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Fire It Up: Heat Mapping for Broken Data Sources

Fire It Up: Heat Mapping for Broken Data Sources

Here we are. Another #MarcoMonday, another route on our Q+A quest.

Piggy backing on last week’s journey to finding those users responsible for the most broken Map Documents and Layer Files, let’s delve more into those datasets associated with broken Map Documents. For instance…

What are the most commonly referenced datasets in broken map documents?

To better answer this, we will need a lot of help from our friend Integrated Marco Desktop and maybe a teensy nudge from Integrated Marco Commander.

Fire It Up

Although all inventory-friendly applications within Integrated Marco Studio make it possible to determine this type of information, Integrated Marco Desktop is one of our favorites for finding this information quickly. Within this Add-In to ArcGIS Desktop, there lives a hot method for determining dataset and Map Document brokenness relationships – Heat Mapping.

With the help of the Heat Map from Inventory Log tool, you may create a heat map diagram based on the output generated by the Data Inventory Logger tool. The output shows the path to each dataset (both full and short), whether it is broken, and which Map Document or Layer File in which it is used. Those colors associated with each swatch help you to quickly identify their status as well, with orange tones associated with broken data and blues with working connections.

Honorable Mention – See the Data for the Trees

Is finding the specifics of each connection not on your To Do list? Rather just see a high-level view of what these counts look like inside of each container? Well, Integrated Marco Commander can give you hand with just that.

The marco tree tool may be run from the application, producing a spreadsheet of statistics on each level under the specified node. An example of this may be seen below.

This output provides details of brokenness and data types as they relate to individual nodes and locations. It is helpful in determining which containers require the most focus during a clean-up or audit – which is even more beneficial if you are looking to address a list longer than your own personal To Do list.

What are other ways you have used these applications to solve these dilemmas? Short of waving a wand and conjuring your own data dynamic magic, of course.

About This Series

In the coming weeks, we will explore ways in which to answer these common questions and get the most out of your spatial data. Want to jump ahead and tackle these for yourself? Follow the links below or view the cheatsheet here.

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