baj23_GIS

The area of interest in my home state of California. California is special because it is the only state that has 6 state plane zones divided within the NAD 1983 State Planar Zone. Most state planar divisions in the United States are divided vertically, but California is vertically shaped, necessitating that it be divided horizontally instead. On the other hand, if we used a UTM projection the zonation for projecting CA would be divided into only two ungainly pieces. Within California’s state planar zone, Placer county falls under Zone 2.  What are Projections?   In GIS, there are different projections a map can be displayed that can present different data from a different view around the Earth. In the Mercator projection, the linear scale is equal in all directions around any point, thus preserving the angles and the shape of smaller objects. The distortion in the size of the object increases as the latitude increases from the equator to the poles. Mercator is a cylindrical conformal

  Legibility – I tried using a Time New Roman approach with the text to explore a different font and have more formality. Figure-Ground Orientation – For this map, one map feature I worked on having a good figure-ground orientation is the buffer zone for the preservation of goshawk nest using a dashed dot to circle it to showcase the buffer zone of the species preservation. I could better re-organize the legend so the protected area and the buffer feature would be first in the list. Hierarchal Orientation – I choose a landscape style to present the data better. I also decided against using an insert map by adding additional information on the location of Tongass National Park. Hierarchal Organization – To emphasize my major map elements, I weighed which parts of the map would I want my readers to focus on. The main focus in the areas where the Goshawk nest and habitat need preservation. Then to address the business proposal of which area would they take lumber I highlighted the areas d

  Learning all the different interpolation methods to take the known values to estimate values of other points gave me a different perspective on how to analyze datasets like elevation, rainfall, chemical concentrations, and water quality. A challenge I found between the different interpolation values in which interpolation method would be best to apply to the different datasets if I had data with fewer sample sites and how to correct them so the data can be better represented. Believe elevation data would be easiest to apply the Spline interpolation method due to the simple data values and best representation in elevation change. Data such as data points of chemical values would be better represented with Thiessen interpolation values.  1.      The difference between two interpolation methods; Spline and IDW (Inverse Distance Weighted) is that Spline estimates values using a mathematical function that minimizes overall surface curvature, resulting in a smooth surface that passes exact

 This week, we explored the applications and used for DEM ( Digital Elevation Models) and TINs ( Triangulated Irregular Networks). They are often used for imagery and elevation models but learning more in-depth about their advantages and disadvantages to showcase data.  TINs are not generalize compared to DEMs and can showcase more data with higher densities of data. TIN can form more accurate contour lines based on the terrain. TINs generate vector points from x,y, and z coordinates and then bind the three points creating the triangles which can form the triangulated patterned model.  DEMs on the other hand create smoother and rounded contour lines. DEMs oftentimes in projects are used to create a grayscale gradient of elevation and simple elevation models in the 3D application of Arc Pro. DEM has limitations in its presentation and since it generalizes its models it has the possibility to misses terrain factors such as hills and dipped along a mountainside. 

 How are roads monitored and maintained?  The United States has a very intricate and developed road system that has continuously evolved over the years. As changes occur roads may become worn down, unused, or discontinued. The road can be repaved and also adjusted to the new development of buildings in the years.  In this week's lab, we learned how to calculate road completeness and analyzing its completeness in excel using reference data to determine how much percentage difference between the analyzed data to the reference. We were given a segment of road data from the TIGER road network from the U.S Census Berua and compared it to the local road centerline dataset from the Jackson County orgeon. To prepare the data given we had to clip the Centerline roads and the TIGER roads to the Jackson, OR county borders. I then split both roads datasets by whichever GRID box they fell into so I could properly calculate the percentage of the difference between the roads per grid. I created a

 How accurate is data?  Many times, we receive data, and as we make the assumption that the data is accurately applied into ArcPro, it is not always the case. The National Standard for Spatial Data Accuracy (NSSDA) creates a methodology to measure and describe the positional accuracy of data features. For our lab assignment, we were given two data sets for roads from USA street data and ABQ street data of Albuquerque, New Mexico. In order to analyze the road data, we were instructed to create 20 points to serve as reference according to the areial imagery. Then gathering intersection points from the USA street data and the ABQ street data and gathering their XY coordinates to enter into excel to calculate SUM, AVERAGE, Root Mean Square Error (RMSE) values.  SUM - The set of the squared differences between the test and the independent data sets.  AVERAGE - The average of the sum.  ROOT MEAN  SQUARE ERROR - The square root of the average. To find the RMSE value at a 95% confidence level