Topic 3 Module 1: Scale Effect and Spatial Data Aggregation

Figure 6: Worst Offender- State 24, County District 8. Lowest Polsby Propper score. 

This lab demonstrated how scale, resolution, and geographic boundaries can significantly influence spatial analysis results. Through both vector and raster datasets, it became clear that the level of detail represented in spatial data affects the measurements and conclusions that can be drawn from an analysis. 

 For vector data, the hydrographic analysis showed that reducing scale leads to greater generalization. As scale decreased, smaller streams, ponds, and shoreline details were omitted, resulting in lower line lengths, polygon counts, perimeters, and total area. These results illustrated how cartographic generalization simplifies geographic features and can alter measured geometric properties.

 The raster analysis demonstrated the impact of resolution on derived terrain products. As DEM cell size increased, average slope values decreased because larger cells smoothed the elevation surface and reduced local variability. This showed that coarser raster resolutions can mask important terrain characteristics and produce significantly different analytical results compared to finer resolution datasets. 

 The MAUP analysis further highlighted the importance of geographic scale and aggregation. The relationship between percent non-white population and percent below poverty varied substantially depending on whether the analysis was performed at the block group, voting district, zip code, or county level. Smaller geographic units preserved local variation and produced stronger statistical relationships, while larger units averaged out differences and weakened those relationships. This demonstrated that statistical conclusions can change simply because data are aggregated into different geographic boundaries. 

 Finally, the gerrymandering exercise illustrated how political district boundaries can influence spatial patterns and representation. Gerrymandering refers to the manipulation of electoral district boundaries to favor a particular political outcome. While visual inspection can identify unusually shaped or fragmented districts, compactness measures provide a more objective approach. The Polsby-Popper compactness score uses area and perimeter to quantify how compact a district is, with values closer to 1 indicating compact districts and values closer to 0 indicating irregular shapes. By calculating compactness scores, districts that deviate significantly from an ideal compact shape can be identified and evaluated as potential examples of gerrymandering. 

 Overall, this lab demonstrated that spatial analysis results are not only influenced by the data themselves but also by the scale, resolution, and geographic boundaries used during analysis. Understanding these effects is essential for interpreting spatial data accurately and avoiding misleading conclusions.

Module 2.2: Surface Interpolation

 In this lab, mapping water quality in Tampa Bay required transforming point based sampling data into a continuous surface that shows how conditions vary across the bay. Interpolation methods make this possible by estimating values in areas where no measurements were taken. Each method approaches this task differently, and these differences influence how the final surface below looks.

Thiessen interpolation creates polygons around each sampling point, assigning each area the value of its nearest neighbor. This method is simple and preserves the original data exactly, but it produces abrupt boundaries and does not model gradual changes across space. In contrast, IDW interpolation creates a smooth surface by weighting nearby points more heavily than distant ones. This results in a more realistic representation of how water quality transitions across the bay without introducing extreme values. Spline interpolation fits a smooth mathematical surface through all points, which can produce visually appealing results when sampling is dense. However, spline can overshoot in areas with sparse data, creating unrealistic peaks or depressions.

For Tampa Bay, where sampling locations vary in density, IDW provides a balanced and reliable surface that reflects both local variation and overall trends. Thiessen is useful for understanding zones of influence, while spline requires careful point spacing to avoid distortions.

Below is an example of one of the interpolated surfaces created during the analysis:

Figure 1. IDW Interpolation

Interpolation is a powerful tool for visualizing water quality, but choosing the right method depends on the nature of the data and the patterns you want to reveal.

Module 2.1 Lab: Surfaces - TINs and DEMs

 

Figure 1: Comparison of TIN (Top) and DEM (Bottom) Contours

This week I explored two different elevation data models in ArcGIS Pro: a TIN surface created from mass points and a DEM generated using Spline interpolation. Working with both helped me understand how the same elevation dataset can produce different representations of terrain. The TIN model captured sharp, localized changes in elevation because each triangle is built directly from the original sample points. The DEM produced a smoother and more continuous surface, especially in areas where interpolation fills in gaps between points.

Comparing the contour lines made these differences easy to see. The TIN contours showed angular transitions that closely followed the elevation points, while the DEM contours were more rounded and gradual. Overlaying the elevation points on the DEM helped me visualize how point density and interpolation influence the final surface. Overall, this exercise showed the strengths of each model and how they can be used depending on the level of detail needed.

Module 1.3: Data Quality - Assessment

 

Figure 1: Percentage difference in road network completeness between Tiger Road and Street Centerlines, separated by grid 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

The horizontal precision of 68% was 4.5 m.

The distance between the average waypoint location and reference point was 3.2 m; horizontal accuracy.

 

The estimated horizontal accuracy was 3.2 m, based on the distance between the average waypoint location and the reference point. The 68% horizontal precision was 4.5 m. Therefore, the horizontal accuracy error was 1.3 m smaller than the 68% precision estimate. The average GPS position was closer to the reference location than the distance representing the spread of 68% of the repeated observations. Based on these results, the difference between horizontal accuracy and precision does not appear substantial.

 

Vertical Accuracy at 68% was 5.96 m.

The vertical precision is 5.9 m.

The difference between these two values is only approximately 0.07 m, indicating that vertical accuracy error and vertical precision are very similar.

Overall, the results do not provide strong evidence of significant horizontal bias because the 3.2 m displacement of the average waypoint from the reference point is smaller than the 4.5 m horizontal precision estimate.

Vertically, the average GPS elevation was 5.96 m higher than the reference elevation, which indicates a positive vertical offset. However, because the 68% vertical precision was also approximately 5.9 m, the magnitude of this offset is similar to the variability observed among the elevation measurements. Therefore, the results should be interpreted cautiously rather than as evidence of a strong systematic bias.

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.