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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.
Public Domain Mark 1.0https://creativecommons.org/publicdomain/mark/1.0/
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This dataset holds all materials for the Inform E-learning GIS course
ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
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The Hills of Governor's Island Dataset for GRASS GIS
This geospatial dataset contains raster and vector data for the Hills region of Governor's Island, New York City, USA. The top level directory governors_island_hills_for_grass is a GRASS GIS location for NAD_1983_StatePlane_New_York_Long_Island_FIPS_3104_Feet in US Surveyor's Feet with EPSG code 2263. Inside the location there is the PERMANENT mapset, a license file, data record, readme file, workspace, color table, category rules, and scripts for data processing. This dataset was created for the course GIS for Designers.
Instructions
Install GRASS GIS, unzip this archive, and move the location into your GRASS GIS database
directory. If you are new to GRASS GIS read the first time users guide.
Data Sources
Maps
License
This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon.
I’d love to begin by saying that I have not “arrived” as I believe I am still on a journey of self-discovery. I have heard people say that they find my journey quite interesting and I hope my story inspires someone out there.I had my first encounter with Geographic Information System (GIS) in the third year of my undergraduate study in Geography at the University of Ibadan, Oyo State Nigeria. I was opportune to be introduced to the essentials of GIS by one of the prominent Environmental and Urban Geographers in person of Dr O.J Taiwo. Even though the whole syllabus and teaching sounded abstract to me due to the little exposure to a practical hands-on approach to GIS software, I developed a keen interest in the theoretical learning and I ended up scoring 70% in my final course exam.
https://www.scottsdaleaz.gov/AssetFactory.aspx?did=69351https://www.scottsdaleaz.gov/AssetFactory.aspx?did=69351
Please click here to view the Data Dictionary, a description of the fields in this table.Certificates of Occupancy issued by the City of Scottsdale.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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This repository contains two Microsoft Excel documents:A quiz with eight questions, assigned to students in a graduate-level GIS programming course as part of Homework Assignment 2. The quiz assesses students' understanding of basic Python programming principles (such as loops and conditional statements).An Excel document with three worksheets, each corresponding to one homework assignment from the same graduate GIS programming course. The document includes self-reported background information (e.g., students' prior programming experience), details about the use of various resources (e.g., websites) for completing assignments, the perceived helpfulness of these resources, and scores for the homework assignments and quizzes.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about
In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.
Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data. - There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects). - If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS. - You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.
Through the Department of the Interior-Bureau of Indian Affairs Enterprise License Agreement (DOI-BIA ELA) program, BIA employees and employees of federally-recognized Tribes may access a variety of geographic information systems (GIS) online courses and instructor-led training events throughout the year at no cost to them. These online GIS courses and instructor-led training events are hosted by the Branch of Geospatial Support (BOGS) or offered by BOGS in partnership with other organizations and federal agencies. Online courses are self-paced and available year-round, while instructor-led training events have limited capacity and require registration and attendance on specific dates. This dataset does not any training where the course was not completed by the participant or where training was cancelled or otherwise not able to be completed. Point locations depict BIA Office locations or Tribal Office Headquarters. For completed trainings where a participant location was not provided a point locations may not be available. For more information on the Branch of Geospatial Support Geospatial training program, please visit:https://www.bia.gov/service/geospatial-training.
This feature class represents locations of properties that should have elevation certificates in Baltimore County. The points were compiled based on the address information provided on the certificate or other source material. Scanned copies of elevation certificates and other relevant documents are stored in the database as attachments. The attached documents are in PDF format.
Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
License information was derived automatically
The goal of the Lead Safe Certificate program is to prevent lead poisoning by ensuring that all rental homes built prior to 1978 are compliant with the city's Lead Safe Ordinance and maintained free of lead hazards.Any home built before 1978 is reasonably presumed to contain lead-based paint. Residential rental units built before 1978 must have a Lead Safe Certification from the City of Cleveland’s Department of Building and Housing.The Lead Safe Certification is only valid for two years, after which rental property owners must re-apply for certification.For more information about the City's Lead Safe Certification program, please visit this Building & Housing page.RelatedLead Safe Certificate ExplorerData GlossaryCOLUMN | DESCRIPTIONRECORD_ID | Unique ID produced by the Accela system.STATUS | Status of the certificate. IS_ACTIVE | Flag that is true if the certificate has a status of Certified, Active, About to Expire, or Exempt, all of which indicate that the associated property is lead safe. All other statuses are coded as false.RECORD_FILE_DATE | Date when the certificate was originally filed.RENTAL_REG_ID | ID of the associated rental registration record in Accela.RENEWAL_RECORD_ID | ID of an associated renewal record in Accela, if applicable.RENEWAL_RECORD_FILE_DATE | File date of the renewal, if applicable.STATUS_DATE | Time of last status update for the certificate.EXPIRATION_DATE | Date on which the certificate will expire.PrimaryAddress | Primary address associated with the certificate.PrimaryAddressZip | Zip code in the primary address.YEAR_BUILT | Year the associated building was constructed.TOTAL_UNITS | Total units in the building associated with the certificate.TOTAL_UNITS_INSPECTED | Total units inspected.INSPECTION_TYPE | Type of inspection.INSPECTION_DATE | Date of inspection. INVESTIGATOR_CERTIFICATION_ID | ID of the investigator who conducted the inspection.REVIEW_DATE | Date of last review.ACCELA_CITIZEN_ACCESS_URL | Link to the record in the Accela Citizen Access portal.DW_Parcel | Associated parcel number. DW_Ward | Associated ward (pre-2025 boundaries).DW_Tract2020 | 2020 Census tract.DW_Neighborhood | Neighborhood.IS_GEOLOCATED | True if the City's geocoder could locate the address. False otherwise.ContactCity of Cleveland, Building and Housing Lead Compliance ProgramUpdate FrequencyWeekly on Sundays at 7 AM EST (6 AM during daylight savings)
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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In successful geoinformatics education, students’ active role in the learning process, e.g. through applying self-assessment, show an increasing interest but the evidence of benefits and challenges of self-assessment are sporadic. In this article, we examine the usefulness of an online self-assessment tool developed for geoinformatics education. We gathered data in two Finnish universities on five courses (n = 11–73 students/course) between 2019 and 2021. We examined 1) how the students’ self-assessed knowledge and understanding in geoinformatics subject topics changed during a course, 2) how the competencies at the end of a course changed between the years in different courses, and 3) what was the perceived usefulness of the self-assessment approach among the students. The results indicate support for the implementation of self-assessment, both as a formative and summative assessment. However, it is crucial to ensure that the students understand the contents of the self-assessment subject topics. To increase students’ motivation to take a self-assessment, it is crucial that the teacher actively highlights how it supports their studying and learning. As the teachers of the examined courses, we discuss the benefits and challenges of the self-assessment approach and the applied tool for the future development of geoinformatics education.
Through the Department of the Interior-Bureau of Indian Affairs Enterprise License Agreement (DOI-BIA ELA) program, BIA employees and employees of federally-recognized Tribes may access a variety of geographic information systems (GIS) online courses and instructor-led training events throughout the year at no cost to them. These online GIS courses and instructor-led training events are hosted by the Branch of Geospatial Support (BOGS) or offered by BOGS in partnership with other organizations and federal agencies. Online courses are self-paced and available year-round, while instructor-led training events have limited capacity and require registration and attendance on specific dates. This dataset does not any training where the course was not completed by the participant or where training was cancelled or otherwise not able to be completed. Point locations depict BIA Office locations or Tribal Office Headquarters. For completed trainings where a participant location was not provided a point locations may not be available. For more information on the Branch of Geospatial Support Geospatial training program, please visit:https://www.bia.gov/service/geospatial-training.
All applicants for a Basic Business License operating from a commercial location in the District of Columbia must provide a Certificate of Occupancy (C of O) for the premise address from which the business activity is conducted in order to demonstrate the activity does not conflict with building and zoning codes. A certificate of occupancy is needed to occupy any structure other than a single family dwelling. To include the following uses: two family flat, apartment house, and all commercial uses.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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This vector dataset provides points that represent significant golf course facility locations in Suffolk County. These courses can be publicly (State, County, Town, Village) or privately owned. This dataset can be linked with the GolfCoursePolygon feature class by the FACILITYID field. In some cases, there may be multiple Golf Course Points for a single Golf Course Polygon. These data are organized for consumption in desktop and web applications.
Seattle Parks and Recreation Golf Course locations. SPR Golf Courses are managed by contractors.Refresh Cycle: WeeklyFeature Class: DPR.GolfCourse
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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A Moodle Backup FIle (.mbz) of a course (SB33102 version Semester 1, 2018/19) is a compressed archive of a Moodle course that can be used to restore a course within Moodle. The file preserves course contents, structure and settings, but does not include student work or grades.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Input shapefiles for the Weighted Overlay Lab of UWSP's WATR 391 GIS course.
The Fire Certificate of Occupancy is a document issued by the City of Saint Paul’s Department of Safety and Inspections, Fire Safety Inspection Division, indicating the existing structure complies with all state and local safety codes allowing its use as a commercial building or residential occupancy.
The City of Saint Paul requires that all buildings, except for owner-occupied single family and duplex structures, are required to have and maintain a Fire Certificate of Occupancy issued by the Department of Safety and Inspections.
The Fire Certificate of Occupancy shall be an indication that the building meets, at the time of inspection, all relevant codes to maintain the health, safety and welfare of the building's occupants and the general public. After each inspection, a property is assigned a letter grade that corresponds to the number of years before the next inspection is required. Different occupancies may need to be on a tighter schedule due to the perceived hazard level.
For more information about the different occupancy types and inspection schedules for one and two family residential, multi- family residential and commercial properties, please visit the following link:
Certificate of Occupancy Information and Fees
A Provisional Fire Certificate of Occupancy is required for single family and duplex structures converting to nonowner-occupied status, allowing the structure to be temporarily occupied, pending an inspection.
Property owners are encouraged to conduct a pre-inspection before their scheduled inspection date using the provided checklists for residential and commercial properties:
One or Two UnitsThree + UnitsCommercial Properties
For more information please see Saint Paul’s Fire Inspections Page.
MIT Licensehttps://opensource.org/licenses/MIT
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Certificate of Occupancy
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.