DIVA-GIS's admin2 file of South Africa

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DATASET: Alpha version 2010 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/). REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. DATE OF PRODUCTION: January 2013

This data is part of the series of maps that covers the whole of Australia at a scale of 1:250 000 (1cm on a map represents 2.5km on the ground) and comprises 513 maps. This is the largest scale at which published topographic maps cover the entire continent. Data is downloadable in various distribution formats.

This data is part of the series of maps that covers the whole of Australia at a scale of 1:250 000 (1cm on a map represents 2.5km on the ground) and comprises 513 maps. This is the largest scale at which published topographic maps cover the entire continent. Data is downloadable in various distribution formats.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.

A major challenge to bedrock mineral exploration in many parts of South Australia is the need to be able to explore efficiently and effectively through extensive and thick regolith. Understanding and mapping regolith distribution and composition... A major challenge to bedrock mineral exploration in many parts of South Australia is the need to be able to explore efficiently and effectively through extensive and thick regolith. Understanding and mapping regolith distribution and composition can be a very cost-efficient and powerful exploration tool. The Geological Survey of South Australia has recently compiled a seamless statewide 1:2 million scale regolith map of South Australia and an accompanying detailed GIS dataset that portrays regolith materials, as well as landforms and regolith overprints (induration, lag). The regolith map aims to provide a statewide representation of regolith materials and landforms and thus presents a broad-scale framework for guiding geochemical prospecting for a wide range of minerals both beneath and within the regolith, as well as addressing land use, groundwater and other environmental issues.

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The GIS market share in EMEA is expected to increase to USD 2.01 billion from 2021 to 2026, and the market’s growth momentum will accelerate at a CAGR of 8.23%.

This EMEA GIS market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers GIS market in EMEA segmentation by:

Component - Software, data, and services
End-user - Government, utilities, military, telecommunication, and others

What will the GIS Market Size in EMEA be During the Forecast Period?

Download the Free Report Sample to Unlock the GIS Market Size in EMEA for the Forecast Period and Other Important Statistics

The EMEA GIS market report also offers information on several market vendors, including arxiT SA, Autodesk Inc., Bentley Systems Inc., Cimtex International, CNIM SA, Computer Aided Development Corp. Ltd., Environmental Systems Research Institute Inc., Fugro NV, General Electric Co., HERE Global BV, Hexagon AB, Hi-Target, Mapbox Inc., Maxar Technologies Inc., Pitney Bowes Inc., PSI Services LLC, Rolta India Ltd., SNC Lavalin Group Inc., SuperMap Software Co. Ltd., Takor Group Ltd., and Trimble Inc. among others.

GIS Market in EMEA: Key Drivers, Trends, and Challenges

The integration of BIM and GIS is notably driving the GIS market growth in EMEA, although factors such as data viability and risk of intrusion may impede market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the GIS industry in EMEA. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key GIS Market Driver in EMEA

One of the key factors driving the geographic information system (GIS) market growth in EMEA is the integration of BIM and GIS. A GIS adds value to BIM by visualizing and analyzing the data with regard to the buildings and surrounding features, such as environmental and demographic information. BIM data and workflows include information regarding sensors and the placement of devices in IoT-connected networks. For instance, Dubai's Civil Defense Department has integrated GIS data with its automatic fire surveillance system. This information is provided in a matter of seconds on the building monitoring systems of the Civil Defense Department. Furthermore, location-based services offered by GIS providers help generate huge volumes of data from stationary and moving devices and enable users to perform real-time spatial analytics and derive useful geographic insights from it. Owing to the advantages associated with the integration of BIM with GIS solutions, the demand for GIS solutions is expected to increase during the forecast period.

Key GIS Market Challenge in EMEA

One of the key challenges to the is the GIS market growth in EMEA is the data viability and risk of intrusion. Hackers can hack into these systems with malicious intentions and manipulate the data, which could have destructive or negative repercussions. Such hacking of data could cause nationwide chaos. For instance, if a hacker manipulated the traffic management database, massive traffic jams and accidents could result. If a hacker obtained access to the database of a national disaster management organization and manipulated the data to create a false disaster situation, it could lead to a panic situation. Therefore, the security infrastructure accompanying the implementation of GIS software solutions must be robust. Such security threats may impede market growth in the coming years.

Key GIS Market Trend in EMEA

Integration of augmented reality (AR) and GIS is one of the key geographic information system market trends in EMEA that is expected to impact the industry positively in the forecast period. AR apps could provide GIS content to professional end-users and aid them in making decisions on-site, using advanced and reliable information available on their mobile devices and smartphones. For instance, when the user simply points the camera of the phone at the ground, the application will be able to show the user the location and orientation of water pipes and electric cables that are concealed underground. Organizations such as the Open Geospatial Consortium (OGC) and the World Wide Web Consortium (W3C) are seeking investments and are open to sponsors for an upcoming AR pilot project, which seeks to advance the standards of AR technology at both respective organizations. Such factors will further support the market growth in the coming years.

This GIS market in EMEA analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth st

A core function of Region 6 Science Applications is to strategically identify and support applied landscape science, and to support its partners' capacity to interpret and use the science. These pages contain information on how projects are selected, project details and products, including the partnerships that are engendered and funds leveraged.LR6 Science Applications has funded various strategic science support projects since 2010 that are closely tied to the Region, FWS Program, and partner priorities. This dataset has cataloged much of the information needed to track the projects and distribute information. One layer (Projects polygon) and 12 non-spatial tables (ConsvTargets,Deliverables,Deliverables_File_Detail,EcotypicArea,Funding_Recipients_Dispersal,Funding_Source,FundReportingInKind,Goals,PrjContacts,PrjContacts_additional_groups,Stressors,StatesProvinces) make up the dataset.

Abstract

The Master List of Schools is a record of all schools in South Africa. The data forms part of the national Education Management Information Systems (EMIS) database used to inform education policymakers and managers in the Department of Basic Education (DBE) and the Provincial education departments, as well as to provide valuable information to external stakeholders. The list is maintained by provincial departments and regularly sent to DBE for updating. A key function of the master list is to uniquely identify each school in the country through a school identifier called the EMIS number. Additionally, the list contains data on school quintiles - categories (quintiles) based on the socioeconomic status of the community in which the school is situated. Analyses comparing schools' performance often use school quintiles as control measures for socioeconomic status, to take into account the effect of, for example, poor infrastructure, shortage of materials and deprived home backgrounds on school performance. There are also other basic data fields in the school master list that could provide the means to answer some of the most frequently asked questions about learner enrolment, teachers and learner-teacher ratio of schools. It is a useful dataset for education planners and researchers and is even widely used in the private sector by those who regularly deal with schools.

Geographic coverage

The data has national coverage

Analysis unit

Individuals and institutions

Universe

The survey covers all schools (ordinary and special needs) in South Africa, both public and independent.

Kind of data

Administrative records and survey data

Mode of data collection

Other

Research instrument

Data from the SNAP survey and ANA that are used to compile the Master List of Schools is collected with a survey questionnaire and educator forms. The principle completes the survey questionnaire and each educator (both state paid and other) in each school completes an educator form. Schools record their EMIS number provided by the DBE on the questionnaire and form for identification.

Data appraisal

The 2023 series only includes data for quarter 2 and quarter 3. The GIS coordinates for schools in the Eastern Cape are incorrectly entered in the original data from the DBE. The data entered in the GIS_long variable is incorrectly entered into the GIS_lat variable. This issue only occurs for schools in the Eastern Cape (EC), all other GIS coordinates for all the other provinces is correct. Therefore, for geospatial analysis, users can swap the GIS coordiate data only for the Eastern Cape.

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This data release contains the GIS data supporting U.S. Geological Survey Open-File Report (OFR) 2005-1252, "The Geologic Map of Seattle—A Progress Report," published in 2005 by Kathy Goetz Troost, Derek B. Booth, Aaron P. Wisher, and Scott A. Shimel (https://doi.org/10.3133/ofr20051252). The OFR was prepared for the 2005 Washington Hydrogeology Symposium and describes the status of geologic mapping for Seattle, Washington, at the time. The map is the result of field mapping and compilation of subsurface geologic data during the years 1999–2004 and was funded by the City of Seattle and the U.S. Geological Survey. Data from more than 36,000 exploration points, geotechnical borings, monitoring wells, excavations, and outcrops were used in making the map. The northern part of the 2005 OFR and the supporting GIS data were subsequently published as two geologic maps: Booth, D.B., Troost, K.G., and Shimel, S.A., 2005, Geologic map of northwestern Seattle (part of the Seattle North 7.5’ X 15’ Quadrangle), King County, Washington: U.S. Geological Survey Scientific Investigations Map 2903, https://doi.org/10.3133/sim2903. Booth, D.B., Troost, K.G., and Shimel, S.A., 2009, Geologic map of northeastern Seattle (part of the Seattle North 7.5' x 15' quadrangle), King County, Washington: U.S. Geological Survey Scientific Investigations Map 3065, https://doi.org/10.3133/sim3065. The southern part of the 2005 OFR and the supporting GIS data were not subsequently published for various reasons. With the original authors' permission, the GIS data used to create the map shown in OFR 2005-1252 are being released here to best meet modern open-data standards and to allow for use in future studies and mapping. The data included in this data release are only those components necessary to create the map shown in OFR 2005-1252. The following map features were not available and are not included in this data release: bedding point data, faults, anticlines, and contact lines. OFR_2005-1252.gdb is an Esri geodatabase containing the following feature classes: ofr_2005_1252_geology_poly (1,068 features); ofr_2005_1252_fill_poly (424 features); ofr_2005_1252_seattle_fault_zone_poly (1 feature); ofr_2005_1252_wastage_landslide_deposits_poly (188 features); ofr_2005_1252_beds_line (6 features); and ofr_2005_1252_scarp_line (351 features). Metadata records associated with each of these elements contain more detailed descriptions of their purposes, constituent entities, and attributes. A shapefile (non-geodatabase) version of the dataset is also included, although due to character limits, some field names and text cells in the attribute tables were truncated relative to the equivalent values in the geodatabase. The authors ask that users of the geologic map data cite both the open-file report and the GIS data release: Open-File Report: Troost, K.G., Booth, D.B., Wisher, A.P., and Shimel, S.A., 2005, The geologic map of Seattle—a progress report: U.S. Geological Survey Open-File Report 2005-1252, https://doi.org/10.3133/ofr20051252. GIS data: Troost, K.G., Booth, D.B., Wisher, A.P., and Shimel, S.A., 2024, GIS data for U.S. Geological Survey OFR 2005-1252, The geologic map of Seattle—a progress report: U.S. Geological Survey data release, https://doi.org/10.5066/P93L6SPS.

The Soil and Terrain database for South Africa primary data (version 1.0), at scale 1:1 million (SOTER_South_Africa), was compiled of enhanced soil information within the framework of the FAO's program Land Degradation Assessment in Drylands (LADA). Primary soil and terrain data for South Africa were obtained from the SOTERSAF database (ver. 1) at scale 1:2 million. This version of SOTER_South_Africa includes some changes in the GIS file, based on the SRTM-DEM derived data and a changes of the attributes database.

SOTER forms a part of the ongoing activities of ISRIC, FAO and UNEP to update the world's baseline information on natural resources.The project involved collaboration with national soil institutes from the countries in the region as well as individual experts.

The tabular and visual dataset focuses on South African basic education and provides insights into the distribution of schools and basic population statistics across the country. This tabular and visual data are stratified across different quintiles for each provincial and district boundary. The quintile system is used by the South African government to classify schools based on their level of socio-economic disadvantage, with quintile 1 being the most disadvantaged and quintile 5 being the least disadvantaged. The data was joined by extracting information from the debarment of basic education with StatsSA population census data. Thereafter, all tabular data and geo located data were transformed to maps using GIS software and the Python integrated development environment. The dataset includes information on the number of schools and students in each quintile, as well as the population density in each area. The data is displayed through a combination of charts, maps and tables, allowing for easy analysis and interpretation of the information.

The main objectives was to investigate the role of fire and the compnents of fire regime,as a detrmining factor for woody vegetation cover of savannas. Use was made of remote sensing and GIS to look at fire and fire regime ,as a determining factor for woody vegetation cover of savannas. Use was made of rempote sending and GIS to look at fire and fire regime over a range of conditions differing in rainfall and geology.

Historical holdings data showing quarterly positions, market values, shares held, and portfolio percentages for GIS held by AXA S A from Q3 2013 to Q1 2025

South Australia MGA Geoscientific GIS DVD The South Australia MGA GIS DVD is a consolidation of the spatial data previously available in the State and Provincial CD data packages, presented as MGA Zones 52, 53 and 54 projections.

New Orleans Dataset for GRASS GIS
This geospatial dataset contains raster and vector data for New Orleans, Louisiana, USA. The top level directory new-orleans-dataset is a GRASS GIS location for the North American Datum of 1983 (NAD 83) / Louisiana South State Plane Feet with EPSG code 3452. Inside the location there are the PERMANENT mapset with citywide data, a vieux_carre mapset with data for the French Quarter, Python scripts for data processing, data records, a color table, a license file, and readme file.

Instructions
Install GRASS GIS, unzip this archive, and move the location into your GRASS GIS database directory. If you are new to GRASS GIS read the first time users guide.

Data Sources

• U.S. Army Corps of Engineers 2012 Lidar Survey of New Orleans
• New Orleans Open Data

License
This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon. The scripts are licensed under the GNU General Public License 3.0 by Brendan Harmon. The graphics are licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0) by Brendan Harmon.

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