Module 1.3: Data Quality - Assessment

 

Figure 1: Percentage difference in road network completeness between Tiger road and Street Centerlines, separated but by grid cell cell to show spatial variation network completeness.

The goal of this lab was to evaluate the horizontal accuracy and completeness of two road network dataset, county Street Centerlines and Tiger Roads, using a grid‑based comparison approach. By measuring how much road length each dataset contained within every grid cell, we were able to identify where the networks aligned closely and where one was significantly more complete than the other. This type of assessment is important for understanding data reliability, especially when road networks are used for routing, emergency response, or spatial modeling.

To complete the analysis, I first clipped both road datasets to the county boundary and intersected them with the grid so that each road segment was split and assigned to the correct grid polygon. After calculating segment lengths in kilometers, I summarized total road length per grid using the grid code field as the unique identifier. These per‑grid totals allowed me to compute percentage differences between the two networks and classify which dataset was more complete in each cell. Finally, I created a graduated‑color map using to visualize spatial patterns in completeness across the county.

Module 1.2: Data Quality - Standards

 

Figure 1: Overall Data Points

To complete the NSSDA accuracy assessment, I started by preparing three point layers: the ABQ city test points, the StreetMapUSA test points, and a set of reference points digitized directly from the orthophotos. I made sure all three point layers were in the same projected coordinate system and verified that each set of points aligned correctly on the map. 

After digitizing the reference intersections, I added XY coordinates to all three layers and exported the attribute tables into Excel. In Excel, I calculated the change in X and Y coordinates for each intersection, squared those values, and computed Error², the average Error², RMSEr, and finally the NSSDA 95% accuracy value for both datasets. 

I initially ran into issues with a few mismatched rows, but once corrected, the results stabilized and produced realistic accuracy values. Based on the final calculations, the ABQ city streets tested at 20.31 feet horizontal accuracy, and StreetMapUSA tested at 154.38 feet horizontal accuracy, both at the 95% confidence level according to NSSDA standards. Thus, the ABQ city map was much more accurate than StreetMapUSA.

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. 

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 

Module 3 - Cartographic Design

 

Figure - Map of Schools in Ward 7

This week, we used Gestalt’s Principles of Organization along with the other cartographic design principles we have learned so far in the course to create a map showing the locations of schools in Ward 7. The main map displayed this information along with general contextual features such as roads, highways, parks, and other environmental elements. Additionally, there was an inset map to show where Ward 7 is located in the context of Washington, D.C., and the surrounding areas. In our map, we had to demonstrate hierarchy, use contrast to show the relative importance of features, apply figure, ground principles to make certain features appear closer to the viewer, and accomplish all of this while maintaining a balanced, harmonious map that effectively communicates its purpose to the end reader.

Different tools were used, such as the clipping tool to select only the schools within the Ward 7 boundary. We also referred back to last week’s lab, where we practiced labeling. In this lab, we labeled neighborhoods, road systems, and the Anacostia River. To make the map more visually engaging, I took an additional step and learned how to convert labels to graphics, particularly useful for creating the shield symbols commonly used for different road and highway systems.

Since there were so many principles to remember, along with the challenge of using ArcGIS Pro, which can be tricky, I decided to tackle all of the labeling first. I fine‑tuned the labels and placed them exactly where I wanted them before moving on to the other map elements, such as fonts, sizing, and layout adjustments. It took some time, but breaking the project into these separate tasks made the overall process far less daunting.

Here are some of my design decisions and the reasoning behind them. 

In designing my map of schools in Ward 7, I focused heavily on visual hierarchy, contrast, figure ground, and balance. To establish hierarchy, I used different‑sized pushpin symbols for the schools so viewers could easily distinguish elementary, middle, and high schools. Their dark red color helped them stand out as the most important features. For the roads and highways, I used standard gallery symbols but adjusted line thicknesses to show their relative importance without overwhelming the map.

I also applied hierarchy through typography. The title used the largest font, while elements like the north arrow and scale bar were kept subtle. Although I wanted a larger font for the school list, space limitations required me to keep it at 11‑point.

To create contrast, I relied on both color and symbol differences. The school symbols were dark and bold, while the ward boundary was intentionally much lighter than its surroundings to highlight the area of interest. I used the same approach in the inset map, making Ward 7 even lighter so it remained visible at a smaller scale. For labels, I used Corbel and Tahoma in medium gray, readable but not overpowering and used bold or italic styles only when according to cartographic principles.

 I made Ward 7 noticeably lighter than the surrounding areas so it would visually “pop” forward. The rest of the map used a cohesive, neutral palette of beiges and grays to keep the focus on the schools and major features.

Balancing the layout required some trial and error. Since heavy elements at the top can make a map feel top‑heavy, I placed the school list at the bottom. Although the legend and school list on the right side made the layout slightly right‑heavy, the bold title and inset map on the left helped counterbalance this. I also made use of the natural triangular spaces created by the map extent to place elements efficiently.

Along the way, I refined several technical details: reorganizing the street drawing order so major roads weren’t covered, experimenting with gradient strokes to add subtle depth to the county layer, converting highway labels to graphics for easier editing, and using 75% transparency behind the school list so it wouldn’t block the map beneath it.

Overall, these choices helped create a map that is visually clear, balanced, and easy for viewers to interpret.

Module 2 - Typography

 

Figure 1 - Map of the Important Features of the State of Florida

The purpose of this lab was to create a map that included essential map elements, follow basic mapping principles, and test our new labeling and annotation skills by creating a map of Florida with specific required features. The following considerations were applied when producing the final map.

Color Schemes and Feature Enhancements

For the county layers, I chose an earthy tan color that blended well with the rest of the map’s color scheme and followed standard GIS color guidelines, where land is represented with neutral tones. I also reduced the thickness of the state and county borders from the original 1 pt to 0.25 pt and changed them to a light gray instead of the original dark gray. This prevented them from distracting the reader or being confused with the river features. In contrast, I made the river features a thicker 1 pt line so they would stand out clearly on the map.

For the swamp and marsh areas, I opted for a green color to represent vegetation and applied a diagonal stripe pattern to give the polygons texture and help differentiate them from the rest of the map. I attempted to use the “swamp” pattern in ArcGIS Pro, but it appeared too sparse and did not look appropriate.

Labeling and Annotation

For areal features such as the Okefenokee Swamp, the polygon was too small to fit the label inside. Following best practices, I used a leader line to point to the center of the polygon. I could not place the text in the upper‑right corner because it conflicted with surrounding land areas and overlapped another state, so I oriented it on the bottom instead.

The city of Tallahassee is the state capital, so I assigned it a star point symbol, which is a common convention used in many state maps. To represent the rest of the major cities, I kept the red circle symbol used in the lab because it paired well with the overall color scheme and was simple and effective.

Because some river labels were repeated when the font size was reduced, and others did not align properly or even fell off the map, I converted all river labels to annotation.

Font Selections

Ordinal values help represent importance and hierarchy on a map, which is why I used the following font sizes. Since Tallahassee is the state capital and therefore the most important feature, it is in the largest font (12 pt), followed by major cities (11 pt), then the swamps (8 pt), and finally the rivers, which are the smallest features (6 pt). Because it is best practice to limit a map to two font types, the main map features were in Constantia, while the other map components,the title, legend, etc., were in Tahoma.

General Map Feature Selections

Although Florida is much taller than it is wide, the portrait layout did not allow me to enlarge the map as much as I wanted so I pursued the landscape layout and also provided usable space in the bottom‑left corner for displaying necessary map elements such as the legend.

For the background, I chose a light gray color that added subtle contrast, matched the rest of the color scheme, and helped the map stand out.

Finally, I added an inset map showing Florida’s location relative to the rest of the United States. I initially considered leaving it out to avoid violating the “minimize map crap” principle, but I thought about the intended audience, likely visitors or tourists,and decided that providing geographic context would be helpful. I used neutral colors to keep the inset simple and unobtrusive.

Important Items Along the Way

The lab assignment required only specific cities, rivers, and features, so all others had to be removed. To do this, I edited the attribute tables of each feature class and deleted the unwanted features. It is important to remember to save changes to the attribute table after making deletions otherwise the deleted features will reappear on your map.

To edit or move annotations, this version of ArcGIS Pro requires using the dropdown options in the Editor tool gallery. This is important as in older versions of ArcGIS Pro the “move” button is noticeable on the ribbon. 

I originally left the major city labels as they were, since they did not require major modification. However, I could not get the font size to change using the labeling properties. I am not sure why this occurred. To fix it, I converted the labels to annotation and manually adjusted the size of each major city name.

I also struggled with the river annotations because I could not easily place the labels in my ideal positions, and when I did, they still followed the original annotation geometry, which no longer aligned with the new placement. I attempted to use the “Follow This Feature” tool to align the annotations, but the tool was not working for me. As a last resort, I used the “Edit Vertices” tool, which allowed me to manually adjust the angle of each letter and word. It is not perfect, but it was better than leaving them unedited. I look forward to properly learning how to use the “Follow This Feature” tool in the future.

Module One- Introduction to Cartography and Map Design

Figure 1- Well Designed Map - SC Wildlife Zones

The map I selected is the Wildlife Map from the South Carolina Department of Natural Resources, found in the R‑Drive. I chose it because, despite a few areas that could be improved, it is simple, easy to understand, and communicates its purpose effectively.

Purpose, Look, and Audience

The map’s purpose is to show the different game zones used for wildlife management in South Carolina, which is made clear through the title and legend. Its look and feel are simple, practical, and informative, presenting a lot of information in a way that anyone can quickly interpret. The intended audience is the general public—people interested in hunting, wildlife conservation, or anything requiring awareness of the state’s game zones. The educational level needed is minimal since the zones are color‑coded and clearly labeled.

Cartographic Design

The map emphasizes the main theme through its use of color. Only the game zones are colored, which makes them stand out against the white background. The symbology is simple but effective: each zone is represented by a different color, outlined with thick borders, and labeled with a number inside the zone. This makes it easy to identify each area without relying heavily on the legend.

Improvements: Some colors are too similar—especially between Zones 1 and 6, and Zones 2 and 5—which may confuse some viewers. Blue and lilac are also not ideal choices for land areas. A neutral background color instead of white would give the map a more finished look.

The symbols and labels are mostly legible, except for a few city names. The symbols are intuitive, and the thick borders and centered numbers make the zones easy to understand. The map also uses graphics and text blocks appropriately, including the official state seal and important information like the source and publication date.

Map Elements and Layout

The map is generally well balanced, and the creator used the landscape layout effectively to fit the shape of the state. The state fills most of the page, and the north arrow, scale bar, and text boxes are placed in a way that doesn’t distract from the main map. The legend is close to the state and sized appropriately. The only change I would make is swapping the north arrow and scale bar with the seal and text boxes to reduce clutter in the bottom‑left corner.

The map has appropriate borders, though slightly thicker ones would give it a more polished look.

Scale and Legend

The map extent is appropriate because it shows the entire state and includes enough detail to display the cities within each zone. The scale bar is simple, uses miles, and is placed near the state for easy reference.

The legend includes all necessary symbols and details and is organized logically from Zone 1 to Zone 6. The labels are clear, though adding “Game” before each zone name is redundant since the legend title already states that these are game zones.

Titles and Subtitles

The title is brief, descriptive, and clearly communicates the map’s purpose. It is the largest text on the page and is positioned well in the open space near the top right. The subtitles are smaller, non‑distracting, and easy to read.

Figure 2 - Poor Map Choice: "Bellevue"

 For this assignment, I chose a map from the R‑Drive that wasn’t obviously terrible at first glance. I wanted to challenge myself with something that looked acceptable to a non‑GIS viewer but still had real design issues. I even asked a non‑GIS friend to look at it, and her impressions matched mine.

Purpose, Look, and Audience

The map’s purpose isn’t clearly communicated. Although the legend suggests it is meant to show public facilities across Bellevue, the title doesn’t say this, and the layout doesn’t help clarify it. The overall look and feel of the map is overwhelming—there is so much going on visually that instead of helping the viewer understand where key facilities are, it creates confusion.

The intended audience seems to be everyday residents or visitors who need general location information, assuming they can read basic directional cues.

Cartographic Design

Some visual themes are emphasized well, such as the contrast between urban areas, green spaces, and surrounding water. These help give a sense of the city’s layout. But the map is so cluttered that even these distinctions start to blur.

The symbology for public facilities is mostly effective. Points are used for specific locations, and color‑coded areas represent broader features like parks. However, some color‑coded areas—like the dark and light gray regions—aren’t explained in the legend, leaving the viewer to guess their meaning.

The color scheme generally works: it’s earthy, not distracting, and highways stand out clearly from smaller streets. But the labels and symbols are hard to read because the font is tiny and the map is visually crowded. Some symbol colors blend into the background, making them difficult to distinguish.

The symbols themselves are intuitive—fire stations in red, schools with a flag symbol, police stations in blue, parks in green, water in blue. There are no extra graphics, but there is a text box in the bottom right corner that is nearly unreadable because it clashes with the map.

Map Elements and Layout

The page layout feels unbalanced. The title and legend are both on the left, making that side feel heavy, while the logo and text box on the right are too small to balance it out. The borders are inconsistent as well, with the bottom border thicker than the others.

Most map elements support the map’s goals, but the scale bar does not. It is shown in feet, which is unusual for a city map, and it is placed awkwardly under the north arrow, making it easy to miss. The north arrow itself is too large and ornate. Both elements could have been placed more thoughtfully.

Scale and Legend

The map extent is reasonable because it includes the whole city, though some might prefer a closer view that excludes surrounding water. Including the water does help show the city’s context, so either choice could be justified.

The legend is only partially complete. Several layers, like water, gray areas, purple areas, and smaller streets, are missing. The structure of the legend is logical, though, and the labels themselves are intuitive.

Titles and Subtitles

The title is not descriptive enough and doesn’t explain the map’s purpose. It is also too small, and the subtitles are even smaller and nearly impossible to read.

Story Map About Me!

 Hello everyone!

I’m Zenia, and I couldn’t be more excited to be here today!

I’m a grad student pursuing my GIS analyst degree, and my undergraduate degree is in Environmental Sciences from Grand Canyon University. School is one of my favorite things in life (weird, I know) but I genuinely enjoy learning new skills and exploring parts of the academic world I haven’t experienced before.

I fell in love with GIS during my undergrad and was thrilled to discover that UWF offered a master’s program. At the end of the program, I hope to secure a GIS job in the public sector and bring some much-needed skills. Once I complete my master’s, I hope to continue on to my PhD where I will use GIS as an additional tool to help government plan and support the most important life sustaining operations, agriculture.

If there were an easy way to describe me, it would be “different” … ha-ha. I bounce between being very extroverted and friendly in public, yet incredibly introverted at home and in my personal life. I enjoy nature, painting, reading, singing, and dancing.

I also feel incredibly lucky to be alive and grateful for the opportunities I’ve been given.

Check out my story map for more about me :)

Story Map Link - https://arcg.is/11bnfW5

Module 5 - Unsupervised and Supervised Image Classification

 

Figure 1: Map of Current Land use in Germantown Maryland

Exercise 1:

This exercise focused on completing unsupervised classifications. In supervised classification, there is a training phase where pixels from known classes are used to inform the classification process. The software groups pixels based on their spectral characteristics, and at the end of the process, the user classifies the grouped classes. The accuracy of the classification is influenced by maximum iteration settings and the convergence threshold. The iterations enable repeated analysis of the area, while the convergence threshold determines how confident the software is that the pixels are accurately classified. Additionally, the skip factor governs how pixel analysis is conducted and affects processing time. For example, a skip factor of 1 analyzes pixels one by one, whereas a skip factor of 2 means that only every other pixel will be analyzed.

The most challenging aspect of this exercise was selecting the appropriate training pixels and ensuring they were from the correct areas. This led to issues later when random pixels from incorrect areas—denoted as "mixed"—appeared in blatantly incorrect locations.

We also explored different comparison methods for analyzing the original and reclassified images using the toggle, flicker, blend, and highlight tools. These tools help identify areas that might have been misclassified.

The best tool introduced in this lab was the record tool, which allows us to combine multiple classes into fewer classes. For example, we took the UWF50 image and reduced it from 50 classes to 8 classes.

Exercise 2:

Exercise Two focuses on supervised classification, where the analyst trains the software to select classes based on pixel values and their surrounding neighborhoods. We were introduced to the Signature Editor, utilizing the polygon tool to select pixels in areas of interest, or we could use the AOI Seed Tool to expand a region around areas of known land cover.

Before adding signatures, we needed to create an Area of Interest (AOI) layer, allowing us to conduct inquiries on this layer to specify where signatures would be added. To create this AOI, we used the Enquire Tool along with known coordinates. While we had coordinates for most of our selected classes, we had to identify our water and road features without specific coordinates. Initially, this was challenging, but I realized I could move the cursor and adjust the Enquire Tool to target the road and water features for the Grow Tool.

I attempted this three times to achieve a satisfactory classification and found that the AOI Seed Tool was the most effective for this task, as it captured the pixels with similar spectral values better than my polygon drawing skills.

Another important skill gained from this exercise was analyzing histogram plots and mean plots to minimize spectral confusion by identifying bands with the least separation between signatures.

We then proceeded to classify the images using Maximum Likelihood Classification, a parametric method based on the probability that a pixel belongs to a specific class, as it computes the likelihood of a pixel corresponding to a particular spectral signature.

Next, we created a Distance File that calculated the spectral Euclidean distance. In this file, brighter pixels indicated a higher probability of misclassification. Once we analyzed, confirmed, and refined our results, we merged the multiple classes using the Record Tool, just like in Exercise 1. Finally, we used the Calculate Area Tool to determine how much of the area was affected.

Module 4 - Spatial Enhancement, Multispectral Data and Band Indices

In this week's lab, we encountered four tools that will be key in helping identify different features during image analysis: Histogram Analysis, Image Grayscale Analysis, Multi-spectral Band Experiments/Analysis, and Image Brightness Analysis. To practice this, we were tasked with finding features that fit certain criteria using the aforementioned methods. Furthermore, once those features were selected, we needed to choose multispectral band combinations that helped distinguish these features on the map. Below are the maps generated for each of those features.

Figure 1- Identification of water features. 

Feature 1: WATER

For identifying the feature in Layer_4, there is a spike between pixel values of 12 and 18. This is quite straightforward: the larger the feature, the larger the spike in the histogram due to the high concentration of pixels at that brightness level. Additionally, the fact that it is on the left side of the histogram indicates that these are dark features. The large dark body of water explains this spectral signature in the histogram. 

To highlight this feature in the image, the chosen filter was False Color Infrared (bands 4/3/2). This is because water appears dark, creating a stark contrast with the surrounding vegetation. Another acceptable band combination would have been False Natural Color (bands 5/4/3), where the dark blue water would be greatly contrasted against its green surroundings.

Figure 2 - Identification of Snow on the Mountaintops

Feature 2 : SNOW: 

The features that represent both A) a small spike in layers 1-4 around pixel value 200, and B) a large spike between pixel values 9 and 11 in Layer_5 and Layer_6, were deduced to be snow on the mountaintops.

The small spike in layers 1-4 around pixel value 200 represents a small amount of pixels at a high brightness level on the left side of the histogram. These correspond to the small caps of bright snow on the mountaintop. As for the large spikes between values 9 and 11, these represent the dark areas of the mountain surrounding the snow, which outnumber the amount of snow on the mountaintop.

To highlight this feature, the TM True Color combination of bands 3/2/1 was used, as the bright white snow highly contrasted with its dark mountain top surroundings.

Figure 3- Identification of varying water depth. 

Feature 3: Varying Water Depths

To represent the gradient in color of water in relation to its depth, the shallower the water, the lighter the color, and the more noticeable the changes in brightness. Once the water becomes deeper, which is the case for the vast majority of the water features in this image, the bands remain the same color as they correspond to those darker values.

To highlight these features, a custom band combination of 5/2/1, where one can distinctly see the depth dependent gradient from the lighter to the darker water features, was selected.  This combination also somewhat neutralized features surrounding the water and contrasted them at the same time.