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GIS5935 M3.1 - Scale Effect and Spatial Data Aggregation

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  Understanding the Scale Effects on Vector Data, Basic Resolution Effects on Raster Data, and Gerrymandering Scale Effects on Vector Data : Scale significantly influences how vector data is portrayed. Smaller-scale maps (such as 1:100,000) offer less detail, resulting in the generalization or exclusion of more minor features. Conversely, larger-scale maps (like 1:1,200) provide greater precision, capturing intricate details such as complex boundaries. As the scale reduces, polygons become simplified, and more complex boundaries—like those of irregular coastlines—are smoothed out. This has a direct effect on the accuracy of calculated areas and perimeters. Regarding hydrographic features, moving from a 1:1,200 scale to a 1:100,000 scale demonstrated a notable reduction in detail. Basic Resolution Effects on Raster Data : Raster data divides the world into a grid where each cell holds a single value. The resolution of raster data refers to the dimensions of these cells. A higher resolu
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GIS5935 M2.2 - Surface Interpolation

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 Interpolating Water Quality Data for Tampa Bay In this exercise, we applied various interpolation techniques to explore water quality conditions in Tampa Bay, Florida, using a dataset of samples collected over a short period. The primary focus was on Biochemical Oxygen Demand (BOD) concentrations, measured in milligrams per liter, a key indicator of water quality. By using methods like Thiessen , IDW , and Spline interpolation, I was able to generate surfaces that estimate BOD levels across Tampa Bay, providing insight into the spatial distribution of pollution. Each method offered a different approach—some emphasizing sharp transitions between points and others providing smoother, more continuous surfaces that better represent gradual changes in water quality conditions. Performing Interpolation Analysis  Thiessen interpolation   assigns each location the value of the nearest point, resulting in abrupt boundaries that  may not be suitable for continuous phenomena like pollution. Inv
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GIS5935 M2.1 Surfaces - TINs and DEMs

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In my exploration of elevation data models, I examined both Triangulated Irregular Networks (TINs) and Digital Elevation Models (DEMs) to understand their unique properties and applications. TINs are formed by connecting data points into triangles, providing highly detailed but angular representations of terrain. On the other hand, DEMs are grid-based and create smoother surface representations through interpolation techniques. I compared these models by analyzing contour lines generated from both TIN and DEM data. One key difference I found is that DEM contours are much smoother, especially in flatter areas with less elevation variation, while TIN contours are more angular, especially in areas of steeper terrain due to the triangular structure. The smoother DEM is more suitable for continuous surface representation, while the TIN excels in areas requiring more detailed elevation information. A comparison between DEM and TIN contours can be seen below: DEM TIN This exploration highligh
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GIS5935 M1.2 Data Quality Assessment

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 Accuracy Assessment of Road Network Completeness Goal of the Accuracy Assessment This accuracy assessment aims to compare the completeness of two road network datasets: Street_Centerlines and TIGER_Roads shapefiles for Jackson County, Oregon. The objective of evaluating the total length of roads within a uniform 5km x 5km grid is to determine which dataset provides more comprehensive coverage for the area. This analysis allows for identifying areas where one dataset may be more detailed than the other, contributing to a better understanding of road network quality in the region. Analysis Methodology The analysis methodology involved multiple steps to compare the two road networks. First, the road datasets— Street_Centerlines.shp and TIGER_Roads.shp —were clipped to the boundaries of a 5km x 5km grid that spans Jackson County. The Clip  tool ensured that the roads outside the grid cells were removed. The next step used the Intersect tool to divide road segments at the grid boundaries
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GIS5935 M1.2 Lab Data Standards

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  Assessing Positional Accuracy of Road Networks: A Case Study In this project, I conducted an accuracy assessment of road networks using data from the city of Albuquerque and StreetMap USA. The goal was to determine the positional accuracy of the two datasets, following the methodology outlined by the National Standard for Spatial Data Accuracy (NSSDA).  Below is a screenshot of the test point locations for the data from the city of Albuquerque: Steps Taken to Complete the Accuracy Assessment: Data Exploration : I began by exploring both datasets, comparing road centerlines from the City of Albuquerque and StreetMap USA against orthophotos of the area to check for differences. Selection of Test Points : Using NSSDA guidelines, I selected 20 well-defined points, such as road intersections, that were visible in both datasets and the reference orthophotos. Independent Dataset : The orthophotos were treated as the independent reference data due to their high level of accuracy. Coordinate
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GIS5935 M1.1 Lab

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Exploring GPS Data Accuracy and Precision: A Brief Analysis Numerical Results for Horizontal Accuracy and Precision Horizontal Accuracy: 3.24 meters Horizontal Precision (68%): 4.47 meters Understanding Horizontal Accuracy and Precision Horizontal Accuracy: This is the measure of how close the average of the collected waypoints is to the true location (not shown in the map). In this case, the average waypoint is 3.24 meters away from the true reference point, indicating reasonably accurate measurements. Horizontal Precision: Precision refers to the spread or variability of the GPS points around the average. Here, a precision of 4.47 meters suggests that the individual measurements are spread out, meaning that while the average position is accurate, the data points themselves are not consistent. This difference between accuracy and precision highlights the GPS unit's performance: it provides a generally accurate estimate of the location, but has low precision with variability in
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M6 - Least Cost

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  Habitat Suitability and Corridor Analysis Introduction In this post, I’ll share the process and results of a habitat suitability and corridor analysis aimed at identifying the most suitable areas for conservation and the optimal paths between protected regions. This analysis involved creating a habitat suitability model based on key environmental factors, followed by a corridor analysis to determine the best routes for wildlife movement between two National Forests. Analysis Steps The first step in the analysis was to create a habitat suitability model using three criteria: land cover, elevation, and proximity to roads. Each criterion was reclassified on a scale of 1 to 10 to reflect its relative suitability for wildlife habitat. I combined these reclassified layers using the Weighted Overlay tool, with land cover given a weight of 60%, elevation 20%, and distance to roads 20%. This produced a suitability raster highlighting the most appropriate areas for conservation. Next, I invert
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