Essential Guide to Geospatial GIS

Time to step back and look at the big picture. Too often I watch folks dive into the details without a clear view (or understanding) of the foundation or 'big blocks'. Whether you are a geospatial GIS solution provider, solution user, or in search of a solution: This is for you.

In this first article, I will provide a high-level overview. My plan is to follow-up with more detail in future articles, possibly an upcoming webinar.

The Big Picture ..

Below is diagram which shows each of the core elements upon which geospatial GIS solutions are built:

The Core Elements ..

Let's walk through each of the individual pieces in the above diagram.

a) Raw Data

Data collection is in the midst of a revolution. We are moving away from time-consuming, too often inaccurate, manual data collection, to highly accurate automated data collection. LiDAR, IoT sensors and more are new large-scale, raw data collection methods which are helping to empower the geospatial GIS industry.

b) Data Access

Once raw data has been processed (corrected, changed etc), there need be a way to access that data. Web services and/or endpoints are popular ways to publish data; providing both public and private data access.

c) Data Discovery

Data can be thought of as the fuel powering the engine. In our case that is the solutions engine. But to solve any problem you need the right combination of data. Having access to data is one thing, but that is of little use if you cannot actually find the data you need. Today there are dramatically better ways to discover data. New tools providing ways to catalogue, organize and search. 

d) Data Visualization

Data-driven or data-informed are terms you will often bump into. These point to the use of data to derive information; upon which decisions are made. Data visualization is how data is combined (sometimes called data fusion) in a single interface. In the geospatial GIS world, that is often either a map-driven or dashboard-powered user-interface (UI). Data visualization is a key element of the decision-making process.  

e) Data Analytics

A deeper dive into data, as part of decision-making, may require data analytics. Great leaps are taking place in this field. GIS now provides a wide array of geospatial analytical services. Machine learning and artificial intelligence (which combines machine learning with deep learning) have helped revolutionize the field of analytics.

Next ..

There you have it. This first article, hopefully lays things out nicely. Next we will move into the details. Examples, or use cases, will help to truly paint the picture.

Read the next article in the series: Data: The Lifeblood of Geospatial GIS.

You can reach me at: [email protected]