Navigational Map Construction

Nathan Sylte

Developing a Field Navigational Map 

Introduction:

The objective of this lab was to construct a set of navigational maps that are to be used in a future navigation activity. Navigation requires several things. First, the navigator must be able to orient his/her self. Second, there must be a projection of some kind so that they can reference their position. In the case of our navigation activity we will be using our navigation maps and a compass to traverse a course on the grounds of the UW-Eau Claire Priory.

Figure 1. Above is the area of interest outlined in red. The Priory is labeled in white. 

Methods:

Proper navigation requires the navigator to know their direction and distance they have traveled. For the future navigation exercise a pace count will be implemented to determine distance traveled. The pace count involved the measuring our of a 100 meter stretch. The walker (myself) then walked the 100 meters twice and took the average amount of steps it took to travel 100 meters. To create the navigation map a database containing aerial imagery and 2 foot contour lines of the area of interest (Priory) was provided to us.  

The map coordinate system and projection are the essential components of any map. In the case of this navigational map the coordinate system used was the Nad83 datum. The Nad83 or North American Datum 1983 coordinate system was implemented to replace the aging Nad27 datum. The Nad83 coordinate system defines a geodetic network in North America and is commonly used throughout North America. In the case of map projection the map projection used for this navigational map was the UTM (universal transverse Mercator) projection. The UTM system divides the Earth into 60 zones. Each zone is six degrees in longitude and distortion is minimized in each zone. The Priory is located in UTM zone 15 so that is the zone that was selected to minimize distortion.

After the map coordinate system and projection was set a grid was placed over the top of the map. Two separate maps were generated utilizing two different types of grids. The two types of grids that were used included the graticular and UTM grids. The graticular grid utilizes latitude and longitude points while the UTM grid simply uses easting and northing coordinates. After the grids were selected and created the last step involved removing some of the contour lines. The map was very cluttered so every third contour line was removed. Below is the selection that was used to select for every third contour line (Figure 2). 

Figure 2. The select by attributes function was used to select for every third contour line. The function to select for every third contour line is shown above. 

Results:

The final maps are displayed with the grids imposed over the top of them for reference during navigation (Figure 3, Figure 4). Each grid square represents 0 degrees, 0 minutes, and 1 second in figure three. While each grid square represents 50 meters in figure four. Although the clutter from the contour lines was reduced by removing every third contour line. There is still clutter from the contour lines in areas where there is severe elevation change. This can be seen in the west central portion of the maps. 

Figure 3. The graticular style grid laid over-top of the Priory map. The contour lines are shown in blue. Key map information such as the north arrow, step count, coordinate system, projection, map scale, and data source is located on the right side of the map. 

Figure 4. The UTM style grid laid over-top of the Priory map. The contour lines are shown in blue. Key map information such as the north arrow, step count, coordinate system, projection, map scale, and data source is located on the right side of the map. 

Conclusion:

This activity proved to be useful in developing the skills associated with proper navigational map construction. Map coordinate systems and projections are critical in the creation of a useful navigation map. This activity certainly reinforced this reality. Although the information on the maps appear small, when printed out on a 11 inch by 17 inch surface the maps should prove to be very informative and useful. 

Cartographic Fundamentals: Map Creation, Description, and Interpretation

Nathan Sylte
Geospatial Field Methods

Cartographic Fundamentals 

Introduction:

In the previous two labs we have been learning about surveying techniques, grids and coordinates, and different interpolation techniques. This lab (lab 3) is a continuation of the previous visualization of our terrain survey lab. Lab three also reviews some of the key fundamentals of creating quality and informative maps. Some of the objectives of this lab are the following. First, create a new cartographically pleasing map of our terrain survey data. Second, review some of the key aspects of creating quality maps. Third, generate several maps of the Hadleyville Cemetery in Eau Claire County using the key fundamentals of map making. 

Proper maps should incorporate the following fundamentals. Maps should include a north arrow, scale bar, locator map, watermark, and data sources. A north arrow is critical for the viewers orientation, and is also important for reference purposes. Scale bars are a way in which the viewer can judge distance on the map. A scale bar is especially important if the area of interest is of an unknown size (a cemetery). Locator maps are also important. Locator maps are crucial for the viewers orientation and reference. They give the viewer information on the whereabouts of the area of interest. A the map creators "watermark" should also be included in the map. This states who generated the map and helps prevent plagiarism. Data sources are often under-included. It is important to know how the data was collected, the precision of the collected data, the data sets coordinate system, and the time and date the data was collected. 

Methods:

Using the data from our sandbox terrain survey we first generated a map of our terrain features. With the use of the interpolation method that best portrays our data, we created a hillshade map of our terrain features. ArcScene allowed us to take four oblique angles of our map which were included in our final map. The natural neighbors interpolation technique was selected as the interpolation method that best portrayed our terrain features and a cartographically pleasing map was then created. 

The next part of the lab involved the creation of four separate maps of the Hadleyville Cemetary. Nominal data labeling the year of death, labeling the last name on the grave, and showing whether the graves were standing or not were included in the first three maps. The last map was supposed to show the year of death using different size points. 

Results and Discussion:

When looking at the results from the sandbox survey the first striking feature that jumps out is the hill feature (Figure 1). The hill feature is located slightly north of the center of the map and is a height of 21 centimeters (cm) above sea level (sea level is the bottom of the sandbox). Directly west of the hill feature runs the valley. The valley resembles a banana in shape and is at an elevation of 7 cm at the bottom of the valley. If the viewer shifts their view to the south of the valley he/she will notice the plain feature. The plain takes up a majority of the space in the south west quarter of the sandbox. The elevation of the plain is 12 cm. It should also be noted that the elevation of the plain is very close to the average elevation of the sandbox (12.48 cm). The most common terrain feature found in the sandbox is the depression feature. This feature can be found several times in the sandbox. The largest depression can be found directly east of the hill feature. This depression has an elevation of 10-11 cm. Another important piece of statistical information that should be included is the range in elevation. The range was a value of 14 cm. This means that the distance from the top of the hill to the bottom of the valley was 14 cm which shows a drastic change in elevation proportionate to the map. 

Figure 1. Natural neighbors interpolation and hill shade map of the sandbox survey terrain features. Elevation is represented by seven different colors with the lowest elevation shown in light blue and the highest elevation shown in white. 

The results from the cemetery maps show several pieces of information. First, the data show that the cemetery is very old. Hadleyville Cemetery is a very old cemetery containing graves dating back to the mid 1800s (Figure 2). Figure two below demonstrates this by displaying the ages of the grave by labeling the year of death. It should also be noted that there are many family members burred in the cemetery. This can be seen in (Figure 3). For example, in the south west corner of the map there are several members of the Dickerson family that are buried.

Overall, the cemetery is in good shape. Despite containing many graves from the 1800s, a majority of the graves are standing (Figure 4). Only five graves in the entire cemetery are not standing. All the graves that are not standing are located at least 20 meters from the road. An interesting observation that can be described relates to the distributions of the graves themselves. It can be seen that many new grave sites (post 1940) are located among the old grave sites (pre 1900) (Figure 5). Another interesting observation is that there are many old grave sites that located far from the road near the south end of the cemetery. These observations are likely a result of family members being buried next to one another (Figure 3). 

Figure 2. The above map displays the year of death. Grave sites are marked by orange circles. 

Figure 3. Above the names of the people buried are listed next to the grave sites. Grave sites are marked by orange dots. Some grave sites do not have information for names.  

Figure 4. This figure shows whether or not the grave sites are standing or not. Standing graves are shown in green while graves that have fallen over are shown in red. Graves that do not have information on whether or not they are standing are shown as smaller dots. 

Figure 5. This image shows a graduated symbols map of the year of death. The most recent years of death are shown in red while the oldest years of death are small black dots. 

Conclusion:

The last several weeks have resulted in the development and improvement of many skills. Interpolation and surveying techniques are now very familiar. These are very practical and important skills to possess in the geospatial field. ArcScene is another program that has become familiar. Generating 3-D maps in ArcScene will prove to be a useful skill to have in the future. Re familiarization with cartography was another important feature of this lab. Generating cartographically pleasing maps is extremely important in the geospatial field and lab three did a good job of refining those skills. 

Visualizing our Terrain Survey: Interpolation, ArcMap, and ArcScene

Nathan Sylte
Geo-spatial Field Methods

Interpolation

Introduction:

The previous lab involved a survey of various terrain features generated in a sandbox. In the sandbox we created multiple terrain features which included a ridge, hill, depression, valley, and plain. Survey points were sampled by the use of the systematic surveying technique. A total of 400 survey points were collected using this technique. Data was then recorded and normalized. The normalization of the data requires turning the data into a standard measure. This was done by transforming the surveyed data points into x,y, and z coordinates in excel. These coordinates were then used to map out the surface of our surveyed area (the sandbox 114 by 114 cm). We used multiple interpolation methods to generate estimated values at points not sampled during the survey based off of the known values of the surrounding points.  The use of various interpolation techniques allowed us to generate accurate visualizations of our terrain features. However, certain interpolation techniques were more accurate than others. 

Methods:

First, survey data had to be normalized into a format that could be used in ArcMap. This involved transforming the data into x, y, and z coordinates in excel. 

Figure 1. Above is the format of our normalized survey data points. 

Data was imported into ArcMap as points feature class. Ultimately, a layer file was generated which was to be put into our geodatabase. ArcScene could now be used to view the data in a 3-dimensional format. 

Figure 2. Above is our data points shown in ArcMap.

After the data was imported into ArcMap, interpolation techniques were used to visually display our data as an accurate representation of the terrain features that we created. The two interpolation methods that most accurately displayed our terrain features were selected. Data interpolation is designed to estimate the values of cells in a raster that could potentially have missing data points/values. Below is our data points in ArcScene before any interpolation techniques were applied. 

Figure 3. Above is our data points in ArcScene before any interpolation methods were used. 

We applied a total of five different interpolation techniques to our data to determine which technique most accurately portrayed our terrain features. The descriptions of the five interpolation techniques that we used are listed below. 

IDW Interpolation: IDW (inverse distance weighted) interpolation presumes that points/cells close together are more similar than points that are far apart from one another. Therefore, IDW predicts cell values based on the averaging of data values in the neighboring cells. Points nearest to the center of the cells being estimated have the most leverage. IDW does have its draw backs. Surfaces cannot be accurately generated if there are points that possess smaller values than the lowest value in the data set. The same holds true with data points that possess larger values than the highest value in the data set. This means that if a very high or low point is not surveyed then IDW will inaccurately display the data. Dense sampling techniques must be used in order for IDW to accurately display what you are surveying. We used a systematic sampling technique that resulted in the collection of over 400 data points in a sandbox. IDW is a good interpolation technique to use in our situation. (ArcGIS Help) 

Natural Neighbors Interpolation: The natural neighbors technique of interpolation simply takes the closest subset of samples for a specific point, and then assigns weights to them. The weights are assigned off of proportionate areas so that unrepresented points can be given values. This method has the same draw back of IDW. If there are peaks or extremely low points that are not survey the information will not be accurately represented. Natural neighbors is a solid interpolation technique for our situation because of the large volume of sample points that we took. (ArcGIS Help) 

Kriging Interpolation: Kriging interpolation uses statistical models that possess auto-correct functions. This means that kriging interpolation is able to predict a surface, and inform the operator about the accuracy of the predicted surface. (ArcGIS Help)

Spline Interpolation: Spline incorporates a function that reduces surface curvature, which results in the production of a smooth surface. However, the surface must pass through the data points. The greater the number of points the more smooth the surface will be. Since we collected so many data points the spline method proved to be a great method to display our terrain features. (ArcGIS Help)

TIN Interpolation: The raster to TIN tool generates a triangulated irregular network where the surface is not allowed to deviate from the input raster by more than a specified Z tolerance. One downfall of TIN is that the generated image will not have any smooth features. TIN only shows the surveyed points which means there will be a lot of peaks and valleys displayed in the image. This will not accurately show the landscape. (ArcGIS) 

ArcScene was then used to generate a 3-D image of our terrain features. Corrections for drastic peaks and valleys had to be used on all the interpolation methods. 

Results/Discussion:

Shown below are all of the different interpolation methods results. The first image shown below are the two best interpolation methods that most accurately display our terrain features. 

Figure 4. Above is the spline and natural neighbors interpolation methods. These methods produced smooth terrain features that do a great job in displaying our terrain features. 

Spline and natural neighbors interpolation best displayed our data (Figure 4). The reason spline and natural neighbors were the best methods was because we sampled a large amount of data points. Since we used a dense sampling technique these methods were able to accurately display our data. 

IDW Interpolation 

Figure 5. Above is the IDW method of interpolation. 

IDW does a great job in showing our hill and ridge feature (Figure 5). However, IDW produced an image that is very bumpy which isn't the most pleasing. IDW does not show the smooth changes in elevation like the natural neighbors method does. 

Natural Neighbors Interpolation 

Figure 6. Above is the natural neighbors method. 

Spline Interpolation 

Figure 7. The Spline Interpolation method. 

The spline along with the natural neighbors method did the best job in displaying our data (Figure 6) (Figure 7). However, the hill has rounded off slightly. This is one of the down sides to these methods is that when a smooth image is produced drastic changes are not always shown. In our case the hill is less defined than it otherwise would be in a TIN (Figure 8). 

TIN Interpolation

Figure 8. The TIN interpolation method. 

The TIN image shows a very jagged landscape (Figure 8). This is because the TIN interpolation method only takes into account points that were surveyed which leads to a jagged landscape. However, the TIN most accurately shows our hill while the natural neighbors method shows a more rounded hill (Figure 6). 

Kriging Interpolation 

Figure 9. Kriging Interpolation. 

Kriging interpolation does not to a horrible job of displaying our terrain features (Figure 9). It is certainly a better method than TIN (Figure 8). Kriging does a good job showing features in the 2-D model, but the 3-D model just isn't as nice as the spline and natural neighbors models (Figure 4). 

Conclusion:

Overall, it is not always practical to perform an extremely detailed grid survey. However, it is important to concentrate survey points in locations that have drastic elevation changes. This activity greatly improved my knowledge of various interpolation techniques as well as surveying techniques. Understanding the advantages and disadvantages of the different interpolation techniques will help in future geospatial projects. It should also be noted that interpolation can be used many different types of data such as temperature and rainfall data. Although we simply surveyed a sandbox the concepts we learned can be applied to real wold situations. The only difference is the sandbox is much smaller. 

Sandbox Survey: Understanding Grids and Coordinates

Nathan Sylte
Sandbox Survey

Understanding Grids and Coordinates 

Introduction: 

Our task was to create several geological terrain features to be surveyed using various spatial sampling techniques. But what does the term sampling mean? Sampling is essentially a simplified means to collect information (1). In our case, "sampling" would refer to an efficient, and organized way to collect spatial information. Three common methods of sampling spatial information would include random, systematic, and stratified techniques. Each of these techniques has their own advantages, however, we chose the systematic technique for its ability to be easily used with a grid. 

Overall, our lab objectives were to first, set up a sandbox with certain terrain features. The terrain features to be included in our sandbox included a ridge, hill, depression, valley, and a plain. Second, we were to survey/sample the entire sandbox using a specific sampling method. In our case we chose the systematic method for reasons preciously stated. In the coming weeks a digital terrain model of our sandbox environment will be generated. 

Methods:

Our study area included a sandbox located outside of Phillips Hall. Specifically, our study area was located east of Phillips Hall near Little Niagara Creek. The sandbox measured 114 by 114 cm, and was 15 cm deep. The terrain features as shown bellow included a ridge, hill, depression, valley, and a plain. These features were hard to create due to the freezing temperatures. 

Figure 1. The picture above shows the terrain features 

As previously mentioned we decided to use the systematic sampling technique to survey our sandbox. This meant that our survey points had to be equally distributed throughout the sandbox. To evenly distribute our survey points a grid was placed on top of the sandbox. The use of this technique insured that our study area was evenly surveyed. The grid that we used included 20 x and 20 y coordinates meaning that every 5.7 cm there was a node. To generate a z (elevation) coordinate the depth of the sandbox was measured (15 cm). The bottom of the sandbox was declared sea level (0 cm) to insure no negative values were recorded. The yarn making up the grid was located at a height of 15 cm from sea level so all elevation (z) values were recorded based off of their distance from the yarn (15 cm). For example, the top of the hill was measured 6 cm above the yarn meaning the height was 21 cm, and the values below the yarn were measure by subtracting from 15 cm. A total of 400 points were surveyed, one point for each node. The materials used to survey the sandbox included one meter stick, yarn for the grid, and one surveying flag (for marking the node which was to be sampled). 

Figure 2. The image above shows our grid pinned to our study area (sandbox). The grid included 400 nodes (places of intersection) and possessed 20 x and 20 y coordinates. 

Results:

A total of 400 values were recorded. The tallest point in our study area measured 21 cm high, while the lowest point measured 7 cm high. Therefore, the range was 14 cm which showed a fair change in elevation respectively. The most common elevation measurement (mode) was 12 cm which was also the height of our plain terrain feature. Furthermore, the average elevation measurement was 12.48 cm. This was very close to the most common elevation. It should also be reported that the standard deviation was 2.14. 

Figure 3. The photo above shows the hill terrain feature in our study area. The hill measured 21 cm above sea level (the bottom of the sandbox). 

Discussion/Conclusion:

Overall, the systematic surveying technique implemented did a good job of surveying the entire grid equally and effectively. However, some of the areas with greater elevation changes may have been under surveyed. This is where the stratified surveying method may have worked better. There was also some difficulty initially in determining how to resolve the sea level issue. Specifically, how would we make sure that we did not record any negative values, and where would our measurements be taken from. However, this issue was resolved and the sampling went smoothly. In the future the surveying should be conducted in warmer temperatures, for it was below 10 degrees F when this exercise took place. These cold temperatures decreased the accuracy of the measurements and decrease length of time we could stay outside. 

References:

1. Royal Geographical Society. (n.d.). Sampling techniques. Retrieved from http://www.rgs.org/OurWork/Schools/Fieldwork+and+local+learning/Fieldwork+techniques/Sampling+techniques.htm