LAB 6 Scale Effect and Spatial Data Aggregation

In regards to the effects of scale on vector data I learned that as the larger the scale the larger the units that are measured come out to be. Specifically with lengths, count, and perimeter of the hydrological features we were using in this lab. For raster data I was able to see that when resolution increased the average slope decreased.

Additionally in this lab we learned about gerrymandering which is the process in which people manipulate the boundaries of something in order to get results that are favorable to them. It can be visually measured through a variety of means such as calculating the compactness of an area using the Polbsy-Popper test or by simply looking at the shape, dimensions, or the number of enclaves that are associated with it. As you can see from the image attached the Congressional District 12 of North Carolina is by far the worst offender based on the Polbsy-Popper test and the fact it almost stretches the entire length of the state with a very thin width throughout its shape.

LAB 5 Surface Interpolation

The use of interpolation for water quality in Tampa Bay is actually a excellent use of this kind of data. Interpolation is the creation of new data based one actual data points. For this particular scenario four different interpolation techniques were used on this data IDW, Thiessen, and Spline( Regularized and Tension). Each of the methods are unique in their approach in creating a layer suing the given points. For instance Spline creates data based off a line that curves through the points and uses that information to create the rest of the data with the regularized creating a smoother surfaces and the tension creating stiffer layer that are more constrained with the sample data (as seen above). While Thiessen creates polygons based on their proximity to the closest point and IDW runs off the assumption that proximity means there more things in common which is similar to dispersion. 

   

  

LAB 3 Data Quality - Assessment

The goal of the accuracy assessment is to determine the completeness of the roads compared to each other in each gridcode in the study area. Specifically how different the Centerline shapefile is from the Tiger shapefile in the grided area. In order tot do this I had to clip the two shapefiles by the grid to only calculate the roads in that area. Then I used the intersect tool between the grid layer with each of the road layers. Then after that I used the percent difference formula in excel with the centerline as the base line to calculate the difference in length within each grid between the two road shapefiles.

LAB 2 Data Quality - Standards

In order to the begin the process for the accuracy statement first I had to create reference points based off the satellite images. Then I created IDs for each of the sets of points from the intersections of the lines and for the reference points that were created from the visuals. After that I created columns in the attribute tables of each set of points for their XY coordinates then I inserted the coordinates into the worksheets that calculated the data and from that I determined the accuracy.

ABQ CITY DATA
Using the National Standard for Spatial Data Accuracy, the data set tested 13 feet horizontal accuracy at 95% confidence level
STREETMAP DATA
Using the National Standard for Spatial Data Accuracy, the data set tested 180 feet horizontal accuracy at 95% confidence level

LAB 4 Surfaces: TINs and DEMs

During this lab I worked with DEMs and TINs in ArcGIS Pro. I learned about the difference between DEM and TIN where I learned that TINs are created from elevation points and they create face of triangles on the surface. While the DEMs represent a continous surface and the cell sizes are all the same unlike a TIN. Each though can make contour lines with some differeneces for example the TIN contour lines have an index lines and the lines are more jagged. The DEM contour line are much smoother though surprsingly their accuracy is similar.

Calculating Metrics for Spatial Data Quality LAB 1

 Horizontal precision : 4.5m, Horizontal Accuracy: is over 99% accurate

Vertical precision : 5.9m, Vertical Accuracy: is about 73% accurate

Accuracy is measured by taking the acutal value and comparing it to the calcualted/observed value then calculating the percent difference and subtracting from 100. Precision is measured how closely the calculated results are and is found by finding the point where the 68th percentile of the data is located. 

Lab 6 Proportional & Bivariate

As you can see from the first map proportional images can be quite useful for mapping information that can be negative and positive. For this map we created two layers in order to show a negative growth and a positive one with appropriate symbology for both and legend that accurately shows the size of differences. For the second map we used a  bivariate choropleth mapping method to show the relationship between two variables. In order to do this correctly the data had to be normalized and had to be reorganized in a way to create a new symbol for the new relationship data. This also involved creating a 3X3 color chart based on the percentage of each variable the color had to be complementary and be able to blend well so that any patterns could be easily distinguished in the map.

 

Analytics Lab 5

 

For my map I chose to have simple but unique color palettes for each variable so they could be distinguished from each other. Additionally I chose to make the corresponding infographics and charts the same color to avoid confusion and enhance the understanding.  I used simple charts the both shared similar designs so that they were understood they were showing similar data but the data was labeled clearly so that it was clear what the information was. In general I used the five principles of map making such as visual contrast, and balance so that the map is easy to understand. Though in hindsight the scatterplot could have been better in terms of color design and title as it appears to stick out.

Color Choropleth Lab 4

                   

For the first image you will see the color ramps that I created. You can see that the first two ramps are similar in the sense they seem to follow the same color hue but have a different shade. This is base on the RGB values having predictable changes throughout the ramp such as increasing the value by 20 each time. While the color brew is a computer generated color ramp that appears to have two colors of similar hues one for the light side and the other for the dark side. Each have a various amount of work to create with the first two having various calculations with some trial and error while the last have very little.

In the second image you can see the choropleth map I created from population change as well as the legend. I chose to have five classes to make the map simpler to understand with zero being the center of the legend and the two extremes as the ends. I chose colors that were associated with a negative and positive change like green and purple.

 

Terrain Visualization Lab 3

 

In this lab I worked with terrain of maps. Specifically with methods on how to use hillshade to show the topography and terrain features of the natural landscape. For my map that is shown above I chose to use a traditional hillshade features since it worked best with a transparent layer that shows landcover over the topography. Though the multidirectional was viable ai found that with a layer over it it wasn't as visually pleasing. So I managed to create a more visually appealing map with the other hillshade and using the essential map elements to create balance and a good hierarchical structure.   

Lab 2 Coordinate System

 For this lab we worked with Coordinate systems and how to determine which is appropriate for our area of interest. My area of interest is Texas and I determined that a State System would be best. The NAD 1983 Texas Centric Mapping System Albers worked the best since Texas is too large to fit into a UTM and it contains 5 state planes.

Map Design and Typography Lab 1

For the first map I used the 5 design principles to create it. To stat I used different colors to create contract between the area of interest and the background. This contrasting color also helps with the figure ground design principle since the are that is being show is clearly distinguishable. Then I used the balance to space out the required feature like the north arrow, legend etc to fill in the empty space. Then I use font size and weight to create a hierarchy organization and maintain a legible map for the readers. For the second made we explore labels and use individualized text for each of the types of labeling that was to be used.  For example I used font, style, size, and color to organize the categories of the labels into groups like water features with more elegant text that was a dark blue and italicized while natural features were labeled with solid text. I also create general labels to be more bold and large to help with area determination and gave parks more official fonts to increase legibility and help show their importance.
 

Lab 5 Unsupervised and Supervised Image Classification

 

In this lab we learned how to use Unsupervised and Supervised classification techniques in ERDAS Imagine. In this exercise we create signatures using Areas of Interest to show the software what each pixel was and to which class it belonged to so that we could create an image. Once that image was create we recoded the classes in a manageable amount of classes and corrected an spectral confusion using the mean statistical plots, distance maps, and histograms to determine areas that could cause an issue. We corrected this using different combinations of the spectral bands Red, Green, and Blue. And then created the image and maps above. 

Lab 04 Spatial Enhancement, Multispectral Data, and Band Indices

 For this lab we worked with both ERDAS Imagine and ARCGIS pro to gather satellite data. Then use it in identifying features using band indices and histograms to determine the reflection of light shown by the images. This also included using a variety of filters to help to identify features an highlight certain features so that they stand out more and can be accurately identified. Additionally we learned to gather data from online sources and work with bands to display and find certain features like in the images below.

Lab03

 For this Lab I worked with ERDAS Imagine and learned how to work in it and manipulate the data. For example I learned how to open it and edit it to fit it to the viewer.  I also learned about how the number of pixels can affect the different types of resolution of the raster and how to retrieve and manipulate the attribute table in ERDAS. Then I learned how to take an raster and bring it from ERDAS to ARCGIS and us the data gathered to make a map like in the image shown above. Additionally I learned how to calculate the area and display it in both software programs.

Lab 02 Ground Truthing

 For this lab we classified areas of the Pascagoula Mississippi based on level 2 Land Use and Land Cover classifications giving each classification a different color to distinguish it from other classifications. Then afterwards we check our work for using google maps and created 30 random points within the boundaries of the image using the create random points tool to check if my classification was correct via the street view feature.  Then we marked the points accordingly with correct points being green and incorrect being represented with red. We then measured the accuracy of my classification  and corrected the point in the attribute table if it wasn't. For my map I was able to get a 90% accuracy rating based off the points that were mapped.

 

Lab01 Visual Interpretation

 

In this lab we were tasked with identifying some of the basic features of images that are used to identify objects in these images. For example I used my own institution to determine to classify each of the different level of tone from very dark to very light and textures from very fine to very coarse. In the second map I used one of four strategies to identify objects such as Shadow, Association, Pattern, and Shape/size. For example I was able to identify parking lots, cars, water towers, and light poles using one of the strategies and additionally see how false and true color can differ on the same image.

Module 6 Suitability Part 2

 For this lab I worked with suitability map and creating a corridor based on the suitability criteria from 3 different layers. This was done by reclassifying the data and then combining it into a single weighted layer then using the cost distance twice to create a corridor. Then I created different thresholds that created the boundaries of the corridor suitability area that connected the two halves of the national forest based on roads, land cover, and elevation for the black bears based on their preferences.

Module 6 Suitability Part 1

 In this module we created suitability maps using a variety of tools and concepts. From the image here you can see that two scenarios were used to find the least cost path from two locations. This was accomplished by creating and reclassifying certain rasters to show the best location for the route. The rasters were then added together and used two different weights of the inputs which generated two maps as you can see above in which the green pixels are the most suitable and the red are the least suitable.  

Damage Assessment

 

In order to create this data and the analysis I used ArcGIS Pro to create several layers one point layer and a coastal line layer. The point layer had several different domains so I had to go through and create points for each of the properties then edit them to display and contain the damage information from the hurricane. Then after that I created a line layer and a multi ring buffer based on 3 different distances then I joined it with the points layer to get accurate counts where the majority of each damage type was located and display that data.