Yuan Ding
Nanjing Normal University
6 Papers
23 Citations
Yuan Ding is an academic researcher from Nanjing Normal University. The author has contributed to research in topics: Data model (GIS) & Automatic label placement. The author has an hindex of 3, co-authored 5 publications. Previous affiliations of Yuan Ding include Nanjing University.
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Papers
Measuring the Spatial Allocation Rationality of Service Facilities of Residential Areas Based on Internet Map and Location-Based Service Data
TL;DR: Wu et al. as discussed by the authors characterized the spatial allocation rationality of the service facilities of residential areas from Internet map and location-based service (LBS) data, which can provide micro-scale knowledge about residential areas.
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Extrusion Approach Based on Non-Overlapping Footprints (EABNOF) for the Construction of Geometric Models and Topologies in 3D Cadasters
TL;DR: A new extrusion approach based on non-overlapping footprints (EABNOF) that supports relatively complex 3D situations and is particularly suited to areas with 2D cadastral data to establish 3D cadasters with low costs.
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A grid algorithm suitable for line and area feature label placement
TL;DR: A new grid algorithm, in contrast to traditional vector-based methods, is proposed, which searches for the numerical label of its corresponding polygon (area) in a maximal inclusive rectangle and shows that the algorithm is simple and appropriate.
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A two-phase algorithm for point-feature cartographic label placement
TL;DR: An algorithm for PFCLP based on the four-slider (4S) model that generated better solutions relative to all methods previously reported in the literature and executes at a reasonable speed and is more stable than most other methods.
6
Patent
Implementation method for point feature cartographic label placement based on cartographic related group ant colony algorithm
Changbin Wu,Zhou Xinxin,Yuan Ding +2 more
- 07 Jan 2015
TL;DR: In this paper, an implementation method for point feature cartographic label placement based on a cartographic related group ant colony algorithm is proposed, which is applicable to a map with a large point scale and large point cluster density variation difference.
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