M7: Google Earth Pro

 While the coursework for this certificate has focused primarily on ArcPro and ESRI technologies, several other GIS platforms exist, including Google Earth and Google Earth Pro, which are free to download or use with a student license (Google Earth Pro). The primary file type associated with Google Earth is KMZ files, while ArcPro utilizes .gdb, .atx, and aprx files. In order to display files created in ArcPro on Google Earth, they must be converted with geoprocessing tools to KMZ. 

I opened the Surface Water layer in ArcPro, used the symbology pane to customize symbology for the different water bodies, and then with the Layer to KMZ tool I exported a KMZ. Adding the KMZ file to Google Earth was very similar to opening a word document in a processor, and navigating around Google Earth was very similar to most navigation in ArcPro. Using the toolbars in Google I was able to add other layers for population dot density, as well as a vector layer showing counties and their borders. I appreciate that the layer drawing order can be determined by height relative to the ground. While this is possible in ArcPro I feel like it is mor intuitive in Google Earth. I also added a legend with image overlay. With 3D Buildings turned on I was able to add placemarks and create a tour of some of the populated areas in South Florida. This was occasionally difficult with the computer lag; I would turn off a data layer so it wouldn't show in the tour, but then the display would take so ling to process the data removal that the change wasn't clear except on the layer pane. 

I can see how Google Earth would be helpful in producing end products or proposals for third parties, perhaps stakeholders who are not well versed in GIS and want to see a spatial display that they can navigate easily without ArcPro. The ability to create tours would work well for examining watersheds, build sites, or research areas without worrying about accidentally changing the layer or view. 

Below is the map of South Florida I created in Google Earth, complete with legend. 

A map of South Florida showing population densities (red dots) and water bodies.

GIS Internship: Industry and Professional Highlights

 During the month of February we were asked to complete professional interviews and garner industry highlights to get additional views of the GIS industry and what job applications may look like using this technology. For my highlight I chose to focus on Natural Resource applications in GIS, specifically Forestry applications. While I am most familiar with marine GIS and marine remote sensing operations, forestry makes up one of the widest swaths of natural resource GIS users. I rarely have the opportunity to speak with individuals in this sector of the industry, and I especially appreciated the insights on how closely databases and systems can be tailored to individual uses. 

I came to GIS after already working in the marine biology field for several years, and my experience with the technology and its uses had - prior to this certificate - been almost exclusively marine applications. Though this has made me expressly familiar with my industry's standards for map making, layer inclusion, and common geoprocessing, it was fascinating to hear how the technology is customized for forestry applications. Some of the uses discussed in the interview such as databases to track the status and location of a single trees I could easily see having applications for marine GIS to track a single coral head or survey point. With artificial reefs we already do something similar, but I would be keen to implement some of the metadata options discussed in the interview. 

An additional point touched on in the interview was the disconnect sometimes faced between technicians and managers over when a numerical database like excel would be most appropriate, or when migrating to a geodatabase would be best for spatial components. This is a situation I have navigated myself as a professional in the field, and I believe it is best to have the tools for operating in both systems. As I gain more experience I feel that my ability to clearly describe the benefits of a spatial database has improved, as well as my ability to design accessible databases. These skills make it smoother when transitioning a team or introducing managers to the benefits of geodatabases. 

Some of the same reef locations displayed both in a geospatial database and in a traditional spreadsheet database. While both are able to display location, name, deployment date, and reef material, the geodatabase also provides at-a-glance location relative to landmarks and other reefs. 

LinkedIn

I am not encouraged to post publicly about some of the work I currently do, so I created this LinkedIn for my GIS certificate and experience. If my work situation changes, I plan to import these details into a LinkedIn with more complete career and identifying information. 

www.linkedin.com/in/emily-jack-gis 

M6: Isarithmic mapping

The focus of Cartography Module 6 was Isarithmic mapping. These are thematic maps where colors are used to express continuous values across an area, such as elevation or temperature. Creating isarithmic maps is a task I have done frequently when planning marine operations or displaying data gained from a sonar or magnetometer scan. 

This lab utilized data downloaded from US Department of Agriculture which displayed annual average precipitation for a 30 year span in Washington state. It makes sense to express this as an isarithmic map, however there are two primary options when determining symbology for the color gradient in an isarithmic map. 

Hypsometric Tinting (with classified values)

Hypsometric tinting with classified values assigns a specific color to individual values or a range of values in a DEM and results in hard or "stepped" delineations over an area. This is especially beneficial in situations where determination of individual values is desired, or value at a discrete point should be able to be estimated. This type of display is typically partnered with contour lines to delineate between different values. The most common type of shading on an isarithmic map, this symbology is easier to display if there is not an extremely wide range of values. For this lab switching between Continuous to classified Hypsometric was just a matter of changing the data classification and then structuring the classes and labels to reasonable parameters. 

For this lab the final product included a map showing the annual averages of rainfall in Washington State over the course of 30 years. Hypsometric tinting and contour lines create clear delineations between ranges of rainfall values, and the PRISM interpolation is explained within the map.

Continuous Tone

Continuous tone symbology displays a smooth color gradient between values in a DEM. While it may make it difficult to identify a discreet value within a gradient, this method works well for datasets with a wide range of values and in situations where the overall picture is the takeaway over any single value. Creating continuous tone symbology for this lab was fairly straightforward, with a hillshade calculation added to what was otherwise simply selecting and formatting the appropriate color ramp. 

This example of continuous tone symbology uses the same data shown above, but the smooth color gradient between values gives a broader view of rainfall patterns over the entire region. With continuous symbology the range of the total dataset is emphasized more than values within any one area. 

Interpolation and PRISM

Isarithmic maps must show continuous data, so often interpolation must be used to model the data between discrete observed points. In other courses I have discussed how DEMs may be used to interpolate elevation over an area like this. For climate and precipitation data in the United States the Parameter-elevation Regression on Independent Slopes Model (PRISM) is a widely accepted method of conducting interpolation. Originally developed by University of Oregon and then refined and expanded in collaboration with USDA Natural Resources Conservation Service, PRISM uses factors like face orientation, elevation, atmospheric layers, position, and proximity to mountains or coast to create a slope regression in each square of the DEM. For the data in this lab PRISM was given point data from several monitoring stations over the course of 30 years, and produced the continuous interpolated data housed on the USDA NRCS data portal.