Module 1.1 Lab: Calculating Metrics for Spatial Data Quality

Horizontal Accuracy and Precision Results From the GPS data collected in the field, I calculated two key metrics: Horizontal Precision (68%): 10.20 meters Horizontal Accuracy: 29.4 meters.  These values tell an important story about how the GPS unit performed. 

The precision value shows that most of the GPS points were clustered within about 10 meters of each other, meaning the device was consistent. 

However, the accuracy value shows that the average GPS location was nearly 30 meters away from the true surveyed reference point, meaning the device was not very accurate in determining the correct position. 

 Accuracy vs Precision: Precision describes how close repeated measurements are to each other. Accuracy describes how close the measurements are to the true location. 

 In this dataset, the GPS unit was precise but not accurate, the points were tightly grouped, but grouped around the wrong location.

Suitability Analysis: Scenario 2 - Development

 

Figure 1 - Suitability Analysis Map

In this suitability analysis, I evaluated potential development land using five key criteria: land cover, soils, slope, distance to streams, and distance to roads. Each dataset was converted or reclassified into a standardized 1–5 suitability scale, where higher values represent more favorable conditions for construction. After generating individual suitability rasters, I performed two weighted overlay analyses one using equal weights and another emphasizing slope as the dominant factor. The results reveal how weighting decisions influence the distribution of highly suitable land. A final map layout presents both scenarios side‑by‑side, illustrating the spatial impact of each weighting strategy and providing the developer with a clear visual foundation for decision‑making.

Module 4 - Damage Assessment

 

Figure 2 - Hurricane Sandy Track 

This lab guided me through mapping Hurricane Sandy’s track, symbolizing the storm intensity, and preparing a layout that highlights affected states and the hurricane’s path. I then create a citizen damage‑assessment survey using Survey123, followed by building pre‑ and post‑storm imagery mosaics for visual comparison. Next, I design attribute domains and a structure‑damage feature class to support consistent data entry. Using these tools, I digitized structures within the study area, assigned damage categories, and symbolized the results. Finally, I digitized a simple coastline, calculated distances from each structure to the shore, and analyzed whether damage patterns correspond to proximity to the coast.

Damage Assessment

To examine patterns in the damage, I first created a simple polyline feature class called Coastline in the geodatabase and digitized the shoreline using the pre‑storm imagery. It didn’t need to be super detailed, just enough to mark where the water meets the beach along the study area. After saving those edits, I used that coastline to measure how far each structure was from the shore. From there, the goal was to figure out which tools would help summarize the number of structures in each damage category within the 0–100 m, 100–200 m, and 200–300 m distance bands. Once the distances were calculated, it was just a matter of organizing the counts into the table so I could see whether any clear patterns showed up in how damage relates to proximity to the coastline.

The damage assessment denoted the following results; the closer the homes were to the coast, the higher the likelihood of being destroyed or experiencing major damage, which is expected since these homes are closest to the shoreline and most likely to experience major wind and water impacts. Although these measurements certainly reveal a trend showing that the closer a structure is to the water, the more destruction and structural damage it will incur, I don’t know if this numerical pattern will always translate across the entire coastline, because different areas experience different building densities, differently sized lots, and other variations. However, if there were a way to standardize these differently sized areas and create a ratio comparison based on area density and similar factors, we could potentially use those numbers to extrapolate conditions in nearby areas.

Table 1- Summary of Structural Damage

Figure 1 - Structural Damage Assessment Features and Attribute Table

To View a Summary of this two-week Coastal Flooding Assessment, please check out my Story Map!

 : https://arcg.is/1qmuC54

Module 3 - Coastal Flooding

 

Figure 1 - NJ Coastal Flooding

Figure 2 -  Fl Coastal Flooding

In Part 1 of the lab, I explored how GIS and LiDAR can be used to analyze natural disasters, focusing on Hurricane Sandy’s impact on Mantoloking, New Jersey. My goal was to measure erosion caused by the storm using pre‑ and post‑event LiDAR data. After loading both datasets into a 3D scene, I compared the pre‑ and post‑storm point clouds to observe visible differences.

Next, I created DEMs by converting each LAS file to a TIN and then to a raster. Using Raster Calculator, I subtracted the pre‑storm DEM from the post‑storm DEM to highlight areas of erosion and deposition. The red‑to‑blue color ramp helped me visualize where the storm removed sand, destroyed structures, or deposited debris. I then compared these changes with building footprints and imagery using bookmarks to answer questions about rebuilding, damage patterns, and data reliability

 In part 2 of the lab, I modeled a 1‑meter storm surge using both the LiDAR DEM and the USGS DEM to see how each dataset affects flood mapping results. I created flood rasters by selecting all areas below the surge height, used Region Group to identify connected flood zones, and kept only the region that was truly connected to open water. I then converted the flood areas to polygons and performed spatial joins to determine which buildings were flooded according to each DEM. All in an effort to determine the accuracy of the DEM’s.

Throughout the process, I had to troubleshoot several issues, including incorrect data paths, confusion about Region Group values, verifying that “0” meant not flooded, and fixing queries for omission and commission errors. I also resolved problems with map layout, such as removing unwanted basemap labels. After calculating errors, I found that the USGS DEM produced extremely high commission rates, meaning it greatly overestimated flooding. Because of this, I used the LiDAR results for my final map since they were more accurate and realistic.

Module 2 - Lidar

 

Figure 1 - Forest Analysis Part 1

Figure 2- Forest Analysis Part 2

 

This week’s lab represented a decent challenge for me! Technology was not cooperating, but overall, with proper troubleshooting, I was able to overcome and complete the assignment.

We conducted a LiDAR‑based forest analysis in Virginia to better understand canopy structure, terrain, and vegetation patterns relevant to forest management. We began by downloading and converting the Virginia LiDAR tile into an uncompressed LAS dataset, then explored the point cloud in a 3D local scene to observe landscape form, topography, and vegetation distribution. Using ground and non‑ground returns, we generated a DEM and DSM and subtracted them to estimate tree height across the study area. We evaluated height accuracy, identified outliers, and interpreted negative values in relation to roads and clearings. To assess biomass‑related canopy density, we converted LiDAR classes to multipoint features, rasterized them, and calculated vegetation to total return ratios to produce a canopy density surface. We then visualized height distribution with a histogram and created a series of maps to illustrate forest structure, highlight man‑made features, and support forestry applications such as biomass estimation, forest health assessment, and terrain‑based planning.

Module 1 - Crime Analysis

 

Image 1. Grid Overlay Hotspot Mapping

Image 2. Kernel Density Hotspot Analysis

Image 3. Local Moran's Hotspot Mapping

This first lab used our GIS skills to map crime incidences in both Washington and Chicago. In the last part of the lab, we explored three different techniques, Grid Overlay, Kernel Density, and Local Moran’s I hotspot mapping, to analyze which would be most efficient in predicting future crime from the perspective of a police department allocating resources.

To complete this analysis, I created three different hotspot maps using the 2017 homicide data for Chicago. I began with the grid‑based hotspot method by joining the homicide points to the half‑mile grid cells, selecting only the cells with at least one homicide, and then identifying the top twenty percent with the highest counts. I dissolved these selected cells into a single polygon to represent the grid‑based hotspot. Next, I created a kernel density hotspot by running the Kernel Density tool with the appropriate parameters for Chicago, adjusting the symbology to isolate values at or above three times the mean, reclassifying the raster into two classes, converting it to polygons, and selecting only the highest‑density areas. For the Local Moran’s I hotspot, I joined the homicide data to the census tracts, calculated homicide rates per one thousand housing units, and ran the Local Moran’s I tool to identify statistically significant high‑high clusters. I selected those clusters and dissolved them into a single boundary. These three hotspot methods provided different outlines of where homicides were concentrated in 2017, which I later compared to the 2018 homicide locations to evaluate how well each method predicted future crime.

The Local Moran’s hotspot would not be a great future predictor because, although it has a high number of 2018 homicides, this occurred over a very large area of 52.67 square miles. This is too broad and unable to pinpoint resource allocation accurately.

The Grid Overlay and Kernel Density data are relatively similar, with smaller areas and a large number of 2018 homicides. However, the density calculation is the dealbreaker here, with a high concentration of crime that is useful for a police chief to allocate his resources. Due to this, the Kernel Density hotspot analysis technique is the best to use as a production of future crime and allocation of resources.

Module 7- Neocartography

Figure 1 - View of South Florida Google Earth Tour

This week we used Google Earth Pro to create an interactive 3D map of South Florida. Google Earth Pro is widely used because it provides a simple way to view and distribute geographic information without requiring GIS software or training. Data in Google Earth can be saved and shared as KML or KMZ files, which are compatible with many GIS platforms. 

 In this lab, we first converted map layers from ArcGIS Pro into KMZ files, then loaded them into Google Earth Pro. We also add a legend as an image overlay and organize all layers into a folder. Much like ARC GIS Pro, one can edit layers, points and fonts, ect on Google Earth Pro.  Amongst the layers included population density data points and surface water. Then, we create a guided video tour in Google Earth Pro by adding placemarks for key locations in South Florida and the Tampa Bay region, including the Miami metropolitan area, Downtown Miami, Downtown Fort Lauderdale, Tampa Bay, St. Petersburg, and Downtown Tampa. Once all placemarks are created, we recorded the tour that flew to each location, adjusting layers, and exploring 3D buildings.