<http://www.dep.state.fl.us/waste/quick_topics/database_reports/pages/misc/dryclean.htm> [February 2, 2007] Dry Cleaning Facilities All Current Owners (by County) Excel spreadsheet of the dry cleaning facilities registered with the Department. Information includes facility identification number, site location information, related party (owner) information, and facility type and status. Data is taken from the Storage Tank & Contamination Monitoring database, the registration repository of dry cleaner facility data.
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>
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>
Dry Cleaning Facilities All Current Owners (by County): <http://www.dep.state.fl.us/waste/quick_topics/database_reports/pages/misc/dryclean.htm>
The original spreadsheet contained multiple listings for the same address. These multiple listings contained information such as property owner, account owner, tank owner, facility owner, and tank operator. Where possible only the point with property owner information was used. Some unique points existed only with other information, so those points were used. As a result a majority of the points are listed with property owner information. There are a number however, with other listings in the OWN_ROLE field. If you are looking for other owner role information please see the original spreadsheet available from the FDEP: <http://www.dep.state.fl.us/waste/quick_topics/database_reports/pages/misc/dryclean.htm>
- All fields were upcased - A DESCRIPT field was added based on TYPEDESC - The field FGDLAQDATE was added based on date downloaded from source
1 ALACHUA 2 BAKER 3 BAY 4 BRADFORD 5 BREVARD 6 BROWARD 7 CALHOUN 8 CHARLOTTE 9 CITRUS 10 CLAY 11 COLLIER 12 COLUMBIA 13 DADE 14 DESOTO 15 DIXIE 16 DUVAL 17 ESCAMBIA 18 FLAGLER 19 FRANKLIN 20 GADSDEN 21 GILCHRIST 22 GLADES 23 GULF 24 HAMILTON 25 HARDEE 26 HENDRY 27 HERNANDO 28 HIGHLANDS 29 HILLSBOROUGH 30 HOLMES 31 INDIAN RIVER 32 JACKSON 33 JEFFERSON 34 LAFAYETTE 35 LAKE 36 LEE 37 LEON 38 LEVY 39 LIBERTY 40 MADISON 41 MANATEE 42 MARION 43 MARTIN 44 MONROE 45 NASSAU 46 OKALOOSA 47 OKEECHOBEE 48 ORANGE 49 OSCEOLA 50 PALM BEACH 51 PASCO 52 PINELLAS 53 POLK 54 PUTNAM 55 ST. JOHNS 56 ST. LUCIE 57 SANTA ROSA 58 SARASOTA 59 SEMINOLE 60 SUMTER 61 SUWANNEE 62 TAYLOR 63 UNION 64 VOLUSIA 65 WAKULLA 66 WALTON 67 WASHINGTON