Geodatabases, Attributes and Domains

Introduction

• This week we recorded points on a GPS and recorded aspects that had to do with microclimate. At these points we recorded things such as temperature, wind direction, dew point, wind speed, relative humidity and of course the date. These details are considered attribute data, which help "describe the geographic characteristic of features" and are sorted into a table (Esri). Attributes tie notes and facts to a point, so it is not just a point floating in space. We also discovered the importance of domains and how they set rules and boundaries to what values are acceptable to enter into the attribute table. This entire system of the geodatabase is the newer object oriented model that has completely transformed the geographic information system. It is completely organized with the attributes and domains tied to each point. Ensuring data integrity and accuracy. 

Study Area

• The area we studied is located in Eau Claire, Wisconsin on the UW-Eau Claire college campus, outside the front door of the Phillips building. We took a few points in the campus mall area with the arcpad collector. On March 23rd at about 5:00 pm the day was relatively warm out, around 40 degrees fahreinheit and raining slightly; it was an overcast sky with a slow breeze ranging from 1-2 miles per hour. 

Methods

• Before setting out and gathering our points we set up a geodatabase and made sure it was well organized. This is easier said than done, there are many specific details that you must make sure are correct before you can continue. Within a geodatabase there is a feature dataset and within that there is a feature class. Domains further organize a feature class by defining the valid values to each attribute. Values are predefined as to ensure that correct data has been entered. Some issues I came across was that "type" field must be the same when you are entering domains for the attributes. For example if you say the type of a certain field is "text" in the domain than that corresponding field in the attributes must be "text" in order for the domain to work. Without proper set up in the geodatabase this will not be transferred over to the Trimble Juno which will then cause issues in the field when collecting data. You may be able to gather points, but attribute qualities and information may be missing which then tells you nothing about what you collected. Re-Collecting data would have to be done. So if you don't want to create more work for yourself, make sure the geodatabase, domains and attributes are set up correctly. 
• Once we had the geodatabase set up we went out to collect a couple of points on the Trimble Juno. We had to load the geodatabase with a basemap onto the arc collector. We then set out to collect a few points in the campus mall to see how the Juno was working. Dr. Hupy explained to us how to collect a point on the map, measure the values on the kestrel device and then record those values in the arc collector attributes. 

Figure 1: Kestrel device that measures many aspects of the weather

Figure 2: Trimble Juno device we used to collect point and record attribute data.

Results/Discussion

• I only recorded two GPS points which limited the results I had. We also did not define the domain for wind speed well enough because we did not state that there could be the option of no wind which also meant no wind direction. For these values I just had to enter something and then go back manually and correct those values. From these mistakes I have learned a lot and how to correct these for next time. If we could combine our own data points with other groups we would have a better understanding of the microclimate on campus. To do this we would upload the data to ArcMap and then use the merge tool. This would bring in all the data from all different groups (as long as they put it in a shared folder) and place them into one feature class with all the same attributes. To merge all the data there would have to be data collecting standards. We would have to know what each value represented (dew point, wind speed, average wind speed, highest wind speed, relative humidity, temperature etc) because there are many values the kesrel device can measure. We would then have to make a standard of where we would hold the device, 2 feet off the ground 4 feet off the ground, it wouldn't make sense to hold it at shoulder height because obviously we're all different heights. 

Conclusion

• Collecting a point from a GPS doesn't tell you anything about that specific point. Was it hard to get to? What day was it? How was the weather? Is there anything that should be noted about that point? Collecting a point on a GPS and then mapping it with domains and attributes allows you to see data collected with that point and you can make sure that it is valid from the domains you entered beforehand. Obviously database setup has to be correct and accurate in order for all of this to happen. Data normalization is important in these steps as well, this is "dividing one numeric attribute value by another to minimize differences in values based on the size of areas or the number of features in each area" (Esri). For example this is very important with population density (diving the population by the area). Data setup and data normalization must occur in order for the data that you collected to have data integrity (meaning the data is trust worthy and was collected with standards.

Navigation Map

Methods

• Before constructing the navigation map we first had to determine our pace count. This is the number of steps we took with our right foot in the given distance of 100 meters. We will be using our pace count later on in the navigation lab.

Map Construction

• I did not want my maps to be overwhelming, I had this in mind when I was constructing them. I added a terrain map as the base for both maps so we as a group know what terrain we will be navigating through. Next I added a grid to each map, they are slightly different from each other.

     GCS Grid Map

•  The GCS Grid Map uses the geographic coordinate system, more specifically GCS North American 1983 HARN (High Accuracy Reference Network), using decimal degrees as its measurement of distance. The spacing of the grid is .001 decimal degrees. This created a grid that is useful for navigation but is not overwhelming the map.

      UTM Grid Map

• The UTM Grid Map uses the NAD 1983 UTM Zone 15N as its coordinate system. This stands for North American Datum 1983(NAD 1983),  Universal Transverse Mercator (UTM), and Zone 15N refers to zone 15 in the northern hemisphere (over western Wisconsin). The spacing of this grid is 50 meters, again creating a nice grid for navigation but not too overwhelming to the map. The UTM Grid Map is projected as Transverse Mercator. This is a cylindrical projection with the central meridian placed in a particular region (the center of the region of interest) that minimizes distortion of all properties (size, distance, area, shape) in that region. 

 Figure 1: Navigation Map in Geographic Coordinate System.

 Figure 2: Navigation Map in NAD 1983 UTM Zone 15N Coordinate System.

Sand Survey Revisited

Part 1: Introduction

• Discuss what you did in the previous lab.

• In the previous week of the sand (snow) survey lab we created a snow scene that included a:
• Ridge
• Hill
• Depression
• Valley
• Plain
• Our entire snow scene was 112 centimeters x 88 centimeters. We then conducted our sampling technique, which we chose to do systematic sampling. With this method we sampled in a grid every 8 centimeters. As a group we created a table with x, y and z values and typed those values into excel. We then uploaded the x, y, z table into ArcScene to allow us to see our sampling technique of our snow scene in 3D. Each of us experimented with the following interpolation methods:
• IDW
• Kriging
• Natural Neighbors
• Spline
• TIN
• Interpolation: "is a method of constructing new data points within the range of a discrete set of known data points" (https://www.google.com/gws_rd=ssl#q=interpolation+defintion).
•  Later on I will describe each of these interpolation methods individually.

• Discuss what the term 'Data Normalization' means, and how that relates to this lab.

• "The process of dividing one numeric attribute value by another to minimize differences in values based on the size of areas or the number of features in each area. For example, normalizing (dividing) total population by total area yields population per unit area, or density" (http://support.esri.com/en/knowledgebase/GISDictionary/term/normalization). The important parts of this definition that I take away are:
•  the purpose of normalizing data is to minimize the differences in values.
• You do this by dividing your values by another value.
• Data Normalization related to this lab because we are trying to create a 3D model of our snow scene, this means our values should have minimal differences depending on which interpolation method we thought represented our original snow scene the best.

• Discuss your data points, and how the interpolation procedure in todays lab will help to visualize that data.

• After uploading our data to ArcScene our data points turned out really well. We created a perfect grid, next was to interpolate the data and create a 3D model of our snow scene. Using the interpolation tools it created a sort of blanket that laid over our data points, showing where elevations were high and where elevations were low with the use of color. 

Part 2: Methods

• Write about the advantages and disadvantages of each. Also, provide your own observations as to how well the method ‘fits’ within a realistic representation of your terrain.
  

•   IDW: Inverse Distance Weighted, is a interpolation method directly based on surrounding measured values or on specified mathematical formulas that determine the smoothness of the resulting surface. "IDW interpolation explicitly implements the assumption that things that are close to one another are more alike than those that are farther apart. To predict a value for any unmeasured location, IDW will use the measured values surrounding the prediction location. Those measured values closest to the prediction location will have more influence on the predicted value than those farther away" (ArcHelp). 
• Advantage: Really only works well with dense evenly spaces sample points.
• Disadvantage: IDW is an exact interpolation method, meaning the highest and lowest values can only come from the sample points that were actually taken and uploaded to the program. This is why there is a sort of bumpy look to the resulting picture.

•   Natural Neighbors: "Estimates cell values for the unknown location by finding the closest known measured values and weighting them according to area (as opposed to distance)" (file:///Users/rachelhopps/Downloads/lecture7_interpolation.pdf) 
• Advantage: Works equally well with regularly and irregularly distributed data.
• Disadvantage: Not as smooth as actual surface.
•   Kriging: Kriging is a geostatistical method which is based on statistical models that include autocorrelation, these techniques provide some measure of the certainty or accuracy of the predictions. "Kriging measures distances and direction between all possible pairs of sample points" (file:///Users/rachelhopps/Downloads/lecture7_interpolation.pdf). 
• Advantage: Creates a fairly smooth suface and therefore a more realistic picture of the sampled landscape. "Output contains a table and surface of error values" (file:///Users/rachelhopps/Downloads/lecture7_interpolation.pdf). 
• Disadvantage: Difficult equations to understand. Many different ways to apply kriging, and you should understand which kriging method works best for your data.
•   Spline: "An interpolation method in which cell values are estimated using a mathematical function that minimizes overall surface curvature, resulting in a smooth surface that passes exactly through the input points" (http://support.esri.com/en/knowledgebase/Gisdictionary/term/spline-interpolation). 
• Advantage: Captures trends, the lows and highs even if they are not measured. 
• Disadvantage:"If points are too closely clustered and their values are extremely different, spline has trouble" (file:///Users/rachelhopps/Downloads/lecture7_interpolation.pdf)
•   TIN: Triangulated Irregular Network,  
• Advantage: All unknown area can be calculated and given a relatively reasonable output.
• Disadvantage: Said to be one of the least accurate interpolation methods. (http://www.geos.ed.ac.uk/~gisteac/gis_book_abridged/files/ch34.pdf).

• You were shown how to bring your data into 3D scene. You were also shown how to export that scene for use in a map layout.

• What format did you export you 3D scene image.
• I first exported the image as a 2D image so I could later open it in ArcMap and add map elements such as the title, legend, north arrow and scale. I saved it as a jpeg and opened it as a picture in ArcMap. 
• What orientation did you decide upon?
• I chose two orientations to represent the interpolation methods when I created my maps. One orientation is from a birds eye view, to clearly see the colors and at what elevation they represent. Then I turned the orientation of the 3D map at an angle to show the positive elevation and negative elevation more clearly. 
• How did you decide to reflect scale? Why does one need to place scale and orientation in these exports?
• I decided to show the scale by drawing in straight lines parallel to our snow scene and then write in the dimensions next to these lines. Scale and orientation are important because they change a simple picture to an actual map. 

Part 3: Results/Discussion 1

• Discuss the results of each method in detail, and refer to the figure, noting where there are issues with the output.
• Revisit your previous lab and make sure you do a detailed job of combining what you did previously with this lab to have what you did carefully documented
• Discuss with your group what you could do differently in a follow up survey

Figure 1: IDW interpolation method. In this particular method we can see the shape of our snow scene being formed. Every 8 centimeters we took a measurement of the z value (elevation) at that particular point. This method shows a little hill at each of the points that we recorded. Our snow scene was not "lumpy" as this interpolation method would have you think. As you can see the hill and valleys are not well defined and rounded by this method. 

Figure 2: Kriging Interpolation method. Using this particular method you can now see our snow scene has become more smooth and looks like the real snow scene we originally created. There are still some minor "bumps" where we took our sampling points, but overall kriging represents our data fairly well.

Figure 3: Natural Neighbors Interpolation. This interpolation method is fairly similar to kriging but is not quite as smooth as kriging. There are still minor "bumps" where we recorded our points. 

Figure 4: Spline Interpolation Method. this by far created the smoothest overall look of our snow scene. I think this method is the best for accurately showing our snow scene, but with that being said, this method may also tend to over smooth things, and not accurately show the highest and lowest points because those values have been averaged. 

Figure 5: TIN Interpolation method. While this method does show the elevation of our snow landscape better than the IDW interpolation method it still has its disadvantages. The snow landscape that we created was obviously not made of triangles. If this had been smoother it may have accurately shown the snow landscape we created as a group.

Part 4: Revisit your survey (Results Part 2)

Evaluate your best interpolation method and assess where you have data that is lacking.

Figure 6: Spline Interpolation with the new added points. The brighter green points are the new points we as a group recorded in the second week.

• Discuss the results of this 'redo', and relate the quality of the output to your previous survey.
• Now that we have gone back and looked at our results from the various interpolation methods, we decided to do a stratified sample method to create a more accurate image of our snow landscape. We recorded more points from areas that needed a little more help such as the hill, ridge and valley regions. The flat regions of our landscape did not need any more points recorded because those regions would still remain flat regardless of how many points we noted. 

Part 5: Summary/Conclusions

• How does this survey relate to other field based surveys? How is the concept the same? How is it different?
• For many surveys you have to take in the landscape and make sure it has been measured well for accuracy purposes. Deciding how you will gather the information is very important. With the first week we chose to do systematic sampling, after seeing this graphed in 3D we realized we needed to record more points in "problem areas." In the second week we chose to add more points by doing stratified sampling which covered these "problem areas" more and made them a little more accurate.
• Is it always realistic to perform such a detailed grid based survey?
• No, I can imagine working in the field you are working at a much bigger scale. If you have more land to sample this costs more money for equipment and workers and also costs more resources.
• Can interpolation methods be used for data other than elevation? How so? Provide examples?
• Yes, I read that interpolation methods can be used for many other things, mostly continuous data such as precipitation. Precipitation can not be measured in every possible location, so many locations are set up to gather information and then that information must be interpolated to "fill in the gaps." This is also done when recording the temperature of the ocean. There are many buoys in the ocean gathering information on the oceans temperatures, this information must also be interpolated to "fill in the gaps" and to create a general picture of where the oceans are heating up, cooling down and remaining the same.