Jason's GeoJourney

This week in GIS 5100 we continued the study of hazards with a focus on damage assessment. For the first part of the assignment, we were directed to create a map based on NOAA data of the track of Hurricane Sandy, also known as "Superstorm Sandy" showing its progression from a tropical storm in the southern Caribbean Sea on October 22, 2012 to its landfall on the northeastern coast of the United States as a Category 1 hurricane on October 29, 2012.  The path of the storm is symbolized by its type along with labels showing MPH winds and barometric pressure in specific points. The storm's maximum wind speed reached 105 MPH and the lowest barometric pressure was 940. For the next part of the assignment, I created a brief survey with ArcGIS Survey 123, which is an online platform that allows individuals to report damage and submit photographs in the field that are geocoded and can help complement remote-sensed data: Hurricane Sandy Citizen Damage Assessment* *This survey is o

This week in GIS Applications, we examined the topic of Coastal Flooding by learning how to assess this type of hazard to communities through the delineation of coastal flood zones via various digital elevation models (DEMs).   Here we used a variety of techniques and tools such as overlay analysis in vector and raster domains while examining the differences in different types such as traditional USGS DEM and LiDAR derived DEM data. For the first part, I determined the level of erosion that occurred on the New Jersey coastline, particularly in Mantoloking, as the resulting from Hurricane Sandy in 2012. Here I compared pre and post sandy LiDAR imagery of the area by creating DEMs of the area. This involved converting the .las files to TINs and using the Raster Calculator tool in order to highlight the greatest areas of erosion through a symbolized color ramp (going from red to blue). In the second part of the lab, I analyzed storm surge in Cape May County, New Jersey, by determining

This week in GIS 5100, we learned about visibility analysis with a focus on two main types: viewshed and line of sight. As part of our assignment, we were directed to complete four different Esri courses through ArcGIS Online.  This included: Introduction to 3D Visualization, Performing Line of Sight Analysis in ArcGIS Pro, Performing Viewshed Analysis in ArcGIS Pro, and Sharing 3D Content using Scene Layer Packages. For the first course, Introduction to 3D Visualization, I learned about using local and global scenes, and their differences, cartographic offset, vertical exaggeration, extrusion and applying 3D symbology, and making visual enhancements, such as displaying shadows, adjusting altitude, and ambient occlusion.  For one of this course's activities, I used an ESRI authored global scene of Downtown San Diego, CA, by applying 3D symbology. Here I was able to work with shadows, illumination, time of day and ambient occlusion.  This was the most interesting scene I created for

This week in GIS applications we learned about LiDAR.  LiDAR stands for "Light Detection and Ranging", and while it has been traditionally used in forestry science, as related to this week's lab topic, it is increasingly being used in archaeological research, which is my primary area of study. For this week's lab, we worked with LiDAR data from the Shenandoah National Park, Virginia, to examine tree height and tree canopy density.  To do this, LiDAR data was obtained from the USGS in the form of an .las file, and then converted to a digital elevation model (DEM). To do this, I used the LAS Dataset to Raster tool in ArcGIS. This map shows the LiDAR point cloud along with DEM produced: Next, I calculated forest height from the DEM and created a map from it along with an accompanying tree height chart: Finally, I calculated biomass density by using the following tools in order: LAS to MultiPoint, Point to Raster, Is Null, Con, Plus, and Divide: Overall, I found this wee

For the first main topic in Applications in GIS, we covered crime analysis, which I found interesting for a few reasons. First, I have not covered the use or application of GIS in the crime field in the certificate program courses up until now, so I enjoyed learning how the technologies can be applied to this type of phenomena.  Another reason of interest is that part of my academic background is in the field of sociology. The first sociology department founded in the U.S.A. was at the University of Chicago (in 1892) and some of the earliest research conducted by pioneering sociologists in this organization focused on the relationship between demography, geography, and crime, such as the work of Shaw and Mckay.  While sociologists use some of the same analytical and statistical methods as geographers/GIS professionals in the analysis of crime (rates, incidences, etc.), there are some fundamental differences!  I cannot recall covering kernel density estimation or Moran I’s i