GIS Programming - Module 2 - Python Fundamentals

Module 2 for GIS Programming introduced us to the fundamentals of Python programming. This included but was not limited to using data types such as strings and lists, utilizing various methods and functions, and using for and loop structures.  

In the lab, we were assigned 4 tasks that built upon each other ultimately running a script for a dice game. Step 1 involved creating a variable for your full name using a string. This was followed by splitting the string into a list and then using indexing to print your last name.

Step 2 involved correcting two errors in a pre-written script for a dice game followed by Step 3 which required creating a loop that added 20 random numbers between 0 and 10 to a list. I began to struggle at this point but after several attempts, I successfully ran a script. After this, I was completely overwhelmed and burnt out. Unfortunately, I was unable to complete Step 4. Below is a screenshot depicting my outputs as well as a flowchart showing the basic steps and what I completed.

GIS Programming - Module 1 - Python Environments and Flowcharts

Module 1 for GIS Programming built upon the principles of Python we were introduced to in our reading and lecture content. This consisted of learning the basics of the Python programming language, learning to write pseudocode, create flowcharts, and learning the basics of how Python editors work. This was all very intimidating considering I have no experience in computer programming languages however after completing this lab the intimidation factor has decreased.

We began the module by running a python the Python script file CreateModFolders.py using the IDLE. Running this script resulted in the creation of the folder directory needed that will be used throughout this course. This saved a great amount of time compared to having to create this directory of folders manually.

The second portion of this module consisted of answering our process summary questions which included creating a flowchart that illustrates converting 3 radians to degrees followed by printing the result. The formula provided for this task was as follows:

          degrees = radians*180/pi 

I referred to Chapter 3 in Agarwal et al. to complete this task. Following the examples in the text I first created the pseudocode and then created the flowchart using draw.io since we used it in the examples we completed in our lecture. My results can be seen below.

We concluded the module by reading "The Zen of Python" by Tim Peters and writing a paragraph on what we thought it meant.  My personal thoughts after reading it were that it was apparent that aside from being functional Python code should be written in a manner that is easily interpreted, straight forward as possible, and should be consistent and implicit. I was hearing that the harmony of the language is important otherwise the value and reliability would be greatly diminished becoming difficult for others to utilize. I can appreciate this framework since I have worked in many different occupational fields, with each having their own "language". 

Overall, this was a great introduction and I'm looking forward to building upon the foundation of what I have now learned.

Computer Cartography - Module 7 - Google Earth

This week's lab module for Computer Cartography 5007L involved converting ArcGIS Pro feature classes to KML files, creating Google Earth maps, and recording Google Earth tours. 

To complete the tasks we utilized ArcGIS Pro and Google Earth Pro. We first converted some ArcGIS feature classes using the Layer to KML geoprocessing tool in ArcGIS Pro. This results in a KMZ file we could open in Google Earth Pro. For our map of South Florida, we now had data for population density and surface water features. Next, we added a legend image overlay. The following is my finished product.

In the second portion of the lab, we recorded a Google Earth Tour of South Florida. This involved creating some placemarks for the Miami metropolitan area and the major cities of South Florida. These placemarks served as the stops on the recorded fly-through video tour. One of the more interesting aspects of the tour is that the downtowns, especially of Miami and Tampa feature 3D renderings of many of the buildings which is really neat. I could definitely use some fine-tuning of my video recording controls, but with practice, I'm sure I can improve significantly. 

Computer Cartography - Module 6 - Isarithmic Mapping

This week's lab module for Computer Cartography 5007L introduced Isarithmic mapping. Isarithmic maps are another type of thematic map that is used to depict continuous and smooth geographic phenomena. There are two types of Israthmic maps, isometric and isopleth. Isometric maps are constructed with true point data in which data values are measured at a point location. Isopleth maps on the other hand are produced using conceptual point data which is collected over an area and presumed to be at point locations. Since Isarithmic maps depict continuous and smooth geographic phenomena throughout a region based on data collected at control points these maps use interpolation which is the process of determining data values for points or areas between those with a known data value.

For our lab, we utilized precipitation data for the State of Washington that was obtained and calculated using the PRISM (Parameter-elevation Relationships on Independent Slopes Model). This interpolation model leverages point data and an underlying grid, such as a Digital Elevation Model (DEM), to generate gridded estimates of monthly or annual precipitation. Our precipitation dataset was generated by applying this method to point data collected from weather monitoring stations and the calculated climate elevation regression for each grid cell within the DEM. The dataset takes into account physiographic factors that may influence climate patterns.

For our first map, we created an average precipitation map for the state of Washington using continuous tones. Our continuous tone map shows a smooth transition for all values and each point is shaded with a hue proportional to the data value at that point. In our second map, we used hypsometric tint by using the Int Spatial Analyst tool and contour lines using the Contour List Spatial Analyst tool. The Hypsometric Tint creates color-scaled areas between the contour lines that enhance the map viewer's ability to visualize a 3D surface. The lighter colors are associated with lower values and the darker colors with higher values. The raster data is separated into bands using a range of values that have upper and lower limits which produces a visual that clearly depicts the changes in data.

Below is my final map utilizing hypsometric tint and contoured overlay.

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.

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.

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.