Representation Of Spatial Data Research Articles

Toward 3D spatial dynamic field simulation within GIS using kinetic Voronoi diagram and Delaunay tetrahedralization

Geographic information systems (GISs) are widely used for representation, management, and analysis of spatial data in many disciplines. In particular, geoscientists increasingly use these tools for data integration and management purposes in many environmental applications, ranging from water resources management to the study of global warming. Beyond these capabilities, geoscientists need to model and simulate three-dimensional (3D) dynamic fields and readily integrate those results with other relevant spatial information in order to have a better understanding of the environmental problems. However, GISs are very limited for the modeling and simulation of spatial fields, which are mostly 3D and dynamic. These limitations are mainly related to the existing GIS spatial data structures that are static and limited to 2D space. In order to overcome these limitations, we develop and implement a new kinetic 3D spatial data structure based on Delaunay tetrahedralization and a 3D Voronoi diagram to support a 3D dynamic field simulation within GISs. In this article, we describe in detail the different steps from discretization of a 3D continuous field to its numerical integration, based on an event-driven method. For validation of the proposed spatial data structure itself and its potential for the simulation of a dynamic field, two case studies are presented in the article. According to our observations, during the simulation process, the data structure is maintained and the 3D spatial information is managed adequately. Furthermore, the results obtained from both experiments are very satisfactory and are comparable with the results obtained from other existing methods for the simulation of the same dynamic field. To conclude, we discuss the current challenges related to the development of the 3D kinetic data structure itself and its adaptation to 3D dynamic field simulation and suggest some solutions for its improvement.

Urban Delivery Distribution Routing Optimizing Key Technology Based on Web GIS

Development of information technology, can provide efficient, safe, convenient support platform to logistics and distribution companies, improve logistics and distribution of economic benefits. The problem of the lack of geographic information and the actual line with the calculation of line, distribution routing optimizing, the introduction of Web GIS, and research key technologies. First, given a graphical Web GIS which based on distribution routing optimizing; secondly, study distribution routing optimizing algorithm, including model representation and reverse dynamic programming algorithm; again, study spatial data representation and data structure, including vector and raster data structure; then, study the structure of Web application framework, in the description based on the MVC framework, Spring MVC framework focuses on the component structure; finally, realize the Web GIS technologies, including Java Applet major technical analysis and implementation techniques. The results show that Web GIS technology will be combined with optimization algorithm on the city line distribution routing optimizing, is technically feasible, has some theoretical and practical prospects, an important future research direction.

Effectiveness of Cartogram for the Representation of Spatial Data

<title/>Cartogram is a technique for visualisation of the geographical distribution of spatial data. It has two main types, i.e. distance cartogram and area cartogram. Area cartogram is a transformed map in which areas are resized in proportion to an attribute value. A number of techniques have been developed for the generation of area cartograms. Some researchers consider cartogram as a very effective technique for visualisation of spatial data, while others doubt about the effectiveness because of the possible distortion in shape and/or disconnectivity in topology. This study aims to evaluate the effectiveness of area cartogram for visualizing spatial data. In this study, two comparative experiments have been conducted. One is the comparison between thematic maps and cartograms, and the other is the comparison among different types of area cartogram. Two sets of data with different characteristics are used, i.e. 2005 China population data and 1996 US election data. Results show that cartogram is more effective in the representation of the 1996 US election data which provides a qualitative result (i.e. in binary form or nominal data), but thematic map is far more effective in the representation of 2005 China population data which provides a quantitative result (in classes or ordinal data). It is also found that among different types of area cartogram, pseudo-cartogram is the most preferred technique and Dorling cartogram is the least preferred one.

Constraints of progressive transmission of spatial data on the web

The progressive transmission of map data on the World Wide Web provides the user with a self-adaptive strategy to access remote data. It not only speeds up the web transfer but also offers an efficient navigation guide for information acquisition. The key technology in this transmission is the efficient multiple representation of spatial data and pre-organization on the server site. This paper aims at progressive transmission investigating some constraints from three aspects: data organization on server site, data control in the transmission process and data restore after reaching the client. Two strategies, namely on-line map generalization and off-line map generalization, are examined respectively for this kind of progressive transmission.

Environmental risk mapping of pollutants: State of the art and communication aspects

Risk maps help risk analysts and scientists to explore the spatial nature of the effects of environmental stressors such as pollutants. The development of Geographic Information Systems over the past few decades has greatly improved spatial representation and analysis of environmental information and data. Maps also constitute a powerful tool to communicate the outcome of complex environmental risk assessment to stakeholders such as the general public and policy makers. With appropriate cartography one can improve communication and thus bridge the gap between experts and users. Appropriate risk communication is pivotal to risk management, decision making and implementation and may prevent unnecessary concern about environmental pollutants. However, at present few risk maps are specifically tailored to meet the demands of such defined uses. This paper presents an overview of the most important types of risk maps that can be distinguished using examples from the scientific literature: contamination maps, exposure maps, hazard maps, vulnerability maps and ‘true’ risk maps. It also discusses, in a general way, the most important issues that need to be addressed when making risk maps for communication purposes: risk perception, target audience, scale and spatial aggregation and visualisation such as use of colours and symbols. Finally, some general rules of thumb are given for making environmental risk maps for communication purposes.

R-Tree Index Structure for Multi-Scale Representation of Spatial Data

R-Tree Index Structure for Multi-Scale Representation of Spatial Data

R-Tree Index Structure for Multi-Scale Representation of Spatial Data

Aiming at multi-scale representation of spatial data not supported by R-tree, a transmutation index structure of R-tree used for multi-scale representation of spatial data is proposed: (1) Spatial objects may appear at non-leaf nodes; (2) Depth of the tree is made use for reflecting change of spatial resolutions, and resolution dimensions is supported; (3) Structure of the tree's branches supports algorithms of automatic cartography generalization. Query of spatial datas among multiple scales of based on the R-tree's transmutation index structure is analyzed, and constraint conditions, insert algorithms and divide algorithms among creations of the index structure are emphasized. For the same data source, compared with multi-scale index based on quadtree for spatial data, experiments prove that the transmutation structure can search spatial data organized by multi-resolution and memorize generalization results with high efficiency.

VASA: An algebra for vague spatial data in databases

Many geographical applications deal with objects in space that cannot be adequately described by determinate, crisp spatial concepts because of their intrinsically indeterminate and vague nature. Geographical information systems and spatial database systems are currently unable to cope with this kind of data. To support the efficient representation, querying, and manipulation of vague spatial data in a database context, we present a formal data model called vague spatial algebra ( VASA). This algebra comprises a set of vague spatial data types for vague points, vague lines, and vague regions together with a comprehensive collection of vague spatial operations and vague topological predicates. One of VASA's main benefits is that its formal framework is based on well known, general, and exact models of crisp spatial data types. This enables an exact definition of the vague spatial model since we can build upon an already existing theory of spatial data types. In particular, crisp spatial data types turn out to be a special case of their vague counterparts. In addition, our approach enables executable specifications for the operations, which can be immediately used as implementations. The article offers a precise and conceptually clean foundation for implementing a DBMS extension for vague spatial data and demonstrates the embedding of these new data types as attribute data types in a database schema as well as the incorporation of vague spatial operations and predicates into queries formulated in an SQL-like query language. All concepts have been verified in a prototype implementation.

PARM—An Efficient Algorithm to Mine Association Rules From Spatial Data

Association rule mining, originally proposed for market basket data, has potential applications in many areas. Spatial data, such as remote sensed imagery (RSI) data, is one of the promising application areas. Extracting interesting patterns and rules from spatial data sets, composed of images and associated ground data, can be of importance in precision agriculture, resource discovery, and other areas. However, in most cases, the sizes of the spatial data sets are too large to be mined in a reasonable amount of time using existing algorithms. In this paper, we propose an efficient approach to derive association rules from spatial data using Peano Count Tree (P-tree) structure. P-tree structure provides a lossless and compressed representation of spatial data. Based on P-trees, an efficient association rule mining algorithm PARM with fast support calculation and significant pruning techniques is introduced to improve the efficiency of the rule mining process. The P-tree based Association Rule Mining (PARM) algorithm is implemented and compared with FP-growth and Apriori algorithms. Experimental results showed that our algorithm is superior for association rule mining on RSI spatial data.

EVALUATION OF THE INFLUENCE OF UNCERTAINTY IN RAINFALL AND DISCHARGE DATA ON HYDROLOGICAL MODELING

The objectives of this study are to analyze the influence of systematic and random error inrainfall data, discharge data, and the spatial representation of rainfall data on the performance of a distributed hydrologicalmodel, BTOPMC. A framework for uncertainty analysis was developed using a Monte Carlo approach, which was applied to the Kalu River basin in Sri Lanka. Findings show that a systematic error exceeding +/-10% in rainfall or discharge data is detrimental to model results. A random error with standarddeviation is equal to10% ofrainfall or discharge isnot substantial. Calibrationof parameters can compensate for some error. The impact of systematic error in rainfall in terms of Nash-Sutcliffe Efficiency (NSE) is higher than that in discharge. The impact of random error in discharge in terms of NSE is higher than that inrainfall. The impact of the random error is the lowest for the gauge network of the highest density, but the impact of systematic error is not the lowest for this case.

Large-area mountain pine beetle infestations: Spatial data representation and accuracy

Point data generated from helicopter surveys are used to determine the location and magnitude of mountain pine beetle infestations. Although collected for tactical planning, these data also provide a rich source of information for scientific investigations. To facilitate spatial research, it is important to consider how to best represent spatially explicit mountain pine beetle infestation data. This paper focuses on the spatial representation of point-based aerial helicopter surveys, which can be difficult to represent due to issues associated with large data quantities and data uncertainty. In this paper, the benefit of using a kernel density estimator to convert point data to a continuous raster surface is demonstrated. Field data are used to assess the accuracy of the point-based aerial helicopter survey data and the kernel density estimator is extended to incorporate data uncertainty. While the accuracy of point-based aerial surveys is high, with 92.6% of points differing by no more than ± 10 trees, there is a general tendency to overestimate infestation magnitude. The method developed for incorporating uncertainty into the kernel density estimator reduces overestimation and improves the correspondence between estimated infestation intensities and field data values. Key words: mountain pine beetle, data representation, visualization, kernel density estimators, uncertainty

Hyperspectral NIR imaging for calibration and prediction: a comparison between image and spectrometer data for studying organic and biological samples

A hyperspectral image in the near infrared contains thousands of position-referenced spectra. After imaging reference materials of known composition it is possible to build Partial Least Squares (PLS) regression models for predicting unknown compositions from new images or spectra. In this paper a comparison is made between spectra from a hyperspectral image and spectra from two spectrometers: a scanning grating instrument with rotating sample holders and an FT-NIR instrument utilizing a fiber-optic probe. The raw spectra and the quality of the PLS calibration models and predictions are compared. Two sample datasets consist of a set of 13 designed artificial mixtures of pure constituents and a selection of 13 sampled cheeses. The prediction error from the hyperspectral image spectra is between that of the two spectrometers. For a typical food sample, the average bias [and replicate standard deviation] was -0.6% [0.5%] for protein and -0.2% [1.3%] for fat. Comparable values for the best spectrometer were -0.2% bias for protein and -0.5% for fat. Some of the advantages of working with hyperspectral images are highlighted: the simultaneous exploration of representations of both spectral and spatial data, and the analysis of concentration profiles and concentration maps all contribute to better characterization of organic and biological materials.

Speaking with spatial relations

Natural language descriptions are an important step in bridging the gap between numerical representations of spatial data and the human user. In this work, we present a system for generating linguistic descriptions of the spatial relationships between two-dimensional objects. The most pertinent relations for the description are chosen based on a fuzzification of the set relations DISJOINT, OVERLAP, SUBSET, SUBSETi and EQUAL. A handful of relevant Allen relations is then selected and their Allen F-histograms are analysed to extract further topological and directional information. The approach is validated using several sets of real and synthetic data.

Personal space requirements of mares versus geldings (Equus caballus): welfare implications and visual representation of spatial data via Spatial Web diagrams)

Increasing numbers of mares are competing at elite levels, and are recognised as being crucial to the production of high calibre sport horses. The management strategies utilised to look after horses affect their welfare and performance. By its very nature the domestication of horses has resulted in their confinement and also in major alterations to their natural social group structure. Most domestic pasture groups comprise mares and castrated males (Van Dierendonck, Sigorjonsdottir and Thorhallsdottir, 2002) and in the absence of a mature stallion are non-reproductive (Boyd and Kieper, 2002). Many equine behaviour studies have been directed at improving welfare and performance, but few centre on Personal Space Requirements (PSR). This study examines whether equine PSR varies between gender. Human personal space (PS) is defined as ‘the geographical component of interpersonal relations’ (Gifford, 1983) and its invasion is shown to cause instantaneous physiological stress (increased blood pressure and heart rate) and longer-lasting psychological stress (anxiety and tension), particularly when individuals can only endure exposure to stress passively (Sawada, 2003).

Analysis of land use drivers at the watershed and household level: Linking two paradigms at the Philippine forest fringe

Land use and land cover change (LUCC) is the result of the complex interactions between behavioural and structural factors (drivers) associated with the demand, technological capacity, social relations and the nature of the environment in question. Although no general theory of land use change exists, different disciplinary theories can help us to analyse aspects of LUCC in specific situations. However, paradigms and theories applied by the different disciplines are often difficult to integrate and their specific research results do not easily combine into an integrated understanding of LUCC. Geographical approaches often aim to identify the location of LUCC in a spatially explicit way, while socio‐economic studies aim to understand the processes of LUCC, but often lack spatial context and interactions. The objective of this study is to integrate process information from a socio‐economic study into a geographical approach. First, a logistic regression analysis is performed on household survey data from interviews. In this approach the occurrence of the land use types corn, wet rice and banana is explained by a set of variables that are hypothesised to be explanatory for those land use types, with fields as the unit of analysis. The independent variables consist of household characteristics, like ethnicity and age, and plot and field information, like tenure, slope and travel time. The results of these analyses are used to identify key variables explaining land use choice, which subsequently are also collected at watershed level, using maps, census data and remote sensing imagery. Logistic regression analysis of this spatial dataset, where a ten percent sample of a 50 by 50 m grid was analysed, shows that the key variables identified in the household analysis are also important at the watershed level. Important drivers in the study area are, among others, slope, ethnicity, accessibility and place of birth. The differences in the contribution of the variables to the models at household and watershed level can be attributed to differences in spatial extent and data representation. Comparing the model with a mainstream geographical approach indicates that the spatial model informed by the household analysis gives a better insight in the actual processes determining land use than the mainstream geographic approach.

Finding and Filling the "Cracks" In Resistance Surfaces for Least-cost Modeling

Least-cost modeling is an increasingly popular method used to measure the effective distance between habitat patches and to assess the connectivity of existing landscapes or potential reserves. For least-cost models to be reliable and credible, however, the validity of input data must be demonstrated. Least-cost modelers must also exercise extreme caution when using any GIS-based analysis of this kind. Technical issues associated with the raster-based representation of spatial data may introduce errors in otherwise correct data that nullify results. In this paper, I address the potential presence of in the resistance input layer of least-cost modeling exercises. Cracks result when narrow, costly features, such as roads or train tracks, are represented in raster form. These cracks lead to the erroneous identification of nonexistent shortcuts across truly expensive barriers. In this paper, I use a hypothetical example to explain resistance-layer cracks, how they occur, and the errors they generate with respect to least-cost modeling. I then present a simple algorithm to reveal the location of cracks and one approach to filling the cracks. Finally, these methods are demonstrated on a real data set in which more than 1% of the raster cells are shown to be cracks. The negative repercussions of faulty resistance layers when conducting least-cost modeling can not be overstated. On the one hand, unimportant or nonexistent least-cost paths on a landscape may be mislabeled as important. As a result, conservation resources committed to protecting these paths will be wasted. Conversely, truly critical corridors may be overlooked, jeopardizing the organisms that rely on them.

WaterScope: an innovative water quality information management system

SA Water is a State owned organisation that owns and manages South Australia's water supplies, providing reliable drinking water to nearly 1.4 million South Australians. A major issue affecting SA Water's ability to manage water quality effectively has been the difficulty accessing water quality information which has been stored in separate, generally inaccessible databases with poor reporting and decision support capability. To improve SA Water's ability to make timely and effective decisions regarding water quality, an integrated business system has been developed which provides water managers with direct access to comprehensive water quality information. The system includes improved field data collection units which incorporate a barcode system; sample point images and workflow support tools; an integrated water quality data warehouse with automated standard and ad hoc reporting capabilities; a geographical information system containing comprehensive coverages of natural resources and system infrastructure information; and water incident exception reporting and incident management support through a corporate incident management system. Major benefits of the system will include improved management of public health risk through quicker and more accurate reporting of incidents; improved customer confidence in SA Water; improved knowledge capture and visibility of water quality information; increased efficiency of capital utilisation and better understanding of system performance through spatial representation of data and trending of results. WaterScope can also be used and shared by data partners and regulators, making optimal use of the State's limited water quality data sets. It can also be made available commercially to other water management organisations. Future challenges include the integration of wastewater and recycled water data, linking of continuous (on-line) water quality data and links to water demand management systems.

Representation of spatial data in an OODB using roughand fuzzy set modeling

This paper introduces and formally defines a fuzzy rough object-oriented database (OODB) model based on a formal framework using an algebraic type system and formally defined constraints. This generalized model incorporates both rough set and fuzzy set uncertainty, while remaining compliant with object-oriented database standards set forth by the Object Database Management Group. Rough and fuzzy set uncertainty enhance the OODB model so that it can more accurately model real world applications. Spatial databases have a particular need for uncertainty management that can be achieved through rough and fuzzy techniques.

What lies beneath?—issues in the representation of air quality management data for public consumption

Policy developments in the UK and the European Union (EU) now require local authorities to engage the general public within the whole process of local air quality management (AQM). Indeed, this is considered to be one of the means by which air quality issues can gain public support and help ensure future improvements. One of the outcomes of this is that data sets associated with air quality management must now be disseminated to nonscientific audiences. This is a problematic task for a number of reasons. One of these relates to the fact that air quality data are complex and variable, yet the public demand representations that are clear and unambiguous. Another important issue is associated with the increasing use of geographical information systems (GIS) and mapping tools, which allow data to be generated and summarised in many different ways without due regard to the effects that the choice of methodology can have on the way data are interpreted. The variation in information obtained using different techniques can represent a problem, but is also an opportunity to further explore data sets and to draw out specific information for complementary air quality management tasks. However, at present, the lack of a well-grounded methodology and guidance for handling and representing the spatial aspects of data sets means that consistency between areas and authorities is not maintained. Such a situation fosters ambiguity at several levels, from the individual's perception of public health-related information to an Authority's rationale for the selection of air quality management areas (AQMAs). This paper investigates a number of issues relating to spatial data generation and representation in the field of air quality management, particularly in relation to emissions inventory data. The examples are UK based, but the issues raised are applicable to other examples and areas. One case study examines the difference in information gained through a number of common mapping techniques and shows how different ‘problems’ can be identified merely as an artefact of the dissemination technique itself. To further illustrate the difficulties and conflicts faced in representing and explaining these data in a practical context, reference is then also made to the methods recently considered by an example London authority. The paper concludes with a call for the development of a more standardised method for representing different types of air quality management-related data, which may help to overcome these problems in the future.

Hierarchical representation of digitized curves through dominant point detection

Hierarchical data structures play a key role in representation of spatial data. This paper proposes an algorithm for constructing hierarchical representations of digitized curves based on the dominant points of the curve. The comparison of the proposed scheme with the other hierarchical representation schemes viz. Arc Tree and Strip Tree reveals that the proposed algorithm is generally better than the Arc Tree and compares well with the Strip Tree scheme.