Lab 9: Topographic Survey with a Total Station.

Introduction

In last weeks lab previous lab, students conducted a survey using the TopCon station to collect feature locations. This method was rather simple, since all of the features  were in an open area with little to no obstruction from land forms of building for satellite triangulation.  Sometimes, however, a situation will call for more accuracy in  X, Y and Z (elevation) locations.  Just as well, sometimes a survey will have to be conducted in area in places where adequate triangulation will be hard to get (like in places right next to a building or at the bottom of a long/steep slope).  In either case, an approach that could be taken is to do a topographic survey using a total station, which is what this lab activity teaches students to do.  In the sections to follow, the methods and results of a total station topographic survey will be outlined.  Following the methods and results, there will be a discussion about this specific method and the specifics of the lab conditions, and ultimately end with a conclusion giving a full rundown of the newly acquired skills from the laboratory activity.

Study Area 

In this particular lab demo where students conducted a topographic survey of the landscape  on the Phillips Science Building's North Lawn using a total station.  The study was conducted by the full class, and was guided in part by Professor Jo Hupy.  The surveying began at roughly 325 PM and ended at around 530 PM.  The conditions on that day, April 19th, were wet, with on going rain.  The temperature was not to cold, however, and hung around the 60's.  Toward the end of the survey the rain did dissipate and the temperature began to raise, but just slightly.  The specific area where the survey was an area to the North West of the main body of the Phillips building, and to the North East of the Davies Center building. Cutting through the study area is the Little Niagara Creek, which runs East/West through the campus.  The land on both sides of the creek gradually moves from more consistent level ground, into a gradual slope as the land approaches the creek.  Trees were surveyed from both sides of the creek.   The point from which data was recorded was on the south side of the creek. At the time of the Survey, there was heavy foot traffic as students were coming and going from their classes, the atmosphere was someone chaotic and high paced as many were trying to escape the damp and cold.  

The point that anchored this land survey was anchored was on  grass portion of the campus mall.  The viewing station was anchored in the same place, and it was very important that students not move any of the legs or move the base of the device.  directly to the west of the station, not more than 3 meters away was the TopCon Differential GPS and Tesla Home Station device, which were connected to the viewing station via a blue tooth connection. below in figure 1 is a map that displays the general area that composed the study are for this survey.

Methods

Field Collection

The collection process of the data for this lab, much like last weeks lab, involved groups of two or three going out into the study area with Dr. Hupy to get one on one instruction on how to work with the equipment.  Before going into the details about data collection, let us first look at how this type of survey is done and what must be accounted for in order to produce meaningful results. because the viewing station has no spatial reference system Incorporated within it, before conducting a survey one must collect back points to provide the system directional points to go off of.  This was done using a GPS the TopCon Survey GPS.  These points provide directional orientation for the total station to get the most accurate results.  Ideally,  one would collect a breadth of back points at varying distances and orientations relative to the occupied point, which is the the precise location of the total station during a survey. Like with ground control points in remote sensing, the more back points a total station survey uses, and the more diverse their orientation, the more accurate your survey will be.  Similarly, it is very important that while establishing back points and conducting a survey that the total station not be moved at all, the occupied point must remain in a steadfast location and orientation in order for the survey to have a meaningful output.

figure 3: TopCon total station

figure 2: Prism at the top of adjustable point rod

The actual collection of the data points was conducted in groups of two, and each group took roughly 20 points with each partner taking turns using both the total station and prism rod.  Figure 2 and 3, to the right, show what each of these components look like.  The total station communicates the data collected via Bluetooth to the Tesla handheld field unit.

During the data collection process, one student would take the prism rod and move about as instructed by Dr. Hupy and the other student would aim the viewer on the total station to line up with the circular portion of the prism, trying to get the cross hairs as central of the total station as centered to the middle of the prism as possible.  When lined up, the student running the total station would tell professor Hupy, and using the Tesla handheld unit (which is connected via Bluetooth to the total station) would command the total station to record/save the information in the the Tesla's memory memory.

Processing Data in ESRI

From the data collected by all the groups, a text file was created containing the UTM easting, northings, and elevation values collected during the time in the field.  With that data, students were to input that data into ArcMap and interpolate it into a raster format using any method they so desired.  Before this was done, the text file was converted into a table and all extra fields other than an ID, X, Y and Z field were deleted.  Once that was complete, the table was exported into a geodatabase and turned into point features using the XY to point tool.  

Now that the data has been turned into a feature class, the points elevation values can be used to create a surface raster.  The Interpolation method that was used was IDW interpolation.  Once complete, the data was manipulated to show relevant significant digits and symbolized in a way that best represents changing elevation.  

Results

The resulting raster shows how the landscape around the Little Niagara Creek slopes down until eventually meeting the water. Figure 4 below shows the raster created that represents the elevation changes within the AOI.

Figure 4: Map showing elevation survey of a portion of Little Niagara Creek's Bank 

Discussion 

Despite this lab being a good introduction to the methods and techniques associated with conducting land surveys, there are a couple of discrepancies associated with the data collected.  The data indicates that the landscape is more or less a sink hole rather than a continuous bank that converges onto a creek.  The data is displayed as such because of obstacles on the both the eastern and western edges of the AOI that prevented students from taking point locations of the ground. To the west, there is a good bridge that hangs low over the water that blocks students from getting a reading of the ground height near the waters edge.  To the east, thick brush covers the ground until it eventually runs the land around the creek converges to a very small strip of land between the Phillips science hall and the creek itself.  Because no/few points were taken near these edge locations, the interpolation of the raster aggregates the higher elevation values from the more open areas further away  from the bank, thus producing the perceived sink whole seen in the raster created.

In terms of the survey set up, this survey only employed the use of 3 back points relative to the occupied point.  In actual survey, this would not be acceptable as there needs to be a much more wide range of points.  So although the survey yielded data that produced points that overall showed lower elevation near the creek and higher elevation away from it, its hard to comment on the accuracy of each point in the X/Y direction because so few back points were used to orientate the total station.  Had more points, there would be much more confidence in the accuracy of the data. 

Conclusion 

Becoming familiar with such a highly accurate technology is a very valuable skill to have for ones geospatial tool kit as surveying work can become a very useful tool to have.  Some times a GPS unit will not have the triangulation capabilities to locate itself over and and over again in the field, and by using this equipment, a full survey can be conducted using angles and distances from a single known point, allowing for collection to occur in hard to collect places where triangulation would otherwise be difficult, thus making traditional methods of simply collecting points with a GPS unit be impossible to do.  

Lab 8: Topographic Survey of UWEC's Southern Portion of Lower Campus

Introduction 

For this lab exercise, students were paired into groups of two to conduct a topographic survey of the University of Wisconsin Eau Claire's Lower Campus.  During the lab, groups took turns going out onto the campus mall with Dr. Hupy to take point locations of a combination of lights, trees, garbage cans, or bike racks.  To take these measurements, a highly accurate Top-Con survey grade GPS device was used to create these point features.  The partner that I was to conduct my survey with was Andrew Faris, and we were the the 5th group of 6 to go to the field and conduct a survey.  Once each group has collected their points, the class was then to combine the data collected from all the groups into one, complete dataset, and export the data into ArcMap where it can be converted into a features that represent the totality of what the class collected. 

Study area and Methods 

The Area where this Activity was conducted was on the lower campus of the University of Wisconsin of Eau Claire, in and around the parking lot that is located to the south of both the Phillips Science Building and the Davies Center.  The device which was used to collect the features in this area was a TopCon Survey Grade GPS unit.  In past activities, GPS units were used that provided accuracy that could be off by a meter or more when pinpointing locations.  The TopCon utilizes RFC technology that can provide sub centimeter accuracy. 

This device comes with a complete built in interface that allows for the user to manipulate the data being collected in the field in real time.  Apart of this interface is a reassembled set of field attributes that identify the point of which is being identified with the device.  By doing this, exporting the data into a GIS software program becomes very symbol, and essentially only requires that you bring it into a geodatabase, provide appropriate symbology, and than make/publish the map the document that shows the location and desired attributes of each feature. 

Collecting the point locations was very similar to using a small handheld unit or cellphone, except for the fact that the TopCon set up is much more bulky.  The device from which the user selects the inputs and other options is a Tesla unit that is about 8 inches wide and 4 inches high.  The Tesla device is portable, but during survey operations it is mounted to a tripod.  Atop the tripod is a the GPS device, which is securely mounted to a long pole with a fixed l.  The GPS atop and the Tesla communicate via a MiFii device which is connected to the 4G network.  Figure 1, below, shows the full set of components that were used in this field operation. 

figure 1: Top Con Survey Grade GPS Unit

The GPS unit atop takes the point location from the geographic or projected coordinate system specified by the user prior to the beginning of survey activity.  The x position, Y position, and Z (elevation position) are taking from the point where the middle black pole in the middle meets the surface below.  The other two poles, seen in figure 1 as the poles colored a bright yellow color, provide support as steaks that can penetrate the surface if the ground material a soft material.  Using these three legs, the users need to adjust the legs until the device is level.  To aid with this process, the tripod is equipped with a bubble leveler near where all three legs meet.  Doing this helps assure that the GPS unit collects the most accurate point possible.  

Collecting the point is a very simple process as well, All that one needs to do is select the feature they are mapping, using a touchscreen pen.  Once you have a feature type selected, and have a confirmed GPS fix from the satellites above, a point can be taken with the simple push of a button.  Collecting a point can be done by simply having the device take a single point, or by taking the average of a specified number of readings.  In the instance of this lab acclivity, the point that was recorded was recorded as an average of 30 points per feature identification.  This was not done in this activity, but the Tesla station also allows the user to take a picture when taking a feature location, which would than be attributed to that point location when exported into a computer desktop. 

In terms of the collection process, this was done during a single class period on April 12th, 2016 in groups of two.  Dr. Hupy and the departments GIS technical adviser, Martin Geotell, took two students at time into the study area to get experience doing topographic work with the TopCon station.  The collection process took roughly 1.5 hours, and was a smooth and easy process since there was no inclement weather (partly cloudy at around 55 degrees F) or technical issues.  

Once the collection process was complete, the locations and attributes were provided to students by Dr. Hupy as a comma delineated text file, which was than imported into excel so that it could be turned into a table.  Once in table format, the table could be imported into a geodatabase, projected to WGS 1984 UTM Zone 15N, and shown as features using the XY tool. 

Results

Below, in figure 2,  is a map of the features collected from the topographic survery that was conducted on a April 12th, 2016 using the TopCon total station. 

Figure 2: Topographic UWEC campus survey

Conclusion 

The TopCon total station had failed on students in the past and it has since been fixed with a new and improved interface.  When functioning, it is a very smooth and intuitive tool that provides users with sub-centimeter locations of features  in a variety of geographic/projected categories.  Such a knowledge of this tool will be useful going forward working with construction based projects where highly accurate GPS data is required as apart of a project.  

 

Lab 7:Distance/Azimuth Tree Survey: UW-Eau Claire Campus, Phillips North Lawn

Introduction 

Much of the field work that is currently done in the professional geospatial industry is contingent upon the full functionality of the newest, shiniest technology.  The thing is though, with these amazing tools come amazing headaches, and at times the technology can completely  fail the user, and become useless.  When this happens, one must resort to more traditional methods of geospatial surveying to complete the task assigned.  With that being said, the method that will be discussed and demonstrated in this lab does still make use of some electronic technology, it can still be done with tools that are completely independent of electronics/satellite connections.  This method is what is known as a distance-azimuth survey, and it is conducted by taking distance and azimuth degree measurements from a single/known point.  With this information, after a few simple operations in ArcGIS, the locations of what ever it is that is being recorded  can be identified with moderate accuracy.   In the sections that follow, methods and results will be discussed that pertain to a distance azimuth survey that was conducted on Tuesday, April 5th, 2016.  

Study Area

In this particular lab demo, students conducted a distance azimuth survey of trees on the Phillips Science Building's North Lawn.  The study was conducted by the full class, and was guided in part by Professor Jo Hupy.  The surveying began at roughly 325 PM and ended at around 440 PM.  The conditions on that day, April 5th, were cold and damp.  The weather was roughly 35 degrees, with gusting winds and a constant drizzling rain that lasted the entire survey.  The specific area where the survey was an area to the North West of the main body of the Phillips building, and to the North East of the Davies Center building. Cutting through the study area is the Little Niagara Creek, which runs East/West through the campus.  The land on both sides of the creek gradually moves from more consistent level ground, into a gradual slope as the land approaches the creek.  Trees were surveyed from both sides of the creek.   The point from which data was recorded was on the south side of the creek. At the time of the Survey, there was heavy foot traffic as students were coming and going from their classes, the atmosphere was someone chaotic and high paced as many were trying to escape the damp and cold.  

The point that anchored this distance azimuth survey was anchored by a man made crack that was created as a gap between slabs of concrete on the sidewalk, directly adjacent from a light pole.  The light pole was intended to be the anchor point of this distance azimuth survey, but was changed to the crack because at its widest point, allowed for people to stand unobstructed by the pole. The crack was roughly a foot and a half long, on one side was grass and on the other side, a complete concrete slap. This was useful because one could place there feet at the edges of the crack, with their toes behind crack, and make consistent measurements from the same point.  Below, in figure 1, one can see the general layout of the study area.  It should be noted, however, that the trees shown in this map do not highlight any part of the trees that were used as apart of this survey. 

figure 1: Study Area for Distance Azimuth Tree Survey.

Methods - Part 1: Collecting Tree Data 

To conduct this survey, the class needed to record three things in pertaining to each tree. The distance from the anchor point in meters, its azimuthal location on a scale of 0 to 360 from the anchor point, the trees common name, and the diameter of its trunk. The distance measurements were taken by two separate devices.  One device was a pulsating device which recorded distances based off of sound pulse that was originated at the anchor point and traveled to the tree where another student was holding a counter part of that device, which than deflected the sound back to the main device.  Given that the speed of sound is relatively constant, the time between when the pulse leave the device, bounces off of the the counterpart, and returns, providing a distance value between the the observation point and the tree. This distance provides distance and distance alone.  However, the second device, which makes use of a laser and an internal compass, provides both a distance and an azimuthal direction. 

In terms of measuring the width of tree trunks, this was done with a simple measuring tape with a hooked end. The hooked end allowed for the measures to stick the tip into the wood, where it remain steadfast in the bark, while the rest of the tap could be rapped around the trunk to record the distance. The identification of the tree was done by relying on the good judgement of the professor.  

Since the class was all recording the information individually, there needed to be someone to relay the information  from the the party that was conducting the tree girth measurements and species types, to the others by the anchor point. After about two tree measurements, the two students that were accompanying the professor would switch out, and the two that were previously with the professor would join the others.  This ensured that everyone would would have experience with all components of the equipment.

Once professor Hupy dubbed the survey complete, students than returned inside so that the classcould record the data into a digital format in ArcMap.

Part 2: Creating Data in ArcMap

Now that the dataset has been created of trees in the Phillips North Lawn, the class can now input this data into an excel table so that it could be turned into features and eventually mapped.  Figure 2 below shows the table that was made. 

figure 2: table that was made in Excel from data collected in the field.  The X and Y field are all the same values because that represents the anchor point 

Once the data was in this digital format, it could then be turned imported into a geodatabase in ArcMap and be transformed into a recognizable set of features, relative to the data that was mapped.  With that, now the workflow will be laid out as to how to create the features desired from this survey.

1. Using the Barring Distance Line Command, input the table and populate the X, Y, Distance, and Bearing fields appropriately. 

2. The output of this operation will be the lines at the appropriate distance and bearing from the anchor point, as seen in figure 3 below.

figure 3: output of distance to line tool

3. Now that this feature has been created, students were to use the feature vertices to point commands to create the endpoints at these lines that represented the tree locations. 

4. Within this tool, the input needs to be the feature that was previously created using the barring distance line tool. 

5. VERY IMPORTANT - change the option at the bottom titled "point type" from 'all', to 'end'. Doing this populates the outputted field with the right number of points.  Leaving this option as all will create two points, one at the beginning and one at the end. 

figure 4: output of feature to vertices to point tool

6. The output of this tool will create the point locations of the tree locations. figure 4 to the right shows these points, with out the lines which provided them. 

7. Now the feature that were desired have been created,  however, the fields associated with these features are not complete.  This point field does not have the species or tree width attributed to the original data. 

8. To complete this feature, it will be necessary to conduct a table join with a table that contains this information.  The join for this field should be based of off Tree_Number.

9. Export the feature to make the join permanent.  The best place to export the feature to is the geodatabase that has been used prior.

10 (optional).  If publishing these feature to web service, project the feature to web mercator.  

Once this is complete, the final feature is a point location with all the data that was recorded at the initial onset of this survey.  

Results and Discussion 

Below is an embedded map that shows  the results of survey.  The point locations visible are tree locations.

as you can see, there are some clear discrepancies in the location of these trees.  This is likely as result of human error, since most of the class had never conducted a survey such like this, let alone work with the equipment ever before.  It is more than likely that somewhere in the process, one, if not all of the students made an error in producing the data with the equipment at hand.  In that sense, this lab could be viewed as a disappointment. However, looking past these errors, with more practice and experience on individual levels, this sort of skill set could be very useful in the future, for it is never easy to predict when and where technology will fail.    

Conclusion

A distance/azimuth survey is handy skill for any survey technician, GIS specialist, or all-purpose-geographer to have in the toolkit.  In this day in age, technology is a crutch that we tend to depend on very heavily, often to a fault.  When it fails, there is a tendency for users of the technology to give up and say today is not my day, and wait for the technology that was intended to be used in the survey to be available for use again.  This is sometimes necessary, but if able one could consider using this method to save the the time that it would take to leave the sight , fix the equipment, reorganize and return to the field sight.  Being able to operate with lower grade technology is a skill in itself, and this is just one example of how it can be helpful 

Lab 6: ArcCollector Data Collection - Street Lights of UWEC off Campus Housing Area.

Introduction

In the previous lab, students used ArcCollector to collect microclimate data in small groups of 2-3 during a portion of a class period.  This activity was mainly tailored to get students familiar with the interface of the mobile software while also exemplifying how easy the process of combining the classes various data sets was.  Prior to this activity (lab 5) being started, a geodatabase was created for students which was then exported to ArcGIS online and accessed through collector - students had to do very little initial set up prior to collecting data.  In this lab, however, students will have to formulate their own geodatabases and collect their own data with the intent of illustrating a certain geographic trend or answer a particular question, with a geographic scope.  

The power of Arc Collector is that it is intertwined with ArcGIS online which allows users to connect with other users to share data.  Organizations then have the capability to collect data and instantly share it to corresponding parts of their operations, provided that they have access to the same ESRI group account.  As the account manager, one can create groups that field workers can then access and interact with maps/features that have been previously created.  

The specific question that is going to be answered in this lab via the use of ArcCollector is where are the street lights in the Randall Park neighborhood that are not working, faulty, obstructed, dammaged, or dimming relative to the majority that are fully functioning?  This question is important because having a well lit neighborhood can prevent crime from occurring, as crooks tend to avoid places where they can be identified or spotted.  Street lights failing is not an uncommon issue within the city of Eau Claire, and the Randal Park Area is of no exception. Sighting the City of Eau Claire web sight, this is the first paragraph on their page about street lighting:

"Reports of non functioning lights, commonly found in residential areas, which are mounted on a wooden pole, should be directed to Excel Energy by calling 1-800-628-2121 or by going to http://www.xcelenergy.com/Outages/Report_Outage "

What this implies is that the city is not well equipped with the resources to handle such issues, and thus has the cites major energy provider deal with this reoccurring issue.  In theory, this application could be a tool help identify faulty street lights. Going forward, This lab will walk through the steps of how this ArcCollector application was created, and will also present the data collected in an embedded map, followed by a discussion of the findings.

Study Area

The study area for this application will be the Randall park neighborhood located across the the Chippewa River from the University of Eau Claire main campus.  The area is primarily residential, populated with low value college properties, with a few pockets of nicer homes.  Below, in figure 1, one can see a map of this area.  Notice that the streets run very straight and form a uniform block system throughout the neighborhood. 

figure 1: Randall Park Neighborhood and the localized area of interest for street light data acquisitions  

Because the Randall Park Neighborhood is rather large for one person to collect street light data, the AOI was appropriately  localized to a smaller extent within the neighborhood.  As such, this localized section of the neighborhood is roughly 3 blocks long, beginning at Hudson St. and going south to Chippewa St, and roughly 7 blocks wide, beginning at 1st Ave and going west to 8th Ave. Let us now move on to how this application was produced. 

Methods

To discuss all the parts of this lab that brought this application to life, the methods section will be divided into a pre-publishing and post-publishing section.  The pre-publishing portion will discuss what went into creating the features before they were exported to ArcGIS online and used in ArcCollector. Post-publishing will discuss aspects related to the data acquisition process. 

Pre Publishing

Prior to this app being created, there is much to consider in terms of how to go about creating this application. The first step is to create the appropriate feature class and populate that feature class with the appropriate field information.  To get a better idea of what fields should bed created, it was necessary to look into what common damages occur to street lights and what issues tend to arise from those damages.  Referring to the link mentioned in the introduction of this lab (  http://www.xcelenergy.com/Outages/Report_Outage) the more common issues that occur with street lights tend to be as follows:

• light out 
• light on and off 
• light on during the day 
• light is dim 
• globe is broken
• globe is hanging 
• light pole is broken 
• light pole is leaning

Going off of this information, five fields were created in association with  this feature class that go off of the information provided by the Excel Energy damage web-form.  Four out of the five fields created make use of domains as limiting factors. The one that has no domain (obstruction_comments) was left as text field without domains so that users could be specific when describing the nature of obstructions to street lights.  Three other Fields were assigned domains, these can be seen below in figure 2 in the section titled default values and domains.

figure 2: domain and sub-type information 

Two types of domains were used for these fields that were apart of the street lights feature class - range and codded value domains. Here is a description of the domains used

Mnt_type: Describes the material that makes up the pole (wood or metal)

Glow_range: A range value that will be used to help identify dim lights, quantitatively (0-100 meters)

Globe: Provides categorical options that describe the globe outside of the bulb (intact, cracked, or hanging off)

Along with using domains, the final field that was created was made as a short integer so that it could be used as a sub-type, even though its value in the application is categorical and not numerical. The field light_type was chosen to be a subtype mainly to provide a variation in symbology that best represents the different types of lights that are used throughout the AOI.  The two options for this sub type are orange light and white light.  Orange light tends to be used in residential areas with low traffic and white lights tend to be used more in areas where there is a higher amount of car traffic.  Once the feature class was created and assigned appropriate symbology for each sub type, it was time to publish the map to ArcGIS online and begin taking point locations of street light locations.

The reason these fields were employed was in order to create a simple set of features that could encapsulate the status of a street light. The most important field is the quantifiable glow radius, as this allows for the identification of lights that are under performing either because of obstructions or defections within the fixture itself.  With appropriate symbology, this can directly show viewers the lights that are not adequately lighting their surrounding areas. Not only that, simply having the geographic locations shown also shows how their are certain block sections that have a single light, or sometimes none at all.

Post Publishing

Once published, the data was ready to be collected.  Data was collected was on the night of Sunday the 4th of April, 2016.  The data collection process took  roughly 2 hours, starting at 9 pm and ending shortly after 11 pm.  A bike was used as the the means of transportation throughout the neighborhood, from light to light. The night itself was rather damp and cold, hovering around 20 degrees, with high winds.  That being said, the default values, domains and sub-types allowed for the data collection process to happen relatively smoothly/quickly and as a result, the cold did not to need to be endured for too long.   More than 40 street light locations were recorded during this two our period.  The data was collected on Galaxy Prime - an Android product operating on a T - Mobile 3G network.

Results 

Below is an embedded map of the data that was collected on Sunday the 3rd of April, symbolized by the glow radius of the light on the ground and light type.  The map also has several toggle-able features, including a legend, zoom bar, address search bar, and base map selector.  Also, by clicking on the individual features, a pop window will appear showing the details about that specific street light 

If one clicks on the majority of street lights with smaller ground light radii, they have some sort of associated damage to the globe or obstruction that could account for their smaller light spread.  However, there are also several lights  that are simply dim with no sort of visible damage or obstruction from any outside objects.


As for the obstructions, the only obstructions that were observed in this study were caused by tree branches.  This perhaps does not have so much of an effect now, in early spring, but very well could have one in later seasons when these branches grow leaves and will have more material which could block light from shedding through.

For the most part, very little damages were found, yet that is not much of a surprise since there is a known forum which people can report damages directly too the company in charge of their maintenance.  That being said, the main damages that were found were cracked globes that surrounded the light, but nothing extensive to the point where a light was not functioning or flickering.  Below is  map that shows the lights that are damaged amidst the ones that are intact and functioning.  Like in the previous map, clicking on the feature will open up a pop up window that provides further detail about that individual street light

Discussion 

Working with ArcCollector going forward will be very useful tool as a field tool.  If using this, however, its very important that the individual taking data have a very solid understanding of the ins and outs of the subject matter.  If not, there is a chance that certain aspects of the collection process and pre-processing will be done incorrectly, redundantly, or be unnecessary. My knowledge of street lights is very little, and the tools that were at my disposal was essentially my phone and my person.  I thought that measuring the ground radius of light that shined onto the ground from the light fixture would be straight forward and an adequate way to to measure the functionality of the light.  However, I came to find out that the area that the light's glow was not uniform, as many of the poles are leaning in one way or another, causing the lights ground glow shape to be skewed or uniform in such a way that made it hard to measure definitively.

Although these results do not yield anything too note worthy in itself, imagine if this was a study that was done over a broader area, over a larger period of time, and/or with more than one field worker inputting features on the system.  This study was done in two hours with bike.  With a car, or two, with some more advance measuring equipment, these same results could be produced faster, with more meaningful results.  This is meant not just in the case of measuring the functionality of street lights, it could just as well be applied to measuring other infrastructure like telephone poles, street signs, road ways, bridges, and many other things.  The potential is very high. On top of the actual data collection.  ArcGIS online resources have the capability of being optimized further by employing custom widgets and tools via the use ArcGIS API java script.  For example, a query widget in this map would allow for viewers to look for certain traits contained within the fields, or features that occupy a certain area.

Conclusion 

Within this small area in the center of city of Eau Claire, the Randall Park Neighborhood, most of the lights are functioning just fine.  With the exception of three lights that had cracks and minor obstructions, most of the lights were found to functioning with little hindrance to the ground below.  It was not expected that this study would reveal that half of the neighborhood had lights that did not work, and the findings a of a just a several broken globes and obstructions form objects near the light was to be expected. The results, however, in this instance is not necessarily the focal point of this lab, even though the question was posed as to are there lights under preforming within the area of interest specified.  The broader goal was exemplify how seamless and efficient this technology can be when pre-processing is done with careful consideration.  A forum like what was presented in the link to Excel website could work in many locations, but in an area predominately occupied by younger people, students, and people of low income, its hard to imagine that many would use the sight.  As such, the absence of good lighting could go unreported and as a result, crime could become attracted to these areas with low viability. Utilizing this technology could be a proactive way to identify areas where lights are not functioning or under performing.