The land area of Palm Beach County, FL was 1,970 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 Palm Beach County, FL
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Beach and Creek Monitoring Results
datahub.smcgov.org | Last Updated 2023-07-01T01:00:14.000ZWater samples from natural recreational waters in San Mateo County are sampled each week for concentrations of indicator bacteria including E. Coli, Enterococcus, and Coliform bacteria. If concentrations of indicator bacteria exceed State or County standards, the area is posted to warn users that they may become ill if they engage in water contact activities in the posted area. More information about results and testing can be found on the San Mateo County Health System site: http://smchealth.org/environ/beaches This dataset contains readings from January, 2012 to the present and is updated weekly.
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Water Quality
data.kingcounty.gov | Last Updated 2024-05-14T00:10:12.000ZThis dataset contains water quality samples collected from Puget Sound, lakes, and streams in the region which can be filtered by "Site Type" and "Area". To see where water quality samples are collected, see the <a href="https://data.kingcounty.gov/dataset/WLRD-Sites/wbhs-bbzf">WLRD Water Quality Collection Sites</a> dataset.
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County to CBSA Mapping for Large Metros
data.bayareametro.gov | Last Updated 2022-08-26T07:12:04.000ZData contains counties in the following list of CBSAS (per OMB Mar 2020 definition): Bay Area CBSAs: San Francisco-Oakland-Berkeley, CA San Jose-Sunnyvale-Santa Clara, CA Napa, CA Santa Rosa-Petaluma, CA Other CBSAs: Los Angeles-Long Beach-Anaheim, CA Washington-Arlington-Alexandria, DC-VA-MD-WV Denver-Aurora-Lakewood, CO Detroit-Warren-Dearborn, MI Philadelphia-Camden-Wilmington, PA-NJ-DE-MD Boston-Cambridge-Newton, MA-NH New York-Newark-Jersey City, NY-NJ-PA Phoenix-Mesa-Chandler, AZ Houston-The Woodlands-Sugar Land, TX Seattle-Tacoma-Bellevue, WA Atlanta-Sandy Springs-Alpharetta, GA Chicago-Naperville-Elgin, IL-IN-WI Austin-Round Rock-Georgetown, TX Dallas-Fort Worth-Arlington, TX Miami-Fort Lauderdale-Pompano Beach, FL
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Iowa Geographic Names
mydata.iowa.gov | Last Updated 2024-03-15T22:00:53.000ZThis 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.
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State Parks
data.pa.gov | Last Updated 2023-11-08T21:59:46.000ZPA State Parks Point Locations
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Recreation & History Related Locations Statewide Current Various PA Agencies
data.pa.gov | Last Updated 2024-05-14T07:06:59.000ZVarious Groupings of Services for Pennsylvanians to find a service and information near any given address. These are in the Recreation & History group
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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.
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Liquefaction zones (HESS)
data.bayareametro.gov | Last Updated 2023-06-09T23:59:16.000ZLiquefaction zones for development of the Parcel Inventory dataset for the Housing Element Site Selection (HESS) Pre-Screening Tool. This feature set is a subset of the complete feature set for the San Francisco Bay Region. It only provides features for areas at either High or Very High susceptibility to liquefaction. The features delineate different types and ages of Quaternary deposits for the region and their susceptibility to liquefaction. The data provides a framework for the architecture and history of the Quaternary sedimentary basins, which is used in estimating earthquake shaking. **This data set represents the entire San Francisco Bay Region by combining both Open-File Report 00-444 and Open-File Report 2006-1037 data. The area covered by Open-File Report 2006-1037 was erased from Open-File Report 00-444 and the two data sets were merged. A column has been added to the attribute table to label which report each polygon was originally from. Other than this supplemental information paragraph, all the metadata is from Open-File Report 2006-1037.** This report presents a map and database of Quaternary deposits and liquefaction susceptibility for the urban core of the San Francisco Bay region. It supercedes the equivalent area of U.S. Geological Survey Open-File Report 00-444 (Knudsen and others, 2000), which covers the larger nine-county San Francisco Bay region. The report consists of (1) a spatial database, (2) two small-scale colored maps (Quaternary deposits and liquefaction susceptibility), (3) a text describing the Quaternary map and liquefaction interpretation (part 3), and (4) a text introducing the report and describing the database (part 1). All parts of the report are digital; part 1 describes the database and digital files and how to obtain them by downloading across the internet. The nine counties surrounding San Francisco Bay straddle the San Andreas fault system, which exposes the region to serious earthquake hazard (Working Group on California Earthquake Probabilities, 1999). Much of the land adjacent to the Bay and the major rivers and streams is underlain by unconsolidated deposits that are particularly vulnerable to earthquake shaking and liquefaction of water-saturated granular sediment. This new map provides a consistent detailed treatment of the central part of the 9-county region in which much of the mapping of Open-File Report 00-444 was either at smaller (less detailed) scale or represented only preliminary revision of earlier work. Like Open-File Report 00-444, the current mapping uses geomorphic expression, pedogenic soils, inferred depositional environments, and geologic age to define and distinguish the map units. Further scrutiny of the factors controlling liquefaction susceptibility has led to some changes relative to Open-File Report 00-444: particularly the reclassification of San Francisco Bay mud (Qhbm) to have only MODERATE susceptibility and the rating of artificial fills according to the Quaternary map units inferred to underlie them (other than dams ? adf). The two colored maps provide a regional summary of the new mapping at a scale of 1:200,000, a scale that is sufficient to show the general distribution and relationships of the map units but not to distinguish the more detailed elements that are present in the database. The report is the product of cooperative work by the National Earthquake Hazards Reduction Program (NEHRP) and National Cooperative Geologic Mapping Program of the U.S. Geological Survey, William Lettis & Associates, Inc. (WLA), and the California Geological Survey. An earlier version was submitted to the U.S. Geological Survey by WLA as a final report for a NEHRP grant (Witter and others, 2005). The mapping has been carried out by WLA geologists under contract to the NEHRP Earthquake Program (Grant 99-HQ-GR-0095) and by the California Geological Survey. The original reports and data are available at Open-File Report 2006-1037 (https://pubs.usgs.gov/of/2006/
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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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SLR Potential Economic Loss - 0.5 Ft. Scenario
highways.hidot.hawaii.gov | Last Updated 2023-03-24T01:37:53.000ZVulnerability was assessed for the main Hawaiian Islands using the outputs of coastal hazard exposure modeling (provided separately). Potential economic loss was based on the value of the land and structures from the county tax parcel database permanently lost in the sea level rise exposure area (SLR-XA) for four future sea level rise scenarios: 0.5 foot, 1.1 foot, 2.0 feet and 3.2 feet based on the upper end of the IPCC AR5 RCP8.5 projections. This particular layer depicts potential economic loss using the 0.5-ft (0.1660-m) sea level rise scenario. While the RCP8.5 predicts that this scenario would be reached by the year 2030, questions remain around the exact timing of sea level rise and recent observations and projections suggest a sooner arrival. Potential economic loss was analyzed individually for each hazard (passive flooding, annual high wave flooding, or coastal erosion) at the parcel level and subsequently aggregated in 1-hectare (100 square meter or 1,076 square foot) grids. For the islands of Hawaii, Lanai, and Molokai, the potential economic loss was based solely on passive flooding. Potential economic loss in the SLR-XA area was determined from the highest loss value of any one hazard within the 1-hectare grid, thus avoiding double counting a loss of a particular asset from multiple hazards. Those maximum values for each sector are then summed to determine the total economic loss to property in each grid. Assumptions and Limitations: The vulnerability assessment addressed exposure to chronic flooding with sea level rise. Key assumptions of the economic analysis for the SLR-XA included: (a) loss is permanent; (b) economic loss is based on the value in U.S. dollars in 2016 as property values in the future are unknown; (c) economic loss is based on the value of the land and structures exposed to flooding in the SLR-XA excluding the contents of the property and does not include the economic loss or cost to replace roads, water conveyance systems and other critical infrastructure; and (d) no adaptation measures are put in place that could reduce impacts in the SLR-XA. Economic value data were not available for length of roads, water and wastewater lines, and other public infrastructure due to the variable cost of such infrastructure depending on location, and the complexity and uncertainty involved in design, siting, and construction. Additionally, environmental assets such as beaches and wetlands were not assessed economically due to the complexity in valuing ecosystem services. The loss of both public infrastructure and environmental assets from flooding would result in significant economic loss. Therefore, the total potential economic loss figures estimated in these data are likely an underestimate. Data compiled by the Pacific Islands Ocean Observing System (PacIOOS) for the Hawaii Sea Level Rise Viewer hosted at https://pacioos.org/shoreline/slr-hawaii/. For further information, please see the Hawaii Sea Level Rise Vulnerability and Adaptation Report: https://climateadaptation.hawaii.gov/wp-content/uploads/2017/12/SLR-Report_Dec2017.pdf