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.

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 table shows total attendance data by month for the Rec Check program. The Rec Check program is a free after-school and other out-of-school time care for youth and is offered for free to all youth who live in or attend school in Saint Paul and are in grades 1st through 5th. An estimated 80% of the children who participate in Rec Check are from low-income families. Children participate in a variety of structured, supervised activities that are recreation focused. Activities include arts and crafts, community building games, quiet time, homework help during the school year, and more.To promote health and fitness, staff engage participants in daily physical activity and provide a snack. The program is staffed with community recreation leaders on a 15-1 participant-to-staff ratio. Rec Check is also offered for extended periods during Non-School Days.Link to more information: https://www.stpaul.gov/departments/parks-and-recreation/recreation-centers/parks-recreation-programs/rec-check

In this course, you will explore the concepts, principles, and practices of acquiring, storing, analyzing, displaying, and using geospatial data. Additionally, you will investigate the science behind geographic information systems and the techniques and methods GIS scientists and professionals use to answer questions with a spatial component. In the lab section, you will become proficient with the ArcGIS Pro software package. This course will prepare you to take more advanced geospatial science courses. You will be asked to work through a series of modules that present information relating to a specific topic. You will also complete a series of lab exercises, assignments, and less guided challenges. Please see the sequencing document for our suggestions as to the order in which to work through the material. To aid in working through the lecture modules, we have provided PDF versions of the lectures with the slide notes included. This course makes use of the ArcGIS Pro software package from the Environmental Systems Research Institute (ESRI), and directions for installing the software have also been provided. If you are not a West Virginia University student, you can still complete the labs, but you will need to obtain access to the software on your own.

In this course, you will learn to work within the free and open-source R environment with a specific focus on working with and analyzing geospatial data. We will cover a wide variety of data and spatial data analytics topics, and you will learn how to code in R along the way. The Introduction module provides more background info about the course and course set up. This course is designed for someone with some prior GIS knowledge. For example, you should know the basics of working with maps, map projections, and vector and raster data. You should be able to perform common spatial analysis tasks and make map layouts. If you do not have a GIS background, we would recommend checking out the West Virginia View GIScience class. We do not assume that you have any prior experience with R or with coding. So, don't worry if you haven't developed these skill sets yet. That is a major goal in this course. Background material will be provided using code examples, videos, and presentations. We have provided assignments to offer hands-on learning opportunities. Data links for the lecture modules are provided within each module while data for the assignments are linked to the assignment buttons below. Please see the sequencing document for our suggested order in which to work through the material. After completing this course you will be able to: prepare, manipulate, query, and generally work with data in R. perform data summarization, comparisons, and statistical tests. create quality graphs, map layouts, and interactive web maps to visualize data and findings. present your research, methods, results, and code as web pages to foster reproducible research. work with spatial data in R. analyze vector and raster geospatial data to answer a question with a spatial component. make spatial models and predictions using regression and machine learning. code in the R language at an intermediate level.

Third grade English Language Arts (ELA) and Math test results for the 2016-2017 school year by school district for the state of Michigan. Data Driven Detroit obtained these datasets from MI School Data, for the State of the Detroit Child tool in July 2017. Test results were originally obtained on a school level and aggregated to school district by Data Driven Detroit. Student data was suppressed when less than five students were tested per school. Student data was suppressed when less than five students were tested per school.Click here for metadata (descriptions of the fields).

Third grade English Language Arts (ELA) and Math test results for the school year 2016-2017 by city for the state of Michigan. Data Driven Detroit obtained these datasets from MI School Data, for the State of the Detroit Child tool in July 2017. Test results were originally obtained on a school level and aggregated to city by Data Driven Detroit. Student data was suppressed when less than five students were tested per school.Click here for metadata (descriptions of the fields).

Third grade English Language Arts (ELA) and Math test results for the 2016-2017 school year by House of Representative districts for the state of Michigan. Data Driven Detroit obtained these datasets from MI School Data, for the State of the Detroit Child tool in July 2017. Test results were originally obtained on a school level and aggregated to districts by Data Driven Detroit. Student data was suppressed when less than five students were tested per school. Click here for metadata (descriptions of the fields).

Third grade English Language Arts (ELA) and Math test results for the 2016-2017 school year by school district for the state of Michigan. Data Driven Detroit obtained these datasets from MI School Data, for the State of the Detroit Child tool in July 2017. Test results were originally obtained on a school level and aggregated to school district by Data Driven Detroit. Student data was suppressed when less than five students were tested per school.Click here for metadata (descriptions of the fields).

This GIS layer file represents the currently adopted 2020-2021 Elementary (Primary), Middle & High School attendance zone, parent responsibility zone boundaries and FDOE Transportation Membership Category assignments under the jurisdiction of the Lake County School Board. The primary purpose of this GIS layer file is to assist District administrative staff and school staff with the appropriate school assignment, transportation eligibility, and Full Time Equivalent (FTE) transportation category assignments accurate to an individual tax parcel or residential address for general education students under the responsibility of the Lake County School District. Because of programmatic reason these zones may not apply to special need students, magnet programs or students within special state funded programs. This GIS layer is not intended for general public use, but for internal use only for administrative purposes.

AssignmentStatus

An ArcGIS Workforce project used by operations and maintenance supervisors organize and manage routine and ah-hoc field assignments.

A map used in Tree Assignments ArcGIS Workforce project to assign field work activity.

A layer view used to symbolize assignments for individual assistance damage assessments.

A map used in the Address Assignments ArcGIS Workforce project to track field work activity.

Note, information is based on location of the school, not students’ community of residence.Children found to have low math skills are at increased risk for poor academic outcomes, which can have profound consequences for future health and longevity.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

A feature layer view used to symbolize assignments for public assistance damage assessments.

A feature layer view used to symbolize assignments for individual assistance damage assessments.

This feature class is updated every business day using Python scripts and the Permit database. Please disregard the "Date Updated" field as it does not keep in sync with DWR's internal enterprise geodatabase updates. Water Right Specialists, also called Basin Engineers, manage the water rights in their assigned basins across the state. This dataset was designed to provide the public with an easy to use map showing the preferred DWR employee to contact for questions about water rights in a specific area of interest. This dataset is used for the Basin Specialist Assignment wall map and the Water Rights web map. For contact information, please see water.nv.gov/contactinformation.aspxBackground Info on Basins:The U.S. Geological Survey and the Nevada Division of Water Resources divided the state into discrete hydrologic units for water planning and management purposes. These administrative boundaries have been identified as 232 Hydrographic Basins (1-232; hydrographic sub-basins designated A, B, C, etc.) within 14 major Hydrographic Regions. There are a total of 256 hydrographic basins and sub-basins.

The Antarctic Imagery map provides 15m TerraColor imagery for the polar region. It is designed to be used as a basemap for overlaying other data for the Antarctic region. The Antarctic Imagery map includes imagery from 90 to 50 degrees south latitude, though the projection will support display of data to lower latitudes.Coordinate System: WGS 1984 Antarctic Polar Stereographic (WKID 3031)Scale Range: 1: 902,590,245 down to 1: 110,179