Master of Urban Spatial Analytics

Contents

Spatial Statistics and Data Analytics

MUSA 500

Eugene Brusilovskiy

The goal of this course is to familiarize students with a number of statistical and data mining techniques commonly used for analyzing different types of urban data. The course will have two key parts: 1) regression analysis for urban data, and 2) identifying patterns in urban data. Even though the course will cover the theory and mathematics behind the majority of the statistical methods, it will focus mostly on their applications. Students will learn when each statistical test should be used and the assumptions behind each test. In addition, while the R software will be used for most of the course, students will also learn to use several other software packages, such as GeoDa and VIS-STAMP. Finally, students will learn how to describe and interpret the output of the analyses that they run.

Geospatial Software Design

LARP 743 / CPLN 670

Dana Tomlin

This course will introduce students to the programming in Python and related programming languages for use in building geo-spatial data models and web-based applications.

Public Policy Analytics

MUSA 507

Ken Steif

Data scientists convert data into actionable intelligence. While most private sector data scientists optimize for profit, their public sector counterparts must address multiple complex bottom lines including economics, equity, politics, bureaucracy and social cohesion. This course teaches students how to wrangle government data; how to mine it for descriptive and predictive intelligence and how to communicate results to non-technical decision-makers. Broadly, coursework is focused on spatial analysis and geospatial machine learning and taught 70/30 in R and ArcGIS. Use cases include home price prediction, forecasting in criminal justice, land use modeling, transportation modeling and real estate site suitability. Prerequisites include vector and raster GIS and introductory statistics.

Modeling Geographic Space

LARP 741

Dana Tomlin

The major objective of this course is to explore the nature and use of raster-oriented geographic information systems (GIS) for the analysis and synthesis of spatial patterns and processes. In contrast to the fall semester course, Modeling Geographical Objects, this Modeling Geographical Space course is oriented more toward the qualities of geographical space itself (e.g. proximity, density, or interspersion) than the discrete objects that may occupy such space (e.g. water bodies, land parcels, or structures).

Land Use and Environmental Modeling

CPLN 675

Ken Steif

Urban and environmental planners are using spatial data and increasingly sophisticated empirical models to analyze existing patterns, parameterize key trends and processes, forecast alternative futures, and visualize key results for non-technical decision-makers. This course focuses explicitly on these themes. By the end of the course, students will understand how to parameterize spatial data specifically for modeling as well as how to take top down and bottom up approaches to modeling comparable phenomenon. This is a GIS-oriented class that will rely on statistical modeling. We will use a variety of software packages including ArcGIS, R, the HEC Suite of hydrological tools and NetLogo.

Javascript Programming for Planning

MUSA 610

Jeff Frankl; Nathan Zimmerman

Throughout this 14 week course, you'll be learning to program web-based mapping and dashboard applications using HTML, CSS, and javascript. In addition to programming skills, we will stress the 'tools of the trade': you will use a text editor designed for programming; your code will be turned in with git and managed through github.

Geospatial Data Science in Python

MUSA 850

Nick Hand

This course will provide students with the knowledge and tools to turn data into meaningful insights, with a focus on real-world case studies in the urban planning and public policy realm. Focusing on the latest Python software tools, the course will outline the "pipeline" approach to data science. It will teach students the tools to gather, visualize, and analyze datasets, providing the skills to effectively explore large datasets and transform results into understandable and compelling narratives. The course is organized into five main sections: Exploratory Data Science; Introduction to Geospatial Data Science; Data Ingestion & Big Data; Geospatial Machine Learning; Data Visualization & Storytelling.

Capstone Course

MUSA 800

Dana Tomlin

This course offers students an opportunity to work closely with faculty, staff, local practitioners, and each other on independent projects that involve the development and/or application of geographic information system (GIS) technology.

Musa/Smart Cities Practicum

Ken Steif

This course pairs students with cities to work on real world data science projects. Students will develop the entire pipeline over the course of the semester. This includes the collection of unstructured data, development of spatial and statistical analytics and the visualization of these analytics by way of data dashboards.