Visualizations are a powerful tool for communicating results to end-users and stakeholders. Their development and life-cycle management are no less challenging than the underlying processes producing the results they communicate.

Databricks explains how this is helping with the COVID pandemic.

Our team overcomes these challenges by leveraging Vega-Lite to encode visualizations as JSON objects and using the MLflow model registry as a visualization registry. During this presentation, we will walk through the process of creating a multi-layered Vega-Lite visualization using COVID-19 and Geodata, then managing it with the MLFlow model registry.Visualizations are a powerful tool for communicating results to end-users and stakeholders. Their development and life-cycle management are no less challenging than the underlying processes producing the results they communicate. Our team overcomes these challenges by leveraging Vega-Lite to encode visualizations as JSON objects and using the MLflow model registry as a visualization registry. During this presentation, we will walk through the process of creating a multi-layered Vega-Lite visualization using COVID-19 and Geodata, then managing it with the MLFlow model registry.

Azure Maps Weather services (https://aka.ms/iotshow/AzureMapsWeatherService) add a new layer of real-time, location-aware information to Azure Maps portfolio of native Azure geospatial services.

Bringing Weather Services to Azure Maps means IoT developers have a simple means of integrating highly dynamic, real-time, historic and forecasted weather data and visualizations into their applications through their existing Azure subscriptions.

The Weather Service APIs for Azure Maps are brought to life in partnership with Accuweather. Weather is a critical factor for many scenarios—whether it’s to ensure the safety of mobile assets, model and forecast need for renewable energy, or predict risk and assess claims in the insurance industry.

Outi demonstrates how a few of these APIs can be used to enhance data visualizations with radar and infrared map overlays with an Azure Maps Web SDK, and how call APIs to make weather-based decisions with current and forecast based weather, as well as weather along route.

Geofencing has many practical applications. A geofence is a virtual boundary defining an area on a map. Using tools in Azure Maps, Jim demos how to test if a coordinate is inside or outside the Microsoft Redmond campus boundary.

It can be used to send an alert if an expensive piece of machinery leaves a construction site unexpectedly or to send a warning if a worker enters an unsafe location within a factory.

Jim Bennett shows us how to code geofencing applications with Visual Studio, Python, and Azure Maps.

Jim explains why and how to manage a buffer around your geofence boundary. (Hint: GPS is not that accurate!) He also demos how to set a notification using a web hook and an Azure Logic App when a person or item enters or leaves a geofence area.

Next steps:

Step through the tutorial: Set up a geofence by using Azure Maps

Visit Jim’s blog on geofencing: are you where you should be?

If you have large data sets that seem too big to map, then watch this IoT show to learn how to cluster data in Azure Maps so your users can rapidly extract insights from very large data sets.

Ricky Brundritt, Principal Technical Program Manager, Azure Maps, takes you on a historical journey from grid-based clustering to radius-based clustering. You’ll learn how the power of the open source community has contributed to the supercluster library which Azure Maps leverages extensively. Watch Ricky demo and step through Azure Maps code for clustering using large data sets of shipwrecks and earthquakes. Learn how to use cluster aggregates to perform calculations based on properties of the children of each cluster. Ricky wraps up with a demo visualizing clustered map data in the form of pie charts—again, to enable your users to extract insights quickly.

Access demo source code here: https://aka.ms/AzureMapsSamples

Learn how to use Azure Maps (aka.ms/MapsTechCommunity) to analyze a catchment area around a retail store. ShiSh

Shridhar, Principal PM in the Azure IoT Team , who spent 15 years on the Microsoft Industry Retail team, will demo how to build a catchment analysis for a café in downtown Seattle.

Learn how Azure Maps can pull in data on locations, competitors, traffic, and public transit through Microsoft partnerships with TomTom and Moovit.

Azure Maps also ingests data from any data provider including companies that offer human mobility data.

Watch the video below to see how a catchment analysis can help a retailer analyze business disruptors, decide where best to open a store, or identify opportunities for expanding business.

Related Resources

Here’s an interesting look on the use of AI and machine learning in the geospatial world.  Given the huge datasets found in remote sensing, it’s not surprising to see that field leading the way in cutting edge data analytics.

From a geospatial perspective, machine learning has long been in wide use. Remote sensing datasets have always been large, so the large data processing power of Machine Learning has been a natural fit. For example, processing satellite images using K Means or ISODATA clustering algorithms was one of the first uses of remote sensing software.

Ricky Brundritt, PM in the Azure Maps team, walks Olivier through data driven styling with Azure Maps. Data driven styling allows you to dynamically style layers at render time on the GPU using properties on your data. This provides huge performance benefits and allows large datasets to be rendered on the map. Data driven style expressions can greatly reduce the amount of code you would normally need to write and define this type of business logic using if-statements and monitoring map events.

Related links:

In this video, learn about the heat map and image layer visualizations in side of Azure Maps. Heat maps are used to represent the density of data using a range of colors. They are often used to show the data “hot spots” on a map and are great to help understand data. The heat map layer also supports weighted data points to help bring the most relevant information to the surface.

The image layer allows you to overlay georeferenced images on top of the map so that they move and scale as you pan and zoom the map. This is great for building floor plans, overlaying old maps, or imagery from a drone.

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