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

Abstract

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.

Performance Testing on Vector vs. Raster Map Tiles—Comparative Study on Load Metrics

“…J. Geo-Inf. 2020, 9,101 3 of 25 The tile in the first zoom level is indicated as 0, and the four tiles in the next zoom level are indicated as 00, 01, 02, and 03. [1].…”

“…According to Netek et al [25], the most commonly used formats are GeoJSON, TopoJSON, and Mapbox Vector Tile (mvt). The GeoJSON format is based on JSON (JavaScript Object Notation), which is a universal data format, and stores data as points, lines, polygons, multipoints, multilines, multipolygons, and geometric collections [26].…”

Evaluating the Performance of Three Popular Web Mapping Libraries: A Case Study Using Argentina’s Life Quality Index

“…Complementary to our work, [6] measured the performance and ability to cope with a big number of data points of five JavaScript mapping libraries. The paper shows diverse results in point rendering performance and severe performance degradation in some libraries when rendering more than 100,000 points.…”

“…here.com/) or OSM (https://www.openstreetmap.org), and then draw over this map additional information as markers, lines, or polygons. The practical impact of these technologies has been important, since they provided a simple way to enrich maps with geographic information from third parties [5,6], making Web mapping applications more flexible and even more widespread [7]. Accompanying this evolution, standard formats for representing and transferring geographic data for the Web emerged.…”

Análise espacial de eventos pontuais para estimar o potencial produtivo da tilápia do Nilo (Oreochromis niloticus)

“…Portanto, consiste num estimador probabilístico de intensidade do processo pontual não paramétrico de função Kernel (Carneiro & Albuquerque, 2019), justamente por visar estimar densidades com base em informações locais ao invés de estimar parâmetros globais para modelo de dados (Parzen, 1962). O KDE é um dos métodos mais usuais para a visualização de grandes conjuntos de dados utilizando a feição ponto (Netek et al, 2019). A utilização do algoritmo oferece muitas vantagens na leitura, interpretação e análises de mapas temáticos, cujo resultado da origem a um arquivo raster em tons de cinza (Santos et al, 2017).…”