Context

Presenting data on the most densely populated islands, with a population density surpassing 1,000 individuals per square kilometer. Precision in estimating density for very small islands (less than 1 km2) proves challenging, given uncertainties in both population and landmass measurements. Furthermore, the populations of these islands are frequently transient, with numerous residents also holding residences on larger landmasses and utilizing the island as a seasonal dwelling.

World Countries is a detailed layer of country level boundaries which is best used at large scales (e.g. below 1:2m scale). For a more generalized layer to use at small-to-medium scales, refer to the World Countries (Generalized) layer. It has been designed to be used as a layer that can be easily edited to fit a users needs and view of the political world. Included are attributes for name and ISO codes, along with continent information. Particularly useful are the Land Type and Land Rank fields which separate polygons based on their areal size. These attributes are useful for rendering at different scales by providing the ability to turn off small islands which may clutter small scale views.This dataset represents the world countries as they existed in January 2015.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Education is one of the most powerful instruments for reducing poverty and inequality and lays a foundation for sustained economic growth. The World Bank compiles data on education inputs, participation, efficiency, and outcomes. Data on education are compiled by the United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics from official responses to surveys and from reports provided by education authorities in each country.

This scene contains the relative heat severity for every pixel for every city in the United States, from this source layer. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summers of 2018 and 2019.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this scene is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource Center: https://www.epa.gov/heat-islands/heat-island-resourcesDr. Ladd Keith, University of Arizona: https://www.laddkeith.com/ Dr. Ben McMahan, University of Arizona: https://www.climas.arizona.edu/about/people/ben-mcmahan Dr. Jeremy Hoffman, Science Museum of Virginia: https://jeremyscotthoffman.com/about-me-shift#about Dr. Hunter Jones, NOAA: https://cpo.noaa.gov/News/News-Article/ArtMID/6226/ArticleID/971/CPOs-Hunter-Jones-delivers-keynote-on-Climate-and-Extreme-Heat-at-Design-for-Risk-Reduction-Symposium-in-NYC Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and Resiliency: https://youtu.be/sAHlqGDU0_4 Disclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Pete.Aniello@tpl.org with feedback.

This dataset describes known introductions of snakes to islands outside of their respective historical, native ranges as of May, 2022. It was created during the preparation of the chapter "Welcome to paradise: snake invasions on islands" for the upcoming book Islands and Snakes, Vol. II. Details for this accompanying summary text will be provided upon final publication.

It contains a spreadsheet/database of individual documented introductions (Island_Snakes_data_cleaned), and vignettes organized by species that summarize introductions and provide references.

Island_Snakes_data_cleaned Database Details

The database contains several categories of information; categories are listed in bold below.

• General Introduction information

Introduced Country = the country authority over the geographic location of introduction event

Introduced to Island = the name of the island where a snake was introduced

Island Group Name = the name of the island grouping, if any exists

Introduced Ocean of Sea = the name of the body(ies) of water surrounding the island where a snake was introduced

Native Range = a general description of where the snake is native to, if known

Date Introduced Note = any details in addition to the year provided in Date Introduced column

Date Introduced = the date a snake was considered or recorded as introduced to an island. These dates are often approximate of based on year of publication.

Established = a binary variable where 1 indicates a snake has established a population on that island, and 0 indicates it has not established or there is not enough evidence to determine this status.

Currently Present on island = a binary variable where 1 indicates a snake is present on that island, and 0 indicates it is not present or there is not enough evidence to determine this status.

• Pathway information - these variables describe known pathways of introduction to an island. All variables are binary; 1 indicates the pathway likely contributed to the introduction of the snake, 0 indicates it likely did not or there is not evidence to support that pathway.

Nursery Trade - introduced as a result of nursery or plant trade

Cargo - introduced as a result of cargo that is not specifically associated with nursery or plant trade

Pet Trade - introduced as a result of importation for eventual keeping as a pet or in captive hobby herpetoculture, or escape

Intentional - introduced intentionally to the wild by a person intending to establish a population or releasing an animal for non-religious purposes

Industrial - introduced as a result of an industry not described in other pathways; e.g. entertainment industry, skin trade

Research - introduced as a result of escape or release from captive animals used for research purposes

Medicinal - introduced as a result of medicinal trade in animals

Food - introduced as a result animals traded or imported for consumption

Pathway comment - additional details about the pathway associated with the introduction event

• Introduced island characteristics - attributes of the ecology and geography where snakes have been introduced. Binary variables 1 indicate there is evidence for the category, 0 indicates there is not evidence for that category. *Note*: we did not consider fully-aquatic sea-snakes in our determination of island ecology characteristics

introduced to historically snake-free - 1 indicates that prior to the snake's introduction, no other snakes were present on the island.

introduced to island with native snakes already there - 1 indicates that prior to the snake's introduction, native snakes were already present

introduced to island with ecologically similar snake - 1 indicates that prior to the snake's introduction, a snake with similar ecotype was already present (native or non-native).

introduced island with same family - 1 indicates that prior to the snake's introduction, another snake of the same family was already present (native or non-native).

introduced island with same genus - 1 indicates that prior to the snake's introduction, another snake of the same genus was already present (native or non-native).

island area km2 - a rough estimate of the island's total geographic area in km²

nearest large landmass (>10000km2) - the name of the nearest landmass (greater than 10,000 km² in area) to the island where a snake was introduced (as determined by linear distance).

distance to nearest large landmass/mainland (km) - an estimate of the linear distance from the island where a snake was introduced to the nearest landmass greater than 10,000 km² in area.

distance to native origin (rough km) - a rough estimate of the linear distance from the snake's native range (and source of non-native introduction, if known) to the island where the snake has been introduced.

Native_to_nearest_large_landmass - a binary variable where 1 indicates the snake is native to the nearest large landmass names in the nearest large landmass (>10000km2) column.

island status comment - any additional information about the ecology of geography of the island where a snake was introduced.

• Snake characteristics - Attributes associated with the ecotype and diet of the snakes introduced to islands. Binary variables indicate whether there is evidence to support a snake's membership to a specific category.

Constrictor - 1 indicates the snake can be classified as a constrictor- using strangulation and squeezing to subdue prey.

Venomous - 1 indicates the snake can be classified as venomous.

Fossorial - 1 indicates the snake can be classified as fossorial; dwelling on ground in soil or leaf litter

Terrestrial - 1 indicates the snake can be classified as terrestrial; living on the ground but generally not in soil or leaf litter

Aquatic - 1 indicates the snake can be classified as aquatic, living at the water's edge or near the water.

Arboreal - 1 indicates the snake can be classified as arboreal; living mostly in trees

Cave-dwelling (troglodytic) - 1 indicates the snake can be classified as Cave-dwelling or troglodytic

max SVL in mm (estimate) - the maximum snout-to-vent length recorded for the introduced snake species; this information may be derived from either native or introduced ranges

Generalist - 1 indicates the snake can be classified as having a generalist diet

Specialist - 1 indicates the snake can be classified as having a specialist diet

Mammals - 1 indicates the snake is documented as consuming mammals

Birds - 1 indicates the snake is documented as consuming birds

Amphibs - 1 indicates the snake is documented as consuming amphibians

Reptiles - 1 indicates the snake is documented as consuming reptiles

Inverts - 1 indicates the snake is documented as consuming invertebrates

• Impacts of introduction - a summary of any documented impacts associated with the introduction of the snake to the island.

Ecological Impacts - 1 indicates there is documentation to support an impact of the snake's introduction to the island's ecology

Health Impacts - 1 indicates there is documentation to support an impact of the snake's introduction to human health on the island

Economic Impacts - 1 indicates there is documentation to support an impact of the snake's introduction to the local economy of the island

Impacts not measured - 1 indicates there is no formal documentation of impacts of the snake to any of the previous categories

Impact Comment - any details about impacts, or speculated impacts

• Management

Previous eradication Efforts - 1 indicates there have been measures taken in the past to attempt to remove the snake species from the island

Current eradication effort - 1 indicates that as of May 2022, attempts to remove the snake species from the island are ongoing.

Cool Stuff! - a category with comments on introductions that do not fit neatly elsewhere

Vignette details

Vignettes are organized by species, using the most up-to-date accepted species name according to the Reptile Database in May, 2022. The vignette describes documented introductions to islands, which includes multiple locations for some species.

All vignettes follow a similar format.

Species name and any relevant synonyms or colloquial names are given.
Where native- describes the native range of the snake, if known.
Where introduced, when - describes the islands where a snake has been documented as introduced, and the associated dates of introduction (if they differ from the publication date)

Introduced Island characteristics - gives any relevant information about the ecology or geography of the island(s) where the species has been introduced

Pathways of introduction - gives information about pathway(s) relevant to introductions

Why successful introduction - if the introduction was successful (i.e. established), what factors may have played a role in this success

Any failed island introductions elsewhere, why? - If introductions are recorded as not established or failed, any information that may help understand the failure of the introduction to establish.

Documented

About this dataset

Context

The dataset tabulates the population of Islamorada, Village Of Islands by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Islamorada, Village Of Islands. The dataset can be utilized to understand the population distribution of Islamorada, Village Of Islands by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Islamorada, Village Of Islands. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Islamorada, Village Of Islands.

Key observations

Largest age group (population): Male # 70-74 years (487) | Female # 50-54 years (454). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Islamorada, Village Of Islands population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Islamorada, Village Of Islands is shown in the following column.
• Population (Female): The female population in the Islamorada, Village Of Islands is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Islamorada, Village Of Islands for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Islamorada, Village Of Islands Population by Gender. You can refer the same here

Our Heard And Mcdonald Islands zip code Database offers comprehensive postal code data for spatial analysis, including postal and administrative areas. This dataset contains accurate and up-to-date information on all administrative divisions, cities, and zip codes, making it an invaluable resource for various applications such as address capture and validation, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including CSV, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Product features include fully and accurately geocoded data, multi-language support with address names in local and foreign languages, comprehensive city definitions, and the option to combine map data with UNLOCODE and IATA codes, time zones, and daylight saving times. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

Abstract

Estache and Goicoechea present an infrastructure database that was assembled from multiple sources. Its main purposes are: (i) to provide a snapshot of the sector as of the end of 2004; and (ii) to facilitate quantitative analytical research on infrastructure sectors. The related working paper includes definitions, source information and the data available for 37 performance indicators that proxy access, affordability and quality of service (most recent data as of June 2005). Additionally, the database includes a snapshot of 15 reform indicators across infrastructure sectors.

This is a first attempt, since the effort made in the World Development Report 1994, at generating a database on infrastructure sectors and it needs to be recognized as such. This database is not a state of the art output—this is being worked on by sector experts on a different time table. The effort has however generated a significant amount of new information. The database already provides enough information to launch a much more quantitative debate on the state of infrastructure. But much more is needed and by circulating this information at this stage, we hope to be able to generate feedback and fill the major knowledge gaps and inconsistencies we have identified.

Geographic coverage

The database covers the following countries: - Afghanistan - Albania - Algeria - American Samoa - Andorra - Angola - Antigua and Barbuda - Argentina - Armenia - Aruba - Australia - Austria - Azerbaijan - Bahamas, The - Bahrain - Bangladesh - Barbados - Belarus - Belgium - Belize - Benin - Bermuda - Bhutan - Bolivia - Bosnia and Herzegovina - Botswana - Brazil - Brunei - Bulgaria - Burkina Faso - Burundi - Cambodia - Cameroon - Canada - Cape Verde - Cayman Islands - Central African Republic - Chad - Channel Islands - Chile - China - Colombia - Comoros - Congo, Dem. Rep. - Congo, Rep. - Costa Rica - Cote d'Ivoire - Croatia - Cuba - Cyprus - Czech Republic - Denmark - Djibouti - Dominica - Dominican Republic - Ecuador - Egypt, Arab Rep. - El Salvador - Equatorial Guinea - Eritrea - Estonia - Ethiopia - Faeroe Islands - Fiji - Finland - France - French Polynesia - Gabon - Gambia, The - Georgia - Germany - Ghana - Greece - Greenland - Grenada - Guam - Guatemala - Guinea - Guinea-Bissau - Guyana - Haiti - Honduras - Hong Kong, China - Hungary - Iceland - India - Indonesia - Iran, Islamic Rep. - Iraq - Ireland - Isle of Man - Israel - Italy - Jamaica - Japan - Jordan - Kazakhstan - Kenya - Kiribati - Korea, Dem. Rep. - Korea, Rep. - Kuwait - Kyrgyz Republic - Lao PDR - Latvia - Lebanon - Lesotho - Liberia - Libya - Liechtenstein - Lithuania - Luxembourg - Macao, China - Macedonia, FYR - Madagascar - Malawi - Malaysia - Maldives - Mali - Malta - Marshall Islands - Mauritania - Mauritius - Mayotte - Mexico - Micronesia, Fed. Sts. - Moldova - Monaco - Mongolia - Morocco - Mozambique - Myanmar - Namibia - Nepal - Netherlands - Netherlands Antilles - New Caledonia - New Zealand - Nicaragua - Niger - Nigeria - Northern Mariana Islands - Norway - Oman - Pakistan - Palau - Panama - Papua New Guinea - Paraguay - Peru - Philippines - Poland - Portugal - Puerto Rico - Qatar - Romania - Russian Federation - Rwanda - Samoa - San Marino - Sao Tome and Principe - Saudi Arabia - Senegal - Seychelles - Sierra Leone - Singapore - Slovak Republic - Slovenia - Solomon Islands - Somalia - South Africa - Spain - Sri Lanka - St. Kitts and Nevis - St. Lucia - St. Vincent and the Grenadines - Sudan - Suriname - Swaziland - Sweden - Switzerland - Syrian Arab Republic - Tajikistan - Tanzania - Thailand - Togo - Tonga - Trinidad and Tobago - Tunisia - Turkey - Turkmenistan - Uganda - Ukraine - United Arab Emirates - United Kingdom - United States - Uruguay - Uzbekistan - Vanuatu - Venezuela, RB - Vietnam - Virgin Islands (U.S.) - West Bank and Gaza - Yemen, Rep. - Yugoslavia, FR (Serbia/Montenegro) - Zambia - Zimbabwe

Kind of data

Aggregate data [agg]

Mode of data collection

Face-to-face [f2f]

Response rate

Sector Performance Indicators

Energy The energy sector is relatively well covered by the database, at least in terms of providing a relatively recent snapshot for the main policy areas. The best covered area is access where data are available for 2000 for about 61% of the 207 countries included in the database. The technical quality indicator is available for 60% of the countries, and at least one of the perceived quality indicators is available for 40% of the countries. Price information is available for about 41% of the countries, distinguishing between residential and non residential.

Water & Sanitation Because the sector is part of the Millennium Development Goals (MDGs), it enjoys a lot of effort on data generation in terms of the access rates. The WHO is the main engine behind this effort in collaboration with the multilateral and bilateral aid agencies. The coverage is actually quite high -some national, urban and rural information is available for 75 to 85% of the countries- but there are significant concerns among the research community about the fact that access rates have been measured without much consideration to the quality of access level. The data on technical quality are only available for 27% of the countries. There are data on perceived quality for roughly 39% of the countries but it cannot be used to qualify the information provided by the raw access rates (i.e. access 3 hours a day is not equivalent to access 24 hours a day).

Information and Communication Technology The ICT sector is probably the best covered among the infrastructure sub-sectors to a large extent thanks to the fact that the International Telecommunications Union (ITU) has taken on the responsibility to collect the data. ITU covers a wide spectrum of activity under the communications heading and its coverage ranges from 85 to 99% for all national access indicators. The information on prices needed to make assessments of affordability is also quite extensive since it covers roughly 85 to 95% of the 207 countries. With respect to quality, the coverage of technical indicators is over 88% while the information on perceived quality is only available for roughly 40% of the countries.

Transport The transport sector is possibly the least well covered in terms of the service orientation of infrastructure indicators. Regarding access, network density is the closest approximation to access to the service and is covered at a rate close to 90% for roads but only at a rate of 50% for rail. The relevant data on prices only cover about 30% of the sample for railways. Some type of technical quality information is available for 86% of the countries. Quality perception is only available for about 40% of the countries.

Institutional Reform Indicators

Electricity The data on electricity policy reform were collected from the following sources: ABS Electricity Deregulation Report (2004), AEI-Brookings telecommunications and electricity regulation database (2003), Bacon (1999), Estache and Gassner (2004), Estache, Trujillo, and Tovar de la Fe (2004), Global Regulatory Network Program (2004), Henisz et al. (2003), International Porwer Finance Review (2003-04), International Power and Utilities Finance Review (2004-05), Kikukawa (2004), Wallsten et al. (2004), World Bank Caribbean Infrastructure Assessment (2004), World Bank Global Energy Sector Reform in Developing Countries (1999), World Bank staff, and country regulators. The coverage for the three types of institutional indicators is quite good for the electricity sector. For regulatory institutions and private participation in generation and distribution, the coverage is about 80% of the 207 counties. It is somewhat lower on the market structure with only 58%.

Water & Sanitation The data on water policy reform were collected from the following sources: ABS Water and Waste Utilities of the World (2004), Asian Developing Bank (2000), Bayliss (2002), Benoit (2004), Budds and McGranahan (2003), Hall, Bayliss, and Lobina (2002), Hall and Lobina (2002), Hall, Lobina, and De La Mote (2002), Halpern (2002), Lobina (2001), World Bank Caribbean Infrastructure Assessment (2004), World Bank Sector Note on Water Supply and Sanitation for Infrastructure in EAP (2004), and World Bank staff. The coverage for institutional reforms in W&S is not as exhaustive as for the other utilities. Information on the regulatory institutions responsible for large utilities is available for about 67% of the countries. Ownership data are available for about 70% of the countries. There is no information on the market structure good enough to be reported here at this stage. In most countries small scale operators are important private actors but there is no systematic record of their existence. Most of the information available on their role and importance is only anecdotal.

Information and Communication Technology The report Trends in Telecommunications Reform from ITU (revised by World Bank staff) is the main source of information for this sector. The information on institutional reforms in the sector is however not as exhaustive as it is for its sector performance indicators. While the coverage on the regulatory institutions is 100%, it varies between 76 and 90% of the countries for more of the other indicators. Quite surprisingly also, in contrast to what is available for other sectors, it proved difficult to obtain data on the timing of reforms and of the creation of the regulatory agencies.

Transport Information on transport institutions and reforms is not systematically generated by any agency. Even though more data are needed to have a more comprenhensive picture of the transport sector, it was possible to collect data on railways policy reform from Janes World Railways (2003-04) and complement it with

Polygon feature class of the California County boundaries obtained from CALFIRE's Fire and Resource Assessment Program (FRAP).

Background: In late 1996, the Dept of Conservation (DOC) surveyed state and federal agencies about the county boundary coverage they used. As a result, DOC adopted the 1:24,000 (24K) scale U.S. Bureau of Reclamation (USBR) dataset (USGS source) for their Farmland Mapping and Monitoring Program (FMMP) but with several modifications. Detailed documentation of these changes is provided by FMMP and included in the lineage section of the metadata. A dataset named cnty24k97_1 was made available (approximately 2004) through the California Department of Forestry and Fire Protection - Fire and Resource Assessment Program (CDF - FRAP) and the California Spatial Information Library (CaSIL). In late 2006, the Department of Fish and Game (DFG) reviewed cnty24k97_1. Comparisons were made to a high-quality 100K dataset (co100a/county100k from the former Teale Data Center GIS Solutions Group) and legal boundary descriptions from ( http://www.leginfo.ca.gov ). The cnty24k97_1 dataset was missing Anacapa and Santa Barbara islands. DFG added the missing islands using previously-digitized coastline data (coastn27 of State Lands Commission origin), corrected a few county boundaries, built region topology, added additional attributes, and renamed the dataset to county24k. In 2007, the California Mapping Coordinating Committee (CMCC) requested that the California Department of Forestry and Fire Protection (CAL FIRE) resume stewardship of the statewide county boundaries data. CAL FIRE adopted the changes made by DFG and collected additional suggestions for the county data from DFG, DOC, and local government agencies. CAL FIRE incorporated these suggestions into the latest revision, which has was renamed cnty24k09_1.

Current Version: This version of the county dataset was created as a result of an effort to improve the coastal linework. It uses the previous interior linework from the cnty24k13_1 data, but replaces the coastal linework (including islands and inlets) based on NOAA's ERMA coastal dataset (which used NAIP 2010). In addition to the improved linework, additional coding was added to differentiate inlets and bays, islands, and manmade structures such as piers and breakers. This dataset is one of several available datasets that were created as a group designed to work in topological sync with each other. These "paired" datasets include a basic county dataset (cnty15_1_basic), a basic state dataset (state15_1), an ocean dataset (ocean15_1), and country/state datasets (both full and neighbor-only - cntrystate15_1_full and cntrystate15_1_neighbor, respectively). Further details about these paired datasets can be found in their respective metadata. This specific dataset represents the full detailed county dataset with all coding (islands, inlets, constructed features, etc.) The user has the freedom to use this coding to create definition queries, symbolize, or dissolve to create a more generalized dataset as needed. In November 2015, the dataset was adjusted to include a change in the Yuba-Placer county boundary from 2010 that was not yet included in the 14_1 version of the dataset (ord No 5546-B). This change constitutes the difference between the 15_1 and 14_1 versions of this dataset.

This dataset compiles most of the data from the "BIO" database for the Seychelles islands. It has three main objectives: 1.To share a nationally agreed taxonomic index of all species recorded in the country 2.To associate with that checklist key data on the conservation value of these species or their invasiveness in Seychelles. This includes a National Red List using IUCN threat statuses assessed at the National level. 3.To share all data available on the distribution of these species, including occurrences with exact coordinates except for species considered sensitive (which are provided here without exact coordinates, and for which the complete data are shared separately in a private GBIF dataset shared only with chosen conservation actors in Seychelles). In the first version, and in the short term, this dataset is restricted to plants, but in future the plan is to extend it to all taxonomic groups. In addition, because the species listed, and their taxonomic and conservation statuses need to be reviewed and discussed with the local scientific community in Seychelles, we use this dataset publication as an opportunity to strengthen partnerships in Seychelles. The pre-published first version was presented to stakeholders and discussed. We agreed to form a Key Biodiversity Areas (KBA) National Coordination Group (NCG) (see list of contacts in the section "Associated Parties"), which is the collective author of this dataset and whose participants are involved in verifying and improving the dataset. This dataset will therefore serve as an open source repository for a formal KBA review in Seychelles. The group will eventually take part to and be complemented by a GBIF National Node which is being developed simultaneously.

This dataset is accompanied by 3 R scripts available online (https://github.com/bsenterre/seychecklist): •The first shows how the BIO database manager converted the BIO data into text files ready for upload in the IPT •The second downloads the dataset from GBIF and compile it into an enriched format that is used for a Shiny app •The third creates a Shiny app that allows users to explore the data and to verify the status of Key Biodiversity Areas based on the distribution of species triggers. The app also provides users with a nationally agreed checklist of species of the Seychelles along with their conservation value or invasion status. These scripts provide therefore a fully transparent approach to identifying KBAs, where the data is open source and where the data analysis and synthesis are also explicit and open source.

To prepare this dataset, we have reviewed the various standards available with GBIF through the main 'cores' and extensions (http://rs.gbif.org/extension/gbif/1.0/). Based on that review, considering the content of our BIO database and differences between our taxonomic backbone and GBIF backbone, we have decided to prepare a dataset using the Taxon Core and the following extensions (i.e. a checklist based on occurrences): •Occurrence: for the core of the BIO database •Species Distribution: for biogeography aspects, of native range (endemic to what) •Species Profiles: for basic ecology (marine, freshwater, terrestrial), basic invasion ecology at species level (isInvasive) and basic functional biology. •Vernacular names (although still in development) •Alternative identifiers: to link to GBIF IDs and to IUCN Red List IDs

Complementary data are spread over the following other datasets: •seysensitive: a private dataset providing the exact geographic coordinates for sensitive taxa (sharing their occurrenceID with the obscured duplicate found in the current seychecklist dataset) •seynotinchecklist: an occurrence dataset containing all species occurrence data from the BIO database which are not linked to a species name listed in the current seychecklist dataset (https://www.gbif.org/dataset/99ccf1cc-03e3-4bd4-8a78-50d46dee8cb7) •seyvegplot: dataset compiling vegetation plots, with eventID linking to the seychecklist dataset (https://www.gbif.org/dataset/4fc42f17-eaeb-4296-949d-34b8414eb1c1) •ecosystemology: a dataset providing an index of ecosystem types, their names and synonymies (https://www.gbif.org/dataset/f513fe98-b1c3-45ee-8e14-7f2a5b7890bf). The ID of each individual stand (ecosystem occurrence) is referred to in the seychecklist dataset using the field eventRemarks (while locationID is used to store the code for the location of the stand).

The Southern Islands Vascular Flora (SIVFLORA) dataset is a globally significant, open-access resource that compiles essential biodiversity data on vascular plants from islands across the Southern Ocean. Covering major taxonomic divisions, SIVFLORA provides a comprehensive overview of plant diversity in these isolated and environmentally sensitive regions, with a total of 14,589 records representing 886 species, 95 families, and 42 orders. This dataset highlights the rich biodiversity of these remote islands, documenting that 58.62% of taxa are native, 9.61% are endemic, and 31.77% are alien species. Such diversity underscores the islands’ ecological importance as sentinels of global environmental change. The Falkland/Malvinas Islands, the most species-rich, contrast sharply with less diverse islands like the South Orkney Islands. SIVFLORA offers critical insights into island biogeography, endemism, and the impacts of anthropogenic pressures on Southern Ocean flora, providing a foundational tool for researching biodiversity patterns and ecosystem responses to climate change in extreme environments.

Since 1976, islands have been surveyed periodically (not annually) for seabirds. Seabird nesting islands (SNI) are defined as an island, ledge, or portion thereof in tidal waters that has 25 or more documented nests, adult seabirds associated with nests, or combination thereof (single species or aggregate of different species) in any nesting season since 1976 provided that the island, ledge, or portion thereof continues to have suitable nesting habitat. In addition, seabird nesting islands are also defined as an island, ledge, or portion thereof in tidal waters that has one or more documented nests of a seabird that is a Maine endangered or threatened species in any year since 1976 provided that the island, ledge, or portion thereof, continues to have suitable nesting habitatThis dataset was developed in accordance with Maine's Natural Resources Protection Act (NRPA). Under this Act, the Maine Department of Inland Fisheries and Wildlife (MDIFW) is designated as the authority for determining Significant Wildlife Habitats (SWHs). Seabird Nesting Islands have been formally adopted as a NRPA Significant Wildlife Habitat through the Maine Department of Environmental Protection (MDEP) regulatory process. The SNI dataset contains polygons that represent NRPA regulated coastal seabird nesting islands or portions thereof along the coast of Maine. This coverage is included in the statewide seabird nesting island database maintained by MDIFW. See metadata for "Seabird Islands (LUPC)" for descriptions of seabird islands under Land Use Planning Commission (LUPC) jurisdiction (available through Maine Department of Inland Fisheries and Wildlife (MDIFW)). Other seabird nesting islands may exist that do not qualify for regulation under NRPA. The dataset is a subset of the statewide coverage.

All island polygons. Islands may overlap as there are islands within islands (e.g., a lake on an island contains an island). GNIS_NAME_1 contains the most atomic name for the island. For example, there are 3797 "Haida Gwaii" islands. If the island has not been named as part of a more specific group or with an individual name, "Haida Gwaii" is the GNIS_NAME_1 value. GNIS_NAME_2 and GNIS_NAME_3 values are null. If the island has a more specific name, "Haida Gwaii" moves to GNIS_NAME_2, and the more atomic name, such as "Moresby Island" is the GNIS_NAME_1. If the island has an individual name, belongs to a group, and is part of Haida Gwaii, the same logic of naming from most to least specific applies. For example, GNIS_NAME_1 = "George Island", GNIS_NAME_2 = "Copper Islands", GNIS_NAME_3 = "Haida Gwaii".

NOAA's National Centers for Environmental Information is building high-resolution digital elevation models (DEMs) for select U.S. coastal regions. These integrated bathymetric-topographic DEMs are used to support tsunami forecasting and warning efforts at the NOAA Center for Tsunami Research, Pacific Marine Environmental Laboratory (PMEL). The DEMs are part of the tsunami forecast system SIFT (Short-term Inundation Forecasting for Tsunamis) currently being developed by PMEL for the NOAA Tsunami Warning Centers, and are used in the MOST (Method of Splitting Tsunami) model developed by PMEL to simulate tsunami generation, propagation, and inundation.

About this dataset

Context

The dataset tabulates the population of Islamorada, Village Of Islands by race. It includes the population of Islamorada, Village Of Islands across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Islamorada, Village Of Islands across relevant racial categories.

Key observations

The percent distribution of Islamorada, Village Of Islands population by race (across all racial categories recognized by the U.S. Census Bureau): 83.56% are white, 0.65% are Black or African American, 0.80% are Asian, 0.09% are Native Hawaiian and other Pacific Islander, 5.25% are some other race and 9.65% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Islamorada, Village Of Islands
• Population: The population of the racial category (excluding ethnicity) in the Islamorada, Village Of Islands is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Islamorada, Village Of Islands total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Islamorada, Village Of Islands Population by Race & Ethnicity. You can refer the same here

Time series data for the statistic Population, ages 6-9, male and country Solomon Islands. Indicator Definition:Population, ages 6-9, total is the total population age 6-9.The indicator "Population, ages 6-9, male" stands at 29.43 Thousand as of 12/31/2015, the highest value at least since 12/31/1991, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 0.5501 percent compared to the value the year prior.The 1 year change in percent is 0.5501.The 3 year change in percent is 1.91.The 5 year change in percent is 3.75.The 10 year change in percent is 10.99.The Serie's long term average value is 24.92 Thousand. It's latest available value, on 12/31/2015, is 18.10 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1990, to it's latest available value, on 12/31/2015, is +51.35%.The Serie's change in percent from it's maximum value, on 12/31/2015, to it's latest available value, on 12/31/2015, is 0.0%.

The boundaries of the CLME Project encompass the Caribbean Sea LME and the North Brazil Shelf LME and include 26 countries and 19 dependent territories of France, the Netherlands, United Kingdom and United States. These countries range from among the largest (e.g. Brazil, USA) to among the smallest (e.g. Barbados, St. Kitts and Nevis), and from the most developed to the least developed. Consequently, there is an extremely wide range in their capacities for living marine resource management. Throughout the region, the majority of the population inhabits the coastal zone, and there is a very high dependence on marine resources for livelihoods from fishing and tourism, particularly among the small island developing states (SIDS), of which there are 16. In addition 18 of the 19 dependent territories are SIDS. The region is characterized by a diversity of national and regional governance and institution arrangements, stemming primarily from the governance structures established by the countries that colonized the region. Physical and geographical characteristics The Caribbean Sea is a semi-enclosed ocean basin bounded by the Lesser Antilles to the east and southeast, the Greater Antilles (Cuba, Hispaniola, and Puerto Rico) to the north, and by Central America to the west and southwest. It is located within the tropics and covers 1,943,000 km2. The Wider Caribbean, which includes the Gulf of Mexico, the Caribbean Sea and the adjacent parts of the Atlantic Ocean encompasses an area of 2,515,900 km2 and is the second largest sea in the world. (Bjorn 1997, Sheppard 2000, IUCN 2003). It is noted for its many islands, including the Leeward and Windward Islands situated on its eastern boundary, Cuba, Hispaniola, Puerto Rico, Jamaica and the Cayman Islands. There is little seasonal variation in surface water temperatures. Temperatures range from 25.5 °C in the winter to 28 °C in the summer. The adjacent region of the North Brazil Shelf Large Marine Ecosystem is characterized by its tropical climate. It extends in the Atlantic Ocean from the boundary with the Caribbean Sea to the Paraiba River estuary in Brazil. The LME owes its unity to the North Brazil Current, which flows parallel to Brazil’s coast and is an extension of the South Equatorial Current coming from the East. The LME is characterized by a wide shelf, and features macrotides and upwellings along the shelf edge. It has moderately diverse food webs and high production due in part to the high levels of nutrients coming from the Amazon and Tocantins rivers, as well as from the smaller rivers of the Amapa and western Para coastal plains. The Caribbean Sea averages depths of 2,200 m, with the deepest part, known as the Cayman trench, plunging to 7,100 m. The drainage basin of the Wider Caribbean covers 7.5 million km2 and encompasses eight major river systems, from the Mississippi to the Orinoco (Hinrichsen 1998). The region is highly susceptible to natural disasters. Most of the islands and the Central American countries lie within the hurricane belt and are vulnerable to frequent damage from strong winds and storm surges. Recent major natural disasters include hurricanes Gilbert (1988) and Hugo (1989), the eruptions of the Soufriere Hills Volcano in Montserrat (1997) and the Piparo Mud Volcano in Trinidad (1997), as well as drought conditions in Cuba and Jamaica during 1997-98, attributed to the El Niño phenomenon. More recently Hurricane Georges devastated large areas, as did Hurricanes Mitch and Ivan (2004). In the case of Ivan, damages were extensive to both natural and infrastructural assets, with estimates reported by Grenada of US$815 million, the Cayman Islands US$1.85 billion, Jamaica US$360 million and Cuba US$1.2 billion. Although the intense category 5 hurricanes Katrina and Rita did not make landfall in the Caribbean, in 2005, Hurricane Wilma devastated the Yucatan peninsula and has the distinction of being the most intense hurricane on record in the Atlantic. Ecological status The marine and coastal systems of the region support a complex interaction of distinct ecosystems, with an enormous biodiversity, and are among the most productive in the world. As mentioned above, several of the world's largest and most productive estuaries (Amazon and Orinoco) are found in the region. The coast of Belize has the second largest barrier reef in the world extending some 250 kilometers and covering approximately 22,800 km2. The region's coastal zone is significant, encompassing entire countries for many of the island nations. Fish and Fisheries A wide range of fisheries activities (industrial, artisanal and recreational) coexist in the CLME Project area. Overall landings from the main fisheries rose from around 177,000 tonnes in 1975 to a peak of 1,000,000 tonnes in 1995 before declining to around 800,000 tonnes in 2005. The total landings from all fisheries shows the decline over the last decade. In the reef fish fisheries, declines in overall landings are rarely observed; instead, there are shifts in species composition. For instance a decline in the percentage of snapper and grouper in the catch, the larger, long-lived predators, is an indication of over exploitation; although not in the Caribbean Large Marine Ecosystem, this pattern was evident in Bermuda between 1969 and 1975 where the percentage of snappers and groupers declined from 67% to 38% and also on the north coast of Jamaica between 1981 and 1990 where the 11 decline was from 26% to 12%. According to an FAO assessment, some 35% of the region's stocks are overexploited. The fisheries of the Caribbean Region are based upon a diverse array of resources. The fisheries of greatest importance are for offshore pelagics, reef fishes, lobster, conch, shrimps, continental shelf demersal fishes, deep slope and bank fishes and coastal pelagics. There is a variety of less important fisheries such as for marine mammals, sea turtles, sea urchins, and seaweeds. The management and governance of these fisheries varies greatly and is fragmented with incomplete or absent frameworks at the sub-regional and regional levels and weak vertical and horizontal linkages. The fishery types vary widely in exploitation; vessel and gear used, and approach to their development and management. However, most coastal resources are considered to be overexploited and there is increasing evidence that pelagic predator biomass has been severely depleted (FAO 1998, Mahon 2002, Myers and Worm 2003). Recreational fishing, an important but undocumented contributor to tourism economies, is an important link between shared resource management and tourism, as the preferred species are mainly predatory migratory pelagics (e.g. billfishes, wahoo, and dolphinfish). This aspect of shared resource management has received minimal attention in most Caribbean countries (Mahon and McConney 2004). Pollution and Ecosystem Health Pollution, mainly from land-based sources, and degradation of nearshore habitats are among the major threats to the region’s living marine resources. The CLME is showing signs of environmental stress, particularly in the shallow waters of coral reef systems and in semi-enclosed bays. Coastal water quality has been declining throughout the region, due to a number of factors including rapid population growth in coastal areas, poor land-use practices and increasing discharges of untreated municipal and industrial waste and agricultural pesticides and fertilizers. Throughout the region, pollution by a range of substances and sources including sewage, nutrients, sediments, petroleum hydrocarbons and heavy metals is of increasing concern. The GIWA studies identified a number of pollution hotspots in the region, mainly around the coastal cities. Pollution has significant transboundary implications, as a result of the high potential for transport across EEZs in wind and ocean currents. Not only could this cause degradation of living marine resources in places far from the source, but it could also pose a threat to human and animal health by the introduction of pathogens. Pollution has been implicated in the increasing episodes of fish kills in the region, although this is not conclusive. Socio-economic situation The physical expanse of the region's coastal zone is significant, encompassing the entire land mass for many of the islands. Additionally, for countries such as the island nations of the Caribbean, Panama and Costa Rica, marine territory represents more than 50% of the total area under national sovereignty. In general, the region’s coastal zone is where the majority of it human population live and where most economic activities also take place. In 2001, the population of the Caribbean Sea region (not including the United States) was around 102 million, of which it is estimated that 59% is in Colombia and Venezuela, 27% is in Cuba and Hispaniola, 10% is in Central America and Mexico, and 3% is in the Small Islands. Taking into account the population growth rate for each country in the Caribbean Sea region, it is expected that the number of inhabitants would be close to 123 million in 2020. When the population for Guyana, Suriname, French Guiana, and the regions of Brazil and Florida that comprise the CLME Project are included, this number is expected to increase to approximately 130 million. Almost all the countries in the region are among the world’s premier tourism destinations, providing an important source of income for their economies. The population in the Caribbean Sea region swells during the tourist season by the influx of millions of tourists, mostly in beach destinations. In 2004, for example, the Mexican state of Quintana Roo received 10.8 million tourists with over 35% of those arriving by cruise ships. There is a high dependence on living marine resources for food, employment and income from fishing and tourism, particularly among the SIDS. Although its contribution to GDP is relatively low, marine

About this dataset

Context

The dataset presents median household incomes for various household sizes in Islamorada, Village Of Islands, FL, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Islamorada, Village Of Islands did not include 6, or 7-person households. Across the different household sizes in Islamorada, Village Of Islands the mean income is $113,694, and the standard deviation is $53,311. The coefficient of variation (CV) is 46.89%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $43,795. It then further increased to $193,382 for 5-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/islamorada-village-of-islands-fl-median-household-income-by-household-size.jpeg" alt="Islamorada, Village Of Islands, FL median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Islamorada, Village Of Islands median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Islamorada, Village Of Islands by race. It includes the population of Islamorada, Village Of Islands across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Islamorada, Village Of Islands across relevant racial categories.

Key observations

The percent distribution of Islamorada, Village Of Islands population by race (across all racial categories recognized by the U.S. Census Bureau): 83.78% are white, 0.61% are Black or African American, 0.80% are Asian, 0.10% are Native Hawaiian and other Pacific Islander, 3.99% are some other race and 10.72% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Islamorada, Village Of Islands
• Population: The population of the racial category (excluding ethnicity) in the Islamorada, Village Of Islands is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Islamorada, Village Of Islands total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Islamorada, Village Of Islands Population by Race & Ethnicity. You can refer the same here

Marshall Islands administrative level 0-2 boundaries (COD-AB) dataset.

NOTE: WARNING: The geoservices are not scaled correctly. Users are advised to rely on the shapefiles or geodatabase. The COD-AB omits MH0609 feature contained in COD-PS. The disbursed nature of this archipelago country does not make it suitable for EMF mapping.

This COD-AB was most recently reviewed for accuracy and necessary changes in October 2024. The COD-AB does not require any update.

Sourced from Secretariat of the Pacific Community, Statistics for Development Division

Vetting by Information Technology Outreach Services (ITOS) with funding from USAID.

This COD-AB is suitable for database or GIS linkage to the Marshall Islands COD-PS.

As this is an island country, no edge-matched (COD-EM) version of this COD-AB is required.

Please see the COD Portal.

Administrative level 1 contains 25 feature(s).

Administrative level 2 contains 153 feature(s).

These administrative boundaries were established in: 2021

Recommended cartographic projection: Oceania