Trish Duce, Ellen Voth and Patricia Andrews
A link between the Oracle database management system and IBM's Data Explorer (DX) scientific visualization software was developed. DX tools provide the capability to create more sophisticated visualizations than may be possible with a Geographic Information System (GIS). The technology is demonstrated on visualization of historical wildfire data from an Oracle database overlayed on data layers from a GIS.
Historical wildfire data are used for a variety of applications including post-season analysis of fire activity and evaluation of fire danger rating indexes. Fire location according to administrative unit (e.g. Forest or Wilderness area) is sufficient resolution for some applications. There is location information (latitude and longitude) in the Forest Service wildfire database, however, that has not been used to full advantage. Forest Service Regions produce annual fire reports summarizing the fire activity for each year (e.g. USDA Forest Service 1994). The report includes tables such as number of fires and acres burned for lightning and person-caused fires for each Forest. A visualization that displays both time and space components of fire activity would supplement summary tables.
Fire occurrence and size are related to fire danger rating indexes to evaluate the effectiveness of the National Fire Danger Rating System (Deeming and others 1977). The relationship between fire activity and fire danger rating is used to evaluate severity of the fire season (Bradshaw and Andrews, in press) and also to better use fire danger rating in decisionmaking (Andrews and Bradshaw 1995). Because the current fire danger rating system is based on weather observations taken at a fixed station and applied to a large area, fire location according to administrative unit is appropriate. The next generation system, the Wildland Fire Assessment System (WFAS), will be based on spatial data for fuel, fuel moisture, and weather (Andrews and others, in press). Using fire location according to latitude and longitude is critical for evaluation and use of WFAS.
In this paper we describe demonstration of software that was developed to extract data from an Oracle data base and display it with Data Explorer (DX) scientific visualization software. Wildfire data are displayed on elevation, land cover classification, and a biweekly live fuel greenness layer, providing a visualization of the fire activity as it varies in both space and time.
USDA Forest Service historical wildfire data (1970-present) is stored in the National Interagency Fire Management Integrated Database (NIFMID), an Oracle database at the USDA National Computer Center in Kansas City (USDA Forest Service 1993). Of the information included for each fire, only the following is used in this demonstration: fire identification number, ownership (region and forest), location (latitude and longitude), discovery date (month/day/year), statistical cause (lightning or human), and final fire size. The fire location information is recorded in latitude/longitude from 1986 to present.
Several data layers are used in conjunction with the fire data for display: elevation, land cover classification, and a biweekly greenness index. The land cover characterization map is available for the conterminous 48 States of the U.S. (Loveland and others 1991). It is derived from AVHRR satellite data and is raster data at 1 km resolution. Land cover classification is being interpreted as fuel type for wildfire (Burgan and Hardy 1994). The greenness maps, which indicate state of live vegetation, are also derived from AVHRR satellite data (Burgan and Hartford 1993). The 1 km resolution maps are updated and made available to the field on a weekly basis. Historical images are available from 1989 to present.
Other ancillary data from a GIS data base that can be used for reference in visualization of fire location include administrative boundaries (state, county, land ownership), lakes and rivers, roads, and cities. In this demonstration, we use only state and county boundaries.
Oracle is a modern relational database management system. It acts as an interface between the physical storage and the logical presentation of data. It provides a set of sophisticated tools for handling information. If you want to access and manipulate Oracle data, you need SQL - Structured Query Language. SQL has become the database language of choice because it is flexible, powerful, and easy to learn.
An Oracle Precompiler is a programming tool that allows you to embed SQL statements in a high-level source program. The precompilier accepts the source program as input, translates the embedded SQL statements into standard Oracle runtime library calls, and generates a modified source program that you can compile, link, and execute in the usual way.
When designing the DX-Oracle interface it was obvious there was a need to access the Oracle data. Pro*C was the Oracle Precompiler choosen for this project. Therefore the language C was used with embedded SQL commands to construct the DX-Oracle interface.
Data Explorer, or DX, is a software package developed by IBM for scientific data visualization and analysis. It provides an extensive library of "modules" -- already written blocks of code which can be tied together into visual programs through the graphical user interface. The user 'writes' a visual program for rendering an image, based on the data, by choosing and connecting modules through the click and drag graphical program editor.
DX is similar to a GIS, in that it can map data to geographic locations, but in addition, it offers new capabilities for investigating large quantities of complex data. It can be used to explore geographic data sets but has also been used to visualize data from disciplines as varied as physics, geology, medicine, and meteorology. The aspects of DX which allow it this flexibility are described below. DX is:
Multidimensional - 3D objects can be created and manipulated just as easily as 2D plots. Other parameters or variables can be mapped onto objects as another dimension through the use of color, 'glyphs', or 'isosurfaces', thus allowing visual discovery of juxtapositions, continuities or irregularities.
Interactive - User interactivity allows the scientist to explore the data more completely. For example, created visual objects can be rotated to allow different points of view or the user can interactively modify the imagery by changing input values.
Temporal - DX allows time sequencing or animation so temporal processes can be viewed directly.
Modular - DX allows various levels of sophistication. Generic software building blocks allow a user to create a visual program or 'network' without ever writing a line of code. Although programming skills can be utilized in converting data files to DX format, and in creating new modules for use within the system they are not necessary to create visualizations of complex data.
A simple visual program is pictured below in Figure 1.
This network imports data from a file, colors and 'glyphs' an object based on the data values and then renders the object in an image window. This same network can be used to depict different data sets, e.g., carbon monoxide levels measured at different locations or the position and size of fires burning on a landscape. A sequencer module could be added to animate the image and view how the values change over time.
An interface between DX and the Oracle database management system has been developed and implemented. The graphical user interface allows the user to connect to oracle and retrieve data from the database. The user has the option of scrolling through all of the tables of the database or just those tables the user owns. When the user double-clicks on a table, a box with the specified table's columns appears. The user can scroll through this list and select any columns he or she wants to retrieve. This can be done with multiple tables depending on what information the user is trying to retrieve. The user has the option of retrieving a subset of the columns he or she has selected by specifying a condition. Once the tables, columns and conditions are specified the user clicks on the "retrieve" button and a window pops up that asks the user to specify a file to save the information in. Once this has been done a window with the select statement created by the user appears. The user can press "OK" and retrieve the information or can press "cancel" if there was an error.
The output of the program is a DX file that has an object for each column the user specifies. A dummy positions component is created so DX will load the data without any problems. It is up to the user to create a positions component in DX. If an Oracle column is of type "date" the program will automatically generate two objects. One with the actual date values and one with the same date values in the Julian format.
One of the hardest parts of visualizing the historical fire data generated by the DX-Oracle interface was referencing it with other data sets. The fire's location is recorded in geographic longitude and latitude coordinates. All of the data sets we wanted to reference the fire data to were in cartesian coordinates. Cartesian coordinates come in many different projections. Digital Line Graphs (DLGs) and Digital Elevation Maps (DEMs) were the two types of data sets used to create visualizations with the fire data. Gerald I. Evenden had developed Cartographic Projection Procedures for the UNIX environment. These procedures will convert geographic longitude and latitude coordinates into many cartesion projections and many of the inverse conversions can also be performed. A Data Explorer module that uses the cartographic projection utilities was created by Dave Thompson at the University of Montana. This module was used to convert the fire data into cartesian coordinates.
The states of Montana and Arizona were chosen as demonstration sites. The first visualization uses the discovery dates and fire out dates to show the number and location of fires burning on any particular day in Montana for the year 1988. The background is colored and scaled according to elevation (Figure 3). The second Montana visualization is a comparison of the 1993 and 1994 fire seasons using simultaneous display of two maps with glyphs indicating fire occurrence (based on fire discovery date) and size throughout the season. The background is colored according to elevation. A plot of number of fires is updated as the display of the fire season progresses (Figure 4). The two seasons are very different, 1994 being one of the most active on record. The visualization is an effective display of that difference.
If you have an MPEG player click here to see an animation of the fire coverage through the fire season.
The background for the Arizona fire data is a greenness map derived from satellite data. It is an indication of fire potential and changes biweekly. Fire location and size is displayed in two week blocks corresponding with the change in greenness (Figure 5). The animation of greenness and fire activity is animated for March through October of the year selected (1990-1993). Developers of the next generation fire danger rating system will be able to use this as the first step in assessing the relationship between fire activity and spatial indicators of fire potential.
This demonstration showed the success of a link between Oracle and DX
and of the benefit uf using DX to visualize historical fire data in
conjunction with GIS data layers.
Software development and visualizations were done by Trish Duce and Ellen Voth under an internship program with IBM in Boulder Colorado.
Vijayant Palaiya from the University of Montana contributed to implementation of the DX-Oracle user interface.
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