ROADIDS_APR26 ROADIDS_APR26.DBF GeoPlan Feature Class8363 OBJECTIDOBJECTIDOID4100Internal feature number.EsriSequential unique whole numbers that are automatically generated. Shape Feature geometry. ESRI Coordinates defining the features. SHAPEGeometry800 ROADWAY Road system designation used by RCI as the roadway identifier. FDOT ROADWAYString3200 MILEPOST Mile post number FDOT MILEPOSTDouble8388 TYPE All values should be equal to MIDPOINT FDOT TYPEString800 MP_ID Mile post ID number FDOT MP_IDInteger4100 LAT_DDLatitudeGeoPlanLAT_DDDouble8388LONG_DDLongitudeGeoPlanLONG_DDDouble8388MGRSMilitary Grid Reference System (MGRS) Coordinate of the Facility. The MGRS is the geocoordinate standard used by NATO militaries for locating points on the earth. The MGRS provides a means to represent any location on the surface of the Earth using an alphanumeric string. Hierarchical references are based on the Universal Transverse Mercator (UTM) coordinate system. The MGRS is used for the entire earth. http://mgrs-data.orgGeoPlanMGRSString1500GOOGLEMAP Webpage hyperlink to location in Google Maps.GeoPlanGOOGLEMAPString10000DESCRIPTFGDL added item based on NONEGeoPlanDESCRIPTString2500FGDLAQDATEDate GeoPlan acquired data from source.GeoPlanDateFGDLAQDATE800AUTOIDAUTOIDInteger410Unique ID added by GeoPlan. This field is for historic tracking of features.0 Sam Palmer University of Florida - GeoPlan Center GIS Specialist 3529328686 Architecture Building Gainesville Florida 32611 US 2026-04-11 ArcGIS Metadata 1.0 0.000Florida Geographic Data Library (FGDL)Jonathan and Melanie Antevy Hall1480 Inner Road, Room 431PO Box 115706GainesvilleFL32611-5706UShttps://fgdl.org/info/contact/NoneDOWNLOADABLE DATAhttps://www.fgdl.org/Enterprise Geodatabase Feature Class Florida Department of Transportation RCI Derived Roadway IDs - April 2026 20260411T00:00:00 University of Florida - GeoPlan Center University of Florida - GeoPlan Center Gainesville, FL vector digital data State of Florida This dataset contains Road ID points from the Florida Department of Transportation Roads Characteristics inventory (RCI) dataset. This dataset is designed for use with the ETDM project and is associated with the other layers RCIROADS and MILEPOSTS. It is used for labeling the roadway ids for the RCIROADS layer on the ArcIMS maps. The FDOT GIS Functional Classification Roadways feature class provides spatial information on the assignment of roads into systems according to the character of the service they provide in relation to the total road network. Sam Palmer University of Florida - GeoPlan Center GIS Specialist 3529328686 Architecture Building Gainesville Florida 32611 US Florida transportation Florida Department of Transportation ISO 19115 Topic Category - April 2026 transportation Florida This product has been compiled from the most accurate source data from the Florida Department of Transportation's Transportation Statistics Office. However, this product is for reference purposes only and is not to be construed as a legal document or survey instrument. Any reliance on the information contained herein is at the user's own risk. The Florida Department of Transportation assumes no responsibility for any use of the information contained herein or any loss resulting therefrom. THE DATA INCLUDED IN FGDL ARE 'AS IS' AND SHOULD NOT BE CONSTRUED AS LEGALLY BINDING. THE UNIVERSITY OF FLORIDA GEOPLAN CENTER SHALL NOT BE LIABLE FOR ANY DAMAGES SUFFERED AS A RESULT OF USING, MODIFYING, CONTRIBUTING OR DISTRIBUTING THE MATERIALS. A note about data scale: Scale is an important factor in data usage. Certain scale datasets are not suitable for some project, analysis, or modeling purposes. Please be sure you are using the best available data. 1:24000 scale datasets are recommended for projects that are at the county level. 1:24000 data should NOT be used for high accuracy base mapping such as property parcel boundaries. 1:100000 scale datasets are recommended for projects that are at the multi-county or regional level. 1:125000 scale datasets are recommended for projects that are at the regional or state level or larger. Vector datasets with no defined scale or accuracy should be considered suspect. Make sure you are familiar with your data before using it for projects or analysis. Every effort has been made to supply the user with data documentation. For additional information, see the References section and the Data Source Contact section of this documentation. For more information regarding scale and accuracy, see our webpage at: http://geoplan.ufl.edu/education.html The Florida Geographic Data Library is a collection of Geospatial Data compiled by the University of Florida GeoPlan Center with support from the Florida Department of Transportation. GIS data available in FGDL is collected from various state, federal, and other agencies (data sources) who are data stewards, producers, or publishers. The data available in FGDL may not be the most current version of the data offered by the data source. University of Florida GeoPlan Center makes no guarantees about the currentness of the data and suggests that data users check with the data source to see if more recent versions of the data exist. Furthermore, the GIS data available in the FGDL are provided 'as is'. The University of Florida GeoPlan Center makes no warranties, guaranties or representations as to the truth, accuracy or completeness of the data provided by the data sources. The University of Florida GeoPlan Center makes no representations or warranties about the quality or suitability of the materials, either expressly or implied, including but not limited to any implied warranties of merchantability, fitness for a particular purpose, or non-infringement. The University of Florida GeoPlan Center shall not be liable for any damages suffered as a result of using, modifying, contributing or distributing the materials. A note about data scale: Scale is an important factor in data usage. Certain scale datasets are not suitable for some project, analysis, or modeling purposes. Please be sure you are using the best available data. 1:24000 scale datasets are recommended for projects that are at the county level. 1:24000 data should NOT be used for high accuracy base mapping such as property parcel boundaries. 1:100000 scale datasets are recommended for projects that are at the multi-county or regional level. 1:125000 scale datasets are recommended for projects that are at the regional or state level or larger. Vector datasets with no defined scale or accuracy should be considered suspect. Make sure you are familiar with your data before using it for projects or analysis. Every effort has been made to supply the user with data documentation. For additional information, see the References section and the Data Source Contact section of this documentation. FDOT GIS: http://www.fdot.gov/statistics/gis/default.shtm Environment as of Metadata Creation: Microsoft [Unknown] Version 6.2 (Build 9200) ; Esri ArcGIS 10.7.1 (Build 11595) Service Pack N/A (Build N/A) true -87.5866 -79.8734 31.036 24.5121 publication date 20260411T00:00:00 The RCI is a computerized database of physical and administrative data related to the roadway networks that are either maintained by or are of special interest to the Department. In addition to data required by the Department, the RCI contains other data as required for special Federal and State reporting obligations. The RCI is maintained by District and Central Office personnel. While there are many other important databases maintained by the Department (several that contain more highly technical data such as bridge specifications, highway design, or pavement) the RCI remains the largest database with over 1 million records. (Chapter 1 – Page 1 RCI Office Handbook) 1-87.586590-79.87338731.03596124.512146 The data set was taken from the Florida Department of Transportation Statistics Office from the following webpage: http://www.dot.state.fl.us/planning/statistics/gis The data set was originally downloaded in April 2026 as the RCI basemap. Specific instructions, created by FDOT, their consultants, and the GeoPlan Center, were carried out. The data set is then to be updated on a regular basis. The data was originally in UTM Zone 17, Nad83, units meters. -Project the shape file to the standard FGDL projection (EPSG = 3087). GeoPlan 2026-04-17T00:00:00 University of Florida - GeoPlan Center Unknown Spatial and Attribute Information This data is provided 'as is'. GeoPlan relied on the integrity of the original data layer's topology.This data is provided 'as is' by GeoPlan and is complete to our knowledge.GeoPlan relied on the integrity of the attribute information within the original data.This data is provided 'as is' and its horizontal positional accuracy has not been verified by GeoPlan.This data is provided 'as is' and its vertical positional accuracy has not been verified by GeoPlan.0 8363 1.0 24000 Unknown 2026050609111600FALSEArcSDE ConnectionROADIDS_APR2600257672.306900793907.00320062954.243300780841.7341001ProjectedGCS_North_American_1983_HARNLinear Unit: Meter (1.000000)NAD_1983_HARN_Florida_GDL_Albers<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/3.5.0'><WKT>PROJCS[&quot;NAD_1983_HARN_Florida_GDL_Albers&quot;,GEOGCS[&quot;GCS_North_American_1983_HARN&quot;,DATUM[&quot;D_North_American_1983_HARN&quot;,SPHEROID[&quot;GRS_1980&quot;,6378137.0,298.257222101]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Albers&quot;],PARAMETER[&quot;False_Easting&quot;,400000.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,-84.0],PARAMETER[&quot;Standard_Parallel_1&quot;,24.0],PARAMETER[&quot;Standard_Parallel_2&quot;,31.5],PARAMETER[&quot;Latitude_Of_Origin&quot;,24.0],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;EPSG&quot;,3087]]</WKT><XOrigin>-19550100</XOrigin><YOrigin>-7526400</YOrigin><XYScale>10000</XYScale><ZOrigin>0</ZOrigin><ZScale>1</ZScale><MOrigin>-1053.8383612570201</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>3087</WKID><LatestWKID>3087</LatestWKID></ProjectedCoordinateSystem>20260515094303002026051509430300 /9j/4AAQSkZJRgABAQEAAAAAAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCADIASwDAREA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD27n1/ SgAOQO1AC0AFABQAHoaAAdBQAUAYniPxTY+GIYZb6K5dJW2qYUBAPuSQKTaW5MpqKuzFg+KvhaeQ Kbm4izj5pIDgZzjpn0pc8Sfaw7j4/ij4XkJH2qZR2JiPzc4yMc0ueIvbQ7ksXxL8Jyu6DVNuzu8L qG+mRzT5kV7SPcdF8SPCk0mxdUweh3wSKAfTJWjnj3D2sO5O/j3wwjlDqseR6Ix/kPY0c8e4e1h3 GN8Q/CqoHOrx4OeiOcY7njge9HMu4e0j3J28ceGVRHOsW21yApye/wDKnzIfPHubFpe219F51rOk 0ZJG5DkZFO9xpp7FigYUAFABQAUAFABQAUAFABQBVvHMaZU80IDKJycmqAKACgAoAKACgAoAKACg AoA36kBO4oAWgAoAKAA9KAAdBQAxZVd2VTkr1oA5zxh4Ug8TW8JmupoRb5YiMbgwx6etTKPMROCn ufP16Ibe6uILbzYYcYBf75KjHPHHXpWDOOW9kWLVLJrS6MsVxLcqcIythE553DqaFawK1rNGbDby mbKRPISwVBjlieAo7dqLXCzehv3PhzVdMS+u/skQWyKpMCRjLDOBnrVWaL5ZJt9jAlDII52xiUn5 VIPAOenoB/OlYjluMkBWUZAZAVxkZPSkJD0uJRyG34fLZO3nH/1+lAeR0nhXxVe6Prdruv5I7WSX Mq7Sysvc4qotpl05NPyPeU8S6JIEK6pa4kXcpMgAI/H6VtdHZzLuWYdU0+4VGhvrZw5IXbKpzj05 phdFhJopHZEkRmTG5QwJGemaBj6ACgAoAKACgCOeUQW8kzKzBFLEKMk49KAOH074o6dezSpJZ3UY jZtzBM7FHdvSoUzJVU90dBL4l0e5soXg1C3f7Q/lxDdyxyRjHXsapNGikmMqxhQAUAFABQAUAFAB QAUAFAGuL2EybMn0z2qbATnkcUALnjNADPNQLuLDHrmgBi3ULPtDjNAD/MQ7gGHHWgBPNQxFlYHA 7UAU9PYGSQk/Me1NgX2ICkk4GKQHyzfySQalfRFo3zOw+6GJ+bjngHJxyOgOeaw62OJr3rHb+B9M 046jZo10konZmntmOEVOwbjlsjpVRRdOK73N/wCJnifTtMtU0Wyto/7Q8yNw21VWLGCDyRnqB6cn NVJ9DWo18KFtPFYuvhtdT6jJZXN9lt8UwByM8Fh0z36ke9F/dFze5fdnDad4f0bUtHnv7m9nsvKl jULJBuR3YMcYQZxhT83Ix29M7Kxjyqzd7MXxB4Yl0hzaxXSzWvlR3AneIIW3DpknJ/T8KJRtsE4W 0WxirpWovay3S6e5gjXLSANtUe59MZ7nP60rMnlZWsYTeX9rAJkgMh/d7unXpnHP5URVxQjd2NiX wjfqiTw3sEscvyecrMVXJwBg7SO5xgjI69qpxbNHTb16FdNPvLWKacSmQxEbWizscHjjjoOenpS5 bCULakw1PVI1W7tEuLNQg+a2Zhux03FiSeo6Y60aglLfY0tO8U+Kba6mL3Fy8TzL5uHOTzzjJOD/ AJNNOQ1Kp3L3iLxHrmn6yyW3iCS5imjLqIpWHlhugbsGx2A4zRJtPcc5STtch/4TXX9A3Qzay04X 5klYNICxH3CWAHHOcZ5ovJBzzXW52Ok/EW91G2sVEdoLj5WmMkwUTJkglc4CnocHnnp60ps0VVux 6BetdmwkbT2ga525j87Ow/XHPStH5GrvbQoLq41HQbq60yaCWeJWQE5VBIB3z29/SlfsF7rQ8E1e 9lt/EEs2pJDPO6nzFhcBJCemNvB//XWPXU5G9dR2gC0uNVtJgLkSxXikxKuU2Z52k4Ocj2J49KI2 vcIWumu57g00auEZ1DHoCeTXSdg/OaACgAoAKACgAoAKACgAoAcY3BwVOfpQBcje5gALYKkZwx6U gEe+do2Crj3HaiwFMsT1JNMBKAFDEZwTz1oAAxGcEjNADkcIwYZyPSgBdQma/sprbJjWVChZTyAa VgZyjeA9EeC2iaJz5DFtwblyeufyqeSJmqUUadp4d0qy/wBTaKGyDuJJJI6GmopFKEVsi1dabY3z 77q0gmfaV3OgJwe2aZVjMuvB+h3No1v9iSMHoyZyKlwiyHTi+hDdaLaaJ4LvLKwsnuJTtdejNvGA GGfQc4ocbLQTjaLSPNdf8U3N9DHYC+upraLDyi5QBzJjnHsKyk+iOepJ2smby2E1l4Whv7/WRPbX sKtBay7tqSgDsOgC59skU7aastr3dXuZ+heHZLqBdUh+y3cqMGZBL5bW+Oc56c4pRXUmEXa61O78 NaV4eeFXXUFkBfatpLIuQ+Pu5/iwTwRWisbxaetziPF11/Z09lZWcPkQ7A7wuQzggsPvDqeTUSdt DGpJqyRqWGhQ6jplhLLLb/Zbl5F2JlnjYoAFPocqOaa1Ra1S8yOS3s7Lw+dRtNVhmuo5E82ARnam eOnBNK9le5N7Ruma+q+HvDdz4JuPEAlDNNGkpmXqOB8gGeOeKbirXLlCLjzM8yu7WNdhhlVYJV3J vbJU9MZ9azOZrsNaW50mZYlvEuQ6fPjDKrYAI56Hgcj1obsNuzZ6Pp/ibUte0Kz0zTYY7S2crau3 mEyKcZyCOlaJ30RspOSstjMj+32J1PylliW3IFyHBUFWDANtz838NGw72TaOe1d4bJwLpcvJAJAA hO4dfw6VLsRJrqQwXGn7JGgiWKaMO+4rsBPO1Rj6j64ouhOSaPXLCze8sra7MzK0kYc56glea3Wp 1J3VzTtLb7LB5e4sc5yTTGT0AFABQAUAFABQAUAFAG/ipAr3Nqs4yOHxgHtQBDLCsdmEJGRySO9M DOpgFABQAUAFABQAUAFABQAUAFAGbf6JYXttOhsbRpZAcNJED82OtJq4mro4iz0uztdb/sfVLuAW lirXJE6N/pDuFBC9eAdo+mcc9MeWzs+hzqKi+V7L8TZ8G+GtLvvDd5/adsLczXP3o2KKQvTaTyR9 acUmtSoQTi0zmNZGk6f4jtbe3McsUzFmvbUCMx9lwOmVx1HJqXa5nLlUkJovhlPt0B1fd5M8zrCq k+YzA8MfanGP8xUY6rmPVbSKKxLfZoo497b32qBubpk+9bWR0JJbGF48tEu/DtxcW8cccir++VY1 zKvYbuowelRNaXM6q91tFXwTo+n2un3Wk3aSXhv7VbyVpUCqqdAm0scHOTn3pRSWgqcUlY4seCNW ntLvVbJVjsbXcIVeUM5QHnke309qjle5l7Nu8kcpJBKYFnZGEUo4JJ55Of8Ax7t+HUVnbqY2e5YN vfWEISOXaZE3hY5QG5J2jBHUf59arUqzWqY6D+0JtQt2s2a6upDudGG5s7gcY6N27YH60ddBq99H dnQax4W1fW9R/tJZVvXm3B0tQD5TKM7COxGMAc81Ti2aOEpMzbAw6YZDd2TmQr80jxb1gPchTjsA Oc9TUqyM42R6f4N1tNa0cyCWWSWNyrmVQp9sAdq3g7o66cro6KrLCgAoAKACgAoAKACgAoA2hcJ5 hjPykdM96kAlc+SzR4Y0AZDOxyD0z0qgGUAFABQAUAFABQAUAFABQAUAFABQBwnifRNZvPEcF9aw LOtvIjxlsfdHJX3Gf6+tZSTbMZxk5XRpa8PFF3p6vLHYybEZvLRSShzxt9TjNNpjala4eGNFgk0O 0Oo6SsVzC5f94ASzEDLEf09qcYq2qHCCsro6gqpIJAJHQ46VZoLQBHPBFcwvDMiyRuMMrdCKAOW1 W1Xw9pzwWMZlfULlYkXOGjTrtBHOM4rKS5fmYyXKtOpS8VeIrnQvDS+HW84akygySRptQqeSoOOf rSk7KxM5OMeXqcVpMkNzol1aTassUyKAIrlCR5Z+bK+5IqFsZqzg1c53zUEpjdmkX0VccDnipI1W ppS3Hmm3gsbT7Jegf61GILKw754//XTv2HfayO00G/uNG0yZtLv7sLDA0t0JLfKvID0RvXOBVryN o3WzMHTz4g8caw8AaWREZjLztAG773b2qdZGa5qjOz8H3Wkadf3WjRJLBeK2GMsgYSEemOAfatKd loa0uVNx6nbVqbhQAUAFABQAUAFABQAUAX79yQox8pGQaSArwzmNSNxA6gY70wJliju0aQkq+efS kBXmgaIKf4W6UARUwCgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgACRGaKWSGORom3JvUHafUe hpNXCxmXegxT6bqYurP+0Yrm43xRR/6yHOMkMehB5qGjNx0d9Tzrw/4VhuNRvhPeSaffWUhktIbi ENuVRySD17Dv06HArNR18zGMFd62Zn6jrker6vbfa9Ms/wDnkGQGMMemTg9e/GOnSi990Lm5nqit Z30Fl5Znb+0JJn8p4bhvMWOFSQOpznjjGMAD14SfcSk7K+522gWuk3q3OnzJLJJFdt9hh3MADtz8 xH8JPqKqKWxpBR2+4wtMh13w+1zZQ3MVkl6rxpOTkKy4zyuSOQRnJ9e1JXW5MeaOj0uY0en32lC3 8RSTg7LjhWmJeRhySMEDaehOO/NLbUizj7yPZ9Nv4dT063vYD+7mQMPb1H4HiuhO+p2J3Vy1TGFA BQAUAFABQAUAFAD3leTG45wMCgBlADk6/ewO9AE8915qKoGMUgK1MAoAKACgAoAKACgAoAKACgAo AKACgAoAKACgAoAKAKeoy6jHHbnTnYMLhDKARymfm61Mr9CZX6FrV4bDUUguryc2gtJBIZVOCw/u 564NKS6sJJbs8v8AEVno0s9zLBHB9quLjbAlpOGHbDFR1yePaspI55xRw9/p8+marNbzqy3MZCsC TkAjOM/mah6aMyldaM9P+G2utPrCw3AhZ3tyvmeXtZNp6ZHB4rSD1N6U7uxrXtjLrGrXOmYXSYbK YXVmyrkzjB3nryDwfbFVvoXu3HY4Ga6g8QW2qLKbWS+hnVoHd9i7MH5UUZA5A4qN0zF+8n3NT4Y+ Iis0mh3Hyg/vIMsDgnJK/wAvxzV05aWNKMtOVnp9am4UAFABQAUAFABQAUAFABQAUAFABQAUAFAB QAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQBHPBHc28kMsUcqOpBSRdyn6ikwZ4DDZaidRuWtsi SzBkZV4KhTznn1I6frXNY4OV32Oj1/X9M13RJNVGleTqs5WG5nOWWRQoK45wrEAHjnA61Td0aynz Rv1OTg1C4gt5ovMdFLEhVGASM5J6kevHao6GXSyOl8J+N73Tr+GO4tzf2sIaMDyvMkCkDIQnkd+M 456VcW7mlOTTSZlaDcRp4rjuVuE023e4MmVTIiB5+6Ovbrn9KV7yEpXnfY9E8QTWI8S2OpW1k1pd reNbANGNt2Bj5+OhGePXPtxbte5rK3MmlqdrHLHMm+J1dexU5FbG46gAoAKACgAoAKACgAoAKACg AoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAI7icW1vJORkRqWx64oBnjHiTxVbXv iJdU02DyJSgWRWO4MehzjtXM5Xd0ccp8zuivpM+palcJommPDGZZPOMYQeW7jK5Hp0AoTb2BNvRd Sxf6fqOm6lNaXNskdyVDx71G0AsNw9OlD0B6aMhvJtNkWCBFjU7I1dwCoJDkN1/P8RTbiU3ArXMW kwTo0ExY7hgiQnPBJ+gBxRpfQPdvodJqs9s+iw3EUrtfW7LI5RjwWVRkA9enX6U5NWuhzknG63O0 8EQ2cmiSXNlNcuu4BvNYEFsZJX8TVwNKSSWh0VaGoUAFABQAUAFABQAUAFABQAUAFABQAUAFABQA UAFABQAUAFABQAUAFABQAUAFABQAUAFAHM+PNTOm+F5xHL5c9wRDHgAk5PzdfbNRN2iRUdos8i0q Wytrd3uoFkecHZL3iBP90YyfToP51htucmi3LvhTUbXTtYZp7Nru0khZDCGIcIc5YHqD+XFEXrqF N2equep3Hi/TbnTLjyjJZ2cdsggvI8GTOQCg3dx9a15tDo5007bHiusQyDVLjbO0hS4f944GSM5y QQfmIPb9aye9jnfxNGp4ZEOleKrM6lGJUSUeckgIyD0JGe2c9KE7PVDi0pK6PXdZ0Cx1yMnUI/7M uLmNoI4IwhaTYcq24DnoOPp+OrV9zocVLfQ86+HNuYfFsiyXKxiJWjWDJG4/TJ549aVPczpfFY9h rY6QoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAPMvif Ml5cx2SuEks4ftB3chtzAAfmtZVDGtqef/2LqBIZ4JNqg7doAxkgevTntUNMylGXY09LuNV0yz2W dvEJZI8CZgCxUrg9eecH8qFdBHmRkSechWKQyqQ+F6bVIP3j6DNQZamro2nw6hfW51C9SKAsDNyc qiqDkj36D6U0l1KSWl2e5WXhm0XULS9tI7Z7A24VllhBdj2bJrax1qOqa2Oj3W005QGF54eSOC0e Rx7jNUWeAra32kfFAQXMLoftgZGQYBVyTkexzj8TWW0zlacaiZ7L0rc6goAKACgAoAKACgAoAKAC gAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAPN/iXpclsP7YgcFZikE6sOijkEHp yQBnGfesqi6mNWP2kZUt/BbeHbSWWJJ9XuRiBeyqWHJGeuRxSvp5i5mo3e5P4Q0qbURI96/2KK02 GaN0xJMvzZAY9T0496S1FG7foZusizlWWd7yY3ENyUs7eRipiQ5Yk/N78fnSbIbtd3JItHht7PT7 6J7eDz52Vp5nzJGef4em3AByAPr2Ct1FbRNHrWhWVw+l6LJYagFggLG5UbmE2R2J9+a1S0R0RWis yXTrO807VtU1CWxtLe3mLMxWRnllIPynJOFBBPHrQlZ3Gk02zgfiVcTXM1nftYmLdacrcISU+fPy 4P3v/r1EzGrrr5Fv4c+I5tWsJdPutzT2iqVkbJ3oR3J7g8VdOV0aUpOS1O4rQ1CgAoAKACgAoAKA CgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgDI8TwzXHh+6SCBJ5NoIjZc7sH PA9fSplsTNXizwcXN5YXw2lobq3ILbwQYyOR2rnaszjaad2er2t3p/iCC3F1Z3Zvp9kM88o4gl2A ghPfufUVpub6S3OX8WaSl5rlzd6UkktrblRdSGMqofIXrn2/Wpkr7GdRczbRh3VlctpouEH2kFyo fcCwVFAwQDwMc/lUtaEtNo9Z+H+v3SaFpdpexoVnkaCAqQu1VHUjOTmtYvQ6ISdlfqVNe+IAe5ut Pn04iyKeVLHNlZckE5x6YApOfQmVXW1tC9pOqxWfw2luNVjguDZoY9jENkH7oJP1FNP3dRxlaGvQ 808Cay+meIgmZRa3bbWREzu2rhTUQlZ2Mqc7Sse110HWFABQAUAFABQAUAFABQAUAFABQAUAFABQ AUAFABQAUAFABQAUAFABQAUAFABQAUAFABQB5D490CLSNYTUhua3upcuqEhl6Zx2PftmsJx1uctW FpX6HWQtA13a3+iahp1tdfcjihwPNtsgAuX/AIxhvfj60+t0XbW8d/0OQ1rWrj/hMJYbaae502K6 DG18zMcmW+YED1x3BFS3qRKT5tNj2hvDujC2uCunWsJnjIdggG0EYOD2/DFa2R0csexw/wAP/Ctz petahcA2txbpmNJWG47x0ZT6YJBqIKxnTi032KF74MuvENvf3mnalbSXomPmwwFlUsw+fLE59D+G PquW5PI5JtMoX+mPoNo9t4pgkuQ+I7aRJ8AD+Jsgc444I6UmrfES1y/GcTquk3Gmm1v47oyrcg3K yRN8wOcHPcHgHqec+tLVakO6sz2jwjq51fQIJZJhJcINkpAwc+/4YreLujqhLmjc3aosKACgAoAK ACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAMbxPoq69oc1oAvm/eiL dmH+IyKmSurEzjzRseO2fhHVr6bUhbqUntE3SxNyCQwGAegJwT+dYKLenY5Ywbun0O8+GkDR+H9X ltbeGbV42XbDLg9Bnv8AjVQ2di6Pwtrc7fxdcZ8FTfa7n7BPNEAVyCS+MlB69xxVy2Naj93XQwfD 3iCIWaWFgqxB4NrJFH+8hcD5pGHcdKSfQmL0sjb8E32jXNjMdNu3mnlkMtwJV2Pv6E7fQ4zx6inG 3QuDj0dx3iTUfCsWoRWmu+SZ2iO3ehbap45x0zRJrqKbgn7xx/jjw/o/hrTTcWjpFNcTKyRum75V B+VfbJB5qZJJGdSMYq5i/DTWJm1q7s7h1P2lTKCGHLDg8DpwR+VOm9Wh0pXbR6nWpuFABQAUAO8t sA7Tg80ANoAKACgAoAKACgAoAKACgAoAKAGNJtcLgfjQA0zDHyjNACtKF4PXGaAHqSVBIwfSgBaA CgAoAKACgAoAKACgAoAKAMW9gGji+1O02h7gq0/mt8oCgjIHrUvTUhrlvJHis2t6gbq4nguniaVt 7iP92rAcgELgeuOOtc92cjk7mza6m/iSW1stf1SdrQB3VpZictg7T3x6d8fjTTbeo1JydpHceEvC lj5T2cYuY1l+cX9lOMMF4MZbHr2x+dWkbRjfT8hdN8A263stlLDeQ6jbgyxX8TYgGTlFXPpk9s5J 5oURxp206lLWfA2vXepySX+rifFrl5XAO8LztVQMnpnpzScW92TKnJvVm74z8LyeJfCGnyWEzBrS EMoumILptH3vfgdfeqlG6LnDnirHmvwxiVfF8aFioSGZlCH5ScqOf8KVPczpfEz2ytjpCgAoAlVU kIywQ989KALi3cECiNcsB39aVgM6mAUAFABQAUAFABQAUAFABQAUARvIiEbuuM9KAANGfSgBSYz1 IoAfQAUAFABQAUAFABQAUAFABQAUAZ2t6dNqmmvbW921rISCJAM/mKTVxSV0eY+LfBI0LRVu47rz n3bXZ1OckE8Y7cdKylCyuc86fKrjrGTwneabpcc/20TrbsgRGVirdQcdSMtgfTvzS91iXIzsfCc2 ryW8WiR3csavB5pkMYWS3APA98+9NX2NIOT0O9tLe30TSyj3DmGLc7SzvkgE5OSavY1VookFvZ3U 8F+ESSRU/dSjnCn0o8wsnqYfj3XpfD3hae6gRWnkIij3gbQW7nNEnZEzlyq55V8LALjxFe3OxhiE 8kYGWbJx+NRT3ZlR3Z65Wx0BQAUAFABQAUAFABQAUAFABQAUAFABQAUAFADWRWPIzQA3yUznHfNA AYVPr+dAElABQAUAFABQAUAFABQAUAFABQAUAY3ijRD4g0CewR1SRiGRmHGQc849en40mrqwpK6s Y/wp0KzEN9d3dukt7BdFIpSd6hcA5X33buevSsqcUY0YJK/U7G2MGk64bdme6vL92dpFjUGNAOA2 O3aq2ZorJ+puyRxzRtHKivGwwysMgj3FUWNaEfZmhiPkjYVUoANnGBj6UAfPfxIl1PTNYfR7jVru 5shIkpE8oJZiucgHJwM4x7evNZSvsc0203G9zZ+E8YNzqUrook2IMqoAIyeeKql1KobM9QrU3CgA oAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgDN8P6JZ +GzMbEPmU5bexP4VKikTGCjsXNF0501u+1W+kEk8r7ICGJ2R4Hy4+opctncSjq2zR02LUbDS5RqE 322dHdkKDBZew+tCv1GrpakV1qt2bM7LC4hd7V5fMYAiJgOFPvRcafkeO+K9OgvtaneWZ3uxIIzN I3Oc7WxjsCNo+lRJGNSKNbwDpt9pn2maIfurnywVZDwxbBPHsc/hVQVkVSi4o72ylmmhZ5oyhyMA gj+FT/MmtDUs0AFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAU AFABQAUAFAEtvL5Uyt26GkBtA5GRSAqzX0SWVzcRMJhArMyoe6jOKAufOXhmwvPE3iSKKWN9ok8+ 5LbsgZBznpkgntWMVeRyQjedz3dEWNQqKFUdABXQdYtABQAUAFABQAUAFABQAUAFABQAUAFABQAU AFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAF67vP7OtPtbFntkUFgoy1S9BN2VyS2N hBbs8CIiTHewAwST6iiwJI5TV9AuIr9tV8P3P2Z55FW5iOAjqBjgduKnld7ohwd7xNmtDQKACgAo AKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAFyc YycUAIpDcggj1FAGfp2l/Ybi7uXuHnnuX3M7DGFAwox04FJKxKjbU0KZQUAFABQAUAFABQAUAFAB QAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAGPqviOx0zT3ulkS6KkARw uGOc45x0HvUykkrkymoq5R0/xJquoGaRfD1zHCirtEpKMzE843AZGO4oTfYSlLsTX9hruo6o6C/W z00AbfJ/1jHHOTj1oabYNSb3sjXsbKLT7YQQlyuSxLNkknqaaVikrFmmMKACgAoAKACgAoAKACgA oAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgCK5t47u1lt5gTHKhRwDjI IwaAMqw8NabpIxZWihiDudzuLHOcnP0H5CpUUtiVCMdkahEu4YPGaooVVkDDnjjNAEtABQAUAFAB QAUAFABQB//Z datasetEPSG6.12(9.2.0)