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. 

Module 6 - Isarithmic Mapping

 

Figure 1 - Isarithmic Map for Washington State Precipitation

In this lab, we explored how long‑term precipitation patterns in Washington are modeled using the PRISM interpolation method. The dataset, originally created by the USDA Service Center Agencies and published through the USDA Natural Resources Conservation Service in 2009, was downloaded from the USDA Geospatial Gateway and includes monthly and annual precipitation rasters. PRISM begins with 30 years of rainfall measurements collected from weather stations, then fills in the gaps between stations using interpolation. Because elevation strongly influences rainfall in Washington, PRISM incorporates a digital elevation model and a regression model that relates each station’s elevation to its precipitation values. These combined factors allow PRISM to estimate rainfall for 800‑meter grid cells, producing monthly surfaces that are summed into an annual precipitation map.

Throughout the lab, we worked extensively with continuous raster data and learned how to apply continuous tone symbology to represent smooth, gradual changes in precipitation. We also used legend‑editing tools to create clear, appropriate map legends and relied on the Spatial Analyst Extension to perform raster operations. Hypsometric tinting was implemented to highlight Washington’s terrain by assigning distinct color bands to elevation ranges, and hillshade relief was added to enhance the visual structure of the landscape. We used the Int tool to convert floating‑point rasters to integers, manually classified elevation data, and generated contours using both the Contour List tool and the Spatial Analyst Toolbar. Altogether, this lab strengthened our ability to symbolize continuous surfaces, interpret terrain, apply analytical tools, and clearly communicate the processes and outcomes involved in building a complete isarithmic map.

Module 5 - Choropleth and Proportional Symbol Mapping

Figure 5 -  Map of European Wine Consumption

 For this project, the Albers projection was used because it preserves area accurately, which is essential for choropleth mapping. Since population density depends on the size of each region, an equal‑area projection prevents misleading distortions. Mapping population density instead of raw population counts also ensures the data is standardized and comparable across countries of different sizes.

A neutral tan color scheme was chosen to represent land, with darker shades showing higher population density. This palette is easy to interpret and avoids overwhelming the reader. Five classes were used to keep the map readable, and the data was classified using Natural Breaks because it best reflected the natural distribution of the dataset. Quantile classification was avoided because it would have grouped very different values together and misrepresented the data.

Wine consumption was displayed using red circles, which stand out clearly against the tan land and blue ocean. The data did not need normalization because population density was already calculated using area. SQL queries were used to filter and manipulate the dataset, allowing for cleaner data presentation and more precise symbol placement.

Graduated symbols were chosen over proportional symbols because they were more user‑friendly and communicated the ranges more clearly in the legend. Although proportional symbols are truest to the raw data, they made it harder to distinguish outliers like Vatican City and were less intuitive for readers. Flannery Compensation was not used; even if proportional symbols had been chosen, it would have exaggerated the Vatican outlier, caused symbol overlap in Europe’s dense geography, and made the legend harder to interpret.

Throughout the project, careful attention was given to cartographic design principles: selecting an appropriate color scheme, choosing a meaningful classification method, creating a clear and accurate legend, using effective thematic symbols, and compiling the final map in a way that communicates the data honestly and clearly. Including the projection information on the map reinforces that the spatial representation is accurate and that the data is being presented faithfully.

Module 4 - Data Classification

In this week's lab, we worked with four common data classification methods, Equal Interval, Quantile, Standard Deviation, and Natural Breaks, to see how each one displays spatial data differently. Using ArcGIS Pro, we created a map layout with four data frames and symbolized each one using graduated colors to make the patterns easier to understand. We also practiced normalizing data, saving custom color schemes, and applying cartographic design principles to produce clear, readable maps. After comparing the classification methods, I evaluated which ones work best for different audiences and which data presentation approach most accurately represents the distribution of senior citizens in Miami‑Dade County.

 Then, two maps were generated using the different methods. he four classification methods each display the senior population data differently and reveal different patterns.

Equal Interval divides the data range into evenly sized classes, which is simple to read but often hides variation, especially after normalization, because most values get compressed into similar categories.

Quantile places the same number of features in each class, preventing empty categories but sometimes splitting similar values or grouping very different ones, which can distort the true differences.

Standard Deviation highlights how far values deviate from the mean, making it excellent for identifying unusually high or low concentrations of seniors, though it becomes less meaningful when the data is not normally distributed or when most values cluster near the average.

Natural Breaks finds the most meaningful clusters in the data, revealing the underlying structure, but the irregular class ranges make comparisons across datasets difficult.

For audiences who need to take action, such as planning senior services, the Standard Deviation method is the most useful because it clearly identifies areas with unusually high concentrations of seniors. For a casual viewer who just wants a general sense of the distribution, Natural Breaks is more intuitive and visually clear.

When presenting this information to Miami‑Dade County Commissioners, the population count normalized by area is the most accurate way to show where seniors actually live. Percentages can be misleading because small populations can appear disproportionately high, and equal percentages can represent very different numbers of people. Normalizing by square miles avoids distortions caused by varying tract sizes and provides a clearer picture of where services are most needed.

Figure 1- Normalized Data

Figure 2 - Data in Percentages