The land area of Mint Hill, NC was 24 in 2018.
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 -
Geographic and Area Datasets Involving Mint Hill, NC
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2020 Census Tracts to 2020 NTAs and CDTAs Equivalency
data.cityofnewyork.us | Last Updated 2024-07-05T13:45:38.000ZThis file shows the relationship between New York City’s 2020 census tracts, 2020 Neighborhood Tabulation Areas (NTAs), and Community District Tabulation Areas (CDTAs). 2020 census tracts nest within 2020 NTAs, and 2020 NTAs nest within CDTAs, so each census tract is listed only once. Note that CDTAs sometimes cross borough boundaries, and therefore will not add up to borough totals for the Bronx, Queens, and Manhattan. As they are nested within CDTAs, NTAs will likewise not add up to borough totals. Also note that census tracts in New York City’s water areas are excluded from this file.
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Environmental Sensitivity Project (2015)
data.edmonton.ca | Last Updated 2022-12-13T23:03:09.000ZHistorically, the City of Edmonton has managed ‘natural areas’ within the North Saskatchewan River Valley and the Tablelands separately, guided by inventories such as the Ribbon of Green and Geowest (1993). Over the past decade, City policy has shifted to manage natural areas with consideration of their role within an ecological network. Today, a goal of the City is to protect, preserve and enhance a functioning ecological network throughout the city limits. This network should include lands in both the river valley and the Tablelands. To further this goal, a model was developed in 2015 for determining environmental sensitivity scores across the entirety of the city. This model guided the collection of several digital data layers with coverage across the entire study area (including several ecological assets, threats to assets, and development and cultural constraints). Data layers were then used to develop spatial outputs that summarized the distribution of these assets, threats and constraints. These base layers have been compiled into this dataset to help inform planning, development and conservation throughout Edmonton. Environmental sensitivity analysis incorporated recent mapping of the ecological network of native and non-native vegetation, streams, wetlands and other waterbodies as much as possible, with practical limitations. The City’s urban Primary Land and Vegetation Inventory (uPLVI) and remote sensing data used for this assessment were completed in 2015 and 2013 respectively, which is relatively recent, but not current. Similarly, infrastructure data (roads, subdivision development and stormwater facilities) provided varied in month of acquisition from 2015. Some discrepancy between mapped and actual features may result, due to loss and changes from ongoing development activities.
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Land Use_data
opendata.utah.gov | Last Updated 2024-04-10T19:40:16.000ZThis 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.
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RSBS SMO: Part 2 of 2, New York State Residential Statewide Baseline Study: Single and Multifamily Occupant Telephone or Web Survey
data.ny.gov | Last Updated 2019-11-15T21:50:04.000ZHow does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. This is part 2 (contains: Clothes Washing and Drying; Water Heating; Home Lighting; Pool and Spa; Small Household Appliances; and Miscellaneous Equipment) of 2; part 1 (https://data.ny.gov/d/3m6x-h3qa) contains: Behavior and Demographics; Building Shell; Kitchen Appliances; and Heating and Cooling. The New York State Energy Research and Development Authority (NYSERDA), in collaboration with the New York State Department of Public Service (DPS), conducted a statewide residential baseline study (study) from 2011 to 2014 of the single-family and multifamily residential housing segments, including new construction, and a broad range of energy uses and efficiency measures. This dataset includes 2,982 single-family and 379 multifamily occupant survey completes for a total of 3,361 responses. The survey involved 2,285 Web, 1,041 telephone, and 35 mini-inspection surveys. The survey collected information on the following building characteristics: building shell, kitchen appliances, heating and cooling equipment, water heating equipment, clothes washing and drying equipment, lighting, pool and spa equipment, small household appliances, miscellaneous energy consuming equipment, as well as behaviors and characteristics of respondents.