Azure Maps and heatmaps

This is a continuation of a series of articles on operational engineering aspects of Azure public cloud computing. In this article, we continue the discussion on Azure Maps which is a full-fledged general availability service that provides similar Service Level Agreements as expected from others in the category.  We focus on one of the features of Azure Maps that enables overlay of images and heatmaps.

Azure Maps is a collection of geospatial services and SDKs that fetches the latest geographic data and provides it as a context to web and mobile applications.  Specifically, it provides REST APIs to render vector and raster maps as overlays including satellite imagery, provides creator services to enable indoor map data publication, provides search services to locate addresses, places, and points of interest given indoor and outdoor data, provides various routing options such as point-to-point, multipoint, multipoint optimization, isochrone, electric vehicle, commercial vehicle, traffic influenced, and matrix routing, provides traffic flow view and incidents view, for applications that require real-time traffic information, provides Time zone and Geolocation services, provides elevation services with Digital Elevation Model, provides Geofencing service and mapping data storage, with location information hosted in Azure and provides Location intelligence through geospatial analytics.

The Web SDK for Azure Maps allows several features with the use of its map control.  We can create a map, change the style of the map, add controls to the map, add layers on top of the map, add html markers, show traffic, cluster point data, and use data-driven style expressions, use image templates, react to events and make app accessible.

Heatmaps are also known as point density maps because they represent the density of data and the relative density of each data point using a range of colors. This can be overlaid on the maps as a layer. Heat maps can be used in different scenarios including temperature data, data for noise sensors, and GPS trace.

The addition of heat map is as simple as:

Map.layers.add(new atlas.layer.HeatMapLayer(datasource, null, { radius: 10, opacity: 0.8}), ‘labels’);

The opacity or transparency is normalized between 0 and 1. The intensity is a multiplier to the weight of each data point. The weight is a measure of the number of times the data point applies to the map.

Azure maps provides consistent zoomable heat map and the data aggregates together and the heat map might look different from when it was normal focus. Scaling the radius also changes the heat map because it doubles with each zoom level.

All of this processing is on the client side for the rendering of given data points.