![]() For example, this icon indicates that there are 6001 cameras within the area. Maps that use elements of this functionality are called smart maps., the cameras are grouped and displayed with icons. If you have multiple cameras covering the different areas on the smart map A map functionality that uses a geographic information system to visualize cameras, structures, and topographical elements of a surveillance system in geographically accurate, real-world imagery. If a camera is visible on multiple levels, only the first level specified is displayed, from the bottom and up. If the camera is placed somewhere inside a multistory building, an indication of the level of the camera appears: You may need to zoom out to get a better overview of the surroundings. When you select a search result, the smart map zooms in on the associated camera in its geographic location. While searching for video and related data on the Search tab, you can locate the cameras geographically in the preview area: For more information, see Geographic backgrounds (explained). One key benefit of a smart map is the spatial reference data behind-the-scenes. For example, you can preview footage from cameras at your sales office in Rome, then zoom out, pan across the world with a single drag, and then zoom in to the cameras in your office in Los Angeles. You can zoom out to see all of your locations in multiple cities, regions, countries and continents, and quickly go to each location to view video from the cameras. For more information about adding cameras to the smart map, see Adding, deleting, or editing cameras on smart map. You can find the stand-alone example right here: .Smart map displays alarms if they are triggered by a camera and if the camera is added to the smart map. What are you going to visualize? Let us know on Twitter or via sure to check out the complete source code of the example. Creating visualizations from large datasets just became a lot easier and hopefully, this article helped you get started! Loading data from Google Sheets and creating a Google Map with a deck.gl GeoJsonLayer on top is possible with just a few lines of code. Else, set the content to the data from the clicked power plants properties and open it at its location. When none is found, close any open info window. When the map is clicked, the overlay is checked for elements that can be picked at these coordinates. Map = new (document.getElementById('map'), In the script tag, a callback is defined which will create the map when the API is loaded: let map The Google Maps API is needed to create the map. Of course deck.gl needs to be added to the visualization: Īs a basis to render the data on, we create a Google Map. With the release of version 7 in April 2019, support for Google Maps was added and we’ll explore how easy it is to implement! This is great news for us as it promises fast render performance! At first, it didn’t work with Google Maps though. What is deck.gl?ĭeck.gl was published in 2016 and brought WebGL-based rendering to maps, using the graphics card in our device. Other methods such as using SVG as an overlay show similar performance problems with just a few hundreds of items already. Using the Data Overlay provided by the Google Maps API is not very performant with that many items to render. Visualizing that much data on Google Maps is not without its problems. There are almost 30.000 power plants in this dataset. No native rendering of large datasets on Google Maps Latitude and longitude will, of course, be used to place the dot on the map. ![]() The size of the dot will be defined by the capacity of the power plant. To show how power is produced, we will use the type of power plant to color a dot on the map. You can find the example sheet here and reuse it. We’ll focus on the data in the six columns you see below. This reduces the amount of data the visualization has to load. Get the dataįirst, grab the CSV data from the World Resources Institute and upload it to Google Sheets.Īs we won’t need all the data in the document, it can be slimmed down by removing unneeded columns. Read on to discover how we build this map.
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