UCGIS Winter Meeting 2000
Monday, Feb. 7, 2000, 9:00-12 noon
Capitol Hill Club, 300 1st Street, S.E., Washington D.C.
Speakers:
Dr. Richard D. Wright (SDSU) and Ms. Nina Garfield (NOAA)
Department of Geography
San Diego State University
San Diego, CA 92182
(619) 594-5466, Fax: (619) 594-4938 E-mail: wright@typhoon.sdsu.edu
The Tijuana River watershed (TRW) spans the border between San Diego, California, United States and Tijuana, Baja California, Mexico. The urban activities and environmental problems in this watershed have been problematic for both nations. Through a cooperative effort, San Diego State University (SDSU), El Colegio de la Frontera Norte (COLEF), the Tijuana National Estuarine Research Reserve (TRNERR), and the National Oceanic and Atmospheric Administration (NOAA) are using geographic information science and technologies to help address several critical land-related problems in this local yet transnational region. Other federal agencies involved in the project include USGS and FEMA while private partners include Sun Microsystems, Inc. and ESRI, Inc.
The project is being accomplished in several phases. The first involved identifying the most critical needs of the region and establishing priorities for the project. This was accomplished by engaging stakeholders in two user workshops and a "needs assessment" survey. From the results it was agreed that funding from the National Spatial Data Infrastructure (NSDI) Demonstration Project program should focus on flood hazards and risk assessment. Flooding is a source of serious damage to property and life and the primary source of sediments impacting the environment of the Tijuana Estuary. The second phase of the project emphasized the formation of advisory committees, upgrading of hardware and GIS software at SDSU and COLEF, and the generation of a high resolution digital elevation model. Geospatial data sets to augment the existing TRW system are also being gathered and a methodology for modeling flood hazards and risk in a data poor situation is being developed. In the third phase, flood hazard and risk models will be applied to assess the extent and impact of flooding in the TRW. In the final phase, end users in Mexico and the United States will be trained in the use of GIS software and the application of one or more flood hazards and risk models to the study area. Recommendations will be made for making improvements in the model and using it for forecasting flood hazards and risk as a part of land use planning processes in the cities of San Diego and Tijuana. This research offers solutions to many of the technical and institutional problems associated with geospatial data integration and GIScience applications across international boundaries.
Funding Sources and Collaborators: The principal funding source is the Federal Geographic Data Committee/National Partnership for Reinventing Government. Nina Garfield of NOAA is the U.S. federal coordinator for the project while Rafael Vela of COLEF is the principal Mexican collaborator. In 1998 the TRW was selected by the National Partnership for Reinventing Government and the Federal Geographic Data Committee as one of six pilot projects nationally to demonstrate the application of the National Spatial Data Infrastructure (NSDI) in a transborder setting and illustrate the use of geospatial technologies in solving local problems.
This presentation illustrates the role of GI Science in several ongoing research projects concerned with risks in our urban environments. Research by faculty and graduate students at Rutgers University address a variety of important questions:
Funding sources: Partnerships between federal and state funding sources, including federal agencies (FEMA, DOE, EPA) and state agencies (New Jersey Department of Environmental Protection, New Jersey State Police, New Jersey Office of State Planning).
The design of a spatial-similarity search engine has multiple aspects that were investigated in different phases of this research project. First is a mathematical model that compares different geometries and determines which ones fit best with a target configuration. The geometric similarity model we developed is based on the spatial relations among objects. It allows an operator to tailor search and retrieval for different spatial criteria, such as the importance of directions or the preservation of distances between the search and the results. The second component captures differences in the types of objects. We are developing a sophisticated computational model to consider, for instance, the conceptual difference between a hospital and a warehouse, and how such differences can be assessed under different contexts. The consideration of such meaning enhances the geometric similarity search. The third part of our spatial-similarity search engine is an advanced user interface through which geospatial analysts sketch the configuration they want to find. With a pen on a screen or wallboard users can draw a spatial scene as if they were making a sketch on a piece of paper. Through this research, a novel method has been developed that analyzes such a freehand drawing and translates it into a representation that can be processed with the spatial-similarity search engine.
The presentation will include a demonstration of a prototype of Spatial-Query-by-Sketch, using freehand sketching to find the most similar configurations.
Funding Sources and Collaborators: The funding sources for this project have been the Air Force Research Laboratory (1995-1998), the National Imagery and Mapping Agency (since 1997), the National Science Foundation (since 1996), and Lockheed-Martin MD&S. Dr. Richard Berg and Dr. Walter Senus are the contacts at NIMA; Dr. Gary Strong is the cognizant NSF program officer; and Mr. Jonathan Gregory from the Air Force Research Laboratory at Rome, NY, provided project oversight. Collaborators are Dr. Robert Rugg (Virginia Commonwealth University), Dr. Elise Turner and Dr. Roy Turner (both University of Maine).
The goals of the project have been to: build digital map data sets describing the historical growth and change of urban areas in North America; use the historical data to calibrate a cellular automaton model of urban spread and land cover change; and use the model to predict future urban area coverage, land use changes, and the environmental consequences attributable to them. The work has involved linking models together as well as applying those models. Gigalopolis has been supported by USGS, EPA, and NSF. It involves also collaborations with Los Alamos National Laboratory, the National Autonomous University of Mexico (UNAM), and the Santa Barbara Urban Modeling Project.
Broader goals of immediate practical impact have been to (1) release to the public FGDC-compatible data that describe the historical changes in selected urban areas, (2) build and test a cellular automaton model of urban growth and land cover change, (3) calibrate, test and predict the future urban patterns for several key urban areas using the model, and (4) examine how local results may be scaled up into national and global land cover change estimates that would be of use in climate and global change modeling. Digital maps and animation sequences are used to visualize the results of the predictions.
Funding Sources and Collaborators: Funding has been provided by USGS, the State of California, and the National Science Foundation. Collaborators include William Acevedo, Dave Hester, Jeannette Candau (USGS), Helen Couclelis (UCSB), Steen Rasmussen (LANL and the Santa Fe Institute), Ron Matheny (EPA), and Louis Bojorquez (UNAM).
Project Page: www.ncgia.ucsb.edu/projects/gig
In addition to the lack of spatial data, smaller units of government often embark on land use planning exercises with few tools at their disposal. While computer simulation models, draft ordinances, and decision support tools are receiving wider use in planning, these tools are often out of reach of local governments, who lack the equipment and expertise required for their use. Further, in natural resource management, the general public is often faced with information that has been slanted in favor of the perspectives of industrial or environmental advocacy groups. There is a critical need for sources of data gathered for general non-advocacy purposes to allow the public to develop informed opinions on current issues.
To this end, we have initiated the Lake Superior Decision Support Project to help resolve issues of data accessibility and interpretation with respect to land use planning in the Lake Superior Basin. We have three key objectives:
Funding Sources and Collaborators: Minnesota Dept. of Natural Resources through the U.S. Environmental Protection Agency, Region 5. Further project participants, collaborators and supporters may be found at the project web site at www.nrri.umn.edu/lsgis.
Recent advances in GIS technology are beginning to aid in resource allocation and other law enforcement decisions. Computer-aided crime analysis and mapping is blossoming into a valuable resource for law enforcement agencies. Local law enforcement agencies, with significant encouragement from the national government, are beginning to recognize the value of these GIS technologies. This interest has fueled a growing demand for a GIS crime application toolbox that GIS providers, such as ESRI, have been working hard to create. Academic researchers, as well, have been expanding the field of GIScience to help create better tools and methods for crime analysis and mapping. In this spirit, we have been researching theoretical methods for predicting the displacement of crime in response to police enforcement so that resource allocation decisions will have the most impact.
Many crimes, such as burglary, robbery and auto theft, are committed by mobile criminals with economic motivations. Using qualitative observations of this type of criminal behavior, we have developed a socio-economic quantitative model that attempts to predict the number of crime incidents within a police jurisdiction. In this model, criminals are assumed to compare the expected reward for committing crime in any specific neighborhood against a minimal acceptable reward. The expected reward in a neighborhood is a function of its wealth and the number of competing criminal agents in the neighborhood as well as the level of police enforcement and likelihood of arrest. The minimal acceptable reward may be viewed as the reward for alternative activity such as gainful employment.
Our goal is to use this model to help determine the best allocation of police enforcement resources among multiple adjacent neighborhoods. The "best" allocation, of course, depends on the objective involved. We examine two plausible objectives: (i) minimizing the total number of crimes among the neighborhoods, and (ii) minimizing the difference in the number of crimes between neighborhoods. Since the allocation policies for these two objectives may not coincide, we also explore policies that yield a comprising solution.
Funding Sources and Collaborators: This research is supported by Grant No. 98-IJ-CX-K008 awarded by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice. We are currently developing a case study through data collected in collaboration with local law enforcement agencies including the Buffalo New York Police Department.