Piece of Eden GIS Blog

In the first part of this lab, we used proportional symbols to represent positive and negative values in job increases/decreases in the USA.  Because there were negative values in this data set, I created a new map to "fix" the data. In this new map, I created a new field and copied the negative job loss data. I then used the Calculate field data and multiplied it by one to make it positive. Lastly, I overlaid both maps on the data and was able to accurately represent the increase and decrease of jobs in the USA by state.   In the second part of this lab, we delved into how to prepare data for a bivariate choropleth map, choose colors for the legend, and create a good layout.  I created three separate fields to analyze the data: Class Obese, Class Inactivity, and Class Final. I used the symbology tool to create 3 Quantile for the Obese and Inactivity classes and used each quantile to set the three classifications in the fields I created using the Select by Attributes...

 This week was a fun and challenging week as we learned about and created infographics. It was fun to create the infographics themselves, but challenging to figure out the best methods and practices in analyzing raw data.  We used 2018 County Health Rankings National Data from countyhealthrankings.org. I chose to move forward with the two values: Unhealthy Mental Days and Premature Dealth.  I   choose these two variables because those that struggle with mental health die before their time due to depression, anxiety, and/or a combination of similar issues. Both variables are normalized by relating their value to all counties within each state in the USA. For example, the poor mental health days is normalized as the average number of reported mentally unhealthy days per month per ctizen. The normalized premature rate is the “age-adjusted years of potential life lost rate per 100,000.”  Below, I created a scatterplot of the normalized data.  I choose to k...

This lab was very interesting as we dived into color theory.  In the first part of the lab, we created and compared linear and adjusted progression color ramps to themselves as well as a color ramp from the website colorbrewer.org.  I found, the colorbrewer color ramps are not as rhythmic when compared to the other methods, as they don’t step up at set intervals or rates. However, I don’t think that a set rate is needed to go from color to color. I preferred the colorbrewer ramp because each color was distinct from its neighbors. In the linear and adjusted color ramps, the colors looked too similar to each other and were not distinct enough for each step. I think that as long as the color ramp is moving in the opposite direction of the same color hue, the step rate or interval is not as relevant. When I first was completing the linear step I started with the purple hue option but had a difficult time, as each step in the color ramp looked the same. At one point, I created my o...

 In this lab, we explored different types of tools to enhance topography. One tool we focused on is Hillshade. Hillshade utilizes the different light source directions to enhance different topographic features like elevation on a DEM map. While composing the map below, I compared traditional and multidirectional hillshade options. I think that Multidirectional is more beneficial with features that have subtle and sharp changes in topography, that are accentuated with lighter ombre shadow sketching, like a mountain face. The traditional hillside tool tends to shadow in pretty dark, and takes away from find details of a mountain face, but does well with cliffs and flatter topography that doesn’t have huge elevation gains. Therefore, I used the multidirectional option below.  I also tried to keep the area map as big as possible, since there are a lot of different subareas covered in the legend. I centered the North Arrow in the top right corner, and the legend and other map eleme...

 This module was extremely helpful, as we dived deep into projection systems. Through the lab, we analyzed UTM, State Plane, Albers and determined when they are the best fit, as well as how to project them properly.  A handful of tools we utilized are listed below:  Projection Wizard Geodetic Parameter  Search For our final map, we choose a state in the USA, and determined which projection to use; UTM vs State Plan.  I choose the state Utah because I have always wanted to go to Zion! I decided not to use the State Plane because there are three different ones that subdivide the state. Since state plane’s do not work here, I opted for UTM over Albers because we are looking at a relatively smaller area not close to the poles. I tried a couple of different UTM NAD83 Zones and landed on UTM NAD83 Zone 12. In this zone, Utah completely lies within one UTM Zone.  

This module explored the use of typography in map-making. For this assignment, I had to label larger cities/islands, waterways, parks, landmarks, neighborhoods, and topographical features in San Francisco, California.  My thought process for labeling Map 1 below is as follows.  General cities and islands: I used basic large black font, for these main locations' features to stand out. I placed them centrally, in a spot they wouldn’t block other features. I used a blockier font (century gothic) for easier legibility. Water Features: I used italicized blue font to signify water. I used a lighter blue for the Pacific Ocean, as it’s not local information. I used the font commonly used for water features, Bodoni MT Italic, for distinction and legibility. Park Names: I used a green front placed above the area of the parks. I used a blockier font (century gothic) for easier legibility. Landmarks: I used a bright orange to stand out and placed it over the bridge. I used a blockier font...

 In this lab we first looked at how the scale at which we analyze data can affect geometric properties. Looking at data at three different scales (1:1200, 1:24000, 1:100000) I was surprised by my final calculations. I thought that there would be a lineal proportional relationship along the scales, but I did not find this. It makes sense that the greatest resolution [1:1200] would have the most detail of geometric properties, but I was surprised that 1:100000 had greater geometric properties than the medium resolution. I resampled the data using the Bilinear technique and found the following effect on DEM resolution.  As DEM resolution increases the average slope in degrees decreases. This makes sense because as you get closer, more in detail by getting closer, you are zooming in as actively seeing less features altogether.  In the last part of the lab, we looked into the Gerrymandering of districts in the USA. Gerrymandering essentially breaks up congressional distri...

In this assignment, we learned about four different interpolation methods: Thiessen, IDW, and Spline Regularized/Tension. We used these methods to analyze the Biochemical Oxygen Demand (BOD) in milligrams per Liter in the Tampa Bay of Florida.  The first method, Thiessen interpolation, is a widely used method mainly because of its ease of use and high accuracy when using a large sampling density. Because this method assigns a value based on the nearest sample point, it does not do well with continuous data. However, because of this same attribute, this method can be highly beneficial when dealing with data that is oddly shaped or ends abruptly. Model 1: Thiessen IDW interpolation draws in the theory that points closer to each other are more alike.  Model 2: IDW The biggest difference in the Spline layer from the other two methods is it has a much smoother expression. This makes sense as the spline interpolation runs through each data point and aims to smooth out the surface el...

  This assignment delved into TIN and DEM’s elevation data models. The main difference between the two is that raster uses DEM while vector data uses TIN. I began by visualizing a 3D TIN of a valley/gorge using a TIFF image. (Image 1 below) For my second analysis, I converted a DEM to TIN, reclassified the data, and used a weighted overlay to develop a suitability model for a ski run. When comparing the DEM and TIN layers side by side, the TIN has more dimensionality and elevation texture than DEM does. Because of this, I would think that the TIN layer is more accurate. Overall, the smaller the elevation slope the greater the difference between TIN and DEM. In this area, DEM understates the slope while TIN overstates it.  I created a couple more models with TIN, and really enjoyed playing around and getting to know how to work with TIN models. The biggest differential element I learned about was in the last step. I found that when creating a modified TIN, using Edit TIN ...

The goal of this analysis was to determine which road dataset (TIGER or Street Centerlines) was more complete for Jackson County, Oregon. Completeness in this case is defined by which dataset layer had a greater total length per grid subsection (267 grid subsections total) and greater coverage of the entire area of study. These two main points of my study were derived from the Haklay study. ”After gauging the level of positional accuracy of the OSM dataset, the next issue is the level of completeness. While Steve Coast, the founder of OSM, stated ``it's important to let go of the concept of completeness'' (GISPro, 2007, page 22), it is important to know which areas are well covered and which are notöotherwise, the data can be assumed to be unusable. ” (Haklay, 2009) To begin with, I used the Summary Statistics tool to find the overall completeness of each layer within the study area. Tiger roads had a total of 11382.7 km of length while Street Centerlines only had 10873...

 In this analysis, I compared the street and road intersect data collected for Alburquerque, NM by the City of Alburquerque and the application StreetMaps. I used an orthophoto base layer as the reference for this analysis, to compare and determine the accuracy of both the City and Streetmap layers using NSSDA procedures. The most difficult part of this analysis for me was how to determine what 20% per quadrant looks like. Because the reference map was divided into 208 quadrants, I had to determine how to subdivide all the quadrant's equality into 20%. After multiple trials and error, I decided to subdivide the entire area (208 sub-quadrants) into 4 equal-area subsections. In this way, I could do 5 random right intersection points per subsection or 20% per subsection.  Map 1: City of Albuquerque city map data.  Map 2: City of Alburquerque SteetMap data When selecting a random intersection to place the points within each quadrant, I choose a location that had data f...

 In this assignment, we analyzed 50 points collected at the same location via a GPS handheld device. Through these collected points we determined their precision and accuracy.  I first determined the mean of the points collected ("waypoints") by using the summary statistics tool, and found the exact coordinates of the "Average Waypoint" via the Absolute X,Y,Z tool. I re-projected and spatially joined the layers. Lastly, I created three new fields to determine the 50th, 68th, and 90th percentile.  Map1: GPS datapoint distribution and precision/accuracy analysis My horizontal precision for the 68th percentile is 4.5 meters. The distance between the "Average Waypoint" and the true reference point is 3.78 meters. Horizontal precision looks at the "consistency of a measurement method,"  and aims to provide "tightly packed results." (Bolstad, 2016) Horizontal accuracy on the other hand "measures how close a database representation of an ...

This week I learned how to utilize ArcGis Pro to make out the pathway of a past hurricane, Hurricane Sandy, along the North East coast of the USA in 2012. Later, I was also able to assess structural damage on the coast of New Jersey from Hurricane Sandy.  Below you can see the map I created of Hurricane Sandy's pathway/timeline. If you look closely, you can see via the symbology what category storm Sandy was along its path along with information on its wind (mph) and pressure (barometer) information.  In the second part of the lab, I assessed the structural damage of Hurricane Sandy in a select area in New Jersey. I created a new point feature class and subsequent domain with categories no damage, affected, minor damage, major damage, and destroyed to categorize each parcel. After creating the points and designating their damage category, I analyzed the data future by determining what damage feel 0-100m, 100-200m, and 200-300m from the coast.  1.     ...

 For the first part of this module, I compared before and after pictures of the New Jersey coast, pre and post Hurricane Sandy. I created DEM's from lidar data to compare the before and after. Below, you can see the magnitude of erosion and accretion on the Jersey coast due to the Hurrican Sandy in 2012.  In the second portion of this lab, I created a storm surge impact model for Collier County, Fl. This was a very educational assignment and I learned a lot. We not only created a storm surge model for the coast of Florida, but we also did so utilizing both Lidar and USGS DEM data to compare accuracy. I first began by converting the Lidar layer to meters, and then reclassified both to only include data where the elevation is under 1 meter. I used the region group tool to clump together into one large area the land along the coast, and isolated it (via select by attributes) to only analysis this single large connected area. Lastly, converted that large area for both Lidar and US...

 This was an interesting module, as I analyzed homicide data in 2017/2018 Chicago. I created three different hotspot analysis maps to anticipate homicide hotspots in Chicago year 2018 using data from 2017. After creating each map, I determined the accuracy of each map by comparing how large a map's hotspot area was to the actual crime density of 2018.  Beginning this portion of the assignment, I first made sure to change the environmental parameters to that of the city of Chicago boundaries.  Below I summarize all the technical steps I took to perform my analysis.  1)       Grid Overlay: a.        Spatial join Chicago grid and 2017 homicides b.       Select attributes greater than zero and make new layer c.        Select attributes in the top 20% and create a new layer d.       Dissolve layer into one polygon     ...