GIS 5100 Visibility Analysis

 This weeks lab changed things up a bit by having us take 4 courses on  the ESRI site. 

Intro to 3D Visualization

There were many new techniques in this ESRI lab.  One of my favorite techniques was Global scene effects. Using the Illuminations settings to display shadows and then taking it further to be able to choose the specific date and time was super fun. I was impressed by the realism that added to the scene. Interesting tip: If you want to be able to tee through the front or back of a 3D object use Face Culling. To do this you select the layer in the contents pane. In the appearance tab navigate to the faces group, click on the down arrow and it gives you choices of none, back, front, and built-in.  Although it was not helpful in this exercise, I can see opportunities for application.

Performing Line of Sight Analysis

This lab was involved more usage of the geoprocessing tools. It was very interesting to see how line of sight scenes are constructed. I learned how to use add Z information tool to calculate the sight line lengths and append the results to the attribute table.  This allows you to use Select By Attributes and Delete Features to remove unwanted lines.  In the case of our exercise that was lines longer than 1,100 feet.   The variety of applications discussed was very interesting. I like how easily you can determine the blind spots at a glance.

 

Performing Viewshed Analysis in ArcGIS Pro

In this exercise we used viewshed (3D Analyst Tool). We were examining a campground and illumination from pole lights.  We used Raster Functions and modified attributes to see how campground light coverage differed with lights at different heights. We found that by changing the OFFSETA (attribute table field) from 3m to 10m the coverage was significantly increased.

 

Sharing 3D Content Using Scene Layer Packages

In this exercise we authored a 3D scene. We chose Global Scene because of the extent and the importance of the curvature of the earth for this project. We converted 2D data to 3D data. We gave the trees and buildings height by adding elevation sources and surface information. The exercise required utilizing the Layer 3D to Feature Class and Feature to 3D By Attribute tools. Once we had the completed scene we moved to the next phase of creating a scene layer package to publish and share through the use of the Create 3D Object Scene Layer Content tool. The package can be viewed on ArcGIS Online Scene Viewer, ArcGIS Earth, and ArcGIS Pro. I think this is a great way to present new information and to collaborate with the person, group or public you choose to allow access to.

This is an example of slide capture and my utilization of the Daylight tool to select the level of sun exposure/shadowing I liked the best

GIS 5100 Forestry & LiDAR Lab

 This week we are onto the area of LiDAR.  What is LiDAR you ask?  Well it is defined as Light Detection and Ranging. There are countless application for this technology. For the purposes of this lab we focused on its application in forestry. We used LiDAR data .las file to create DEMs (Digital Elevation Models) and DMSs (Digital Surface Model). We used this information to calculate forest height and biomass density for the Shenandoah National Park in Virginia.

We started by importing a .las file into our local scene in ArcGIS Pro. this was my first result. 

Seeing this my initial response was panic and asking myself what I did wrong. Fear not, just zoom in and the transformation occurs. You get that rainbow of color you were hoping for.  From there we went on to calculate for forest height by first using the Point File Information tool. Next, we created a DEM and a DSM using additional geoprocessing tools.  Here is my DEM layout.

Our next step involved using the LAS to Multipoint tool and the Pont to Raster tool to calculate the Biomass density. After multiple calculations I was able to a tree height  and a tree canopy layout.

It was important to review and refine map navigation skills as well. When dealing with scenes you need to be able to tilt the map.  I created a LiDAR layout map for this lab and attempted to tweet the colors here and work on the map navigation to better illustrate the way it lays over the topography.

The uses of LiDAR are really endless. This lab sparked my interest to see some of the ways it is being utilized.

I found this article very interesting and hope you will check it out:

https://geoslam.com/blog/2022/01/25/where-in-the-world-is-lidar-being-used/

Until next week ....

 

GIS 5100 Module 4

This is our first lab for this course and we are using hotspot analysis for crime maps. To introduce us to crime mapping we started out with making some basic maps of burglaries and assaults in the Washington DC area. Once we were introduced to the basic concepts we were tasked with making 3 maps of Chicago homicides using grid overlay, kernel density and Local Moran's I methods. after completing the maps we had to evaluate which method we felt was most predictive based on the comparison of the 2017 homicide maps and 2018 homicide locations.

Here are my three 2017 Chicago Homicide Maps:

When examining the table above it appears that the Kernel density would be the best predictive source for the police chief.  The chief has to take into consideration the vast area he has to cover and find away to best allocate his resources to protect the citizens in the most efficient manner. Both the Kernel Density and Local Moran’s I have relatively high % of 2018 homicide occurrences in the 2017 hotspot areas. However, the Kernel Density map is based on the point data directly. It provides a good balance between the methods.  A more concise area than Local Moran’s I and better results than the Grid  which make it a better choice for the police chief to use when allocating protective resources.