Real-Time Data: Looking Beyond GIS

Here is an interesting fact: new Ford vehicles will come loaded with 1500 sensors. 1500! Incredible. There is almost nothing that will not be collected on the what and where of each Ford vehicle. And much of this data will be streamed in real-time.

Another fact. Radar can detect 1/10 mm movement in a slope from over 2 miles away. Amazing. Today there are ground-based radar sensors which are streaming data in real-time on the state of at risk slopes such is in mines and road embankments.

My point in sharing these facts?

We are seeing an explosion in the development and use of sensors. These devices are collecting and streaming a tsunami of real-time data: Welcome to the Internet of Things (IoT).

And every sensor, whether moving or stationary, has a location: Welcome to the world of geospatial.

How then do we visualize and analyse this new stream of real-time geospatial data?

The basics of real-time data visualization ..

The diagram below shows the typical architecture of a real-time data visualization and analytics solution:

On the left of the diagram are all the sensor feeds: imagine hundreds of Ford vehicles streaming data. That data needs to be collected in one place, aggregated, and often manipulated (cleaned, processed etc). From here the data can be visualized in a map-centric dashboard and analysed (that might be AI or other algorithms).

Real-time data: looking beyond GIS ...

The geospatial revolution is being driven by the availability of new data. That includes IoT sensor data. New innovative solutions are emerging. These use advances in technology to provide ways to visualize and analyse massive streams of real-time data.

Before we dig deeper, some context. Real-time or 4D data provides a snapshot of now. Primarily real-time data is of two types: moving (tracking a person or thing) and monitoring (current state). In the past most geospatial data has been 2D. Real-time and 3D data, made viewable over the web (this is important), are relatively new. But 2D, 3D and 4D do not live in isolation. As the diagram below suggests, often they are presented in combination.

As we will describe below, the viewing frame for real-time data is critical. Whether you combine 4D data with 2D or 3D depends on the problem you are looking to solve.

A quick note. In some cases the solutions below use data served up by a GIS, but none use a traditional GIS directly. From my experience to date, I have encountered a variety of challenges in the GIS world when it comes to real-time data. Thus this articles title: looking beyond GIS.

Real-time data use cases ..

In this use cases section we will show some solutions built around real-time data. We will be describing both 4D/2D and 4D/3D solutions.

1. Tracking Snow Plough's in Real-Time (2D/4D)

Many local and state authorities are equipping their snow ploughs with sensors. That allows vehicle tracking, and the public sharing of cleared and uncleared roads. This data helps authorities with planning, particularly during big storms. Sensors are also monitoring the state of the vehicles and resources. That includes salt levels, letting the drivers know when they need to refill.

Below we show a real-time app monitoring a fleet of 75 trucks. Location, average vehicle speeds are being monitored as are active (non-stationary) vehicles. This app also allows citizens to provide feedback, in near real-time, mapped directly from social media. This might be a tweet indicating a problem: unploughed road or snow plough caused damage.

2. Tracking the Path of Atmospheric Plumes (4D/3D)

Let's now switch to 4D/3D; in this case 3D digital abstraction, that is a 3D representation of reality. What might happen if a chemical weapon were released in a neighbourhood? Where might that poisonous cloud or plume move horizontally and vertically. How about a smoke plume, and monitoring its spread in real-time?

In the video demo below we show in a 3D landscape the spread of a plume.

Note, this demo was built using Luciad and M.App Enterprise.

3. Monitoring Slope Movement in Real-Time (4D/3D)

Radar is fascinating technology. Different to LiDAR or imagery, data familiar to me. As a mechanism for monitoring the smallest of movements, radar is hard to beat. As I mentioned at the beginning of this article: Radar can detect 1/10 mm movement in a slope from over 2 miles away. Bingham Canyon is the largest copper mine in the US. Radar technology from IDS Radar helped support the prediction of a massive slide at Bingham Canyon in 2013.

As shown in the diagram below, data collected by radar can be streamed in real-time for visualization and analysis. The red points in the image show the areas of most surface movement, while the green points indicate areas of stability. This is potentially life saving technology.

Note, this demo was built using M.App Enterprise.

4. Tracking the Movement of Buses in San Francisco (4D/3D)

Many public transit authorities have begun to explore bus tracking. Improving routing, scheduling and overall management have been key drivers. UDOT and UTA here in Utah have been conducting a fascinating experiment with bus locations, scheduling (on time or delayed) and controlling traffic lights. If a bus is behind schedule lights are being held at green as the bus approaches. This has resulted in a dramatic improvement in on-time arrivals.

Below is a video showing a real-time data visualization of bus movements in San Francisco. The frame of reference is 3D digital reality using LiDAR and mesh: A digital twin. Not only is the location of the bus being tracked, but users can get data from other sensors on the bus by simply mousing over the moving icon. Amazing.

Note, this demo was built using Luciad.

5. Monitoring Parking Availability in the city of Ghent (4D/2D)

Parking can be a major problem in large cities. Ghent in Belgium took an innovative approach to solving parking headaches. They implemented a smart parking solution. Through a mobile app, drivers can see in real-time parking availability across the city. Key features of the application include:

• Real-time updates on available parking spots in various locations around the city
• A routing tool, guiding users to selected parking areas
• Based on historic data, a user can receive information on the likelihood of a parking lot having the same, more, or less parking available for a given time of day. So predictive analysis.

See the application in action in the video below:

Note, this demo was built using M.App Enterprise and Xalt.

Parting thoughts ..

Real-time data combined with 2D or 3D data respectively, opens new perspectives and potential paths to solutions. Working with geospatial technologies which work seamlessly with any geospatial data, has vastly expanded my world as a geospatial solution provider.

Who said work isn't fun?

I'd be happy to tell you more. Reach me at [email protected]