The water area of Taylor Mill, KY was 0 in 2010.

Land Area

Water Area

Land area is a measurement providing the size, in square miles, of the land portions of geographic entities for which the Census Bureau tabulates and disseminates data. Area is calculated from the specific boundary recorded for each entity in the Census Bureau's geographic database. Land area is based on current information in the TIGER® data base, calculated for use with Census 2010.

Water Area figures include inland, coastal, Great Lakes, and territorial sea water. Inland water consists of any lake, reservoir, pond, or similar body of water that is recorded in the Census Bureau's geographic database. It also includes any river, creek, canal, stream, or similar feature that is recorded in that database as a two- dimensional feature (rather than as a single line). The portions of the oceans and related large embayments (such as Chesapeake Bay and Puget Sound), the Gulf of Mexico, and the Caribbean Sea that belong to the United States and its territories are classified as coastal and territorial waters; the Great Lakes are treated as a separate water entity. Rivers and bays that empty into these bodies of water are treated as inland water from the point beyond which they are narrower than 1 nautical mile across. Identification of land and inland, coastal, territorial, and Great Lakes waters is for data presentation purposes only and does not necessarily reflect their legal definitions.

Above charts are based on data from the U.S. Census American Community Survey | ODN Dataset | API - Notes:

1. ODN datasets and APIs are subject to change and may differ in format from the original source data in order to provide a user-friendly experience on this site.

2. To build your own apps using this data, see the ODN Dataset and API links.

3. If you use this derived data in an app, we ask that you provide a link somewhere in your applications to the Open Data Network with a citation that states: "Data for this application was provided by the Open Data Network" where "Open Data Network" links to http://opendatanetwork.com. Where an application has a region specific module, we ask that you add an additional line that states: "Data about REGIONX was provided by the Open Data Network." where REGIONX is an HREF with a name for a geographical region like "Seattle, WA" and the link points to this page URL, e.g. http://opendatanetwork.com/region/1600000US5363000/Seattle_WA

Geographic and Area Datasets Involving Taylor Mill, KY

  • API

    Aquatic Biological Monitoring Sampling Locations: Beginning 1980

    data.ny.gov | Last Updated 2024-05-02T15:02:49.000Z

    The Division of Water Stream Biomonitoring Unit (SBU) dataset contains the point sampling locations at which benthic macroinvertebrates, field chemistry, and at some locations, sediment, fish or diatoms have been collected as part of the Rotating Integrated Basin Studies (RIBS) program, Rapid Biological Assessments (RAS), or special studies. The data collected are used for water quality assessment (input to the Waterbody Inventory, completion of the 305(b) report and 303(d) list of impaired Waters) and for track-down of water quality problems. The data set is maintained by the Division of Water, Bureau of Water Assessment and Management, Stream Biomonitoring Unit.

  • API

    GDP by Metropolitan Statistical Area

    data.colorado.gov | Last Updated 2024-10-03T11:07:34.000Z

    Gross Domestic Production (GDP) in millions of 2009 Dollars by industry per Metropolitan Statistical Area (MSA), from 2001 to 2017 in Colorado, as provided by the Bureau of Economic Analysis (BEA).

  • API

    SPDES Multi-Sector General Permit (MSGP) Facilities

    data.ny.gov | Last Updated 2024-08-12T19:36:08.000Z

    The SPDES Multi-Sector General Permit (MSGP), which is administered by the Department of Environmental Conservation (the Department), regulates stormwater discharges associated with industrial activity from a point source. The MSGP covers thirty one different industrial sectors which include activities such as mining, land transportation, and scrap recycling. The dataset displays information on facilities that have active MSGP coverage in New York State. Information included in the data set include the facility’s name, address, contact information, industrial sector(s), discharging waterbody, and location of the facility’s Stormwater Pollution Prevention Plan. For more information, please go to http://www.dec.ny.gov/chemical/62803.html.

  • API

    Land Use_data

    opendata.utah.gov | Last Updated 2024-04-10T19:40:16.000Z

    This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the Northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the Southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe’s Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe’s Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS.

  • API

    Patents by Connecticut Inventors 1800-1890

    internal-ct.data.socrata.com | Last Updated 2024-08-16T15:27:38.000Z

    Connecticut Patents 1800-1890 This list was compiled by Museum of Connecticut History staff in the 1990s, and lists all patents by Connecticut inventors between the years of 1800-1890. This period spans a time of incredible industrial growth and inventiveness in Connecticut, and the Yankee ingenuity on display in these patents helped make the state one of the premier industrial centers of the nation and the world. Many of the pre-1836 patents listed here are only known because of annual lists of patents or patent digests that were published by the Patent Office. The actual records were destroyed in a fire in 1836, and many were never reconstructed. All of the pre-1836 patents are known as the “X-Patents,” and those that have been recovered were given patent numbers beginning with the letter X. Patents up to this time had not actually had numbers of any kind, they were simply known by date and title. About five months before the fire, the Patent Office began numbering their patents; this system, now up to seven digits, is still in use today. Patents found in this dataset can be found using the <a href=”https://www.uspto.gov/patents/search/patent-public-search”>Patent Public Search tool</A> on the U.S. Patent Office’s website. The easiest way to locate a patent is to enter the seven-digit serial number into the basic search. The Connecticut Patents 1800-1890 dataset contains names of inventors, assignees if relevant (inventors could “assign” ownership of patents to another person or entity), descriptions of the invention, patent numbers (if available), and date of issue. It also contains a two-digit class code that corresponds to a general subject area. The subjects are: 01 Firearms, guns, cannons, etc. 02 Metallurgy, metalworking, etc. 03 Woodworking, sawmills, etc. 04 Weaving, sewing, looms, etc. 05 Steam boilers, engines 06 Railroads 07 Ships, boats, canals 08 Clothing 09 Agriculture 10 Hardware, tools, etc. 11 Tanning 12 Rubber 13 Musical instruments 14 Medical 15 Foods and cooking 16 Business and labor 17 Clocks 18 Fireplaces, stoves, furnaces, etc. 19 Lanterns, lamps, and lighting 20 Furniture 21 Household items 22 Doors, windows, drawers, trunks, etc. 23 Grinding mills 24 Stonemasonry, mining, etc. 25 Vehicles, non-motorized 26 Chemical processes? Explosives, distilling, paintmaking, etc. 27 Plumbing, pipes, hoses, etc. and water-powered devices 28 Printing, bookmaking, and paper 29 Engines, coolers, elevators, pneumatics, etc. 30 Ivory 31 Military equipment (non-weapons) 32 Sewing machines 33 Toys, games, and leisure (includes bicycles) 34 Equipment for horses 35 Photography 36 Glassware 37 [doesn't exist] 38 Electricity and electronics 39 Telephones 40 Fire safety 41 Construction 42 School equipment 43 Art

  • API

    Ethiopia Pastoralist Areas Resilience Improvement and Market Expansion (PRIME) Project IE--Household Information: Section 1

    datahub.usaid.gov | Last Updated 2024-06-08T01:11:43.000Z

    This dataset contains the data describing the household roster collected as part of the baseline survey generated in support of an impact evaluation of the Ethiopia Pastoralist Areas Resilience Improvement and Market Expansion (PRIME) Project. In the process of migrating data to the current DDL platform, datasets with a large number of variables required splitting into multiple spreadsheets. They should be reassembled by the user to understand the data fully.

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    Ethiopia Pastoralist Areas Resilience Improvement and Market Expansion (PRIME) Project IE--Household Information: Section 2

    datahub.usaid.gov | Last Updated 2024-06-08T01:45:41.000Z

    In the process of migrating data to the current DDL platform, datasets with a large number of variables required splitting into multiple spreadsheets. They should be reassembled by the user to understand the data fully. This is the second spreadsheet of three in the Ethiopia Pastoralist Areas Resilience Improvement and Market Expansion (PRIME) Project IE--Household Information.

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    Feed the Future-Ethiopia PRIME-Interim Survey-October 2015 through October 2016

    datahub.usaid.gov | Last Updated 2024-07-12T10:15:36.000Z

    This dataset is the second of two interim surveys administered from October 2015 through October 2016 to enable the Feed the Future PRIME project in Ethiopia to monitor program performance by reviewing changes in a number of standardized indicators. These indicators reflect data collected through population-based surveys (PBS) in the geographic areas targeted by Feed the Future interventions, known as the Feed the Future Zones of Influence (ZOI). Each survey was administered to a sample of over 400 households in 17 kebeles (communities) once a month over a 6-month period creating for a total of six rounds of data for each survey.