EPA 122A Spatial Data Science#

Overview#

Urban planners, policymakers, and key decision-making stakeholders use data and data-based infrastructures to govern various urban systems from operations to planning, optimization, and distribution of resources. This course will introduce you to practices in data analysis and computational methods in the context of urban planning. It will illustrate how data can be used and misused, and how to critically evaluate datasets, models, and questions that arise from them. While learning how to collect, transform, and analyze data using machine learning techniques for understanding urban phenomena, you will learn about the process of data science and its positive and negative impacts on people and places.

Learning Outcomes#

After successful completion of this course, you will be able to:

  • Interpret and discuss spatial data sources that are usable and relatable for a problem presented.

  • Transform spatial data and consolidate all information into a dataset that is manageable, informative, and relates to your problem.

  • Describe and analyze the consolidated spatial datasets to support your problem with evidence.

  • Apply models using statistical techniques and machine learning to infer results in the process of turning spatial data into valuable information.

  • Report results and reflections through visualization, mapping, storytelling, and interpretable summaries, especially when faced with a new dataset.

Career Prospects#

  • Hopefully get great data-driven policy, governance, or civil society jobs in the future.

  • Go on adventurous and sustainable journeys with an open mind.

Ethical Considerations#

  • Most importantly, become mindful of how ML and data can disrupt and impact the lives of multiple communities.