Mapping out a future through spatial data

The term data tends to throw up mental images of sales reports or possibly more abstract information such as social media sentiment. While such information is useful, it is far from the only form of data available to organisations.

One key growth area is linking data to the physical world, and new technology has created the opportunity to use spatial data and to share it within organisations.

Rob Morrison, head of technology at GIS mapping software provider and spatial data analytics specialist Esri Ireland, said that spatial data has a variety of applications including the creation of sophisticated models called ‘digital twins’.

“One of the more interesting developments for me involves some of our utility customers and their efforts to create a virtual representation of their physical infrastructure by creating a digital twin of their network,” he said.

“By representing the detailed network connectivity between their assets, and modelling the operational characteristics and behaviour of the individual components that make up that network, they can plan and manage their infrastructure more efficiently, leading to optimised operations and ultimately improved service delivery.”

Others are automating spatial data by embracing the integration between Esri’s ArcGIS geographic information system (GIS) software and Microsoft Power Automate.

This means, he said, Esri Ireland’s customers are doing “everything from monitoring changes in spatial data and then triggering actions such as sending an email, to automating spatial analysis and data processing, to integrating ArcGIS with other applications such as SharePoint”.

Despite the fact that spatial data touches each of us, few outside the field think much about it, creating something of a disconnect about something so fundamental.

Geospatial professionals understand the potential to use spatial data as a source of information to deliver insights and ultimately value to their organisation, or externally to other organisations, or the public. However, knowledge among the public is starting to grow, Morrison said.

“I think, outside the world of geospatial professionals, there is probably less understanding, although that is changing. People are becoming more familiar with spatial data and are comfortable using it to solve simple everyday problems and make decisions.”

One of the reasons for this is the ubiquity of location-based technology driven by the smartphone and GPS.

“Almost everyone has a smartphone and is now familiar with using maps and using their location within apps to undertake all sorts of context based actions, like order a taxi or find the nearest Starbucks.

“I think organisations and industries that could be considered non-traditional users of geospatial data are waking up to the huge potential that spatial data can bring to their business,” he said.

The ongoing artificial intelligence (AI) revolution is also playing a role, as data is its foundational layer. As a result, using AI on spatial datasets to make data-driven decisions is becoming more and more common.

This has resulted in the field of GeoAI, which combines geospatial data with data science and technology to accelerate and automate workflows.

“GeoAI facilitates the analysis and processing of this data using data science and big data technology, via machine learning and deep learning techniques, to derive insight where humans would struggle,” Morrison said.

However, he noted that spatial datasets can be large and complex, particularly in the case of remotely-sensed multi-spectral imagery from satellites or drones and Lidar data, and processing this requires a lot of computational power.

As a result, Esri Ireland offers pre-trained deep learning models and spatial machine learning tools, for tasks such as detecting clusters and patterns and calculating change, as well as for object detection, feature extraction and semantic segmentation.

Ultimately, whether AI is used or not, the goal of spatial data-use is to derive insights from reality.

How you do that depends largely on your need, Morrison said, as well as the problem you are looking to solve and the spatial data you have access to. These will differ for, say, an environment agency, a police force, or an insurance company.

“Generally though, you can turn spatial data into actionable information by performing spatial analysis or geospatial analytics on the data. This allows [users] to solve complex location-oriented problems.

“It is the process of applying analytical techniques to the various input datasets to discover what relationships, patterns or trends exist, and to then extract meaningful information to make predictions or decisions from the results,” he said.