Thursday, April 25, 2024
Computer Cartography - Module 6 - Isarithmic Mapping
Friday, April 19, 2024
Computer Cartography - Module 5 - Choropleth Mapping
This week's lab module for Computer Cartography 5007L focused on enhancing our understanding of the use of choropleth maps as well as proportional and graduated symbology.
A choropleth map is a type of thematic map that is used to present data in relative values such as percentages or rates per capita. Choropleth maps are not to be used when mapping raw data counts as this will result in a misleading representation of the data. For instance, if you wanted to map cases of disease infection by state using raw data totals in a choropleth map this would result in larger more populous states appearing as though they have the highest case rates which would only be a partial truth. If you made the same map with normalized data by dividing total cases by the population of each state your map would tell a different story with the large more populous states likely showing to be less impacted.
In this week's lab, we were tasked with creating a choropleth population density map of Europe with graduated or proportional symbols for wine consumption. This involved choosing an appropriate color ramp to present our population density data and choosing the right classification method to present it. For my map, I chose a sequential single hue 5-class orange ramp and my method of classification was natural breaks. I think the natural breaks method did an adequate job of maximizing class differences. I chose the sequential 5-class orange ramp scheme using Color Brewer ensuring that the colors would be colorblind safe. I chose graduated/range-graded symbols for my wine using a wine glass picture symbol for customization. I think my choice of wine glass could have been better as the color does not offer an adequate contrast to the population density colors.
I created my map using ArcGIS Pro and utilized SQL expressions to manipulate the data for optimal presentation in both my main and inset maps. I also converted my labels to annotation in both maps to appropriately place my labels. The most challenging part of creating this map was getting all the SQL expressions right and placing the labels appropriately. I thoroughly confused myself a few times but was able to regain my bearings.
Below is my final map after numerous revisions.
Friday, April 12, 2024
Computer Cartography - Module 4 - Data Classification
This week's lab module for Computer Cartography 5007L focused on understanding and comparing four different data classification methods. We were tasked with reviewing and preparing Miami-Dade County Census tract data and presenting it in two separate map compilations using ArcGis Pro. The first map showed the percentage of senior citizens in each census tract, while the second map showed the normalized count of senior citizens per square mile. Both maps used four classification methods: equal interval, quantile, standard deviation, and natural breaks. For my maps, I used a blue continuous choropleth scheme with the exception of the standard deviation maps. I originally had my initial classes as white however I changed this after reading in our textbook Cartography that this is not recommended. I found the layout to be a little bit more challenging than I anticipated but it was a great learning experience. The four classification schemes we followed are described below:
Equal Interval - The Equal Interval classification method divides attribute values into equal-sized ranges. While these ranges might be evenly spaced the number of records in the range or category can differ. This method is great for emphasizing the number of attributes relative to one another however, your data will not be distributed properly. This can lead to many features being in one class while in another class there might be none.
Quantile - The Quantile classification method divides classes into evenly filled ranges. This method is ideal for ordinal data with a clear ordering of variables. This method can be misleading because interval sizes can vary and similar values can end up in different classes. Also, widely differing features can end up in the same class. This distortion can be minimized by increasing the number of classes.
Standard Deviation - The Standard Deviation method shows the variation of the attribute values from the mean. This splits the values into above and below the mean value. The class breaks are created with equal value ranges from the mean value. These might be unevenly distributed but are not skewed toward either end. Standard Deviation can be advantageous because it uses all possible information however it can be disadvantageous if your data is not normally distributed.
Natural Breaks - The Natural Breaks method divides data into naturally occurring “breaks” found within a data set. This method seeks to minimize class variance and maximize variance between classes. This results in classes that are usually different from one another. This method can be advantageous because it can create a more accurate or unique class scheme for each map. A few disadvantages are that it is not ideal for data that has low variance and due to the data-specific classifications produced by the method comparing multiple maps with differing data is not useful.
The map below is my final product for the normalized senior citizen population age 65+ per square mile in Miami-Dade County, Florida. I believe the normalized population count best represents the data set because it more accurately depicts the distribution allowing the map reader to see that there is a concentration of seniors in the northeast portion of the county. The maps based on a percentage above age 65 contain some potentially misleading outliers that could distract the reader. The population count by normalized data does a better job at eliminating some of the outliers seen in the percent above 65 maps which leads to a better overall map that is easier to interpret especially for a novice map reader. I think the quantile method does a great job of presenting the data showing a clearer distribution than natural breaks which has an initial class that goes up to 864 in comparison to the quantile which only goes up to 440. For this reason, the quantile provides a better idea of the concentration of seniors and would likely be easier for novice map readers to interpret.
Overall, I found this lab to be very helpful in beginning to understand data classifications as they pertain to cartography. I have very little experience in this topic and hope to be able to continue to understand and grasp the concepts as I move forward.
Wednesday, April 3, 2024
Computer Cartography - Module 3 - Cartographic Design
This week's lab module for Computer Cartography 5007L continued to build upon the foundational principles of sound cartographic design. The focus of the exercise was to design a map of public schools in Ward 7, Washington D.C. We utilized Gestalt principles to make an aesthetically pleasing map. The main aim of Gestalt theory is to understand how humans visually perceive and organize individual components of a graphical images into a whole. We used concepts such as visual hierarchy, contrast, figure-ground, and balance. Below is my final map.
To create this map I used ArcGIS Pro and data provided by UWF. I employed several tools such as the clipping tool and SQL expressions to isolate the data that I wanted to present in the map. To establish visual hierarchy, I made my base layer for Ward 7 a pale yellow and surrounded it by grey to accentuate it. I also used three different-sized school symbols where the smallest represent elementary schools and the largest represent high schools.
To achieve contrast in my map I used colors like the red of my school symbols that contrast well against the pale yellow of my Ward 7 layer. The use of pale yellow in the Ward 7 layer creates a contrast with the surrounding grey color of the negative space. Similarly, I used a slightly lighter shade of grey for the inset map and legend, which makes them stand out from the darker grey.
To establish figure-ground relationship, I used lighter colors for important features and darker colors for the surrounding areas. This creates contrast and makes the important features appear closer to the reader therefore drawing the readers attention. I employed a similar design for my inset map.
In designing my map, I took care to ensure balance by placing the map elements in each quadrant where negative space occurred. However, due to the unusual shape of Ward 7, this proved to be more challenging than I anticipated. After using the measuring tool, I found that Ward 7 was almost the same length from North to South as it was from Southwest to Northeast. I debated between using a landscape or portrait layout but ultimately opted for landscape. I placed my inset in the northwest quadrant since there was enough space to make it the appropriate size. The legend, scale bar, and north arrow were placed in the southwest quadrant because it was the largest area and they fit well there. I placed the map title in the northeast quadrant after considering making a rectangle box across the top. I'm still not sure if I like it, but it fills the quadrant and maintains consistency. Lastly, I placed my name and data sources in the southwest quadrant because it was the smallest area and they have the least visual weight.
Applications in GIS - Module 6 - Suitability & Least Cost Analysis
In Module 6, we learned about Suitability and Least Cost Path Analysis. We were introduced to performing suitability analysis using both vec...
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Hello everyone! My name is Rick Schmidt and I was born and raised in northwestern Pennsylvania in a small town called Franklin. In 2011, my ...
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Hello everyone! My name is Rick Schmidt and I was born and raised in northwestern Pennsylvania in a small town called Franklin. In 2011, my ...
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This week's lab module for GIS 5027L Remote Sensing and Photo Interpretation focused on supervised vs unsupervised classification of ima...