Tank Facility - All Locations & Lat/Long Information (by County) [November 7, 2007]
Excel spreadsheet of the regulated and unregulated facilities registered with the Department and tracked for active storage tanks, storage tank history, or cleanup activity. Information includes facility identification number, site location information with latitude/longitude, and facility type and status. Data is taken from the Storage Tank & Contamination Monitoring database.
FLORIDA DEPARTMENT OF ENVIRONMENTAL PROTECTION STORAGE TANK AND CONTAMINATION MONITORING DATABASE CODES LIST <ftp://ftp.dep.state.fl.us/pub/reports/codes/fs_codes.pdf>
SPECIAL NOTE: This dataset was created from a database dump, which included regulated and unregulated tanks, not the spreadsheets by county, which include only regulated tanks, and are available at: <http://www.dep.state.fl.us/waste/quick_topics/database_reports/pages/stcm/storagetank/stcm_allll.htm>
Like previous datasets, except this dataset includes unregulated tanks as well as regulated tanks, also this dataset includes tanks that contain petroleum as well as other substances. The database provides more point locations and latitude and longitude for every point. The county spreadsheets, available online, only contain regulated tanks and do not contain as many latitude and longitude coordinates.
What is Geocoding? Geocoding is term used to describe the act of address matching. Geocoding is the process of finding a geographic location (x, y point) for an address (such as street number and name, city, state, and ZIP Code) on a map. Geocoding is based off the typical address scheme for the US, in which one side of the street contains even house numbers while the other side of the street contains odd house numbers. The geocoding process uses an algorithm to find the geographic location of addresses. First, a street segment is identified using the zip code and street name. Next, the geographic location of the address is matched using the building number to determine how far down the street and on which side of the street the building is located.
Geocoding Accuracy The locational accuracy of geocoded addresses may vary from urban to rural areas due to the algorithm used to generate the geographic locations of addresses. The algorithm assumes that the size of parcels are equivalent along a road route. This assumption tends to be more consistent in urban areas, where the size of parcels vary less than in rural areas. Consequently, the results of geocoded addresses in urban areas are usually more reliable than those in rural areas.
For example, the locational accuracy of rural addresses can be slightly off because some parcels along a rural route may be 15 acres while others may be 2.5 acres, but the geocoding algorithm assumes that the addresses are distributed evenly along the route.
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>
All fields were standardized to match the existing dataset
Fields were added X-Double Y-Double COUNTY-Short FAC_ID-Double, length of 14 FAC_NAME-Text, length of 71 FAC_ADDR-Text, length of 45 FAC_CITY-Text, length of 25 FAC_ZIP-Long, length of 7 FPHONE-Text, length of 20 FAC_STAT-Text, length of 10 FAC_TYPE-Text, length of 5 TYPEDESC-Text, length of 32 DESCRIPT-Text, length of 32 FGDLAQDATE-Date AUTOID-Long
Fields were calculated X=X coordinate of point in meters Y=Y coordinate of point in meters COUNTY=[CC_COUNTY] FAC_ID= [FACILITY_I] FAC_NAME=[NAME] FAC_ADDR= [ADDRESS1] FAC_CITY= [CITY] FAC_ZIP=[ZIP5] FPHONE= [old_petroleumtanks_oct07.FPHONE] (The FPHONE field in a previous dataset that was provided to the GeoPlan Center. Some fields have blank values) FAC_STAT= [FSC2_FAC_S] FAC_TYPE= [FTC1_FAC_T] TYPEDESC= [DESCRIPTIO] DESCRIPT=TYPEDESC FGDLAQDATE=11/07/2007 AUTOID=[FID] +1
A number of fields were also deleted to maintain consistency. These included CC_COUNTY_, FACILITY_I, NAME, ADDRESS1, CITY, SC1_STATE_, ZIP5, FSC2_FAC_S, FTC1_FAC_T, DESCRIPTIO, OID_1, FACSUB_1, Cnt_FACSUB_, Min_AUTOID; many of these were in the original dataset and were not required after new standardized fields were added and calculated based on their contents.
Added a "Dummy" field to calc [FAC_NAME] & " [" & [SC5_SUBSTA] & "--" & [Tank_Count] & "]" Found longest record from the "Dummy" and added Descript based on the expression above. Deleted "Dummy" field Upcased fields