GIS6005: Lab 3 - Terrain Visualization

 

For this map i chose a design that focused on clarity, hierarchy, and visual balance. Base maps, along with thematic layers on top, ensure the data, not the background, remains the focus. A muted color palette with clear contrast helped to contrast the features while remaining readable and visually cohesive. My color choices were consistent across the map to reinforce the meaning. The typography shows readability, using simple fonts with clear distinctions between the titles, labels, and the supporting text. Overall, I think the design balances looks with functionality.

GIS6005 - Module 2: Coordinate Systems

 

For this map, I used NAD 1983 Oregon Albers (Meters). There were multiple for the State, but I decided on this one. Oregon spans multiple state plane zones, so the state plane is unsuitable for Oregon’s statewide mapping. The state also crosses two UTM zones, which would distort if just one zone were used. For this reason, I used a custom statewide conic projection. Oregon is a mid latitude state with a strong east-west orientation, so the conic projection works well. 

GIS5935: Module 4 - TINs and DEMs

 

In this week's lab, we created 3d visualizations of elevation models and compared TINs and DEMs. The TIN followed the original elevation points exactly, resulting in boxy contours that reflected this. In contrast, the DEM produced smoother contours that generalized the original elevation points. The DEM is better for visualization, but doesn't reflect the raw data. The differences between the two models were most noticeable in steeper areas and terrain. The TIN showed sharp bends in the contours while the DEM smoothed them out. In flatter areas, the two models were almost identical. This comparison shows the tradeoffs between the two sets of models.

GIS5935: Module 3 - Assesment

 

Map Layout

The goal of the analysis in this lab was to evaluate the relative completeness of two road network shapefiles in the same county. Street Centerlines and TIGER Roads. To see which data set provided a more complete representation of the county's road system 

GIS5935: Mod 2 - Standards

 

For this lab, I added ABQ_Streets, StreetMapsUSA, and the 2006 Orthophotos into a new map and made sure they were projected into the same coordinate system. Next, I found 20 random points correlating to a good intersection. Then I digitized a new feature class that correlated to the true locations of the other two sets of 20 points. I used the Add XY tool to add coordinates and exported the data to Excel. In Excel, I then converted the two data sets into a 95% confidence measure using the given NSSDA Table.

My final Accuracy statement for the data sets is:

ABQ_Streets: Based on 20 test points, the positional accuracy of the ABQ_streets layer data is 36,187 feet at 95% confidence level. determined using NSSDA methodology.

StreetMapsUSA: Based on 20 test points, the positional accuracy of the Street Map USA data is 33,784 feet at 95% confidence level. determined using NSSDA methodology.

GIS5935: Module 1 - Fundamentals

 

Numerical Results for Horizontal accuracy and precision. 

Horizontal Accuracy: 4.5

Horizontal Precision: 0.9

In this week's lab, we learned about horizontal accuracy and precision. Horizontal precision is the measurement of how close GPS measurements are to each other. In this case, the measurement is summarized by the percentage they fall within a location. (68%) Horizontal accuracy is the measurement of how close the GPS location is to a reference point. In this case, it is also summarized by 68%.

GIS5100: Module 5 - Damage Assesment

 

The goal of this Lab was to assess structural damage from Hurricane Sandy using pre- and post-storm imagery. I created mosaic datasets for both pre- and post-storm imagery using .SID and JPG files. I then added domains like Structure Damage, Wind Damage, and Inundation, and created a point feature class. Then I created points for each structure in the study area polygon. I compared pre- and post-storm imagery using the Swipe tool and assigned a damage level to each point individually using the attribute table. Once those steps were completed, I assigned points based on damage. This is the screenshot you see above. Overall, this was a constructive lab in which I learned a lot.