The land area of Pocatello, ID was 32 in 2014. The land area of Twin Falls, ID was 18 in 2014.
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 Pocatello, ID or Twin Falls, ID
- API
Iowa Geographic Names
mydata.iowa.gov | Last Updated 2024-09-20T22:00:21.000ZThis dataset provides the geographic names data for Iowa. All names data products are extracted from the Geographic Names Information System (GNIS), the Federal Government's repository of official geographic names. The GNIS contains the federally recognized name of each feature and defines its location by State, county, USGS topographic map, and geographic coordinates. GNIS also lists variant names, which are non-official names by which a feature is or was known. Other attributes include unique Feature ID and feature class. Feature classes under the purview of the U.S. Board on Geographic Names include natural features, unincorporated populated places, canals, channels, reservoirs, and more.
- API
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.
- API
OLAS/SCL WASH Household Survey Dataset
mydata.iadb.org | Last Updated 2024-09-20T19:54:24.000ZThe OLAS/SCL Household Survey Data Set contains 47 water and sanitation related indicators generated from microdata from national household surveys throughout the region. The data set contains information from 2003-2022 for 22 countries throughout Latin America and the Caribbean. Indicators are provided in terms of household percentage and total households that fall into each category, and can be broken down by various socioeconomic dimensions, including area (urban or rural community), income quintile, migratory status, ethnicity, and disability status. This dataset is the result of a collaboration between INE/WSA and SCL, and is a subset of the larger IDB SCL Indicators dataset.
- API
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.
- API
2024 Connecticut Parcel and CAMA Data
data.ct.gov | Last Updated 2024-10-14T03:04:25.000ZThis dataset contains statewide CAMA information for the parcels in Connecticut, created by the GIS office in accordance with CGS Sec. 4d-90-92 and 7-100L. It is part of the 2024 data collection effort, which involved gathering CAMA data from all municipalities through the Council of Governments. The parcel layer is provided as a zipped folder containing a File Geodatabase, encompassing information from all 169 towns organized into a seamless parcel layer. The CAMA dataset include details about real property within Connecticut towns, which can be linked to the parcel data using a GIS software. The linking is facilitated through a designated column called ‘link’ that contains unique codes for each town and the designated values provided by the assessors and COGs. While the data was gathered from Connecticut towns and submitted to CT OPM by the COGs, it’s important to note that not all towns adhered to the established schema. As a result, some attribute names, primary and secondary keys, naming conventions, and file formats were inconsistent. Cleaning and reorganization were performed to align the data with the state schema, though some limitations remain. This file was generated on 09/28/2024 from data collected throughout 2024. Additional Note: Some towns were unable to verify which entries were suppressed pursuant to Connecticut General Statute Sec. 1-217. As a result, all related information has been fully suppressed. The owner and co-owner fields have been replaced with "Current Owner" and "Current Co-Owner," respectively, and the mailing address has been updated to reflect the location address.
- API
Feed the Future Malawi Interim Survey in the Zone of Infuence, Children's File
datahub.usaid.gov | Last Updated 2024-07-12T09:57:38.000ZThis dataset contains records describing for all children under 5 years of age sampled during the 2015 Feed the Future Malawi Interim Survey in the Zone of Influence. The survey was designed to monitor program performance by periodic assessments of a number of standardized indicators. A total of 1,021 households were interviewed, which provided data for the target sample size of 1,007 households and ensured the sample is representative of the seven districts covered in the interim assessment. This dataset includes data from Module I for children’s anthropometry and infant and young child feeding practices. The anthropometry Z-scores were calculated in SAS during the data management process, using the World Health Organization (WHO) SAS “igrowup” package. The unique identifiers for this file are pbs_id + idcode.
- API
RVA Community Survey 2014 Data GEO
data.richmondgov.com | Last Updated 2023-03-31T18:17:55.000ZDataset is the anonymized responses from the 2014 Community Survey conducted by ETC Institute. Random surveys were sent to residents across our city to create an equal representation of at least 150 responses per Council District. Most answers are scored: 5- Very Satisfied 4- Satisfied 3- Neither 2- Dissatisfied 1- Very Dissatisfied 9- Don't Know While Most Important ranked questions refer to the preceding questions services and items ordered by letter. Not all Ranking questions are required and might not equal total number of surveys.