Problem Set 6 Options

ENV 859 - Geospatial Data Analytics   |   Fall 2023   |   Instructor: John Fay  

Acknowledging limited time for both section 6 and course project work, I’ve scaled back the assignments associated with the various pathways you can select for section 6. These assignments are described below.

Insights/Dashboards | Spatial Statistics Earth Engine | Object-Detection/Deep Learning | Web-based GIS

Topic: Insights/Dashboards

The learning objectives of this session are to become acquainted with ESRI’s on-line data visualization options, namely Insights, Dashboards, & Experience Builder. The assignment will be based on which technology you want to explore.

Assignment

♦ Insights

  • Complete the insights tutorial (interactive recordings), outlined here. (Example of finished product.)
  • On completion, submit a PDF or Word document with links to your shared Insights page(s) to Sakai.
  • You will be awarded full points for a properly shared Insights dashboard that includes at least 3 items (cards), including one map card. The cards should have interactive element such that selection of features on one card causes selection in other cards. This can be the one developed on watching the provided tutorial, or you can construct one using different sets of data. However, your Insights document should include at least one dataset derived from an analysis (e.g. spatial aggregation).

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♦ ESRI Dashboards & Experience Builder Apps

  • Complete the ESRI dashboard tutorial, found here. (Example of final product.)
  • Complete the ESRI Experience Builder tutorial, found here. (Example of final product.)
  • On completion, submit a Word document (the same as above) with links to your shared AGOL dashboard to Sakai. Include in this document a paragraph or two describing the the advantages and limitations among the two technologies. Include some examples where one might be better suited than the other.
  • You will be awarded full points for creating and successfully sharing of the products of your tutorial (they should resemble the final products linked above)) as well as thoughtful evaluation of these products as outlined in the previous bullet.

Topic: Spatial Statistics

The learning objectives of this session are to become familiar with some of the more advanced spatial statistics methods available within ArcGIS Pro and ArcGIS Online. Materials from ESRI’s Spatial Statistics MOOC are available on a box drive with a link to those materials and descriptions of the different sub-topics listed here.

Assignment

  • Complete three of the six sub-sections in the MOOC. Each section includes short videos, some exercises, and a quiz. Data for the exercises are provided in the Box drive.
  • You will schedule an “exit interview” with me sometime between Dec 1-6. In this interview, be prepared to discuss your quiz answers (on the topics you chose), your thoughts on the uses, applications, and limitations of the techniques covered, and how you might apply those to various situations (actual or hypothetical).
  • Full points will be awarded for rational answers to the quiz, and for articulating your understanding of the methods and applications of the spatial statistical techniques you chose to explore.

Topic: Earth Engine

The learning objectives of this session are to lay a foundation for successfully using Google Earth Engine in an analysis of your choosing. This will include creating your Earth Engine account, navigating the Earth Engine JavaScript Code interface, and executing Earth Engine analyses in Python via the geemap package.

Assignment

  • Complete the tutorial linked here in which you will write and execute code in the JavaScript interface as well as within a Jupyter notebook.
  • Submit a link to your JavaScript EE Code as well as your Jupyter Notebook file to Sakai.
  • Write and submit a short summary (one or two paragraphs) sharing your thoughts on this lesson. This should include your opinion on the JavaScript vs the geemap coding approaches, what you see are the advantages and disadvantages to using Earth Engine, and where you might take the knowledge learned.
  • Full points will be awarded to successful completion of these exercises as well as thoughtful articulation in your summary.

Topic: Deep Learning & Object Detection

This session is more independent and unguided than the others. The learning objectives are simply to dive deeper into the technology of deep learning. Those choosing this path should have a specific dataset and analysis in mind. (If you just want a cursory look at deep learning/object detection you should consider the Spatial Statistics option which has a deep learning module). It will be up to you which technology you want to pursue; you aren’t limited specifically to ESRI’s technology.

Assignment

  • Arrange a discussion with me (sometime between Nov 20-29) why you want to pursue this topic and what your personal objectives are. Through this discussion, we will develop a workplan for your assignment, given your objectives, what’s realistic given time constraints, and what materials and technologies are available.

Topic: Web-based GIS

The learning objectives of this session are to build a deeper understanding of the differences between desktop and cloud based GIS. Given time constraints, this can either involve a deeper dive into exploring ArcGIS Online as a data repository and on-line analysis platform, or using Python to explore, access, and analyze the vast amount of spatial datasets via APIs.

This topic also include the ArcGIS API for Python which is an up and coming alternative to Google Earth engine

Assignment

ArcGIS Online

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GIS & Web Services