20240408
21591600
1.0
FALSE
FDOT_Five_Yr_Work_Program
002
59490.849500
793804.684500
62975.153400
781198.818000
1
Projected
GCS_North_American_1983_HARN
Linear 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["NAD_1983_HARN_Florida_GDL_Albers",GEOGCS["GCS_North_American_1983_HARN",DATUM["D_North_American_1983_HARN",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Albers"],PARAMETER["False_Easting",400000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-84.0],PARAMETER["Standard_Parallel_1",24.0],PARAMETER["Standard_Parallel_2",31.5],PARAMETER["Latitude_Of_Origin",24.0],UNIT["Meter",1.0],AUTHORITY["EPSG",3087]]</WKT><XOrigin>-19550100</XOrigin><YOrigin>-7526400</YOrigin><XYScale>225743210.67916927</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-1041.9043234915948</MOrigin><MScale>4194304001953.124</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>3087</WKID><LatestWKID>3087</LatestWKID></ProjectedCoordinateSystem>
20260331
13493600
20260331
13493600
50000
5000
FGDC
Sam Palmer
University of Florida GeoPlan Center
GIS Specialist
n/a
431 Architecture Building
Gainesville
FL
32611
data@fgdl.org
ArcGIS Metadata
1.0
0.000
Florida Geographic Data Library (FGDL)
431 Architecture PO Box 115706
Web site: http://www.fgdl.org
Gainesville
FL
32611-5706
US
None
DOWNLOADABLE DATA
https://www.fgdl.org/
File Geodatabase Feature Class
Florida Department of Transportation Five Year Work Program - 2023
2022-01-01
University of Florida GeoPlan Center
Gainesville, FL
University of Florida GeoPlan Center
Vector Digital Data Set (Polyline)
State of Florida
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>This data set contains the FDOT Adopted Work Program (2024 - 2028) roadways. This data was created based on linear referencing, using the five year work program spreadsheet in conjunction with RCI Basemap Roads. Roadways are designated in one of at least 16 phases. Of particular importance to ETDM are the following 7 phases: Construction (CST) phase - Project actively in construction; Design Build (DSB) phase - Project in a condensed process where the firm completing the design also construct the project; Environmental (ENV) phase - Projects where multiple alternatives are being evaluated; Project Development and Environment (PDE) phase - Projects where multiple alternatives are being evaluated; Preliminary Engineering (PE) phase - Project has single preferred alternative that has been selected, and during this phase, the project is actually being designed; Planning (PLN) phase - Project is in the concept and information gathering stage; Right of Way (ROW) phase - The Right of Way office is identifying and acquiring right-of-way for construction of the project.</SPAN></P></DIV></DIV></DIV>
The work program spreadsheet is used by the FDOT to report on its Work Program of road, bridge, freight logistics and passenger operations, maintenance, planning, research, and other transportation related projects. The Department strives to maintain the stability of the Work Program; however, updated cost estimates, revised schedules, and other events outside the Department's control, such as changes in revenue estimates, all necessitate modifications to the Work Program during the course of the year. Users of the data contained in the accompanying file should bear this in mind and may wish to consider confirming project specific information with the Department's District Offices (Or at our Work Program Application site) for changes that may have occurred since the last posting.
Sam Palmer
University of Florida GeoPlan Center
GIS Specialist
n/a
431 Architecture Building
Gainesville
FL
32611
work program
ISO 19115 Topic Category
transportation
transportation
work program
FDOT only
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.
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>FDOT only</SPAN></P></DIV></DIV></DIV>
publication date
2023-01-01T00:00:00
-87.567513986
-79.874497155
24.512308932
31.034263923
Esri ArcGIS 13.5.5.57366
1
-87.567656
-79.874333
31.039180
24.512364
This data is provided 'as is'. GeoPlan relied on the integrity of the original data layer's topology
GeoPlan center acquired the work program spreadsheet in August 2023 from William Jimenez of FDOT. The Item Segment field was used from the above spreadsheet to execute linear referencing to create line work from the spreadsheet records. The event theme used for the linear referencing command was the RCI_GAMING layer. This layer is generated daily based on a csv file that is received daily from FDOT work program and is created via linear referencing with RCI Basemap as the event theme. The particular csv file that was used was: GISROAD20230801.CSV Records were removed that had BEGIN_MILE = 0 AND END_MILE = 0 (Please note: COUNTY_CODE = 99 are now present, in FDOT_WP_2014 and earlier these records were removed) The RCI Basemap that was used was from July 2023.
2023-08-03T00:00:00
GeoPlan
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
FDOT_Five_Yr_Work_Program
fdot_wp_2023
FDOT
Feature Class
0
OBJECTID
Internal feature number.
ESRI
Sequential unique whole numbers that are automatically generated.
OBJECTID
OID
4
0
0
Shape
Feature geometry.
ESRI
Coordinates defining the features.
Shape
Geometry
0
0
0
DISTRICT
This field indicates the District where the project is located.
FDOT
DISTRICT
String
10
0
0
FundingTier
FundingTier
Double
8
0
0
WPITEM
This field is the identifying sequential number assigned to each work program item. This number is assigned by the system. This is a six digit field in character format, with numeric data.
FDOT
WPITEM
String
10
0
0
WPITMSEG
The Item segment may be used to segment a project for scheduling or funding reasons. Item segments may be used when the boundaries of construction projects are different from the PE and/or ROW phase of a 'Project', or it is desirable to use the same item number to track projects along a corridor. For example, the department may want or need to let different segments at different times. Item segments may not cross county lines, except for district wide items.
FDOT
WPITMSEG
String
10
0
0
ID
ID
String
255
0
0
COUNTY_CODE
County Code
FDOT
COUNTY_CODE
String
10
0
0
COUNTY_NAME
County Name
FDOT
COUNTY_NAME
String
20
0
0
DESCRIPTION
This represents the Local Name of the roadway or other.
FDOT
DESCRIPTION
String
80
0
0
BEGIN_MILE
This represents the beginning milepost to 3 decimal places.
FDOT
BEGIN_MILE
Double
8
0
0
END_MILE
This represents the ending milepost to 3 decimal places.
FDOT
END_MILE
Double
8
0
0
MIN_LENGTH
MIN_LENGTH
Double
8
0
0
This field describes the length of the project to 3 decimal places.
PROJECT_TYPE
Type of Work
FDOT
PROJECT_TYPE
String
40
0
0
PHASE_NAME
Phase name. Three letter code and written description.
FDOT
PHASE_NAME
String
40
0
0
FISCAL_YEAR
The year range of the record (Department's Fiscal Year Range is July through June. Example: Fiscal Year 2013 = 7/1/2012 - 6/30/2013)
FDOT
FISCAL_YEAR
Double
8
0
0
AMOUNT
The dollar amount (to the nearest dollar) of the record.
FDOT
AMOUNT
Double
8
0
0
BOXNAME
Further Description of the Project.
FDOT
BOXNAME
String
40
0
0
CATEGORY
Undefined by Source
FDOT
CATEGORY
String
30
0
0
WPITEM_SEG
Item number and item segment combined. This field created by concatenating WPITEM and WPITMSEG.
GeoPlan
WPITEM_SEG
Double
8
0
0
PHASE_YEAR
Three letter phase code concatenated with year
GeoPlan
PHASE_YEAR
String
8
0
0
PHASE
Three letter phase code.
GeoPlan
PHASE
String
3
0
0
LOC_ERROR
This field is created from the linear referencing command. Values indicate if there were errors during linear referencing process.
GeoPlan
LOC_ERROR
String
50
0
0
DESCRIPT
GeoPlan added field based on phase name
GeoPlan
DESCRIPT
String
50
0
0
FGDLAQDATE
Date GeoPlan acquired data from source.
GeoPlan
FGDLAQDATE
Date
8
0
0
SHAPE_Length
Shape_Length
Double
8
0
0
Length of feature in internal units.
Esri
Positive real numbers that are automatically generated.
dataset
EPSG
6.12(9.2.0)
0
20260331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==
Simple
FALSE
0
TRUE
TRUE