Standard Geographic Information System Research Articles

Comparing multiple criteria decision methods to extend a geographical information system on afforestation

The main goal of the AFFOREST project (EU 5th Framework Programme for Research & Technological Development) is the development of a spatial decision support system (SDSS) capable of providing advice for policy and planning decisions pertaining to afforestation of agricultural land. The system addresses questions on ‘where,’ ‘how,’ and ‘how long’ to afforest, based on changes in environmental performance (EP) of the land. EP is defined as the combination of three environmental impact categories: total carbon sequestration (i.e. carbon sequestration in the soil and in biomass), nitrate leaching, and groundwater recharge. The core of the SDSS is a spatial database, storing the location of agricultural land, its environmental performance before, and up to 100 years after, afforestation. EP values, as both a function of time and afforestation strategy, are obtained via a metamodel that uses site characteristics as inputs. Using standard geographical information system (GIS)-software and query tools, the spatial database can be queried to provide answers to questions expressed in terms of thresholds, possibly combined by Boolean operators. Multiple criteria decision making (MCDM) methods are, however, required to search for locations, afforestation strategies, or time lags. In this paper, we compare three common numerical techniques to help decision makers choose among the afforestation strategies for a given class of agricultural land. We find that all three methods succeed in providing answers to the ‘how’ question. However, PROMETHEE II is slightly preferable to both ELECTRE III and AHP in AFFOREST, based on user friendliness, simplicity of the model strategy, variation of the solution, and implementation. To test the robustness of the selected methods, the results are compared with a goal programming technique. A general finding for all three MCDM methods is that standard GIS-software packages and their programming facilities are not well suited for a full integration of MCDM-routines. Therefore, a loose coupling with the GIS-environment is required.

Four-Dimensional Interactive Visualization System for Transportation Management and Traveler Information

This research developed a system for visualizing four-dimensional (4-D), real-time transportation data for the major road networks of Washington, D.C., Northern Virginia, and the entire state of Maryland. The effort employed a combination of OpenGL and other modeling techniques to develop a scalable, highly interactive 4-D model using available geographic information system (GIS) and transportation infrastructure data in conjunction with real-time traffic management center data. The prototype system interacts with real-time traffic databases to show animations of real-time traffic data (volume and speed) along with incident data (accident locations, lane closures, responding agencies, etc.). A user can “fly” or “drive” through the region to inspect conditions at an infinite number of angles and distances. The program also allows users to monitor the status of and interact with traffic control devices such as dynamic message signs, closed-circuit television feeds, and traffic sensors and even view the location of emergency response vehicles equipped with Global Positioning System transceivers. Because the system uses standard GIS data and relatively standard transportation databases to derive traffic measures, it can be scaled to incorporate other states and agencies.

Buffering Fuzzy Maps in GIS

In this paper, we show how standard GIS operations like the complement, union, intersection, and buffering of maps can be made more flexible by using fuzzy set theory. In particular, we present a variety of algorithms for operations on fuzzy raster maps, focusing on buffer operations for such maps. Furthermore, we show how widely-available special-purpose hardware (in particular, z-buffering in graphics hardware) can be used for supporting buffer operations in fuzzy geographic information systems (GIS).

Closing the loop: A spatial analysis to link observed environmental damage to predicted heavy metal emissions

In many cases, the link between industrial emissions and damage to the environment can only be inferred. The Environment Agency of the United Kingdom imposes emissions limits on industrial sites so that predicted concentrations and deposition rates remain below standard thresholds. Estimates of appropriate critical levels and loads are usually based on laboratory results and rarely estimate synergistic effects between pollutants or consider biological adaptation or selection in the target receptor organisms. The Avonmouth smelter has been emitting zinc and other heavy metals since 1929. It has been the subject of a number of detailed and synoptic studies, especially the impact on soil invertebrates. Damage was assessed using both physiological and ecological measurements. Two methods of spatial analysis were investigated, namely interpolation using standard geographical information system (GIS) operators and atmospheric dispersal modeling using an off-the-shelf model. Both methods can be used to compute contours (isolines) of predicted biological effect. Correlation results show that dispersal modeling is at least as good as kriging but requires much less data. This article demonstrates the usefulness of GIS and dispersal models as tools in decision making to determine the most suitable sampling sites in the assessment and monitoring of the impact of contamination around major point sources.

The Australian Oil Spill Response Atlas Project

ABSTRACT Australia is among the top five shipping nations of the world based upon cargo and kilometres travelled. Australia also has vast, remote and environmentally sensitive coastlines ranging from tropical to subantarctic. Unfortunately shipping accidents and illegal discharges of oil and chemical pollutants into our marine environment do occur. To support spill response management in Australia, over the past four years the Australian Maritime Safety Authority (AMSA) has coordinated the development of a uniform and integrated national Oil Spill Response Atlas (OSRA) based upon an ArcView® geographic information system (GIS). The main aims of the OSRA project were to:Develop a mutually agreed national GIS specification for the development of the digital atlas to assure consistency and compatibility Australia wide;Fast track the collation, capture and conversion of all relevant geographical and textual data into a standard digital GIS format for the majority of Australia's marine and coastal environments (particularly for highly sensitive environments such as, world heritage areas, and marine parks and reserves); andCreate a user-friendly series of GIS system tools specially designed for the particular needs of spill response managers, operational staff and environmental agencies. This paper highlights some of the advantages of GIS based systems for spill response management, the OSRA system development and features, as well as the GIS automation tools that assist spill response managers and operational personnel.

Using Synthetic Aperture Radar Imagery for Flood Modelling

Airborne radar technology has traditionally been largely devoted to military applications. In recent years, applications in telecommunications, oil exploration and agriculture have proved that radar technology can also be used commercially. This paper focuses on an application of airborne radar technology in the insurance industry and describes the development of a large‐scale flood risk assessment model for the River Thames. The model is based upon airborne Synthetic Aperture Radar (SAR) data and was built with commonly used Geographic Information Systems (GIS) and image processing tools. From the Orthorectified Images (ORIs) a land cover map was produced, from which a surface roughness map could be derived. A Digital Elevation Model (DEM) was processed to remove trees and other soft barriers and obtain the effective ground level. This was achieved by using standard GIS processes. The methodology may be applicable to any organisation exposed to flood risk.

STORMFLOW SIMULATION USING A GEOGRAPHICAL INFORMATION SYSTEM WITH A DISTRIBUTED APPROACH1

ABSTRACT: With the increasing availability of digital and remotely sensed data such as land use, soil texture, and digital elevation models (DEMs), geographic information systems (GIS) have become an indispensable tool in preprocessing data sets for watershed hydrologic modeling and post processing simulation results. However, model inputs and outputs must be transferred between the model and the GIS. These transfers can be greatly simplified by incorporating the model itself into the GIS environment. To this end, a simple hydrologic model, which incorporates the curve number method of rainfall‐runoff partitioning, the ground‐water base‐flow routine, and the Muskingum flow routing procedure, was implemented on the GIS. The model interfaces directly with stream network, flow direction, and watershed boundary data generated using standard GIS terrain analysis tools; and while the model is running, various data layers may be viewed at each time step using the full display capabilities. The terrain analysis tools were first used to delineate the drainage basins and stream networks for the Susquehanna River. Then the model was used to simulate the hydrologic response of the Upper West Branch of the Susquehanna to two different storms. The simulated streamflow hydrographs compare well with the observed hydrographs at the basin outlet.

GIS and geostatistics: Essential partners for spatial analysis

Initially, geographical information systems (GIS) concentrated on two issues: automated map making, and facilitating the comparison of data on thematic maps. The first required high quality graphics, vector data models and powerful data bases, the second is based on grid cells that can be manipulated by suites of mathematical operators collectively termed “map algebra”. Both kinds of GIS are widely available and are taught in many universities and technical colleges. After more than 20 years of development, most standard GIS provide both kinds of functionality and good quality graphic display, but until recently they have not included the methods of statistics and geostatistics as tools for spatial analysis. Recently, standard statistical packages have been linked to GIS for both exploratory data analysis and statistical analysis and hypothesis testing. Standard statistical packages include methods for the analysis of random samples of cases or objects that are not necessarily co-located in space—if the results of statistical analysis display a spatial pattern then that is because the underlying data also share that pattern. Geostatistics addresses the need to make predictions of sampled attributes (i.e., maps) at unsampled locations from sparse, often expensive data. To make up for lack of hard data geostatistics has concentrated on the development of powerful methods based on stochastic theory. Though there have been recent moves to incorporate ancillary data in geostatistical analyses, insufficient attention has been paid to using modern methods of data display for the visualization of results. GIS can serve geostatistics by aiding geo-registration of data, facilitating spatial exploratory data analysis, providing a spatial context for interpolation and conditional simulation, as well as providing easy-to-use and effective tools for data display and visualization. The value of geostatistics for GIS lies in the provision of reliable interpolation methods with known errors, methods of upscaling and generalization, and for supplying multiple realizations of spatial patterns that can be used in environmental modeling. These stochastic methods are improving understanding of how errors in models of spatial processes accrue from errors in data or incompleteness in the structure of the models. New developments in GIS, based on ideas taken from map algebra, cellular automata and image analysis are providing high level programming languages for modeling dynamic processes such as erosion or the development of alluvial fans and deltas. Research has demonstrated that these models need stochastic inputs to yield realistic results. Non-stochastic tools such as fuzzy subsets have been shown to be useful for spatial analysis when probabilistic approaches are inappropriate or impossible. The conclusion is that in spite of differences in history and approach, the linkage of GIS, statistics and geostatistics provides a powerful, and complementary suite of tools for spatial analysis in the agricultural, earth and environmental sciences.

Development of a Seamless Multisource Topographic/Bathymetric Elevation Model of Tampa Bay

Many applications of geospatial data in coastal environments require knowledge of the near-shore topography and bathymetry. However, because existing topographic and bathymetric data have been collected independently for different purposes, it has been difficult to use them together at the land/water interface owing to differences in format, projection, resolution, accuracy, and datums. As a first step toward solving the problems of integrating diverse coastal datasets, the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA) are collaborating on a joint demonstration project to merge their data for the Tampa Bay region of Florida. The best available topographic and bathymetric data were extracted from the USGS National Elevation Dataset and the NOAA hydrographic survey database, respectively. Before being merged, the topographic and bathymetric datasets were processed with standard geographic information system tools to place them in a common horizontal reference frame. Also, a key part of the preprocessing was transformation to a common vertical reference through the use of VDatum, a new tool created by NOAA's National Geodetic Survey for vertical datum conversions. The final merged product is a seamless topographic/bathymetric model covering the Tampa Bay region at a grid spacing of 1 arc-second. Topographic LIDAR data were processed and merged with the bathymetry to demonstrate the incorporation of recent third party data sources for several test areas. A primary application of a merged topographic/bathymetric elevation model is for user-defined shoreline delineation, in which the user decides on the tidal condition (for example, low or high water) to be superimposed on the elevation data to determine the spatial position of the water line. Such a use of merged topographic/bathymetric data could lead to the development of a shoreline zone, which could reduce redundant mapping efforts by federal, state, and local agencies by allowing them to customize their portrayals of the shoreline using a standard baseline elevation dataset.

Runoff and drainage pattern derived from digital elevation models, Finsterwalderbreen, Svalbard

AbstractFinsterwalderbreen (77°26’ N, 15° 15’ E) is a 35 km2 polythermal, surge-type glacier in southern Spitsbergen, Svalbard. Extensive field investigations have been carried out on the glacier, including mapping of bed topography by radio-echo soundings, mass-balance measurements and hydrological and meteorological registrations. In this paper we have used surface and bed topography and observations of summer ablation to estimate total runoff and subglacial drainage pattern by means of spatial map analysis in standard geographic information system software. The location and relative importance of the water-channel outlets from the glacier were estimated, as well as the change in drainage pattern over the period 1970–90. The results could be compared to direct observations of drainage pattern and discharge recordings. The analyses indicate that the location of the subglacial channels is dominated by the surface topography and thus that the channels are partly pressurized.

A national register of historic and contemporary anthrax foci.

Anthrax in Russia has for a long time posed a serious problem for public health and veterinary services. At the beginning of the century, 40-60 thousand cases of this infection were annually reported in the country in agricultural animals and about 10-20 thousand cases in people where each fourth (25%) was dying. In the Russian Federation the registration of anthrax foci is obligatory for veterinary as well as for sanitary-epidemiological services. So our initial project, funded by the International Technical and Scientific Center in Moscow, has envisaged the collection of all data of known anthrax foci, including the village name, agricultural council, region, oblast and year of occurrence. The objective is to assemble a reference handbook, "Register of stable anthrax sites in the Russian Federation", containing organized information on more than 10 000 anthrax foci occurring during the past 100 years. Such a study makes it possible to identify regions characterized by the highest concentrations of stationary anthrax sites in Russia, to identify trends in expressed activity of such sites through the periodic emergence of disease in humans and animals, and to determine the factors contributing to the formation of such trends. In doing this, it makes it possible to develop contingency plans for different risk locations (i.e. high risk of persistent infection, high risk of sporadic occurrence, low risk areas, etc.) in terms of anthrax in Russia, to identify high risk areas and develop a differentiated strategy of vaccination and other control strategies, and to develop preventive recommendations to reduce risk in high risk areas. It is now important to develop the second step of the project: to develop in depth studies of particular clusters to determine which factors are associated with Russian anthrax outbreaks. Maps will greatly enhance the value of this work in terms of spatial analysis. Furthermore, this supplementary project will allow the incorporation of powerful Geographic Information System (GIS) electronic mapping technology so that natural geographic features, such as soil type, climate, etc., can be compared with anthrax distributions in Russia using standard GIS and statistical analysis. At the present time, it is imperative to develop a detailed understanding of the world's distribution of anthrax and what geographic factors determine its prevalence.

Toward a Conceptual Framework for the Cartographic Visualization of Network Information

Geographic information systems (GIS) are now widely used to manage and visualize transportation and other network information and to plan for changes in network infrastructure. As GIS technology becomes more portable and as planning styles evolve, these network data and derived analyses will be presented, sometimes interactively, not only to professional analysts but also to a wider audience of administrators, planners, and the general public. Such presentations require the effective communication of a combination of static and dynamic attributes of complex spatial networks to a diverse audience for a variety of purposes. A large quantity of data that describes the geometry and attributes of network infrastructures is already accumulated on a regular basis and is readily available in digital formats. Like most raw data, however, this massive digital resource is often not used to its fullest potential because methods for rendering it and synthesizing it for visual display are not well developed or implemented within standard GIS software. In this paper we develop a framework in which the formal functions of networks are tied to the information that must be communicated given a set of demands that influence the choice of a basic representational strategy.

Crop area monitoring within an advanced agricultural information system

Abstract This paper describes a framework for an image processing procedure for operational agricultural crop area estimation. This operational framework has been conceived within the development of an Advanced Agricultural Information System (AAIS) for the “Regione del Veneto “ (RdV ‐ Veneto Region) in northeastern Italy. The objective of this program is to develop the ability to generating timely and accurate area estimates and production information for four major agricultural crops: soybeans, sugar beets, corn, and small grains. AAIS uses state of the art methods in remote sensing and geographic information systems (GIS) technology and integrates a variety of data types including satellite imagery. This paper describes the methodology developed for image and ancillary data processing for the production of crop area statistics. Using a combination of standard unsupervised classification and GIS operations that incorporate knowledge about the agricultural system, a “sequential masking” classification pr...

Emulating the prospector expert system with a raster gis

Recent enhancements to the MAPS public-domain raster geographic information system (GIS) allow it to emulate the methodology of the Prospector Expert System. This pragmatic implementation deviates from the classic methods of expert systems, forward and backward chaining, but uses the standard GIS method of cartographic modeling. This allows faster processing of cell maps, but requires that the rule base be ordered before use. There is some evidence in the original description of Prospector of deviation from the reported method of backward chaining. The Prospector methodology is discussed, and step-by-step examples of Bayesian inference, fuzzy logic, and certainty calculations are presented. Expert systems provide the ability to process a cartographic model with missing or incomplete data. A simplified model of the original Prospector rules determining a favorable location to drill for copper was used to test this methodology. This model can accept up to 26 maps as input. The example in this paper uses 23 maps. The results are compared with those of the original Prospector report.

The Role of Artificial Intelligence in the Integration of Remotely Sensed Data with Geographic Information Systems

Although databases for geographic information systems (GIS) have been developed to manage digital map data, the integration of remotely sensed imagery and other collateral non-map information is rarely performed. For the most part, the use of sophisticated intelligent spatial databases, in which the user can query interactively about map, terrain, or associated imagery, is unknown in the GIS and cartographic community. In standard GIS systems, the ability to formulate complex queries requiring dynamic computation of factual and geometric properties is severely limited, often reflecting its origin as collections of thematic map overlays. Spatial database research requires the integration of ideas and techniques from many disciplines such as computer graphics, computational geometry, database methodology, image analysis, photogrammetry, and artificial intelligence. In this paper we discuss some ideas on how the scope of geographic information systems can be expanded by utilizing techniques from the Al community that may remedy deficiencies in user interfaces, spatial data representation, and its utilization. We draw on ongoing research at Carnegie Mellon University for examples of these techniques in the areas of image/map database and knowledge-based image interpretation.

Requirements and principles for the implementation and construction of large-scale geographic information systems

This paper provides a brief survey of the history, structure and functions of 'traditional' geographic information systems (GIS), and then suggests a set of requirements that large-scale GIS should satisfy, together with a set of principles for their satisfaction. These principles, which include the systematic application of techniques from several subfields of computer science to the design and implementation of GIS and the integration of techniques from computer vision and image processing into standard GIS technology, are discussed in some detail. In particular, the paper provides a detailed discussion of questions relating to appropriate data models, data structures and computational procedures for the efficient storage, retrieval and analysis of spatially-indexed data.