Performance Testing on Marker Clustering and Heatmap Visualization Techniques: A Comparative Study on JavaScript Mapping Libraries

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F19%3A73598467" target="_blank" >RIV/61989592:15310/19:73598467 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2220-9964/8/8/348/htm" target="_blank" >https://www.mdpi.com/2220-9964/8/8/348/htm</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/ijgi8080348" target="_blank" >10.3390/ijgi8080348</a>

Result language

angličtina

Original language name

Performance Testing on Marker Clustering and Heatmap Visualization Techniques: A Comparative Study on JavaScript Mapping Libraries

Original language description

We are now generating exponentially more data from more sources than a few years ago. Big data, an already familiar term, has been generally defined as a massive volume of structured, semi-structured, and/or unstructured data, which may not be effectively managed and processed using traditional databases and software techniques. It could be problematic to visualize easily and quickly a large amount of data via an Internet platform. From this perspective, the main aim of the paper is to test point data visualization possibilities of selected JavaScript Mapping Libraries to measure their performance and ability to cope with a big amount of data. Nine datasets containing 10,000 to 3,000,000 points were generated from the Nature Conservation Database. Five libraries for marker clustering and two libraries for heatmap visualization were analyzed. Loading time and the ability to visualize large data sets were compared for each dataset and each library. The best-evaluated library was a Mapbox GL JS (Graphics Library JavaScript) with the highest overall performance. Some of the tested libraries were not able to handle the desired amount of data. In general, an amount of less than 100,000 points was indicated as the threshold for implementation without a noticeable slowdown in performance. Their usage can be a limiting factor for point data visualization in such a dynamic environment as we live nowadays

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10511 - Environmental sciences (social aspects to be 5.7)

Project

<a href="/en/project/GA18-05432S" target="_blank" >GA18-05432S: Spatial synthesis based on advanced geocomputation methods</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

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

Name of the periodical

ISPRS International Journal of Geo-Information

ISSN

2220-9964

e-ISSN

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Volume of the periodical

8

Issue of the periodical within the volume

8

Country of publishing house

CH - SWITZERLAND

Number of pages

8

Pages from-to

"348-1"-"348-8"

UT code for WoS article

000482985000047

EID of the result in the Scopus database

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