23 April, 2019

Store and visualise big data collected from Smart City technologies

Cities all over the world are striving to be smarter and more efficient in an effort to improve the quality of urban life.

The advancement of technology in cities has seen a surge in the amount of data collected and intelligence that can be derived from the data.

Cities are now deploying technology, such as IoT sensors on bins and parking bays, to detect activity and manage assets more effectively.

For example, bin sensors alert local councils when to empty the bin if it is detected as full or urban planners can provide a rationale for extra parking spaces where they can see congestion in certain areas.

However, the massive amounts of data collected in smart cities needs to be quickly analysed in order for organisations to react and make useful decisions.

So how do you efficiently store and visualise big data in a smart city context?

Use Google Cloud Platform to store, manage and analyse big data

Google Cloud Platform (GCP) is a collection of public cloud computing services, which includes storage, networking, big data, machine learning and the internet of things (IoT), as well as cloud management, security and developer tools.

GCP is serverless and therefore, removes operational overheads for organisations. Performance, scaling, availability, security and compliance is handled automatically in GCP so organisations can focus on programming instead of managing server clusters.

When it comes to the intelligence collected from smart city technologies, Google Cloud services such as Dataflow and BigQuery are a powerful partnership for the storage, management and analysis of big data.

Dataflow helps process data once it is collected and BigQuery acts as a data warehouse that connects to other cloud services and provides management and analytics of the data.

The ability to add your data in streams or batches to an existing query will mean you are always making your analysis on the latest available data. Moreover, the speed and power of BigQuery allows users to glean insights from their data quickly.

Read the Smart Parking success story from Google!

Visualise your data on a map

Maps are a great way to visualise your processed and queried data.

For example; Using Geoscape data, the largest built environment dataset for Australia, NGIS built a BigQuery and Google Maps tool that allows users to set criteria about building attributes such as size, height, roof area, council zoning.

The tool demonstrates the speed of BigQuery, visualises the results on a map and uses a hover tool to show the detailed building information and Google street view to the user.  

If you’re looking to leverage high quality aerial imagery in your map, NGIS can integrate your data with other products such as Nearmap.

NGIS recently integrated the data obtained from IoT sensors on parking spaces in Melbourne with CARTO and Nearmap.

Applying the real time parking data collected over the top of Nearmap’s current high resolution imagery provides great context for urban planners and car parking companies as they assess the rationale for additional parking spaces or garage’s in the city.

How do I get started?

At NGIS we focus on the integration of your data with the most relevant geospatial product for your project, creating you a bespoke mapping tool that helps you make sense of your data quickly.

Whether it is Google Cloud, Google Maps, Nearmap, Esri or CARTO, we can help find the right solution for you.

If you have a smart city project you want to discuss, get in touch

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