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.

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Property Description

Hylak_id Unique lake identifier. Values range from 1 to 1,427,688.

**Lake_name ** Name of lake or reservoir. This field is currently only populated for lakes with an area of at least 500 km2; for large reservoirs where a name was available in the GRanD database; and for smaller lakes where a name was available in the GLWD database.

Country Country that the lake (or reservoir) is located in. International or transboundary lakes are assigned to the country in which its corresponding lake pour point is located and may be arbitrary for pour points that fall on country boundaries.

Continent Continent that the lake (or reservoir) is located in. Geographic continent: Africa, Asia, Europe, North America, South America, or Oceania (Australia and Pacific Islands)

Poly_src The name of datasets that were used in the column. Source of original lake polygon: CanVec; SWBD; MODIS; NHD; ECRINS; GLWD; GRanD; or Other More information on these data sources can be found in Table 1.

Lake_type Indicator for lake type: 1: Lake 2: Reservoir 3: Lake control (i.e. natural lake with regulation structure) Note that the default value for all water bodies is 1, and only those water bodies explicitly identified as other types (mostly based on information from the GRanD database) have other values; hence the type ‘Lake’ also includes all unidentified smaller human-made reservoirs and regulated lakes.

Grand_id ID of the corresponding reservoir in the GRanD database, or value 0 for no corresponding GRanD record. This field can be used to join additional attributes from the GRanD database.

Lake_area Lake surface area (i.e. polygon area), in square kilometers.

Shore_len Length of shoreline (i.e. polygon outline), in kilometers.

Shore_dev Shoreline development, measured as the ratio between shoreline length and the circumference of a circle with the same area. A lake with the shape of a perfect circle has a shoreline development of 1, while higher values indicate increasing shoreline complexity.

Vol_total Total lake or reservoir volume, in million cubic meters (1 mcm = 0.001 km3). For most polygons, this value represents the total lake volume as estimated using the geostatistical modeling approach by Messager et al. (2016). However, where either a reported lake volume (for lakes ≥ 500 km2) or a reported reservoir volume (from GRanD database) existed, the total volume represents this reported value. In cases of regulated lakes, the total volume represents the larger value between reported reservoir and modeled or reported lake volume. Column ‘Vol_src’ provides additional information regarding these distinctions.

Vol_res Reported reservoir volume, or storage volume of added lake regulation, in million cubic meters (1 mcm = 0.001 km3). 0: no reservoir volume

Vol_src 1: ‘Vol_total’ is the reported total lake volume from literature 2: ‘Vol_total’ is the reported total reservoir volume from GRanD or literature 3: ‘Vol_total’ is the estimated total lake volume using the geostatistical modeling approach by Messager et al. (2016)

Depth_avg Average lake depth, in meters. Average lake depth is defined as the ratio between total lake volume (‘Vol_total’) and lake area (‘Lake_area’).

Dis_avg Average long-term discharge flowing through the lake, in cubic meters per second. This value is derived from modeled runoff and discharge estimates provided by the global hydrological model WaterGAP, downscaled to the 15 arc-second resolution of HydroSHEDS (see section 2.2 for more details) and is extracted at the location of the lake pour point. Note that these model estimates contain considerable uncertainty, in particular for very low flows. -9999: no data as lake pour point is not on HydroSHEDS landmask

Res_time Average residence time of the lake water, in days. The average residence time is calculated as the ratio between total lake volume (‘Vol_total’) and average long-term discharge (‘Dis_avg’). Values below 0.1 are rounded up to 0.1 as shorter residence times seem implausible (and likely indicate model errors). -1: cannot be calculated as ‘Dis_avg’ is 0 -9999: no data as lake pour point is not on HydroSHEDS landmask

Elevation Elevation of lake surface, in meters above sea level. This value was primarily derived from the EarthEnv-DEM90 digital elevation model at 90 m pixel resolution by calculating the majority pixel elevation found within the lake boundaries. To remove some artefacts inherent in this DEM for northern latitudes, all lake values that showed negative elevation for the area north of 60°N were substituted with results using the coarser GTOPO30 DEM of USGS at 1 km pixel resolution, which ensures land surfaces ≥0 in this region. Note that due to the remaining uncertainties in the EarthEnv-DEM90 some small negative values occur along the global oce...

Time series data for the statistic GDP deflator (base year varies by country) and country Marshall Islands. Indicator Definition:The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency. The base year varies by country.The indicator "GDP deflator (base year varies by country)" stands at 128.44 as of 12/31/2024, the highest value at least since 12/31/1971, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 5.21 percent compared to the value the year prior.The 1 year change in percent is 5.21.The 3 year change in percent is 10.92.The 5 year change in percent is 25.29.The 10 year change in percent is 24.50.The Serie's long term average value is 68.33. It's latest available value, on 12/31/2024, is 87.97 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1973, to it's latest available value, on 12/31/2024, is +603.89%.The Serie's change in percent from it's maximum value, on 12/31/2024, to it's latest available value, on 12/31/2024, is 0.0%.

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

Time series data for the statistic GDP deflator (base year varies by country) and country Solomon Islands. Indicator Definition:The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency. The base year varies by country.The indicator "GDP deflator (base year varies by country)" stands at 129.81 as of 12/31/2024, the highest value at least since 12/31/1981, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 4.37 percent compared to the value the year prior.The 1 year change in percent is 4.37.The 3 year change in percent is 12.94.The 5 year change in percent is 5.31.The 10 year change in percent is 22.34.The Serie's long term average value is 58.40. It's latest available value, on 12/31/2024, is 122.27 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1980, to it's latest available value, on 12/31/2024, is +3,151.61%.The Serie's change in percent from it's maximum value, on 12/31/2024, to it's latest available value, on 12/31/2024, is 0.0%.

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

The data comprehends the list of bird species that occur on 3,713 across the globe. The data was compiled from two data sources: 1) BirdLife International and HBW (2018) (BirdLife hereafter). This dataset consists of expert range maps (polygons of geographical distributions) of all bird species globally (11,125). Every species has information related to its presence (e.g., extant, probably extant, possibly extant, possibly extinct, extinct), origin (e.g., native, reintroduced, introduced, vagrant, origin uncertain, assisted colonization), and seasonality (e.g., resident, breeding season, non-breeding season, passage, seasonal occurrence, uncertain). 2) eBird (2020). This database consists of occurrence points where birds were observed. This database documents the distribution, abundance, habitat use, and trends of birds through species lists collected by birders. These observations were collected on a global scale, and experts reviewed unusual records. For our focal islands, we obtained a total of 54,731,644 records with more than 947,862,800 observations). To compile a birds-on-islands dataset, we separately intersected the expert range maps (BirdLife) and the occurrence points (eBird) of each bird species with the island polygons. Doing so resulted in two occurrence matrices with information on the presence and absence of each bird species on each island. For the Birdlife data, we kept all attributes that describe the status of the species, resulting in a total of 7,289 island bird species on 17,771 islands. For the eBird data, we kept the occurrence information, and we found a total of 7,318 species on 4,030 islands. The database is very comprehensive in many locations but does not provide systematic global coverage, so it is known to underestimate species’ presence in some regions of the world. To increase the likelihood that our bird dataset only contains actual species presences with the correct presence, origin and seasonality, i.e., to reduce the number of false presences and increase the accuracy of the distributions, we only kept species records from the BirdLife range maps which were also confirmed by species presence records from eBird and vice versa. We decide this, because the distribution maps created by Birdlife estimate the distribution of the species, extrapolating data according to the habitat of the identified species. Thus, Birdlife maps do not provide the actual presence of the species but its potential distribution. These expert range maps are extent-of-occurrence estimates, so are known to overestimate species’ presence. However, these maps provide ecological and seasonal information on birds that is not available in eBird.

To match the two occurrence matrices in this way, we intersected them using the species names and the island identity, allowing us to validate the presence of birds on islands. Here we obtain a resulting common dataset of 5,123 species on 3,713 islands.

This data set contains quantities and values for all seafood products that are reported landed in St. Thomas, St. Croix or St. John, U.S. Virgin Islands. These data are reported by fishermen that are licensed by the Territorial Government and are required to complete and submit fishing logs for each trip. There have been several major changes in the reporting forms throughout the program history which affect the data. These are documented separately. These are raw data are not corrected for non-compliance with the reporting regulations unless otherwise specified. For the purpose of these metadata, the word 'confidential' generally refers to sensitive data.

The Open Budgets Portal is the first effort to create a one-stop shop for budget data worldwide with the hope of bringing visibility to countries’ efforts in this field, facilitating access and promoting use of spending data, and motivating other countries into action. The purpose of the BOOST project is to enhance the transparency and efficiency of public spending across the globe by improving access to government expenditure data and linking spending to relevant results. The BOOST team has collected and processed detailed government expenditure data from more than a dozen countries, creating easy-to-use databases that have been employed by World Bank researchers on a variety of projects.

This data set is part of an ongoing project to consolidate interagency fire point data. The incorporation of all available historical data is in progress.The InFORM (Interagency Fire Occurrence Reporting Modules) FODR (Fire Occurrence Data Records) are the official record of fire events. Built on top of IRWIN (Integrated Reporting of Wildland Fire Information), the FODR starts with an IRWIN record and then captures the final incident information upon certification of the record by the appropriate local authority. This service contains all wildland fire incidents from the InFORM FODR incident service that meet the following criteria:Categorized as a Wildfire (WF) or Prescribed Fire (RX) recordIs Valid and not "quarantined" due to potential conflicts with other recordsNo "fall-off" rules are applied to this service.Service is a real time display of data.Warning: Please refrain from repeatedly querying the service using a relative date range. This includes using the “(not) in the last” operators in a Web Map filter and any reference to CURRENT_TIMESTAMP. This type of query puts undue load on the service and may render it temporarily unavailable.Attributes:ABCDMiscA FireCode used by USDA FS to track and compile cost information for emergency initial attack fire suppression expenditures. for A, B, C & D size class fires on FS lands.ADSPermissionStateIndicates the permission hierarchy that is currently being applied when a system utilizes the UpdateIncident operation.CalculatedAcresA measure of acres calculated (i.e., infrared) from a geospatial perimeter of a fire. More specifically, the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands. The minimum size must be 0.1.ContainmentDateTimeThe date and time a wildfire was declared contained. ControlDateTimeThe date and time a wildfire was declared under control.CreatedBySystemArcGIS Server Username of system that created the IRWIN Incident record.CreatedOnDateTimeDate/time that the Incident record was created.IncidentSizeReported for a fire. The minimum size is 0.1.DiscoveryAcresAn estimate of acres burning upon the discovery of the fire. More specifically when the fire is first reported by the first person that calls in the fire. The estimate should include number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.DispatchCenterIDA unique identifier for a dispatch center responsible for supporting the incident.EstimatedCostToDateThe total estimated cost of the incident to date.FinalAcresReported final acreage of incident.FinalFireReportApprovedByTitleThe title of the person that approved the final fire report for the incident.FinalFireReportApprovedByUnitNWCG Unit ID associated with the individual who approved the final report for the incident.FinalFireReportApprovedDateThe date that the final fire report was approved for the incident.FireBehaviorGeneralA general category describing the manner in which the fire is currently reacting to the influences of fuel, weather, and topography. FireCodeA code used within the interagency wildland fire community to track and compile cost information for emergency fire suppression expenditures for the incident. FireDepartmentIDThe U.S. Fire Administration (USFA) has created a national database of Fire Departments. Most Fire Departments do not have an NWCG Unit ID and so it is the intent of the IRWIN team to create a new field that includes this data element to assist the National Association of State Foresters (NASF) with data collection.FireDiscoveryDateTimeThe date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.FireMgmtComplexityThe highest management level utilized to manage a wildland fire event. FireOutDateTimeThe date and time when a fire is declared out. FSJobCodeA code use to indicate the Forest Service job accounting code for the incident. This is specific to the Forest Service. Usually displayed as 2 char prefix on FireCode.FSOverrideCodeA code used to indicate the Forest Service override code for the incident. This is specific to the Forest Service. Usually displayed as a 4 char suffix on FireCode. For example, if the FS is assisting DOI, an override of 1502 will be used.GACCA code that identifies one of the wildland fire geographic area coordination center at the point of origin for the incident.A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.IncidentNameThe name assigned to an incident.IncidentShortDescriptionGeneral descriptive location of the incident such as the number of miles from an identifiable town. IncidentTypeCategoryThe Event Category is a sub-group of the Event Kind code and description. The Event Category further breaks down the Event Kind into more specific event categories.IncidentTypeKindA general, high-level code and description of the types of incidents and planned events to which the interagency wildland fire community responds.InitialLatitudeThe latitude location of the initial reported point of origin specified in decimal degrees.InitialLongitudeThe longitude location of the initial reported point of origin specified in decimal degrees.InitialResponseDateTimeThe date/time of the initial response to the incident. More specifically when the IC arrives and performs initial size up. IsFireCauseInvestigatedIndicates if an investigation is underway or was completed to determine the cause of a fire.IsFSAssistedIndicates if the Forest Service provided assistance on an incident outside their jurisdiction.IsReimbursableIndicates the cost of an incident may be another agency’s responsibility.IsTrespassIndicates if the incident is a trespass claim or if a bill will be pursued.LocalIncidentIdentifierA number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year.ModifiedBySystemArcGIS Server username of system that last modified the IRWIN Incident record.ModifiedOnDateTimeDate/time that the Incident record was last modified.PercentContainedIndicates the percent of incident area that is no longer active. Reference definition in fire line handbook when developing standard.POOCityThe closest city to the incident point of origin.POOCountyThe County Name identifying the county or equivalent entity at point of origin designated at the time of collection.POODispatchCenterIDA unique identifier for the dispatch center that intersects with the incident point of origin. POOFipsThe code which uniquely identifies counties and county equivalents. The first two digits are the FIPS State code and the last three are the county code within the state.POOJurisdictionalAgencyThe agency having land and resource management responsibility for a incident as provided by federal, state or local law. POOJurisdictionalUnitNWCG Unit Identifier to identify the unit with jurisdiction for the land where the point of origin of a fire falls. POOJurisdictionalUnitParentUnitThe unit ID for the parent entity, such as a BLM State Office or USFS Regional Office, that resides over the Jurisdictional Unit.POOLandownerCategoryMore specific classification of land ownership within land owner kinds identifying the deeded owner at the point of origin at the time of the incident.POOLandownerKindBroad classification of land ownership identifying the deeded owner at the point of origin at the time of the incident.POOProtectingAgencyIndicates the agency that has protection responsibility at the point of origin.POOProtectingUnitNWCG Unit responsible for providing direct incident management and services to a an incident pursuant to its jurisdictional responsibility or as specified by law, contract or agreement. Definition Extension: - Protection can be re-assigned by agreement. - The nature and extent of the incident determines protection (for example Wildfire vs. All Hazard.)POOStateThe State alpha code identifying the state or equivalent entity at point of origin.PredominantFuelGroupThe fuel majority fuel model type that best represents fire behavior in the incident area, grouped into one of seven categories.PredominantFuelModelDescribes the type of fuels found within the majority of the incident area. UniqueFireIdentifierUnique identifier assigned to each wildland fire. yyyy = calendar year, SSUUUU = POO protecting unit identifier (5 or 6 characters), xxxxxx = local incident identifier (6 to 10 characters) FORIDUnique identifier assigned to each incident record in the FODR database.

This study examines the status of plant conservation in Oceania, where most islands have experienced two waves of anthropogenic habitat alteration and extinction, following Austronesian and European contact.

Heard-Island-and-McDonald-Islands Whois Database, discover comprehensive ownership details, registration dates, and more for domains registered in Heard-Island-and-McDonald-Islands with Whois Data Center.

Time series data for the statistic Urban population (% of total population) and country British Virgin Islands. Indicator Definition:Urban population refers to people living in urban areas as defined by national statistical offices. The data are collected and smoothed by United Nations Population Division.The indicator "Urban population (% of total population)" stands at 50.17 as of 12/31/2024, the highest value at least since 12/31/1961, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 0.8503 percent compared to the value the year prior.The 1 year change in percent is 0.8503.The 3 year change in percent is 2.56.The 5 year change in percent is 4.27.The 10 year change in percent is 8.56.The Serie's long term average value is 33.36. It's latest available value, on 12/31/2024, is 50.39 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1960, to it's latest available value, on 12/31/2024, is +310.30%.The Serie's change in percent from it's maximum value, on 12/31/2024, to it's latest available value, on 12/31/2024, is 0.0%.

The following catalogue was compiled in support of an article focusing on the islands of the Saronic Gulf and in particular, small islands termed there the ‘Little Saronics’. In the first part of this catalogue, the evidence is listed first by island/region, and then by site. If there are substantial chronological horizons to the site, then this forms the next division. The second part of this catalogue presents a short overview of each chronological period. This database (and the article it supports) were funded by the Enbomsdonantionsfond of Vitterhetsakademien, Sweden, under the project: 'Island-hopping in the Saronic Gulf: preliminary research in advance of a seascape-focused survey' (ENB2022-0011). I am most grateful for their support of this project.

This data set contains interviews of three hunters from Sanikiliaq, Belcher Islands, in the territory of Nunavut, Canada. The hunters describe their observations of sea ice conditions around the Belcher Islands gathered from their many hunting expeditions. The local and traditional knowledge (LTK) gathered from these interviews reveals that the ice is changing and becoming more dangerous and less predictable than it once was.In addition to the three video interviews, the hunters drew important ice features and changes on maps which are included as part of the data set. Photographs of ice conditions at specific places on the maps are available. Two of the three interviews are in English; the third is in Inuktitut and English.Data are available via the Product Web Site as edited video files, maps, and photographs. Full video interviews, maps, and photographs are on ftp as a compressed file via the Access Data link.

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.

This dataset provided by SPREP contains per capita generation of municipal solid waste for 21 Pacific islands countries and territories.

Find more Pacific data on PDH.stat.

Techsalerator Covers all firmographic fields of companies and businesses ( B2B) in Caroline Islands( Business/Company Data) in its Caroline Islands B2B Database.

For more information on this dataset, please contact us at info@techsalerator.com or via https://www.techsalerator.com/contact-us

We can select the Perfect set based on location, revenue, number of employees, revenue, years in business as well as 40 other fields.

We cover all the following elements on the businesses + 200 more fields :

UniqueID UniversalPublicationId CompanyName TradeName DirectoryName Address1 Address2 PostCode City CityCode Province ProvinceCode Region RegionCode Country CountryCode Language PhoneOrMobile Phone DNCMPhone Fax Mobile DNCMMobile Email Website WebDomain WebSocialMedialinksFacebook WebSocialMedialinksTwitter GenericLinlkedInLink WebsiteIpAddress NationalID NationalIdentificationTypeCode NationalIdentificationTypeCodeDescription NationalIDIsVat PrimaryLocalActivityCode LocalActivityTypeCode MarketabilityIndicator YearStarted NumberOfFamilyMembers CEOName CEOTitle CEOFirstName CEOLastName CEOGender CEOLanguage EmployeesHereReliabilityCode EmployeesHereReliabilityCodeDescription EmployeesTotalReliabilityCode EmployeesTotalReliabilityCodeDescription EmployeesHere EmployeesTotal ImportExportAgentCode ImportExportAgentCodeDescription LegalStatusCode LegalStatusCodeDescription StatusCode StatusCodeDescription SalesVolume Currency SalesVolumeDollars SalesVolumeEuros SalesVolumeReliabilityCode

Notice: this is not the latest Heat Island Severity image service. For 2023 data, visit https://tpl.maps.arcgis.com/home/item.html?id=db5bdb0f0c8c4b85b8270ec67448a0b6. This layer contains the relative heat severity for every pixel for every city in the United States. 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 layer 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.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): 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 CenterDr. Ladd Keith, University of Arizona Dr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAADaphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/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 Dale.Watt@tpl.org with feedback.