This is a link to the QGIS website where you can download open-source GIS software for viewing, analyzing and manipulating geodata like our downloadable shapefiles.

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.

QGIS is a Free and Open Source Geographic Information System. This dataset contains all the information to get you started.

Maps have always been a powerful tool for visualizing data. Participants will learn how to link the static data of census tables to census geographies by using open-source GIS software. Participants will learn how to join data, calculate new attributes, symbolize geography and create maps. No prior GIS experience is necessary. QGIS will be required to be downloaded prior to the workshop, and laptops will be required. Download instructions https://qgis.org/en/site/forusers/download.html. Download data files https://drive.google.com/drive/folders/1xrAj_BrPtMDBgdi9MXWGcrcuVGfTsGgi?usp=sharing

The datasets represent topographic description (cost and accessibility maps) of Khabarovsk and Primorsky Krais of the Russian Far East divided into unit areas with a 10x10 km grid in WGS84. The datasets are in MID/MIF formats to be processed in QGIS with use of self-written open source software. The datasets are used to model single or multiple socio-economic scenarios of regional spatial development and inter-regional economic cooperation.

In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

The GEOL-QMAPS digital geological mapping solution comprises a QGIS field data entry template, designed as an open-source, collaborative tool with user-driven updates. It integrates with a custom QGIS plugin that facilitates the import of existing field data, fieldwork preparation, and field database management (available at https://github.com/swaxi/WAXI_QF). Although developed within the framework of stage 4 of the West African eXploration Initiative project (https://waxi4.org/), this template and plugin are not region-specific and can be adapted to any mapping guidelines. The downloadable archive includes: - a folder containing the customised QGIS mapping project template and related files for field data collection, - a .docx log file providing updates for the various releases of the QGIS template project, - a .txt file with a link to the UserGuide (https://github.com/swaxi/GEOL-QMAPS/blob/main/README.md).

Abstract In this work, we report the mapping of electrical equipotential lines (1D) and equipotential surfaces (3D) using the free Quantum Geographic information system (QGIS) software. For this purpose, experiments taking into account, four different electrical configurations were performed on physics classes of undergraduate students, using two conductors of opposite electrical charges for each experiment. For the first experiment two copper parallel linear conductors; for the second, a copper parallel linear conductor with a small circular ring acting as a point charge; for the third, two concentric circular ring and for the fourth one a semicircular ring with a small circular ring acting as point charge. The experimental data were treated and interpolated in the, open source, QGIS software, used in geoprocessing, to map the electrical equipotential planes and surfaces.

Discover the booming Satellite Remote Sensing Software market! This in-depth analysis reveals market size, CAGR, key drivers, trends, and restraints, including regional breakdowns and leading companies. Explore the opportunities in agriculture, water management, and more. Learn about the growing impact of AI and open-source solutions.

Tracking an animal's location from video has many applications, from providing information on health and welfare to validating sensor-based technologies. Typically, accurate location estimation from video is achieved using cameras with overhead (top-down) views, but structural and financial limitations may require mounting cameras at other angles. We describe a user-friendly solution to manually extract an animal's location from non-overhead video. Our method uses QGIS, an open-source geographic information system, to: (1) assign facility-based coordinates to pixel coordinates in non-overhead frames; 2) use the referenced coordinates to transform the non-overhead frames to an overhead view; and 3) determine facility-based x, y coordinates of animals from the transformed frames. Using this method, we could determine an object's facility-based x, y coordinates with an accuracy of 0.13 ± 0.09 m (mean ± SD; range: 0.01–0.47 m) when compared to the ground truth (coordinates manually recorded..., Please see the description in the associated research publication., Please see the included README file.

This .zip file contains pre-configured files for members of the public to interact with Kendall County's public GIS layers in a desktop environment. Included are:An ArcGIS Pro PackageA QGIS Project FIleArcGIS Pro requires an ESRI license to use. See the ArcGIS Pro product page for more information.QGIS is free, open-source software that is available for a variety of computing environments. See the QGIS Downloads page to select the appropriate installation method.With the appropriate software installed, users can simply open the corresponding file. It may take a minute or two to load, due to the number of layers that need to load. Once loaded, users will have read-only access to all of the major public layers, and can adjust how they are displayed. In a desktop environment, users can also create and interact with other data sources, such as private site plans, annotations, and other public data layers from non-County entities.Please note that the layers included in these packages are the same live data sources found in the web maps. An internet connection is required for these files to function properly.

This data package includes two related data files that can be used as input for habitat network analyses on amphibians using a specific habitat network analysis tool (HNAT; v0.1.2-alpha):

1. AmphibianHabitatNetwork_Parameters.xlsx
2. BiotopeMap_GothenburgRegion_withPondsRoadsAndBuildings.tif

HNAT is a plugin for the open-source Geographic Information System QGIS (https://qgis.org/en/site/). HNAT can be downloaded at https://github.com/SMoG-Chalmers/hnat/releases/tag/v0.1.2-alpha. To run the habitat network analyses based on the input data provided in this package one must install the plugin HNAT into QGIS. This software has been created by Chalmers within a research project financed by the Swedish government research council for sustainable development, Formas (FR -2021/0004), within the framework of the national research program "From research to implementation for a sustainable society 2021". The Excel-file contains the parameters for amphibians and the GeoTiff-file is representing a biotope raster map covering the Gothenburg region in western Sweden. SRID=3006 (Sweref99 TM). Pixel size =10x10 metres. The pixel values of the biotope map correspond to the biotope codes listed in the in the parameter file (see column “BiotopeCode”). For each biotope the parameter file holds biotope specific parameter values for two alternative amphibian models denoted “Amphibians_NMDWater_ponds” and Amphibians_NMDWater_ponds_NoFriction”. The two alternative parameter settings can be used to demonstrate the difference in model prediction with or without the assumption that amphibian movements are affected by barrier effects caused by roads, buildings and certain biotopes biotope types. The “NoFriction” version assumes that amphibian dispersal probability declines exponentially with increasing Euclidian distance whereas the other set assumes dispersal to be affected by barriers. Read the readme file for details on each parameter provided in the parameter file.

The GeoTiff-file is a biotope mape which has been created by combining a couple of publicly available geodata sets. As a base for the biotope map the Swedish land cover map NMD was used (https://geodata.naturvardsverket.se/nedladdning/marktacke/NMD2018/NMD2018_basskikt_ogeneraliserad_Sverige_v1_1.zip). To achieve a greater cartographic representation of small ponds, streams, buildings and transport infrastructure relevant for amphibian dispersal, reproduction and foraging, NMD was complemented by information from a number of vector layers. In total, 20 new biotope classes representing buildings of different height ranging from less than 5 m up to 100 m, were added to the basic land cover map. The heights were obtained by analyzing the LiDAR data provided by Swedish Land Survey (for details see Berghauser Pont et al., 2019). The data was rasterized and added on top of existing pixels representing buildings in the Swedish land cover map. The roads were separated into 101 new biotope classes with different expected number of vehicles per day. Instead of using statistics from the Swedish Transport Administration on observed number of vehicles per day relative traffic volumes were predicted based on angular betweenness centrality values calculated from the road network using PST (Place Syntax Tool, Stavroulaki et al. 2023). PST is an open-source plugin for QGIS (https://www.smog.chalmers.se/pst). Traffic volumes are expected to be correlated to the centrality values (Serra and Hillier, 2019). The vector layer with the centrality values was buffered by 15 m prior to rasterization. After that the new pixel values were added to the basic Land cover raster in sequence following the order of centrality values. Information on small streams with a maximum width of 6 m was added from a vector layer of Swedish streams (https://www.lantmateriet.se/en/geodata/geodata-products/product-list/topography-50-download-vector/). These lines where rasterized and added to the land cover raster by replacing the underlaying pixel values with new class specific pixel values. Small pondlike waterbodies was identified from the NMD data selecting contiguous fragments of the original NMD biotope class 61 with a smaller area than 1 hectare. Pixels representing the smaller water bodies was then changed to 201.

References Berghauser Pont M, Stavroulaki G, Bobkova E, et al. (2019). The spatial distribution and frequency of street, plot and building types across five European cities. Environment and Planning B: Urban analytics and city science 46(7): 1226-1242. Serra M and Hillier B (2019) Angular and Metric Distance in Road Network Analysis: A nationwide correlation study. Computers, Environment and Urban Systems 74: 194-207. Stavroulaki I, Berghauser Pont M, Fitger M, et al. (2023) PST Documentation_v.3.2.5_20231128, DOI:10.13140/RG.2.2.32984.67845.

The current legal situation obliges all municipalities to register and provide ‘new’ development plans and land use plans in accordance with XPlanungs and, if possible, also to do so for ‘old’ plans. This is currently strongly supported by NRW-URBAN. In any case, for each plan, there is the time when the capture is considered complete and the result is generated as an xplan-gml file. Among other things, it often happens that a service provider (internal or external) has created the file and the client does not have any software to check the delivered file for content, errors and inconsistencies. The XPLAN reader closes this gap. What is the XPLAN reader technically? * The XPLAN reader requires the installation of a current version of the open source GIS ‘QGIS’ and can then be used within QGIS as an extension from the official QGIS plugin repository. * The XPLAN reader is a script that reads and interprets the xplan gml file and assigns the order and optical design (currently 274) of the various themes.

QGIS Intro and Instructions for Mapping Species Occurrences

A. current conditions; B. RCP 2.6 by 2050; C. RCP 8.5 by 2050; D. RCP 2.6 by 2070; E. RCP 8.5 by 2070. Warm areas: suitable; Cold areas: unsuitable, for tiger mosquito. The maps were built using the free and open source QGIS software version 3.10.11 (https://www.qgis.org/en/site/about/index.html) and shapefiles were obtained from the free and open source DIVA-GIS site (https://www.diva-gis.org/gdata). (ZIP)

Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains diffuse horizontal irradiation (DIF) in kWh/m² covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characeristics: DIF LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 198.94 MB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).

Discover the booming satellite remote sensing software market! This in-depth analysis reveals key trends, growth drivers, regional market shares, and leading companies shaping this $2.2 billion (2025 est.) industry. Explore the potential of precision agriculture, water resource management, and more.

A. current conditions; B. RCP 2.6 scenario by 2050; C. RCP 8.5 scenario by 2050; D. RCP 2.6 scenario by 2070; E. RCP 8.5 scenario by 2070. The maps were built using the free and open source QGIS software version 3.10.11 (https://www.qgis.org/en/site/about/index.html) and shapefiles were obtained from the free and open source DIVA-GIS site (https://www.diva-gis.org/gdata). (ZIP)

Developed by SOLARGIS (https://solargis.com) and provided by the Global Solar Atlas (GSA), this data resource contains photovoltaic power potential (PVOUT) in kWh/kWp covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characteristics: PVOUT - LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 3.6 GB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).

The Los Angeles County Storm Drain System is a geometric network model representing the storm drain infrastructure within Los Angeles County. The long term goal of this network is to seamlessly integrate the countywide drainage infrastructure, regardless of ownership or jurisdiction. Current uses by the Department of Public Works (DPW) include asset inventory, operational maintenance, and compliance with environmental regulations.

GIS DATA DOWNLOADS: (More information is in the table below)

File geodatabase: A limited set of feature classes comprise the majority of this geometric network. These nine feature classes are available in one file geodatabase (.gdb). ArcMap versions compatible with the .gdb are 10.1 and later. Read-only access is provided by the open-source software QGIS. Instructions on opening a .gdb file are available here, and a QGIS plugin can be downloaded here.

Acronyms and Definitions (pdf) are provided to better understand terms used.

ONLINE VIEWING: Use your PC’s browser to search for drains by street address or drain name and download engineering drawings. The Web Viewer link is: https://dpw.lacounty.gov/fcd/stormdrain/

MOBILE GIS: This storm drain system can also be viewed on mobile devices as well as your PC via ArcGIS Online. (As-built plans are not available with this mobile option.)

More About these Downloads All data added or updated by Public Works is contained in nine feature classes, with definitions listed below. The file geodatabase (.gdb) download contains these eleven feature classes without network connectivity. Feature classes include attributes with unabbreviated field names and domains.

ArcMap versions compatible with the .gdb are 10.1 and later.

Feature Class Download Description

CatchBasin In .gdb Catch basins collect urban runoff from gutters

Culvert In .gdb A relatively short conduit that conveys storm water runoff underneath a road or embankment. Typical materials include reinforced concrete pipe (RCP) and corrugated metal pipe (CMP). Typical shapes are circular, rectangular, elliptical, or arched.

ForceMain In .gdb Force mains carry stormwater uphill from pump stations into gravity mains and open channels.

GravityMain In .gdb Underground pipes and channels.

LateralLine In .gdb Laterals connect catch basins to underground gravity mains or open channels.

MaintenanceHole In .gdb The top opening to an underground gravity main used for inspection and maintenance.

NaturalDrainage In .gdb Streams and rivers that flow through natural creek beds

OpenChannel In .gdb Concrete lined stormwater channels.

PumpStation In .gdb Where terrain causes accumulation, lift stations are used to pump stormwater to where it can once again flow towards the ocean

Data Field Descriptions

Most of the feature classes in this storm drain geometric network share the same GIS table schema. Only the most critical attributes are listed here per LACFCD operations.

Attribute Description

ASBDATE The date the design plans were approved “as-built” or accepted as “final records”.

CROSS_SECTIN_SHAPE The cross-sectional shape of the pipe or channel. Examples include round, square, trapezoidal, arch, etc.

DIAMETER_HEIGHT The diameter of a round pipe or the height of an underground box or open channel.

DWGNO Drain Plan Drawing Number per LACFCD Nomenclature

EQNUM Asset No. assigned by the Department of Public Works’ (in Maximo Database).

MAINTAINED_BY Identifies, to the best of LAFCD’s knowledge, the agency responsible for maintaining the structure.

MOD_DATE Date the GIS features were last modified.

NAME Name of the individual drainage infrastructure.

OWNER Agency that owns the drainage infrastructure in question.

Q_DESIGN The peak storm water runoff used for the design of the drainage infrastructure.

SOFT_BOTTOM For open channels, indicates whether the channel invert is in its natural state (not lined).

SUBTYPE Most feature classes in this drainage geometric nature contain multiple subtypes.

UPDATED_BY The person who last updated the GIS feature.

WIDTH Width of a channel in feet.