lab 6 part 1

 For this portion of Lab 6 I learned how to use and create meaningful legends for proportional data. Below is a map I created of the USA showing different states increase or decrease in employment.

To achieve this map I had to do some data manipulation. I first had to separate the jobs that decrease and the jobs that increase. I had to do this because negative numbers are not allowed to be used in proportional symbology. I then use proportional symbology for my newly created layers and match the classes to each other. I then had to create the legend by converting the legend made by ArcPro into graphics. For my legend I wanted to have a legend similar to the example in the lab. To achieve this legend I first added all of my symbology, matching the circle sizes by creating 4 classes for my jobs lost group and 5 for my jobs gained group. I then made my minimum the same for both layers. For my symbology I used the Jobs gained layer and changed the circles to no color fill and a black outline. After I changed my symbology back to the green color I wanted it to be. I then used the graphic to nest the circles within each other as I did previously in this lab

Lab 5, the Link between childhood poverty and teen birth rates

 For this lab I was given an excel spreadsheet of data from all the counties in the United States. I decided to compare teen birth rates and childhood poverty. Using the data provided i created 2 maps, 2 bar graphs, 2 pie charts, and a scatter plot comparing the 2 factors. 

For my layout I wanted to have a large title with a small paragraph explaining why teen birth rates and childhood poverty were linked. I wanted to have at least one academic journal article as a reference when writing this paragraph to show my sources. I decided to stack the 2 maps on top of each other with their correlating bar graphs and pie charts next to them to have a consistent flow of information.

Lab 4 part 2

 

For this map I chose the classification method of Natural Jenks. This choice was through trail and error of my other leading choice of manual intervals. At first I set mutual intervals of 8 classes breaking the data up by 10s. This ended up being a not so great choice when discovering that most of the data fell between below 0 and 10. To be able to see the variations of data I went with natural jenks. This allowed the data to be conveyed in a more intuitive way. With the formula choosing naturally occurring clusters so the value changes from below 0 and 10 are in different classes and are represented in the map. I chose 8 classes to not group all large values in one class break.

For my legend I changed the percentages to whole numbers to make it easier to read. The colors I chose were red to blue, because these are the colors of the American flag. I added the percent sign after each values to clearly depict these values being percentages

Lab 4 Color Concepts and Choropleth Mapping

 

The purpose of this lab was to calculate rgb values for different color ramps. I was provided the values for the darkest color in the color ramp and was tasked to create a linear and adjusted progression color ramp. When creating my linear color ramp I had to find the average value increase from darkest to lightest with 6 colors in total. When finding the lightest color I changed my color mode form RGB to HSV to keep the hue around the same but change the saturation and value. I then subtracted the values of the darkest and lightest RGB values and got the average by dividing by 5.

For the adjusted progression color ramp I took the average and got ⅓ of it. I then added ⅓ and subtracted ⅓ from the average. In an excel spreadsheet I had 5 rows, the top being the ⅓ higher, middle being the average, and last being ⅓ lower. In between in spots 2 and 4 i got the average of the middle and highest and middle and lowest. I then added these to the rgb values of the darkest color, continuing to add until I got to the lightest color.

For the color brew ramp I went to colorbrew.com and selected of the color ramps provided

While doing the linear color progression the color increases gradually, increasing by the same amount with every color. When calculating the adjusted progression the rbc values increase from light to dark. From the lightest colors there is not a very large difference between the colors. Once we get to the larger colors the color has a greater more visible difference. When using the color brewer I chose a multi hue color ramp. The lightest color in the color ramp is almost white. The colors rgb values very much more sporadically than with the ramp I calculated. The hue changes cause the R values to decrease in the middle, then increase again after that. This color ramp has the most distinct color variation than both that I calculated, which I believe allows the colors to be more easily distinguishable.

Lab 3 Elevation and Hillshade

 

This weeks lab had an emphasis on hillshading and visualize 3d elements of maps. In the first part of this lab we learned how to use a visualize contour lines in a mountain range. We smoothed out the more jagged contour lines using the geoprocessing tool "Focal Statistics" to make the contours easier to interpret. 

In the second and third part of this lab I learned how to hillshade and what hillshading means. I learned the importance of azimuth and how different parts of the day can cause your hillshading to be different. 

In the final part of this lab I had a short look at local scenes in ArcPro. I used tin data to visualize a 3d scene of a valley.

Lab 2 Coordinate Systems

 This week the lab was focused on coordinate systems. I really enjoyed this lab because projections have always left me a little confused. I feel like this lab gave me much more  clarity on appropriate projections for study areas. 

the map I created was a accurately projected map of Massachusetts

I chose Massachusetts for my area of interest because this is where I grew up. When looking between UTM_Zones and the state_plane_zones, the clear answer was state plane. UTM zones cut Massachusetts in half so any zone would not fit all of Massachusetts perfectly. State plane has all of mainland Massachusetts in one area. This allows all of Massachusetts to be projected accurately in the projection. I chose NAD 1983 (2011) State Plane Massachusetts FIPS 2001 (Meters).

Lab 1 Communicating GIS

 This week I learned the a lot about the 5 basic map design principles:

▪ Visual contrast

▪ Legibility

▪ Figure-ground organization

▪ Hierarchical organization

▪ Balance

I created 5 maps looking into different symbology and labeling features.

This map highlight different areas in San Francisco. In the map I kept all my text font to be Calibi to have the map remain consistent. For water I made my font Italic and chose a dark blue to contrast against the lighter blue water. For parks I chose a dark green with a white halo around it to increase visibility against the green symbology of the park. When labeling the general areas I chose a black with a white halo to improve visibility and contrast. With the smaller islands I used a line callout to avoid having the font very small causing it to be illegible. With the neighborhoods I used a black font with a balloon callout because these are very small areas and I wanted the neighborhoods to stand out and not look like the general areas. For the twin peaks and hills I created curved labels with a light gray font since these are geographic landmarks.

Module 6

Scale can have a large effect on vector data. When studying hydrographic data with varying scales it is clear the line water will change depending on scale, a higher scale giving much more accurate data. Resolution, just like scale, will have a large effect on data. When you have a better resolution your data will be smoother and more accurate. 

Gerrymandering is the manipulation of boundaries to favor one party over another. One way to measure this is using the Polsby-Popper score to find the compactness of a district. The lower the score the worst the 'offender' is. Below is a screenshot of the worst offender in the data.

Module 5

 

Image of Spline interpolation method using tension. 

In this weeks lab we used different methods of interpolation to map data from water qualify samples.

The first method was non-spatial, which was just getting the data from the actual points. The next method was Thiessen. this method draws lines from neighboring points and create polygons with the resulting polygon having the value of the center point. The next method was IDW. This method estimates unknown values from points nearby. In this method the further away the point is, the less weight it has in determining the unsampled value. The last method is the spline method. In the spline method the data is used to create smooth curves from sample to sample. 

This weeks lab was pretty straightforward and I know feel like I have a good understanding of interpolation methods. 

Module 4

 The purpose of this weeks lab was to explore TIN and DEM data sets. I had to create 3d visualizations of elevation model. I learned to purpose of TIN data and how it can be used as an elevation source creating these 3d models. Using different symbology's and the reclassify tool allowed me to make the models more presentable. In the image provided I used a DEM to develop a Ski Run Suitability Map:

Module 3

 In this blog I did data analysis to see which road dataset was more complete within a grid. The first thing I did was use the Projection  tool to project the Tiger Road data to be the same as the Centerline.

 Next I used the summarize within tool. When using this tool I set the input polygon as grid and the input summary feature at the tiger roads polygon. For the summary fields I used the LengthinKM field I created to generate the length in Km of the roads and for the statistic I chose sum. In shape unit I used KM. This left me with a table with the sum of roads within each grid. I did the same analysis for the centerline roads data. When both of these were complete I created 2 new fields in the grid: Tiger, for the tiger roads sum, and centerline, for the centerline roads sum.

I joined the summary table of each of the roads to the grid and calculated the field with the summary data. I then created another field for my percent difference calculation. I used the calculate field tool to run this formula.

For symbology I wanted to have a graduated color ramp to show the difference in percentage. I chose to use Natural Breaks with 7 different breaks in the data.

Module 2

 

Location of 20 test points

In this lab I used the Positional Accuracy Handbook to find the horizontal accuracy of 2 datasets of streets in Albuquerque: the cities street data and StreetMap USA's data. To complete my analysis I found 20 test points. In finding the right test points I looked out for right angle intersections. I placed a point for each dataset at all 20 locations, then using aerial imagery provided, I placed another point where the intersection should be. I got the latitude and longitude of all points and exported the 3 new tables into .csv tables.

I used the worksheet provided in the Positional Accuracy Handbook to find the RSME and the National Standard for Spatial Data Accuracy.

Formal Accuracy Statements:

Using the National Standard for Spatial Data Accuracy, the street data from the city of 

Albuquerque tested 28.93 meters horizontal accuracy at 95% confidence level

Using the National Standard for Spatial Data Accuracy, the street data from StreetMap USA

tested 206.30 meters horizontal accuracy at 95% confidence level

Module 1

 

The distance between the average location and the reference point is 3.18 meters. This lands in the 68% buffer. This tells me that the gps data averages to be within 68% of the actual point location.

For horizontal accuracy the distance from the average and the reference was 3.14 meters. For the results of the horizontal precision looking at the 68% buffer value the distance is 4.4 meters. These 2 values are very close giving a difference of 1.26 meter. This could mean that the gps data is more accurate with horizontal accuracy than it  is precise.

Horizonal accuracy and precision are measured by first getting the average of all of the points longitudes and latitudes. This will leave you with one average waypoint. To get the horizontal precision you will find the difference between the average waypoints longitudes and latitudes and all of the gps points longitudes and latitudes. you will then section these differences out into percentiles, 50th, 68th, and 95th. Using the percentiles you create buffers around the average points. the get the Horizontal accuracy you find the distance between the actual point location and the average of all the gps points.