USGS developed The National Map Gazetteer as the Federal and national standard (ANSI INCITS 446-2008) for geographic nomenclature based on the Geographic Names Information System (GNIS). The National Map Gazetteer contains information about physical and cultural geographic features, geographic areas, and locational entities that are generally recognizable and locatable by name (have achieved some landmark status) and are of interest to any level of government or to the public for any purpose that would lead to the representation of the feature in printed or electronic maps and/or geographic information systems. The dataset includes features of all types in the United States, its associated areas, and Antarctica, current and historical, but not including roads and highways. The dataset holds the federally recognized name of each feature and defines the feature location by state, county, USGS topographic map, and geographic coordinates. Other attributes include names or spellings other than the official name, feature classification, and historical and descriptive information. The dataset assigns a unique, permanent feature identifier, the Feature ID, as a standard Federal key for accessing, integrating, or reconciling feature data from multiple data sets. This dataset is a flat model, establishing no relationships between features, such as hierarchical, spatial, jurisdictional, organizational, administrative, or in any other manner. As an integral part of The National Map, the Gazetteer collects data from a broad program of partnerships with federal, state, and local government agencies and other authorized contributors. The Gazetteer provides data to all levels of government and to the public, as well as to numerous applications through a web query site, web map, feature and XML services, file download services, and customized files upon request. The National Map download client allows free downloads of public domain geographic names data by state in a pipe-delimited text format. For additional information on the GNIS, go to https://www.usgs.gov/tools/geographic-names-information-system-gnis. See https://apps.nationalmap.gov/help/ for assistance with The National Map viewer, download client, services, or metadata.

USGS developed The National Map Gazetteer as the Federal and national standard (ANSI INCITS 446-2008) for geographic nomenclature based on the Geographic Names Information System (GNIS). The National Map Gazetteer contains information about physical and cultural geographic features, geographic areas, and locational entities that are generally recognizable and locatable by name (have achieved some landmark status) and are of interest to any level of government or to the public for any purpose that would lead to the representation of the feature in printed or electronic maps and/or geographic information systems. The dataset includes features of all types in the United States, its associated areas, and Antarctica, current and historical, but not including roads and highways. The dataset holds the federally recognized name of each feature and defines the feature location by state, county, USGS topographic map, and geographic coordinates. Other attributes include names or spellings other than the official name, feature classification, and historical and descriptive information. The dataset assigns a unique, permanent feature identifier, the Feature ID, as a standard Federal key for accessing, integrating, or reconciling feature data from multiple data sets. This dataset is a flat model, establishing no relationships between features, such as hierarchical, spatial, jurisdictional, organizational, administrative, or in any other manner. As an integral part of The National Map, the Gazetteer collects data from a broad program of partnerships with federal, state, and local government agencies and other authorized contributors. The Gazetteer provides data to all levels of government and to the public, as well as to numerous applications through a web query site, web map, feature and XML services, file download services, and customized files upon request. The National Map download client allows free downloads of public domain geographic names data by state in a pipe-delimited text format. For additional information on the GNIS, go to httpS://nationalmap.gov/gnis.html.

Corals of the World online is an interactive program, which captures global information about corals and makes it readily accessible to conservationists, educators and research scientists alike. The …Show full descriptionCorals of the World online is an interactive program, which captures global information about corals and makes it readily accessible to conservationists, educators and research scientists alike. The program is divided into two linked components, Coral ID and Coral Geographic.

Coral ID is founded on the three-volume book, "Corals of the World" (Veron, 2000) and incorporates updated and expanded species data from the electronic publication "Coral ID" (Veron and Stafford-Smith, 2002). The sophisticated electronic key in this program makes coral identification easy. Species pages give summaries of key characteristics as well as taxonomic detail.

The Coral ID program includes:

1. A sophisticated (Lucid-based) key taking users directly to species pages. (coming soon)

2. Species pages containing: -A character summary -Underwater in situ photographs -Photographs of skeletons -Data on colour, similar species, habitats and abundance -Detailed account of skeletal and live coral characteristics (coming soon) -A map showing global distribution from Coral Geographic -Reference information -Links to other identification products

3. A link to Coral Geographic.

   Coral Geographic has not been previously published although it has been providing geographic data about corals since the early 1990s. Geographic information can be obtained in a many formats allowing analyses and interfacing with other programs and datasets. The program divides the world's coral regions into 141 named ecoregions. Features of Coral Geographic will be released progressively over the next few years and will be available (a) interactively on the internet, (b) for downloading in available formats and (c) on DVD.

   The Coral Geographic program includes:

4. For each coral species: -A GIS map showing global distribution -Types of presence/absence records available -Comprehensive references -A link to the relevant species page in Coral ID

5. For each ecoregion: -A comprehensive list of species -Geographic information and photographs -Links to other information sources

6. Multiple species maps which can be selected according to user needs.

7. Species maps which can be interfaced with other programs according to user needs.

   Additional information is available in the form of explanatory chapters about coral classification, coral structure and growth, coral taxonomy, coral environments, reefs, mass bleaching, coral reproduction and coral evolution.

   Also, a space where users can insert information for personal use and a link where information and photographs can be submitted for inclusion in program updates is included. (coming soon) This program has been developed to provide global information about corals to conservationists, educators and research scientists alike. Early taxonomic classifications were published by J.E.N Veron and Michael Pichon in 'Scleractinia of Eastern Australia' Monograph Series, Vol I-V. These publications have been digitised by Atlas of Living Australia, and made available on the Ocean Biogeographic Information System (OBIS: http://www.iobis.org/explore/#/dataset/3088)

Abstract

This dataset is derived from GA TOPO 250K Series 3 features clipped to the BA_SYD and environs extent for the purpose of providing geographic context in BA_SYD report map images. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Selected features currently include:

Lakes

PlaceNames*

PopulatedPlaces

Railways

Roads

WatercourseLines

additional features may be included as required (relevant feature classes asterisked).

Currently the only addition has been to PlaceNames with the addition of Census Spring (see Lineage).

Purpose

providing geographic context in BA_SYD report map images.

Dataset History

A rectangular mask polygon feature was manually drawn around the BA_SYD (ie NSB+SSB) boundary extending approximately 100km beyond the BA_SYD extent. This mask is included in the dataset (SYD_clip).

Selected features from the national GEODATA TOPO 250K series 3 were overlaid with the mask and intersecting features extracted.

Extracted feature classes have the same names as the source features.

The additional feature of "Census Spring" was added to place names. It's approximate location was sourced from

Fig 4, p172 of the document :

Duralie Coal (2013) Duralie Coal Mine - Water Management Plan (Document No. WAMP-R02-D) Appendix 3 - Groundwater Management Plan . September 2013 Document No. GWMP-R02-C (00519574) . Fig4 pp13

http://www.gloucestercoal.com.au/documents/community_environment/duralie/Duralie_Coal_Mine_Water_Management_Plan.pdf

Dataset Citation

Bioregional Assessment Programme (2014) BA SYD selected GA TOPO 250K data plus added map features. Bioregional Assessment Derived Dataset. Viewed 09 October 2018, http://data.bioregionalassessments.gov.au/dataset/ba5feac2-b35a-4611-82da-5b6213777069.

Dataset Ancestors

• Derived From GEODATA TOPO 250K Series 3

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This dataset includes the current boundary data required for the bioregional assessment impact analysis for the Namoi (NAM) subregion. These data are (1) the current Preliminary Assessment Extent (PAE), (2) the Analysis Extent (AE) and (3) and the Analysis Domain Extent (AD).

The PAE is defined and explained in the BA submethodology (1.3 Description of the water-dependent asset register) and, specifically for the NAM subregion in product 1.3 Water-dependent asset register for the NAM subregion. The Analysis Extent (AE) is defined as the geographic area that encompasses all the possible areas that may be reported as part of the impact analysis component of a bioregional assessment, specifically, the subregion boundary and the PAE. The Analysis Domain extent (AD) is defined as the geographic area used for geoprocessing and data preparation purposes that encompasses the Analysis Extent plus additional areas sufficient to ensure all relevant data is included for the impact analysis component of a bioregional assessment. For NAM, the ADE had at least an additional 20 km geographic buffer added to the AE boundary.

All data are in the Australian Albers coordinate system (EPSG 3577).

Purpose

The purpose of the various boundary polygons are to assist in the efficient spatial analysis of the impact of coal resource development in the Namoi subregion.

Dataset History

This dataset includes the current boundary data required for the bioregional assessment impact analysis for the Namoi (NAM) subregion. These data are (1) the current Preliminary Assessment Extent (PAE), (2) the Analysis Extent (AE) and (3) and the Analysis Domain Extent (AD).

The PAE is defined and explained in the BA submethodology (1.3 Description of the water-dependent asset register) and, specifically for the NAM subregion in product 1.3 Water-dependent asset register for the NAM subregion. The Analysis Extent (AE) is defined as the geographic area that encompasses all the possible areas that may be reported as part of the impact analysis component of a bioregional assessment, specifically, the subregion boundary and the PAE. The Analysis Domain extent (AD) is defined as the geographic area used for geoprocessing and data preparation purposes that encompasses the Analysis Extent plus additional areas sufficient to ensure all relevant data is included for the impact analysis component of a bioregional assessment. For NAM, the ADE had at least an additional 20 km geographic buffer added to the AE boundary.

All data are in the Australian Albers coordinate system (EPSG 3577).

Dataset Citation

Bioregional Assessment Programme (XXXX) NAM Analysis Boundaries 20160908 v01. Bioregional Assessment Derived Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/b71e38ac-a7cd-4781-a255-0b13548e6a90.

Dataset Ancestors

• Derived From Groundwater Zone of Impact for the Namoi subregion

• Derived From Gippsland Project boundary

• Derived From Bioregional Assessment areas v04

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From Bioregional Assessment areas v06

• Derived From Bioregional Assessment areas v05

• Derived From GEODATA TOPO 250K Series 3

• Derived From Preliminary Assessment Extent (PAE) for the Namoi subregion - v04

• Derived From Surface water Preliminary Assessment Extent (PAE) for the Namoi (NAM) subregion - v03

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Victoria - Seamless Geology 2014

• Derived From Geological Provinces - Full Extent

• Derived From Groundwater Preliminary Assessment Extent for the Namoi subregion

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a 3.75 minute quadrangle format and include a detailed, field verified inventory of soils and nonsoil areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

Abstract

This database was produced for a research work published in: Das, Do et al. (2009): "US and Them: The Geography of Academic Research" World Bank Policy Research Working Paper Number 5152, Washington DC: December 2009.

It covers over 76,000 empirical economic papers published between 1985 and 2004 in the top 202 economics journals, and was used to study a number of relationships between GDP and research output, countries covered in these articles, and publication rates.

Geographic coverage

The database covers 180 countries.

Kind of data

Aggregate data [agg]

Mode of data collection

Internet [int]

Data appraisal

The field "CountryName" might be subject to coding errors, and will be updated regularly. Please inform us if you identify coding errors.

Abstract

The National Household Survey - PNAD investigates annuall and permanently, general characteristics of the population, education , labor, income and housing, and others with varying regularity, according to the information needs for the country. Topics include characteristics on migration, fertility , marriage, health, food security, among other topics. The survey of these statistics is an important instrument for the formulation, validation and evaluation of policies to socio-economic development and the improvement of living conditions in Brazil.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey is conducted by a random sample of households. The information is provided by person resident or non-resident, considered capable of providing information for the whole neighborhood and the home. The interviewer is instructed not to accept a person under 14 years of age as an informant. The sampling plan uses cluster sampling, self-weighted in three stages (respectively municipalities, census tracts and households) with geographical stratification of the units of the first stage set for each state. The large municipalities in terms of population and those belonging to the metropolitan areas were each treated as a stratum and therefore included in the sample with certainty, being called autorrepresentativos. The other municipalities within the same geographic microregion were grouped into strata of approximately equal size, and designated non autorrepresentativos. Strata in these municipalities were selected systematically with probability proportional to size (ppt).

Sectors are the unit of selection in the second stage and also are selected systematically and ppt, in which case the size is measured by the number of households. The sectors were stratified according to the situation of urban and rural states of the northern region, except for Tocantins, to allow comparison of indicators from PNADs after 2004 with those performed before insertion of the rural area of the northern states. In other regions this stratification is only implicit, ie, there is an ordering for the situation of the sector before the systematic selection. Municipalities and selected sectors are kept in the sample until they are available new Census data, when they are selected new units for the sample.

Each year, in each sector selected for the sample is prepared (or updated) in the field a listing of households, producing an updated register for selection. An important characteristic of this listing operation refers to the Register of New Buildings, which is prepared to contain the buildings account for large changes in the sizes of sectors. The inventory of new construction is done in the municipalities of the sample, both in the sectors selected for the sample as those not selected. An area of new construction is excluded from the area of the original sector and is dealt with separately at the time of selection of households in this case is performed according to the sample fraction of the area. Households, which are units of the third selection stage, are formed by private households and the housing units in collective households occupied during the listing operation. The initial number of households per sector in the sample was set at 16. The sampling fraction indicates the proportion of the population constituting the sample. Currently fractions ranging from 1/50 (rural area of Roraima) to 1/800 (Sao Paulo). How the selection of households in each selected sector for the sample is done systematically to ensure self-weighting sample, the selection range of households remains fixed from year to year. This procedure entails an annual increase in the number of households in the sample, it depends on the number of households upgraded the sector by listing operation. In PNAD 2008, approximately 151,000 households were selected. The final size of the sample of PNAD 2009 was approximately 851 municipalities, 7818 153837 sectors and households. In 2007 PNAD introduced the use of electronic collector ( Personal Digital Assistant - PDA) for carrying out data collection, making it possible to improve the research operating system. Also during PNAD 2007 the DIA system was used, which is an imputation system that automatically detects qualitative data errors. Developed by the National Institute of Statistics - INE of Spain, the software aims to facilitate debugging censuses and large statistical research. In this first year of use of the application, all steps of criticism usually applied to data from the National Household Survey core questionnaire were performed, followed by a process of simultaneous validation of the data collected. In 2008 PNAD used only the Canadian Census Edit and Imputation System - CANCEIS already including the procedures usually applied to critical data from the questionnaires. Starting from PNAD 2011 sample selection of Rondônia, Acre, Amazonas, Roraima, Pará and Amapá followed the same methodology in other units of the Federation.

Mode of data collection

Face-to-face [f2f]

Large Scale International Boundaries

Version 11.1 Release Date: August 22, 2022

Overview

The Office of the Geographer and Global Issues at the U.S. Department of State produces the Large Scale International Boundaries (LSIB) dataset. These data and their derivatives are the only international boundary lines approved for U.S. Government use. They reflect U.S. Government policy, and not necessarily de facto limits of control. This dataset is a National Geospatial Data Asset.

Details

Sources for these data include treaties, relevant maps, and data from boundary commissions and national mapping agencies. Where available, the dataset incorporates information from courts, tribunals, and international arbitrations. The research and recovery of the data involves analysis of satellite imagery and elevation data. Due to the limitations of source materials and processing techniques, most lines are within 100 meters of their true position on the ground.

Attributes

The dataset uses the following attributes: Attribute Name Explanation Country Code Country-level codes are from the Geopolitical Entities, Names, and Codes Standard (GENC). The Q2 code denotes a line representing a boundary associated with an area not in GENC. Country Names Names approved by the U.S. Board on Geographic Names (BGN). Names for lines associated with a Q2 code are descriptive and are not necessarily BGN-approved. Label Required text label for the line segment where scale permits Rank/Status Rank 1: International Boundary Rank 2: Other Line of International Separation Rank 3: Special Line Notes Explanation of any applicable special circumstances Cartographic Usage Depiction of the LSIB requires a visual differentiation between the three categories of boundaries: International Boundaries (Rank 1), Other Lines of International Separation (Rank 2), and Special Lines (Rank 3). Rank 1 lines must be the most visually prominent. Rank 2 lines must be less visually prominent than Rank 1 lines. Rank 3 lines must be shown in a manner visually subordinate to Ranks 1 and 2. Where scale permits, Rank 2 and 3 lines must be labeled in accordance with the “Label” field. Data marked with a Rank 2 or 3 designation does not necessarily correspond to a disputed boundary. Additional cartographic information can be found in Guidance Bulletins (https://hiu.state.gov/data/cartographic_guidance_bulletins/) published by the Office of the Geographer and Global Issues. Please direct inquiries to internationalboundaries@state.gov.

Credits

The lines in the LSIB dataset are the product of decades of collaboration between geographers at the Department of State and the National Geospatial-Intelligence Agency with contributions from the Central Intelligence Agency and the UK Defence Geographic Centre. Attribution is welcome: U.S. Department of State, Office of the Geographer and Global Issues.

Changes from Prior Release

This version of the LSIB contains changes and accuracy refinements for the following line segments. These changes reflect improvements in spatial accuracy derived from newly available source materials, an ongoing review process, or the publication of new treaties or agreements. Changes to lines include: • Akrotiri (UK) / Cyprus • Albania / Montenegro • Albania / Greece • Albania / North Macedonia • Armenia / Turkey • Austria / Czechia • Austria / Slovakia • Austria / Hungary • Austria / Slovenia • Austria / Germany • Austria / Italy • Austria / Switzerland • Azerbaijan / Turkey • Azerbaijan / Iran • Belarus / Latvia • Belarus / Russia • Belarus / Ukraine • Belarus / Poland • Bhutan / India • Bhutan / China • Bulgaria / Turkey • Bulgaria / Romania • Bulgaria / Serbia • Bulgaria / Romania • China / Tajikistan • China / India • Croatia / Slovenia • Croatia / Hungary • Croatia / Serbia • Croatia / Montenegro • Czechia / Slovakia • Czechia / Poland • Czechia / Germany • Finland / Russia • Finland / Norway • Finland / Sweden • France / Italy • Georgia / Turkey • Germany / Poland • Germany / Switzerland • Greece / North Macedonia • Guyana / Suriname • Hungary / Slovenia • Hungary / Serbia • Hungary / Romania • Hungary / Ukraine • Iran / Turkey • Iraq / Turkey • Italy / Slovenia • Italy / Switzerland • Italy / Vatican City • Italy / San Marino • Kazakhstan / Russia • Kazakhstan / Uzbekistan • Kosovo / north Macedonia • Kosovo / Serbia • Kyrgyzstan / Tajikistan • Kyrgyzstan / Uzbekistan • Latvia / Russia • Latvia / Lithuania • Lithuania / Poland • Lithuania / Russia • Moldova / Ukraine • Moldova / Romania • Norway / Russia • Norway / Sweden • Poland / Russia • Poland / Ukraine • Poland / Slovakia • Romania / Ukraine • Romania / Serbia • Russia / Ukraine • Syria / Turkey • Tajikistan / Uzbekistan

This release also contains topology fixes, land boundary terminus refinements, and tripoint adjustments.

Copyright Notice and Disclaimer

While U.S. Government works prepared by employees of the U.S. Government as part of their official duties are not subject to Federal copyright protection (see 17 U.S.C. § 105), copyrighted material incorporated in U.S. Government works retains its copyright protection. The works on or made available through download from the U.S. Department of State’s website may not be used in any manner that infringes any intellectual property rights or other proprietary rights held by any third party. Use of any copyrighted material beyond what is allowed by fair use or other exemptions may require appropriate permission from the relevant rightsholder. With respect to works on or made available through download from the U.S. Department of State’s website, neither the U.S. Government nor any of its agencies, employees, agents, or contractors make any representations or warranties—express, implied, or statutory—as to the validity, accuracy, completeness, or fitness for a particular purpose; nor represent that use of such works would not infringe privately owned rights; nor assume any liability resulting from use of such works; and shall in no way be liable for any costs, expenses, claims, or demands arising out of use of such works.

Geographic coverage

National coverage

Analysis unit

households/individuals

Kind of data

survey

Frequency of data collection

Quarterly: average based on 3 monthly data points

Sampling procedure

Sample size:

overview

The project extends the long-term, LULC datasets to facilitate environmental change monitoring and social-ecological studies regarding urban sprawl and dynamics, urban heat islands, and outdoor water consumption, among others. Six land-use/land-cover (LULC) maps at 30 m resolution were previously created from 1985 to 2010 at five-year intervals (Zhang and Li 2017). This project updates that suite with maps for 2015 and 2020. As with the prior set, systematic object-based classification was utilized to ensure map consistency and direct comparison capability over time. The maps comprise 11 land-use/land-cover classes with an overall accuracy of 89.1% for 2015 and 89.6% for 2020.

literature cited

• Zhang, Y. and X. Li. 2017. Land cover classification of the CAP LTER study area at five-year intervals from 1985 to 2010 using Landsat imagery ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/dab4db27974f6c8d5b91a91d30c7781d (Accessed 2022-07-13).

This project provides the first systematic assessment of non-governmental vacant parcels for potential greening (VPPG) the Phoenix metropolitan area—land parcels that are or can be privately owned but which contain no buildings, are unpaved, have no apparent use, and are potential candidates for urban greening. To achieve the data, a new method for the identification of vacant lands was employed that combines remote sensing techniques and cadastral data and trains the computer to distinguish different forms of vacant land. The classification result proved to be an effective approach for open land identification and identified approximately 19500 ha of open land in the metro area. The model achieved an average accuracy of 90.67%. This dataset only includes VPPG and does not include other vacant land determined to be inappropriate for potential greening (developed/abandoned or impervious surface). (Overall accuracy for all classes was 87.20%).

PLEASE NOTE: If choosing the Download option of "Spreadsheet" the field PIN is reformatted to a number - you will need to format it as a 10 character text string with leading zeros to join this data with data from King County.King County Assessor data has been summarized to the tax parcel identification number (PIN) and City of Seattle spatial overlay data has been assigned through geographic overlay processes. This data is updated periodically and is used to support the analytical and reporting functions of the City of Seattle long-range and policy planning office.The table includes attribute data from the King County Assessor as well as spatial overlay data for various City of Seattle reporting geographies. These geographic attributes are assigned as "majority rules" by land area in cases where multiple geographies span a single tax parcel.KCA tax parcels are created by King County for property tax assessment and collection and may not match development sites as defined by the City of Seattle (single buildings may span multiple tax parcels), may be stacked on top of each other to represent undivided interest and vertical parcels, or may be made up of several sites that are not contiguous. Every effort is made to accurately summarize key tax parcel attributes to a single PIN. Attributes include parcel centroid locations in latitude/longitude and Washington State Plane X,Y. To get polygon representation of the data please see King County's open data page for parcels and join this table through the PIN field. Please be aware that the King County Assessor site address is not a postal address and may not match other address sources for the same property such as postal, utility billing, and permitting.See the detailed data dictionary for more information.

The seven NTL-LTER primary study lakes in the Trout Lake Region include Allequash Lake, Big Muskellunge Lake, Crystal Bog, Crystal Lake, Sparkling Lake, Trout Lake, and Trout Bog. This data set provides locations for these lakes, as well as attributes including the lake names, NTL-LTER lake identification codes, Water Body Identification Codes (WBICs), and other fields that may be used to reference individual lakes.

Abstract

This research of businesses with one to four employees was conducted in Madagascar from Sept. 15, 2008 to Feb. 13, 2009, at the same time with 2009 Madagascar Enterprise Survey. Data from 113 establishments was analyzed.

Micro-Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, registration, and performance measures. The questionnaire also assesses the survey respondents' opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for registered establishments in Madagascar was selected using stratified random sampling. Two levels of stratification were used in the Madagascar Micro-Enterprise Survey sample: firm sector, and geographic region.

Industry stratification was designed as follows: the universe was stratified into three manufacturing industries (food, textiles, and other), one services industry (retail) and one residual sector.

Regional stratification was defined in terms of the geographic locations with the largest commercial presence in the country. Antananarivo, Mahajanga, Toamasina, and Antsiranana were the four metropolitan areas selected in Madagascar.

Two frames were used for Madagascar. The first was an extract from the database of active establishments provided by Institute National de la Statistique du Madagascar [INSTAT]. The second frame (the panel sample) consisted of enterprises interviewed for the Enterprise Survey in 2005, which were to be re-interviewed where they were in the selected geographical regions and met eligibility criteria.

The quality of the frame was assessed at the onset of the project and was not immune from the typical problems found in establishment surveys: positive rates of non-eligibility, repetition, non-existent units, etc. Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 26.3% (558 out of 2,120 establishments for the ES and micro samples, including panel establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Enterprise Survey MICRO Module Questionnaire - Screener Questionnaire.

Micro-Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, registration, and performance measures. The questionnaire also assesses the survey respondents' opinions on what are the obstacles to firm growth and performance.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

The Micro-Enterprise Surveys, along with all other surveys, suffer from both survey non-response and item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Different strategies were used to address these issues.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially sampled. When the survey frame was extracted from the sampling frame, several establishments with the same strata characteristics were randomly selected for each interview and each establishment was assigned a preference number. Substitutions of replacement establishments were made in order to help achieve targets on the number of interviews for each stratum. Extensive efforts were made to complete interviews with each first preference establishment before contact with a replacement establishment was allowed. At least four attempts were made to contact each sampled establishment for an interview at different times/days of the week before a replacement establishment was allowed to be contacted for an interview.

For micro firms the number of contacted establishments per realized interview was 3.65. For each establishment eligible for an interview, 0.45 refused to participate.

In completed surveys, item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information on important questions including total sales, cost figures and employment levels were re-contacted in order to complete this information. However, re-contacts did not fully eliminate low response rates for some items.

Complete information regarding the sampling methodology, sample frame, weights, response rates, and implementation can be found in "Description of Madagascar Implementation 2009" in "Technical Documents" folder.

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GIS Market Size 2025-2029

The GIS market size is forecast to increase by USD 24.07 billion at a CAGR of 20.3% between 2024 and 2029.

The Global Geographic Information System (GIS) market is experiencing significant growth due to the integration of Building Information Modeling (BIM) software and GIS, enabling more accurate and efficient construction projects. The increasing adoption of GIS solutions in precision farming for soil and water management is another key trend, with farmers utilizing sensors, GPS, and satellite data to optimize fertilizer usage and crop yields. However, challenges persist, such as the lack of proper planning leading to implementation failures of GIS solutions. In the realm of smart cities, GIS plays a crucial role in managing data from various sources, including LIDAR, computer-aided design, and digital twin technologies. Additionally, public safety and insurance industries are leveraging GIS for server-based data analysis, while smartphones and antennas facilitate real-time data collection. Amidst this digital transformation, ensuring data security and privacy becomes paramount, making it a critical consideration for market participants.

What will be the Size of the GIS Market During the Forecast Period?

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The Global Geographic Information System (GIS) market encompasses a range of software solutions and hardware components used to capture, manage, analyze, and visualize geospatial data. Key industries driving market growth include transportation, smart city planning, green buildings, architecture and construction, utilities, oil and gas, agriculture, and urbanization. GIS technology plays a pivotal role in various applications such as 4D GIS software for infrastructure project management, augmented reality platforms for enhanced visualization, and LIDAR and GNSS/GPS antenna for accurate location data collection. Cloud technology is transforming the GIS landscape by enabling real-time data access and collaboration. The transportation sector is leveraging GIS for route optimization, asset management, and predictive maintenance.
Urbanization and population growth are fueling the demand for GIS in city planning and disaster management. Additionally, GIS is increasingly being adopted in sectors like agriculture for precision farming and soil mapping, and in the construction industry for Building Information Modeling (BIM). The market is also witnessing the emergence of innovative applications in areas such as video games and natural disasters risk assessment. Mobile devices are further expanding the reach of GIS, making it accessible to a wider audience. Overall, the market is poised for significant growth, driven by the increasing need for data-driven decision-making and the integration of geospatial technology into various industries.

How is this GIS Industry segmented and which is the largest segment?

The gis industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Software
  Data
  Services


Type

  Telematics and navigation
  Mapping
  Surveying
  Location-based services


Device

  Desktop
  Mobile


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France


  APAC

    China
    Japan
    South Korea


  South America

    Brazil


  Middle East and Africa

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

The market encompasses desktop, mobile, cloud, and server software solutions, catering to various industries. Open-source software with limited features poses a challenge due to the prevalence of counterfeit products. Yet, the market witnesses an emerging trend toward cloud-based GIS software adoption. However, standardization and interoperability concerns hinder widespread adoption. Geospatial technology is utilized extensively in sectors such as Transportation, Utilities, Oil and Gas, Agriculture, and Urbanization, driven by population growth, urban planning, and sustainable development. Key applications include smart city planning, green buildings, BIM, 4D GIS software, augmented reality platforms, GIS collectors, LIDAR, and GNSS/GPS antennas. Cloud technology, mobile devices, and satellite imaging are critical enablers.

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The software segment was valued at USD 5.06 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 38% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during th

Ice shelf surface elevation data from an oversnow ground-based traverse along the centre of the Amery Ice Shelf from A509 (69.06 S, 72.15 E) to T4 (71.22 S, 69.48 E), including two transverse arms; between G1 (69.49 S, 71.72 E) and A119 (69.81 S, 73.28 E); and between T3 (70.79 S, 68.89 E) and T2 (71.00 S, 70.75 E) during the 1968 spring-summer season. More information can be found at the BEDMAP website.

The fields in this dataset are:

Mission ID Latitude Longitude Ice Thickness Surface Elevation Water Column Thickness Bed Elevation

Abstract

This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied.

This is a virtual dataset as the original is too large to upload to the registry. It may be accessed via the State of Victoria www.data.vic.gov.au

Part of the Vicmap Features of Interest dataset series. This layer is derived from the Register of Geographic Names.

Named locations described in this layer include town names, buildings/structures and place names in general.These locations are stored as named points. The layers primary function is to support production of map annotation and as a general reference for localities.

The Register is the primary reference for official names and their applications. The Register holds the status of names (e.g. official; official alternative; official historical; etc). To provide the official legal name of a place or feature or asset as in section 18 of the Geographic Place Names Act 1998.

Purpose

To provide accurate locations to named features of interest in Victoria

Dataset History

This data was derived from the Register of Geographic Names.

Description: 1:25 000 maps (approx. 100m accuracy); 1:100 000 maps (approx. 1000m accuracy) Determination: Comparison to independent source Vertical Accuracy (m): N/A (no height data maintained)

Dataset Citation

Victorian Department of Environment, Land, Water and Planning (2016) Vicmap towns, virtual dataset.. Bioregional Assessment Source Dataset. Viewed 05 October 2018, http://data.bioregionalassessments.gov.au/dataset/bc953598-7bb2-4667-8ff2-e2849234b82d.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This dataset includes the current boundary data required for the bioregional assessment impact analysis for the Hunter (HUN) subregion. These data are (1) the current Preliminary Assessment Extent (PAE) which for Hunter is the current subregion boundary, (2) the Analysis Extent (AE) and (3) and the Analysis Domain Extent (AD).

The PAE is defined and explained in the BA submethodology (1.3 Description of the water-dependent asset register) and, specifically for the HUN subregion in product 1.3 Water-dependent asset register for the HUN subregion. The Analysis Extent (AE) is defined as the geographic area that encompasses all the possible areas that may be reported as part of the impact analysis component of a bioregional assessment, specifically, the subregion boundary and the PAE. The Analysis Domain extent (AD) is defined as the geographic area used for geoprocessing and data preparation purposes that encompasses the Analysis Extent plus additional areas sufficient to ensure all relevant data is included for the impact analysis component of a bioregional assessment. For HUN, the ADE had at least an additional 20 km geographic buffer added to the AE boundary.

All data are in the Australian Albers coordinate system (EPSG 3577).

Purpose

The purpose of the various boundary polygons are to assist in the efficient spatial analysis of the impact of coal resource development in the Hunter subregion.

Dataset History

This dataset includes the current boundary data required for the bioregional assessment impact analysis for the Hunter (HUN) subregion. These data are (1) the current Preliminary Assessment Extent (PAE) which for Hunter is the current subregion boundary, (2) the Analysis Extent (AE) and (3) and the Analysis Domain Extent (AD).

The PAE is defined and explained in the BA submethodology (1.3 Description of the water-dependent asset register) and, specifically for the HUN subregion in product 1.3 Water-dependent asset register for the NAM subregion. The Analysis Extent (AE) is defined as the geographic area that encompasses all the possible areas that may be reported as part of the impact analysis component of a bioregional assessment, specifically, the subregion boundary and the PAE. The Analysis Domain extent (AD) is defined as the geographic area used for geoprocessing and data preparation purposes that encompasses the Analysis Extent plus additional areas sufficient to ensure all relevant data is included for the impact analysis component of a bioregional assessment. For HUN, the ADE had at least an additional 20 km geographic buffer added to the AE boundary.

All data are in the Australian Albers coordinate system (EPSG 3577).

Dataset Citation

Bioregional Assessment Programme (XXXX) HUN Analysis boundaries 20170106 v03. Bioregional Assessment Derived Dataset. Viewed 28 August 2018, http://data.bioregionalassessments.gov.au/dataset/20d25db8-75fd-46f2-a64c-c249c8b40a95.

Dataset Ancestors

• Derived From Hunter bioregion (IBRA Version 7)

• Derived From Gippsland Project boundary

• Derived From Bioregional Assessment areas v04

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From Victoria - Seamless Geology 2014

• Derived From Bioregional Assessment areas v05

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From GEODATA TOPO 250K Series 3

• Derived From Bioregional Assessment areas v06

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From Interim Biogeographic Regionalisation for Australia (IBRA), Version 7 (Regions)

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)