Smart Cities

Smart Cities

The ‘smart city’ refers to the increasing role of new digital technologies in understanding and addressing an array of problems facing contemporary cities.

Smart Cities, Big Data, and the Matrix

Read  a reflection by Djuan Coleon, Executive Director of PURE (Project Urban Renewable Energy) on the recent SLS/INSS Conference: Smart, Connected Cities, as he writes about Smart Cities, Big Data, and the Matrix.

Project Studio (Local Data Design Lab)

This course seeks to engage graduate students (and advanced undergraduates) from across Georgia Tech in exploring what Atlanta looks like through civic data. Today, data on the city are increasingly available. Micro and macro changes in the makeup of local neighborhoods can be tracked through demolition/construction permits, tax records, and community surveys, among other sources; all of which might be easily downloaded by anyone with an internet connection. But data can be available, without necessarily being accessible or actionable.

Principles of Interaction Design

In this course we will study and explore the principles and practices of interaction design. You will be introduced to a number of different techniques and tools for understanding particular interaction design challenges, you will develop scenarios and storyboards, create low-fidelity prototypes, and iterate on those prototypes to create a final design project.

Urban and Regional Economics

In Urban Economics, Atlanta is an interesting city. It is one of the most segregated cities ethnically and economically. It is one of the most sprawled cities in the US. The unique features affect your life. Atlanta shows very low inter-generational income mobility. Drivers spend so much time stuck in traffic. We study urban economic theory to explain how the city characteristics affect your life.

Big Data and Public Policy

The School of Public Policy is offering a new cross-listed course with the School of Economics in Big Data and Public Policy. This course will provide an introduction to data science tools and methodologies for social science applications. Students will learn to conduct experiments and to identify causal mechanisms in large-scale social and administrative data. The course is targeted for Ph.D. or advanced M.S. students in Public Policy; M.S. students in Economics, and M.S. students in Cybersecurity

Urban Sociology

The purpose of this course is to introduce students to the field of urban sociology by exploring the history and current conditions of cities. This course will be geared toward viewing the city as a simultaneously social, cultural, and political economic phenomenon, with particular attention to the following: a) urbanization and the structure of cities; b) suburbanization; c) sustainable urban growth and economics; d) race and segregation; e) immigration; g) culture; h) gender and sexuality; i) gentrification and housing policy; j) environmental justice; and k) sustainable communities.

Data Science for Public Policy

Data Science for Public Policy introduces big data for social science and public policy applications. Students learn foundations of data science and learn to
conduct field experiments with an aim to solve social, environmental problems in major policy areas.

Urban and Regional Economics

In Urban Economics, Atlanta is an interesting city. It is one of the most segregated cities in terms of races and incomes. It is one of the most sprawled cities in the US. This unique features affect your life. For example, children from families at the 25th percentile income in Seattle, have economic outcomes comparable to children from families at the median in Atlanta (Raj et al. 2014). Why does Atlanta kids show this poor performance? We study urban economic theory about your life and city.

Virtually Remaking Cities

In addressing their sustainability agenda through design and construction, cities are subject to unique challenges, which requires effective exchange of knowledge and subject matter expertise among distributed project teams. At the same time, design and construction projects are dynamic and uncertain, requiring considerable coordination, communication and leadership to execute. Executing such projects in a virtual environment can offer many advantages and facilitate the design efforts. Yet, coordination, communication and leadership become increasingly difficult.

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