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 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

This is a set of three files to be used in a workshop on spatial SQL in SpatiaLite

This example demonstrates how to use PostGIS capabilities in CyberGIS-Jupyter notebook environment. Modified from notebook by Weiye Chen (weiyec2@illinois.edu)

PostGIS is an extension to the PostgreSQL object-relational database system which allows GIS (Geographic Information Systems) objects to be stored in the database. PostGIS includes support for GiST-based R-Tree spatial indices, and functions for analysis and processing of GIS objects.

Resources for PostGIS:

Manual https://postgis.net/docs/ In this demo, we use PostGIS 3.0. Note that significant changes in APIs have been made to PostGIS compared to version 2.x. This demo assumes that you have basic knowledge of SQL.

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

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”.

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.

The Geographic Management Information System (GeoMIS) is a FISMA Moderate minor application built using ArcGIS Server and portal, Microsoft SQL, and a web-facing front-end. The system can be accessed over the internet via https://www.usaidgiswbg.com using a web browser. GeoMIS is based on a commercial off-the-shelf product developed by Esri. Esri is creates geographic information system (GIS) software, web GIS and geodatabase management applications and is based in California. GeoMISIt is maintained by an Israeli company, Systematics (see Attachment 3) which is EsriI's agent in Israel. The mission has an annual maintenance contract with Systematics for GeoMIS. GeoMIS has 100 users from USAID staff (USA Direct Hire and Foreign Service Nationals) and 200 users from USAID contractors and grantees. The system is installed at USAID WBG office in Tel Aviv/Israel inside the computer room in the DMZ. It has no interconnections with any other system.

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

This polygon dataset shows Public Works, Parks, and Facilities open work orders in the City of Kirkland that Utility Superiors have tagged to share. This layer is meant to show the location, spatial extent, and related information of work orders projects. Data is derived from a spatial enable table views from the Lucity SQL Database. This layer has a definition query applied to show the location of open work orders.

This dataset consists of 24-hour traffic volumes which are collected by the City of Tempe high (arterial) and low (collector) volume streets. Data located in the tabular section shares with its users total volume of vehicles passing through the intersection selected along with the direction of flow.Historical data from this feature layer extends from 2016 to present day.Contact: Sue TaaffeContact E-Mail: sue_taaffe@tempe.govContact Phone: 480-350-8663Link to embedded web map:http://www.tempe.gov/city-hall/public-works/transportation/traffic-countsLink to site containing historical traffic counts by node: https://gis.tempe.gov/trafficcounts/Folders/Data Source: SQL Server/ArcGIS ServerData Source Type: GeospatialPreparation Method: N/APublish Frequency: As information changesPublish Method: AutomaticData Dictionary

OR-Trans is a GIS road centerline dataset compiled from numerous sources of data throughout the state. Each dataset is from the road authority responsible for (or assigned data maintenace for) the road data each dataset contains. Data from each dataset is compiled into a statewide dataset that has the best avaialble data from each road authority for their jurisdiction (or assigned data maintenance responsibility). Data is stored in a SQL database and exported in numerous formats. Additional metadata resouce: https://geoportalprod-ordot.msappproxy.net/geoportal/catalog/main/home.page

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-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

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

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

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. The GIS layer symbology is based on the the maximum concentration for the year preceeding the last sampling event for that contaminant as summarized in the evaluation field and further categorized in the s_value field. Symbology is represented as follows: no data submitted, not sampled, non-detect (ND), detected below standard, reporting limit > standard, 1 - 10 times the standard, 10 - 100 times the standard, 100 - 1000 times the standard and greater than 1000 times the standard. The standard is the NJDEP Ground Water Quality Standard and/or MCL for this contaminant. 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.

The Kid Zone Enrichment Program provides a safe and enriching place for students to be in out-of-school time. This dataset provides the percentage of students who are currently enrolled in after school community programs. Data are broken down by school, including school and Kid Zone enrollment.This page provides data for the Kid Zone Participation performance measure. These data are the source of the summary values for Performance Measure 1.18The performance measure dashboard is available at 1.18 Kid Zone ParticipationAdditional InformationSource: SQL ServerContact: Jeremy KingContact E-Mail: jeremy_king@tempe.govData Source Type: SQL ServerPreparation Method: Extracted to Excel and combined with data given by the Tempe and Kyrene School districts (school enrollment, Free and Reduced Lunch Percentages). These data are generated from from SQL counts the participants by site with an enrollment end date greater or equal to the report date.Publish Frequency: AnnuallyPublish Method: ManualData Dictionary

description:

This dataset consists of 24-hour traffic volumes which are collected by the City of Tempe on arterial and collector streets.

Contact: Sue Taaffe

Contact E-Mail: sue_taaffe@tempe.gov

Contact Phone: 480-350-8663

Link to embedded web map: http://www.tempe.gov/city-hall/public-works/transportation/traffic-counts

Link to site containing historical traffic counts by node: https://gis.tempe.gov/trafficcounts/Folders/

Data Source: SQL Server/ArcGIS Server

Data Source Type: Geospatial

Preparation Method: N/A

Publish Frequency: As information changes

Publish Method: Automatic

Data Dictionary: https://gis.tempe.gov/traffic-count-dictionary/

This dataset consists of 24-hour traffic volumes which are collected by the City of Tempe on arterial and collector streets.

Contact: Sue Taaffe

Contact E-Mail: sue_taaffe@tempe.gov

Contact Phone: 480-350-8663

Link to embedded web map: http://www.tempe.gov/city-hall/public-works/transportation/traffic-counts

Link to site containing historical traffic counts by node: https://gis.tempe.gov/trafficcounts/Folders/

Data Source: SQL Server/ArcGIS Server

Data Source Type: Geospatial

Preparation Method: N/A

Publish Frequency: As information changes

Publish Method: Automatic

Data Dictionary: https://gis.tempe.gov/traffic-count-dictionary/

2017, 2016. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project city-level data in GIS-friendly format can be joined with city spatial data (https://chronicdata.cdc.gov/500-Cities/500-Cities-City-Boundaries/n44h-hy2j) in a geographic information system (GIS) to produce maps of 27 measures at the city-level. There are 7 measures (all teeth lost, dental visits, mammograms, Pap tests, colorectal cancer screening, core preventive services among older adults, and sleep less than 7 hours) in this 2019 release from the 2016 BRFSS that were the same as the 2018 release.

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.