M2 Python Environments & Flowcharts

 This week’s lab is the beginning of learning to find errors, run simple scripts, and put some longer scripts together. As a first step, we needed to simply use a script to print our name. Print in Python means just showing results. The second step was to figure out errors that were intentionally entered into a script. We needed to find the errors and correctly run the script. In step 3 for this assignment we had to create random numbers using a while loop and in step 4 use an if-else script to remove a chosen random number.

Writing a script is straightforward in that you type and hit run. However, the caveat to it all is “Does it work?”. It is quite frustrating at times when you look at so many characters on a screen and cannot errors. That said I look forward to the next week’s module. It sounds cliched but it is the only way to learn sometimes, jump in, and you will sink or swim. The first of two screenshots shows the results of scripts we had to fix random errors built into the script. This was a challenge and fun, it helped to enlarge the screen and font to better see any errors. included in this are also the random numbers that were generated and the removal of a chosen integer. Below this is the flowchart I created to help break down the steps. Doing this is helpful in visualizing what needs to be done with writing the script. It also is an asset in the creative thinking process.

M1 Python Environments & Flowcharts

 

This lab is about Python programming and is my first venture into the world of programming. The readings for this week tout how easy Python is as a programming language. Any endeavor into programming, writing script, or using syntax, in general, can seem daunting. Fortunately, Mr. Zandbergen’s words in Python Scripting for ArcGIS Pro are true so far. Putting the script together is deciding what to answer and ensuring the correct formula contains the correct words. There is a sequential order to things in IDLE with the programming language. This order is structured with pseudocode, flowcharts, and Python code. Creating a flowchart and using Python in IDLE is our first task with an input of print (‘Hello String”) and (“Hello World”). Very simple I know but it felt good. Implementing the code correctly can be confusing as well as interpreting the formula to enter it into flowchart format. Another thing to remember is how the shapes correspond to what task/step in the script is taking on. We created flowcharts as well in a program called Drawio, remember to save them as a png file. As I said it is easy enough but just make sure things are organized and placed correctly. I am on my way with programming. The first flowchart was saved with a transparent background.

M7 Google Earth

 

This week’s lab assignment was to create a tour of South Florida and its major cities on Google Earth (GE). First, we had to add a surface water layer into ArcGIS, symbolize the layer, choose the appropriate color, and then convert it to a KMZ file. For this action, we used the Layer to KML tool in the analysis toolbox kit. Afterward, we added the file as New Image Overlay into Google Earth. In doing this I learned that the sizing of the legend should be finished and correct up front because there is no going back when you begin to add new folders, create the tour, and save the map or at least that is my experience. I was unable to figure out how to change the size of the legend which seems too large for the map extent. Also, the colors were different in ArcGIS but were not differentiated in the same way for GE, although they are different in the legend. When setting up the tour I needed to play around with a proper location for a downtown area, not plopped on a corner staring directly at a traffic sign. Details!

Recording a tour in GE was really interesting. Putting everything together for this assignment was fun since I have never done anything like it. There are so many wonderful places in South Florida that could have been added to this tour but this is a good start. Google Earth is free and includes many layers that could be added to a map or tour. The great thing is files from ArcGIS can easily be added once in the correct format. Google Earth also has the ability to display buildings in 3D which creates a fascinating entry for a tour into a downtown area. This aspect makes it that much better and gives a great aerial perspective.

M6 Isarithmic Mapping

 This week’s lab is working with raster data and mapping the annual rainfall in Washington state with the end result of an Isarithmic map. These are maps of continuous data presented smoothly across a geographic region or area. They are primarily used for temperature or precipitation.

I used hypsometric tinting and a continuous tone to display the data. In ArcGIS, the analysis tool used was INT and Dynamic Range Adjustment for the raster layer to adjust color shading for better visibility. The data were classified into 10 classes with a default color scheme for precipitation. After adding all of the data to the map contours were added which aided in understanding the rainfall amounts if one is familiar with the topography of Washington. 

Oregon State University created the data we used for this lab using an analytical method called PRISM, Parameter-elevation Regressions on Independent Slopes Model. According to the National Geospatial Center of Excellence, “PRISM is an analytical model that uses point data and a digital elevation model (DEM) to generate gridded estimates of annual, monthly, and event-based climatic parameters.” The data was taken over a 30-year span from 1981-2010. It opened the door to a better way of studying and observing precipitation data.

M5 Choropleth and Proportional Symbol Mapping

This week’s lab assignment is about Choropleth and Proportional Symbol mapping of wine consumption and population density in European countries. It is a straightforward process but delving into the task of creating the map one must realize the importance of color schemes and establishing proper symbology. It has to be correct to convey the message to the audience properly and much thought needs to go into the process. It is important to consider the audience's ability to interpret the map results with the data and colors presented.
It was fun mapping a different continent. We were given the data to add to ArcGIS and allowed free will in completing this map which is a lot of responsibility. In my opinion, the biggest challenges as I stated were ensuring colors and symbols were ideal enough to deliver a message without overpowering the reader. 

I used the quantile method to map the data separating the classifications into five categories. The blue discrete color scheme for the five classes of population density was chosen because the blue shows balance and good contrast against the gray background. More importantly, it better represents the values of population density per square kilometer mapped. The blue discrete color shows a smooth gradation making it ideal for the population density. Beginning with the light color of a faint blue the scale moves toward a dark blue hue. Choosing to use five classes of population density is an amount that does not overwhelm the map reader and offers enough information about the displayed content. In mapping wine consumption of the European countries graduated symbols were a better option to display the differences in wine consumption. I used the linear arrangement as opposed to the nesting arrangement. 

Europe Albers Equal Area Conic is the projection used for the map. It is good for continental Europe because it maintains accurate area measurements and it keeps distortion to a minimum which helps normalize the data. It has a central meridian and two parallels and Europe is small enough for the accuracy of the area with the projection to hold true for the intent of this map. Although Europe has countries that spread out to the north and south it is still oriented mostly in an east-west fashion. This setup for the projection makes it ideal for the choropleth map because the enumeration areas are the country borders. The enumeration areas of the country borders make it where the data does not need to be normalized. 

This lab, like others, offers an insight into the psychology of map-making. What colors to use? What sizes of symbols for comparison? Is the message conveyed correctly? It is always a fun adventure.

M4 Data Classification

 

This week’s lab is about data classification. Using census data from Census.gov and UWF GIS we analyzed the data of the over-65 age group in Miami-Dade County in south Florida. The classification methods for performing this task are equal interval, quantile, standard deviation, and natural break (Jenks) data. After the data are added to the map ArcGIS Pro does all the work with classification breakdown in the symbology pane. Rainbow color schemes were not used and should not be used when classifying this type of data, they should be a low to high intensity (light to dark).

We created one map for each classification method with two different layouts for a total of eight individual classification method maps. It is interesting to see how the methods compare to each other for each map. The first set of maps, layout 1, was data for the census tract and not normalized. The colors used for each one aid in presenting the data and display how skewed the information can be conveyed in maps. It brings to mind Monmonier’s book How To Lie With Maps, it can be easy to do unknowingly, or purposefully if one does not understand the data and the methods used to classify it. 

In creating these maps I found the standard deviation best defines the data. Because it emphasizes the variations above and below the mean it is ideal for population density. It might prove to be a slight challenge to an audience unfamiliar with standard deviation and the mean but in this case, I believe it is the best choice when compared to the other data classification methods.

In map 2 we normalized the data by population per square mile. In the normalized maps each classification method might offer a possibility to be a final choice in presenting the data to an audience. However, this is where understanding the methods and how they represent the data on the maps is crucial. Admittedly I need to understand more about all of these methods by creating maps with other types of data. Standard deviation seems to be the best choice because of how data relates to the average, whether above or below. It offers better statistical analysis calculated through ArcGIS Pro which makes it easier to display and analyze.

M3 Cartographic Design

This week's module was about Gestalt principles and how to create a map keeping with the principles in place. It is not hard to create a map but it is a challenge to create one that adheres to the point of the map ensuring the point is made with balance. A map with colors that do not overwhelm and offer an invitation to look further into what is being conveyed in symbols and colors. I enjoyed this week's assignment because of how challenging it was, if for anything just picking the right colors. 

Figure Ground, Visual Hierarchy, and Contrast are three of Gestalt’s principles that are evident in this map. The public school symbols are smaller for the lower-level elementary schools and then get larger through junior high and high school. This is representative of the Figure Ground principle. The Visual Hierarchy principle is also implemented in the schools being shown at the front and on top of the University campus. As it says in the description of the figure the symbols reflect “intellectual hierarchy”. 

I made the colors and the symbol size of the schools different with a circle symbol. The size is smallest for the elementary increasing in size for the middle to the high school. Using different colors and sizes for the circles the visual hierarchy is evident that the focus of the map is on the schools in Ward 7 shown in the increasing sizes of the symbol of school grade levels. 

My map shows contrast with various colors representing the different elements of the map. The colors are not a variation of the same color but they offer a differentiation that is easy to follow for the different schools. The background of Washington, D.C. is grayed out making it easy to distinguish from the focus area of Ward 7. This contrast ensures immediate interest in the title and answers any questions about the point of the map.

The Figure-Ground relationship is evident in the colors of the school symbols. The grayed-out background is subdued enough to make the symbols stand out to the reader of the map. They are obviously different colors representing the schools in the area of Ward 7. While the river and parks offer balance to the neighborhoods and schools in Ward 7 they do not overwhelm the intent of the map, instead offering good figure-ground cohesion.

Balance was achieved in the final map design by adding and placing the feature layers in a specific order. Also making the colors blend in a gradient of gray or a shade similar to it. Also with certain layers, I made them transparent enough for the layer to be seen but not enough to become irrelevant to the map, i.e. parks and Ward 7. This offers balance when using certain colors to enhance the blend of colors used in the map. A subtle nudge towards a specific school may be ideal for a family with a school near a park. Having balance offers insight into what the point of the map is and also offers information about the area. It informs the reader of the whole picture presented in the map but also states the true point of the map, Ward 7 public schools.