Introduction:
Micro-climates consist of data associated with the weather patterns within restricted areas. With this knowledge in mind, the purpose of this assignment was to as a class, collect data points and their corresponding attribute information from around the UW - Eau Claire campus area. This data, which was obtained through the Arc Collector App that was downloaded to each students' smart phone or tablet. This data was then added into ArcMap where it was further analyzed for any micro-climate patterns within the area.
Study Area:
The location in which data was to be found, was the entire UW - Eau Claire campus area (Figure 6.1) which is located along the Menomonie River, in the City of Eau Claire, Wisconsin.
The UW-Eau Claire campus consists of three main regions that were then divided into seven study zones, in which at least two students were assigned to collect data in. Upper campus was split into two zones (4 and 5), lower campus was divided into three separate zones (3,6, and 7), and finally "Water Street" campus was then allocated into two zones as well (1 and 2).
Methods:
The first step in collecting the data was to download the Arc Collector App onto everybody's individual smart phone or tablet. Arc Collector is an Esri product that meant to collect, edit, and manage field data, which can then be directly added into ArcMap for further editing and analysis.
From here, a pre-constructed geodatabase, that included domains and feature classes was added onto ArcMap. This map was then shared through Esri online. Once logged into the Arc Collector service (again found on each individuals smart phone), using our school enterprise information, the previously shared map was then deployed onto each smart phone and opened up for collecting and editing purposes. To become more familiarized with the app the website:
The purpose of this exercise was to capture the micro-climate on UW-Eau Claire's campus. To do so, field data was collected, edited, and analysed to produce functional maps that visually represent the data at hand. To better improve this data, a normalized plan to collect more data points over a longer time frame would be needed, as climate is based on the trends of weather over time.
Micro-climates consist of data associated with the weather patterns within restricted areas. With this knowledge in mind, the purpose of this assignment was to as a class, collect data points and their corresponding attribute information from around the UW - Eau Claire campus area. This data, which was obtained through the Arc Collector App that was downloaded to each students' smart phone or tablet. This data was then added into ArcMap where it was further analyzed for any micro-climate patterns within the area.
Study Area:
The location in which data was to be found, was the entire UW - Eau Claire campus area (Figure 6.1) which is located along the Menomonie River, in the City of Eau Claire, Wisconsin.
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| Figure 6.1: The red lines show the rough outline the UW - Eau Claire campus, which is then broken broken into seven separate group study zones. |
The UW-Eau Claire campus consists of three main regions that were then divided into seven study zones, in which at least two students were assigned to collect data in. Upper campus was split into two zones (4 and 5), lower campus was divided into three separate zones (3,6, and 7), and finally "Water Street" campus was then allocated into two zones as well (1 and 2).
Methods:
The first step in collecting the data was to download the Arc Collector App onto everybody's individual smart phone or tablet. Arc Collector is an Esri product that meant to collect, edit, and manage field data, which can then be directly added into ArcMap for further editing and analysis.
 |
| https://katiefaull.files.wordpress.com /2014/05/photo.png |
From here, a pre-constructed geodatabase, that included domains and feature classes was added onto ArcMap. This map was then shared through Esri online. Once logged into the Arc Collector service (again found on each individuals smart phone), using our school enterprise information, the previously shared map was then deployed onto each smart phone and opened up for collecting and editing purposes. To become more familiarized with the app the website:
http://doc.arcgis.com/en/collector/
was provided. From this point each student was give one hour to collect as many points as possible with attributing information on; group number, temperature, dew point, wind chill, wind speed, wind direction (azimuthal), and time. This data was collected using a hand held kestrel wind unit and a compass.
Once the hour was up, the class reconvened inside to connect the newly acquired data onto ArcMap to be managed and analyzed. The first step in analyzing the data was to share each individual data set in one group folder so that all the class data could be found on one map. To make this data easier to manage, all the separate data sets were merged into a single functional one. With this new data set, three maps were created using the multiple attributes symbology found under layer properties.
Results and Discussion:
*Note: Any points that are mentioned as outliers or anomalies are still included in all maps and statistical analysis, as one of the goals of this assignment was to assess all the points that were collected by the class.
During the one hour time frame, a total of 186 points were collected. From the data collected, temperatures ranged from 54 to 74 degrees Fahrenheit. However it should be noted that temperature is continuous from 65 degrees to 74 degrees Fahrenheit. This would mean that point 118 ( located in zone 6), which was the only temperature that was recorded to be 54 degrees Fahrenheit, could be considered to be an outlier.* This most likely due to an error of data entry as the two data points recorded directly after (points 119 and 120) have corresponding temperatures of 71 and 72 degrees Fahrenheit.When looking at the statistical data for temperature, a range of 20, a mode of 71, and a mean temperature of 70.41 degrees Fahrenheit can be deducted.
As for dew point, attributing data was found to vary from 48 to 75 degrees Fahrenheit (with one <Null> entry). This would give dew point a range of 27, a mode of 50 and a mean of 54.29. By assessing the natural breaks of this data, it can be noted that 75% of all the data points fall between 49 and 53 degrees Fahrenheit, and that of the 25% that fell outside that range were collected mainly by the group that surveyed zone one*. This indicates that there was probably a misunderstanding as to which symbol on the kestrel unit was meant to be recorded for dew point.
From these two feature classes, the first micro-climate map was made (Figure 6.2).
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| Figure 6.2: This map depicts the micro-climate on UW-Eau Claire's campus as it relates to temperature (symbol color) and dew point (symbol size). |
Within Figure 6.2, it can be seen that the majority of the points collected have a temperature between 70 and 73 degrees Fahrenheit and a corresponding dew point between 48 and 53 degrees Fahrenheit. What this means is that relative humidity would be about 47% (when calculated with the mode of both temperature and dew point). This then directly corresponds with the forecast for the day which was rather warm and humid for the week.
The next feature class, wind speed, had wind speeds from zero to 18 mph (which then equals its range), a mode of 2 mph, and a mean of 3.65 mph. This is where the second map (Figure 6.3) comes into play.
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| Figure 6.3: This map depicts the micro-climate on UW-Eau Claire's campus as it relates to temperature (symbol color) and wind speed (symbol size). |
Figure 6.3 is a multiple attribute map that looks at temperature (symbol color) and respective wind speed (symbol size) for each data point. What this map shows is that the wind speeds vary greatly usually in respect to location. Areas that were more open (i.e. parking lots, campus court yard and the walking bridge) often showed higher wind speeds then areas where there was more buildings or tree cover. It could also be noted that the day in which data was collected, was rather gusty and thus varied heavily in wind speed.
To continue with the micro-climate found on UW-Eau Claire campus, the next feature class to be assessed was wind chill. When first reviewing the data, two discrepancies were found. The first was a value of zero degrees for one point, which was probably due to a typo while collecting data.* The second was for the data points that were collected in zone 3 and that extend along the rive on Lower Putnam Park (Figure 1.4).*
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| Figure 6.4: This map depicts the micro-climate on UW-Eau Claire's campus as it relates to temperature (symbol color) and wind chill (symbol size). |
When compared with the rest of the the data points collected in Figure 6.4, zone 3 had much a lower wind chill than the recorded temperatures. In general, wind chill's mode temperature was 71 degrees Fahrenheit and a mean of 68.9 degrees Fahrenheit (compared with temperatures mode and mean, which again was 71 and 70.41 respectively). This shows that the wind chill often fell within 1-3 degrees of the recorded temperature. However the points collected in zone 3 and Lower Putnam, did not follow this trend, as wind chill was frequently recorded between 48 and 64 degrees Fahrenheit. Although this may be just a simple case of data entry error, it could also be based on the geology of that zone, which is wooded cut bank along the rivers edge. To better determine cause of discrepancy, an analysis of that zone and the effects on climate from rivers would need to be conducted.
The final feature class in discussion is based on wind direction. For the sake of data integrity, this feature class was purposely unrepresented in the three analysis maps created. This is due to the over all incompleteness of the data (over 1/3 was <Null>), along with the inconsistency of data that was recorded (ranging from 0 degrees to 360 degrees). The inconsistency of data can be attributed to the lack of data collection normalization that occurred between the students. In future studies, Kestrel symbols along with the correct method for collecting wind direction should be discussed among the students before entering the field.
The final feature class in discussion is based on wind direction. For the sake of data integrity, this feature class was purposely unrepresented in the three analysis maps created. This is due to the over all incompleteness of the data (over 1/3 was <Null>), along with the inconsistency of data that was recorded (ranging from 0 degrees to 360 degrees). The inconsistency of data can be attributed to the lack of data collection normalization that occurred between the students. In future studies, Kestrel symbols along with the correct method for collecting wind direction should be discussed among the students before entering the field.
Conclusion:
The purpose of this exercise was to capture the micro-climate on UW-Eau Claire's campus. To do so, field data was collected, edited, and analysed to produce functional maps that visually represent the data at hand. To better improve this data, a normalized plan to collect more data points over a longer time frame would be needed, as climate is based on the trends of weather over time.
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