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!

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!

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

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

GIS 5007L - Module 8: Isarithmic Mapping

Hello Everyone! 

After an agonizing week last week with choropleth mapping, I am happy to say that I am back on track and mapping ever forward! This weeks lab was all about isarithmic mapping. Isarithmic mapping is a mapping style that utilizes continuous data to give the reader a visual idea of the data in question. Continuous data can include temperature, elevation, precipitation, and many others. In this weeks lab, I was tasked with creating two maps using isarithmic mapping for precipitation levels in the state of Washington. This data was collected over a 30 year period from various weather locations to show the average annual rainfall in the state by the Oregon State University PRISM Group. This data was then interpolated (analytically estimated the areas between monitoring stations) using the PRISM method which takes factors such as elevation, slope orientation, and coastal proximity into account and shows how said factors can influence precipitation levels. During the lab I made two different maps, one in continuous tones, and the other in hypsometric tints with contours, so let's get right into it!

The map above is a Continuous Tone color scheme for precipitation with a hillshade (elevation) under the color ramp. As you can see the transitions between areas of higher precipitation levels and lower are quite smooth and the legend reflects this. Areas in dark blue are areas where precipitation levels are highest, and areas in red areas where precipitation amount is lowest.

The second map I made was a Hypsometric Tint map. As you can see, the data is broken up into 10 classes allowing for a more distinguishable map to be made. These classes are separated by contour lines which allow the viewer to distinguish between the amounts of rain across the state. For this map, I believe that this map style is better at representing this data as the contour lines really emphasize the changes in elevation and precipitation that reflect said elevation changes. In this map is a brief description of how the data was derived and interpolated so the target audience and you, the reader can understand. This project has been my all time favorite this semester and a much-needed sigh of relief after the choropleth disaster. I look forward to sharing my future progress with you and as always,

~Map On!

GIS 5007L - Module 7: Choropleth Maps

Hello Everyone,

This week was a very interesting week in terms of my lab. Murphys Law was very present in my work this week. This week I was tasked with making a choropleth map. Choropleth maps are maps that utilize color and proportional symbology to show quantities in map variables. For this map, I was tasked with creating a choropleth map of Europe that shows the population density and wine consumption. By the time I got to my final product my map was beyond broken, and I was beyond frustrated. My map can be seen below:

In this map, I used a continuous color ramp from light blue to dark to illustrate the variations in population density across Europe broken into 5 classes and classified using the Quantile method as I thought it would be best for the visualization of population density. For wine consumption, I used graduated symbols in order to show the difference in consumption across Europe also classified by the Quantile method. During this lab, I ran into more problems than I can count. The lab, unfortunately, had to be completed in ArcMap which I find is far inferior to ArcGIS Pro due to label issues. Even after migrating to pro, none of my labels would even load or work properly. Once a new update released for Pro, I attempted to recreate the project but would receive fatal crashes along the way making it impossible. I wanted to make an inset map, but that would not even work in Adobe Illustrator nor ArcMap...Overall this project has been a real challenge and an absolute pain because I am limited by the software I use, but I am reminded that software does not work as we always want it to. I look forward to sharing my next map on Isarithmic Mapping next module, and as always, ~Map On.

GIS 5007L - Module 6: Data Classification

Hello Everyone! 

It's hard to believe that we are at the halfway point of the Spring semester and almost in March of 2019! Time has really flown by since I have started GIS here at the University of West Florida. After a much needed week off to prepare for my 8-week term class finals, this week I came back to hit the ground running with Data Classification. Data classification is when data is taken and combined to create groups that are called classes. These classes are then represented by unique symbols such as color ramps.

In this weeks lab, I was tasked to create two sets of choropleth maps that show the distribution of the population that is 65 years old and older in the Miami Dade area from Census Tract information using four common methods of data classification. The first set of maps was classified by the percentage of the population that is over the age of 65 in each census tract, and the second map series was classified by the number of citizens per census tract that are over the age of 65 and then normalized by the area of square miles to get the density. These classifications can be broken down as follows:

1. Equal Interval - Each class within the data has an equal range. For example, if you have five total classes with 200 data records, each class range would be 40.
2. Quantile - Each class has an equal number of observations within it by equal distribution. If you have 50 data points and you want five classes, each would have 10 points.
3. Standard Deviation - The classification of the data is based on the Standard Deviation bell curve graph with most points falling within the avg of one deviation away from the mean in either direction and fewer point in deviations of 2 or more from the mean.
4. Natural Breaks - Each class in the data is made to be as similar as possible for values, but as unique as possible compared to other classes for illustration distinction.

I believe that the normalized data map set based on the number of citizens over the age of 65 (pictured above) is the better series of maps to use because it shows a better representation of the distribution of senior citizens in the Miami Dade area. The percentage map can be misleading because it is only showing the percentage of the population in each census tract that is above the age of 65 whereas the map of the actual number of senior citizens per census tract normalized by the area illustrates where the heaviest densities of senior citizens are located.

Out of all my assignments so far, this one has to be one of my favorites. Even though it was all created in ArcGIS Pro, I am eager to get back into working with Adobe Illustrator as I have witnessed first hand how much more freedom you have with cartographic design and editing in Illustrator. I look forward to continuing on with an extensive segment on Choropleth maps in next weeks module. In the midst of final exams and cartography, I am grateful for all the encouragement that I have received to keep pushing, to strive for the best maps I can make, and most importantly: to ~Map On!