Design and Evaluation of WebGL-Based Heat Map Visualization for Big Point Data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F17%3A43931422" target="_blank" >RIV/49777513:23520/17:43931422 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/978-3-319-45123-7_2" target="_blank" >http://dx.doi.org/10.1007/978-3-319-45123-7_2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-45123-7_2" target="_blank" >10.1007/978-3-319-45123-7_2</a>

Result language

angličtina

Original language name

Design and Evaluation of WebGL-Based Heat Map Visualization for Big Point Data

Original language description

Depicting a large number of points on a map may lead to overplotting and to a visual clutter. One of the widely accepted visualization methods that provides a good overview of a spatial distribution of a large number of points is a heat map. Interactions for efficient data exploration, such as zooming, filtering or parameters’ adjustments, are highly demanding on the heat map construction. This is true especially in the case of big data. In this paper, we focus on a novel approach of estimating the kernel density and heat map visualization by utilizing a graphical processing unit (GPU). We designed a web-based JavaScript library dedicated to heat map rendering and user interactions through WebGL. The designed library enables to render a heat map as an overlay over a background map provided by a third party API (e.g. Open Layers) in the scope of milliseconds, even for data size exceeding one million points. In order to validate our approach, we designed a demo application visualizing a car accident dataset in the Great Britain. The described solution proves fast rendering times (below 100 ms) even for dataset up to 1.5 million points and outperforms mainstream systems such as the Google Maps API, Leaflet heat map plugin or ESRI’s ArcGIS online. Such performance enables interactive adjustments of the heat map parameters required by various domain experts. The described implementation is a part of the WebGLayer open source information visualization library.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

R - Projekt Ramcoveho programu EK

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

The Rise of Big Spatial Data

ISBN

978-3-319-45122-0

ISSN

1863-2246

e-ISSN

1863-2351

Number of pages

14

Pages from-to

13-26

Publisher name

Springer

Place of publication

Heidelberg

Event location

Ostrava

Event date

Mar 16, 2016

Type of event by nationality

EUR - Evropská akce

UT code for WoS article

000419321700002