This dataset holds all materials for the Inform E-learning GIS course

This is a full-day training, developed by UNEP CMB, to introduce participants to the basics of GIS, how to import points from Excel to a GIS, and how to make maps with QGIS, MapX and Tableau. It prioritizes the use of free and open software.

This is GIS course announcement flier.

In this course, you will learn about some common types of data used for GIS mapping and analysis, and practice adding data to a file geodatabase to support a planned project.Goals Create a file geodatabase. Add data to a file geodatabase. Create an empty geodatabase feature class.

This seminar is an applied study of deep learning methods for extracting information from geospatial data, such as aerial imagery, multispectral imagery, digital terrain data, and other digital cartographic representations. We first provide an introduction and conceptualization of artificial neural networks (ANNs). Next, we explore appropriate loss and assessment metrics for different use cases followed by the tensor data model, which is central to applying deep learning methods. Convolutional neural networks (CNNs) are then conceptualized with scene classification use cases. Lastly, we explore semantic segmentation, object detection, and instance segmentation. The primary focus of this course is semantic segmenation for pixel-level classification. The associated GitHub repo provides a series of applied examples. We hope to continue to add examples as methods and technologies further develop. These examples make use of a vareity of datasets (e.g., SAT-6, topoDL, Inria, LandCover.ai, vfillDL, and wvlcDL). Please see the repo for links to the data and associated papers. All examples have associated videos that walk through the process, which are also linked to the repo. A variety of deep learning architectures are explored including UNet, UNet++, DeepLabv3+, and Mask R-CNN. Currenlty, two examples use ArcGIS Pro and require no coding. The remaining five examples require coding and make use of PyTorch, Python, and R within the RStudio IDE. It is assumed that you have prior knowledge of coding in the Python and R enviroinments. If you do not have experience coding, please take a look at our Open-Source GIScience and Open-Source Spatial Analytics (R) courses, which explore coding in Python and R, respectively. After completing this seminar you will be able to: explain how ANNs work including weights, bias, activation, and optimization. describe and explain different loss and assessment metrics and determine appropriate use cases. use the tensor data model to represent data as input for deep learning. explain how CNNs work including convolutional operations/layers, kernel size, stride, padding, max pooling, activation, and batch normalization. use PyTorch, Python, and R to prepare data, produce and assess scene classification models, and infer to new data. explain common semantic segmentation architectures and how these methods allow for pixel-level classification and how they are different from traditional CNNs. use PyTorch, Python, and R (or ArcGIS Pro) to prepare data, produce and assess semantic segmentation models, and infer to new data.

Seattle Parks and Recreation ARCGIS park feature map layer web services are hosted on Seattle Public Utilities' ARCGIS server. This web services URL provides a live read only data connection to the Seattle Parks and Recreations Golf Courses dataset.

The Python language offers an efficient way to automate and extend geoprocessing and mapping functionality. In ArcGIS 10, Python was fully integrated into ArcGIS Desktop with the addition of the Python window and the ArcPy site package. This course introduces Python scripting within ArcGIS Desktop to automate geoprocessing workflows. These skills are needed by GIS analysts to work efficiently and productively with ArcGIS for Desktop.After completing this course, you will be able to:Create geoprocessing scripts using the ArcPy site package.Identify common scripting workflows.Write Python scripts that create and update data.Create a script tool using built-in validation.

Maps exist to convey information to people, whether that information is how to get from one point to another or how many oil fields are located in a given region. Effective cartography can convey that information efficiently to map users.In this course, you will be introduced to a five-step workflow for designing and creating maps. This workflow can be applied to any map or output medium (print or digital). This course will cover all steps of the workflow in general terms, emphasizing the first two steps: the cartographic planning process and data evaluation.After completing this course, you will be able to perform the following tasks:Identify and describe the cartographic workflow steps.Explain cartographic design controls and how they drive map creation.Apply the planning step of the cartographic workflow.Evaluate data sources to determine applicability.Discuss why basemap and operational layers are important.Assign the correct coordinate system to data based on the geographic extent and map objective.Assess the level of detail required for a map and apply generalization techniques when appropriate.

Golf Courses in Henderson

This vector dataset provides polygons 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 GolfCoursePoint 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.

Climate data and geographic data from Madagascar for learning multi-criteria analysis in GIS courses.

• MultiCriteriaAnalysis_ENG_v2022_v3.pdf - summary of Multi-Criteria Analysis methods for suitability of mango tree fruitculture.
• Country borders: https://www.naturalearthdata.com/downloads/50m-cultural-vectors/50m-admin-0-countries-2/
• Rivers: https://www.naturalearthdata.com/downloads/10m-physical-vectors/
• Roads: https://www.naturalearthdata.com/downloads/50m-cultural-vectors/
• GLOBCOVER: http://due.esrin.esa.int/page_globcover.php
• GlobCover Legend and More XLSX - sheets with support material from videos in lecture channel
• Temperature and precipiatation - climate variables: clipped from WorldClim database rasters (see http://www.worldclim.org/)
• DEM from EarthExplorer web portal (see https://www.cirgeo.unipd.it/didattica/GIS/01x_access_geodata.html#USGS:_EarthExplorer
  

You have been assigned a new project, which you have researched, and you have identified the data that you need.The next step is to gather, organize, and potentially create the data that you need for your project analysis.In this course, you will learn how to gather and organize data using ArcGIS Pro. You will also create a file geodatabase where you will store the data that you import and create.After completing this course, you will be able to perform the following tasks:Create a geodatabase in ArcGIS Pro.Create feature classes in ArcGIS Pro by exporting and importing data.Create a new, empty feature class in ArcGIS Pro.

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.

Seattle Parks and Recreation Golf Course locations. SPR Golf Courses are managed by contractors.Refresh Cycle: WeeklyFeature Class: DPR.GolfCourse

Golf Courses, as of 2014

Layer displaying golf course fairways.

This training module drives the user through the installation and configuration of a Desktop GeoDatabase

As GIS and computing technologies advanced rapidly, many indoor space studies began to adopt GIS technology, data models, and analysis methods. However, even with a considerable amount of research on indoor GIS and various indoor systems developed for different applications, there has not been much attention devoted to adopting indoor GIS for the evaluation space usage. Applying indoor GIS for space usage assessment can not only provide a map-based interface for data collection, but also brings spatial analysis and reporting capabilities for this purpose. This study aims to explore best practice of using an indoor GIS platform to assess space usage and design a complete indoor GIS solution to facilitate and streamline the data collection, a management and reporting workflow. The design has a user-friendly interface for data collectors and an automated mechanism to aggregate and visualize the space usage statistics. A case study was carried out at the Purdue University Libraries to assess study space usage. The system is efficient and effective in collecting student counts and activities and generating reports to interested parties in a timely manner. The analysis results of the collected data provide insights into the user preferences in terms of space usage. This study demonstrates the advantages of applying an indoor GIS solution to evaluate space usage as well as providing a framework to design and implement such a system. The system can be easily extended and applied to other buildings for space usage assessment purposes with minimal development efforts.

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.

The thesis the data comes from analyses patterns of growth, decline, clustering and dispersal of live music in Sydney and Melbourne between the 1980s and 2000s. It demonstrates the use of historical Geographic Information Systems, combined with interviews, as a methodological approach for understanding the impacts of restructuring in cultural industries. It offers a practical example of applied social research with GIS.

The project developed a novel methodology combining GIS with interviews with music scene participants. A substantial part of the research project comprised the development of a historical geodatabase, leveraging the spatial and temporal data embedded in historical live music performance listings (‘gig listings’) sourced from archived publications in Sydney and Melbourne. This geodatabase ultimately incorporates over 20,000 live music listings and over 2500 geocoded venues.

The historical geodatabase was built incrementally to adapt to the format of the historical data. The structure maintains a one-to-one relationship to primary sources from different publications, allowing for quality checks, but can produce normalised outputs that allow live music venues, performances, and bands to be analysed separately. Outputs from the geodatabase have facilitated the quantitative analysis and geovisualisation of live music data over the study time frame in Sydney and Melbourne.