Final_Report_GRAM

Team Members:
Daniel Cohen
Sam Boesch
Matt Riggan
Team Members:
Daniel Cohen
Sam Boesch
Matt Riggan

TABLE OF CONTENTS
Introduction..................................................................................1
Data..............................................................................................2
Table 1. .........................................................................5
Table 2. .........................................................................6
Table 3. .........................................................................8
Table 4. ........................................................................9
Image 1. ......................................................................10
Methods......................................................................................11
Image 2. .....................................................................15
Results and Discussion ..............................................................16
Image 3. .....................................................................17
Image 4. .....................................................................17
Image 5. .....................................................................17
Image 6. .....................................................................19
Conclusion .................................................................................20
References..................................................................................21
Appendix I: Group Members Contribution................................22
Appendix II: Metadata...............................................................23

1
Final Report
Introduction
The Soccer Complex at Five Mile Dam is a 43-acre property located in Hays County and
maintained by the City of San Marcos. Providing many park goers with an area for recreational
activities, the features and amenities receive a natural amount of wear and tear. This results in the
necessity for a geographic database that assists the maintenance process for specified features
within the complex. The city’s Parks and Recreation Department is currently creating geographic
information system (GIS) inventories for its sports facilities in order for cheaper and more
efficient amenity maintenance to be performed on a regular basis. Establishing the best method
for data collection, processing, and portrayal is crucial to this project’s success, and that is
exactly what Geographic Analysis and Maintenance (G.R.A.M.) aims to do. There are different
methods of acquiring specific amenity’s spatial and attribute data in an area. There are also
different procedures that can be put forth to correctly file that data within a geographic database
for analysis and visual aids. Using global positioning systems (GPS) to provide accurate
coordinates for each feature is much quicker and more precise than alternative methods of
recording spatial data. Having many tools and functions, GPS units can be preset before
collecting data, ultimately making the entire process simpler. “A GPS receiver is provided with
an input/output port that retains any date and time stamp information often associated with
position determinations, and therefore is more useful in archiving data for later analysis” (Woo
and Sprague, 1995). With a high quality GPS unit spatial data can be recorded, saved, and shared
with ease due to its compatibility with popular software. In comparison with other techniques of

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data processing, GIS methods are not only the quickest for feature database creation but also the
most “frequently used in professional businesses around the globe” (Grimshaw, 2000). Although
there are many, “ a few advantages to utilizing GIS includes improved communication, enhanced
record keeping, cost saving, increased efficiency, and decision making” (AustinTexas.gov,
2015). Before starting this work we believed if we apply specific GPS and GIS methods to this
project, then we will successfully provide a geodatabase consisting of the most accurate and
efficiently collected data points while establishing a repeatable methodology for future use.
During the process, visual aids will be created for better comprehension of the project.
Data
Creating this type of integrated geographic database involves capturing a large amount of
spatial and attribute data. We aimed to collect coordinates for the specified features requested by
the City as well as features that contributed in building an aesthetically pleasing layout for visual
aids. This data was required and explicitly asked to be collected in regards for the creation of the
geographic database and visual representations. Without the specifically requested data we
collected, there would neither be a database and methodology to create, nor any maps to provide.
All data collected was performed first hand by the members of our team making it primary data.
Secondary data is data collected by people other than the members in our team. In only one
aspect of the project did we incorporate secondary data, which originated from the City of San
Marcos and was used as a reference for both the collection and creation of the complex’s
irrigation structure. G.R.A.M. strived to collect the best primary data while refraining from the
use of other data sources in order to insure optimal accuracy and reliability. Working at the

3
complex also proved extremely helpful toward understanding how a geodatabase for the
amenities there should be constructed. Acquiring the feature data needed within this large
complex demanded an advanced GPS unit. Using Pathfinder Office G.R.A.M. and another
group, Central Texas Geospatial Consulting, created and uploaded a data dictionary with similar
GPS classification requirements appropriately named in accordance with the waypoints we
needed to collect. We worked with the Trimble Geo XT GPS unit which provides accuracy under
one meter during data collection. Our data dictionary allowed each collected point to be filed
under a pre-described classification system.
Table 1. The menu classification options we used for our data dictionary is shown below.
Feature Data Type Primary Attributes SecondaryAttribute
Field
Polygon
Usage Active, Inactive
Type Small, Large
Point
Type Small, Large
Irrigation System Point
Type
Valve Box, Sprinkler
Head
Stationary Garbage
Receptacles
Point Usage
Recycling
Non-Recycling
Trail Line Type Running
Building Polygon Name
(Text Box) –
Concession Stand
Stationary Benches Point Type Path Bench
Light Poles Point Type Field Light Pole
This data dictionary is relatively basic and allows for quick data collection. During the
database creation process we made changes for future specifications regarding features with

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multiple attributes. For unexpected features believed to have significance in this project,
“generic” point, line, and polygon data classifications are programmed within the GPS unit.
These generic features are primarily used when there is uncertainty in needing an amenities
coordinates and it doesn’t have a designated place in the unit, or if there is a need to record the
same amenity in a different data form. A text box option is provided and written in describing the
generic feature’s attributes. We used the generic option in the beginning of our collection when
failing to add a light pole class, and also when we wanted to take both line and point data for the
fields to see which method was easier and more accurate. A data collection time table including
leeway for disruptions such as poor weather conditions came in handy and kept the project on
pace. The Trimble Geo XT unit is not water proof and is quite expensive, so we did not take any
chances on collecting data in weather that was questionable. We used another time table for the
irrigation points, which was the majority of the data collected, during the second business week
of April to ensure an organized completion of work within a short amount of time.
Table 2. Data Collection Time Table for the second business week of April, 2015.
Monday Tuesday Wednesday Thursday Friday
10am – 11am Class Class Class Class Class
11am – 12pm Collect Data Class Collect Data Class Class
12pm – 1pm Collect Data Class Collect Data Class Class
3pm or Later Charge
Trimble
Collect Data Charge
Trimble
Collect Data Charge
Trimble

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For each GPS feature collected, between twenty and thirty points were taken to ensure a
consistent level of accuracy. Satellite connection is crucial when utilizing GPS methods. The
fewer the satellites, the tougher it is to find a signal and establish a point on the globe. Depending
on the time of day, as few as two satellites and as many as nine were available to help pinpoint
our data. In only one instance did we stop and come back another day to continue the particular
area’s data collection, because there were not enough satellites connecting with our position at
the time. The most difficult part of this data collection arose when attempting to locate sprinkler
heads within the path surrounding the fields and concession building. The sprinklers were
installed to project out of the ground from the irrigation pipelines below. This system results in
water runoff accumulating and completely covering any signs of the sprinkler over time. After
having trouble finding the majority of the sprinkler heads we turned to field methods and paced
the distances between each sprinkler, dropped an irrigation flag once we located the head and
proceeded to collect all the sprinkler data that way. Using provided PDF layouts of the irrigation
given to us by the City, we were able to flag important sprinkler heads and pace in the direction
the next head would be located according to the layouts. All together we ended up recording over
seven hundred data points to use as features for our geographic database. Having only one GPS
unit among the three project members allowed one to two of us to go in the field and collect
points while the other(s) provided research and prepared the database for imminent use.
Once we finished collecting all of the needed coordinates, the locations and feature types
were officially recorded within the data dictionary and were ready to be exported from the unit.
This positioning system works using Terrasync and Pathfinder office version 5.40 to export the
collected data onto the computer and ultimately the ArcGIS software version 10.2.2. In regards
to the coordinate system applied, the Soccer Complex at Five Mile Dam is located within the

6
State Plane Coordinate System 4204 and is based on the North American Datum 1983 (unit is in
US feet). After uploading the data to Pathfinder, we strengthened its accuracy by differentially
correcting it. All of our GPS data is based on the times it was taken and where the unit was when
the satellites connected with it. In order to increase the overall accuracy of our data the
differential correction method uses a nearest base station to compare timing and accuracy. These
base stations are all over the world and contain exact times, intervals, and information regarding
satellite placement. The base station used for this project was TxDOT, San Marcos. Comparing
our unit’s data with the spatio-temporal accuracy of the base station ultimately provides better
results for the project. After differentially correcting our data points, we receive a report of
accuracy showing what percentage of our points were within a certain range of the points
intended. Simply put, the correction shows how well our GPS unit performed, and how well we
used it from a statistical standpoint.
Table 3. Below are the calculated accuracies of all our data points collected with the Trimble.
Range Percentage
0-5cm -
5-15cm 6.71%
15-30cm 1.32%
30-50cm 66.48%
0.5-1m 24.24%
1-2m 1.25%
2-5m 0.01%
>5m 0.01%

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After differentially correcting our data, we found that over seventy four percent was as
accurate as fifty centimeters. After correcting our data we needed to properly export it into
shapefile format so it can be applied and utilized within ArcGIS and Adobe Illustrator. In order
to do this we manually formatted export setup properties to represent our data in an organized
fashion. These properties include all of the feature types that will shape how the data is filed
inside of our geographic database. Critical information is applied in this step such as the names
for each group of data, the specific qualities of the data, and the general format to be exported.
The setup properties are shown in a list of check boxes within the group of features it belongs.
This made it very easy to pick which features we wanted to use and not use when exporting all of
our extensively collected data.
Table 4. This table shows the feature options we chose during our export setup properties step.
Feature
Type
Data File
Name
Data
Dictionary
Name
Feature
Name
Correction
Status
Date
Recorded
Point
Features
Horizontal
Precision
Point ID
Line
Features
Length Line ID Arc ID Area Perimeter

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When we look at our differentially corrected, and exported data in ArcMap we see all of
the points we collected in a concise format. We can open up attribute tables for any data point
and it will show that the location and attributes in correlation to all the other data points
collected. By simply adding a base map of the Five Mile Dam Soccer Complex to the layers we
created with our exported data, we can see how all of our data is mapped in a spatial frequency.
At this point we were able to finally see all of the points we collected correctly integrated with
the satellite imagery of the complex.
Image 1. This map shows every data point collected with its proper coordinates on the complex.

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Methods
Providing a methodology focused on the creation of the complex’s geographic database is
best comprehended when outlined in procedure descriptions. This methodology contains
reiterations of previously explained project procedures. As one of the primary deliverables
required for this project, this methodology is constructed to explain and the entirety of our
methods from beginning to end. Planning the steps for this type of database involves full
understanding of what the project assignment entailed. Our general method for success with this
project was simply staying focused on exactly what our final deliverables were and what the best
methods for providing each of those deliverables would be. Primary objectives included
collecting GPS coordinates of specified amenities throughout the entire complex grounds, as
well as the creation and organization of a geographic database focused around those specified
amenities/feature attributes. Also, we needed to construct visually detailed maps showing the
attribute data that was collected and exported. Upon completion of those objectives the task of
providing a final report including this scientific methodology for this geodatabase design process
was to be devised.
The methodology began with a team discussion covering the most effective ways to
accomplish each task. We determined a plan and a number of agendas that would enforce the
best possible product. We turned down many ideas, although they would satisfy our objective,
we deemed them inefficient due to unnecessary obstacles involved. Time spent reviewing and
researching similar projects allowed our team to compile ideas for a strong delivery product,
which ultimately would be comprised of the best strategies for each step. After planning and
researching we constructed a clear outline to show our clients what our plans were and how we
intended to accomplish each step.

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Once an outline was established we began the data acquisition portion of the project. A
GPS unit capable of recording sub-meter accuracy was provided by our teacher assistant. Within
this global positioning unit we created a data dictionary that provided menu options for when we
actually collected the data. We labeled these menu options to organize and speed up the data
collection process. Without the data dictionary we would have had to manually label each feature
point collected, which ultimately elongates the duration of the project. After programming the
unit’s data dictionary the group devised a time schedule to collect points. This schedule needed
to be implemented around each of our class times as well as other interfering activities, such as
work schedules and weekend travel plans to home cities. We followed this schedule for all of the
collection of all data required except irrigation. For the irrigation data we ambitiously made a
week long timetable for the second week of April (refer to Table 2) to collect all of the sprinkler
heads and other water related features. The irrigation data eventually compiled to be the vast
majority of all our collected data points.
At this point everything we needed to officially go out to the soccer complex and begin
collecting points for our geodatabase had been accomplished. The first points we collected were
non-irrigation points. This included features requested by our client that were not affiliated with
the irrigation structures, such as stationary trash cans, light poles, and field dimensions. This took
a decent amount of time due to how large the area is and how many fields there are out there. By
the time we had collected those points it was the second week of April, which meant it was time
to follow our irrigation data collecting schedule. We began by collecting valve boxes and main
water source points, because they were much more visible than the embedded sprinkler heads.
The hardest part of the data collection were the sprinkler heads. They were extremely hidden
under the fields, and we found out the hard way that simply walking around and trying to spot

11
every sprinkler head was not a sound method. This resulted in us using field methods and
irrigation flags to find a consistent pattern and direction for the sprinkler irrigation dynamic. The
City’s PDF documents showing the infrastructure and hierarchy of the irrigation were also used
as references for our data collection techniques. The data collection process was lengthy and
physically demanding, but it defined what this project’s GIS aspect would be focused on.
The uploading of all the Five Mile Dam’s requested data points were then ready to be
imported from the GPS unit software Terrasync to the computer’s software Pathfinder. This
simply requires the GPS unit’s charging cradle and USB connection cord. Within utilities in the
Pathfinder software we transferred the data by choosing our labeled files containing the data we
collected and adding it to the transfer area. We then chose to ‘transfer all’ files selected, and after
the software processes it our data was provided in a spatially recorded format. The shown data
points are the “raw” data from our collection which then needed to be differentially corrected.
This process uses a nearest base station to where the points were collected that contains exact
temporal satellite records. Differentially correcting our raw data shows how well our GPS unit
worked in terms of accuracy, and ultimately corrects slight inaccuracies for every point collected
at the soccer complex.
At this point we had corrected all of our data and were ready to export it as shapefiles in
order for it to be compatible with ArcMap. We wanted to export the data into ArcMap because
this software is where we will create a geographic database around the collected data. The export
process requires an organization step labeled “Export Setup Properties.” Within this property
window we selected features that organized the data for when it appears inside the ArcMap
database. For the feature selections chosen refer to “Table 4.” After exporting the data, we
organized the layers of each feature within the database. This involved selecting attributes for

12
certain amenities and grouping them with similar data. For example, if we used the “general
point” option within the data dictionary, we often misspelled the attributes of that point. We then
would determine what the point represented and relocate it within the group layer it belonged.
Once the data was organized and placed in their appropriate layers, we established the state plane
coordinate system 4204 to ensure the data corresponds with its true location on the globe. From
there we scaled a base map within our coordinate projection which showed the surrounding roads
and rivers of the soccer complex. Adding the base map simply revealed the surroundings of our
data for a more aesthetic perspective. At that point we officially had all of our corrected data in
the correct coordinate system, and shown in the correct area of the world. The data is bland and
basic looking at this point. Exporting the map as an Adobe Illustrator file (AI) was what we did
next. By exporting the map as an AI file we were able to open it up inside of the Illustrator
software and symbolize all of the features in much greater detail. This software also allowed us
to create a visual layout of the irrigation pipe hierarchy between important points, such as
sprinklers and valve boxes.
In order to make an impressive map, we spent hours of work in Adobe Illustrator
rendering our data points into symbolic visuals that represented their attributes and functions.
Creating a professional, effective visual aid to represent data collected by a GPS unit proved
meticulous. When trying to represent data points through another software we strived to maintain
data accuracy. Without the accuracy of the data a project of this caliber becomes unorganized
and incredible. For areas not requested for the collection of data we used a TIFF image to find
the features in those areas and then correctly modeled their location around the areas we
collected data in. The types of maps we provided included our data’s feature classes within a
clean representation of the complex’s layout all in the requested ANSI D format.

13
Image 2. Below is a flowchart representing this project’s methodology.

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Results and Discussion
Successfully collecting and utilizing data for this project proved to be as time consuming
and technically oriented as we anticipated. Results for each task were accomplished and
accumulated to meet all the project’s needs. Our results also show that it is possible to collect
specifically requested data points in an area with a GPS unit, and construct a method that turns
that data into a functional geographic database. Using GPS and GIS methods to achieve a
structural understanding of the requested amenities on the Five Mile Dam Soccer Complex
worked phenomenally. Our team, G.R.A.M., learned how to use the specific GPS system
required for this project, and after a few minutes of collecting data we were able to consistently
and accurately record the rest of the points needed to facilitate a sound geographic database.
Creating a data dictionary for the unit was complicated and confusing at first, because of the
need to apply it for both my team’s project and another team’s as well, but after utilizing its
purpose we found it quite convenient. Charging the Trimble was an issue in the beginning of this
project, but our team made a point to remember and recharge the device after a lengthy day of
data collection. At first, the GPS portion of this project was time consuming and unorganized,
but as the semester progressed our team became very familiar and confident in our ways of
collecting the data we needed. Finding the sprinkler heads were an issue until we established an
irrigation-flag-line method to locate all the heads. By looking at irrigation blueprints and using
field method techniques, we were able to understand a consistent spatial pattern for each of the
fields for the entire irrigation system on the soccer complex. Originally finding the sprinkler
heads in a timely fashion was the only issue that did not conform to our beginning hypothesis for
this project.

15
Images 3, 4, and 5. Below images show our flag method of finding sprinkler heads.
Some GIS portions of this project were fairly new to our team as well. The entire
exporting of data from Pathfinder to ArcMap process was learned and applied multiple times.
Knowing how to conform the points we collected to an organized and compatible format was not
profoundly difficult, but definitely needed to be instructed for it to be properly preformed. Once
the data was correctly displayed in the ArcGIS software, there were no more limitations to our

16
process, and we were able to smoothly transition from collecting data to filing and utilizing the
data. This gave us comfort knowing that the remaining portion of the project was attainable by
spending time in the lab applying our GIS and map making knowledge to produce a desired
outcome that meets the needs of our employer. If we were assigned this project again, we would
approach very similarly to how we have, but with a more precise method for each and every task.
We are very happy with our results, but if possible, cutting down the time it took to collect all of
our data and create our database and visual aids would benefit our team in the long run. Knowing
the procedures for every step during this project was crucial and informative. If we had used a
less advanced GPS system we would not have been able to record such accurate data, therefore
being uncertain and unhappy with the outcome of this project. Luckily, we were equipped with
our unit were successfully able to collected excellent points all throughout our study area.
Utilizing GIS to solve our geospatial problem worked very well. The knowledge our team used
from previous GIS classes came in handy every step of the way. Knowing how satellites operate
and are utilized in ArcMap supported our need to understand the GPS device we used so that it
could be correctly applied upon completion of collecting data. The majority of our data was
irrigation related, and using GIS methods to understand the spatial layout of the soccer
complex’s water system became a huge part of this project, and helped our team keep a
consistent accuracy of every amenity that we needed to record. The immensity and spatial
determination of our collected data set became precisely organized and represented in our visual
aids toward the end of this project. Creating a database focused around the data we collected and
then establishing a methodology to repeat that process was proven obtainable and successful.
Symbolizing requested amenities for the entirety of our subject area’s irrigation system was also
a success, and our methods to obtain this goal proved to be both effective and efficient.

17
Image 6. This is our team’s map symbolizing our study area’s amenities and irrigation system.
The map above was made using Adobe Illustrator, after data was accurately collected,
transferred from a GPS unit to ArcMap through various procedures, and meticulously traced
along the collected data points. This maps intention is to provide a clean visual aid that precisely
symbolizes specifically requested amenities within the Soccer Complex at Five Mile Dam.

18
Conclusion
This project provided our team with real world tasks that needed to be completed as
professionally and efficiently as possible. Interacting with clients from the City of San Marcos
provided an aspect to our GIS education that had yet to be incorporated. This allowed our team
to speak about information and directions with our employer regarding the successful completion
of this assignment. The methods used to complete our procedures required a large amount of
group communication and employer to employee communication. We gained a stronger insight
on professional communication with clients, and achieved a unanimously desired outcome in the
best way we all thought was possible. Understanding what kind of jobs are out there and how
those jobs are completed is exactly what we got out of the project assigned in this course. What
little uncertainty our team had during this project was quickly vanquished by the knowledge and
guidance our professor, teacher assistant, and employer provided. By accumulating our team
member’s skills to achieve the success of this project, we not only provided a professional
outcome, but also gained individual experience for the careers that lie ahead of us. Our
completion of this project ultimately allows the City of San Marcos to be better equipped when
maintaining the Soccer Complex at Five Mile Dam’s original functionality and aesthetic beauty
for decades to come.

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References
David J. Grimshaw. Bringing geographical information systems into business. John
Wiley & Sons, 2000.
“GIS and Maps AustinTexas.gov – The Official Website of the City of Austin.”GIS and
Maps AustinTexas.gov – The Official Website of the City of Austin. N.p., n.d. Web. 24 Apr,
2015.
Woo, Arthur, and David S. Sprague. "GPS receivers with data ports for the uploading and
downloading of absolute position information." U.S. Patent No. 5,450,344. 12 Sep. 1995.

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Appendix I: Group Members Contribution
Matt:
Led the team through our planned outline for the duration of the project;
scheduled data collection times for irrigation points; assisted in data dictionary construction; held
responsibility for the GPS unit the second half of the semester; co-collected all data points;
devised method to find sprinkler heads more efficiently; created detailed final map in adobe
illustrator showing the complex and it’s irrigation hierarchy; created final poster.
Danny:
Idea discussion assistance; created tables regarding scheduling and budget;
organized data for geodatabase creation and created the geodatabase; co-created presentations
and previous reports; filed and analyzed all data for correct geographic database construction;
formatted all materials to be applied within the website; created and specified entire metadata.
Sam:
co-scheduled data collection times for all data points (non-irrigation and irrigation);
assisted in data dictionary construction; created dictionary table; created methodology flowchart;
created April’s data collection time table; co-collected all data points; held responsibility of the
GPS unit for the first half of the semester; uploaded data from unit to Pathfinder; differentially
corrected the data; exported all data as shapefiles using export setup properties; Created final
report.

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Appendix II: Metadata
Benches
Tags
Benches, Resting, Sitting
Summary
The purpose of the benches are to show the benches at the five-mile dam.
Description
The benches are every 100 feet or so around the trail, and each have their own awning.
Credits
Danny Cohen, Matt Riggan, Sam Boesch
Use limitations
There are no access and use limitations for this item.
Extent
West -97.903924 East -97.898334
North 29.945397 South 29.940456
Scale Range
Maximum (zoomed in) 1:5,000
Minimum (zoomed out) 1:150,000,000
ArcGIS Metadata ►
Topics and Keywords ►
* CO NTENT TYPE Downloadable Data
Hide Topics and Keywords ▲
Citation ►
* TIT LE Benches
PRESENTATION FORMATS * digital map
Hide Citation ▲
Resource Details ►
DA TASET LANGUAGES * English (UNITED STATES)
SP A TIAL REPRESENTATION T YPE * vector

22
* PRO CESSING ENVIRONMENT Microsoft Windows 7 Version 6.1 (Build 7601) Service Pack 1; Esri
ArcGIS 10.2.2.3552
CREDITS
ARC GIS ITEM P ROPERTIES
* NA M E Benches
* LO CATION file://TAG316040ESpring2015GISDesignGIS
mapsFinalGeodatabaseFive_Mile2.gdb
* AC CESS P ROTOCOL Local Area Network
Hide Resource Details ▲
Extents ►
EXT ENT
GEO GRAPHIC EXTENT
BO U NDING RECTANGLE
EXT ENT T YPE Extent used for searching
* WEST LONGITUDE -97.903924
* EA ST LONGITUDE -97.898334
* NO RT H LATITUDE 29.945397
* SO UTH LATITUDE 29.940456
* EXT ENT CONTAINS T HE RESOURCE Yes
EXT ENT IN T HE ITEM'S COORDINATE SYSTEM
* WEST LONGITUDE 2315642.437140
* EA ST LONGITUDE 2317395.987439
* SO UTH LATITUDE 13891146.248389
* NO RT H LATITUDE 13892926.538135
Hide Extents ▲
Spatial Reference ►
ARC GIS COORDINATE SYSTEM
* TY P E Projected
* GEO GRAPHIC COORDINATE REFERENCE GCS_NAD_1983_2011
* PRO JECTION NAD_1983_2011_StatePlane_Texas_South_Central_FIPS_4204_FtUS
* COORDINATE REFERENCE DETAILS
PRO JECTED COORDINATE SYSTEM
WELL-KNOWN IDENTIFIER 103161
X O RIGIN -126725700
Y O RIGIN -77828800
XY SC ALE 3048.0060960121918
Z O RIGIN -100000
Z SC ALE 10000
M O RIGIN -100000
M SC ALE 10000
XY T O LERANCE 0.0032808333333333331
Z T O LERANCE 0.001
M T O LERANCE 0.001
HIGH P RECISION true

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LA T EST WELL-KNOWN IDENTIFIER 103161
WELL-KNOWN T EXT
PROJCS["NAD_1983_2011_StatePlane_Texas_South_Central_FIPS_4204_FtUS",GEOGC
S["GCS_NAD_1983_2011",DATUM["D_NAD_1983_2011",SPHEROID["GRS_1980",63781
37.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]
],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",1968500.0],P
ARAMETER["False_Northing",13123333.33333333],PARAMETER["Central_Meridian",-
99.0],PARAMETER["Standard_Parallel_1",28.38333333333333],PARAMETER["Standard_
Parallel_2",30.28333333333334],PARAMETER["Latitude_Of_Origin",27.83333333333333
],UNIT["Foot_US",0.3048006096012192],AUTHORITY["ESRI",103161]]
REFERENCE SYSTEM IDENTIFIER
* VA LUE 103161
* CO DESPACE ESRI
* VERSION 10.2.1
Hide Spatial Reference ▲
Spatial Data Properties ►
VEC TOR ►
* LEV EL OF TOPOLOGY FOR T HIS DATASET geometry only
GEO METRIC OBJECTS
FEA TURE C LASS NAME Benches
* OBJECT TYPE point
* OBJECT COUNT 20
Hide Vector ▲
ARC GIS FEATURE CLASS PROPERTIES ►
FEA TURE C LASS NAME Benches
* FEA TURE TYPE Simple
* GEO METRY TYPE Point
* HA S T OPOLOGY FALSE
* FEA TURE COUNT 20
* SP ATIAL INDEX TRUE
* LINEAR REFERENCING FALSE
Hide ArcGIS Feature Class Properties ▲
Hide Spatial Data Properties ▲
Distribution ►
DIST RIBUTION FORMAT
* NA M E File Geodatabase Feature Class
Hide Distribution ▲
Fields ►

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DET AILS FOR OBJECT Benches ►
* TY P E Feature Class
* RO W C OUNT 20
FIELD OBJECTID ►
* ALIAS OBJECTID
* DA TA T YPE OID
* WIDT H 4
* PRECISION 0
* SC ALE 0
* FIELD DESCRIPTION
Internal feature number.
* DESCRIPTION SOURCE
Esri
* DESCRIPTION O F VALUES
Sequential unique whole numbers that are automatically generated.
Hide Field OBJECTID ▲
FIELD Shape ►
* ALIAS Shape
* DA TA T YPE Geometry
* WIDT H 0
* PRECISION 0
* SC ALE 0
* FIELD DESCRIPTION
Feature geometry.
Esri
Coordinates defining the features.
Hide Field Shape ▲
FIELD GPS_Date ►
* ALIAS GPS_Date
* DA TA T YPE Date
* WIDT H 8
* PRECISION 0
* SC ALE 0
Hide Field GPS_Date ▲

25
FIELD GPS_Time ►
* ALIAS GPS_Time
* DA TA T YPE String
* WIDT H 10
* PRECISION 0
* SC ALE 0
Hide Field GPS_Time ▲
Hide Details for object Benches ▲
Hide Fields ▲
Metadata Details ►
* MET ADATA LANGUAGE English (UNITED STATES)
MET A DATA CHARACTER SET utf8 - 8 bit UCS Transfer Format
SC OPE O F T HE DATA DESCRIBED BY T HE M ETADATA * dataset
SC OPE NAME * dataset
* LA ST UPDATE 2015-05-04
ARC GIS M ETADATA P ROPERTIES
MET A DATA FORMAT ArcGIS 1.0
MET A DATA STYLE FGDC CSDGM Metadata
ST A NDARD O R PROFILE U SED TO EDIT M ETADATA FGDC
CREATED IN ARCGIS FOR T HE ITEM 2015-04-27 13:29:50
LA ST MODIFIED IN ARCGIS FOR T HE ITEM 2015-05-04 12:27:10
AU T OMATIC U PDATES
HA V E BEEN PERFORMED Yes
LA ST U PDATE 2015-05-04 12:23:32
Hide Metadata Details ▲
Metadata Maintenance ►
MA INTENANCE
TIM E P ERIOD BETWEEN U PDATES
TIM E DURATION N/A
Hide Metadata Maintenance ▲
FGDC Metadata (read-only) ▼
DET AILED DESCRIPTION
ENT ITY TYPE

26
ENT ITY TYPE LA BEL Benches
AT T RIBUTE
AT T RIBUTE LA BEL OBJECTID
AT T RIBUTE DEFINITION
AT T RIBUTE DEFINITION SOURCE Esri
AT T RIBUTE DOMAIN VA LUES
UNREPRESENTABLE DOMAIN
AT T RIBUTE
AT T RIBUTE LA BEL Shape
Feature geometry.
AT T RIBUTE
AT T RIBUTE LA BEL GPS_Date
AT T RIBUTE
AT T RIBUTE LA BEL GPS_Time
Hide Entities and Attributes ▲
Disposal
Tags
Trash, Litter, Recycling, Waste, Bin
Summary
The purpose of having a Disposal layer is to show all the litter and recycling boxes at the five-
mile dam
Description
The trash and recycling bins are in a permanent location, located mostly in the middle of the
five-mile dam where people would eat their foods.
Credits
Use limitations
Extent
West -97.902967 East -97.899680
North 29.943104 South 29.940940
Scale Range
ArcGIS Metadata ►

27
Citation ►
* TIT LE Disposal
Hide Citation ▲
ArcGIS 10.2.2.3552
CREDITS
* NA M E Disposal
Extents ►
EXT ENT
GEO GRAPHIC EXTENT
* SO UTH LATITUDE 13891318.271995
* NO RT H LATITUDE 13892095.323262
Hide Extents ▲

28
* TY P E Projected
X O RIGIN -126725700
Y O RIGIN -77828800
XY SC ALE 3048.0060960121918
Z O RIGIN -100000
Z SC ALE 10000
M O RIGIN -100000
M SC ALE 10000
XY T O LERANCE 0.0032808333333333331
Z T O LERANCE 0.001
M T O LERANCE 0.001
WELL-KNOWN T EXT
* VA LUE 103161
* CO DESPACE ESRI
* VERSION 10.2.1
VEC TOR ►
GEO METRIC OBJECTS
FEA TURE C LASS NAME Disposal
* OBJECT TYPE point
* OBJECT COUNT 31
Hide Vector ▲
FEA TURE C LASS NAME Disposal

29
* FEA TURE COUNT 31
Distribution ►
Fields ►
DET AILS FOR OBJECT Disposal ►
* RO W C OUNT 31
FIELD OBJECTID ►
* ALIAS OBJECTID
* DA TA T YPE OID
* WIDT H 4
* PRECISION 0
* SC ALE 0
* FIELD DESCRIPTION
Esri
FIELD Shape ►
* ALIAS Shape
* WIDT H 0
* PRECISION 0
* SC ALE 0
* FIELD DESCRIPTION

30
Feature geometry.
Esri
FIELD Type ►
* ALIAS Type
* WIDT H 60
* PRECISION 0
* SC ALE 0
Hide Field Type ▲
FIELD GPS_Date ►
* ALIAS GPS_Date
* DA TA T YPE Date
* WIDT H 8
* PRECISION 0
* SC ALE 0
FIELD GPS_Time ►
* ALIAS GPS_Time
* WIDT H 10
* PRECISION 0
* SC ALE 0
FIELD Trash_Temp ►
* ALIAS Trash_Temp
* DA TA T YPE SmallInteger
* WIDT H 2
* PRECISION 0
* SC ALE 0
Hide Field Trash_Temp ▲

31
Hide Details for object Disposal ▲
Hide Fields ▲
ST A NDARD O R PROFILE U SED TO EDIT M ETADATA ItemDescription
LA ST U PDATE 2015-05-04 12:29:46
FGDC Metadata (read-only) ►
Entities and Attributes ►
ENT ITY TYPE
ENT ITY TYPE LA BEL Disposal
AT T RIBUTE
AT T RIBUTE
Feature geometry.

32
AT T RIBUTE
AT T RIBUTE LA BEL Type
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE LA BEL Trash_Temp
MAIN LINE WATER VALVES
Tags
Water Valves, Main Water Pipes, Valves
Summary
The purpose of the Main_Line_Water_Valves is to show the locations of the Main Water Valves
Description
The main water valves are the source of irrigation from the city main system.
Credits
Use limitations
Extent
West -97.900504 East -97.899942
North 29.943054 South 29.942544
Scale Range
ArcGIS Metadata ►
Citation ►
* TIT LE Main_Line_Water_Valves
Hide Citation ▲

33
ArcGIS 10.2.2.3552
CREDITS
* NA M E Main_Line_Water_Valves
* SIZE 0.000
Extents ►
EXT ENT
GEO GRAPHIC EXTENT
* SO UTH LATITUDE 13891900.603179
* NO RT H LATITUDE 13892084.502746
Hide Extents ▲
* TY P E Projected
X O RIGIN -126725700
Y O RIGIN -77828800
XY SC ALE 3048.0060960121918
Z O RIGIN -100000

34
Z SC ALE 10000
M O RIGIN -100000
M SC ALE 10000
XY T O LERANCE 0.0032808333333333331
Z T O LERANCE 0.001
M T O LERANCE 0.001
WELL-KNOWN T EXT
* VA LUE 103161
* CO DESPACE ESRI
* VERSION 10.2.1
VEC TOR ►
GEO METRIC OBJECTS
FEA TURE C LASS NAME Main_Line_Water_Valves
* OBJECT TYPE point
* OBJECT COUNT 7
Hide Vector ▲
FEA TURE C LASS NAME Main_Line_Water_Valves
* FEA TURE COUNT 7
Geoprocessing history ►

35
PRO CESS
PRO CESS NAME
DA T E 2015-04-15 11:55:33
TO O L LOCATION c:program files (x86)arcgisdesktop10.2ArcToolboxToolboxesData
Management Tools.tbxDefineProjection
CO MMAND ISSUED
DefineProjection Point_ge
PROJCS['NAD_1983_2011_StatePlane_Texas_South_Central_FIPS_4204_FtUS',GEOGCS[
'GCS_NAD_1983_2011',DATUM['D_NAD_1983_2011',SPHEROID['GRS_1980',6378137.0,29
8.257222101]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PRO
JECTION['Lambert_Conformal_Conic'],PARAMETER['False_Easting',1968500.0],PARA
METER['False_Northing',13123333.33333333],PARAMETER['Central_Meridian',-
99.0],PARAMETER['Standard_Parallel_1',28.38333333333333],PARAMETER['Standard
_Parallel_2',30.28333333333334],PARAMETER['Latitude_Of_Origin',27.8333333333
3333],UNIT['Foot_US',0.3048006096012192]]
INC LUDE IN LINEAGE WH EN EXPORTING M ETADATA No
Hide Geoprocessing history ▲
Distribution ►
TRA NSFER OPTIONS
* TRA NSFER SIZE 0.000
Fields ►
DET AILS FOR OBJECT Main_Line_Water_Valves ►
* RO W C OUNT 7
FIELD OBJECTID ►
* ALIAS OBJECTID
* DA TA T YPE OID
* WIDT H 4
* PRECISION 0
* SC ALE 0
* FIELD DESCRIPTION
Esri

36
FIELD Shape ►
* ALIAS Shape
* WIDT H 0
* PRECISION 0
* SC ALE 0
* FIELD DESCRIPTION
Feature geometry.
Esri
FIELD Comment ►
* ALIAS Comment
* WIDT H 32
* PRECISION 0
* SC ALE 0
Hide Field Comment ▲
FIELD Corr_Type ►
* ALIAS Corr_Type
* WIDT H 36
* PRECISION 0
* SC ALE 0
Hide Field Corr_Type ▲
FIELD GPS_Date ►
* ALIAS GPS_Date
* DA TA T YPE Date
* WIDT H 8
* PRECISION 0
* SC ALE 0

37
FIELD Feat_Name ►
* ALIAS Feat_Name
* WIDT H 20
* PRECISION 0
* SC ALE 0
Hide Field Feat_Name ▲
FIELD Datafile ►
* ALIAS Datafile
* WIDT H 20
* PRECISION 0
* SC ALE 0
Hide Field Datafile ▲
FIELD Data_Dicti ►
* ALIAS Data_Dicti
* WIDT H 20
* PRECISION 0
* SC ALE 0
Hide Field Data_Dicti ▲
FIELD Horz_Prec ►
* ALIAS Horz_Prec
* DA TA T YPE Double
* WIDT H 8
* PRECISION 0
* SC ALE 0
Hide Field Horz_Prec ▲
FIELD Point_ID ►
* ALIAS Point_ID
* DA TA T YPE Integer
* WIDT H 4
* PRECISION 0
* SC ALE 0
Hide Field Point_ID ▲

38
Hide Details for object Main_Line_Water_Valves ▲
Hide Fields ▲
* MET ADATA C HARACTER SET utf8 - 8 bit UCS Transfer Format
LA ST U PDATE 2015-05-04 12:30:35
ENT ITY TYPE
ENT ITY TYPE LA BEL Main_Line_Water_Valves
AT T RIBUTE
AT T RIBUTE
Feature geometry.

39
AT T RIBUTE
AT T RIBUTE LA BEL Comment
AT T RIBUTE
AT T RIBUTE LA BEL Corr_Type
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE LA BEL Feat_Name
AT T RIBUTE
AT T RIBUTE LA BEL Datafile
AT T RIBUTE
AT T RIBUTE LA BEL Data_Dicti
AT T RIBUTE
AT T RIBUTE LA BEL Horz_Prec
AT T RIBUTE
AT T RIBUTE LA BEL Point_ID
Path
Tags
Path, Trail, Outline
Summary
The purpose of the Path is to show not only the outline of the five-mile dam, but to show where
the path is located.
Description
The path is a 1 mile sidewalk trail surrounding the complex, enabling runners to jog, and soccer
players to know their soccer boundaries.
Credits
Use limitations
Extent
West -97.903947 East -97.898239
North 29.945408 South 29.940387
Scale Range
ArcGIS Metadata ►

40
Citation ►
* TIT LE Path
Hide Citation ▲
ArcGIS 10.2.2.3552
CREDITS
* NA M E Path
Extents ►
EXT ENT
GEO GRAPHIC EXTENT
* SO UTH LATITUDE 13891121.144765
* NO RT H LATITUDE 13892930.358338
Hide Extents ▲

41
* TY P E Projected
X O RIGIN -126725700
Y O RIGIN -77828800
XY SC ALE 3048.0060960121918
Z O RIGIN -100000
Z SC ALE 10000
M O RIGIN -100000
M SC ALE 10000
XY T O LERANCE 0.0032808333333333331
Z T O LERANCE 0.001
M T O LERANCE 0.001
WELL-KNOWN T EXT
* VA LUE 103161
* CO DESPACE ESRI
* VERSION 10.2.1
VEC TOR ►
GEO METRIC OBJECTS
FEA TURE C LASS NAME Path
* OBJECT TYPE composite
* OBJECT COUNT 24
Hide Vector ▲
FEA TURE C LASS NAME Path
* GEO METRY TYPE Polyline

42
* FEA TURE COUNT 24
Distribution ►
Fields ►
DET AILS FOR OBJECT Path ►
* RO W C OUNT 24
FIELD OBJECTID ►
* ALIAS OBJECTID
* DA TA T YPE OID
* WIDT H 4
* PRECISION 0
* SC ALE 0
* FIELD DESCRIPTION
Esri
FIELD Shape ►
* ALIAS Shape
* WIDT H 0
* PRECISION 0
* SC ALE 0
* FIELD DESCRIPTION
Feature geometry.

43
Esri
FIELD type ►
* ALIAS type
* WIDT H 60
* PRECISION 0
* SC ALE 0
Hide Field type ▲
FIELD GPS_Date ►
* ALIAS GPS_Date
* DA TA T YPE Date
* WIDT H 8
* PRECISION 0
* SC ALE 0
FIELD GPS_Time ►
* ALIAS GPS_Time
* WIDT H 10
* PRECISION 0
* SC ALE 0
FIELD GNSS_Lengt ►
* ALIAS GNSS_Lengt
* WIDT H 8
* PRECISION 0
* SC ALE 0
Hide Field GNSS_Lengt ▲

44
FIELD Shape_Length ►
* ALIAS Shape_Length
* WIDT H 8
* PRECISION 0
* SC ALE 0
* FIELD DESCRIPTION
Length of feature in internal units.
Esri
Positive real numbers that are automatically generated.
Hide Field Shape_Length ▲
Hide Details for object Path ▲
Hide Fields ▲
LA ST U PDATE 2015-05-04 12:31:22
ENT ITY TYPE

45
ENT ITY TYPE LA BEL Path
AT T RIBUTE
AT T RIBUTE
Feature geometry.
AT T RIBUTE
AT T RIBUTE LA BEL type
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE LA BEL GNSS_Lengt
AT T RIBUTE
AT T RIBUTE LA BEL Shape_Length
Sidewalk
Tags
Sidewalk, Path, MIddle
Summary
The purpose of the Sidewalk is to show the pavement in the middle of the complex, seperating
the two soccer areas.
Description
The sidewalk is made out of concrete and allows soccer fans to watch the games from a closer
distance.
Credits
Use limitations

46
Extent
West -97.901325 East -97.900205
North 29.943286 South 29.941784
Scale Range
ArcGIS Metadata ►
Citation ►
* TIT LE Sidewalk
Hide Citation ▲
ArcGIS 10.2.2.3552
CREDITS
* NA M E Sidewalk
* SIZE 0.010
Extents ►
EXT ENT
GEO GRAPHIC EXTENT

47
* SO UTH LATITUDE 13891623.446253
* NO RT H LATITUDE 13892166.470758
Hide Extents ▲
* TY P E Projected
X O RIGIN -126725700
Y O RIGIN -77828800
XY SC ALE 3048.0060960121918
Z O RIGIN -100000
Z SC ALE 10000
M O RIGIN -100000
M SC ALE 10000
XY T O LERANCE 0.0032808333333333331
Z T O LERANCE 0.001
M T O LERANCE 0.001
WELL-KNOWN T EXT
* VA LUE 103161
* CO DESPACE ESRI
* VERSION 10.2.1

48
VEC TOR ►
GEO METRIC OBJECTS
FEA TURE C LASS NAME Sidewalk
* OBJECT COUNT 1
Hide Vector ▲
FEA TURE C LASS NAME Sidewalk
* GEO METRY TYPE Polygon
* FEA TURE COUNT 1
PRO CESS
PRO CESS NAME
DA T E 2015-04-15 11:55:54
CO MMAND ISSUED
DefineProjection slabs
3333],UNIT['Foot_US',0.3048006096012192]]
Distribution ►
TRA NSFER OPTIONS

49
Fields ►
DET AILS FOR OBJECT Sidewalk ►
* RO W C OUNT 1
FIELD OBJECTID ►
* ALIAS OBJECTID
* DA TA T YPE OID
* WIDT H 4
* PRECISION 0
* SC ALE 0
* FIELD DESCRIPTION
Esri
FIELD Shape ►
* ALIAS Shape
* WIDT H 0
* PRECISION 0
* SC ALE 0
* FIELD DESCRIPTION
Feature geometry.
Esri
FIELD type ►
* ALIAS type
* WIDT H 60
* PRECISION 0

50
* SC ALE 0
Hide Field type ▲
FIELD number_of_ ►
* ALIAS number_of_
* DA TA T YPE SmallInteger
* WIDT H 2
* PRECISION 0
* SC ALE 0
Hide Field number_of_ ▲
FIELD material ►
* ALIAS material
* WIDT H 60
* PRECISION 0
* SC ALE 0
Hide Field material ▲
FIELD Corr_Type ►
* ALIAS Corr_Type
* WIDT H 36
* PRECISION 0
* SC ALE 0
FIELD GPS_Date ►
* ALIAS GPS_Date
* DA TA T YPE Date
* WIDT H 8
* PRECISION 0
* SC ALE 0
FIELD Feat_Name ►
* ALIAS Feat_Name
* WIDT H 20
* PRECISION 0

51
* SC ALE 0
FIELD Datafile ►
* ALIAS Datafile
* WIDT H 20
* PRECISION 0
* SC ALE 0
* ALIAS Data_Dicti
* WIDT H 20
* PRECISION 0
* SC ALE 0
FIELD GNSS_Area ►
* ALIAS GNSS_Area
* WIDT H 8
* PRECISION 0
* SC ALE 0
Hide Field GNSS_Area ▲
FIELD GNSS_Perim ►
* ALIAS GNSS_Perim
* WIDT H 8
* PRECISION 0
* SC ALE 0
Hide Field GNSS_Perim ▲
FIELD Area_ID ►
* ALIAS Area_ID
* WIDT H 4
* PRECISION 0

52
* SC ALE 0
Hide Field Area_ID ▲
* WIDT H 8
* PRECISION 0
* SC ALE 0
* FIELD DESCRIPTION
Esri
FIELD Shape_Area ►
* ALIAS Shape_Area
* WIDT H 8
* PRECISION 0
* SC ALE 0
* FIELD DESCRIPTION
Area of feature in internal units squared.
Esri
Hide Field Shape_Area ▲
Hide Details for object Sidewalk ▲
Hide Fields ▲

53
LA ST U PDATE 2015-05-04 12:31:54
ENT ITY TYPE
ENT ITY TYPE LA BEL Sidewalk
AT T RIBUTE
AT T RIBUTE
Feature geometry.
AT T RIBUTE
AT T RIBUTE LA BEL type
AT T RIBUTE
AT T RIBUTE LA BEL number_of_
AT T RIBUTE
AT T RIBUTE LA BEL material

54
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE LA BEL GNSS_Area
AT T RIBUTE
AT T RIBUTE LA BEL GNSS_Perim
AT T RIBUTE
AT T RIBUTE LA BEL Area_ID
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE LA BEL Shape_Area
Area of feature in internal units squared.
Soccer Fields
Tags
Soccer Field, Soccer Outline.
Summary
The purpose of the soccer fields is to show the boundaries of where the soccer outlines are on
the field.
Description
The soccer fields are outlined within the complex, each with their own unique size and shape.
Credits
Use limitations

55
Extent
West -97.903889 East -97.899281
North 29.945230 South 29.941240
Scale Range
ArcGIS Metadata ►
Citation ►
* TIT LE Soccer_Fields
Hide Citation ▲
ArcGIS 10.2.2.3552
CREDITS
* NA M E Soccer_Fields
Extents ►
EXT ENT
GEO GRAPHIC EXTENT

56
* SO UTH LATITUDE 13891428.523743
* NO RT H LATITUDE 13892865.795147
Hide Extents ▲
* TY P E Projected
X O RIGIN -126725700
Y O RIGIN -77828800
XY SC ALE 3048.0060960121918
Z O RIGIN -100000
Z SC ALE 10000
M O RIGIN -100000
M SC ALE 10000
XY T O LERANCE 0.0032808333333333331
Z T O LERANCE 0.001
M T O LERANCE 0.001
WELL-KNOWN T EXT
* VA LUE 103161
* CO DESPACE ESRI
* VERSION 10.2.1
VEC TOR ►

57
GEO METRIC OBJECTS
FEA TURE C LASS NAME Soccer_Fields
* OBJECT COUNT 42
Hide Vector ▲
FEA TURE C LASS NAME Soccer_Fields
* GEO METRY TYPE Polyline
* FEA TURE COUNT 42
Distribution ►
Fields ►
DET AILS FOR OBJECT Soccer_Fields ►
* RO W C OUNT 42
FIELD OBJECTID ►
* ALIAS OBJECTID
* DA TA T YPE OID
* WIDT H 4
* PRECISION 0
* SC ALE 0
* FIELD DESCRIPTION
Esri

58
FIELD Shape ►
* ALIAS Shape
* WIDT H 0
* PRECISION 0
* SC ALE 0
* FIELD DESCRIPTION
Feature geometry.
Esri
FIELD Comment ►
* ALIAS Comment
* WIDT H 32
* PRECISION 0
* SC ALE 0
FIELD GPS_Date ►
* ALIAS GPS_Date
* DA TA T YPE Date
* WIDT H 8
* PRECISION 0
* SC ALE 0
FIELD GPS_Time ►
* ALIAS GPS_Time
* WIDT H 10
* PRECISION 0
* SC ALE 0

59
FIELD GNSS_Lengt ►
* ALIAS GNSS_Lengt
* WIDT H 8
* PRECISION 0
* SC ALE 0
Hide Field GNSS_Lengt ▲
* WIDT H 8
* PRECISION 0
* SC ALE 0
* FIELD DESCRIPTION
Esri
Hide Details for object Soccer_Fields ▲
Hide Fields ▲

60
LA ST U PDATE 2015-05-04 12:32:26
ENT ITY TYPE
ENT ITY TYPE LA BEL Soccer_Fields
AT T RIBUTE
AT T RIBUTE
Feature geometry.
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE LA BEL GNSS_Lengt
AT T RIBUTE
Sprinklers

61
Tags
Sprinklers, Sprinkler Heads, Water
Summary
The purpose of the sprinklers is to show where at the five-mile dam complex the sprinkler heads
are located, giving the five-mile dam complex a nice looking watered field.
Description
There are about 400 sprinkler heads, each located the same distance from each other.
Credits
Use limitations
Extent
West -97.903870 East -97.898298
North 29.945229 South 29.940408
Scale Range
ArcGIS Metadata ►
Citation ►
* TIT LE Sprinklers
Hide Citation ▲
ArcGIS 10.2.2.3552
CREDITS

62
* NA M E Sprinklers
* SIZE 0.019
Extents ►
EXT ENT
GEO GRAPHIC EXTENT
* SO UTH LATITUDE 13891128.648687
* NO RT H LATITUDE 13892865.795147
Hide Extents ▲
* TY P E Projected
X O RIGIN -126725700
Y O RIGIN -77828800
XY SC ALE 3048.0060960121918
Z O RIGIN -100000
Z SC ALE 10000
M O RIGIN -100000
M SC ALE 10000
XY T O LERANCE 0.0032808333333333331
Z T O LERANCE 0.001
M T O LERANCE 0.001
WELL-KNOWN T EXT

63
* VA LUE 103161
* CO DESPACE ESRI
* VERSION 10.2.1
VEC TOR ►
GEO METRIC OBJECTS
FEA TURE C LASS NAME Sprinklers
* OBJECT TYPE point
* OBJECT COUNT 693
Hide Vector ▲
FEA TURE C LASS NAME Sprinklers
* FEA TURE COUNT 693
PRO CESS
PRO CESS NAME
DA T E 2015-04-15 11:55:33
CO MMAND ISSUED

64
3333],UNIT['Foot_US',0.3048006096012192]]
PRO CESS
PRO CESS NAME
DA T E 2015-04-15 12:54:14
Management Tools.tbxMerge
CO MMAND ISSUED
Merge Sprinkler;Sprinklers "E:4427 GPS and ArcMap DataOriginal Exported
DataSprinkler_Join.shp" "Comment "Comment" true true false 32 Text 0 0
,First,#,Sprinkler,Comment,-1,-1;Corr_Type "Corr_Type" true true false 36
Text 0 0 ,First,#,Sprinkler,Corr_Type,-1,-1,Sprinklers,Corr_Type,-1,-
1;GPS_Date "GPS_Date" true true false 8 Date 0 0
,First,#,Sprinkler,GPS_Date,-1,-1,Sprinklers,GPS_Date,-1,-1;Feat_Name
"Feat_Name" true true false 20 Text 0 0 ,First,#,Sprinkler,Feat_Name,-1,-
1,Sprinklers,Feat_Name,-1,-1;Datafile "Datafile" true true false 20 Text 0 0
,First,#,Sprinkler,Datafile,-1,-1,Sprinklers,Datafile,-1,-1;Data_Dicti
"Data_Dicti" true true false 20 Text 0 0 ,First,#,Sprinkler,Data_Dicti,-1,-
1,Sprinklers,Data_Dicti,-1,-1;Horz_Prec "Horz_Prec" true true false 16
Double 1 15 ,First,#,Sprinkler,Horz_Prec,-1,-1,Sprinklers,Horz_Prec,-1,-
1;Point_ID "Point_ID" true true false 9 Long 0 9
,First,#,Sprinkler,Point_ID,-1,-1,Sprinklers,Point_ID,-1,-1;Sprinklers
"Sprinklers" true true false 60 Text 0 0 ,First,#,Sprinklers,Sprinklers,-1,-
1"
Distribution ►
TRA NSFER OPTIONS
Fields ►
DET AILS FOR OBJECT Sprinklers ►
* RO W C OUNT 693
FIELD OBJECTID ►
* ALIAS OBJECTID
* DA TA T YPE OID
* WIDT H 4
* PRECISION 0
* SC ALE 0
* FIELD DESCRIPTION

65
Esri
FIELD Shape ►
* ALIAS Shape
* WIDT H 0
* PRECISION 0
* SC ALE 0
* FIELD DESCRIPTION
Feature geometry.
Esri
FIELD Comment ►
* ALIAS Comment
* WIDT H 32
* PRECISION 0
* SC ALE 0
FIELD Corr_Type ►
* ALIAS Corr_Type
* WIDT H 36
* PRECISION 0
* SC ALE 0

66
FIELD GPS_Date ►
* ALIAS GPS_Date
* DA TA T YPE Date
* WIDT H 8
* PRECISION 0
* SC ALE 0
FIELD Feat_Name ►
* ALIAS Feat_Name
* WIDT H 20
* PRECISION 0
* SC ALE 0
FIELD Datafile ►
* ALIAS Datafile
* WIDT H 20
* PRECISION 0
* SC ALE 0
* ALIAS Data_Dicti
* WIDT H 20
* PRECISION 0
* SC ALE 0
FIELD Horz_Prec ►
* ALIAS Horz_Prec
* WIDT H 8
* PRECISION 0
* SC ALE 0

67
FIELD Point_ID ►
* ALIAS Point_ID
* WIDT H 4
* PRECISION 0
* SC ALE 0
FIELD Sprinklers ►
* ALIAS Sprinklers
* WIDT H 60
* PRECISION 0
* SC ALE 0
Hide Field Sprinklers ▲
Hide Details for object Sprinklers ▲
Hide Fields ▲
LA ST U PDATE 2015-05-04 12:33:26

68
ENT ITY TYPE
ENT ITY TYPE LA BEL Sprinklers
AT T RIBUTE
AT T RIBUTE
Feature geometry.
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE LA BEL Sprinklers
Valve Boxes
Tags
Valve Boxes, Water
Summary
The purpose of the Valve boxes are to show where all the valve boxes are located on the field.
Description
The valve boxes are there to turn the sprinklers off and on at different sections.

69
Credits
Use limitations
Extent
West -97.903773 East -97.898256
North 29.945045 South 29.940510
Scale Range
ArcGIS Metadata ►
Citation ►
* TIT LE Valve_Boxes
Hide Citation ▲
ArcGIS 10.2.2.3552
CREDITS
* NA M E Valve_Boxes
* SIZE 0.003
Extents ►

70
EXT ENT
GEO GRAPHIC EXTENT
* SO UTH LATITUDE 13891166.155956
* NO RT H LATITUDE 13892798.859509
Hide Extents ▲
* TY P E Projected
X O RIGIN -126725700
Y O RIGIN -77828800
XY SC ALE 34994581.165044695
Z O RIGIN -100000
Z SC ALE 10000
M O RIGIN -100000
M SC ALE 10000
XY T O LERANCE 0.0032808333333333331
Z T O LERANCE 0.001
M T O LERANCE 0.001
WELL-KNOWN T EXT
* VA LUE 103161
* CO DESPACE ESRI
* VERSION 10.2.1

71
VEC TOR ►
GEO METRIC OBJECTS
FEA TURE C LASS NAME Valve_Boxes
* OBJECT TYPE point
* OBJECT COUNT 65
Hide Vector ▲
FEA TURE C LASS NAME Valve_Boxes
* FEA TURE COUNT 65
PRO CESS
PRO CESS NAME
DA T E 2015-04-15 11:55:33
CO MMAND ISSUED
3333],UNIT['Foot_US',0.3048006096012192]]
Distribution ►
TRA NSFER OPTIONS

72
Fields ►
DET AILS FOR OBJECT Valve_Boxes ►
* RO W C OUNT 65
FIELD OBJECTID ►
* ALIAS OBJECTID
* DA TA T YPE OID
* WIDT H 4
* PRECISION 0
* SC ALE 0
* FIELD DESCRIPTION
Esri
FIELD Shape ►
* ALIAS Shape
* WIDT H 0
* PRECISION 0
* SC ALE 0
* FIELD DESCRIPTION
Feature geometry.
Esri
FIELD Comment ►

73
* ALIAS Comment
* WIDT H 32
* PRECISION 0
* SC ALE 0
FIELD Corr_Type ►
* ALIAS Corr_Type
* WIDT H 36
* PRECISION 0
* SC ALE 0
FIELD GPS_Date ►
* ALIAS GPS_Date
* DA TA T YPE Date
* WIDT H 8
* PRECISION 0
* SC ALE 0
FIELD Feat_Name ►
* ALIAS Feat_Name
* WIDT H 20
* PRECISION 0
* SC ALE 0
FIELD Datafile ►
* ALIAS Datafile
* WIDT H 20
* PRECISION 0
* SC ALE 0

74
* ALIAS Data_Dicti
* WIDT H 20
* PRECISION 0
* SC ALE 0
FIELD Horz_Prec ►
* ALIAS Horz_Prec
* WIDT H 8
* PRECISION 0
* SC ALE 0
FIELD Point_ID ►
* ALIAS Point_ID
* WIDT H 4
* PRECISION 0
* SC ALE 0
FIELD Shared1 ►
* ALIAS Shared1
* WIDT H 50
* PRECISION 0
* SC ALE 0
Hide Field Shared1 ▲
Hide Details for object Valve_Boxes ▲
Hide Fields ▲

75
LA ST U PDATE 2015-05-04 12:33:52
ENT ITY TYPE
ENT ITY TYPE LA BEL Valve_Boxes
AT T RIBUTE
AT T RIBUTE
Feature geometry.
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE

76
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE
AT T RIBUTE LA BEL Shared1
Water Fountain
Tags
Water Fountain, Water
Summary
The purpose of the water fountain is to give thirst soccer players and fans a place to drink fresh
water,
Description
The water fountain is located in the middle of the five-mile dam complex, enabling for its most
efficient use.
Credits
Use limitations
Extent
West -97.900530 East -97.900530
North 29.942790 South 29.942790
Scale Range
ArcGIS Metadata ►
Citation ►
* TIT LE Water_Fountain

77
Hide Citation ▲
ArcGIS 10.2.2.3552
CREDITS
* NA M E Water_Fountain
Extents ►
EXT ENT
GEO GRAPHIC EXTENT
* SO UTH LATITUDE 13891988.425182
* NO RT H LATITUDE 13891988.425182
Hide Extents ▲
* TY P E Projected
X O RIGIN -126725700
Y O RIGIN -77828800
XY SC ALE 3048.0060960121918

78
Z O RIGIN -100000
Z SC ALE 10000
M O RIGIN -100000
M SC ALE 10000
XY T O LERANCE 0.0032808333333333331
Z T O LERANCE 0.001
M T O LERANCE 0.001
WELL-KNOWN T EXT
* VA LUE 103161
* CO DESPACE ESRI
* VERSION 10.2.1
VEC TOR ►
GEO METRIC OBJECTS
FEA TURE C LASS NAME Water_Fountain
* OBJECT TYPE point
* OBJECT COUNT 1
Hide Vector ▲
FEA TURE C LASS NAME Water_Fountain
* FEA TURE COUNT 1

79
Distribution ►
Fields ►
DET AILS FOR OBJECT Water_Fountain ►
* RO W C OUNT 1
FIELD OBJECTID ►
* ALIAS OBJECTID
* DA TA T YPE OID
* WIDT H 4
* PRECISION 0
* SC ALE 0
* FIELD DESCRIPTION
Esri
FIELD Shape ►
* ALIAS Shape
* WIDT H 0
* PRECISION 0
* SC ALE 0
* FIELD DESCRIPTION
Feature geometry.
Esri

80
FIELD GPS_Date ►
* ALIAS GPS_Date
* DA TA T YPE Date
* WIDT H 8
* PRECISION 0
* SC ALE 0
FIELD GPS_Time ►
* ALIAS GPS_Time
* WIDT H 10
* PRECISION 0
* SC ALE 0
Hide Details for object Water_Fountain ▲
Hide Fields ▲
LA ST U PDATE 2015-05-04 12:34:17

81
ENT ITY TYPE
ENT ITY TYPE LA BEL Water_Fountain
AT T RIBUTE
AT T RIBUTE
Feature geometry.
AT T RIBUTE
AT T RIBUTE

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