I’ve been telling the Geospatial 2.0 story for some time now. Labels have been created. Ideas have been formed and evolved through feedback. Definitions tested. Use cases provided. Validation required. We’ve been through each of these steps. It’s been an interesting journey. My conclusion:
Geospatial 2.0 is real and rapidly evolving
Geospatial 2.0 represents a shift in focus from yesterday (what has happened), to today (what is happening now) and tomorrow (what could/will happen).
To complete this initial article series, there is one crucial area we need to cover; that is the user, and their (important) place in the Geospatial 2.0 universe. Specifically we need to discuss the System of Engagement/Execution.
System of Engagement/Execution ..
A System of Engagement (SoE) facilitates and orchestrates the user journey via seamless interactions across various touch-points.
The System of Engagement is simply the access point made available to users to get answers to their questions. Today there are available many user touch-points. The access point might be through desktop, web, mobile, or car dashboard based apps. The information/insight/communication shared via image, maps, dashboards, text, voice.
Built into the System of Engagement is the System of Execution. That is action driven by information/insight/communication from engagement. That might be:
- The closest maintenance crews sent out to repair a power outage from a real-time notification.
- A consumer acting on a Buy-2-Get-1-Free voucher while in-store sent to their iPhone.
- Reorientation of a retails store, based on the analysis of in-store customer location data, to ensure social distancing.
Geospatial 2.0 Use Case ..
As ever, use cases are the best way to paint the Geospatial 2.0 picture. Let’s briefly discuss a proprietary solution. Fire, and specifically fire risk, has been the focus of a forward thinking group within this organization. The customer question they were looking to answer:
How can we better predict high risk fire areas?
To answer this customer question, the group partnered with an external AI company, and adopted a Geospatial 2.0 approach and implementation. Let’s walk through the key elements of this implementation:
1 – Data, Aggregation and Analysis
Predicting high risk fire areas requires 4 core data sources: weather & climate, satellite imagery, topography, and proximity. To be useful that data needs first to be aggregated. That involves fusing these disparate data-sets. AI was used to automate this process. Next, the aggregated data is analysed, again using AI: high risk fire areas are defined, their boundaries marked.
2 – User Persona’s
From a computational perspective the hard work has been done, The correct data has been discovered, aggregated and high risk areas and boundaries defined. Next those who need this information need to be defined. That means User Persona’s. In this case there are 3 key persona’s: Managers who make the crucial decisions, supervisors who coordinate those who take action, and field crews who take action. Each requires a different system of engagement. And by the way, this is an action-oriented approach, mitigating the predicted risk we will not discuss in this article.
3 – System of Engagement/Execution
The final Geospatial 2.0 system of engagement involved 3 different system, based on user persona. Managers were given a desktop application which provided a range of decision-making tools (shown above). Supervisors were provided a focused web application, which enabled the alerting of crews closest to target areas, given a fire event. Lastly, field crews were given a mobile app which provided focused information on the fire, its boundaries and a feedback and communication mechanism.
This is a wonderful Geospatial 2.0 use case. In articles I try to remain independent where I can, that means not promoting companies unless there is good reason. That said, I am happy to share more information with you on this use case if you contact me directly.
You can reach me on: [email protected]