About: Data model (GIS) is a research topic. Over the lifetime, 413 publications have been published within this topic receiving 4735 citations. The topic is also known as: data model.
TL;DR: A proposed general framework learning to live with errors in spatial databases and the traditional and modern look at Tissot's indicatrix are proposed.
Abstract: Error modelling for the map overlay operation modelling error in overlaid categorical maps user considerations in landscape characterization knowledge-based approaches to determining and correcting areas of unreliability in geographical databases observations and comments on the generation and treatment of error in digital GIS data developing confidence limits on errors of suitability analyses in geographical information systems distance calculations and errors in geographical databases inclusion of accuracy data in a feature based, object- orientated data model accuracy and bias issues in surface representation modelling error in objects and fields frame independence spatial analysis modelling locational uncertainty via hierarchical tesselation minimum cross-entropy convex decompositions of pixel-indexed stochastic matrices - a geographical application of the Ising model the traditional and modern look at Tissot's indicatrix real data and real problems - dealing with large spatial databases the small number problems and the accuracy of spatial databases demand point approximations for location problems modelling realibility on statistical surfaces by polygon filtering scale-independent spatial analysis the effect of data aggregation on a poisson model of Canadian migration statistical methods for inference between incompatible zonal systems statistical effect of spatial data transformations - a proposed general framework learning to live with errors in spatial databases.
TL;DR: The atomic form of geographic information is introduced and shown to provide a foundation for object fields, metamaps, and the association classes of object‐oriented data modelling.
Abstract: Geographic representation has become more complex through time as researchers have added new concepts, leading to apparently endless proliferation and creating a need for simplification. We show that many of these concepts can be derived from a single foundation that we term the atomic form of geographic information. The familiar concepts of continuous fields and discrete objects can be derived under suitable rules applied to the properties and values of the atomic form. Fields and objects are further integrated through the concept of phase space, and in the form of field objects. A second atomic concept is introduced, termed the geo-dipole, and shown to provide a foundation for object fields, metamaps, and the association classes of object-oriented data modelling. Geographic dynamics are synthesized in a three-dimensional space defined by static or dynamic object shape, the possibility of movement, and the possibility of dynamic internal structure. The atomic form also provides a tentative argument that discrete objects and continuous fields are the only possible bases for geographic representation.
TL;DR: Differences between the underlying data models help to explain the variation in results between raster and network-based methods, and advises researchers to use caution in model selection.
Abstract: Inequalities in geographic access to health care result from the configuration of facilities, population distribution, and the transportation infrastructure. In recent accessibility studies, the traditional distance measure (Euclidean) has been replaced with more plausible measures such as travel distance or time. Both network and raster-based methods are often utilized for estimating travel time in a Geographic Information System. Therefore, exploring the differences in the underlying data models and associated methods and their impact on geographic accessibility estimates is warranted. We examine the assumptions present in population-based travel time models. Conceptual and practical differences between raster and network data models are reviewed, along with methodological implications for service area estimates. Our case study investigates Limited Access Areas defined by Michigan’s Certificate of Need (CON) Program. Geographic accessibility is calculated by identifying the number of people residing more than 30 minutes from an acute care hospital. Both network and raster-based methods are implemented and their results are compared. We also examine sensitivity to changes in travel speed settings and population assignment. In both methods, the areas identified as having limited accessibility were similar in their location, configuration, and shape. However, the number of people identified as having limited accessibility varied substantially between methods. Over all permutations, the raster-based method identified more area and people with limited accessibility. The raster-based method was more sensitive to travel speed settings, while the network-based method was more sensitive to the specific population assignment method employed in Michigan. Differences between the underlying data models help to explain the variation in results between raster and network-based methods. Considering that the choice of data model/method may substantially alter the outcomes of a geographic accessibility analysis, we advise researchers to use caution in model selection. For policy, we recommend that Michigan adopt the network-based method or reevaluate the travel speed assignment rule in the raster-based method. Additionally, we recommend that the state revisit the population assignment method.
TL;DR: The paper distinguishes computer-aided mapping from geographic information systems, a link between mapping and information systems is emerging in the form of a data model that relates locational data on features and descriptive data for those features.
Abstract: The paper distinguishes computer-aided mapping from geographic information systems. Mapping systems are display oriented, and they produce plots of selected layers of point and line data. Geographic information systems, on the other hand, are analysis oriented; they analyze relationships among point, line, and area data that describe such geographic features as streets, rivers, buildings, and counties. A link between mapping and information systems is emerging in the form of a data model that relates locational data on features and descriptive data for those features. Planners are cautioned about acquiring computer-aided mapping systems that lack the geographic information systems data models that most spatial analysis requires.
TL;DR: The nature and form of uncertainty is remarkably consistent across various situations, and is approximately equivalent in the three aspects, which will enable consistent solutions for representation and processing of spatiotemporal data.
Abstract: While the presence of uncertainty in the geometric and attribute aspects of geographic information is well known, it is also present in temporal information. In spatiotemporal GIS databases and other formal representations, uncertainty in all three aspects of geography (space, time, and theme) must often be modeled, but a good data model must first be based on a sound theoretical understanding of spatiotemporal uncertainty. The nature of both uncertainty inherent in a phenomenon (often termed indeterminacy) and uncertainty in assertions of that phenomenon can be better understood through the Uncertain Temporal Entity Model, which characterizes the cause, type, and form of uncertainties in the spatial, temporal, and attribute aspects of geographic information. These uncertainties are the result of complexities and problems in two processes: the process of conceptualization, by which humans make sense of an infinitely complex reality, and measurement, by which we create formal representations (e.g. GIS) of those conceptual models of reality. Based on this framework, the nature and form of uncertainty is remarkably consistent across various situations, and is approximately equivalent in the three aspects, which will enable consistent solutions for representation and processing of spatiotemporal data.