Various Geospatial Data and Geographic Information Systems (GIS) presentations, workshops and tutorials. For the live versions of these files and material, please see uoft.me/GIS

This presentation provides an overview of Atlantic provincial and city GIS resources.

The historical GIS layers for the Tokugawa Period (circa 1664 and 1820) were developed for presentation at CEAL, Japanese Librarians Meeting, 2004. This paper will briefly outline existing examples of Japan Historical GIS, the methodology used to develop our demonstration GIS, and the means of searching the data online.

Presentation for AWRA Geospatial Technologies Conference May 10, 2022 https://www.awra.org/Members/Events_and_Education/Events/2022_GIS_Conference/2022_GIS_Conference.aspx

HydroShare is a web-based repository and hydrologic information system operated by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) for users to share, collaborate around, and publish data, models, scripts, and applications associated with water related research. It serves as a repository for data and models to meet Findable, Accessible, Interoperable, and Reusable (FAIR) open data mandates. Beyond content storage, the HydroShare repository also links with connected computational systems providing immediate value to users through the ability to reduce the needs for software installation and configuration and to document workflows, enhancing reproducibility. These approaches have facilitated considerable sharing and publication of data associated with research in HydroShare, enabling its re-use and the integration of data from multiple users to support broader synthesis studies. Data types supported include multidimensional netCDF, time series, geographic rasters and features. For some of these, standard data services, such as OpenDAP services for netCDF or Open Geospatial Consortium web services for geographic data types are automatically established when data is made public, improving machine readability and system interoperability. This presentation will describe geospatial data in HydroShare focusing on the geospatial feature and raster aggregations used to hold geospatial data and the functionality developed to automatically harvest metadata from these data types, simplifying the process of metadata creation for users. We will also describe how geospatial data services established for public resources holding geospatial data in HydroShare enable the data to be accessed by third party web applications adding to the functionality supported by HydroShare as a content storage element within a software ecosystem of interoperating systems.

Powerpoint slides describing factors of linked data, geographic information system, and cartographic integration. This presentation was given at the Earth Science Information Partners (ESIP) Winter Meeting held in Bethesda, MD in January 2020.

Hexbin data (PFAS test results) for public water supplies come from two different sources: 1) EGLE’s statewide PFAS survey, which tested supplies between 2018 and 2020, prior to Michigan’s PFAS maximum contaminant levels (MCLs), and 2) PFAS MCL compliance monitoring results under Michigan’s Safe Drinking Water Act. (2020 – present). Use the TaskType field to differentiate the Statewide Surveillance PFAS data (StateSurv) versus the PFAS Compliance Monitoring data (ComplMon).The locations that have been sampled in this effort include Community Water Supplies (including regional and municipal water supplies, manufactured housing communities, residential apartment buildings, etc.), Nontransient Noncommunity Water Supplies (including schools, businesses, childcare providers, Michigan Head Start locations, motels, resorts, etc.), and Transient Noncommunity Water Supplies (children’s camps and medical care providers).*Results are provided in parts per trillion. A part per trillion (ppt) is the equivalent of 1 drop of water in 20 Olympic-size swimming poolsDefinitions of the information provided in the sampling results table:WSSN: Water Supply Serial Number; unique identifier for each public water supplyCollection Date: The date samples were taken at the water supply.The System Type column lists the type of public water supplyCWS: Community Water Supply (Type I)NCWS: Noncommunity Water Supply (Type II)Non-transient: Non-transient noncommunity water supplies (Type II) are monitored for compliance under Michigan's SDWA.ADFSTC: Non-Community Water Supply (Adult Foster Care Provider)CHLCMP: Non-Community Water Supply (Children's Camp)DAYCARE: Non-Community Water Supply (Child Care Provider)INDUS: Non-Community Water Supply (Industry)MEDCAR: Non-Community Water Supply (Medical Care Provider)MOTEL: Non-Community Water Supply (Hotel or Motel)MUN: Community Water Supply (for example Municipal Supply, Apartment, Nursing Home, Prison, etc.)PFAS analytes are listed in individual columns (ppt). These include the 18 PFAS compounds tested under EPA method 537.1. If the result listed is "ND" the lab did not detect, or find, that PFAS was in the water sample above the method reporting limit.HFPO-DA (Hexafluoropropylene oxide-dimer acid, also referred to as "GenX") is a regulated analyte in Michigan with an MCL = 370 pptPFBS (Perfluorobutane sulfonic acid) is a regulated analyte in Michigan with an MCL = 420 pptPFHxA (perfluorohexanoic acid) is a regulated analyte in Michigan with an MCL = 400,000 pptPFHxS (perfluorohexane sulfonic acid) is a regulated analyte in Michigan with an MCL = 51 pptPFNA (perfluorononanoic acid) is a regulated analyte in Michigan with an MCL = 6 pptPFOA (perfluorooctanoic acid) is a regulated analyte in Michigan with an MCL = 8 pptPFOS (perfluorooctane sulfonic acid) is a regulated analyte in Michigan with an MCL = 16 ppt11Cl-PF3OUdS (11-chloroeicosafluoro-3-oxaundecane-1-sulfonic Acid) is currently a non-regulated analyte in Michigan9Cl-PF3ONS (9-chlorohexadecafluoro-3-oxanone-1-sulfonic acid) is currently a non-regulated analyte in MichiganADONA (4,8-dioxa-3H-perfluorononanoic acid) is currently a non-regulated analyte in MichiganNEtFOSAA (2-(N-ethylperfluorooctanesulfonamido) acetic acid) is currently a non-regulated analyte in MichiganNMeFOSAA (2-(N-methylperfluorooctanesulfonamido) acetic acid) is currently a non-regulated analyte in MichiganPFDA (perfluorodecanoic acid) is currently a non-regulated analyte in MichiganPFDoA (perfluorododecanoic acid) is currently a non-regulated analyte in MichiganPFHpA (perfluoroheptanoic acid) is currently a non-regulated analyte in MichiganPFTA (perfluorotetradecanoic acid) is currently a non-regulated analyte in MichiganPFTrDA (perfluorotridecanoic acid) is currently a non-regulated analyte in MichiganPFUnA (perfluoroundecanoic acid) is currently a non-regulated analyte in MichiganSample Number: The identification number that corresponds to the sample taken.Lab Name: Laboratory providing PFAS analysis via EPA method 537.1.Site Code: Specific sample location, when applicable.na: not applicableTaskType: To be used as a way to differentiate the Statewide Surveillance PFAS data (StateSurv) versus the PFAS Compliance Monitoring data (ComplMon).LabNumber: The certified lab code number to help clarify when the LabNameCode values vary, but are actually the same lab (issue with data coming from two different databases). Statewide Surveillance data has null values for this.TreatmentStatus: MP = The midpoint of the influent and effluent points, Raw = The water was not treated for any analytes or compounds, Treated = The water was treated, but not necessarily for PFAS.The data represents the PFAS sample locations, with each location having multiple samples taken, dates for each sample, and the analytical results for each sample. These analytical results include detections for 14 – 28 PFAS analytes, depending on the analytical method used. Each result has a "flags" column that corresponds to important data qualifiers.Understanding the flags: Contaminant data often include flags, also known as qualifiers, which are notes attached to data that gives detailed information about that particular result. In the drinking water PFAS data layer, PFAS analytes associated with a “U” flag were not detected in the sample and therefore a null value is displayed. “J” flagged results indicates an estimated concentration as the result is above the minimum detection limit (MDL) but below the laboratory reporting limit. “J-“ flagged results indicates an estimated concentration as the result is above the minimum MDL, below the laboratory reporting limit, but biased low. “UJ” flagged results indicates a not detected at the value estimated reporting limit. “R” flagged results indicates a rejected sample as serious quality control issues render the result value bot usable. “*” flagged results indicates that the result values have not been verified.This contains one hexbins layer (3 miles in height) showing the general location of the water sampling. The hexbins were created by EGLE using ArcGIS Online's Aggregate Points tool. The layer contains one field called "Hexagon ID" that connects the general location with the results of samples found within the hexbin area. The results are found in a related table "Statewide Testing Initiative of Public Water Supplies Sampling Results". There is no precise location attached to the results for security. However, the table does contain a field "Hexagon ID" that connects the the result with the general location symbolized by the hexagon layer. The units for all drinking water PFAS results are measured in PPT (parts per trillion).This data is used in the MPART: PFAS Geographic Information System (item details). For more information about Michigan's PFAS response, please visit our State of Michigan PFAS Response website. Call 800-662-9278 for assistance with reading or interpreting this data. Questions regarding the drinking water sampling PFAS data can be directed to Lisa Dygert (DygertL@Michigan.gov). To submit feedback on the data, please reach out to EGLE-Maps@Michigan.gov.Update Information: This data is static and was last pulled 1/31/23.

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/

Geospatial Services Market Outlook

The global geospatial services market size is poised to grow significantly, with an estimated CAGR of 13.4% from 2024 to 2032. In 2023, the market was valued at approximately USD 150 billion and is projected to reach around USD 411 billion by 2032. The primary growth drivers include the rapid advancements in geospatial technologies, increasing adoption of GIS (Geographic Information Systems) across various industries, and the burgeoning demand for spatial data analytics.

One of the key growth factors for the geospatial services market is the escalating demand for precise and real-time geographic information. This demand stems from various sectors such as urban planning, disaster management, and agriculture, where accurate geospatial data is crucial for decision-making. The proliferation of IoT devices and the increasing integration of geospatial data with AI and machine learning algorithms are further fueling market growth. Additionally, the growing adoption of cloud-based geospatial services is providing a significant boost to the market as it offers scalability and cost-efficiency.

Another significant driver contributing to the market's growth is the rapid technological advancements in remote sensing and surveying techniques. The advent of high-resolution satellite imagery, drone technology, and advancements in LiDAR (Light Detection and Ranging) are revolutionizing the way geospatial data is captured and analyzed. These technologies not only enhance the accuracy and granularity of spatial data but also reduce the time and cost involved in data acquisition. Consequently, industries such as defense, utilities, and natural resources are increasingly leveraging these technologies for various applications, driving the market forward.

The integration of geospatial services with big data analytics and AI is also propelling market growth. Organizations are increasingly recognizing the value of spatial data for gaining insights and driving business strategies. For instance, in the transport and logistics sector, geospatial data combined with predictive analytics can optimize route planning and fleet management, leading to significant cost savings and operational efficiency. Similarly, in agriculture, precision farming powered by geospatial analytics can enhance crop yield and resource management. Such integrations are creating new opportunities and expanding the application scope of geospatial services.

The emergence of Geo IoT is transforming the geospatial services landscape by enabling the seamless integration of geographic data with IoT devices. This integration allows for real-time data collection and analysis, which is crucial for applications such as smart city development, environmental monitoring, and asset tracking. Geo IoT facilitates the automation of processes by providing precise location-based insights, thereby enhancing operational efficiency and decision-making. As IoT devices become more prevalent, the demand for geospatial services that can leverage this technology is expected to rise, offering new growth opportunities for market players. The ability to connect and communicate with IoT devices in real-time is revolutionizing sectors like logistics, agriculture, and urban planning, where timely and accurate geographic information is essential.

From a regional perspective, the Asia Pacific region is expected to witness the highest growth rate in the geospatial services market. The rapid urbanization, infrastructure development, and government initiatives promoting the adoption of geospatial technologies are key factors driving the market in this region. Countries such as China, India, and Japan are investing heavily in geospatial infrastructure and innovation, further bolstering market growth. North America and Europe also hold significant market shares, driven by the presence of key market players and advanced technological adoption in sectors like defense, transportation, and natural resources.

Service Type Analysis

The geospatial services market can be segmented by service type, which includes data acquisition & mapping, surveying, remote sensing, and others. Data acquisition & mapping is one of the most critical and high-demand segments within the geospatial services market. This segment involves the collection, processing, and presentation of geographical data, primarily used in urban planning, environmental monitoring, and infrastructure development. The increasing use of satellite and drone-bas

Analysis of ‘500 Cities: City-level Data (GIS Friendly Format), 2019 release’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/2c57306d-7eef-4653-b1c7-13a83b4f0b39 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

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.

--- Original source retains full ownership of the source dataset ---

This presentation describes how data systems, in conjunction with GIS formatting, has provided a dynamic analytical methodology that allows business students to prepare advanced competitive and target market analysis, thereby contributing to the identification of new business opportunities.

NOTE: This file includes data for all 5 boroughs and has a size of 4.60 GB. Individual borough files are available for download from the metadata attachments section. Citywide Geographic Information System (GIS) land cover layer that displays land cover classification, plus pervious and impervious area and percentage at the parcel level, separated into 5 geodatabases, one per borough. DEP hosted a webinar on this study on June 23, 2020. A recording of the webinar, plus a PDF of the webinar presentation, accompany this dataset and are available for download. Please direct questions and comments to DEP at imperviousmap@dep.nyc.gov. This citywide parcel-level impervious area GIS layer was developed by the City of New York to support stormwater-related planning, and is provided solely for informational purposes. The accuracy of the data should be independently verified for any other purpose. The City disclaims any liability for errors and makes no warranties express or implied, including, but not limited to, implied warranties of merchantability and fitness for a particular purpose as to the quality, content, accuracy or completeness of the information, text graphics, links and other items contained in this GIS layer.

Presentation of the studied faunal series and number of percussion marks.

Analysis of ‘500 Cities: Census Tract-level Data (GIS Friendly Format), 2018 release’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/026fa141-2bee-4387-bf52-3095ae0f6b32 on 12 February 2022.

--- Dataset description provided by original source is as follows ---

2016, 2015. 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 census tract-level data in GIS-friendly format can be joined with census tract spatial data (https://chronicdata.cdc.gov/500-Cities/500-Cities-Census-Tract-Boundaries/x7zy-2xmx) in a geographic information system (GIS) to produce maps of 27 measures at the census tract level. There are 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, cholesterol screening) in this 2018 release from the 2015 BRFSS that were the same as the 2017 release.

--- Original source retains full ownership of the source dataset ---

River hydraulic geometry is an important input to hydraulic and hydrologic models that route flow along streams, determine the relationship between stage and discharge, and map the potential for flood inundation give the flow in a stream reach. Traditional approaches to quantify river geometry have involved river cross-sections, such as are required for input to the HEC-RAS model. Extending such cross-section based models to large scales has proven complex, and, in this presentation, an alternative approach, the Height Above Nearest Drainage, or HAND, is described. As we have implemented it, HAND uses multi-directional flow directions derived from a digital elevation model (DEM) using the Dinifinity method in TauDEM software (http://hydrology.usu.edu/taudem) to determine the height of each grid cell above the nearest stream along the flow path from that cell to the stream. With this information, and the depth of flow in the stream, the potential for and depth of flood inundation can be determined. Furthermore, by dividing streams into reaches or segments, the area draining to each reach can be isolated and a series of threshold depths applied to the grid of HAND values in that isolated reach catchment, to determine inundation volume, surface area and wetted bed area. Dividing these by length yields reach average cross section area, width, and wetted perimeter. Together with slope (also determined from the DEM) and roughness (Manning's n) these provide all the inputs needed for establishing a Manning's equation uniform flow assumption stage-discharge rating curve and for mapping potential inundation from discharge. This presentation will describe the application of this approach across the continental US in conjunction with NOAA’s National Water Model for prediction of stage and flood inundation potential in each of the 2.7 million reaches of the National Hydrography Plus (NHDPlus) dataset, the vast majority of which are ungauged. The continental US scale application has been enabled through the use of high performance parallel computing at the National Center for Supercomputing Applications (NCSA) and the CyberGIS Center at the University of Illinois.

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/.

This is a point dataset that reflects the locations of all existing USPS post offices in Lexington-Fayette County. Points were identified utilizing public records and heads-up digitizing. Dataset was created for use in the LFUCG AtLex map book. Attributes include name and address.As part of the basemap data layers, the post office location map layer is an integral part of the Lexington Fayette-Urban County Government Geographic Information System. Basemap data layers are accessed by personnel in most LFUCG divisions for basic applications such as viewing, querying, and map output production. More advanced user applications may focus on thematic mapping, summarization of data by geography, or planning purposes (including defining boundaries, managing assets and facilities, integrating attribute databases with geographic features, spatial analysis, and presentation output).

This presentation shows how to use the geography part of the DLI collection. Mapping software, such as MapInfo, is discussed. Census geography is also discussed.

This dataset is designed to represent and identify the boundaries of public school facilities within Lexington-Fayette County. The dataset is created by leveraging the appropriate boundaries in the GIS parcel dataset. The location of the public school facilities is updated through public record and coordination with the Fayette County Public School. The location for the certified private schools is updated through public record for certified private schools from the Kentucky Department of Education. The public school facilities are continuously updated. This dataset participates in a topology with the parcel dataset to assure coincident geometry during parcel editing.As part of the basemap data layers, the school boundary map layer is an integral part of the Lexington Fayette-Urban County Government Geographic Information System. Basemap data layers are accessed by personnel in most LFUCG divisions for basic applications such as viewing, querying, and map output production. More advanced user applications may focus on thematic mapping, summarization of data by geography, or planning purposes (including defining boundaries, managing assets and facilities, integrating attribute databases with geographic features, spatial analysis, and presentation output).

GeoGratis is a portal provided by the Earth Sciences Sector (ESS) of Natural Resources Canada (NRCan) which provides geospatial data at no cost via your Web browser. The data will be useful whether you're a novice who needs a geographic map for a presentation, or an expert who wants to overlay a vector layer of digital data on a classified multiband image, with a digital elevation model as a backdrop. The geospatial data are grouped in collections and are compatible with the most popular geographic information systems (GIS), with image analysis systems and the graphics applications of editing software.

The railroad dataset is a subset of the statewide railroad dataset held by the Kentucky Division of Geographic Information. The data speficially for Lexington-Fayette County is extracted and corrected if necessary through comparison to aerial phtoography or other attribute data held by the LFUCG.As part of the basemap data layers, the railroad centerline map layer is an integral part of the Lexington Fayette-Urban County Government Geographic Information System. Basemap data layers are accessed by personnel in most LFUCG divisions for basic applications such as viewing, querying, and map output production. More advanced user applications may focus on thematic mapping, summarization of data by geography, or planning purposes (including defining boundaries, managing assets and facilities, integrating attribute databases with geographic features, spatial analysis, and presentation output).

CrimeMapTutorial is a step-by-step tutorial for learning crime mapping using ArcView GIS or MapInfo Professional GIS. It was designed to give users a thorough introduction to most of the knowledge and skills needed to produce daily maps and spatial data queries that uniformed officers and detectives find valuable for crime prevention and enforcement. The tutorials can be used either for self-learning or in a laboratory setting. The geographic information system (GIS) and police data were supplied by the Rochester, New York, Police Department. For each mapping software package, there are three PDF tutorial workbooks and one WinZip archive containing sample data and maps. Workbook 1 was designed for GIS users who want to learn how to use a crime-mapping GIS and how to generate maps and data queries. Workbook 2 was created to assist data preparers in processing police data for use in a GIS. This includes address-matching of police incidents to place them on pin maps and aggregating crime counts by areas (like car beats) to produce area or choropleth maps. Workbook 3 was designed for map makers who want to learn how to construct useful crime maps, given police data that have already been address-matched and preprocessed by data preparers. It is estimated that the three tutorials take approximately six hours to complete in total, including exercises.