20210922
10264700
1.0
FGDC CSDGM Metadata
FALSE
M_PCTARegions_Dissolve
002
-123.741321
-115.982806
32.226768
49.361622
1
Geographic
GCS_North_American_1983
Angular Unit: Degree (0.017453)
<GeographicCoordinateSystem xsi:type='typens:GeographicCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/2.8.0'><WKT>GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433],AUTHORITY["EPSG",4269]]</WKT><XOrigin>-400</XOrigin><YOrigin>-400</YOrigin><XYScale>999999999.99999988</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>8.9831528411952133e-09</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><LeftLongitude>-180</LeftLongitude><WKID>4269</WKID><LatestWKID>4269</LatestWKID></GeographicCoordinateSystem>
20210819
15074100
20210819
15074100
150000000
5000
FGDC
Esri ArcGIS 12.8.0.29751
M_PCTARegions_Dissolve
1
-123.741321
-115.982806
49.361622
32.226768
This feature class represents the six PCTA regions and each region's associated five subregions. The purpose of the feature dataset is to roughly identify area of interests of trail and land protection projects with reference to the PCTA Trail Operations regions.
<DIV STYLE="text-align:Left;"><P STYLE="font-size:16ptmargin:7 0 7 0;"><SPAN STYLE="font-size:10pt">This feature class represents the six PCTA regions and each region's associated five subregions. The purpose of the feature dataset is to roughly identify area of interests of trail and land protection projects with reference to the PCTA Trail Operations regions.</SPAN></P><P STYLE="font-size:16ptmargin:7 0 7 0;"><SPAN STYLE="font-size:10pt">The regions are classified as the following:</SPAN></P><P STYLE="font-size:16ptmargin:7 0 7 0;"><SPAN STYLE="font-size:10pt">1.Southern California</SPAN></P><P STYLE="font-size:16ptmargin:7 0 7 0;"><SPAN STYLE="font-size:10pt">2. Southern Sierra</SPAN></P><P STYLE="font-size:16ptmargin:7 0 7 0;"><SPAN STYLE="font-size:10pt">3. Northern Sierra</SPAN></P><P STYLE="font-size:16ptmargin:7 0 7 0;"><SPAN STYLE="font-size:10pt">4. Big Ben</SPAN></P><P STYLE="font-size:16ptmargin:7 0 7 0;"><SPAN STYLE="font-size:10pt">5. Columbia Cascades</SPAN></P><P STYLE="font-size:16ptmargin:7 0 7 0;"><SPAN STYLE="font-size:10pt">6. North Cascades</SPAN></P><P STYLE="font-size:16ptmargin:7 0 7 0;"><SPAN STYLE="font-size:10pt">Each region was split up into five subregions of equal trail length. They are labeled a through e, with a at the southern most extent of the region and e at the northern most extent. </SPAN></P><DIV><DIV><P><SPAN /></P><P><SPAN /></P></DIV></DIV></DIV>
Pacific Crest Trail Association and USFS Pacific Crest Trail Centerline (2011)
PCTA Regions
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN><SPAN>These features were developed to get a coarse level understanding of where particulary parcels and projects are located with respect to PCTA regions. The polygon feature class regions do not follow public land boundaries, and therefore should not be used as the absolute determination of where the PCTA regions boundaries are located. This feature dataset is just a reference. </SPAN></SPAN></P><P><SPAN /></P></DIV></DIV></DIV>
File Geodatabase Feature Class
dataset
EPSG
6.5(3.0.1)
0
Simple
FALSE
0
TRUE
FALSE
M_PCTARegions_Dissolve
Feature Class
0
OBJECTID
OBJECTID
OID
4
0
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Shape
Shape
Geometry
0
0
0
Feature geometry.
Esri
Coordinates defining the features.
PCTA_Region
PCTA_Region
String
50
0
0
Shape_Length
Shape_Length
Double
8
0
0
Length of feature in internal units.
Esri
Positive real numbers that are automatically generated.
Shape_Area
Shape_Area
Double
8
0
0
Area of feature in internal units squared.
Esri
Positive real numbers that are automatically generated.
20210819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