Visualization of Spatial Data Structures on Different Levels of Abstraction

AbstractSpatial data structures are used to manipulate location data. The visualization of such structures faces many challenges that are not relevant in the visualization of one-dimensional data. The visualized data can be represented using several different types of visual metaphors. These metaphors can be divided into several different levels of abstraction depending on the purpose of the visualization. This paper proposes a division of data structure visualization into four levels of abstraction, and shows how these abstractions can be taken into account in the visualization of spatial data structures

A Benchmark and analysis of spatial data structures for physical simulations

Collision detection is an issue in physical simulations; without it simulations are inaccurate. Unfortunately, effective collision detection can require a significant amount of computational power. To reduce the number of computations and make the problem more tractable, computer scientists have used date structures to partition the system. This removes the need to have every single partical check for possible collisions with every other particle in the system; however, generic data structures typically do not work as well as specialized data structures, so this has led to the creation of multiple spatial data structures. Some spatial data structures and algorithms were customized and created to optimize memory usage while others have been made to increase speed. This project seeks to compare spatial data structures in systems with uniformly and non-uniformly distributed particles, while varying the number of particles and the filling factor. The results of this project should provide useful information to those doing general collisional simulations, such as physicists and engineers

Chapter 4 Geographic information system spatial data structures, models, and case studies

This chapter provides a basic overview of geographic information systems (GISs) as well as a summary of basic concepts encountered with GISs. Specifically, it touches on the various spatial data structures and models used by GISs to represent geographical information. First, general concepts related to information organization and data structure are briefly described and related to the different ways of representing real-world geographical data and information in GISs. Second, different perspectives on information organization are discussed, including different types of spatial relationships processed by GISs as well as the underlying information organization structure within GISs. Finally, the concept of data is investigated, as well as the purpose of databases with respect to GISs, including the various methods of modeling real-world data, relationships, and processes into databases

Spatial Query for Planetary Data

Science investigators need to quickly and effectively assess past observations of specific locations on a planetary surface. This innovation involves a location-based search technology that was adapted and applied to planetary science data to support a spatial query capability for mission operations software. High-performance location-based searching requires the use of spatial data structures for database organization. Spatial data structures are designed to organize datasets based on their coordinates in a way that is optimized for location-based retrieval. The particular spatial data structure that was adapted for planetary data search is the R+ tree

A geometric constraint over k-dimensional objects and shapes subject to business rules

This report presents a global constraint that enforces rules written in a language based on arithmetic and first-order logic to hold among a set of objects. In a first step, the rules are rewritten to Quantifier-Free Presburger Arithmetic (QFPA) formulas. Secondly, such formulas are compiled to generators of k-dimensional forbidden sets. Such generators are a generalization of the indexicals of cc(FD). Finally, the forbidden sets generated by such indexicals are aggregated by a sweep-based algorithm and used for filtering. The business rules allow to express a great variety of packing and placement constraints, while admitting efficient and effective filtering of the domain variables of the k-dimensional object, without the need to use spatial data structures. The constraint was used to directly encode the packing knowledge of a major car manufacturer and tested on a set of real packing problems under these rules, as well as on a packing-unpacking problem

Efficient geographic information systems: Data structures, Boolean operations and concurrency control

Geographic Information Systems (GIS) are crucial to the ability of govern mental agencies and business to record, manage and analyze geographic data efficiently. They provide methods of analysis and simulation on geographic data that were previously infeasible using traditional hardcopy maps. Creation of realistic 3-D sceneries by overlaying satellite imagery over digital elevation models (DEM) was not possible using paper maps. Determination of suitable areas for construction that would have the fewest environmental impacts once required manual tracing of different map sets on mylar sheets; now it can be done in real time by GIS. Geographic information processing has significant space and time require ments. This thesis concentrates on techniques which can make existing GIS more efficient by considering these issues: Data Structure, Boolean Operations on Geographic Data, Concurrency Control. Geographic data span multiple dimensions and consist of geometric shapes such as points, lines, and areas, which cannot be efficiently handled using a traditional one-dimensional data structure. We therefore first survey spatial data structures for geographic data and then show how a spatial data structure called an R-tree can be used to augment the performance of many existing GIS. Boolean operations on geographic data are fundamental to the spatial anal ysis common in geographic data processing. They allow the user to analyze geographic data by using operators such as AND, OR, NOT on geographic ob jects. An example of a boolean operation query would be, Find all regions that have low elevation AND soil type clay. Boolean operations require signif icant time to process. We present a generalized solution that could significantly improve the time performance of evaluating complex boolean operation queries. Concurrency control on spatial data structures for geographic data processing is becoming more critical as the size and resolution of geographic databases increase. We present algorithms to enable concurrent access to R-tree spatial data structures so that efficient sharing of geographic data can occur in a multi user GIS environment