Let’s explore in this blog post the journey from Geospatial 1.0 to 2.0.

We’ve provided definitions in previous articles. By way of review, two parallel geospatial universes have emerged. Geospatial 1.0 has a long history. 1.0 is map-centric, and built for two-dimensional, static data. That is, it provides a ‘what was’ or past view. A foundation. Geospatial 1.0 requires geospatial experts, and has been dominated by the GIS industry.

Geospatial 2.0 is new. It is being driven by new location-based data (IoT sensor data, LiDAR, customer behaviour etc), and new data analytical methods (notably artificial intelligence/machine learning). 2.0 is being built for multi-dimensional (2D, 3D) and dynamic data. Maps are one small part of a larger system of engagement. 2.0 provides ‘what is’ and ‘what will be’ perspectives. Automation is key. GIS is a small part of the 2.0 bigger whole.

Building the foundation ..

We live in a time of change. COVID-19 has disrupted the world. Business is adjusting to a ‘new normal’. Those organisations who most successfully navigate these new waters; survive and flourish, will be those who make the best use of data. The locational component of that data will be key.

Much has been made of the various dashboards published by both GIS and business intelligence (BI) providers. The image below show a COVID-19 related dashboard.

No alt text provided for this image

Image 1 – Geospatial 1.0: COVID-19 Dashboard

The COVID-19 data is historic. There is real value in these types of historic data summaries. They provide a base for understanding. And that is the promise of Geospatial 1.0; a data foundation or beginning.

So, using the COVID-19 dashboard as an example, what was the GIS process needed to build this foundation?

A GIS analyst needed first to find relevant 2D data-sets. Process, convert, publish that data. Bring together or combine these data-sets. Then build a web application (map-based dashboard) to visualize this data.

1.0 helps organizations assemble those large, foundational building blocks. Think of a bricks-and-mortar retailer. Many have put their focus on the point-of-sale: improving a consumers shopping experience with fast-check out etc. Many retailers still know little about me the customer: Where I live, my age, family size etc. Geospatial 1.0 helps build that foundational knowledge.

Retail Demographic Data

Image 2 – Geospatial 1.0: Mapping Retail Demographic Data

Geospatial 1.0 helps start the geospatial data journey. But 1.0 is backward looking. A geospatial analyst is required to assemble the pieces. That process takes time.

Though yesterday remains important. Thanks to COVID-19, what we can learn from yesterday has changed. Today 22% of office space in downtown Salt Lake City is occupied. Most of these folks are today working from home. That is a big change. In contrast, 77% of bars and restaurants have re-opened in downtown Salt Lake. Many rely on office workers. Office worker spending has moved from downtown to the suburbs. Will that remain true? Can retail businesses in downtown Salt Lake City survive? Will retailers in the suburbs benefit, and if so who?

These are questions currently without answers. But, the relevance of today (through these unpredictable, rapidly changing times) has never been greater. Geospatial 2.0 is focused on today (and tomorrow). It is dynamic, and fine grained for fast decision-making.

Insight-driven decision making ..

I regularly visit the Starbucks drive-in on Highland Drive in Cottonwood Heights. It’s Tuesday. It’s 12:30 pm. Outside its 100 degrees. I’ve just made an order through my Starbucks app, which I plan to pick-up.

No alt text provided for this image

What is relevant about the above paragraph to this article?

Data that is what. There are 5 data points in that paragraph: Day, time, outside temperature, order and location. This is data about me. It is not historic data, its about the now.

How might Starbucks use that data? Might they nudge me to influence my behaviour at that moment (“Here is a coupon Matt for $3 off a cooling milk shake”), maybe use it for future interactions or engagements, pool my data with others who use the drive-in on Highland Drive and use predictive analysis to help streamline the products offered over the day.

On the larger scale, today’s technology makes it far easier to gather data on customers current behaviour. And location is a key data component. Using the earlier example, if I’m no longer working out of an office in downtown Salt Lake. What are my new behaviours? Am I visiting new restaurants for lunch, ordering take-out? Is touch free important to me, so curb-side pick-up is a big deal?

Historical data will answer none of these questions.

Using AI to aggregate (static and dynamic) data, then analysing that data, leveraging the location component, and generating an outcome in real or near-real time is incredibly powerful. And that is the promise of Geospatial 2.0.

Geospatial 2.0 enables organizations to be insight-driven delivering immediate outcomes through a deep understanding of a person (or thing) and their behaviour.

Closing thoughts ..

Some call Geospatial 2.0 improved location intelligence (LI). I’ll admit to not being a fan of the LI term. It feels too much like GIS: somewhat meaningless. In a similar vein, most of those operating in the Geospatial 2.0 space (eg. data collectors/providers, AI specialists, end customers), have little understanding of the meaning of that term geospatial. Maybe Geospatial 2.0 is a convenient umbrella term, at least for now.

Geospatial 2.0 collects, and aggregates data both dynamic (eg. my current location and behaviour) and static (eg. what is around me). Processes that data and delivers an outcome (eg. milk shake coupon) through a system of engagement (eg. SMS message).

2.0 is a giant leap from 1.0. But that does not lessen the importance of 1.0.

The promise of 2.0 is to cross the chasm for geospatial: from the 1.0 public sector expert niche to use by all organizations.

Mouth watering possibilities indeed.

You can reach me at matt.sheehan@whyofthere.com

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