Introduction:
Living Atlas is a tool created by ESRI that allows access to an extensive library of geo-spatial data. This data can easily be added to maps in ArcGIS pro using the catalog tab. In this lab we learned how to easily find content within the living atlas, how to view the data online, how to import the data into ArcGIS pro, how to adjust settings to display data from specific times, how to view charts and graphs of the data, how to configure the data in the most optimal way, and how to use the data to create proper layout maps. This tutorial is relevant to unmanned aerial systems because I believe it would be extremely useful in some cases to supplement data collected by UAS with already existing data. It would also allow one to supplement UAS data with other information in real time.
5 lessons that are of interest:
This tutorial involves working with global pollution data. The purpose of the tutorial is to find differences in pollution patterns by relating them to space and time. By the end of the tutorial one would be able to find areas with extreme or abnormal pollution patterns. This lesson is interesting to me because it is related to environmental sustainability. 2. https://learn.arcgis.com/en/paths/combating-crime-with-gis/
This tutorial involves tracking incidents related to prescription drug abuse, graffiti, and card fraud. This data is then used in a way that assists law enforcement in the future. This tutorial is interesting to me because I wouldn't normally expect geo-spatial data to be used in order to stop crime.
This lesson teaches one basics about lidar data, how to create a LAS datasheet, how to take measurements using LAS datasheets, creating mosaic data-sets, creating hill shades, and exporting contours. I think that this tutorial is extremely relevant to UAS because lidar data is used in so many UAS applications.
This lesson teaches you how to map ocean temperatures, build prediction surfaces, and compare cross validation errors to determine which prediction surfaces are the most accurate. The objective is to use this data to find fish in the Bering Sea. I find this tutorial interesting because I enjoy applications of GIS data that relate to wildlife and sustainability.
This lesson walks you through several machine learning applications involving alternate climate zones, using deep learning to assess tree health, and using AI to downscale climate data. This tutorial is interesting to me because I am curious how artificial intelligence can be used with GIS data.
Methods Part 1: Exploring the Living Atlas Website
During this portion of the lab we learned how to filter through the living atlas website in order to locate specific data-sets. We use filters in order to find an item named "GLDAS Soil Moisture 2000 – Present". Figure 1 shows how this data-set looks within the online map viewer.
(Figure 1)
We then dive deeper into the information presented using the living atlas application. First, we locate a specific location using coordinates and find the Grand Ethiopia Renaissance Dam. Once this location is selected we can use the app in order to showcase different graphs displaying data about this specific location. Figure 2 below is an example of one of the graphs, and it specifically displays the soil moisture in the area.
(Figure 2)
The first step in this portion of the lab is to create a base map. In this case we use the light gray canvas as the base. Next, we browse living atlas to find a layer titled "USA NLCD Land Cover". Once this layer is added we focus on Las Vegas, Nevada. This layer can be seen in figure 3 below. We then use the time slider in order to view data collected from different years and observe how the data changes.We then use a renderer in order to only display data for developed land, and then we adjust the symbology of the data.
(Figure 3)
Methods Part 3: Use Living Atlas in ArcGIS Pro
For this portion of the lab we use an existing data-set that contains information about hurricane Irma. We download this data and then import it into ArcGIS pro. We then use the portal in the catalog pane in order to add the data to our map. Once the data is added to the map several layers become available as shown in figure 4. We then use this data to perform analysis in the Geo-Processing pane. Using a forecasting cone we find that 3,523 nursing homes that may be at risk from this hurricane.
(Figure 4)
The first map I created shown in Figure 5 displays minor weather events and othe minor advisories in the united states in real time. The data-set used to create this map had several other layers that included more severe conditions, but I chose to create a map that only displayed minor events.
(Figure 5)
The second map shown below in figure 6 displays the crime index in the state of South Carolina. The crime index is displayed using shades of red to represent each county's crime rate relative to the national average. Since this data-set is organized by county I chose to showcase an individual state, and South Carolina displayed a diverse spectrum.
(Figure 6)
(Figure 7)
The fourth map I created shows the density of roads within the United States. The lighter the color displayed on the map the more roads can be found within that area.
(Figure 8)
The final map shown below showcases the risk for Earthquakes within the United States. It ranks areas on a scale of 0-100; 0 being the lowest risk for earthquakes and 100 being the highest. I made this layer semi-transparent so state borders can still be observed.
Conclusion:
Living Atlas is an extremely useful library of data that can be used within ArcGIS pro. Living Atlas can be used to display data in real time as well as data from the past. This large collection of data would be extremely useful if it is used in conjunction with data collected by UAS.
