Module 1 GIS 5935 Special Topics in GIS 

Our first module for GIS 5935 has us learning about the difference between precision and accuracy and also the calculations and practices involved in determining precision and accuracy. It is a common misconception that precision and accuracy are the same thing, however something can be precise but still be inaccurate. In this weeks assignment we carried out analysis to determine the horizontal accuracy and precision of some data that was collected.

The first part of this weeks assignment had us determine the horizontal and vertical precision of points (from the same location) that were collected with a handheld GPS. The points were compared to a reference point that is the true location of the point that was collected. In order to carry out the analysis we had to carry out a batch processing operation to get all the coordinate systems in the same system, we had to do some field calculations and then we determined target percentiles for our data. Once we had target percentiles we had to create a buffer that showed where 50%, 68% and 95% of our data fell. We used the 68%  ring to determine our precision. Below is my resulting map with results. 

I calculated a horizontal precision of 4.5 meters and a vertical precision of 5.9 meters both at the 68% level. My calculated horizontal accuracy was 3.29 meters and my vertical accuracy was 5.96 meters. Horizontal precision was determined by coming up with an X,Y coordinate that was derived by averaging out all the collected waypoints. An index point was derived from the data and ring was created using the index point. The index point created a buffer which contained 68% of all the collected waypoints. The same method was used to determine the vertical precision. 

For accuracy a reference point was used and the the distance between the reference point and the average waypoint was used to determine the accuracy. The same methods were used to determine the vertical accuracy. 

 GIS Applications Module 6

This week we continued with our coastal flooding theme from last week with a few additions. First we used the point to line tool to create a path for hurricane Sandy. Along the path we added custom symbology that we created to represent different stages of the storm. We added custom labels to our custom symbology by utilizing VBScript. The map below is the final product of the first part of this weeks module.

 

The second part of our assignment this week had us put together a survey to assess damage caused by a hurricane using Survey123. The survey created could be utilized to easily collect data from just about anyone with access to a computer or smart device. 

Along with creating the online survey we also carried out some damage assessment analysis of our own. We started by adding raster mosaics to our geodatabase and populating our mosaics with imagery that was obtained prior to hurricane Sandy and after the hurricane made landfall. We focused on small area within the state of New Jersey. We set up new feature classes to catalog damages that were caused within the given study area. Using the imagery we did our best to classify damage based on analysis of the imager provided. To carry out the analysis I used mostly the Swipe tool to quickly see any changes in the two sets of imagery. The screenshot below shows the points I placed that are associated with my damage assessment.  

Finally we used the imagery and damage assessment data we came up with to examine patterns in our data. We digitized a line using our pre storm imager and used it as reference to measure the distance between the pre-storm shoreline and the damaged properties in our study area. I used a multiring buffer and some clipping of my damage assessment layer to determine the damage that occurred within specific distances from the shore. The table below represents the status of structures at 100, 200 and 300 meters from the shoreline within the study area. 

Link to associated story map:

https://storymaps.arcgis.com/stories/1534176b11ca457bb93424b2c448e2b9

 GIS Applications Module 5

This week we learned how to carry out activities related to coastal flooding. We used Digital Elevation Models (DEM) derived from various sources to map out the effects of storm events. In the first exercise for this module we looked at data from New Jersey associated with Hurricane Sandy. We had to work with LAS datasets and rasters to carry out our evaluations. We had to do conversions using the TIN to Raster tool and we also had to use the Raster Calculator to be able and distinguish differences in raster datasets over time. Once we had all or our data converted and ready to use we were able to map areas where changes in altitude occurred due to either deposition or removal of material by forces created by Sandy. Below is a map I created that shows areas where material was either added or removed by storm activity. 

The second part of the this weeks module continued with the same theme however we moved the study area to the State of Florida and mapped potential storm surge zones and impacts to buildings. We were provided two DEM's one derived from LIDAR data and the other was a conventional USGS DEM. We first had to ensure the units of both DEM's were the same so we had to use the Times tool to change units. We had to reclassify values in the DEM's to represent areas that were flooded (below 1 meter) or not flooded. Rasters were converted to polygon using the Raster to Polygon tool. Once we had derived polygons we spatially joined the DEM derived polygon with a shapefile containing buildings in the study area. The spatial join allowed us to see what buildings were impacted by the predicted storm surge. A series of selections by attribute were used to carry out queries that specified the impacts by building type and DEM type. Once all the analysis was complete we put together a map showing the predicted storm surges derived from each DEM and the potential impacts to buildings. Below is the resulting map from this exercise.