Make Living Atlas Content Your Own

Customize ready-to-use content quickly and easily

The way we think about data within GIS is changing. 

You used to open an empty map and then leave the map to go find raw data from an FTP site somewhere. Then you would spend hours processing it into the format you needed before you could ever start making a map.

Data used to be something that only lived on our local computer storage and had to be mapped on paper to share. And while workflows like this are still incredibly important, the internet has enabled us to map and share spatial information like never before. Instead of starting with spreadsheets or raw data, the  ArcGIS Living Atlas of the World  allows you to start with useful layers that can be customized for our mapping and analysis needs.

Within your existing or new projects, Living Atlas content makes the map-making process easier and can quickly make your maps more powerful. Spend less time working with raw data by using layers that are easy to add and adapt into what you need.

Let’s explore an example in ArcGIS Online. 

I am interested in learning more about the agricultural industry and wages in the U.S. Particularly, those who are working in the fields (as opposed to managerial, equipment, and other agriculture jobs). With no preconceptions of the industry and no data of my own, this is where Living Atlas layers save me an immense amount of time and money.

Photo credit: Karsten Würth - Unsplash images

We will start in a map in ArcGIS Online with nothing but an idea of what we want to learn. This example will step through the major steps to create a unique map using Living Atlas content.

I like to begin making my maps with the Light Gray Canvas basemap. It is easy to explore new data patterns with such a neutral background.

I'll start the exploration process by going into Add > Browse Living Atlas layers

I want to see where crops exist in the U.S., so I use the search phrase "usa crops".

There is a layer called USA Cropland, and when I click on the title, the details for the item appear. The details explain that this layer is authoritative and the data is sourced by the U.S. Department of Agriculture (USDA).

I see that this layer is managed by the user Esri.

I'll add this layer to my map.

Note that there is a symbol that says "Subscriber". This means that you need to be signed into ArcGIS Online with an organizational account to use this layer. To share a map using this layer, check out  this blog .

I type in the phrase "farming" to look for layers related to farming jobs.

I explore the first result: "Farming, Fishing, and Forestry Employment and Wages"

Scrolling down in the description, I learn that this data is sourced from the Bureau of Labor Statistics (BLS). The owner of this item took the time to document and process raw BLS data and turn it into a usable layer for the GIS community.

I add this layer to my map as well.

By just looking at the layers added to my map, I can see visually interesting patterns emerging in the Midwest and West.

However, I want to go further than just overlaying these two layers on top of each other. So now I'll start to customize these layers around my original question: "What is agricultural industry like in the US?"

I'll start by removing the top two state and nation layers, because I am only interested in smaller geographies.

I'll also disable the time animation of the USA Cropland layer.

The legend for the crops layer is broken into the various types of crops, but I am only interested in the general view of where crops exist in the U.S.

Since this is an imagery layer, I can customize the cartography for this layer by going into the Image Display settings.

With just a few changes, I can highlight all crops by showing data values greater than 0. This appears as <= 1 in the classification.

I set the crop color to my own custom green. In this example, I use hex value #91B861 for crops and make all non-crops transparent.

I'll apply my changes and close the image display settings to go back to the Table of Contents.

Looking at the layer with BLS employment data, I see that the current attribute being shown is the total count of employees within farming, fishing, and forestry occupations.

I rename the layer to represent the geography being shown (metro and non-metro areas) and I go into the Change Style settings to explore what other attributes are available in the layer. The way we change the cartography is different than the crops layer because this is vector-based data rather than raster/imagery.

Scrolling through the attributes, I find variables about the median annual wage for various different farming, fishing, and forestry occupations.

Instead of mapping the median wages of all farming, fishing, and forestry, I want to focus on the subset of workers who are out in the fields, actually working in the crops.

I choose the "median annual wage of farmworkers and laborers, crop, nursery, and greenhouse" attribute.

Let's add a second attribute to also see the quantity of farm workers who are out in the crops.

The total workers attribute is "Farmworkers and laborers, crop, nursery, and greenhouse".

Within the color settings, I want to highlight which areas have wages below the average. I select the Above and Below theme in order to easily show this on the map with a diverging color ramp.

To highlight the pattern better, I change the ramp to a red-to-blue so that the red areas highlight the lowest wages for farm workers.

I round the numbers to clean up the legend.

Next, I go into the size settings and notice the map is being driven by outliers in the data (values much larger than the majority of the data).

I change the numbers used for size, which helps bring out the pattern in the data. The areas with more farm workers start to appear more obvious in the map.

To add additional context, I make the metro/non-metro area polygon outlines slightly more visible in order to see which areas they cover in relation to the crops.

I'll also add some overall transparency to allow the map reader to see the crops layer through the circles better.

Visually, my map is starting to tell a more interesting story, but I want to make one final customization to really help the data pattern show.

I make the water more blue by using a new hex value of #aab0b5 and I slightly alter the land to hex value #dadbd9.

These two small changes will transform the overall feel of the map.

Let's see how they look!

With some curiosity and small cartographic changes, the ready-to-use Living Atlas layers have transformed into a completely new map of farm workers and the crops they work in.

Clicking Save at the top will save this map into My Content and this is now a web map I can use and share.

Immediately, we see some interesting patterns in the map. We see that not all farming jobs are equal in the U.S.

Large, crop-diverse fields, manual labor dominated jobs in the West employing a vast low-wage workforce. Heartland areas are mostly low employment, large scale mechanized, higher paying jobs. The South and East have much more complicated crop types along with state and local regulations on labor. There are some interesting higher paying jobs in the West that are typically associated with the wine growing areas.

Another way this map was customized was the pop-up. The language and chart were adjusted in order to cater to the new story being told in the map:

To explore the map further, click  here .

This simple example shows us how to transform the way we make our maps using Living Atlas. We started with no data and a question about the world and ended with a beautiful map that gives us trustworthy insight. This new workflow for GIS removes the hours or days that were normally spent processing data just to get it to be usable. Now you can quickly find usable information in hosted layers that help you make your maps faster than ever.

Contribute Your Content

You can also nominate your own content to Living Atlas so that it can be found by the GIS community. To nominate your layers, maps, and apps, check out the resources below:

If you have map or layers that routinely update, the LEARN lesson below provides information about how to automatically update your services with Python.

Learn More about Living Atlas

You can follow what's new with Living Atlas to see new content and questions others have about using it. Check out the resources below:

Social Media:  Twitter , Esri Community