A Modern Approach to Geographic Problems

Introduction

            A modern approach to geographic problems has given people a very large and accessible toolset that can collect data very accurately. The advanced technology that has become at the disposal of geographers allows data to be collected in a variety of ways depending on the complexity of the information needed. Although advanced GPS units, and professional survey technology can collect incredibly accurate information in optimal conditions, adverse environments can skew and disrupt the signals used to collect the locational information. Often during field collection, the environment is not optimized for geographic signals, especially is the field of study is ecology (dense forest), hydrology (river canyons).

That is why, it is very important to collect spatially accurate data, without the use of advanced technology, in the event of technological failure or disruption. One technique to produce a simple survey is conducted using azimuth and distance. In a bind, techniques like this can make sure that a project is able to be completed, even if technology fails.

            For the purposes of this project, a location was chosen that suits the criteria of making the accuracy of data collection questionable. An area was selected at the base of the hill that surrounds Putnam Park. The steep incline of the hill, plus a dense overhead canopy of trees, greatly limits the accuracy of the data. Here, the azimuth-distance technique is implemented to collect data on the relative distances of tree species in Putnam Park.

Methods

Figure 1: Arial Image of Putnam park, centered on
source points of the study area.
Source of the image is GoogleMaps

On the day the data was collected, the weather was partly cloudy, mostly sunny, and 60 degrees F. The date was October 20^th.

Figure 2:Dr. Joe Hupy, explaining
the proper strategies to use the
laser distance finder and
azimuth collector. 

To implement the azimuth-distance technique, a source point is needed to base all consequent measurements off of. For this reason, this technique is implicitly geographic, meaning the data can only be relatively accurate. There are many sources of inaccuracy, just like the data can only be as accurate as the GPS unit used. For the technique to work, information on an azimuth reading, and distance measurement are needed. Using a laser distance finder, an individual looks down the ocular scope of the tool and points the locator arrows (like the scope of a rifle) at the tree that desired. The device that was used in class also had the capability to record azimuth data as well. Azimuth was collected in a similar format to the distance. The two data recordings were written into a notebook for the creation of a physical copy of the data. Along with azimuth and distance, several other ancillary categories of information were collected as well. To make the data point more valuable biologically, the tree species and diameter at breast height (DBH) were noted.

           

Results and Discussion

Once the distance and azimuth data was collected out in the field, the data was typed into Excel and imported into Esri ArcMap program. 

Figure 3: snapshot of the format
of the Excel spreadsheet used. Showing the
group classifications.

Using the Bearing Distance to Line command (data management, features) to import the table. Next, a Feature Vertices to Points tool (data management tool box, features tool set) is used to convert the data into points. Here is where things got a little messy. In the beginning the project, a source point was collected using a hand held GPS unit. This unit would under normal conditions be accurate to below a meter. But, with the dense canopy and steep incline of the hill, bounced several of the source points to a field a few miles away from the collection area. The correction to this error is a simple one, and that would be to use Google earth, and approximate the source point and use those coordinates to base our map off of. This does lead to a great variety of errors in spatial accuracy of the outcome product; for the purpose of the project it suits the need.

Sandbox Survey; Refining and Visualizing Terrain Survey

Introduction

            Visual information often leads to greater understanding then text information alone. Similarly, 3D information often conveys more then 2D information. When conveying information from one dimension to another (2D à 3D) requires a great deal more time to correctly and accurately display the information.

            As a follow up to the previous activity (Sandbox Survey), the task at hand for this exercise is to create a continuous surface of the sandbox terrain that was sampled. To sample the sandbox, a simple systematic sampling scheme was devised to fit the proportions of the sandbox, and get accurate representation of the features without oversampling or representing. The data was cataloged in a paper notebook (kept as a physical copy), and then transferred to an Excel table. This table was then normalized, to reduce the redundancy and improve the data integrity. Important measures were taken, in Excel, to make sure that the information in the spreadsheet is considered ‘numeric’. Unless the table is specified to be ‘numeric’ data, problems occur when the data table is imported into Esri powered Arcmap. The format of the Excel table is also important, and extra attention is needed in the planning stages to make sure the Y, X, and Z columns are in the right order. This is important because once the table is imported into Arcmap, interpolation techniques are applied to the data to create a smooth surface from the XY (Z) data, and if the table is not formulated correctly, huge inaccuracies can be found, and the product is often unrecognizable and the project must be started again.

Methods

            A major outcome of this project is to come to a better understanding of the various interpolation techniques that can be applied to XY data to produce a continuous surface to represent the area that was surveyed. Each technique utilizes its own mathematical algorithms to produce the surface.  Since each technique has a unique mathematical principle behind constructing it, slight differences in the outcome of the product are experienced, with each one presenting advantages and disadvantages. In order to gain a more in-depth understanding of what each method does, 5 different types (along with a 3D version) of spatial interpolation were performed on the data collected from the sandbox survey.

The first technique explored is the IDW, or inverse distance weighted interpolation. Specifically, this interpolation assumes that each measured point has an influence on other points surrounding, that diminishes with distance from the source point. In order to predict points surrounding the measured value points, IDW weights the points closed to the predicted point, and diminishes the point further away. This method produces a good result, if the assumption of the algorithm is true, that being, objects closer together are more similar then those farther apart. If this assumption is not met, like in our sandbox survey, spots can be noticed in the final product. (Fig. IDW). These anomalies, i.e. high spots, are variations in the elevation occurring around our measured points. Another problematic feature of IDW interpolation is the product does not feature any prediction of standard error, which can make justifying the method a challenge in a professional environment.

The next method that was used is the Natural Neighbors interpolation model. The algorithms for this model works on a multivariate approximation of a point, selecting the nearest point, and not consider the values of neighboring points at all. This model is relatively simple compared to IDW and other models. It has an advantage of being simple, and producing a quality, relatively accurate representation of the points.

The third method used for this project is the Kriging interpolation model, which uses a geostatistical method to estimate and predict points. Kriging interpolation uses the distance between points as a statistical reflection of a correlation between the measured points. Kriging produces a smooth surface from geostatistical methods, which gives it the added benefit of being able to produce a prediction surface, and have the capability to provide accuracy information. This type of interpolation model is best suited for data, which has a spatially correlated distance or directional bias. Although is provides a much more detailed surface, this model is a multistep process, and involves much more time constructing the model than ether model explored so far.

            The next interpolation model for this project is the Spline method. This model works to minimize the curvature of the surface by fitting a formula to a set of sample points, and bending the surface to ‘pass through’ the sample points. Spline is considered to be a deterministic model (along with IDW), by working with the measured points and basing the surface off the extent of similarity. This model works most effectively when it’s applied to gently varying surfaces like elevation or water table information. Spline interpolation does not however produce accuracy information, so if a highly technical report is needed for professional reasons, a more dynamic model might be better to apply. However, for the Sandbox survey project, this model produced the best surface interpolation of the methods we tried.  This is due to the fact that our group intentionally made a very minimal elevation changes (small relief), in order to produce a surface, which is not very drastic.

            The 5^th interpolation model that was produced is a Triangulated Irregular Network (TIN) model. TIN’s use triangle topography to connect nodes (X, Y, Z) and edges to create a continuous surface. TIN models represent elevation topography in a way, which illustrates a change by differing the size and angle of triangles. This particular model produces a surface that can effectively delineate change, and visualize elevation much different then other forms of surface interpolation. TIN models are fantastic at representing very rough surfaces, with many angles. An example of where TIN models are useful is representing a ridge of a mountain. Since the model uses triangles of various size and shape, a much more natural surface is produced. TIN modeling does have a disadvantage in representing surfaces that have gradual elevation change, or when objects are positioned close together. In the sandbox survey, both of these situations are represented, so a TIN surface would not be the best choice to represent our survey.

            A final product that was created is a 3D surface representation.  For this, Arc scene was used. The surface was interpolated in 3D spatial analyst, using the Spline interpolation model, because it produced the best model for the sandbox survey data.  The 3D model was then exported into a .JPEG file. The surface image is presented in 3D, so two different perspectives were taken, the first from a 45-degree angle from the southwestern corner of the surface. The second representation was taken from a 45-degree angle from the opposite side, the northwest corner of the surface. These two orientations were chosen because each one shows the areas with the greatest relief, and the 45 degree angle accentuates the 3^rd dimension even though it is only being presented on a 2D planes (computer screen).

Results and Discussion

Figure 1

         This section will elaborate on the results of the 5 interpolation models that were produced for this project. In each model, the goal is to produce a smooth surface that displays visual evidence of the topographic features that were represented in the sandbox, those being a plateau, a ridge, a hill, a valley, and a depression. The first figure represented (fig. 1), is a product of the IDW interpolation model. This model is deterministic, which measures points from those surrounding. As mentioned before, the calculation of predictive points involves an unevenly distributed weight on the measured points. This uneven weighting to produce the surrounding points, the image displays a dotted surface, (fig.2), giving the illusion that there are tiny bumps of elevation throughout the surface. This is in fact a false representation of the real surface.

Figure 2

These consequent bumps can be diminished with further alteration of the input values, but for the time it would take to produce an output that still is represented with small bumps it is not a worthwhile investment. The IDW interpolation model is better suited for a highly texturized surface like a DEM of a rocky hillside.

           

Figure 3

The next technique for interpolation reviewed, is using the nearest neighbor model. Over all, the nearest neighbor technique produced a well-represented surface of the landscape that was surveyed. Figure 3 shows the image that is produced does have a few problem areas, which are notable. Most prominently, the ridge presents with a decrease in width along the entire axis.

Additionally, the eastern side of the ridge shows a bumpy surface, while the western side of the ridge shows a smooth surface transition. In truth, the surface was smooth across the entirety of the ridge. An explanation for the lumpy appearance of the eastern side of the ridge is found in the settings menu of the tool.

Figure 4

The nearest neighbor tool uses a “neighborhood” selection that defines the area that the tool will consider as a neighbor. A closer inspection, as represented by Fig (4), shows a consistent size of bump over the entire side of the eastern side of the ridge. This is a representation of the circle radius of the “neighborhood” selection. A similar effect can be seen in the western axis of the surface adjacent to the valley.

           

Figure 5

The Kriging technique resulted in the lowest quality product surface representation. Although this product was not a well fitting image of the sand box landscape, a probable cause is that this tool uses a model, which is quite complex, and offers many different selection menus’s to represent the   The depression in the northeast corner of the sandbox also looks much deeper, and the plateau as looks higher than in other images.
surface in more detail. Surface representation is a complicated and detail oriented subject. Much of the selections menus were out of the scope of this project, but the goal of introduction to the method was met in seeing the surface representation image. Fig (5). The kriging tool captured the general shapes of the features in the landscape. All of the features do show in the image, but each one is generalized to a great extent, showing a very washed out look.

The final deterministic interpolation method is using the spline model. This technique produced the smoothest, and most accurate details of the sandbox features of any. Figure 6 shows this model did a great job of representing the depression and the valley, which were approximately the same depth.

Figure 6

Another detail that was well presented in the spline model is the distinction in elevation between the ridge and the plateau. The plateau in the image is displayed as a elevation class lowers then the ridge, which is true of the actual landscape of the sandbox.

Figure 7

The final interpolation method that was used is a TIN. This technique produced a image that gave a much more 3D effect then the other interpolation methods examined today, the Arc scene produced 3D model excluding. The TIN model is very adapted to representing landscapes with large
amounts of relief. In areas of slight change, and in small spaces, the TIN model produced a slightly confusing and harsh image. I particular example of this is found in fig. (7), between the eastern side of the plateau and the western side of the ridge, is a darker area, which is challenging to interpret. Part of that effect is due to a hillshade tool, which helps define the features. But in this circumstance it presents with a challenge to interpret.

The final figures represent the 3D image produced in Arc scene. Fig (8) is presenting the surface produced from a perspective tilted to the southwestern, 45 degrees.

Figure 8

The next image, Fig (9) represents the surface from a 45-degree angle from the northeast corner. Both representations were chosen to show the construction of a real representation of the features. This model did a great job producing the image. Scale was set to a floating model so it could fit the data to a unique surface and not excessively over or under represent features in the sandbox landscape.

Figure 9

Conclusions

In conclusion, a greater knowledge of a survey format to conduct studies will greatly aid the proficiency and accuracy of the final product of the project. Comprehensive survey techniques facilitate the collection of quality data, which is crucial to any projects success. This relates to any kind of study where data is being collected from any time of environment. As often as can be allowed, the level of detail of the survey should be as large as necessary with out over collecting data points. These types of projects relates to many fields, including biology from sample distribution in lakes, or geology to study sedimentary formation, or hydrology looking into aquifers. There are many applications for survey and interpolation images. This type of project is a valuable study for future referencing.

Sandboxed

Introduction

            For this assignment, the main focus was the methodology of sampling, and various techniques involved in best practice. A standardized practice for data collection is needed in order to promote a successful experiment or project involving any time of field collection of data.

            As a loose definition, sampling can be defined as the selection of individuals or individual points within a population to estimate the characteristics of the whole population. If this is thought of in a spatial perspective, sampling can be used to produce an estimate of the whole only using a select number of data points, which can save both time and energy. This is an important concept because the way in which you sample your data affects the workflow of the entire project, and potentially the accuracy of the data in the end product. There are a few common ways to sample spatial data, including random, systematic, and stratified systematic.

            Random sampling involved the completely random selection of a certain amount of points from a specified area. This form of sampling is often used for forest population estimates, or for water quality sample test sites. The next form of sampling, systematic, involved placing the area of interest in a grid pattern, and sampling the entire area only collecting data from the intersections of the grid lines. This technique produces an even distribution of samples throughout the area of interest. This technique is commonly used to collect information about large areas with as few points while still remaining spatially accurate. Expanding on systematic sampling, stratified (systematic) utilizes the same grid technique of the systematic, but features certain areas, which are broken up into smaller grid patterns. This allows researchers to collect more information on areas with large amounts of relief or change, while focusing less on spaces with that are less important or contrasting. Allowing researchers to focus on the areas, which need the more intensive sampling to gather accurate data.

            The project, which we are working on for this assignment, utilizes sampling methods and builds on practical problem solving and team decision making. Our task is to conceive a sampling method to use, to accurately collect the topographic variation of a sandbox, which has the following features: a ridge, a hill, a depression, a valley, and a plateau.

Methods

            The sampling technique, which our group chose, is the simple systematic method.  The rational behind choosing this method over the others is based on the following reasons; the variation of our sandbox featured well defined and separate features, so the need for dynamic sampling (stratified) was low, also the features are presented in a space (sandbox) which is quite small so a grid with a even interval would properly capture most of the relief.

            The sandbox, which we sampled, had 5 features, which were described in the earlier text. Spatially, we designed our features to be located in the four corners, with the plateau to be found in the center of the sandbox. By the way in which we organized the features, the valley is found in along the western side of the sandbox, stretching from the south side of the box to the north side. In the southeast corner of the box are two small hills adjacent to each other paralleling the southern side of the box? Leading from the small hills, a ridge follows along parallel to the eastern side of the box, eventually tapering off. The ridge flows directly into the depression, which is featured in the northeast corner of the sandbox.

            The material which were used in this project are as listed: String, pushpins, a measuring stick (cm), and a sandbox.

Our sampling scheme was directly influenced by the structure and shape of the sandbox. Beause the box was a (near) perfect square, we measured all four sides to find the dimentionality of the actual sandbox space. It measured 144cm for each side. In order to get a properly small enough grid space, with consistant spacing, the group played with the numbers until an even ratio could be found. The grid was then formed, using 19 distinct classifications for X, Y, which spaced them about 6cm apart. This gave us a 6cmx6cm grid box for which to sample. We used the south, western corner of the box as our source point, which means that the X vales increased from left to right, and our y values increased from bottom to top. For the purposes of collecting the Z value, or elevation, we used the sandbox lip as a zero elevation. This means that anything below the lip is below sea level, and anything above is above.

            Once the grid was formed, and set up, data collection took place via manual data entry into a physical notebook. One member of the group took the reading of the elevation at each point, while the others wrote in information down. We choose to do a physical copy initially, to make sure that each member of our group was active in the data collection, and so that we had a physical copy to reference back to if any of our data was corrupted later in the project process.

Results and Discussion

As a result of our data collection, we collected 361 data points across the entirety of the sandbox. While looking at the data, the variation of our elevation was not very pronounced, and this is purposeful. We did not make our sandbox features very drastic, because we are measuring the sandbox with centimeters. The scale is much larger at the centimeter level then the inches or foot level, which most people operate on a daily basis. By not making the features very drastically pronounces, we ensured that the features, which we did produce, would have a consistent scale and be easier to identify once the data is imported into computer systems. The system, which we devised in the beginning of the project, worked in practice, and help up to the collection process, leading to the successful survey of the sandbox landscape.  During the project, we encountered very minimal problems, mostly stemming from time constraints placed by other obligations. Our group was purposeful in the task by making sure that our sampling method was completely thought through, and planned out so would encounter as few problems as possible during the data collection process, because data integrity is of utmost importance.

 Conclusion

Our methodology for the sampling techniques for this procedure flow directly from the definition provided because the spatial sampling is a landscape can provide a greatly cost effective (time and money) way to produce an estimate of the topographic variation captured in the sandbox landscape. Using the numbers, which we gathered, our survey did an appropriate job or representing the landscape. The technique always has a potential to be refined and redesigned for the future, one potential would be to add a stratified section if a more detailed sample is needed for a particularly complex area in the landscape, but for our purposes the simple systematic technique produces a satisfactory result.

Activity 3

Introduction

                The Hadleyville cemetery mapping project is currently in its final stages. The projects goal was to create a GIS enabled map for the Hadleyville cemetery to use for plans which had been lost in a previous move. A major challenge of achieving this goal was the lack of physical records of the grave stones, so data had to be collected in person. The data that was collected was kept in a physical format(notebook), and contained all the pertinent information of the individual graves like name, DOB DOD, and title. The information in the notebook was then compiled into an excel spreadsheet and imported into the GIS map to link with our attribute table, which allows for the capability to have all the attribute information available about each grave, in a spatial map, giving the user a much more powerful tool to work with.

                The specifics of data collection can be found in a previous blog post (http://geospatialoliver.blogspot.com/ ). Overall, the data collection flow stemmed from the manual collection of grave information, and an aerial photograph taken with the Gis capable drone flown above the cemetery. The aerial image was used to then, in a heads-up display, digitize the location of each of the graves. Using the physical copies of the grave locations and information, grave stones which are under the canopy of trees can be successfully mapped and placed spatially. That is why it is of the utmost importance to have physical copies of the information, because while technology is constantly improving our lives, it always has the chance of failing. By building a GIS, around the attribute data of the grave stones, a GIS can solve the problem faced by the cemetery by including individual’s information with a map symbolizing the spatial locations of the grave.

Study Area

                The Hadleyville cemetery is located south of the city of Eau Claire, in west-central Wisconsin. The cemetery is adjacent of a large plot of corn field, and mostly consists of grass, with a few large conifers and other assorted trees around the parameter of the cemetery. Approximately 1 square acre. The data was collected mid-September, between the afternoon hours of 3-6 pm.

Methods

                This project was completed with a combination of geospatial tools. Most simply, the use of a notebook and pencil to gather the information about the grave stones and retain a physical copy. It is important for the collection of data to not only have digital information. A physical copy will next fail, or crash when a program isn’t responding. Additionally, a physical copy can always be returned to as a reference as the project moves forward.  As a compliment to that, a Drone was flown overhead to obtain an aerial image of the cemetery. This specific drone produces a very spatially accurate image, down to below a foot. This advanced technology saves a great deal of time, because the alternative was using a survey grade GPS to map the locations of the grave stones. When the data was first being collected, a survey grade GPS was used, but postponed due to the long amounts of time it took to gather each point.

                To facilitate the transfer of data from the physical copy into Esri’s Arcmap program, an Excel spread sheet use used. excel allowed for the standardization of the attribute data, which varied greatly. When the information was added into the excel, a standardized scheme of labeling had to be established. The class communicated together, and came up with a list of attribute categories which fit all the needs of the attribute data. The process of data normalization, which is the facilitation of a standardized table, produced some problems which the class had to figure around. A primary problem faced by data normalization is what to do with a grave stone which houses a family (sometimes up to six), with only one stone. In order to not exclude any names, each individual had an individual point which corresponded with attribute information. In order to make sure a plan was clearly communicated, a special category was created call notes, which illustrated which graves are attached to which, and what families are placed together. This was a problem which was clearly communicated by the class during discussion, so a cohesive GIS map could be created.

                Once the attribute data table was normalized, it could be combined with the aerial photo taken from the drone be the process of heads-up digitizing. This involves the manual collection of the grave stone locations while looking at the imagery inside of a Arcmap. The grave stone locations are collected, and integrated with the attribute data by a table join. The final product results in a spatially accurate map, which possesses a lot of attribute data. Now each point in the map, is linked to a person, and can be viewed by looking at the information of the point.

                An important aspect of why an aerial image can be used, comes with the advancement of lowing the pixel size so that incredibly accurate information can be collected, directly from the image, because it already is in a projected coordinate system, with a consistent scale throughout the image.

Results

Figure 1: This displays the GIS map which was created. The graves are located by the yellow dots, with a locator map in the lower left corner. 

Figure 2: The final spreadsheet used to attach the attribute data with the points in the GIS.

Figure 3: This image displays the information table which appears
when a data point is selected. Showing the options of information
available.

Our data collection methods needed some refining. Our class was quite unorganized when we initially got out to the site. Several groups of students went off in different directions without a cohesive plan for data collection. This caused for data points to be missed, and attribute data to be miss appropriated to the wrong locations. To remedy this situation, the entire class got together and had a dialog about what specific goals for the project, and individual tasks for people to take. This produced the desired outcome, as the students began to have an open conversation about the project, which was very important when it came time to normalize the data.

                Another facet of the methods which increased the data collection was the complicated, survey grade GPS. This device, can accurately map within the cm, but does have some short fallings, like time to collect each point, and the poor reliability for points under tree canopies. This latter problem, is what caused the most problems with the collection of data. That is why an aerial image was taken from the drone, which greatly decreased the time needed to collect each of the gave stone locations.

Conclusion

                The methodology used was distinctly planned to follow exactly what was needed in the final product. By using a drone to collect the spatially accurate image, and the manual collection by multiple students made the process go much faster. Although, there is great potential for data errors when collecting in the manual way, it allows for the easy manipulation and correction of the error. If the greatest potentials for error are monitored, and several QC checks are done throughout the process, the data error will be essentially null.

                As the project comes to its conclusion, it’s important to evaluate the end product which is going to be delivered. The product which is finalized, offers a successful comprehensive survey of the Hadleyville cemetery, locating each grave stone in residence, and producing a GIS which can be added onto simply and efficiently. This provides a great service to the county, which is now able to electronically keep up with its occupancy, and add information much more easily then every before. The spatially accurate map, contains a great deal of attribute data, which combined with the spatial locations of each grave is a valuable end product which will follow into the future.