Problems with handling spatial data — the voronoi approach

CISM journal ◽  
1991 ◽  
Vol 45 (1) ◽  
pp. 65-80 ◽  
Author(s):  
Christopher M. Gold
Keyword(s):  
Error Estimation ◽  
Voronoi Diagram ◽  
Spatial Data ◽  
Euclidean Plane ◽  
Interpolation Error ◽  
Line Segments ◽  
Definition Of ◽  
Incremental Construction

Experience with the handling of spatial data on a computer led to the identification of a variety of “awkward” problems, including interpolation, error estimation and dynamic polygon building and editing. Many of the problems encountered could be classified as “spatial adjacency” issues. The Voronoi diagram of points and line segments in the Euclidean plane is shown to give a functional definition of spatial adjacency. The basic operations for incremental construction and maintenance of this Voronoi diagram and its dual are described, and a variety of applications are outlined.

ON THE FARTHEST LINE-SEGMENT VORONOI DIAGRAM

2013 ◽  
Vol 23 (06) ◽  
pp. 443-459 ◽  
Author(s):  
EVANTHIA PAPADOPOULOU ◽  
SANDEEP KUMAR DEY
Keyword(s):  
Convex Hull ◽  
Voronoi Diagram ◽  
Line Segment ◽  
Euclidean Plane ◽  
Line Segments ◽  
Polygonal Curve ◽  
Number Of Faces ◽  
Farthest Point ◽  
Construction Algorithms ◽  
Standard Techniques

The farthest line-segment Voronoi diagram illustrates properties surprisingly different from its counterpart for points: Voronoi regions may be disconnected and they are not characterized by convex-hull properties. In this paper we introduce the farthest hull and its Gaussian map as a closed polygonal curve that characterizes the regions of the farthest line-segment Voronoi diagram, and derive tighter bounds on the (linear) size of this diagram. With the purpose of unifying construction algorithms for farthest-point and farthest line-segment Voronoi diagrams, we adapt standard techniques to construct a convex hull and compute the farthest hull in O(n log n) or output sensitive O(n log h) time, where n is the number of line-segments and h is the number of faces in the corresponding farthest Voronoi diagram. As a result, the farthest line-segment Voronoi diagram can be constructed in output sensitive O(n log h) time. Our algorithms are given in the Euclidean plane but they hold also in the general Lp metric, 1 ≤ p ≤ ∞.

scholarly journals Open Code is not enough: Towards a replicable future for geographic data science

2019 ◽  
Author(s):  
Levi John Wolf ◽  
Sergio J. Rey ◽  
Taylor M. Oshan
Keyword(s):  
Spatial Data ◽  
Data Science ◽  
Current Model ◽  
Open Science ◽  
Social Changes ◽  
Working Definition ◽  
Geospatial Cyberinfrastructure ◽  
Geographic Data ◽  
Definition Of ◽  
Healthy Part

Open science practices are a large and healthy part of computational geography and the burgeoning field of spatial data science. In many forms, open geospatial cyberinfrastructure adheres to a varying and informal set of practices and codes that empower levels of collaboration that are impossible otherwise. Pathbreaking work in geographical sciences has explicitly brought these concepts into focus for our current model of open science in geography. In practice, however, these blend together into a somewhat ill-advised but easy-to-use working definition of open science: you know open science when you see it (on GitHub). However, open science lags far behind the needs revealed by this level of collaboration. In this paper, we describe the concerns of open geographic data science, in terms of replicability and open science. We discuss the practical techniques that engender community-building in open science communities, and discuss the impacts that these kinds of social changes have on the technological architecture of scientific infrastructure.

scholarly journals A redesign of OGC Symbology Encoding standard for sharing cartography

2017 ◽  
Author(s):  
Erwan Bocher ◽  
Olivier Ertz
Keyword(s):  
Spatial Data ◽  
Poor Quality ◽  
Use Cases ◽  
General Observation ◽  
Data Infrastructures ◽  
Open Standards ◽  
Definition Of ◽  
Data Requirements ◽  
Basic Level

Despite most Spatial Data Infrastructures are offering service-based visualization of geospatial data, requirements are often at a very basic level leading to poor quality of maps. This is a general observation for any geospatial architecture as soon as open standards as those of the Open Geospatial Consortium (OGC) shall be applied. To improve the situation, this paper does focus on improvements at the portrayal interoperability side by considering standardization aspects. We propose two major redesign recommendations. First to consolidate the cartographic theory at the core of the OGC Symbology Encoding standard. Secondly to build the standard in a modular way so as to be ready to be extended with upcoming future cartographic requirements. Thus, we start by defining portrayal interoperability by means of typical use cases that frame the concept of sharing cartography. Then we bring to light the strengths and limits of the relevant open standards to consider in this context. Finally we propose a set of recommendations to overcome the limits so as to make these use cases a true reality. Even if the definition of a cartographic-oriented standard is not able to act as a complete cartographic design framework by itself, we argue that pushing forward the standardization work dedicated to cartography is a way to share and disseminate good practices and finally to improve the quality of the visualizations.

BASELINE BOUNDED HALF-PLANE VORONOI DIAGRAM

2013 ◽  
Vol 05 (03) ◽  
pp. 1350021 ◽  
Author(s):  
BING SU ◽  
YINFENG XU ◽  
BINHAI ZHU
Keyword(s):  
Voronoi Diagram ◽  
Half Plane ◽  
Line Segment ◽  
Euclidean Plane ◽  
Voronoi Region ◽  
Point Set ◽  
Set Of Points

Given a set of points P = {p1, p2, …, pn} in the Euclidean plane, with each point piassociated with a given direction vi∈ V. P(pi, vi) defines a half-plane and L(pi, vi) denotes the baseline that is perpendicular to viand passing through pi. Define a region dominated by piand vias a Baseline Bounded Half-Plane Voronoi Region, denoted as V or(pi, vi), if a point x ∈ V or(pi, vi), then (1) x ∈ P(pi, vi); (2) the line segment l(x, pi) does not cross any baseline; (3) if there is a point pj, such that x ∈ P(pj, vj), and the line segment l(x, pj) does not cross any baseline then d(x, pi) ≤ d(x, pj), j ≠ i. The Baseline Bounded Half-Plane Voronoi Diagram, denoted as V or(P, V), is the union of all V or(pi, vi). We show that V or(pi, vi) and V or(P, V) can be computed in O(n log n) and O(n2log n) time, respectively. For the heterogeneous point set, the same problem is also considered.

Spatial Autocorrelation and Association Measures

2011 ◽  
pp. 1502-1508
Author(s):  
J. Negreiros ◽  
M. Painho ◽  
I. Lopes ◽  
A.C. Costa
Keyword(s):  
Spatial Analysis ◽  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Regional Analysis ◽  
Geographical Information ◽  
Association Measures ◽  
Share Data ◽  
Definition Of ◽  
Graphic Database

Several classical statements relating to the definition of GIS can be found in specialized literature such as the GIS International Journal, expressing the idea that spatial analysis can somehow be useful. GIS is successful not only because it integrates data, but it also enables us to share data in different departments or segments of our organizations. I like this notion of putting the world’s pieces back together again (ArcNews, 2000). “GIS is simultaneously the telescope, the microscope, the computer and the Xerox machine of regional analysis and the synthesis of spatial data” (Abler, 1988). “GIS is a system of hardware, software and liveware implemented with the aim of storing, processing, visualizing and analyzing data of a spatial nature. Other definitions are also possible” (Painho, 1999). “GIS is a tool for revealing what is otherwise invisible in geographical information” (Longley, Goodchild, Maguire, & Rhind, 2001). Certainly, GIS is not a graphic database.

An UML Profile and SOLAP Datacubes Multidimensional Schemas Transformation Process for Datacubes Risk-Aware Design

2015 ◽  
Vol 11 (4) ◽  
pp. 64-83 ◽  
Author(s):  
Elodie Edoh-Alove ◽  
Sandro Bimonte ◽  
François Pinet
Keyword(s):  
Spatial Data ◽  
New Technologies ◽  
Design Method ◽  
Transformation Process ◽  
Spatial Data Analysis ◽  
Uml Profile ◽  
Previous Proposal ◽  
On Line ◽  
Definition Of ◽  
Spatial Vagueness

Spatial Data Warehouses (SDWs) and Spatial On-Line Analytical Processing (SOLAP) systems are new technologies for the integration and the analysis of huge volume of data with spatial reference. Spatial vagueness is often neglected in these types of systems and the data and analysis results are considered reliable. In a previous work, the authors provided a new design method for SOLAP datacubes that allows the handling of vague spatial data analysis issues. The method consists of tailoring SOLAP datacubes schemas to end-users tolerance levels to identified potential risks of misinterpretation they encounter when exploiting datacubes containing vague spatial data. It this paper, the authors further their previous proposal by presenting different formal tools to support their method: it is an UML profile providing stereotypes needed to add vague, risks and tolerance levels information on datacubes schemas plus the formal definition of SOLAP datacubes schemas transformation process and functions.

Applications of Geographical Information System in E-Government

2011 ◽  
pp. 80-86
Author(s):  
Y. Yongling
Keyword(s):  
Information System ◽  
Spatial Analysis ◽  
Geographical Information System ◽  
Real World ◽  
Spatial Data ◽  
Geographical Information ◽  
The Real ◽  
Definition Of ◽  
At Will

Geographical information system (GIS) is one kind of information system that handles spatial data. It is difficult to give one definitive definition about GIS (Heywood, Cornelius, & Carver, 2002; Maguire, Goodchild, & Rhind, 2001). This variety of definitions can be explained by the fact that any definition of GIS will depend on who is giving it, and their background and viewpoint (Pinkles, 2002). The complete definition of GIS is selected here as: “a set of tools for collecting, storing, retrieving at will, transforming, and displaying spatial data from the real world for a particular set of purposes”(Burrough, 1986, p. 6). As an important part of e-government, is that it has functions of cartography, manages spatial data and spatial analysis.

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