Wednesday, May 29, 2019

GIS Programming - Module 2 Lab

Python 3.6.6 |Anaconda, Inc.| (default, Jun 28 2018, 11:27:44) [MSC v.1900 64 bit (AMD64)] on win32

>>> print ("Hello Everyone!") 

Hello Everyone!


This week’s lab was all about learning the basics of Python! For this week’s lab, I had three separate coding exercises. For the first exercise, I was tasked with printing my last name from a created list. For the second exercise, I had to edit the given script for a dice game with a handful of errors that would prevent the script from running and import a specific Python module. Finally, for the last exercise, I was tasked with creating a script that creates a list of twenty random integers between 0 and 10 and then select and remove every occurrence of a specific integer and print the newly updated list. As you can see below these were the basic flowcharts I created to help me construct my scripts for parts one and three as they were the scripts I needed to write myself and not edit.



After writing and editing my scripts, these were the results.



As you can see the script successfully printed my last name, the prewritten script that I edited and corrected errors ran for the dice game, and it created the list of twenty random integers, selected the unwanted integer, and removed all instances of the integer and printed an updated list. I really enjoyed this week’s lab as it brings back really good memories of when I first started Python, and I look forward to sharing my future progress with you!

~ Code On!

Monday, May 20, 2019

GIS Programming - Module 1 Lab


Python 3.6.6 |Anaconda, Inc.| (default, Jun 28 2018, 11:27:44) [MSC v.1900 64 bit (AMD64)] on win32

>>> print ("Hello Everyone!") 

Hello Everyone!

Well, I can officially say that I have completed my first ever semester of Graduate school and I am loving it! To kick things off for this Summer semester, I am taking a GIS Programming class that will focus heavily on using Python (I just could not resist having a specialized intro for this blog series!) to create various GIS processes. For this weeks lab, I focused on primarily learning about the history of Python and some coding basics that include how to map out our code using flowcharts and pseudo code. I was also tasked with creating the required folder directories on my machine to work through the class from a provided script. To run this script, I had to use Spyder which is the Python interface/editor I will use primarily for this class. This was achieved by simply typing in the word 'spyder' into the Python command prompt. I then opened the script and examined each line to get an idea of what the script would do. Finally, I ran the script and it created my folder directories (seen below).
As you can see, the script I was provided created a series of directories for each module of the class. Since the class is 8 weeks in length, there are 8 modules in total. Within each module folder, there are three subfolders (Data, Results, and Scripts). I am so excited to be taking this class as I can continue to work with my preexisting Python skills and develop them further. I look forward to sharing my progress with you all!

Thursday, May 2, 2019

GIS 5007 - Final Project

Hello Everyone!

It's so hard to believe that my Cartography class is coming to an end, I have learned SO much this semester here at UWF. I never thought I would leave saying Adobe Illustrator is a Life Saver. For my final project, I was tasked to create a map of US national average cumulative SAT scores and participation percentages (seen below):



For this project, a bivariate map using choropleth and graduated symbols was created.  A choropleth map was used to depict the average SAT scores because it excels at emphasizing class-based data where phenomena are grouped together for a means of comparison (Slocum, Thematic Cartography and Geovisualization, 2009).  The comparison of state cumulative average scores would best be seen by the intended audience using a color ramp from red (lower scores) to green (higher scores).  The SAT score data was classified into seven classes to best represent the variance in scores across the nation and show more distinction than five classes.  For this method, equal interval classification was used, because it makes the thematic data easier for the intended audience to understand (Slocum, Thematic Cartography and Geovisualization, 2009).  Since this data pertains to average scores per state, the data was not standardized.
The second thematic method used was graduated symbols to summarize the state participation percentage.  Graduated symbols were used because they best show the change in quantity and the magnitude of participants taking the SAT.  This data was broken down into four manual classes ranging from two to one hundred percent.  This was done because it was the easiest method that would be understandable by the public.  Again, this data was not standardized since the flat percentages were given for each state.  While other more advanced methods of data classification such as natural breaks, quantile, or standard deviation methods could have been chosen, due to the research specifications and public audience, data simplicity was the primary goal.
            To best capture the data in question, the map was created in a portrait view to fit the entire continental United States.  The importance of this orientation was also that the state of Alaska and Hawaii could be fully represented.  It was also decided that the map should be grouped and labeled together by relatively loose regions, the southeastern/northeastern states, the Midwest states, the mountain states, and the Pacific states (Alaska and Hawaii included but not to scale).  An inset map was also included to show the Washington D.C. area that would be obstructed from a normal perspective.  To emphasize the regions, a drop shadow effect was used to draw the viewer's eye to the various regions and make the map have a pop out effect.  In addition to utilizing drop shadow, visual hierarchy rules were implemented.  First, the choropleth and graduated symbols were given the only color aside from the legend elements as these two components were the most important.  Second, the title was clearly visible and concise, but all other text was reduced in size to not distract the viewer from the map.  Finally, any elements that were not of visual importance such as cartographer information, data sources, and north arrows were minimize either in size or opacity.
The results of this project were very interesting, the results show an inverse correlation between the two variables with less participation yielding higher test scores with the highest average scores in North Dakota with the lowest participation (2%) and the lowest scores in D.C. with the highest participation (100%). It just goes to show that using the SAT alone does not give you an accurate portrayal of statewide data with potential unrepresented groups.

Sunday, April 7, 2019

GIS 50007L - Module 11: 3D Mapping

Hello Everyone! 

It's hard to believe that this is the second to last module in my Cartography class! This weeks lab was all about 3D mapping. For this lab, I was lucky to take a 3D mapping class from ESRI to practice my 3D mapping skills. For this educational class, I had 3 main exercises. First, I created a map of crater link with a linked view of a 2D map and a 3D scene. When I zoom or navigate in one of the panes, it zooms and navigates in the other. Second, I created a 3D scene of downtown San Diego to find a suitable hotel with an ocean view and shade for a convention being held there. Finally, I made a 3D scene of San Diego with realistic 3D building textures and realistic trees. I also included two additional layouts, one of the convention center and one of a hotel nearby (seen below).

Finally, I created a building footprint of Boston that I then exported to use in Google Earth that other people can use. 3D mapping has a variety of uses. For example, you wanted to get a good 3D scene representation of a city, you could. One of the advantages of using 3D mapping is that you can visualize features much better in 3D than you can in 2D. With this informational form of mapping comes a challenge. 3D mapping is quite a processive intensive process and sometimes it can be difficult to visualize the data you want without difficulty. While 3D maps can show information in a completely new way, they can be hard to print in a 2D layout. 

I look forward to finishing my last module next week on Google Earth and as always ~Map On!

Sunday, March 31, 2019

GIS 50007L - Module 10: Dot Density Mapping

Hello Everyone! 

For this weeks lab, I created a dot density map. Dot density maps use dots to visualize a certain number of geographic phenomenon. For this map, I created a dot density map of the population of South Florida from the year 2010. To create this map, I fully used ArcGIS Pro. I took the data from south Florida and then joined the census data in order to get the population from each county. After the join, I symbolized the data of population using dot density where the value of each dot was the right size and count. For my map, each dot represents 2,000 people. Next, using the water features feature mask, I was better able to represent the distribution of the population. To add to my map, I labeled four major cities but also classified the four types of surface water features. 


While dot density mapping is an effective thematic mapping style, one needs to take into account the dot size and density as it is easy to misrepresent the data. Stay tuned as we add another dimension to our maps in 3D Mapping and as always ~Map On!

Sunday, March 24, 2019

GIS 50007L - Module 9: Flow Mapping

Hello Everyone!

This weeks lab and material is all about flow mapping! Flow maps are maps that illustrate movement from region to region on the geographic scale. These maps use lines of various proportional widths in order to convey both quantitative or qualitative data. There are five primary types of flow line maps. Distributive flow maps which show the movement of people or goods between geographic regions. Network flow maps which depict network patterns such as transportation systems. Radial flow maps like the one I created for this weeks lab that shows migration to a specific region from other geographic regions. Finally, continuous flow maps which show specific continuous data such as winds or ocean currents and telecommunication flow maps which depict networks such as telecommunication and the internet.

For this lab, I was tasked with creating a flow map that shows migration from the various geographic continental regions to the United States (see below):


This map shows all the regions where people have migrated from to come to the United States. I created flow lines that are proportional to the number of migrants from each region so the largest flow line is from the region of North America and the smallest is from the Oceania region. Included in this map is also an inset choropleth map of the United States showing the percentage of total immigrants per state so the user can get a sense of where most immigrants go when they migrate to the U.S. For this map, I was required to add a stylistic effect so I color coded my flow lines in order to better represent them in my legend. While this effect does make the map look a bit busy, I believe that it can benefit the viewer to help distinguish the change in immigrants from each geographic region. This assignment was fully created in Adobe Illustrator and one thing I particularly struggled with and need to practice is making smoother more continuous lines. Next week I look forward to jumping to the other side of the mapping spectrum to dot mapping and as always ~Map On

Sunday, March 17, 2019

Applications of GIS: Crime Analysis

Hello Everyone! 

This week I've been enjoying a much-needed spring break, and have been focusing on potentially testing out of one of my future classes. For this project, I was assigned a discussion on the assumptions that hot spot maps can make and how that can influence crime analysis decisions and I was also tasked with a lab that had me create several types of crime analysis maps using ArcMap 10.6. While the first two portions of the lab had me create choropleth and hotspot maps the portion I would like to focus on is the last part using three various forms of hot spot maps to predict future crime trends. The three main methods can be seen below and I will briefly discuss them in depth, as well as discuss which I think is the most suitable.
 The map above compares the three types of crime analysis hotspot maps in question. The first is a grid-based thematic map. To create this I took burglaries from the year 2007 and spatially joined them together with a grid of the area. I then selected the top 20% of grids that had the highest counts of burglary crimes in each after excluding cells with a count of '0'. I then created a polygon of this grid that shows where the highest burglary activity is located throughout the region. Next, I created a Kernel Density map. To do this I took and created a density map of the burglaries from 2007 and found where the density was highest, this gave me a completely different result as I used a half-mile search radius to help shape my map. Finally, I made a Local Moran's 1 map that uses cluster and outlier analysis to find areas of high concentrations of crime near other areas of high concentrations of crime. I then stacked these three map types to get a comparison and find which method would best suit crime forecasting.

After looking into the numbers and analyzing the data I found that if I were to be a police chief or a sheriff, I would want to use a Local Moran's map. This is because it is neither too big (like the grid) or too small (like the kernel density) to focus the efforts and resources on. I also notice that the areas of high concentration in the Moran's map are pretty central to my data and share the same geographic location with the areas of crime with the other two analysis maps. When looking at the numbers, I found that when I compared the 2008 burglary data to the data from 2007, the Local Moran's map did the best at forecasting for the next year.

I have been fortunate to have a background in Crime Analysis through an amazing opportunity to be an intern analyst at the Jacksonville Sheriff's Office. Crime analysis is one of my desired fields of interest and creating maps like this has always been one of the things I love most about GIS. It has been an absolute pleasure pursuing this project, and as always, ~ Map On