space and time, and fields are used to model how states of geographic themes vary in a space-time frame. Precipitation is used to demonstrate the construction and application of the proposed framework with digital precipitation data from April 15 to May 22, 1998, for the state of Oklahoma, U.S.A. With the proposed framework, two sets of algorithms have been developed. One set automatically assembles precipitation events and processes from the data and stores the precipitation data in the hierarchy of events, processes, and states, so that attributes about events, processes, and states are readily available for information query. The other set of algorithms computes information about the spatio-temporal behavior and interaction of events and processes. The proposed approach greatly enhances support for complex spatio-temporal queries on the behavior and relationships of events and processes.

[1]  Janusz Niemczynowicz,et al.  Storm tracking using rain gauge data , 1987 .

[2]  Donna Peuquet,et al.  An Event-Based Spatiotemporal Data Model (ESTDM) for Temporal Analysis of Geographical Data , 1995, Int. J. Geogr. Inf. Sci..

[3]  Heikki Mannila,et al.  A database perspective on knowledge discovery , 1996, CACM.

[4]  G. Langran,et al.  A Framework For Temporal Geographic Information , 1988 .

[5]  Michael F. Worboys,et al.  A Unified Model for Spatial and Temporal Information , 1994, Comput. J..

[6]  Max J. Egenhofer,et al.  Why not SQL! , 1992, Int. J. Geogr. Inf. Sci..

[7]  G. Langran Time in Geographic Information Systems , 1990 .

[8]  E. L. Usery A feature-based geographic information system model , 1996 .

[9]  Helen Couclelis,et al.  People Manipulate Objects (but Cultivate Fields): Beyond the Raster-Vector Debate in GIS , 1992, Spatio-Temporal Reasoning.

[10]  J. Hoke,et al.  Map Projections and Grid Systems for Meteorological Applications , 1981 .

[11]  Russ Rew,et al.  NetCDF: an interface for scientific data access , 1990, IEEE Computer Graphics and Applications.

[12]  C. J. Date An Introduction to Database Systems, 6th Edition , 1995 .

[13]  E. L. Usery Category Theory and the Structure of Features in Geographic Information Systems , 1993 .

[14]  D. Peuquet It's About Time: A Conceptual Framework for the Representation of Temporal Dynamics in Geographic Information Systems , 1994 .

[15]  Graeme F. Bonham-Carter Spatial Data Models , 1994 .

[16]  Robert A. Houze,et al.  Mesoscale Organization of Springtime Rainstorms in Oklahoma , 1990 .

[17]  Donna J. Peuquet,et al.  A Conceptual Framework and Comparison of Spatial Data Models , 1984 .

[18]  J. Kelmelis Time and space in geographic information: toward a four-dimensional spatio-temporal data model , 1992 .

[19]  Gail Langran,et al.  A review of temporal database research and its use in GIS applications , 1989, Int. J. Geogr. Inf. Sci..

[20]  Walid G. Aref,et al.  Spatial Data Models and Query Processing , 1995, Modern Database Systems.

[21]  R. J. Marshall The estimation and distribution of storm movement and storm structure, using a correlation analysis technique and rain-gauge data , 1980 .

[22]  Stephen H. Schneider,et al.  Encyclopedia of Climate and Weather , 1996 .