Big Data Analysis (Spatial Statistics and Data Analysis)
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
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.
Applications of Urban Spatial Analysis
This course teaches advanced GIS functionality and spatial analysis in the urban planning domain. The class focuses on real-world GIS applications and, in combination with introductory statistics, provides students a framework for understanding how to efficiently allocate limited resources across space. Specific applications include network analysis; retail site-suitability; spatial housing market analysis; predictive modeling; remote sensing, land use planning and more.
Modeling Geographic Space
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
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.
Jeff Frankl; Nathan Zimmerman
Data wrangling and data visualization
The purpose of this course is to familiarize students with the “pipeline” approach to data science. This involves the process of gathering data, storing the data, analyzing the data, and visualizing the data such that non-technical decision makers can make sense of it. The course is broken down accordingly into four sections. 1. Data collection: Students will learn how to gather data by way of web scraping, APIs, and other unstructured sources. 2. Databases: This part of the course teaches students how to store this data for efficient retrieval and analysis. 3. Analytics: Students will learn a range of machine-driven techniques for analyzing structured and unstructured data. 4. Data visualization: The last part of the course teaches students how to present the results of their analysis visually using R and the web application framework Shiny.
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.
Smart Cities Studio
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.