Module 2.1 - Surfaces: TINs and DEMs

In this week's lab assignment, we began by previewing a Triangulated Irregular Network (TIN) file in ArcGIS Pro. A TIN is a data model used to represent terrain heights by entering the x, y, and z coordinates of measured points. These points can connect to form the smallest triangles possible between any three adjacent points, creating a network of triangles. Few points are sufficient for large, flat areas, while a higher density of points is necessary to accurately capture small-scale variations (Bolstad & Manson, 2022, p. 61).

We started by created a new scene, incorporated the TIN file, and designated it as an Elevation Source. Subsequently, we exaggerated the terrain by increasing the vertical exaggeration and examined the data.

Following that in Part B, we utilized a DEM to create a Ski Run Suitability Map. To achieve this, we first established a new local scene and added the DEM raster. We then employed the Raster to TIN tool to generate a TIN file from the DEM and set it as the elevation source. Next, we applied the reclassify tool to reclassify the elevation from the DEM file, then generated a slope raster from the DEM, and reclassifying it, as well as creating and reclassifying an aspect raster. Ultimately, we merged the three reclassified rasters to produce the final suitability map.

In Part C, we opened a new local scene and added a TIN file, setting it as the elevation source and applying a vertical exaggeration of 2.00. We utilized the symbology options under TIN Layer > Drawing to visualize the TIN file, incorporating slope, aspect, edges, and contour lines, and examining the elevations and points distribution given the nature of the terrain to determine if the distribution is appropriate.

When overlaying el_points shapefile onto the TIN, the density and distribution of points must correspond to the spatial variability of the terrain. The point distribution indicates that the higher uniform area between elevations of 1898.11-2022 meters is flat, resulting in fewer collected points. In contrast, the lower elevations of 637-790.89 meters, which can be viewed as not overly steep, exhibit a high point density. While increased point density in flat regions can enhance accuracy, in smooth terrains, additional points may not significantly improve the surface representation. In these areas, fewer points are sufficient due to the gradual changes in the surface.

Moreover, the steep regions with elevations ranging from 944.78-1252.56 meters have a lower point count. Insufficient sampling in uneven or variable terrains can lead to distortions in the resulting surface. If the TIN shows a hilly landscape characterized by sharp ridges and valleys, we should observe a greater concentration of points along these features.

In the final Part D, we introduced a new scene and incorporated two layers: a points layer and a study area. We utilized the Create TIN tool to generate a TIN layer from the points layer. The symbology was modified to display only contour lines. The next step involves generating contour lines from the DEM based on the points layer using the spline tool, followed by creating contours from the DEM with the same intervals used for the TIN contours. After generating both sets of contours, we compared the contour lines produced by the two distinct methods.

Comparing TIN and DEM contours reveals key differences: TIN contours are jagged and sharp, while DEM contours are smoother and more rounded, particularly evident in steep areas with low elevation point density. In flat regions, both models yield similar contour patterns due to effective interpolation. TINs, based on a Triangulated Irregular Network, offer greater accuracy in complex terrain by using non-overlapping triangles for interpolation. Conversely, DEMs use a raster grid. Thus, the choice between TIN and DEM should align with specific application requirements and data needs.

Screen shot showing TIN and DEM contour lines

Bolstad, P., & Manson, S. (2022). GIS Fundamentals A First Text on Geographic Information Systems. Elder Press.

Module 1.3 - Data Quality : Assessment

The goal of accuracy assessment in mapping involves the verification of data reliability and quality. In the context of road networks, this assessment evaluates positional accuracy, attribute accuracy, completeness, and consistency. Given that road networks are essential to applications such as address geocoding and routing, it is important that the attribute data is both complete and precise.

There are various methods to ensure data accuracy. One effective approach is ground truthing, which utilizes high-precision GPS units to validate data accuracy. Additionally, data can be compared against high-resolution imagery or existing high-accuracy datasets to further determine its reliability.

Both positional accuracy and completeness are critical in the evaluation of data accuracy. Positional accuracy is determined by comparing the data against a higher quality dataset. Completeness, on the other hand, assesses the degree to which real-world objects are adequately represented. This involves evaluating the number of objects that should exist within the database but are absent, as well as identifying data that may need to be excluded.

Thematic accuracy and thematic completeness represent two additional measures of data quality. Thematic accuracy is measured by the extent to which attribute values correspond with reference data, such as the correct recording of street names or speed limits. On the other hand, thematic completeness is assessed by the number of missing attributes.

In the previous lab assignment 1.2 Data Quality – Standards, we evaluated the quality of two road networks: the streets of Albuquerque, derived from a shapefile provided by the city, and the Street Map USA data obtained from the TeleAtlas product. The aim was to evaluate positional accuracy using metrics defined by the National Standards for Spatial Data Accuracy (NSSDA).

In this lab assignment 1.3 Data Quality – Assessment, we initially calculated the total length of roads from two distinct layers: Jackson County Street centerlines and TIGER Roads, in order to evaluate which dataset exhibits greater completeness. 

Comparing the two shape files Street_Centerlines and TIGER_Roads we find that the total length of roads represented in the TIGER_Roads shapefile exceeds that of the street_centerline shapefile by around 509.4 kilometers. This suggests that the TIGER_Roads dataset includes a larger quantity of digitized streets. Typically, TIGER data incorporates unpaved roads and private driveways, which are generally absent from local street centerlines. It can be concluded that although TIGER data may not possess the positional accuracy of local street centerlines, it often provides greater completeness.

In the second step, we employed a 5km x 5km grid to determine the total length of roads contained within each grid for both networks. As a result, we identified the number of grid polygons where the county centerlines are more complete than the TIGER roads, as well as the number of polygons where the TIGER roads surpass the county centerlines in completeness. 

In the final step, we calculated the percentage difference in total length between the two road datasets for each grid polygon, utilizing the centerlines as the reference and applying the formula: 

% difference = (total length of centerlines - total length of TIGER roads) x 100%                                                          (total length of centerlines)

Ultimately, we produced a choropleth map that illustrates the percentage differences between the two datasets.

Map layout showing the percentage difference between the two data sets for each grid polygon

Module 1.2 - Data Quality: Standards

In this lab assignment 1.2 Data Quality – Standards, we measured the quality of two road networks: the streets of Albuquerque, derived from a shapefile provided by the city of Albuquerque, and the Street Map USA data sourced from the TeleAtlas product. The objective was to evaluate the positional accuracy using metrics established by the National Standards for Spatial Data Accuracy (NSSDA). 

The application of the NSSDA standard involves seven distinct steps. 

The first step is to determine whether the assessment apply to horizontal accuracy, vertical accuracy, or both. For this lab, our focus is solely on determining the horizontal accuracy.

The second step requires selecting a study area and identifying a set of test points within that area. These test points correspond to street intersections, ensuring that we select good intersections and include a minimum of 20 street intersection points. The distribution of these points must be such that, when the study area is divided into four quadrants, each quadrant contains 20% of the test points. I selected 24 test points, ensuring that each quadrant included 6 test points. Furthermore, the distance between these points should exceed one-tenth of the length of the study area’s diagonal. Given that the diagonal length of my study area was 10 miles, I ensured that my test points were spaced at least 1 mile apart. The test points were chosen from both Albuquerque Street data and Street Map USA data correspond to the same street intersection. Both layers of test points are populated with identical point IDs that match to the same intersection. 

Screenshot of study area and sampling locations

The third step involves selecting a higher accuracy dataset for comparison with the data collected from the test area. Initially, we created a new layer designated as Reference, utilizing high-resolution aerial photography to digitize all points that correspond to the true locations of the test points. It was imperative to populate the ID field with the same test point number across all three datasets: Albuquerque Street, Street Map USA, and the Reference true point layer.

To determine the coordinate values for each test point for both the test data sets and the reference data set, I ran Calculate Geometry Attributes tool from Data Management on all three layers to add two new fields for the X and Y coordinates for the test points. At this point, we have three datasets of the same street intersections and have a common field “Point ID”.

Our next step is to assess the accuracy of both the Albuquerque Street layer and the Street Map USA layer. To achieve this, I employed the join tool in ArcGIS Pro to first join the Albuquerque Street layer with the Reference layer based on the common field, Points ID. Subsequently, I applied the join tool to join the Street Map USA test points layer with the Reference layer. Finally, I exported both layers into an Excel spreadsheet.

Using NSSDA accuracy statistic worksheet to calculate the positional accuracy statistic. 

For this step I added five new fields to the excel table they are:

DIFF IN X = the difference between the X coordinate from test point and reference true point

SQUARED DIFF IN X (1) = This is the square root of the x difference 

DIFF IN Y = the difference between the Y coordinate from test point and reference true point

SQUARED DIFF IN Y (2) = This is the square root of the Y difference 

(1) + (2) = SQUARED DIFF IN X + SQUARED DIFF IN Y (2)

From field (1) + (2), three key values can be calculated: The sum, the average and the root mean square error (RMSE).

The sum is the total of the squared differences between the coordinate values of the test data set and those of the Reference data set. The average is obtained by dividing the sum by the number of test points. The root mean square error (RMSE) is the square root of the average. 

The NSSDA statistic is then calculated by multiplying the RMSE by a factor that signifies the standard error of the mean at a 95 percent confidence level which is 1.7308 for the horizontal accuracy. Finally, NSSDA accuracy statement is prepared in a standardized report form.

NSSDA accuracy statement for Albuquerque Street Data:

Horizontal Positional Accuracy: Tested 23.25 feet horizontal accuracy at 95% confidence level

NSSDA accuracy statement for Street Map USA Data:

Horizontal Positional Accuracy: Tested 325.69 feet horizontal accuracy at 95% confidence level

Module 1.1 - Calculating Metrics for Spatial Data Quality

Part A: Determining accuracy and precision

Precision indicates the degree to which the collected points are in proximity to one another; it is considered high when the points are closely grouped. Conversely, accuracy assesses how near the points are to the actual reference location. Average accuracy is deemed high when the mean of the points is situated close to the true location. 

In this laboratory exercise, we initially computed the estimated horizontal precision for a set of points gathered using a handheld GPS device. By applying the 68th percentile, we determined that the horizontal precision was 4.47 meters. This suggests that the points are clustered and are closed to each other. 

Subsequently, we assessed the horizontal distance from the average waypoint to the reference point, which represents the true location of the mapped point, in order to determine the horizontal accuracy, which was found to be 3.24 meters. This suggest that the points are near the true location. This minimal discrepancy indicates a high level of accuracy. 

 

Part B: Root-mean-square error (RMSE) and cumulative distribution function (CDF)

Error metrics for the GPS positions:

Minimum = 0.14

Maximum = 6.95

Mean = 2.67

Median = 2.45

RMSE = 3.06

68th Percentile = 3.18

90th Percentile = 4.67

95th Percentile = 5.69

In the first section of part B of the lab, we used metrics to calculate the RMSE (Root Mean Square Error), and also calculated the Mean, Average, minimum, maximum, 68th percentile, 90th percentile and 95th percentile values for error_xy. The error_xy is the distance error from a given point to the benchmark point. In the second section of part B, we created a cumulative distribution function (CDF) graph showing the complete distribution instead of the selected metrics. To accomplish that we used the error_xy data, sorting by smallest to largest values and adding a new column which represent the percentage from 0 to 100. We created a CDF graph showing the entire probability distribution of the error_xy. We can say that the CDF is a distributional description of data, while metrics are summary statistics derived from data.

The cumulative distribution function (CDF) provides significantly greater insight than metrics. The slope of the CDF indicates variations in density. In this analysis, the curve rises steeply until approximately 68%, where the majority of points are situated near the true location, while the flatter sections signify lower density with fewer points present. Additionally, the CDF offers a visual representation of cumulative distribution, enabling the comparison of multiple distributions. In summary, the CDF enables a more detailed interpretation than merely depending on a mean or median obtained from metrics.

Module 5 - Part 2 - Corridor Analysis

In part two of the analysis lab we are asked to create a corridor that model the potential movement of black bears between two protected areas.

The extent environment should first be modified to align with the elevation or land cover layer.

Running Euclidean tool on the road layer and reclassifying the raster layer based on the distance from roads. The further the distance from the road the higher the suitability value. Distance from the roads greater than 500 meter is given the value of 10 while values lower than 100 meter is classified as value 1, and values between 100-500 m are given the value of 5. 

Reclassified Elevation layer based on suitability value. Elevation between 1200-3000m is considered the most suitable, it is given the number 10.

Land Cover are reclassified based on suitability values, landcover I, 2, and 4 are classified as value 10  

Runing the Weighted Overlay tool to integrate the three criteria, assigning weights of 60% to land cover, 20% to elevation, and 20% to distance from roads, thereby generating the suitability model surface. 

To develop a cost surface, I inverted the suitability model surface layer utilizing the reclassify tool. In this context, a higher suitability value corresponds to a lower cost, while a lower suitability value indicates a higher cost. 

Following this, I executed the Cost Distance tool for the Coronado 1 shapefile and repeated the process for the Coronado 2 shapefile. 

I applied the Corridor tool using the two cost distance surfaces. The corridor raster represents cells that hold values indicating the total travel cost between the source and destination points in both directions.

Finally, I assigned an appropriate symbology to the newly created layer.

Module 5 - Part 1 - Rating Locations in Raster

In this laboratory assignment, we are tasked with identifying the most appropriate location for a developer to establish a new project. Our analysis will encompass five distinct layers, each based on the provided suitability ratings: Land Cover, Soils, Slope, Rivers, and Roads. 

To begin, we will utilize the Reclassify tool to categorize the Land Cover into the specified suitability ratings.

Next, we will convert the vector soil layer into a raster format using the polygon to raster method, followed by reclassifying this new raster layer according to the designated suitability ratings.

We will then employ the slope tool to generate a slope raster from the Digital Elevation Model (DEM) and subsequently reclassify this new layer based on the suitability ratings.

Additionally, we will apply the Euclidean tool to create a raster representing the distance from the river, which will also be reclassified according to the provided suitability ratings.

Lastly, we will generate a distance raster from the Roads vector layer using the Euclidean tool and reclassify this new raster based on the specified suitability ratings.

After completing these steps, we will utilize the Weighted Overlay tool, assigning a scale of 1-5 and incorporating all raster layers, with each layer contributing 20% to the overall analysis.

The outcome will be a new map illustrating areas ranked from the most suitable to the least suitable for development.

Alternative Scenario

In the alternative scenario, we applied the Weighted Overlay tool again using the same layers but adjusted the percentage contributions as follows:

▪ Land Cover – 20%

▪ Soils – 20%

▪ Slope – 40%

▪ Distance to Streams – 10%

▪ Distance to Roads – 10%

This adjustment resulted in a new map depicting different suitability areas.

Module 4 - Damage Assessment

In this lab assignment, we are tasked with evaluating the damage caused by hurricanes within a designated study area. We began by creating a new map and establishing a new feature class specifically for structural damage. This new layer incorporates an attribute domain to distinguish between various types of structures and their corresponding damage. I employed a raster image taken prior to the hurricane to assess different properties, placing points on each structure and filling in attributes with the correct building type. Additionally, using raster image taken after the event helped in identifying the structural damage. 

In the last step, we established a new polyline feature class layer intended for digitizing the coastline. This new layer will facilitate the analysis of structural damage based on five categories within distances of 100, 200, and 300 feet from the coastline. The categories include: no Damage, Affected, Minor Damage, Major Damage, and destroyed. There are several methods to evaluate the number of houses according to damage categories and their distance from the coastline. One method I used involved the Multiple Ring Buffer tool to create non-overlapping buffers at 100, 200, and 300 meters from the coastline. Afterward, I employed the Overlay tool alongside the building layer and each buffer to generate three separate layers, each illustrating the houses affected by the hurricane based on the distance specified by each buffer. Finally, I symbolized each layer according to the level of structural damage and activated the Show Count feature to ascertain the number of houses classified by structural damage within each buffer.

Damage Assessment Map

Table showing number of houses damaged based on structure damage category