GIS 6005: Lab 6

 This lab week consisted of working with choropleth mapping, but in a more unique way then what we've done thus far. We ended up comparing multiple values from different statistics/fields, but then putting them in comparison with one another on the same map. As seen in the first map below we compared negative and positive state job growth across the country. This can have numerous applications across multiple sectors to convey and review information. 

The proportional symbol mapping as seen above can take a second to begin to classify the values. But upon inspection you can easily differentiate the negative and positives. Then comes reviewing the legend to get a general ideas of the numbers. Whole these a whole rounded numbers it does convey a good bit of information on this in a simple format. All the sizes have enough variable difference to get an idea of the number of jobs either lost or gained. 

In a more advanced map then the one previously seen this takes choropleth mapping to the next level with bivariate choropleth mapping. Now not only are you comparing two different variables such as the positive or negative growth. But you are adding multiple fields into the same combined values for review. These are then added together and put on a scale as seen on the legend in the bottom left hand part of the map. This allows you to review two different factors simultaneously and see the correlation they may have for one another. This can help to review different effects of causation to see if certain things may affect or contribute to others in a vast scale to determine possible outcomes. A very effective tool for works such as the topics of obesity and activity levels as we have above. This weeks lab was very pragmatic and can be applied to very meaningful topics across the research space.

GIS 6005: Lab 5

 For this I opted for a horizontal slanted bar graph with the 2010 values on top and the 2018 values on bottom. I wanted to show the full 0 to 100 percentage even though either value don’t exceed 20 percent. As seen by the numbers listed above each bar it can be seen that between 2010 to 2018 the percentages didn’t even increase a full percent. It can be seen from this that even during almost a decade the amount of poor or fair health showing that this issue is staying fairly consistent if not slightly increasing over time. While this display doesn’t convey much more information than that I still find that this display does present the information in a simple but informative manner.

My design for the map was positioned for the choropleth maps to be staged on the left side with the severe housing problems on top and the poor or fair health on the bottom. I found having these stacked on top of each other with their legends fitted in gave the best impression for viewing these together. Then to the right is the accompanying bar graph, description box and pie chart for each variable as well. These are color coated to match with each other with the blue colors being paired with the severe housing problems and the orange coloring being paired with the poor or fair health portion. There is also the scatterplot chart in the bottom right-hand side that shows these variables paired together as we did in deliverable 2. I thought this was important to include for comparison purposes. There is also the bar chart in the center right which shows the average US comparison of the counties from 2010 to 2018. With this there was very little discretion between the years. I chose a very particular way to stack each of these products so that they looked organized, but had a good flow on the page. There was a lot of information to stack in here, so I felt that organization was important to keep from looking too over crowded. I didn’t include many colors for this reason as well as I felt it would crowd out the imaging. In the end I felt simple structuring was the best option and gave this the best look.

GIS 6005: Lab 4

 My classification was the basic method of just the population gain or loss from 2010 to 2014. I found this to be a good judge of the movement in these counties. From here you could look into it farther whether for age, diversity, income, etc. Reasons to decide which areas are the hotbeds and which are being deserted. Then to decide the reasons of who and why. I found this to be a good entry level map to begin to interpret these counties from. I chose 5 as usual as I enjoy having two upper and two lower values with a medium value to average it off. I think this allows for a good breakdown and judge of values without offering too little or too much variety. Class breaks were there to break down the zero or below. The first 10. The first 25. The first 50. And then from halfway and above dominated the upper value brackets. This made the most sense and there were very minor movements in some areas which needed to be documented and then in others they had large quantities of population growth which needed to be shown in a way which stood out as the 50 percentile or more. 

For the legend I went with a relatively simple design. It goes from a white to a light blue to a dark blue. For this map I found these made the most sense and looked fairly easy to decipher. This was a basic legend design with a 5 class to have 2 upper and 2 lower values with a medium value to level everything out. The percentages go from negative to near zero. Near zero to near ten. Near ten to near twenty-five. Near twenty-five to near fifty. And finally, near fifty to near seventy-two which was the last percentage rounded too. I found breaking this into these classifications gave a good judge on the growth of these areas by brackets. This gain or loss was based solely on the population change in these counties from 2010 to 2014. 

GIS 6005: Lab 3

 

As seen in the map above this shows a land cover map in Yellowstone being overlaid over a elevation map with the land cover map having its transparency being altered to around 50 percent so, that you can view the elevation map from underneath the land cover map. For these I went with a standard color panel for the land cover map which went from dark green to yellow to brown to a white for the land cover portion. This can be easily viewed and making out all the features is quite easy. The elevation portion is being detailed with a light blue ranging to a dark blue. This blends very well with the landcover map and even helps with the greener areas turning it into a bit of a teal coloring. This allows us to tell the elevation ranges with the tree ranges being blended in with them. After choosing the color maps I went for a standard landscape view map design with the map on the left and legend/north arrow on the right. This helped with this style map for better interpretation and brought a more concise look to the map. Adding the final background color to help hone in on the map portion better rather than having tons of empty space and the land cover map for Yellowstone was complete.

GIS6005: Lab 2

 

Listed above is our last deliverable map for lab 2. In this we had to showcase a state of our choice and mine was the State of Texas. With this I used the NAD 1983 Texas Statewide Mapping System (Meters) projection. There are a couple reasons for this. I tried to use State Plane projections which I normally use, yet Texas has 5 of these as seen above. I also tried to use the UTM projections as well which can also be seen above in the map. There are 3 of those that fall on Texas. So this made using a statewide projection almost impossible with these other formats. I then found the one I ended up using the NAD 1983 Texas Statewide Mapping System (Meters). This is a bit older of a projection, yet the other option that had international feet instead of meters was from 1927. Most of the original State Plane projections were done around 1983 or 1984, but most of these were updated around 2011 as well. Yet, this projection I chose fit in nicely and had all the polygons that I re-projected fall into place quite nicely. It showcase the entire State of Texas falling into the surrounding projections of the continental US. In this map I still showcased the other projections as well or at least the zones that they would have fallen into if used. As a statewide case you would use the projection like I did currently. But, if you wanted to get more regional and break into the inner zones of Texas then it would make more sense to switch over to State Plane or UTM from that point. This was the most appropriate projection I could find to cover the entire state and I would recommend looking for other statewide projections if doing this to any of the other states on a large scale as well. 

GIS: 6005 Lab 1

 For this map I broke down a lot of my details based off of previous maps and how they can be easily perceived on the map itself. Even if some of the colors do or don’t make sense, I still added them if they can be deciphered from the map in a productive manner. For the general-purpose things, I chose red for the font. I changed the size of this based off of the feature which it is trying to convey. This was just because the color stands out well to the backdrop of other random colors and allows you to easily identify them on the map. The fonts were variable for every type and not really anything too specific. Just changed up how they all looked up a little for some slight variation. The water features were a blue italic. I chose this cause most maps have water features with a blue font coloring with italic. Also, a popular font for this is Sans Serf which was the only one that had an intentional font type because of this. The Park features had a brown font type because a lot of parks use the color brown as it is a natural nature-based color. This conveys to most when on maps or signs that it is a natural resource-based thing. The landmark feature I used orange for no other reason than there was only one and I needed a color to fill it. It stood out well on the map though and even somewhat signified the “golden” gate bridge with a low-key variation of that color. The black text was used for topographic features as these stood out well in a crowded map setting without having to use extremely vibrant colors. This is normally a good go-to color for these features and seemed the most appropriate. The rest of the features were used from the shapefiles with parks being green because of the trees. Water is blue cause its water. The streets and highways had a gray to black coloring as these are easily identifiable and usually how they look in real life from concrete to asphalt. All of these features were subjective to the size of the feature, the backdrop that it had, the relative importance of them, as well as how easily they could be spotted off the map compared to their hierarchically status. I feel like this map conveys that fairly well not only from the coloring, but how even some of the same category features had the have their font sizes and locations changed to properly convey the locations and general size of the features they were describing. 

GIS 4035: Final Project

 The final project for GIS 4035 was a do your own project with a base idea being given. The Lake Tahoe Basin is experiencing urbanization surrounding the lake and this is causing storm water run-off which is then in effect causing eutrophication to the lake. This is where the nutrient levels are too rich and cause many problems with the water quality (algae blooms, fish kills, dangerous waters, etc.) The goal of this project was to see can we tell through using satellite imagery whether or not urbanization is increasing over time and if so we can interpret that the amount of storm water run-off is likely increasing as well. On the left you can see a image from 1992 which shows Lake Tahoe with a few features classified which do differ a bit from the image on the right which is the same Lake Tahoe map but from 1999. I used unsupervised classification on the right image and broke it down to 50 classes for identifying. In this I ended up with 5 features (which differed a bit from the left which had snow) and while it appears to be severely different from the 1992 the features are subject to pixel confusion. While some of them were leaning more to the mixed category it was still easy to tell that the urbanization was indeed increasing over time. With being able to interpret this from using satellite imagery we can interpret that our initial quandary is likely true that the storm water run-off is likely increasing as well.