Skip to main content

Module 3: Visibility Analysis

 This week for Module 3, I did four different training classes at ESRI.com regarding 3D data visualization and analysis.

The first training [3D Visualization] taught me how 3D data is represented, different elevation types (on the ground, relative to the ground, absolute height), and how to convert 2D data to 3D via extrusion. Numerical data can be extruded an attribute as z-value to make it 3D using base, min, max, or absolute value of a certain attribute. In the appearance tab -> extrusion group -> choose the field. In our exercise I also used Extrusion Expression to “see the features more clearly.” (ESRI) Additionally in this training, I used different marker types for 3D data like a basic tree, to procedural markers that are more realistic and specific to an given location, fill symbols like grass, and procedural fills “for cities with a well-known architectural style, procedural fills can help create a realistic set of buildings, along with other common city features, such as street light.” (ESRI) I thought it was cool that you could even change the Ocean/water fill symbol layer by degrees to reflect waves and water strength. Lastly, I played around with atmospheric and illumination effects. When using a global scene, you can change the lighting by time! For example, I can choose in the properties to change the lighting to 7p.

Picture 1: Example of 3D visualization for training 1.

 

In the second training, I performed line of sight analysis via the construct sight lines and line of sight tool. This was a fun training exercise, as I was able to create all the sight lines for the view points of a parade, and then narrow down the sight lines given distance. We added the data with the Add Z Info tool, and removed the lines of sight that didn’t met our criteria with the Delete Features tool.  

Picture 2: Example of line of sight analysis for a parade.

The third training was also fun; Viewshed analysis. Viewshed analysis is a bit more detailed then line of sight, as it considers any obstructions surrounding the observer’s point of view. So while line of sight, looks for a line between the observer and target that is unobstructed, viewshed looks at all the areas the observer can see. In the exercise, we looked at the illumination capability of camping areas given the current light provided by a lighting company. We used the viewshed tool and raster functions [greater than in this case] to define and meet our criteria.

Picture: Example for training 3. 


The last and fourth training I did was: Sharing 3D content. One random but very important tid-bit I learned was that you can use the V key and mouse to move among an ArcGIS Pro map in a 3D fashion. I previously struggled with moving into a 3D map, away from an aerial view. In this training, I learned how to use the Create 3D Object Scene Layer Package tool to upload the scene layer to arcgis and the steps to correctly publish the scene layer. 

Picture 4: Example for training 4. 


Labels:

Comments

Post a Comment

Popular posts from this blog

Bivariate Choropleth and Proportional Symbols

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...
Post a Comment
Read more

Positional Accuracy: NSSDA

 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...
Post a Comment
Read more

Infographic's

 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...
Post a Comment
Read more