Data: The Lifeblood of Geospatial GIS

In this, the second article in the Essential Guide to Geospatial GIS series, we will discuss data. That is: the fuel or lifeblood of any geospatial GIS solution.

I often use the analogy of the automobile when I write about data. That beautiful sedan you've been eyeing at your local car dealership would be useless to you, should you buy it, without fuel.

And the right fuel!

Let's discuss that too often overlooked source of any geospatial solution: data.

Data famine ..

Those of you my hairline, or hair colour, will remember a time when finding geospatial data was a detective exercise. Any data, let alone the right data, took an enormous amount of effort to find. Basemap raster or vector data, layers as overlays; incredible amounts of time needed to be spent in the late 90's and early 2000's in data discovery efforts.

And what if you could not find the data you needed?

Data collection could be a very painful process. For those with deep pockets that meant buying hand-held units. For the rest, a mixture of data collection methods were employed.

Manual data collection has its challenges.

A nod to Google ..

In 2005 Google maps was launched. Not only did that mean simple to use slippy maps, accessible over the web. It also meant data. Lots of data. High resolution satellite imagery with an increasing array of overlay data: restaurants, routing, images, on and on.

Open street map gave us a crowd-sourced highly accurate vector base-map.

KML gave all the ability to quickly and easily publish their own data.

From desktop to the web .. the second phase of the geospatial GIS revolution had truly begun.

From famine to feast ..

The popularity of mobile devices, starting around 2009, gave us new ways to collect data. For those in the pure GIS world, mobile apps like Esri's Collector for ArcGIS greatly simplified data collection. Data could be collected in the field, then directly uploaded into an organizations GIS for QA/QC and eventual production data inclusion.

But though made easier, manual data collection was still problematic. Limited in quantity, given how time-consuming it was to collect, and potential inaccuracy were two significant drawbacks. Data collected using mobile apps rely on the GPS built into the device. On smartphones these are accurate to within 10 meters (Note here, high accuracy GPS units can greatly improve this number when connected to smartphones).

Some data is better than no data, but this level of accuracy can be highly problematic.

Automated data collection is relatively new. It is creating a tsunami of highly accurate geospatial data. Though it comes with its own set of challenges, automated data collection is rapidly replacing manual data collection methods.

Two of the more popular methods:

LiDAR is an acronym for 'light detection and ranging'. Sensors mounted in aeroplanes, on drones or vehicles send out laser pulses which collect point data: x,y and z, on a massive scale. This so called point data is cm accurate, and can be used to build 3D representations of reality. So called digital reality.

IoT is an acronym for 'The Internet of Things'. IoT is our new sensor-based world. It provides the ability to report on the state of stationary or moving objects: people and things. That might mean that generator whose temperature is critical and needs monitoring in real-time, or the past (or present) movement of recently diagnosed folks with COVID-19.

We are now in the early stages of the second phase of the geospatial GIS revolution.

This is new phase is dramatically changing the geospatial GIS landscape.

From 2D to multi-dimensional data ..

From paper maps to digital geospatial data, many of us have long seen a static 2-dimensional world. GIS was designed for 2D static data. Why? Because until recently that was the only data available.

That has now changed.

Thanks to automated data collection we now see the world differently: dynamic and in 3D. Using Mats Henrikson phrase: digital reality with a pulse.

In conclusion ..

Data is the lifeblood of any geospatial GIS solution. We've moved from paucity to, as I have heard some describe, data overload. Much of that is thanks to new, automated ways to collect data.

The geospatial GIS revolution is being powered by this data tsunami. This new data provides potentially better ways to solve problems. That is key.

Does this mean the end of 2D data? How will this stream of new data impact the GIS industry? Is too much data now the problem? What new solution paths have opened up thanks to multi-dimensional data?

You'll need to keep reading this article series to get answers to these and many other questions.

Read the next article: Removing the blindfold: Geospatial GIS data access and discovery.

You can reach me at: [email protected]