For a detailed walkthrough of the workflow, go to The Geo-visualization Workflow.
Below we demonstrate the Geo-visualization toolkit with some use cases. Each story links to a fully documented code example on the HERE API Explorer, where you can find further examples that describe the technologies offered.
Use Case: Distribution of Mobile Signal Strength on a Heat Map
This use case displays a visualization of mobile network signal strength on a map. The dataset used for the visualization represents a set of geographical points and signal strength measurements taken at each point. The data points are aggregated per pixel and, where multiple points are represented on one pixel, the points' signal strength measurements are averaged. The data is fetched from the Geo-visualization cloud and represented on a heat map that plots an interpolation of signal strength. Color-coding indicates the average signal strengths, and opacity indicates the confidence in these interpolated averages.
Use Case: Distribution of Averaged Mobile Signal Strengths on a Raster Map
This use case is similar to the previous example: it displays a visualization of mobile network signal strength on a map. In this case, the data points are first mapped to geographical squares of 250x250 meters, and the signal strength measurements of all points in each square are averaged. The data is fetched from the Geo-visualization cloud and represented on a raster map that displays a distribution of average signal strength.
Use Case: Visualize Mobile Network Antennae Grouped by Providers
This use case displays a visualization of groups of mobile network antennae on a map. The dataset used for the visualization contains antenna attributes such as its location, provider and technology. Single antennae are represented by colored circles, while groups of antennae ("clusters") are represented by pie chart symbols ("categorical markers"). The end-user can filter the antennae in the viewport by cell technology (2G, 3G, 4G) using check boxes. The data served to the client is only that required for the current viewport. The data is clustered by location on the client-side, and the clustered data points are categorized and markers are created to represent the categorization.