Integrating Machine Learning into Geographic Research

American Association of Geographers

This workshop will introduce the basic concepts of machine learning, and integrate simple machine learning models into geographic research.

<h1> About the workshop</h1><p>This workshop will introduce students to the fundamental concepts and techniques related to machine learning, and how to integrate machine learning into geographic research. This workshop is designed to help students overcome some of the challenges posted by the pandemic by leveraging a free cloud computing platform, Google Colab, that allows students to build machine learning models on super computers safely at home and for free. The main programming language for this workshop will be Python, and the main machine learning package will be scikit-learn.</p><p></p><h1>Workshop schedule</h1><h3>The workshop will start on Feb 8, and end on Feb 12. All times are in Eastern Time.</h3><p><strong>Monday, asynchronous: Intro to GeoAI and Google Colab</strong></p><p>A pre-recorded lecture consisting of two videos is made available. We will give a brief introduction to geospatial artificial intelligence (GeoAI) and help students set up the working environment on Google Colab.</p><p><strong>Tuesday, synchronous: Python Refresh and GeoPandas</strong></p><p>This session will help you refresh Python programming basics and learn how to work with shapefile data using the GeoPandas library.</p><ul><li>9:00 - 9:50 am: Small Group Interactive Session; Only for selected participants who need research help during the pandemic
</li><li>10:00 - 11:30 am: Open Lecture; Open to all AAG members registered to the workshop; This session will help you refresh Python programming basics and learn how to work with shapefile data using the GeoPandas library.
</li></ul><p><strong>Wednesday, synchronous, 9:00 - 11:00 AM (ET): Machine learning for Geography </strong></p><p>This session will cover the basics of preparing geographic data for machine learning models and implementing a model (Random Forest) using the scikit-learning library.</p><ul><li>9:00 - 10:25 am: Open Lecture; This session will cover the basics of preparing geographic data for machine learning models and implementing a model (Random Forest) using the scikit-learning library.
</li><li>10:30 am - 11:00 am: Small Group Interactive Session; Only for selected participants who need research help during the pandemic</li></ul><p><strong>Thursday, asynchronous: Exercise: Building Your Own Machine Learning Model </strong></p><p>A simple exercise will be released at the end of the session on Wednesday. Students are expected to work on this exercise on Thursday, in which you will be asked to build your own model using scikit-learning.</p><p><strong>Friday, synchronous, 9:00 - 11:30 AM (ET): Recap and the Road Forward</strong></p><p>This session will review the exercise that you have worked on Thursday, and answer any questions. I will also share some resources for studying machine learning and AI beyond this workshop.</p><ul><li>9:00 - 10:25 am: Open Lecture; This session will review the exercise that you have worked on Thursday, and answer any questions. I will also share some resources for studying machine learning and AI beyond this workshop.
</li><li>10:30 am - 11:30 am: Small Group Interactive Session; Only for selected participants who need research help during the pandemic</li></ul><p><strong>Note</strong>: synchronous sessions will not be recorded</p><h1></h1><h1>Eligibility</h1><p>The intended audience is master and PhD students in geography who are interested in learning how to use some basic machine learning in their research and how to utilize the free cloud computing platform Google Colab. <strong>Students must be familiar with the basics of Python programming in order to effectively participate in this workshop.</strong></p><h1></h1><h1>Capacity</h1><p>We will select <strong>up to 20 graduate students</strong> to participate in this workshop based on time of registration, your AAG membership status, and your research needs. If you are selected, we will notify you ahead of the workshop and provide you all the workshop details and session links. If you are selected, the expectation is that you will participate in all sessions of the workshop.</p><p>If you are interested in this workshop, but can not attend one or more of the interactive sessions, you can still register and indicate you are interested in any of the &quot;asynchronous&quot; resources associated with this workshop (articles, recorded videos, exercises, etc.). We will make sure to share them with you.</p><p></p><h1>About the workshop instructor</h1><h3>Dr. Yingjie Hu</h3><p>Dr. Yingjie Hu is an Assistant Professor in the Department of Geography at the University at Buffalo and the National Center for Geographic Information and Analysis (NCGIA). His major research area is in GIScience and more specifically in geospatial artificial intelligence (GeoAI). He was a guest editor of the GeoAI special issue in the International Journal of Geographical Information Science, and a lead organizer of the AAG GeoAI Symposium series.</p>

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