Multiple representations for cartographic objects in a multi-scale tree—An intelligent graphical zoom

Frank, Andrew U.; Timpf, Sabine

doi:10.1016/0097-8493(94)90008-6

Cited by 81 publications

(54 citation statements)

References 9 publications

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“…This work generally involves domain expertise in performing transformations while minimizing loss of detail (e.g., merging nearby roads, aggregating houses into blocks, and blocks into neighborhoods), and is a notoriously difficult problem [12]. Our work can be used to complement cartographic generalization in two ways.…”

Section: Related Workmentioning

confidence: 99%

“…Cartographic generalization deals with selection and transformation of geographic features on a map so that certain visual characteristics are preserved at different map scales [12,28,29]. This work generally involves domain expertise in performing transformations while minimizing loss of detail (e.g., merging nearby roads, aggregating houses into blocks, and blocks into neighborhoods), and is a notoriously difficult problem [12].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Efficient spatial sampling of large geographical tables

Sarma

Lee

González

et al. 2012

Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data

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Large-scale map visualization systems play an increasingly important role in presenting geographic datasets to end users. Since these datasets can be extremely large, a map rendering system often needs to select a small fraction of the data to visualize them in a limited space. This paper addresses the fundamental challenge of thinning: determining appropriate samples of data to be shown on specific geographical regions and zoom levels. Other than the sheer scale of the data, the thinning problem is challenging because of a number of other reasons: (1) data can consist of complex geographical shapes, (2) rendering of data needs to satisfy certain constraints, such as data being preserved across zoom levels and adjacent regions, and (3) after satisfying the constraints, an optimal solution needs to be chosen based on objectives such as maximality, fairness, and importance of data. This paper formally defines and presents a complete solution to the thinning problem. First, we express the problem as an integer programming formulation that efficiently solves thinning for desired objectives. Second, we present more efficient solutions for maximality, based on DFS traversal of a spatial tree. Third, we consider the common special case of point datasets, and present an even more efficient randomized algorithm. Finally, we have implemented all techniques from this paper in Google Maps [6] visualizations of Fusion Tables [14], and we describe a set of experiments that demonstrate the tradeoffs among the algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Efficient spatial sampling of large geographical tables

Sarma

Lee

González

et al. 2012

Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data

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show abstract

“…Coordinate information from location sensing is always subject to uncertainty and varies considerably with devices [6]. Layered multi-resolution spatial databases can store objects having different representations on multiple scales, so that each representation is associated with a certain range of scales [5,12]. The idea of a scale parameter has also been applied for indexing in spatial databases [7] and to diagrammatic visual inference about interval relations [8].…”

Section: Related Workmentioning

confidence: 99%

Positions, Regions, and Clusters: Strata of Granularity in Location Modelling

Schmidtke

Beigl

2010

KI 2010: Advances in Artificial Intelligence

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Abstract. Location models are data structures or knowledge bases used in Ubiquitous Computing for representing and reasoning about spatial relationships between so-called smart objects, i.e. everyday objects, such as cups or buildings, containing computational devices with sensors and wireless communication. The location of an object is in a location model either represented by a region, by a coordinate position, or by a cluster of regions or positions. Qualitative reasoning in location models could advance intelligence of devices, but is impeded by incompatibilities between the representation formats: topological reasoning applies to regions; directional reasoning, to positions; and reasoning about set-membership, to clusters. We present a mathematical structure based on scale spaces giving an integrated semantics to all three types of relations and representations. The structure reflects concepts of granularity and uncertainty relevant for location modelling, and gives semantics to applications of RCC-reasoning and projection-based directional reasoning in location models.

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“…This approach follows the representation principle: one object in the real world translates into one instance in the database. Proposals by Frank & Timpf [11,32], Kidner, Jones & al. [15,17], Bedard [3], and Vangenot [34] represent variations within this trend.…”

Section: One Object One Multi-resolution Instancementioning

confidence: 99%

“…A multi-resolution database has to keep track of all links that are needed to retrieve a consistent subset of database representations for each user interested in data at a specific resolution. Aggregation links, for instance, are necessary to support intelligent zooming [11].…”

Section: One Object One Multi-resolution Instancementioning

confidence: 99%