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.

This GIS layer is based on a SQL query of the groundwater HAZSITE data that resides in COMPASS for each active Site Remediation case. Once the raw groundwater HAZSITE data is extracted from COMPASS, it is summarized such that a maximum concentration for the contaminant is derived for the year preceeding the last sampling event (samp_last_max_conc) and a maximum concentration is also generated for all sampling events (all_max_conc) . Each active Site Remediation case is included in the GIS layer. For the HAZSITE data, there are a number of considerations that need to be taken into account when using this GIS layer for decision making purposes:- Not all SRP cases have provided HAZSITE data to the Department or HAZSITE data that has been provided to the Department may be incomplete;- Additional sampling may have been conducted since the last round of HAZSITE data was submitted that has not yet been provided as HAZSITE data is only required with key document submittals;- HAZSITE data that was submitted may not have been provided in the correct format and therefore could not be uploaded into the COMPASS data repository and would therefore not be returned via the COMPASS SQL query.

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”.

Chicago sites that offer free or affordable technology resources and services, like computers with Internet access, Wi-Fi hotspots and technology training. Call or visit the organization's website before going to the location. For more information, visit http://locations.weconnectchicago.org/.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

The Geodetic Survey Section within WSDOT has installed and maintains a majority of the primary (Geodetic) survey control used by the Department of Transportation in its ongoing construction and road maintenance programs. As part of this process the Survey Section maintains a Survey-Monuments database. The GIS file is updated nightly and sourced from a SQL database. Updates to the SQL database are irregular but the GIS data will be as recent as the most current version of the SQL database. This data is provided for mapping purposes only. This data does not contain the complete range of attributes and information that are available for each station within the database. If you need the physical geodetic coordinates for a monument, please obtain it from the database.Note: if you need the coordinates for a station for survey work you should use the coordinates shown in the datasheet for the station NOT the coordinates contained in this layer for the feature.This service is maintained by the WSDOT GIS & Roadway Data Office. If you are having trouble viewing the service, please contact OnlineMapSupport@wsdot.wa.gov.

Addressing point file for the City of Chattanooga.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Contact E-mailData Source: Versaterm Informix RMSData Source Type: Informix and/or SQL ServerPreparation Method: Preparation Method: Automated View pulled from CADWSQL (SQL Server) and duplicated on the GIS ServerPublish Frequency: WeeklyPublish Method: AutomaticData DictionaryFor prior reporting period datasets, see:2012-2015https://tempegov.maps.arcgis.com/home/item.html?id=ca69de49b1644f4088b681fbf4e1bb692016-2019https://tempegov.maps.arcgis.com/home/item.html?id=141e7069563b4fecae1d868bf95ed0db

This shapefile contains the Cadastral Lines for Williamson County, Texas. This shapefile is created and maintained by the Williamson Central Appraisal District Mapping Department. The data in this layer are represented as lines.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Chattanooga city limits

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This is a collection of layers created by Tian Xie(Intern in DDP) in August, 2018. This collection includes Detroit Parcel Data(Parcel_collector), InfoUSA business data(BIZ_INFOUSA), and building data(Building). The building and business data have been edited by Tian during field research and have attached images.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This dataset contains model-based census tract level estimates in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2021 or 2020 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 estimates. The 2023 release uses 2021 BRFSS data for 29 measures and 2020 BRFSS data for 7 measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours) that the survey collects data on every other year. These data can be joined with the census tract 2015 boundary file in a GIS system to produce maps for 36 measures at the census tract level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software.

https://cdcarcgis.maps.arcgis.com/home/item.html?id=2c3deb0c05a748b391ea8c9cf9903588

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Note: This dataset was deprecated effective April 30, 2025. Please refer to the Calls for Service (Consolidated) dataset for current data and access to historical records, including prior reporting years.The Calls for Service dataset includes police service requests for which patrol officers, traffic officers, bike officers and, on occasion, detectives will be dispatched to public safety response. It also includes self-initiated calls for service where an officer witnesses a violation or suspicious activity for which they would respond.Contact E-mailContact Phone: N/ALink: N/AData Source: Versaterm Informix RMSData Source Type: Informix and/or SQL ServerPreparation Method: Preparation Method: Automated View pulled from CADWSQL (SQL Server) and duplicated on the GIS ServerPublish Frequency: WeeklyPublish Method: AutomaticData Dictionary

The GDAL/OGR libraries are open-source, geo-spatial libraries that work with a wide range of raster and vector data sources. One of many impressive features of the GDAL/OGR libraries is the ViRTual (VRT) format. It is an XML format description of how to transform raster or vector data sources on the fly into a new dataset. The transformations include: mosaicking, re-projection, look-up table (raster), change data type (raster), and SQL SELECT command (vector). VRTs can be used by GDAL/OGR functions and utilities as if they were an original source, even allowing for chaining of functionality, for example: have a VRT mosaic hundreds of VRTs that use look-up tables to transform original GeoTiff files. We used the VRT format for the presentation of hydrologic model results, allowing for thousands of small VRT files representing all components of the monthly water balance to be transformations of a single land cover GeoTiff file.

Presentation at 2018 AWRA Spring Specialty Conference: Geographic Information Systems (GIS) and Water Resources X, Orlando, Florida, April 23-25, http://awra.org/meetings/Orlando2018/

From the US Census Bureau: "The cartographic boundary files are simplified representations of selected geographic areas from the Census Bureau’s MAF/TIGER geographic database. These boundary files are specifically designed for small scale thematic mapping."

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Census tracts from the 2010 census

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

The Clerk-Treasurer’s Office is responsible for collecting payments of utility bills. This data is entered into the accounting system via SQL Server. The Utility account address points are then linked from the GIS to the SQL server accounting system.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This is a worship data from OSM of Pakistan. It is in SQL format for postgresql with postgis extension enabled.

A custom Python Tool Box exclusively for ESRI ArcMap Application. This toolbox contains two tools: 1. Duplicate Value Calculator : - to search duplicate values in a specified Attribute Field of Table /FeautureClass and populate user defined text for such records in another specified Attribute Field of same Table/FeatureClass. If no Attribute Field is selected to populate text, a default Attribute Field will be added with Name as "DUPLICATE_{Name of Field for Search Duplicate values}".

Further, User can imply SQL Expression to limit the records to be searched as per requirement.

Caution : This Tool modifies the SCHEMA of selected Table/FeatureClass if no Attribute Field is selected to populate text for duplicate values. So preconsider to choose both Attribute Fields - One for Duplicate Search and other for Text against duplicate value if You are concerned about to add new field to Your Table/FeatureClass.

1. Delete Rows : - to delete Rows from input Table/FeatureClass. Put an SQL Expression for records filter, otherwise all rows will be deleted.