The land area of Gooding County, ID was 729 in 2018. The land area of Humboldt County, NV was 9,641 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 Gooding County, ID or Humboldt County, NV
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Restaurant Inspections in Tri-County Colorado
data.colorado.gov | Last Updated 2024-05-28T11:01:13.000ZRestaruant Inspection data for food service facilities within Adams, Arapahoe, and Douglas counties in Colorado provided by Tri-County Health Department (TCHD).
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Restaurant Inspections in Tri-County Colorado 2018
data.colorado.gov | Last Updated 2024-05-28T11:01:12.000ZRestaurant Inspection data for food service facilities within Adams, Arapahoe, and Douglas counties in Colorado provided by Tri-County Health Department (TCHD) in 2018.
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SPDES Multi-Sector General Permit (MSGP) Facilities
data.ny.gov | Last Updated 2023-04-14T19:16:46.000ZThe 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.
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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 MOM: Multifamily On-Site Survey, Measure Level, New York State Residential Statewide Baseline Study
data.ny.gov | Last Updated 2019-11-15T22:18:02.000ZHow 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. 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 data collected from a total of 67 on-site inspections of multifamily buildings. Data collected during the inspections covers property characteristics, heating and cooling equipment, water heating equipment, appliances, lighting, clothes washing and drying, miscellaneous energy using equipment, and observable operating behavior. The objective of the on-site inspections was to enhance the residential baseline study with detailed on-site information and, to the degree possible, verify self-reported data from the phone and web surveys. The on-site inspection data is segmented to cover both common space equipment and tenant-unit equipment.
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City of Gainesville 2020 Neighbor Survey - Raw Data
data.cityofgainesville.org | Last Updated 2020-09-01T20:56:17.000ZThis dataset includes the raw results from the City of Gainesville 2020 Neighborhood Survey. Please view the survey, given here: https://tinyurl.com/CoGNeighborSurvey for reference to columns within this dataset. Responses of "9" for questions on a 1-5 scale indicate a non-response or a response of "Don't know".
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Connecticut CAMA Data 2023
data.ct.gov | Last Updated 2024-04-03T16:13:02.000ZThis dataset contains statewide CAMA information for parcels in the State of Connecticut. This dataset was created by the GIS Office as required by CGS Sec. 4d-90-92. This dataset is a result of the 2023 data collection effort, which included collecting CAMA data from all municipalities via the Councils of Government.
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Baseline Study of Food for Peace Title II Development Food Assistance Program in Niger-- Household Sanitation and Maternal Health
datahub.usaid.gov | Last Updated 2018-11-13T05:02:48.000ZThis dataset captures data about the mothers in the households surveyed as part of the Baseline Study of Food for Peace Title II Development Food Assistance Program in the Maradi and Zinder regions in Niger as well as the water and sanitation resources available to the household. It has 200 columns and 7,337 rows. In fiscal year 2012, USAID's Office of Food for Peace (FFP) awarded funding to private voluntary organizations (PVOs) to design and implement a multi-year Title II development food assistance program in Niger. The main purpose of the Title II program is to improve long-term food security of chronically food insecure population in the target regions. FFP contracted a firm, ICF International to conduct a baseline study in targeted areas of the country prior to the start of the new program. The purpose of the study was to assess the current status of key indicators, have a better understanding of prevailing conditions and perceptions of the population in the implementation areas, and serve as a point of comparison for future final evaluations. Results would also be used to further refine program targeting and, where possible, to understand the relationship between variables to inform program design. The study was conducted in 2013, while FFP expects to conduct final evaluations as close as possible to the end of the program five years later.
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Guatemala Maternal health and Sanitation Data: Section 1
datahub.usaid.gov | Last Updated 2024-03-29T19:07:55.000ZData on maternal Health and household sanitation in Segamil and Paisano in Guatemala 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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Baseline Study of Food for Peace Title II Development Food Assistance Program in Karamoja, Uganda--Maternal Health and Household Sanitation
datahub.usaid.gov | Last Updated 2018-11-13T05:02:49.000ZThis dataset captures data about the mothers in the households surveyed as part of the Baseline Study of Food for Peace Title II Development Food Assistance Program in Karamoja, Uganda as well as the water and sanitation resources available to the household. This dataset contains data from Modules F and J of the questionnaire and has 295 columns and 4,766 rows. In fiscal year 2012, USAID's Office of Food for Peace (FFP) awarded funding to private voluntary organizations (PVOs) to design and implement a multi-year Title II development food assistance program in Uganda. The main purpose of the Title II program is to improve long-term food security of chronically food insecure population in the target regions. FFP contracted a firm, ICF International to conduct a baseline study in targeted areas of the country prior to the start of the new program. The purpose of the study was to assess the current status of key indicators, have a better understanding of prevailing conditions and perceptions of the population in the implementation areas, and serve as a point of comparison for future final evaluations. Results would also be used to further refine program targeting and, where possible, to understand the relationship between variables to inform program design. The study was conducted in 2013, while FFP expects to conduct final evaluations as close as possible to the end of the program five years later. The data asset is comprised of six datasets: 1) a description of all members of the households surveyed, 2) data on maternal health and sanitation practices, 3) data about the children in the household, 4) data describing the agricultural practices of the household, 5) data describing the food consumption of the household (broken into 4 smaller spreadsheets), and 6) and a description of the weights that should be applied during the analysis of the other datasets.