Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work. Each tutorial video is also accompanied by a written script, providing a step-by-step reference that users can follow alongside the video or consult afterwards.

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

A listing of web services published from the authoritative East Baton Rouge Parish Geographic Information System (EBRGIS) data repository. Services are offered in Esri REST, and the Open Geospatial Consortium (OGC) Web Mapping Service (WMS) or Web Feature Service (WFS) formats.

This data set was generated through the 2020 LU/LC update mapping effort. The 2020 update is the seventh in a series of land use mapping efforts that was begun in 1986. Revisions and additions to the initial baseline layer were done in subsequent years from imagery captured in 1995/97, 2002, 2007, 2012, 2015 and now, 2020. This present 2020 update was created by comparing the 2015 LU/LC layer from NJDEP's Geographic Information Systems (GIS) database to 2020 color infrared (CIR) imagery and delineating and coding areas of change. Work for this data set was done by Aerial Information Systems, Inc., Redlands, CA, under direction of the New Jersey Department of Environmental Protection (NJDEP), Bureau of Geographic Information System (BGIS). LU/LC changes were captured by adding new line work and attribute data for the 2020 land use directly to the base data layer. All 2015 LU/LC polygons and 2015 LU/LC coding remains in this data set, so change analysis for the period 2015-2020 can be undertaken from this one layer. The mapping was done by USGS HUC8 basins, 13 of which cover portions of New Jersey. This statewide layer is composed of the final data sets generated for each HUC8 basin. Initial QA/QC was done on each HUC8 data set as it was produced with final QA/QC and basin-to-basin edgematching done on this statewide layer. The classification system used was a modified Anderson et al., classification system. Minimum mapping unit (MMU) is 1 acre for changes to non-water and non-wetland polygons. Changes to these two categories were mapped using .25 acres as the MMU. (See entry under the Advisory section concerning additional review being done on NHD waterbody attribute coding and impervious surface estimation.) ADVISORY This data set, edition 20231120, is a statewide layer that includes updated land use/land cover data for all HUC8 basins in New Jersey. The polygon delineations and associated land use code assignments are considered the final versions for this mapping effort. Note, however, that there is continuing review being done on this layer to update several additional attributes not presently evaluated in this edition. These attributes include several from the National Hydrography Database (NHD) that are specific to the waterbodies mapped in this layer, and several attributes containing impervious surface estimates for each polygon. Evaluating the NHD codes facilitates extracting the water features mapped in this layer and using them to update the New Jersey portion of the NHD. Those NHD specific attributes are still being evaluated and may be added to a future edition of this base data set. Similarly, additional review is being done to assess the feasibility of incorporating data on impervious surface (IS) amounts generated from two independent projects, one of which was just completed by NOAA, into this base land use layer. While the NHD and IS attributes will enhance the use of this base layer in several types of analyses, this present layer can be used for doing all primary land use analyses without having those attributes evaluated. Further, evaluating these extra attributes will result in few, if any, changes to the polygon delineations and standard land use coding that are the primary features of this layer. As such, the layer is being provided in its present edition for general use. As the additional attributes are evaluated, they may be added to a future edition of this data set. The basic land use features and codes, however, as mapped in this version of the data set will serve as the base 2020 LU/LC update. As stated in this metadata record's Use Constraints section, NJDEP makes no representations of any kind, including, but not limited to, the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the digital data layers furnished hereunder. NJDEP assumes no responsibility to maintain them in any manner or form. By downloading this data, user agrees to the data use constraints listed within this metadata record.The data for Somerset County data was extracted & processed from the latest dataset by the Somerset County Office of GIS Services (SCOGIS).

This dataset is the first 1: 100,000 desert spatial database in China based on the graphic data of desert thematic maps. It mainly reflects the geographical distribution, area size, and mobility of sand dunes in China. According to the system design requirements and relevant standards, the input data is standardized and uniformly converted into a standard format for various types of data input. Build a library to run the delivery system. This project uses the TM image in 2000 as the information source, and interprets, extracts, and edits the coverage of the national land use map and TM digital image information in 2000. It uses remote sensing and geographic information system technology to 1: 100,000 Thematic mapping requirements for scale bar maps were made on the desert, sandy land and gravel Gobi in China. The 1: 100,000 desert map across the country can save users a lot of data entry and editing work when they are engaged in research on resources and the environment. Digital maps can be easily converted into layout maps The dataset properties are as follows: Divided into two folders e00 and shp: Desert map name and province comparison table in each folder 01 Ahsm Anhui 02 Bjsm Beijing 03 Fjsm Fujian 04 Gdsm Guangdong 05 Gssm Gansu 06 Gxsm Guangxi Zhuang Autonomous Region 07 Gzsm Guizhou 08 Hebsm Hebei 09 Hensm Henan 10 Hljsm Heilongjiang 11 Hndsm Hainan 12 Hubsm Hubei 13 Jlsm Jilin Province 14 Jssm Jiangsu 15 Jxsm Jiangxi 16 Lnsm Liaoning 17 Nmsm Inner Mongolia Gu Autonomous Region 18 Nxsm Ningxia Hui Autonomous Region 19 Qhsm Qinghai 20 Scsm Sichuan 21 Sdsm Shandong 22 Sxsm Shaanxi Province 23 Tjsm Tianjin 24 Twsm Taiwan Province 25 Xjsm Xinjiang Uygur Autonomous Region 26 Xzsm Tibet Autonomous Region 27 Zjsm Zhejiang 28 Shxsm Shanxi 1. Data projection: Projection: Albers False_Easting: 0.000000 False_Northing: 0.000000 Central_Meridian: 105.000000 Standard_Parallel_1: 25.000000 Standard_Parallel_2: 47.000000 Latitude_Of_Origin: 0.000000 Linear Unit: Meter (1.000000) 2. Data attribute table: area (area) perimeter ashm_ (sequence code) class (desert encoding) ashm_id (desert encoding) 3. Desert coding: mobile sandy land 2341010 Semi-mobile sandy land Semi-fixed sandy land 2341030 Gobi 2342000 Saline land 2343000 4: File format: National, sub-provincial and county-level desert map data types are vector shapefiles and E00 5: File naming: Data organization based on the National Basic Resources and Environmental Remote Sensing Dynamic Information Service System is performed on the file management layer of Windows NT. The file and directory names are compound names of English characters and numbers. Pinyin + SM composition, such as the desert map of Gansu Province is GSSM. The flag and county desert map is the pinyin + xxxx of the province name, and xxxx is the last four digits of the flag and county code. The division of provinces, districts, flags and counties is based on the administrative division data files in the national basic resources and environmental remote sensing dynamic information service operation system.

Some of the finest mountain scenery in the Southwest is found in the 1.6-million-acre Santa Fe National Forest. Here, you can find the headwaters of Pecos, Jemez, and Gallinas Rivers; mountain streams; lakes; and trout fishing. Travel into Pecos, San Pedro Parks, Chama, and Dome Wildernesses via wilderness pack trips, saddle, or on 1,000 miles of hiking trails. Try whitewater rafting on the Rio Chama or Rio Grande from May to September. Consider turkey, elk, deer, and bear hunting, or visit one of many nearby Indian pueblos, Spanish missions, and Indian ruins. Golden aspen grace the high country from September to October and snow blankets Santa Fe Ski Basin in winter. The Santa Fe National Forest GIS data available for download includes Santa Fe National Forest Geospatial (GIS) Datasets, Motor Vehicle Use Map (MVUM) Travel Aids - digital maps and data of the SFNF to upload to GPS units or Smart Phones, 7.5 Minute Topographic Maps (PDF and GeoTIFF) - US Forest Service topo maps only, USFS Geospatial Clearinghouse - includes GIS data of vegetation treatments, administrative boundaries, inventoried roadless areas, FSTopo datasets, USGS Map Locator and Downloader - download current and historic topo maps, Hardcopy Maps with information on how to purchase hard copy visitor, wilderness, or topographic maps. Resources in this dataset:Resource Title: Santa Fe National Forest Geospatial Data. File Name: Web Page, url: https://www.fs.usda.gov/main/santafe/landmanagement/gis

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

Observations and subtle shifts of vegetation communities in Lake Erie have USGS researchers concerned about the potential for Grass Carp to alter these vegetation communities. Broad-scale surveys of vegetation using remote sensing and GIS mapping, coupled with on-the-ground samples in key locations will permit assessment of the effect Grass Carp may have already had on aquatic vegetation communities and establish baseline conditions for assessing future effects. Existing aerial imagery was used with object-based image analysis to detect and map aquatic vegetation in the eastern basin of Lake Erie.

This data collection uses a Geographic Information System (GIS) to organize and characterize information about benthic communities and substrates, which are ecological environments of the sea floor. The communities in this data collection include coral, seagrass, bare bottom, hard bottom, tidal flat, saltmarsh, and halophila communities. This coverage shows benthic distributions of various organisms for South Florida. The data provider included a metadata file in the FGDC description with the data files in this collection. Geographic Information System (GIS) software is required to fully utilize the contents of this accession.

Department of Parks and Recreation (DPR) properties identified as polygons. The dataset contains general locations and amenity information about the properties under the jurisdiction of the DC Department of Parks and Recreation. It has been created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. This data is provided by the Department of Parks and Recreation.

Abstract : The search for the most appropriate GIS data model to integrate, manipulate and analyse spatio-temporal data raises several research questions about the conceptualisation of geographic spaces. Although there is now a general consensus that many environmental phenomena require field and object conceptualisations to provide a comprehensive GIS representation, there is still a need for better integration of these dual representations of space within a formal spatio-temporal database. The research presented in this paper introduces a hybrid and formal dual data model for the representation of spatio-temporal data. The whole approach has been fully implemented in PostgreSQL and its spatial extension PostGIS, where the SQL language is extended by a series of data type constructions and manipulation functions to support hybrid queries. The potential of the approach is illustrated by an application to underwater geomorphological dynamics oriented towards the monitoring of the evolution of seabed changes. A series of performance and scalability experiments are also reported to demonstrate the computational performance of the model.Data Description : The data set used in our research is a set of bathymetric surveys recorded over three years from 2009 to 2011 as Digital Terrain Models (DTM) with 2m grid spacing. The first survey was carried out in February 2009 by the French hydrographic office, the second one was recorded on August-September 2010 and the third in July 2011, both by the “Institut Universitaire Européen de la Mer”.

GISCO (Geographic Information System of the COmmission) is responsible for meeting the European Commission's geographical information needs at three levels: the European Union, its member countries, and its regions.

In addition to creating statistical and other thematic maps, GISCO manages a database of geographical information, and provides related services to the Commission. Its database contains core geographical data covering the whole of Europe, such as administrative boundaries, and thematic geospatial information, such as population grid data. Some data are available for download by the general public and may be used for non-commercial purposes. For further details and information about any forthcoming new or updated datasets, see http://ec.europa.eu/eurostat/web/gisco/geodata.

This metadata refers to the whole content of GISCO reference database extracted in June 2020, which contains both public datasets (also available for the general public through http://ec.europa.eu/eurostat/web/gisco/geodata) and datasets to be used only internally by the EEA (typically, but not only, GISCO datasets at 1:100k). The document GISCO-ConditionsOfUse.pdf provided with the dataset gives information on the copyrighted data sources, the mandatory acknowledgement clauses and re-dissemination rights. The license conditions for EuroGeographic datasets in GISCO are provided in a standalone document "LicenseConditions_EuroGeographics.pdf".

The database is provided in GPKG files, with datasets at scales from 1:60M to 1:100K, with reference years spanning until 2021 (e.g. NUTS 2021). Attribute files are provided in CSV. The database manual, a file with the content of the database, a glossary, and a document with the naming conventions are also provided with the database.

The main updates with respect to the previous version of the full database in the SDI (from Jul. 2018) are the addition of the following datasets: - Administrative boundaries at country level, 2020 (CNTR_2020) - Administrative boundaries at commune level, 2016 (COMM_2016) - Coastline boundaries, 2016 (COAS_2016) - Exclusive Economic Zones, 2016 (EEZ_2016)

- Farm Accountancy Data Network based on NUTS 2016, 2018 (FADN_2018)

             Local Administrative Units, 2018 (LAU_2018)

• Nomenclature of Territorial Units for Statistics, 2021 (NUTS_2021)
• Political regions (NB.: defined by the Committee of the Regions), 2018 (POLREG_2018)
• Pan-European Settlements, 2016 (STLL_2016) and 2018 (STLL_2018)
• Transport Networks (NB.: railway lines, railway stations, roads, road junctions, levelcrossings, ferry routs and custom points), 2019 (TRAN_2019)
• Urban Audit Areas, 2018 (URAU_2018) and 2020 (URAU_2020)

NOTE: This metadata file is only for internal EEA purposes and in no case replaces the official metadata provided by Eurostat. For specific GISCO datasets included in this version there are individual EEA metadata files in the SDI: NUTS_2021 and CNTR_2020. For other GISCO datasets in the SDI, it is recommended to use the version included in this dataset. The original metadata files from Eurostat for the different GISCO datasets are available via ECAS login through the Eurostat metadata portal on https://webgate.ec.europa.eu/inspire-sdi/srv/eng/catalog.search#/home. For the public products metadata can also be downloaded from https://ec.europa.eu/eurostat/web/gisco/geodata. For more information about the full database or any of its datasets, please contact the SDI Team (sdi@eea.europa.eu).

Xverum delivers high-utility Point of Interest (POI) data covering 230M+ locations globally. Our data is precision-built for SaaS platforms, AI models, and logistics, ensuring you receive grounded, accurate signals without the need for costly cleanup.

With regular updates and continuous POI discovery, Xverum ensures accurate, up-to-date information on businesses, landmarks, retail stores, and more. Delivered in bulk to S3 Bucket and cloud storage, our dataset integrates seamlessly into mapping, geographic information systems, and analytics platforms.

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Rich Business Insights: ✅ Detailed business attributes, including company name, category, and subcategories. ✅ Contact details, including phone number and website (if available). ✅ Consumer review insights, including rating distribution and total number of reviews (additional feature). ✅ Operating hours where available.

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The City of Norfolk Open GIS Data Site. This site contains various spatial data that can be used by anyone with an interest in geographic information systems (GIS) data for their applications. The City’s datasets are updated regularly and can be downloaded or accessed for free from this site. If you don’t see a particular dataset you are looking for, please check back often, as we will be providing additional data to the site in the future.

The spatial dataset consists of 743 landslide polygons, landslide centroid points, randomly non-landslide points, and 11 landslide-controlling factors. Landslide polygons were delineated through manual interpretation of high-resolution satellite imagery. The landslide-controlling factor data were extracted from topographic maps and Indonesia’s national digital elevation model (DEMNAS). The landslide-event dataset was mapped by comparing pre- and post-event (Tropical cyclone (TC) Cempaka, which occurred on 27–29 November 2017) high-resolution satellite imageries and conducting field surveys. The landslide polygons indicate areas with confirmed landslide occurrences, while the landslide-controlling factors data includes slope aspect, distance to river, distance to road, elevation, lithology, landuse, plan curvature, profile curvature, slope, stream power index, and terrain wetness index. The landslide polygons and points are stored in gpkg format, while the landslide controlling factors are stored in tif format. Files with xml and tfw extensions are text files used to store metadata and georeference of a tif raster file. All data can be opened using GIS software such as QGIS. The datasets can also be accessed and opened using R or Python using specified geospatial libraries such as SF and Terra.

This dataset provides detailed geospatial and operational information for utility assets such as poles, towers, underground cables, manholes, and pad-mounted equipment. It enables precise mapping, network connectivity analysis, and outage management planning for utility infrastructure, supporting both operational efficiency and strategic decision-making.

This hosted feature layer has been published in RI State Plane Feet NAD83.This is a statewide, seamless digital dataset of the land cover/land use for the State of Rhode Island derived using semi-automated methods and based on imagery captured in 2003-2004. The project area encompasses the State of Rhode Island and also extends 1/2 mile into the neighboring states of Connecticut and Massachusetts or to the limits of source orthophotography. Geographic feature accuracy meets the National Mapping Standards for 1:5000 scale mapping with respect to base level data (roads, hydrography, and orthos). The minimum mapping unit for this dataset is .5 acre. The land use classification scheme used for these data was based on the Anderson Level III modified coding schema used in previous land use datasets in Rhode Island (1988 & 1995) with some modifications for the 2003 classification.The dataset is also intended to be incorporated into the Rhode Island Geographic Information System database for use by federal, state and local government and made available to the general public under established RIGIS licensing procedures.This hosted feature service layer replaces the map service https://maps.edc.uri.edu/arcgis/rest/services/Atlas_PLAN/Land_Use_and_Land_Cover_0304/MapServer/0

This boundary dataset complements 13 other datasets as part of a study that compared ancient settlement patterns with modern environmental conditions in the Jazira region of Syria. This study examined settlement distribution and density patterns over the past five millennia using archaeological survey reports and French 1930s 1:200,000 scale maps to locate and map archaeological sites. An archaeological site dataset was created and compared to and modelled with soil, geology, terrain (contour), surface and subsurface hydrology and normal and dry year precipitation pattern datasets; there are also three spreadsheet datasets providing 1963 precipitation and temperature readings collected at three locations in the region. The environmental datasets were created to account for ancient and modern population subsistence activities, which comprise barley and wheat farming and livestock grazing. These environmental datasets were subsequently modelled with the archaeological site dataset, as well as, land use and population density datasets for the Jazira region. Ancient trade routes were also mapped and factored into the model, and a comparison was made to ascertain if there was a correlation between ancient and modern settlement patterns and environmental conditions; the latter influencing subsistence activities. This boundary dataset was generated to define the extent of the study area, which comprises the border between Syria and Turkey, Syria and Iraq, the River Tigris and the River Euphrates. All related data collected was confined within this boundary dataset with the exception of the archaeological dataset. Archaeological sites were identified and mapped along both banks of the River Euphrates. Also, the town of Dayr az-Zawr, where the 1963 precipitation and temperature monthly values were collected for one of the datasets, falls outside the Jazira Region. Derived from 1:200,000 French Levant Map Series (Further Information element in this metadata record provides list of sheets).The boundary line dataset was captured from 11 map sheets, which were based on the French Levant surveys conducted in Syria during the 1930s and mapped at a scale of 1:200,000. The size of each map measures 69 x 59 cm. The boundary line on each sheet was traced to mylar. Subsequently, each mylar sheet was photocopied and reduced in size to an 11 x 17 inch sheet. These sheets were merged to form the contiguous area comprising the full extent of the boundary for the study area. This was then traced again to another mylar sheet and subsequently scanned and cleaned for further processing and use in a GIS as a polygon coverage. Thesis M 2001 MATH, Ohio University Mathys, Antone J 'A GIS comparative analysis of bronze age settlement patterns and the contemporary physical landscape in the Jazira Region of Syria'., French Levant Map Series (1:200,000) for Syrie (Syria). Projected to Lambert grid. These are colour maps measuring to 69 x 59 cm in size. The dataset was created from the following sheet numbers and titles: 1) NI-37 XVII, Abou Kemal 2) NI-37 XVIII, Ana 3) NI-37 XXI, Ressafe 4) NI-37 XXII, Raqqa 5) NI-37 XXIII, Deir ez Zoir 6) NI-37 XXIV, Bouara 7) NI-37-III, Djerablous 8) NJ-37 IV, Toual Aaba 9) NJ-37 V, Hassetche 10) NJ-37 VI, Qamishliye-Sinjar 11) (No sheet number), Qaratchok-Darh Dressepar la Service Geographique des F.F.L. en 1945 Reimprime par l'Institut Geographique National en 1950 (Originally produced by this Geographic Service of the F.F.L. (Forces Francaises Libres) in 1945 and reprinted by the National Geographic Institute in 1950). Paris: France. Institut Geographique National, 1945-1950. Original map series might be traced to Beirut: Bureau Topographique des Troupes francaises du Levant, 1933-1938. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2010-06-09 and migrated to Edinburgh DataShare on 2017-02-21.

The Urban Place GIS Coverage of Mexico is a vector based point Geographic Information System (GIS) coverage of 696 urban places in Mexico. Each Urban Place is geographically referenced down to one tenth of a minute. The attribute data include time-series population and selected census/geographic data items for Mexican urban places from from 1921 to 1990. The cartographic data include urban place point locations on a state boundary file of Mexico. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI) and the Environmental Research Institute (ERI) of Michigan.