Catálogo de publicaciones - libros

Current GIS are often described as rich in functionality but poor in knowledge content and transfer. This paper presents a prototype for communicating data quality in spatial databases using a hybrid design between data-driven and user-driven factors based upon traditional communication and cartographic concepts. The prototype aims to give data users a better understanding of the uncertainty that affects their information by utilizing a knowledge-based method where they can choose from multiple visualizations to represent the uncertainty in their data, as well as access information about why a particular visualization has been proposed. In doing so, decisions become more transparent to data users, which increases the capability of the prototype to act as a training aid. The example case study examines the data quality in a source dataset and illustrates how the concepts apply in an operational environment at different levels of communication.

This paper addresses changes in topological relations as they occur when splitting a region into two. It derives systematically what qualitative inferences can be made about binary topological relations when one region is cut into two pieces. The new insights about the possible topological relations obtained after splitting regions form a foundation for high-level spatio- temporal reasoning without explicit geometric information about each object’s shapes, as well as for transactions in spatio-temporal databases that want to enforce consistency constraints.

The most commonly used topographic vector data are currently two-dimensional. The topography is modeled by different objects; in contrast, a digital terrain model (DTM) is a continuous representation of the Earth surface. The integration of the two data sets leads to an augmentation of the dimension of the topographic objects, which is useful in many applications. However, the integration process may lead to inconsistent and semantically incorrect results.

In this paper we describe recent work on consistent and semantically correct integration of 2D GIS vector data and a DTM. In contrast to our prior work in this area, the presented algorithm takes into account geometric inaccuracies of both, planimetric and height data, and thus achieves more realistic results. Height information, implicitly contained in our understanding of certain topographic objects, is explicitly formulated and introduced into an optimization procedure together with the height data from the DTM. Results using real data demonstrate the applicability of the approach.

Topological relations have been recognized to be very useful for spatial query, analysis and reasoning. This paper concentrates on the topological relations between two lines in . The line of thought employed in this study is that the topological relation between two lines can be described by a combination of finite number of basic (or elementary) relations. Based on this idea, a hierarchical approach is proposed for the description and determination of basic relations between two lines. Seventeen (17) basic relations are identified and eleven (11) of them form the basis for combinational description of a complex relation, which can be determined by a compound relation model. A practical example of bus routes is provided for illustration of the approach proposed in this paper, which is an application of the line-line topological relations in traffic planning.

Spatial distances are the main tools used for data matching and control quality. This paper describes new measures adapted to sinuous lines to compute the maximal and average discrepancy: Discrete Fréchet distance and Discrete Average Fréchet distance. Afterwards, a global process is defined to automatically handle two sets of lines. The usefulness of these distances is tested, with a comparison of coastlines. The validation is done with the computation of three sets of coastlines, obtained respectively from SPOT 5 orthophotographs and GPS points. Finally, an extension to Digital Elevation Model is presented.

This paper introduces the semantic variogram, which is a measure of spatial variation based upon semantic similarity metrics calculated for nominal land cover class definitions. Traditional approaches for measuring spatial autocorrelation for nominal geographical data compare classes between pairs of observations to determine a simple binary measure of similarity (identical/different). These binary values are summarized over many sample pairs separated by various distances to characterize some spatial metric of correlation, or variation. The use of binary similarity measures ignores potentially substantial ranges in similarity between different classes. Through the development of category representations capable of producing quantifiable measures of pair wise class similarity, descriptive spatial statistics that operate upon ratio data may be employed. These measures, including the semantic variogram proposed in this work, may characterize spatial variability of categorical maps more sensitively than traditional measures. We apply the semantic variogram to National Land Cover Data (NLCD) for three different study sites, and compare results to those from a multiple class indicator semivariogram. We demonstrate that substantial differences exist in observed short-range variability for the two metrics in all sites. The semantic variograms detect much lower short-range variability due to the tendency of semantically similar classes to be closer together.

The objective of this paper is to discuss our methodology for comparing, searching and integrating geographic concepts. Searching for spatially oriented datasets could be illustrated by the complexity of the communication between the producer and user. The common vocabulary consists of a set of concepts describing the geographic space called universal ontology of geographical space (UOGS). We have defined the semantic parameters for measuring semantic similarities within the UOGS semantic framework and described our applicative approach to the similarity analyses of spatial databases. In order to test our results we have implemented the entire vocabulary as a set prolog fact. Following this we also implemented functionality such as the querying mechanism and the simple semantic similarity model, again as a set of prolog clauses. In addition to this, we applied prolog rules for the purpose of extracting semantic information describing geographic concepts and extracting it from natural language texts.

In on-demand map generation, a base-map is modified to meet user requirements on scale, resolution, and other parameters. Since there are many ways of satisfying the requirement, we need a method of measuring the quality of the alternative maps. In this paper, we introduce a uniform framework for measuring the quality of generalized maps. The proposed Map Quality measure takes into account changes in all local objects (Shape Similarity), their neighborhoods (Location Similarity) and lastly across the entire map (Semantic Content Similarity). These three quality aspects measure the major generalization operators of simplification, relocation and selection, exaggeration and aggregation, collapse and typification. The three different aspects are combined using user-specified weights. Thus, the proposed framework supports the automatic choice of best alternative map according to preferences of the user or application.

Recent technological advances in urban traffic systems engender the availability of large trajectory data sets. However, the potential of these large urban databases are often neglected. This is due to a twofold problem. First, the volumes generated represent gigabytes of information per day, thus making data processing and analysis a computationally costly operation. Secondly, there is a lack of analysis of the semantics revealed by urban trajectories, at both the representation and data manipulation levels. The research presented in this paper addresses these two issues. We introduce an optimized representation approach that can efficiently reduce trajectory data volumes and facilitate data access and query languages. Our approach is a semantic-based representation model that characterizes significant trajectory points within a network. Key points are selected according to a combination of network, velocity, and direction criteria. This semantic approach facilitates trajectory data queries, the implicit modeling of trajectory processes. The proposed model is illustrated by a prototype implemented in a district of Hong Kong.

Current data models for representing geospatial data are decades old and well developed, but suffer from two major flaws. First, they employ a onesize-fits-all approach, in which no connection is made between the characteristics of data and the specific applications that employ the data. Second, they fail to convey adequate information about the gap between the data and the phenomena they represent. All spatial data are approximations of reality, and the errors they contain may have serious implications for geoprocessing activities that employ them. As a consequence of this lack of information, users of spatial data generally have a limited understanding of how errors in data affect their particular applications. This paper reviews extensive work on spatial data uncertainty propagation. It then proposes development of a data producer focused ontologically-driven GIS to implement the Monte Carlo based uncertainty propagation paradigm. We contend that this model offers tremendous advantages to the developers and users of spatial information by encapsulating with data appropriate uncertainty models for specific users and applications.