An effective algorithm for lines and polygons overlay analysis using uniform spatial grid indexing

The Naval Oceanographic Office has constructed a high-resolution geophysical database (GDB) for the Western Barents Sea describing the properties of the seafloor and subsurface sediment layers important to acoustic modeling. The original implementation of this database used a public domain geographic information system (GIS) called GRASS (Geographic Resource Analysis Support System). Subsequent versions use a commercial GIS, ARC/INFO. While GRASS and subsequently ARC/INFO address the needs of the Naval Oceanographic Office as well as the R&D community currently using GDB, its large data files, GIS system size, and database access time, limit the use of this database on Navy operational systems, where access time and disk space are limited. Additionally, the data stored in GDB are not immediately compatible with the Navy's standard propagation loss models. This paper discusses efforts made toward porting the Western Barents Sea GDB onto an operational system which include regionalizing the data to accommodate severe disk space limitations, new storage and retrieval methods, and on-the-fly conversion of GDB data to data types compatible with Navy standard models. The result is a full implementation of the Barents Sea GDB on a PC-based model operating system, without sacrificing many appealing features of the GIS.

Postscript routines for display of watershed data

Geographic information systems (GIS) provide a variety of tools for the manipulation and display of public health data. Few, however, enable users to interactively evaluate hypotheses on spatial trends in disease risk that may be suggested by maps of measures of disease impact. We addressed this limitation by developing a seamless interface between a commercial GIS and a suite of spatial analysis algorithms. Users of the system can utilize the GIS's capability to interactively select and manipulate geographically referenced data and, through a series of pull-down menus, apply a variety of exploratory analysis methods to this information. In the presented application, we illustrate this capability by including algorithms for the reduction of random noise in observed incidence rates, for the detection of unusual aggregations of disease events, and for the statistical evaluation of inferences drawn from spatial trends. We demonstrate this application by examining lung cancer mortality in the state of Ohio.

Open source origin might appear like a major prospective change which is qualified to deliver in various industries and also competing means in developing countries. The leading purpose of this research study is to basically discover the degree of adopting Open Source Software (OSS) that is connected with Geographic Information System (GIS) application within Malaysia. It was derived based on inadequate awareness with regards to the origin ideas or even on account of techie deficiencies in the open origin instruments. This particular research has been carried out based on two significant stages; the first stage involved a survey questionnaire: to evaluate the awareness and acceptance level based on the comparison feedback regarding OSS and commercial GIS. This particular survey was conducted among three groups of candidates: government servant, university students and lecturers, as well as individual. The approaches of measuring awareness in this research were based on a comprehending signal plus a notion signal for each survey questions. These kinds of signs had been designed throughout the analysis in order to supply a measurable and also a descriptive signal to produce the final result. The second stage involved an interview session with a major organization that carries out available origin internet GIS; the Federal Department of Town and Country Planning Peninsular Malaysia (JPBD). The impact of this preliminary study was to understand the particular viewpoint of different groups of people on the available origin, and also their insufficient awareness with regards to origin ideas as well as likelihood may be significant root of adopting level connected with available origin options.

As of this date, approximately 5,000 horizontal Eagle Ford wells have been completed in South Texas. Still, geologists and engineers question whether their companies are using the most appropriate operating practices. Side-by-side case studies may show value or not, given the challenge of small sample size and hidden influences on outcome. Multivariate statistical analysis of larger data sets offers sound interpretation across larger geographic areas, with the caveat that correlations need to be scaled to local conditions. The purpose of this paper is to apply multivariate statistical modeling in conjunction with Geographic Information Systems (GIS) pattern recognition work to the Eagle Ford. The investigation began by acquiring Eagle Ford data using both proprietary and public information. The different data sets were loaded into a common database and put through quality control sanity checks. Production proxies, such as maximum oil rate in the first 12 producing months and normalized 12 month cumulative production, were selected and merged with the other data. Final data sets were then subjected to analysis in both an open-source multivariate statistical analysis and visualization code and a commercial Geographic Information Systems (GIS) application. Similar to other studies in unconventional reservoirs, integration of the two analysis and interpretation methods highlighted the importance of using well location as a proxy for reservoir quality when working with data sets that lack such measurements. The use of multivariate statistical analysis allowed modeling the impact of particular well architecture, completion, and stimulation parameters on the production outcome by integrating out the impact of other variables in the system. This work is a continuation of the prior work designed to address well optimization in unconventional reservoirs. It is significant in that it takes full advantage of GIS map-based methods and multivariate statistical methods to capitalize on the volume of data available through the public domain.

Relative space-based GIS data model to analyze the group dynamics of moving objects

Adaptive indexing allows for the progressive and simultaneous query-driven exploration and indexing of memoryresident data, starting as soon as they become available without upfront indexing. This technique has been so far applied to onedimensional and multidimensional data, as well as to objects with spatial extent arising in geographic information systems. However, existing spatial adaptive indexing methods cater to static data made available in an one-off manner. To date, no spatial adaptive indexing method can ingest data updates interleaved with data exploration. In this paper we introduce GLIDE, a novel method that intertwines the adaptive indexing and incremental updating of a spatial-object data set. GLIDE builds a hierarchical spatial index incrementally in response to queries and also ingests updates judiciously into it. We examine several design choices and settle for a variant that combines gradual self-driven top-down insertions with query-driven indexing operations. In an extensive experimental comparison, we show that GLIDE achieves a lower cumulative cost than upfront-indexing methods and adaptiveindexing baselines.

Since the 1960s, researchers and practitioners in the geosciences have been working on computer solutions to match their specific needs. Commercial geographic information systems (GIS) are among the most important outcomes of these efforts. In those early years, computer scientists have been involved only marginally in the development of such systems. More generally speaking, cooperations between computer scientists and the geoscientific communities have traditionally been rare.

High-resolution regional and global raster databases are currently being generated for a variety of environmental and scientific modeling applications. The projection of these data from geographic coordinates to a plane coordinate system is subject to significant areal error. Sources of error include users selecting an inappropriate projection or incorrect parameters for a given projection, algorithmic errors in commercial geographic information system (GIS) software, and errors resulting from the projection of data in the raster format. To assess the latter type of errors, the accuracy of raster projection was analyzed by two methods. First, a set of 12 one-degree by one-degree quadrilaterals placed at various latitudes was projected at several raster resolutions and compared to the projection of a vector representation of the same quadrilaterals. Second, several different raster resolutions of land cover data for Asia were projected and the total areas of 21 land cover categories were tabulated and compared. While equal-area projections are designed to specifically preserve area, the comparison of the results of the one-degree by one-degree quadrilaterals with the common equal area projections (e.g., the Mollweide) indicates a considerable variance in the one-degree area after projection. Similarly, the empirical comparison of land cover areas for Asia among various projections shows that total areas of land cover vary with projection type, raster resolution, and latitude. No single projection is best for all resolutions and all latitudes. While any of the equal-area projections tested are reasonably accurate for most applications with resolutions of eight-kilometer pixels or smaller, significant variances in accuracies appear at larger pixel sizes.

Choropleth mapping is an essential visualization technique for exploratory spatial data analysis. Visualizing multiple choropleth maps is a technique that spatial analysts use to reveal spatiotemporal patterns of one variable or to compare the geographical distributions of multiple variables. Critical features for effective exploration of multiple choropleth maps are (1) automated computation of the same class intervals for shading different choropleth maps, (2) dynamic visualization of local variation in a variable, and (3) linking for synchronous exploration of multiple choropleth maps. Since the 1990s, these features have been developed and are now included in many commercial geographic information system (GIS) software packages. However, many choropleth mapping tools include only one or two of the three features described above. On the other hand, freely available mapping tools that support side-by-side multiple choropleth map visualizations are usually desktop software only. As a result, most existing tools supporting multiple choropleth-map visualizations cannot be easily integrated with Web-based and open-source data visualization libraries, which have become mainstream in visual analytics and geovisualization. To fill this gap, we introduce an open-source Web-based choropleth mapping tool called the Adaptive Choropleth Mapper (ACM), which combines the three critical features for flexible choropleth mapping.

Spatial indexing technology is widely used in Geographic Information Systems (GIS) and spatial databases. As a data retrieval technology, spatial indexing is becoming increasingly important in the big-data age. The purpose of this study is to propose a unified indexing strategy for the mixed data of a future marine GIS. First, data organisation of the system is described. Second, the display condition of each type of data is introduced. These conditions are the basis for the construction of a unified indexing structure. Third, a unified indexing structure for mixed data is presented. The construction process and the search method of the indexing structure are described. Finally, we implement the indexing strategy in our system “Automotive Intelligent Chart Three-dimensional Electronic Chart Display and Information Systems” (AIC 3D ECDIS). Our strategy can provide fast and integrated data retrieval. The spatial indexing strategy we propose breaks through the limitation of data types in our system. It can also be applied in other GIS systems. With the advent of the big-data age, mixed data indexing will become more and more important.

We present a unique Geographical Information System (GIS) that seamlessly integrates 2D and 3D views of the same spatial and aspatial data. Multiple layers of information are continuously transformed between the 2D and 3D modes under the control of the user, directly over a base-terrain.Although 2D/3D hybrid (or combination) displays are now widely used in medical applications such as tomography, they have not been explored to any great extent in GIS applications. And although many existing commercial 3D GIS systems have 2D views they are typically isolated from the 3D view in that they are presented in a separate window. In this paper we introduce a 2D/3D hybrid GIS that allows the user to view the 2D data in direct relation to the 3D view within the same view.

Geographic information systems (GIS) are becoming more usual due to the improved performance of computer systems. GIS applications are being developed using the three-tier software architecture traditionally used for general-purpose information systems. Even though this architecture is suitable for GIS applications, the special nature and exclusive characteristics of geographic information pose special functional requirements on the architecture in terms of conceptual and logical models, data structures, access methods, analysis techniques, or visualization procedures. In this paper, we propose a generic architecture for GIS that provides support for the special nature of geographic information and conforms with the specifications proposed by the ISO/TC 211 and the OGC. Our strategy to achieve this goal consists of two steps: (i) we analyze the special characteristics of GIS with respect to traditional information systems, (ii) and we adapt the traditional three-tier architecture for information systems to take into account the special characteristics of GIS. Finally, we have tried to apply the architecture that we propose in the development of a complete and complex real-life GIS application using commercial tools in the analysis, design and implementation. We describe this application, and we use it to describe the limitations of current commercial GIS development tools by analyzing the differences in the architecture of the resulting system with respect to our proposal.

For the performance of geographic information systems, it has been widely recognized that efficient data structures and algorithms supporting proximity based access to spatial data are a crucial ingredient. Because of that, in the last years, a lot of data structures for storage of multidimensional non-point objects have been proposed in the literature (for an overview see our tutorial introduction in this volume). Almost all of these approaches are made to support queries based on spatial neighborhood of objects, so-called proximity queries. Common examples of such proximity queries are range queries, returning all objects intersecting a given (rectangular) region, or nearest neighbor queries, determining the object nearest to a given point. But a geographic or cartographic information system should also support queries that do not exclusively refer to the spatial neighborhood of objects. For example, different versions of spatial data must be organized such that proximity queries to any version of the data can be answered efficiently. Or, given a set of object classes, a spatial query that involves an arbitrary combination of these classes should also be supported. Because of the growing size of data sets to be handled in geographic information systems, an appropriate data organisation to support such and several other application-specific queries becomes more and more important. In this paper, we will present two approaches for access structures supporting the two types of queries mentioned above. Handling of versioned, spatial data is the topic of Section 2, while Section 3 discusses queries on some classes selected from the set of object classes stored in a geographic information system.

Implementing spatial segregation measures in GIS