When life gives you data… make maps.

I’ve started to explore ArcGis and its capabilities. ArcMap is one the most important tools in the GIS professional’s toolkit. It can be intimidating and unintuitive to use at first exposure. That’s partly why I’m taking the initiative to supplement the lectures and labs at school by further familiarizing myself with the program and its capabilities. Below are a few maps I created while practicing.


I decided to go with an energy theme for this first map since I found a energy-themed site that provides plenty of data and shapefiles.


Having the right data is one of the most important parts of GIS work. When data doesn’t exist it might be necessary to create your own. Luckily the website linked above has an abundance of usable data. The map above features the biodiesel plants operating in the United States in 2015. This map was pretty simple to make. I used an ArcMap included basemap, added the data shapefile, added some inset maps to include the noncontiguous states, and added some simple design elements.

The next map required a more complex design process.


Projecting data in the correct coordinate system so it makes sense and is easily interpreted is one of the most critical elements of GIS. The data in the above map, provided by the Bureau of Land Management, used the GCS_1927 coordinate system and the projection had to be transformed to GCS_1983, which the basemap uses in this design. Failure to have matching coordinate system may result in misaligned and uninterpretable data.

I decided to try a more contrasting color scheme to make the color pop. Since the Bureau of Land Management primarily operates in the western United States I figured it would be appropriate to focus on just that area in the map. Some of the features are smaller and may not be visible at this scale. If I wanted to increase the legibility of the map and its data it might be useful to include inset maps of areas with a lot of symbology in close proximity.

An unfortunate characteristic of this basemap is the hard to see state lines which might be useful in the projection of this data. It might be worth creating custom basemaps so the gaudy credits, which can’t be removed, don’t appear in the bottom right of the map when using one of the provided basemaps in ArcMap.

The data was found at:


This next map I attempted something new. Sometimes the best way to learn is to experiment on the fly.


I looked for zip code shapefiles specifically for North Carolina and started to run into shapefiles you had to pay for so I decided to see if I could create some kind of work around.

To begin I imported the National Geographic basemap. Then I acquired to zip code data for the entire United States.


I then imported another shapefile that was specific to North Carolina. In this case it was a county subdivision shapefile that was easy to find and free. This was used as a guide to isolate just North Carolina from the entire United States dataset using the clipping feature. Once the basemap had been clipped. I imported the zip code data for the United States and only the zip codes in North Carolina were displayed.

GIS work can be computer intensive. The United States zip code shapefile had over 40,000 entries and even with an i7 processor it took several seconds to refresh all the features every time adjustments to the map were made.

After isolating the North Carolina zip codes I then wanted to give it that multicolored traditional map look where features are easily distinguished from each other. This was done by changing the symbology for the unique values (another huge processing task because it was changing 43,000 entries) and selecting a color ramp to had a large variety of colors.

The finishing touches were then applied. I couldn’t remove the border on the data frame for some reason which looked like a design disaster. Once I exported the map I loaded it into Microsoft paint for a little post-production where I removed the border.

These were great exercises and with each map that gets made I can feel my comfort with ArcGIS increasing.

The process is sometimes the most interesting part. For that reason, I’ve included a video of the creation of these three maps.

The full resolution maps can be found here:




Interpreting Google Takeout Location History Data

Everybody knows we’re officially in the future and being in the future comes with many perks. Depending on who you are, these perks could include having the largest GPS crowdsourcing initiative ever imagined. That’s the position Google finds themselves comfortably in. In the era of big data, GIS data is one of the biggest.

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Screenshot of the Google Maps layer that plots your historic locations

The proverbial Big Brother has formally arrived and brings with it some interesting concepts. The use of cell phones with GPS functionality allows big data giants like Google and Facebook to request location data from your device and build profiles like the layer seen above. Wifi connections can provide similar information that helps paint a precise location history.

Google makes this data available to the users of its services either in raw data from or viewable in a browser already affixed to a map as we see above.


Of course, this is all in the name of transparency. It’s not the end of the world if someone collects some personal information from you with the intent of giving you a more relavent advertising experience. It’s also not the end of the world if a users wishes to opt-out of this data collection. These services are the backbone to many luxuries including apps that find stolen or misplaced electronics. Microsoft incorporates a “Find My Device” service similar to Google’s “Device Locator”.


The link above is the portal for users to view the data Google collects and manage the settings about the collection. The old takeout.google.com redirects here now. The process is still the same. Creating an archive with the Takeout service will have a compressed archive sent directly to your email from Google. With this data we can do some interesting some fun presentations.

The different data metrics available for export directly from Google

The data isn’t just layed out in a notepad. Many of the metrics exist as .json files which can be tricky to explore and use for an inexperienced user. Luckily to task is made easly by the awesome apps on the following webpage.


Unfortunately it costs $69 to unlock its full functionality. We can, however, take a look at the free heatmap feature. This parses the .json file and plots the coordinates onto a map powered by Google Maps. My map is rather boring compared to what yours may look like.

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A heatmap visualization of the location history data provided by Google

The map achieves a satisfactory resolution at smaller scales.

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Imagine having the data for millions of people and what the heatmaps for entire populations would look like. This is the power of big data and harnessing this great power provides insights that are unimaginable.