Google Earth - Computer Cartography

 Hello cartographers!

This will be the last post for this class, and I have thoroughly enjoyed my time here. I have learned a lot and refreshed my memory on so many concepts that I cannot wait to utilize the concepts in my current job.

For this week's lab, we explored Google Earth and how it can be used for data presentation. We were expected to learn how to convert layers in ArcGIS Pro into KML files, this was pretty straightforward and I also do this all of the time for project managers whenever they need to view property boundaries. The next thing we needed to know how to do was create and share a KMZ map that presented the data we converted into KMZ file format. The last thing we were expected to learn was creating and sharing a KMZ tour of  South Florida. I have never done this before, but it was fun to learn. I fly drones for my job, so when making this tour I tried my best to utilize the angles and shots I would normally get when flying in the field. Note that I said "tried" as it is definitely harder to do with a computer mouse. 

To create the map I am showing you below, we had to add the layer to ArcGIS Pro and select the symbology that relates most to water. We were then told to go to the analysis tab and search up the layer to KML tool. Once the layer was converted, I clicked on the file twice and it opened up to Google Earth Pro. We then needed to add the legend to Google Earth, so I went up to the "Add" tab and selected "Image Overlay". There I added the jpeg file of the legend and shaped the image to not be too large. I then clicked the "Save Image" button at the top of the page which inserted a scale and legend.

Here is how the map turned out:

Thanks for reading about this week's lab!

Isarithmic Mapping - Computer Cartography

 Hello cartographers!

This week's lab was pretty straightforward, but I managed to gain a lot of information from it. We were tasked with creating a precipitation map depicted with hypsometric tints, hillshade effect, and contours. We needed to achieve some learning outcomes to reach this map objective. To begin, we needed to understand the PRISM Interpolation method. PRISM stands for Parameter-elevation Relationships on Independent Slopes Model and it is a model that uses point data and DEM data or a 30-year climatological average to generate gridded estimates of monthly and annual precipitation and temperature. The data that was interpolated from PRISM is continuous raster data which required us to implement a continuous tone symbology using a precipitation color scheme. When formatting the map layouts, we needed to understand how to utilize legend properties to make map-appropriate legends. The next learning outcome section involved us needing to enable spatial analyst licenses. Doing this allowed us to be able to implement hypsometric symbology which is when you are symbolizing with different shades between contours. To be able to see these different shades, we needed to employ hillshade relief and utilize the Int Tool to convert floating raster values to integers. Next, we needed to know how to manually classify our data in the symbology pane to display in certain contour intervals. We needed to use the Contour List Tool to show these contour intervals, which allowed us to automatically create contours per the intervals we set.

Here is how my map turned out:

This lab was an amazing refresher for me and has introduced new data modeling methods that I can utilize in my career. As we are nearing the end of this course for the semester, my amazement at GIS continues to grow.

Thanks for reading this week!

Choropleth and Proportional Symbol Mapping - Computer Cartography

 Hello Cartographers!

This week's lab was for sure an interesting one. We were tasked with making a map that displays population density and the corresponding consumption of wine in each European country. To reach this final outcome, we had to reach these student learning outcomes: Choose an appropriate color scheme for a choropleth map, which entailed finding a color scheme that best represents the data we are trying to display and is color blind friendly. The next thing we were expected to gain from this map was creating an appropriate legend for the classification scheme and map type, which meant we had to consider how we wanted the legend to be presented and would be the easiest for the reader to understand. The next learning outcome was implementing an appropriate classification method for the population data. I chose quantile because I believe it best represents the population data being presented. After all, the map viewer needs to see that all classes are visibly represented. The next thing we had to do was utilize SQL Query language and query classes to manipulate data presentation. I was already familiar with this as I use this in my current job all of the time. But that basically means that if some data does not correlate well with the rest of the data we can exclude it from the overall analysis. The next few learning outcomes go hand in hand, we had to utilize proportional or graduated symbols and also learn how to create effective picture symbols. I went with the graduated symbols because it had the best overall display of the data. It didn’t block out the countries displaying population density like proportional did. I chose it because it also utilizes classes that can help people understand patterns and trends to make viewing the data easier. When it came to creating effective thematic picture symbols, I did not go this route, but I understood the concept. Basically, one way to effectively display wine consumption, you use different picture symbols of wine glasses or bottles at varying degrees of wine levels. A full wine bottle would mean a high amount of wine consumption, a near-empty bottle means that the country has very low wine consumption. It is definitely a creative way to approach this lab, but I am not very articulate so I went the boring route. The final outcome is the one we always try to achieve, compiling the map in accordance with cartographic design principles. I always make sure that my maps follow these principles.

Here is how my map turned out:

To reach this map outcome, here are the steps I took:

I added the data to the project, then right-clicked on the layer and selected the symbology pane. From there I assigned the primary symbology to be graduated colors, and then selected population density in the field section. The base classification method was natural breaks, but like I said above quantile was the best method of displaying the data. Once I completed that, I chose a blue color scheme because it shows the blue in different shades and the color is the most colorblind-friendly color. To make the data not distorted, I removed small countries that would not be visible at the scale the map is displayed in by excluding them in the advanced symbology tab. In that tab, I went to the data exclusion section and created an SQL query that included four countries too small to be viewed. Once complete, I copied the dataset and changed the primary symbology to graduated symbols. When I did that, the data displayed too many decimal places. So I went to the advanced symbology section, selected format labels, and changed the rounding of decimal places to 2. I also followed the same data exclusion steps from before to exclude the countries too small to be displayed. I then began formatting the map layout by having the main map in the center and then an inset map zooming in on the Baltics due to their close proximity to each other. One issue I had with the displaying of data was that the wine consumption points were being placed in weird locations. To combat this issue, I converted the polygons to points which allowed me to fix the placement of points to better display the data. To finish it out I converted the country names to annotations which allowed me to fix the position in which the labels were displayed. Once I completed the presentation of the data, I made sure to include all essential map elements.

This was definitely a cool map concept, and I now have a better outlook on how wine consumption is associated with population density.

Thanks for reading!

Data Classification - Computer Cartography

 Hello, fellow Cartographers!

This week's lab was definitely an interesting one as we got to explore different methods of data classification using census data from Miami-Dade County. We were expected to properly demonstrate the four common data classification methods: Natural Breaks, Equal Interval, Quantile, and Standard Deviation. When it came to presenting the data in layouts, we had to prepare a map with four data frames, symbolize the map for intuitive data acquisition, be able to properly implement cartographic design principles into our final maps, and then compare and contrast the classification methods that were used to find the best representation of spatial data for the specified audience (Miami-Dade County Commissioners). Not only did we have to do this once, but twice! We were also tasked with using the same classification methods but normalizing the data this time around and using a different field. In the first set, we analyzed the percent of the population age 65+ in census tracts, for the second set we analyzed actual population counts age 65+ but by normalizing the data into square mile regions. Once we completed that analysis, we had to compare and contrast the two map sets to decide which is the best data presentation method, and also identify which method is the best suited to present distribution data.

Now that you understand what the objective of this lab is, I will explain my maps and go into more detail about the different classification methods.

The first map I am about to show you is the percentage of the population 65+:

In the first square (top left) I am demonstrating the Natural Breaks classification. In case you don't know how Natural Breaks works, it takes numerical values of ranked data are examined to account for non-uniform distributions. This then results in unequal class width that has a varying frequency of data observations for each class. The second square (top right) is Equal Interval classification, which is the data range of each class being presented is held constant. This in turn gives an equal class width with a varying frequency of observations for each class. The third square (bottom left) is Quantile classification, this is when data observations are distributed equally across the class interval. This then results in unequal class widths but the same frequency of data observations for each class. The fourth square (bottom right) is Standard Deviation classification, this method is for normally distributed data. This means that class widths are defined using standard deviations from the mean of the data array. This then results in giving equal class widths and a varying frequency of data observations for each class. Looking at this map, in my opinion, the best classification method for displaying the percentage of the population 65+ is the Quantile classification method. I believe this because it equally distributes the data over the county and I believe it best displays the data by clearly showing where the more centralized areas of the senior citizen population are located.

For my second map, I used the same classification methods but used a different field to present the data. This time instead of a percentage, I used actual population counts of individuals age 65+. Unlike before, I normalized the data by census tract areas which are measured in square miles. Just in case you don't know what normalization is, it is the process of taking a count (population count) and dividing it by something else (square miles) to make a number more comparable or to put it in context. Here is how my map turned out:

You can tell that this map is very different compared to the first map. In another part of this assignment, we had to figure out which data presentation method would be best to present to the Miami-Dade County commisssioners regarding the distribution of senior citizens, and by looking at the second map it is clear which one is the better option. I believe that the population count normalized by area more accurately depicts the distribution. My reasoning for this is that the population count normalized actually counts every individual living in that area. The reason why percent above 65 doesn’t really work is because percentage growth doesn’t really represent the number of actual people living in an area. Think like this, if a census tract had around 100 people age 65+ in 2010, when the next census tract comes around the population has grown to 175 people. That is a 75% increase in the 65+ population, that may look good and all but it doesn’t mean a lot of people within that demographic live there. With that reasoning, using the population count normalized by area is the best way to present data to the Miami-Dade County Commissioners.

After completing this lab, I am looking forward to figuring out ways I can utilize these different classification methods for my full-time job. Thanks for reading!

Cartographic Design - Computer Cartography

 Hello fellow Cartographers!

In this week's lab, I explored cartographic design and learned how conceptualize and create maps according to the need of a user. During the lab we were tasked to learn how to establish and implement visual hierarchy that emphasizes important map features, and applying contrast that implies their importance. We also learned how to employ figure-ground to make important map features appear closer to the map reader, achieve map balance by utilizing empty map space for placement of map elements. When displaying our data for the lab, we needed to be sure we knew how to symbolize layers by category and when the data was displayed on a figure we needed to know how to utilize an inset map. Below is my map that resulted from learning outcomes we came to understand:

I designed my map in a way that follows some aspects of Gestalt's principles. In case you don't know what Gestalt's principle are, it is the principles/laws of human perception that describe how humans group similar elements, recognize patterns, and simplify complex images. In my case, one aspect of the principles I followed is the figure-ground relationship which you can observe in my map above. I established a figure-ground relationship by making the ward 7 target area a brighter color than the area outside of ward 7. By doing this, I made it seem that ward 7 was closer to the viewer and that emphasizes that this area is important. Another reason I designed my map in this way was by implementing visual hierarchy, which when symbols and map elements are ranked according to their importance. I implemented visual hierarchy on the map by putting an emphasis on the schools. I made sure they were the top layer, and made them into different colors and sizes depending on school type to emphasize the visual importance of each one. The next bit of visual hierarchy I implemented was for the title and legend. For the title I made sure it would be the first thing to catch your eyes by making it a size 24 font in bold. I made the legend title bold as well, and outlined it in a bold black line with a bright white background to draw attention to it. The last bit of visual hierarchy I implemented involved the roadways. I made sure the more important roadways were displayed above the less important roadways and ensured that the line width was displayed in accordance with the importance of the roadway.

The process I followed to achieve my final map outcome involved adding the geodatabase from our lab drive. Once I added the geodatabase to the project in the catalog pane, I added the data within the database to the main map frame. I then setup my symbology for each layer to each color you see above. I did this by finding the color schemes that contrast one another enough and display in a way that emphasizes the importance of each layer. The school dataset we utilized at first displayed all of the schools in Washington, D.C., so I needed to isolate the layer to only display schools within Ward 7 which is the target area. I did this by using the clip tool and created a new layer that only displayed the schools within Ward 7. Next we had to display the schools by the facility type, I did this by changing the symbology type from single layer to unique values. Once I did that, I selected the unique value I wanted to display called "FACUSE", this then displayed the symbology as three different types: elementary school, middle school, and high school. To differentiate between each value type, I organized the symbols by color and size. I also wanted to display the name of each school by a numerical value and including a table with the corresponding number and school name in the final map product. To achieve this, I created labels from the symbology and applied a custom callout to each symbol with their corresponding number and I ensured that the label displayed at the center of each point. Another layer we needed to include was the neighborhood clusters layer, but specifically the neighborhoods you see above. I isolated those neighborhoods by creating a definition query that included only those neighborhoods. One issue I encountered applying symbology occurred with my road layers. When I added an outline to the road layers it would show each intersection of roads and made the map very messy. To combat this issue, I used the merge tool for the Ward 7 roads to make them all one continuous road. This made it so that no more line breaks were being displayed. I applied this merge fix to each road layer I used. After completing the symbology customization I wanted to achieve, I copied each layer I wanted to include onto a new map frame for an inset map. Once that was complete, I added a new 8x11 layout and added both map frames to the layout and began formatting. Every map element you see was added via their corresponding tools, and a lot of effort was put into this map to reach what you see above.

This map was very enjoyable to make and I even utilized some of what I learned from it in my current job.

Typography - Computer Cartography

Hello fellow GIS users! 

This weeks lab was a very fun lab, as it brought me back into my creative thinking mindset. With my current job I make maps all of the time, but more often then not, they are boring maps. The learning outcome for this lab was that us students would have the ability to define and insert essential map elements, label our map in accordance with general typographic guidelines, and making sure we employ proper type placement for different feature types (i.e., point, lines, and polygons) This lab allowed me to mess with different color customizations and font styles to make a map in my own creative style. 

When it came to making labels for my map, I made sure to use the appropriate font style for the feature type. For example, the river labels on my map are italicized because that is the what is established with typographic guidelines. I also had to edit my river labels to be placed in a better position on my map, as a good majority of the labels were offset in weird locations. To fix them, I converted my river labels to annotations, and used the move tool to place them in the location I wanted to place them. Next I used the edit vertices tool to then have the labels move along the curves of the river and not overlap with the river layer itself. I also used the annotation tool for the swamp labels on the map, as that was required by the lab. 

Once we completed the first part of the lab, we got to customize our map as long as we included all of the rivers that you now see on my map, all of the cities included on my map, and two large swamps that are included on my map. That being said here are the three customizations I added to make my map my own:

The first customization I chose to do involved the color scheme for the counties, swamps, and background. I chose these colors for the counties and swamp because they both merged well together and the color for the counties doesn’t really distract from the main point of the map being that it showcases Florida's rivers and swamps, as well as major cities. I decided to go with that sky blue background because the white background color it originally was, made the map very bright and was pulling the attention away from what the map was trying to showcase. The second customization I did was changing the symbology of the major Florida cities. It was important to showcase that these cities are major seats of county governments so that is why I went with ESRI’s government building symbol, because originally it was just a plane old flame orange dot. The third customization I did was choosing the font style for the two swamps that were included on the map. Knowing that we should go with a serif font for natural areas, I decided to go with the Georgia font style. One issue I had with the text while using this font was that the text was merging with the county lines, as a result of that, I added a halo to the annotation to make the text using this font easier to see.

Overall, I really enjoyed this lab and look forward to the next one. Check out my map below!