Большие городские данные

Магистратура 2018/2019

Лучший по критерию «Полезность курса для Вашей будущей карьеры»

Лучший по критерию «Полезность курса для расширения кругозора и разностороннего развития»

Лучший по критерию «Новизна полученных знаний»

Статус: Курс по выбору (Прототипирование городов будущего)

Направление: 07.04.04. Градостроительство

Кто читает: Высшая школа урбанистики имени А.А. Высоковского

Где читается: Факультет городского и регионального развития

Когда читается: 1-й курс, 2 семестр

Формат изучения: без онлайн-курса

Преподаватели: Гончаров Руслан Вячеславович, Кармацкий Андрей Витальевич

Прогр. обучения: Прототипирование городов будущего

Язык: английский

Кредиты: 4

Контактные часы: 72

Full Syllabus

Abstract

Modern cities are complex and dynamic structures, it is challenging to manage cities. First it is very crucial to start observing urban processes through the prism of data. Some of the data sources are obvious, some of them are hidden from glance view. Data from mobile operators, public transport ridership statistics or data from social networks. The Urban Big Data course meets students with a practical view on the issue of using data to understand cities. During this course, students will achieve the theoretical knowledge and an understanding of practical skills needed to manipulate and analyze large datasets derived from urban phenomena. Using urban data analysis and visualization approach students would be able to support urban planning decisions. Urban Big Data course provides a set of practical examples, uses real data from the city, introduces actual technologies and approaches to work with the urban data

Learning Objectives

To develop an understanding of research methodology for data collection and analysis
To develop an understanding of data-driven decision process workflow
To explore and develop research methods around large datasets derived from urban phenomena
To introduction and use of technologies and tools for data analysis and decision support
To develop a skill set in quantitative methods, data science, geospatial analysis, data visualisation and programming

Expected Learning Outcomes

To know basic quantitative methods of analysis
To know the ways where to get urban data
To know how to evaluate urban data
To be able to apply proper ways to work with urban data
To be able to build data structures
To apply data analysis methods in order to extract key trends and metrics on complex processes
To be able to manipulate and analyse large datasets
To understand principles of the data representation in decision support
To have a strong experience in data analysis and visualisation tools

Course Contents

Introduction in Urban Data Analysis: data-driven cities
Introduces how cities utilize different kinds of data. This part of the course meets students the actual issues of urban data analysis. Theme combine three topics: the basic concepts of urban data, the data in the context of cities, efforts of data collection, aggregation and processing workflow. Students will be introduced the projects utilizing data-driven approach.
Data analysis and statistics
It this part of the course students will be introduced to data analytics basic concepts and techniques. As the part of the data analysis discipline course meets participants with general topics of statistics. Students will achieve appropriate knowledge level to be able preparing, cleaning, manipulating and verifying data. The major part of practice activities will be given to learn exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. This approach follows five-steps workflow: – Posing a question – Preparing and converting data for analysis – Exploring the data, finding patterns in it, and building your intuition about it – Drawing conclusions, making predictions – Communicating findings (visualisation) In order to learn data analysis students will get essential programming skills in Python programming language.
Geographic information systems and spatial computing
The location is essential part of the Urban Data analysis activities. This thematic block will describe a basic geo-spatial concepts such as geographic coordinates, projections, types and formats of spatial data. During a practice students will achieve strong knowledge and skills in GIS. Using provided open datasets they will practice on the spatial processing and will be able to apply learnt techniques for their projects. Next part of this block will introduce the possible ways to collect and compute appropriate geo-data.
Data visualisation

Assessment Elements

Classwork
Final Project

Interim Assessment

Interim assessment (2 semester)
0.4 * Classwork + 0.6 * Final Project

Bibliography

Recommended Core Bibliography

Brewer, C. A. (2016). Designing Better Maps : A Guide for GIS Users (Vol. Second edition). Redlands, California: Esri Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1621448
Corbett, J. (2002). Edward Tufte, The Visual Display of Quantitative Information, 1983. CSISS Classics. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.ADE6E165
Frederick Mosteller, Stephen E. Fienberg, & Robert E.K. Rourke. (2013). Beginning Statistics with Data Analysis. [N.p.]: Dover Publications. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1154016
Fry, B. (2008). Visualizing Data : Exploring and Explaining Data with the Processing Environment. Sebastopol, Calif: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=415604
Krygier, J., & Wood, D. (2016). Making Maps, Third Edition : A Visual Guide to Map Design for GIS (Vol. Third edition). New York: The Guilford Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1260131
Ratti, C., & Claudel, M. (2016). The City of Tomorrow : Sensors, Networks, Hackers, and the Future of Urban Life. New Haven: Yale University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1241134
Taieb, D. (2018). Data Analysis with Python : A Modern Approach. Birmingham, UK: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1993344

Recommended Additional Bibliography

Campbell, M. J., & Swinscow, T. D. V. (2009). Statistics at Square One (Vol. 11th ed). Chichester, UK: BMJ Books. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=295794
McKinney, W. (2012). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython. Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=495822

Course Syllabus