As a final assignment at the end of my time with University of West Florida, I have built a GIS portfolio StoryMap. The final product is embedded in this page, but some of the best viewing is by following the link below to the storymap website.
I've greatly enjoyed my time as a student at UWF, and I love continuing to use my skills as a geospatial analyst and resource manager to grow our scientific foundations!
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.
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| A map of South Florida showing population densities (red dots) and water bodies. |
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.
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| 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. |
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| 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. |
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| 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. |
During the last several units we explored different types of maps and mapping criteria. In module 5 we took a deeper dive into the popular thematic map, a choropleth map. For the lab I was given a dataset with European population data as well as the wine consumption per liter for the same countries. Since there were two aspects of the data that needed to be compared, this map assignment required multiple types of data display.
The choropleth aspect of the map was the easiest, and most familiar to me at this point in the certificate course. I chose a natural looking color scheme with colors that went from pale yellow green to dark forest green. I felt this was a great way to make densely populated areas stand out while also lightening up areas that were sparsely populated. Since this is a choropleth map, typically raw population data would need to be normalized first to account for the difference in land size. However, by utilizing the pre-calculated population density class, there was no longer a need to further normalize the data, and that field could be mapped and colored through symbology without further calculations.
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| Choropleth map with graduated symbols for wine consumption. |
For the wine consumption we chose between proportional and graduated symbols. Though I preferred the information that can be gleaned from the map with proportional symbols, I ended up using graduated symbols since they allowed each country to be read as an individual item, rather than requiring that they be compared to other countries in order to get information. I toyed with standard deviation vs natural breaks for classification. The standard deviation is typically one that I am partial to for its mathematic origin, however in this case using standard deviation to classify the data resulted in a large emphasis of the outliers, and made it difficult to assess the average wine consumption between countries. Though natural breaks with five classes made the outliers no longer stand out, it did make it easier to read the map and view the average amount of wine across countries. I had some difficulty with my symbols and label positioning, though I believe I found where to change the label placement in properties, when I did attempt to change how the labels fit they didn't follow the rules I had made in settings. The way to overcome this would have been to make all labels annotations, and customize them to fit within countries and avoid symbols as needed. Unfortunately, I have limited use of my hands and right now fitting each annotation was beyond me.
During this lab I attempted to stick with natural color schemes for the choropleth and the sea, as well as the wine bottle symbology. I feel that the natural tones are most clear and personally easy to read and pleasing to look at. With more time and skill in graphic design I would have wanted to create much more interesting wine bottle symbols, as it was I imported these as image files from a clipart package.
For the fourth lab in Cartography the assignment was to display 2010 Miami Dade census data as a choropleth map with four different data classification methods. When we choose to use a color gradient, there are a few ways that the data can be broken up into different color ranges. The way that the data is classified into different ranges for the choropleth can considerably change how the data is interpreted, and the "main idea" of the map. This assignment provided a great comparison of the different classifications, and practice normalizing data for area or sample size - something that is crucial to make choropleth maps that are not misleading.
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| For the map I used the cartographic principles we had reviewed in other lessons to make the data stand out. |
The first map layout I created was one that looked at the percentage of seniors (65 and above) in census tracts. This layout was composed of four maps, one that classified the data with Natural Breaks, one with Quantile, one with Standard Deviation, and one with Equal Interval method. Though these are each mathematical ways for classifying the data, they were each already familiar to me with previous GIS work, and can be calculated as options in the symbology pane. I manipulated the color ramp and tone, as well as added a background to the layout to make my data stand out and be easy to read.
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| This map shows data that has been presented with a normalization, however it could have benefitted from additional class numbers; perhaps 8-10 instead of 5. |
The second map layout was structured the same as the first, but instead of a percentage this set of maps displayed senior citizens as counts of individuals, normalized (adjusted for) the size of the census tract they were in. This gives a better "big picture view" statistically speaking, for the distribution of seniors through the general population of Miami Dade county. For this map layout I was unsure if I could use a custom number of classes for the data, I feel that some of the classes were swallowed up a bit by only having 5 classes, with most of the maps appearing largely light blue or periwinkle. However, the instructions were not clear on whether or not I could add classes to improve the display. The normalized data showed a belt of areas in the middle of the county where there were higher concentrations of seniors, while the percentage data highlighted an outlier census tract, along with (generally) a similar belt. The least accurate in the displays was the use of Equal Interval for the percentage data, as it makes it appear seniors are only found in on census tract in Miami-Dade County.
In module 3 of cartography we continued our investigations and application of appropriate map techniques and the principles behind them. In this assignment we were given a packet of data spatially describing Ward 7 in Washington DC, and directed to create an aesthetically pleasing map using skills we learned from reading and lecture. Gestalt's Design Principles, visual hierarchy, and the mechanics of certain ArcPro labeling and scale tools were the forefront of this assignment.
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| For this assignment we created maps of Ward 7 public schools, with specific requirements for certain symbols and labels to display. |
One of the first things I did with my map was use the Clip tool to create small subsets of the road, school, and park shapefiles. I clipped these datasets to just the boundaries of Ward 7, which allowed me to focus detail in that area.
For the assignment we also needed to label one neighborhood in each of the seven neighborhood clusters within Ward 7. I knew I didn't want to keep the neighborhood cluster delineations on the map, because it would create too much distraction. I assigned a boundary-only symbology to the neighborhood clusters, then created unique-feature color symbology for the neighborhoods themselves. This allowed me to see each neighborhood border within the neighborhood cluster. By mapping the schools over both of these layers, I was able to make a list of the seven neighborhoods I wanted to label based on which ones seemed most relevant to school locations. Labelling just seven neighborhoods could have been accomplished a number of ways - from annotating, to SQL lines, to creating a new label class based on feature selection. For my map I decided to add an attribute column called "Name Selected" and only populate the column for neighborhoods I had picked to label. This wouldn't have worked for a larger dataset, but for this size it was handy to be able to manipulate the labels by selecting that new attribute.
After toying with color and symbology, the last challenge in the map creation was inserting text that followed the shape of the river for Anacostia River. During map creation I had located the Anacostia, created a new attribute column for naming it, and then used the River Polygon label defaults to label the river. This gave me the results I expected during most of my map work, but once I got to layout the labelling proved very difficult to control and did not always line up with the feature like I expected. This led me to use an annotation, where I went to Layout->Insert->Curved Text and traced the shape of the river before fixing the font and color for my new curved river label.
Overall this was a great exercise in map customization and aesthetics. I think I spent more time on this map than almost any of the maps in the certificate program so far.
One thing I wish I had done was put a shadow on the border of my Washington D.C. shape, since this would have added a new layer of visual hierarchy and helped better establish Feature Ground Relationship.
