Module 6

 

This week I learned about Isarithmic maps, continuous symbology, and hypsometic tint. The map about depicts the the precipitation data for the 30 year period was derived by using point precipitation data. This data was then used in the PRISM analytical model to make gridded estimates of precipitation and temperature throughout months and years of the 30 year period.

I used hypsometic tinting to symbolized the data. Hypsometric tinting uses light and dark shades to add a 3D effect to the map. This uses different colors and shades between contours. I implemented this into our map by using our Annual Precipitation layer and running it through the “Int” spatial processing tool to enhance the shading from the original continuous tone. Then I added the hillshade effect for more depth. 

I enjoyed this weeks lab and learning the many different ways to depict data of a raster image.

Module 5

In this lab we created a map showing population density and wine consumption across Europe.

 

I created a chloropleth map to show the population density across Europe. I used the natural break scheme to represent this data, with 6 classes. I believe the natural break was best for this data class because it excludes outliers, and doesn't give misleading information of the more sparse areas. I chose a graduated color scheme of purples. This color reminds me of wine because it is purple and using shades of one color is easier for the color blind to distinguish and leads to less confusion.

Then i used graduated symbols to show the wine consumptions in each country. I chose the graduated method for this data. I think this gives a better representation of the wine consumptions increase throughout Europe.

I struggled with this map because using all these functions really slowed down arcpro to the point where I had to take many breaks while waiting for it to load.

Module 4: Data Classification

Figure 1. Data Classification of Seniors using percentages

Figure 2. Data Classification of Seniors using counts 

In this module I learned how to use different data classification method to show how populations of seniors are distributed uses percentages and counts.

The data classifications I used were the following: 

1. Equal Interval-this uses the highest and lowest amount of 65 years old's and breaks that up into 5 categories. This showed the left census tract having more 65 year old's than any other method. Equal intervals do not take into account outliers so this can inaccurately display data

2. Quantile-this method breaks the total number of classes into equal categories. This helps eliminate outliers skewing the data. This and the natural break class ended up having very similar looking maps. 

3. Standard Deviation- Standard deviation method is using the standard deviation to add or subtract from the mean of the data. Showing how far data deviates from the mean can cause outliers to stand out more. Using standard deviation is best with “normal” datasets. this map has the least variation than any other map, having the largest amount of the map be in the <-1.5 Std. Dev. category. 

4. Natural Break-Natural breaks uses the natural grouping over data within the dataset. This method used the Jenks optimal method. This method does consider the outliers when creating the classifications. This map ended up looking very similar to the quantile method. 

I believe the population count normalized by area using natural breaks gives the best idea of where most of the seniors are distributed in Miami Dade County. This map shows where the seniors actually are, and not showing misleading data from percentages living in sparse areas. If one area has a small population of people, but most of those people are seniors, the percentage map would show this area as an area of interest, even though across the map, only a very small number of seniors actually live there. The natural break method gives the most accurate representation of the population. This map takes the outliers into consideration.

Module 2, Map of Florida

This lab was about the importants of all the map elements, specifically labeling. We learned the differences of labeling points (cities), lines (rivers), and polygons (swamps and marshes). My map shows all of the important features in florida, including important rivers, cities, the capital of Florida, Okefenokee Swamp, and the Everglades. The water features are in italics and the cities are in regular font. The font I used was Bodoni MT because I thought it looked sleek and easy to read. To label I used ArcGIS pro's labeling function for all labels. For the rivers I customized by placing the labels in river placement curved above the river. I learned how to use the convert labels to annotations to have more control of the location of the labels. I made the Everglades label fit the entirety of the largest part of the everglades. Here are three more customizations I made: The first customization I made was in the label properties for rivers, under position->conflict resolution under remove duplicate labels I removed within a fixed distance of 150 points. I think this made the rivers look less crowded. The second customization I made was to move all point labels to the upper right corner. According to the lecture this is the best location for a point label. The last customization I made was changing the capitals symbol to a star to indicated that it is an important city and is different from the other cities

Good Maps vs. Bad Maps

THis week we are looking into the differences between a good map and a bad map. Here is an example of what a good map would be:

This is a map depicting the largest ancestry in the 2000's in each state and each county. This map is very easy to understand. There is minimal "crap in the map, showing only the important inforation. The important things in the map state out, with the focal point of the map being the map of ancestery with largest population in each county. This depicts the imformation truthfully showing smaller areas to give better understanding of ancestry in america. Here is an example of what a bad map would be:

This is a map depicting the wildlife and game in the US usnig pictures. This map is not easy to understand. So much information is given on this map that I cannot focus on one thing. The labeling is very small to inlcude all of the info. The title is on the bottom of the page and if very small so it goes overlooked. The infomration around the map seems unneeded and difficult to undertand. I do think the map could be pretty to look at, but does not convay information clearly of effieciantly.

A little about me

 Hi everyone! My name is Abby Stack and I’m in the GIS Administration Masters program. I currently work as a GIS technician for a utility pole auditing company. I just started a couple months ago. My goal with this program is to expand my knowledge of GIS and eventually get a job using GIS in some sort of environmental protection field. I work full time and have a 16 moLinks to an external site.nth old son named Eli. I think some adjectives they describe me are fun, happy, creative, and caring. Here's a storymap of my hometown: Story MapLinks to an external site.

Guana Tolomato Matanzas National Estuarine Research Reserve LULC change report

https://docs.google.com/document/d/e/2PACX-1vRk7CW1vqW5702wcNRThcLMiou7KdrJm23p9HyX8ZZphUaI4zE9Ija-CvnNzlR38ETIqO6kjNAu0BHU/pub

Module 5 Lab

 

This weeks map is a supervised classification of Germantown, Maryland. We learned how to used classification tools in ERDAS imagine to classify and recode features. We used histogram plots of different bands to remove spectral confusion. In my map I used TM False Color IR because I believe it caused the least spectral confusion. I did enjoy this lab but I don't feel the most confident on the material.

Module 4

 

The purpose of the module this week was to use ERDAS imagine and ArcGIS to explore band and layer data. We learn to interpret the histogram data that is located in the metadata of the image and identify features. So far out of all the labs I did find this one the most confusing. I do think I need more help figuring out how to interpret the histograms and the purpose of breakpoints. The inquire tool was something I found very useful. 

 I know that the lower pixel values in layer 4 mean darker. I used the inquire tool to hover over the dark areas of the image which was the water. When using this tool the file was 13, which was between 12 and 18, confirming that the water was responsible for the spike.

The small spikes in layers 1-4 around pixel value 200 would have to be a small area that is very bright. When using the inquire tool I looked for the lightest area of the image which was the snow in olympia park. When using this tool I noticed layers 5 and 6 were staying within the range 9-11 which makes sense because this area shows discrimination between snow.

For this feature I also used the inquire tool. I looked over the bodys of water using the inquire tool. At first I looked at the inlets and noticed while the 1-3 layers changed in brightness, layers 5 and 6 where not staying the same and were also changing. I then went over to the bay where there was alot of variation in the water but layers 5 and 6 did not change. 

Module 3 Lab

 

This weeks lab was to introduce us to ERDAS image. We began the lab by calculating EMR properties by using Maxwell's wave theory and Planck Relation. These are pretty simple equations but I struggled with knowing if I got the correct answer since no examples were provided.

Next we open ERDAS image for the first time. It was interesting learning a new program. We learned the basics on how to view images. The third part of the lab was to explore the metadata of the images. This gave me a better understanding of pixels, radiometric resolution, and spatial resolution. 

Module 2 Lab

 

The purpose of this lab was to us LULC levels I and II to classify areas in a aerial image of Pascagoula, Mississippi. I created a feature class to use polygons to cover the entire photograph. I then used statistical Ground truthing accuracy to figure out how accurate out labeling was. I used the create random points to find 30 different points and compared these points to google maps. This lab was time consuming for me. I finish making all of my polygons for my LULC feature class and forgot to save. This was devastating for me since I only have so little time when Im not working or taking care of my 8 month old. This was a good lesson learned, always save, save, SAVE.

Module 1 Lab

 

I created 2 maps this week. The first map was to identify different tones and textures in an aerial photograph. The second map was to identify different features using different identifiers. This lab was not very challenging for me and I enjoyed trying to find different object in the aerial photo. It felt like an adult "eye-spy"

Bobwhite-Manatee Transmission Line

PowerPoint:

https://docs.google.com/presentation/d/1t_lZijUjOdcM1Q09Lp-TcNYDuV07h9lEFvFX8ULoSf4/edit?usp=sharing

transcript:

https://docs.google.com/document/d/1dhoPqv5Vpmw_akSiuGdOU1rDfgcZjrW88fhAvLIry7c/edit?usp=sharing