20260921
10051700
1.0
FALSE
Ga_Hse_2023
002
Projected
GCS_North_American_1983
Linear Unit: Foot_US (0.304801)
NAD_1983_StatePlane_Georgia_East_FIPS_1001_Feet
<ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' 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/10.8'><WKT>PROJCS["NAD_1983_StatePlane_Georgia_East_FIPS_1001_Feet",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]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",656166.6666666665],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-82.16666666666667],PARAMETER["Scale_Factor",0.9999],PARAMETER["Latitude_Of_Origin",30.0],UNIT["Foot_US",0.3048006096012192]],VERTCS["NAVD_1988",VDATUM["North_American_Vertical_Datum_1988"],PARAMETER["Vertical_Shift",0.0],PARAMETER["Direction",1.0],UNIT["Meter",1.0]]</WKT><XOrigin>-17790500</XOrigin><YOrigin>-43703500</YOrigin><XYScale>3048.0060960121928</XYScale><ZOrigin>0</ZOrigin><ZScale>1</ZScale><MOrigin>0</MOrigin><MScale>1</MScale><XYTolerance>0.0032808333333333331</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>102666</WKID><LatestWKID>2239</LatestWKID><VCSWKID>5703</VCSWKID><LatestVCSWKID>5703</LatestVCSWKID></ProjectedCoordinateSystem>
20260921
10052100
20260921
10052100
Version 6.2 (Build 9200) ; Esri ArcGIS 10.8.1.14362
Ga_Hse_2023
Feature Class
dataset
EPSG
5.3(9.0.0)
0
Simple
FALSE
0
TRUE
FALSE
Ga_Hse_2023
Feature Class
0
OBJECTID
OBJECTID
OID
4
10
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
ID
ID
Double
8
12
0
BC_Zoning.DBO.Ga_Hse_2023.AREA
BC_Zoning.DBO.Ga_Hse_2023.AREA
Double
8
14
2
DATA
DATA
Double
8
12
0
DISTRICT
DISTRICT
String
12
0
0
MEMBERS
MEMBERS
Double
8
14
1
LOCKED
LOCKED
String
1
0
0
NAME
NAME
String
43
0
0
POPULATION
POPULATION
Double
8
12
0
DEVIATION
DEVIATION
Double
8
14
4
F_DEVIATIO
F_DEVIATIO
Double
8
14
4
F18_POP
F18_POP
Double
8
12
0
NH_WHT
NH_WHT
Double
8
12
0
NH_BLK
NH_BLK
Double
8
12
0
HISPANIC_O
HISPANIC_O
Double
8
12
0
NH_ASN
NH_ASN
Double
8
12
0
NH_IND
NH_IND
Double
8
12
0
NH_HWN
NH_HWN
Double
8
12
0
NH_OTH
NH_OTH
Double
8
12
0
NH_2_RACES
NH_2_RACES
Double
8
12
0
NH18_WHT
NH18_WHT
Double
8
12
0
NH18_BLK
NH18_BLK
Double
8
12
0
H18_POP
H18_POP
Double
8
12
0
NH18_ASN
NH18_ASN
Double
8
12
0
NH18_IND
NH18_IND
Double
8
12
0
NH18_HWN
NH18_HWN
Double
8
12
0
NH18_OTH
NH18_OTH
Double
8
12
0
NH18_2_RAC
NH18_2_RAC
Double
8
12
0
AP_WHT
AP_WHT
Double
8
12
0
AP_BLK
AP_BLK
Double
8
12
0
AP_IND
AP_IND
Double
8
12
0
AP_ASN
AP_ASN
Double
8
12
0
AP_HWN
AP_HWN
Double
8
12
0
AP_OTH
AP_OTH
Double
8
12
0
F18_AP_BLK
F18_AP_BLK
Double
8
12
0
F_18_POP
F_18_POP
Double
8
14
4
F_NH_WHT
F_NH_WHT
Double
8
14
4
F_NH_BLK
F_NH_BLK
Double
8
14
4
F_HISPANIC
F_HISPANIC
Double
8
14
4
F_NH_ASN
F_NH_ASN
Double
8
14
4
F_NH_IND
F_NH_IND
Double
8
14
4
F_NH_HWN
F_NH_HWN
Double
8
14
4
F_NH_OTH
F_NH_OTH
Double
8
14
4
F_NH18_WHT
F_NH18_WHT
Double
8
14
4
F_NH18_BLK
F_NH18_BLK
Double
8
14
4
F_H18_POP
F_H18_POP
Double
8
14
4
F_NH18_ASN
F_NH18_ASN
Double
8
14
4
F_NH18_IND
F_NH18_IND
Double
8
14
4
F_NH18_HWN
F_NH18_HWN
Double
8
14
4
F_NH18_OTH
F_NH18_OTH
Double
8
14
4
F_AP_WHT
F_AP_WHT
Double
8
14
4
F_AP_BLK
F_AP_BLK
Double
8
14
4
F_AP_IND
F_AP_IND
Double
8
14
4
F_AP_ASN
F_AP_ASN
Double
8
14
4
F_AP_HWN
F_AP_HWN
Double
8
14
4
F_AP_OTH
F_AP_OTH
Double
8
14
4
F_18_AP_BL
F_18_AP_BL
Double
8
14
4
F_PEOPLE_I
F_PEOPLE_I
Double
8
14
4
F_NH18_2_R
F_NH18_2_R
Double
8
14
4
F_NH_2_RAC
F_NH_2_RAC
Double
8
14
4
IDEAL_VALU
IDEAL_VALU
Double
8
14
4
DISTRICT_L
DISTRICT_L
String
11
0
0
Shape
Shape
Geometry
4
0
0
Feature geometry.
Esri
Coordinates defining the features.
Shape.STArea()
Shape.STArea()
Double
0
0
0
Shape.STLength()
Shape.STLength()
Double
0
0
0
20260921
iVBORw0KGgoAAAANSUhEUgAAASwAAADICAYAAABS39xVAAAAAXNSR0IB2cksfwAAAAlwSFlzAAAO
xAAADsQBlSsOGwAAIABJREFUeJzt3elyG1l6JuA390Qm9h0EuIuUqL2q2+52tN1tt2cc4Z4lJuxr
8O+5AkfMDTh8HxMxf+Z3e8IdvbiqurpLVdoXipS4gSQAYksg95xIUKWlRUkURRKZie+pYEjFRUwS
xMuT53znO7zneR5I4Hz9u3XEuFlEwW7jKX7+XxbHfRkkAvhxXwB5W7vdhjWMIRZHJCgJ+p1ITgcF
VgCtrzaQjC8gKhJpDrv1fXQ7fSwuzYJl2XFfEgkpCqyAMQwDvRaDbJJBFHR6e9Bau9hcG6A2XUSz
0YYkSVDjMjiOG/flkUkOrEfP15BW4yhk82CYaDzhzlt9+wDpxDSiIpUoQjbjEHgJ5lDD2oMBBMFB
ecZEpZoZ9+WRSQ2sr777IwZzCdR7e0hZKUiieFr/9MRwXRd63x91RGvkIYnK6E+RTULkk6O/P1/d
QKGUBM9H62slAQ0sx3GgDTT09QEednfAX8hAkkW4MQnfbD3GfLKElBIHz/Oj0ZY/b+H/6S9K0ujr
aMOBAa3nQeKiPcfTHzThchr9HJDzC6znzR3U2SEsxoU4kwP7Yj7C/9OZSuDxoA2u3YTnunANGzzH
IWnxME0TjAeUikWU88WTfvpIOjjog3HjURtgvUXgZXBKDJRX5NQCy7898X8Dup4Lx3HB+j9dox+w
w9fd7+4gszAF4YiPZf1hfjIGJA8/xH/++Qvb+wMdsEWISRX3H28gGU8gJsmjjznub1t/hOaP7l4f
tUVFfbOLmJBH1Lmeha2NVfDyAFeuXB735ZCwB9az+hZ6ngGW5WC4FnqeCdHjwLAA5zFwWSAxlfvo
TyYqh+HkY6spfLH/GDUuBY9nwINFgpNQzBWOXDlr9tr+TzoGpo7dQQeKqiDDxjBXqSEKms0mTE1F
LIHIE7gYcqkFFIsT8MWSsw+se60NJBYq8DwXLCdC4GOjEZL/4ryYg/rU2XpRiQExGXu6BY9lwHgO
1nf2Uet1cGF6fjTCe7T2BJ7rYcfrQ6nm/GEYPH/+tpjGgGFhNYcoGwZkSULYbW+2kUzMYzIwSKRZ
5HLZcV8ICZkjc8exHbACB4Z5+82negvGMOBir1YTlfkiNjYbeHL3C/CCAHnmcBQXj6WO/HBTZtA3
h6EPLH8EOTiQEI9F5/b2fRzXhG1q474MEpXAWs5VsdcegMuo535Bai0/ejkOV2TR7HSQT6QRZvu7
bcjS5Iw2GIaFG/WVBXJ+gbVYnoa2vYpB3AYrBLcYXpAl7DUOcBHh5d/6DjUWzJHLF9HE+F8tPzlf
Lzk9R6aRv2Uiyck46GkQ4wo4MbihNZSA/UYDhXw4V9dsy0G3ZUHmg/s9Pm37B89QVLjDSVFCPsI7
nyXFdBZMn8PWfhuoBveWK1HI4MH9Z6ENrGbzAJ6jTtSuzoSahTHs4s43m7h0vQyRdkWQY3rn0ySh
xNHtdKEzNoLe5cTIiuj1ekgkwrdMvrnehiItYZJwrIiDpgVGOMB1cWbcl0PCHFj9gYbNzv7o77vs
APFS8EcuSj6N+m4zdIHV6/Wh92JQgjuAPROM0EdtLoFUtjBaIfW7NxDyUYHlT/7WDxpY0/bh5GKj
1wny4UbVwGMYDMTDCvgwtSx59nQPmdQcJo3ETcHq2fji/q+wcnkJl69NSv0ZObXA+ur2N9BmVCi1
NMK29ZZhGfQZCwNDR0I5/1KMk7AsC9ZQhRihrUXHxTIsWF7Exdm/hWU/olEW+bjAarUP0E4zSKXC
dUv1Oltk0B70QhNYrUYXjBd7sT9zMu11boPhNWj9MgUWOX5gPe/uI1kLd+cEMR7D5vMGpvNlhIHW
c8F4r/ZWTiJVKkNVUtje3EU2R838yIfx3X4PuhqBrgcMgzZnQtM0qGqwR1l+x4nW/hAxPvgLGmcp
HithqDdRrR699YqQP8XvaW2YKhuJOutUtYgn689xY3EFQdZstuCYykTVXh2l09sFp+yjVLky7ksh
IcFv6W2I+WjsY/NHiU1GH01oC0JwI3jt8T5UZRmTrtVfx9/8xXU6RYccG2/HeYghvxt8XaySxW5r
H7XSFIJoONTROxCg5CL0TT/hbbEaU6HrBmRZClU5ChkfnolY/3C/uaDG2IHtHb/xbA/F7OTVXr3N
g+1qo5bZdPg4OS4+lgz6xpuP43eXOHD7sGwLohCsPWp+Yave58H5rVsnnGkOYQ5FJJOHB5UQchyR
/EkZcC40fRi4wOp2NDhmDJN+92PZBvr2U/zdf7sW6LlGEjyRDCwprWJtfROZRLCWy/s9C3BjkT8V
58M8iNLh8W+EYNIDyz9qrO5po/2RQVqB2tvuQxaisSL7ycd8eRfx5RffYGamgkwmjXgi2LVzJBgi
GVi+xHQBz7Y3MV8LRvuSTqeDYU+AHKxB31j3E8aZa3h6fwPzywIFFpnswOIlEduDBua86UDcemw9
O0A6GYzwDAqeF5GOT8M02uO+FBISkQ0sH19KotfvIznmPln+sv3uloFidvzBGcTQ2t9ikCsdIJOh
/YRkggPL4oGOOUASibGXM7BspL/Vn4xKG8hxRPqnhJV4NNpd1NLFsd4W2raNbq+JfHphbNcQZPEk
F7pusWQ8Ih1YDMuiAwOGaY71sFVZlsEJ1ouzs+m28HWu66DT1XDrD7u4+YPL474cEnCRDiyffxjs
neYzLCoFZNLjmyNZuTaDxtY+JD4Ljm4PX/I8F6ZzgPW1Jq7dvAQuYlvFyOmK/DOHl0WYJQHfbW/i
Cjzk0+Opg5pfmEahqOHBtzsQvDJ4jiq8fRwnoJi+inzKwZ1vV3Hj88k6QYh8nMgHls+fv+Iradyu
b2HZsVBK58Gfw/4YbzAAI8uAX7zqeZA4FrmUhnt//DcUp38MZdKOy3nHCqrtmHCYFmpTtEpI3m8i
AsvnV7zLlQyedrvo7g1xIV89831s/X/5F/A/+xm02Xl0d7vo9PzwTKFc+gyCFK1N5yfVH+5AzZhY
WipDVQ9PayIEkx5YI/5IK6Vgj9ehb67iSm0R4mmGlueNptVd24b2q19jrdVH+98fgP/BDFK5FYjx
wwl3uhk81GxvQLf3sdsc4tpNOuqLfNhkBdYLoiqjx7P4eushljNV5JLpk5c9eN4ooIx799F6uAqj
tox2S4fp1SD/4/9CXpjsgybeJ5URcHF6HpbtjPtSSEhMZGD5BEmEV+Vxb28bS5aJSr703vf3LP9o
9dfGRq4LQ9PQuP0A9T0NsBJg+atgzCLEfAJSALYDBZ1nJdFs1rFyherTyPFMbGD5/G6rQiWN+80W
+hsDzE/NvJyM94ZDuL0euGz25XyUG5OBzz+HtjiPZ9/ch96QEJ+9CaUSkhOyA0bgFRzsW7BtB4Iw
0T+K5Jjop8TvA59LYqc3xHBnDZdKM6PGf8Yvfwn79m2I//RP2P/Nr9HcXofxD/8VRsKFJfah10qo
5S6DZehb+ClUcQbPnzawsFwKxCZ1Emz0bHtBSMTQlWz89tG3+Hz6IkyOwdNcCsbWA+DmDLif/U8g
roDj+VH/PQ99tL7dQT4+Pe5LDzWBj+Fg18Bd4xEuX10KVP8yEjwUWK/hRB7cyhS+2loDe3MWsf98
A8I7Kq9j2Tha03XEd7OQRerl9CliUhp6R8Djh5tYulij0CLvRIF1hHj1eCcyZxZLOHi2g4p44cyv
Ker80O83NXTafWSyNCdIjkaB9Qn8ORdnyoDe6kMWqRD0U4lcDne/WcWVz1zs7TUxPT0FhYpJyWso
sD6RMqeiO9iH7FJgfSqW5ZCJL+Pht/swTQEJVQfHsxAEESxLE/KEAuuT8TERg1IL2lobqkx7A09D
QinAkSw8W61jbX0VpVIFCxcqNLdFKLBOQ7KWxdb2Ayw4P6AuDKfYxUHgYhgOi3Bsd9yXQwKCAuuU
FD+vofkfGyiy81RPdEpiYh6SYEPXWni+1hpt5UmlEzTSmmAUWKeE5Vl4MzaMHQ0y92o+y/Xc0ZFW
5GT8wlzGKaBTN9Cs98DJDcwvFpBMJWBZFkzTRKetwXFt2LaFhYW5cV8yOUMUWKdImpLR2NpAjVsZ
ddLU3T72mWfIm7NQJFqq/6R+ZrwMHjI8K49vv9xAIt3FcGBgOHSQVstwuF30tC7S6TTS6SSNwiKK
AusUcQIPZtbFYK2DodiFOaMhnk2gd7uBmJegW8VT4H8Pc+nD8x1FBUgph00Ah04btsniuz88x+d/
voBkilZto4gC65TFKyns1lchVxWo5cNRlZZtwmla4CGO+/IiG2IyO4VytgRNb6Feb1BgRRQF1hmc
1JP7QfmN16UWstjZeoSafIVGWWdYw+W/JNUStp6uY/niuK+InAUKrPPgz8Gs8Gjf2UUm/maYkdPl
OBYE2RydBUmHs0YPPaLnRC0kcaDuIeUWRiMBcjZ4XkCaX8L9u6ujxoAUWtFCj+Y5UhfjOHi4A8VO
Q+TlUbkDQyUPp4wZ/Wf2pnD31jbmlrJIJlU4jkPhFQH0CJ4jPiXAvKijsb8Ops9C6Mkoxd9sD2zZ
xugJx/M0Qf8pJEGFZfB49F0LSrqO9kEXV29cgKYNMDVFt+VhRYF1jlieg5xTRi++zS+eIGWWIPnr
8y/GBhv9e+h5TVxJ/hQ8R6H1KQRegoAKPM1DQnDxx99uYmomBkyN+8rISVFgjdHUn82j/ughWIsH
63JIOWXIczGkaovYvvMIRXOO2tacAn9llmM4gB2iMn28XmckmCiwxojlOGRXyqOjwhzHhWVoUCR1
NBKT5m3ojzTIoMA6LUm1jH5vSDVaIUaBFQT+CIDn3ng0OPDg3GA8PH4luS/sNWQCH0djr45CyaZT
ekKKHrWgEj302AaGnR4kSUFczIJjz//hcjwHj7ubMOUYph0BGSWDsPK/f+Ywiwe361i+UoQk0Rxh
2FBgBZSYkMB+xsExHehGB/aqiRxbO9drsBwLt/Q6HtQKcHkee402/tLQIEvhPXRD5BXYOo+H9zZx
9cYcbZIOGQqsIFfHx4TRi39L1nq8jxzONrAGtg6bZdA3+jAlGY8HO9icrgHSYV/1jVIO/3frOf6s
L2N+VLEfzltEf/XVGpbw9Mku5hdL4N5xMhIJHgqsENAbAyS9wpl+joGp4TdcD11VhR4XYIkcUL4w
Cs6XOB6D6iz2tvcw67qhrtgXOAX9hot17GL+QolGWiFBgRUC3jqDjFg5y8+ATW+Iei4DR5Lf+55c
u4HrTCrUYfU9gYujs8fgnv4El69eoNAKAQqsIPOA3d9vYNa+BlY4iyeTB9OxsGN2cCfOfDCsfOyg
jzXTQtWJIyWnEIWK+OFBHrd+/xzxlIvlS2/uPCDBQoEVYKZuoKDNQ4wfVsKftp45wC2vjWe5BCz1
eLVJZnkaXxk6PlvfxI0IBJZPllLQjC7yhdy4L4V8AAVWgPX2WijwZ3d02C27iScz1dHc1LEJIjxB
xIY3xA2En2kOoNtt6O4OsjnqBx90FFgB5a8MJss5bPfvo3Zw9dR7wj/pbOBJNfNxYfWa/YtXsP/k
OQq5w3bFYdXRtlCo8rg2d33cl0KOgQIroMyBjrtPNdiWAtZYR8GdBcMLYC1z1JbGZgCTZSCw/OGp
PJaBuHT8vvG/5zVAPXnYKPu7SMTDvy8vk5qBMdiFKNF5kmFAgRUAhztfvDfCxjYsHHA5tFM11PWn
4Kxd6MkC2F4HAliYogAvmQFMY1R6wGhdXO208MPU7Afro3pGD0apeuLr5fpdXO/akLNnM7d2nqPY
neYt/OgnFyGKVPUeBhRYY2YMTbS7JhzLxlTt1XyVP0/kiYerdlZtAdaL17vJLIzX/4EXRZ1ePIXv
7KdYHBwgHUvDdmxw3IvR12tc18FddghPKZ7sgl0Xc3tNLCZPHnhB4f+CUJQ4MpnwbjeaNBRYY9TZ
b+PLTQmmlAXb3kWl+mqU1R/YGDofV+vElGfw77s7uNDtYodzcIPLoBTLvvE+Q0vHvsq/WRD6UZ+E
GYUjZ0ejZskaSOO+BPIRKLDGpN3s4+u9JHq56cNXSCmsPryN+QsFDIcmvt0RYGY+coWQ49GemsbX
rjsKFn17C39hdJAR4qP/txwTPVcH1+sC2ezJQsvUYep9WBDBceGf98kkp/Dw/jqWLs5Q4WgIUGCd
I3e0nYXFztou1jUVHfW1Vr2CiAfmHLQnu2jYKvqZmZOPgl488ZqVCn7VbqNotOAJMnoYoKeKsLzE
ibcBMvvbuNRnIWUPb0XDzu+EoTXjaO53UCjRrWHQUWCdoz/8vzuAKGIrex1uPOP3TH7j7Yaaw6qb
gud3xzyN3lMsh142h96LEReYUyiNKM1gr7uKami3Pr9N5DN4tlpHKqPS5HvAUWCdo65aRrt6871P
de8sel6d4q2OJwh4lEtiwdCQkRKIAv/kIpmt4Osv7+PHP1mhW8MAo8A6RwsVCbe0Dlz17KrXzwMv
iGCt8G9+fp2/2CE4Vayv1jG3WKbQCigKrHOUVgGpp2GIcAeW5zjwPBeGOYQkRmMuy6fEUmjVbaQz
PWTz0dgnGTUUWOeI57nRS9jpiorfGC0w2+v4++mfRGo0YlsebPuwhz0JHgqscxRTJKhWGz2E+yBP
R41jX41D1fvwPMefJENUxFQWMZUm3oOKAusc+b+3vQjULn3PyBXwZGsdS/nFtyrqw8kDJ1iIx98s
tiXBQYF1jga6i74YnVofO5XDV/0OpN4O5kK+VcdxLZhuA+UKja6CjALrHPFwwHs2IoNlYU/N4Xc7
G5j13ty8HSa60YWU7OLShRxUNRbar2MSUGCdI5bxwMGf84kQjodRnMJguws1Fq6VNddzYFgdJPJ9
zF+oUNFomAPr+20k5PRYHgsT0ZnDeokXcNfZwTVLREwIR5mDZetgxAPMzMsolvyWPCRcgeV50Bod
SB6L/sY+Zqo1bLb2kLz8YnMu+WSG5UE3PeDDZz2EC8vi/nQF7a0tpLZ38aPZHyOobNtEW9tAbV5F
dToHWaZuDWHCs7t9uB0d06UKRK6IdDIFsbwyemNRSODbxh7UfLgLHYPCr+9xI1QC8DpXlLA1v4AN
p48fuvboWPggNepzPQseO4Sc1vDDm1NQ1XA3H5xU/I3MLOTK0b/yC4UClEfbcDP+5ln/F2k0n2zn
xfRYuFK0nyhs7QJWH69iuXhxrNfhV+IPhl3YjgU1ziJTYpHOS1DVMjgu/MW7k4qX5fffnywVarj3
YBVxSUEDQyQWK7SKckIZ0Yaq1aFJ84gsWcFAOxj3VaDVv4/ytAKGcTA7OwtRogn1KPjguL2QyeGn
6ewopPbaTTxs7IHPJ6PTW+QcJXMJXBl08E1vH9boAIdofhMfXFzG1v4DVDwBaT6G2VjhXJv9acM2
Zi4kMDdHk+lRc6yJhu9HVPlkBlbLxnfbO0hXC2d9bZFUnYpD2Gvgyz0LdmYKUTRMZzFMpbGrD1Hb
eI6qmznnwGpheprOGIyij5oZ9eewqvkS3LqDR/UmlPKnb2HwXA9mfwA4HsRE7GXvJoaN5uiD5TiU
y0lc6LfxwMoBQgRXqUbNAjkwShxGJg9btyGd49yV7fZoniqiTrSUUytV0N4ZomPZ4AT+xEHlaDpi
mosZPo64Esf2Rh1MXMIAFmyBgeHZcHkWoiqDidKEPwNUkh6e7+5h4G9pidLX9id6sogn7SYusyKE
cwjngbGPv/jp8pl/HjIeJ0ob/xYxKSpoGdqJAsvSTTj1DpbTUyjk0y8rjNPx5Khg1XEcmI4N3TLQ
7BzAtYHdfhO2xIJXZYhqOIoT3yedS+Cm2cFau48daQaQVUSRkUjhO5ZD9/E9/NXUZ2f6uRzXRq4s
Qo2H/+eDHI0/hRNAP25U1Rsi12OwOHUJR61Q+red/osgCFDlGHKJwxqwi9kaGo0GDN3Bdr0BZGLQ
LB1iUgEr8nA9D2yIek3511qZzqBYcfDw0SOsDaahK+E/SfktDAM7nsA2Z2FoaIhJZxfM/pmLlqWf
2b9PQhxYLJjRfz7XcWD481BgICoyPNeF67gvi/b8Q0IZ10PSEVDiEqjWih9d0+WP6vy6MF+tUIZh
GBiaBtq9LlhVQrPfgeaacEUOnsiBVySwXPBvtTiew+WVApw7z/AIEQysF+xCBV2tf6aB5U/stw90
2LYD4YRTFSTYTvyoSgwP7f4mXJbBdGkKJU4eNfNXpQRcfyRlmqP384PLvwWUeAHlQgk8x51KHZck
SaOXdCI5CsWqkoXtOHA8F832ATq9AbZaexDyCXApBaK/BSOo8/gMg27XAMK1d/ij2ByHrqmhdIaf
w/E6mF1MgOcprKLqxI9sMZfH3+f+GkHgB6C/KvT9ypBSqsDfAXmluoBerwfT87D+5DmsGAdX5sDE
RHg8CxcueFE4nSO1PlGM/f4w+mhy8xUceB24nnvqzf78rgu6vYuFSypyeWq+F2WR/lU0WhxIHp7F
l794bXQb6Y/AugMNumWjo/VgCjYsye+iYI/mwcS4MrpNO285ycSaPycYgPA8Gx5afsGsLEMS5FMN
KwgtXFxJI5mK5sIFmZDA+lP+LaRPkQ9XkbxMadS22J+sbTZbsDwH7FDA47VVOHEBQ85FZrr8wZow
x7TQWdtEdqhDdvzf9jYMRYGwsgD2mLcn5UsLwJ3nQDmi1dksh51KGf92+wv8Yv70Ruam1cb8skhh
NSEmKrCOGoH5UcRyPErF4svXT93Mw7IsdLpdtOpdDHkP3UEHguXCVUR4CRnMQAcPDl53iKKgYiW7
hLhtg/c8tP/1X2GwLLZ+8Z9gXJsFm1LAfKCQUVJklLkG6ohoYPnUJA5SqdGGZP4TK98d14TlNpGf
dpHN1k7tEkmwTXRgvY9fVpHP5ZBHblQXNuz3RsHmODaGxgA8l4CiqmCzAkR/5PbarVzmn/95VOha
kiS0Bj2stVqwc8oHR2oJzkA90reFgD23jM2tBuYSlU/6dzy+gaWLKSST8VO7NhJ8FFjH4E/mx1Ov
eoIlPnAQKps+fLs/pipIEmRNxq2HD2DEGcTKBbDC0aOt2mwWT7f24SRfjfaixhEkmIbmfxNPyMNB
7zk++3ERajzarXrI2yiwzkFCjeOvrvwQT+/dxc76HrB09OhCiQlIiQ5aER5lMSwHyxye6GP98hXL
bWN+RaKwmlAUWOdoYeUyYvVtfPvdE6SuX3jr7aLAYlZooe3m4Ubo/MI3sCy2oWPZ0iF85Gqh4+oo
VBlUq592O0nCiwLrPDEMKpUqVG2A331xB4IqQ56fAuOXUQjCqDK/WpJw78EWjHx026MIjgP2JC2U
PRZ7uwcoVlQ64WZCUWCNQfLCEn6aL4y2NG1+/XtYd75De2kW3N/9BLwogh80YSC6gbW9dAkbO9uY
y/jlvce/9eV5CY5RxK2vn2HlahmJ5IknwkhIUWCNifJiYv7iX/4cztXPcffr36Nr2dh6uoth4fAQ
kKgyEkmsajqmPQ/cR87VSaIK3pnFg7sbuHKDh6JQZ4ZJQoE1Zow/oioWsfKzv8Zvb3+FZrwK9ww3
CAeC60LXNUA5+SZn3irj/u1tXL5eRSwWtXPTyLtQYAWEqKpYSVfx1cNd4MoSIo1hwOtDOJIJjj9Z
2IiCgnZXwebzHSxdjPChHuQNFFgBIuVL4Bp8ZEsaXuJ4bNdqeLC3j6rDI5f++FU/w9KQm7KxuBTd
uT7yNgqsAHE8D0ZEe9m/JZkZdSLdqG/jb40EZOnjKtZ5aYjZ+TzYSfl+kREKrABhXBdchPu7/ykr
ngQrtk9wSrQHvxFtjCbcJw4FVoBkMilcrHfQtEx4gl84Gv3Rw1S3D6Hglzccv9q9q+1jeiXiCxPk
SBRYATsC7KfFONxHd3BbKqD/EU/ksGrGpGM19fODykYTguSBcxpIJs+ydykJKgqsgMkVC/jv6RSc
393CV5kywEd0i84LjYSKemsbU5l3t4gxzCFspoHlqxmoagy2RQWjk4oCK4AEUcTncxXc2d3BoDSD
KIuJKtKx95++pDvb+OzPqy/nrOiAiclFj3xAVTMp3Nhew38g2oE19CxorvvOGlLbNlCupiFTcSih
wAquWCqJPPtx5z6G0VDg0GcdHB7g9jaGcxFTDrvDEkKBFWBpWQCMISBFd/nen0z3j4d7F9czcA4n
3JOQoMAKsLQiwDN0MBEOLD+qukYPkI4+nks3hrAt4UWw0Shr0lFgBZhl22Bi0e5Z7iRSuK/rqLS2
UEhX33p7XC5h60kHmxsPcP3mIvXBmnAUWAFmxdP+kcmINgaDfAm/3vwS/3BEYPm3i7KYgW3HceuL
PeQrPIqVJGIx6eXBuWRyUGAFWDWXRubecxwUor7B18Oc9f7CUf9YMI6dQnt3gJ2NTVy6kUMulzu3
KyTBQIEVYElVQXHQxEGEu4/6vMYOrtdufvD9/Cksw+xgdilBYTWhKLAC7rO0iIbWRDOWHp2eHEkc
D898fwmHP+luOHVcuKqgUMyc26WRYKHACrjrKxch77fxf/oD9KSIbkmRFWys3sfCzI0j32zZJmxm
Bxcu55BKR3sRgrwfBVbA+atiy4U0Fvc2cEtQojnKch0Y1tFnFVq2DkE5wOUrU7RCSCiwwsB/ov68
FIM8aGFNZ9BgZDgxv73KO+qSXBfMsOffR8Hzqy4FMdBBx8TT2JYYLNkmeP5VKA30NuLZIWYXMxRW
ZIQCKyQqlQr+h+uiOzRQHxj4w/NHeAoFtm0hE1fQ1U0MHSDOuLiieLhaSsNzPZisic3dLbQHBoZy
Al1ORrPXh8nLsONpMKIUiDDbTCowLf1lYA2MJvI1CzOzJXD+uY2EUGCFh1/lzXMcsnFl9LJSSEMb
DOC5LiRJgmlZsF0XAstCUZQ3qsKvFVIv/25ZFnQ9Bodh0TNM1Pe30Bka0BN5NHQLG64I2bXBCgIs
SYXpuHBYHi7Hj/5923H9Y2vg+V1CT9od1fPe6Fvv2TacQQ931BZWdAYS7yBbGWJuoUbV7eQNFFgh
5T+R4+qrrpvHvWUSBGH04vPnr6dz6VcN8mwbe50eErHE6H2GpoVefwCT4WHxErpDHf3BEK6goGN7
2D70WJ7CAAACT0lEQVTQ0BZU9HkFnj9S85kGWNuAywlgXQeukgBGewU98PoARbOLHO+hywjYNRxY
4OD473P1R7jnOMCjb/CLHy6jUKxSWJG3UGCRET8c/JCq5l/t6YuJwmg09z3PexWQrh9wThqarqNt
2rj7+CEScRULMzWwrj8CY+HZLm7VN/DE4jHlDvFZKYnZQh48y44O3Oj0+rAcD48bdfxSj0NP5lGv
TiOekMBOUG97cnwUWOTYXh/x+Cc2+wdmSEIcfsQt5D478mOmM4nRbeifjgD9H7xi5nB0V81nMPVs
A/+710DDsOH4Iy1CjkCBRc485I5zu7o0O41/rDfwa9PBdvMAyWTyXK6PhAsFFgkEf+y2Us7jUikH
13XHfTkkoCiwSOBGZNSFgbwLBRYhJDQosAghoUGBRQgJDQosQkhoUGARQkKDAosQEhoUWISQ0KDA
IoSEBgUWISQ0KLAIIaFBgUUICQ0KLEJIaFBgEUJCgwKLEBIaFFiEkNCgwCKEhAYFFiEkNCiwCCGh
QYFFCAkNCixCSGhQYBFCQoMCixASGhRYhJDQoMAihIQGBRYhJDQosAghoUGBRQgJDQosQkhoUGAR
QkKDAosQEhoUWISQ0KDAIoSEBgUWISQ0KLAIIaFBgUUICQ0KLEJIaFBgEUJCgwKLEBIaFFiEkNCg
wCKEhAYFFiEkNCiwCCGhQYFFCAkNCixCSGhQYBFCQoMCixASGhRYhJDQoMAihIQGBRYhJDQosAgh
oUGBRQgJDQosQkhoUGARQkKDAosQEhoUWIQQhMX/B6FdVkd9g5uRAAAAAElFTkSuQmCC