2022/2023

Программирование для анализа городских данных

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

Лучший по критерию «Новизна полученных знаний»

Статус: Дисциплина общефакультетского пула

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

Где читается: Факультет городского и регионального развития

Когда читается: 1, 2 модуль

Охват аудитории: для всех кампусов НИУ ВШЭ

Преподаватели: Кульчицкий Юрий Викторович

Язык: русский

Кредиты: 3

Контактные часы: 58

Программа дисциплины

Аннотация

Contemporary urban planner and researcher should be aware of the processes that can be observed with new data sources and analysis tools. In the modern urbanised world, enormous amounts of data are generated daily ranging from citizen complaints and reports to their search queries, daily movements, electricity meter readings, etc. Analysing that data creates new opportunities for studying urban phenomena and enables new scientific approaches in urban planning and management. The extraordinary volume and multidimensionality of urban data require learning new tools and methods for collecting and acquiring such data, shaping it into a specific form appropriate for the analysis, and performing the analysis. The course introduces the students to the types of data (especially spatial data) relevant to urban research, the advanced tools of working with such data, the full process of data analysis from data collection and exploratory visualisation to inferences, conclusions, presentation of the analysis results. Specific topics include data acquisition, data manipulation and preparation, exploratory analysis, statistical analysis (basic regression and introduction to spatial autocorrelation and regression), data visualisation and reproducible reporting. The students will use R statistical programming language and RStudio IDE (integrated development environment) during the course, but the concepts used in the course and the acquired skills can be applied in Python, Julia or any other programming language with data analysis libraries.

Цель освоения дисциплины

• Familiarise students with different types of urban data sources, file and database types used for storage of such data.
• Discuss the origins and associated limitations of various urban data sources.
• Showcase the practices of explanatory data visualisation in urban planning and research.
• Explain the importance of time and space dimensions of urban data.
• Explain how the data is stored and structured.
• Develop basic skills of applying statistical analysis to large and small data sets.
• Teach basic principles of exploratory data analysis.
• Show how to communicate urban data analysis results through explanatory data visualisation.

Планируемые результаты обучения

• Acquire spatial urban data from files, remote servers and databases using R packages, API and web-scraping
• Apply exploratory data analysis (EDA) to reveal time and space variations and patterns in urban data
• Apply linear and spatial regression models to interpret space-time variations and patterns of urban processes.
• Clean and Transform spatial urban data to prepare it for exploratory and statistical analysis.
• Perform geoprocessing and spatial data manipulation and visualisation. Apply linear and spatial regression models to interpret space-time variations and patterns of urban processes.
• Write readable and error-free data analysis code in R that allows a third party to reproduce and interpret the analysis.

Содержание учебной дисциплины

• Introduction to Smart Cities and Urban Data
• Introduction to Scripted Data Analysis and Reproducible Research
• Data Visualisation and Exploratory Data Analysis
• Urban Data Types and Sources. Getting Access to Data
• Tidy Data. Data Cleaning and Transformation
• Statistical Modelling
• Spatial Data Analysis and Statistics
• Working with APIs and Web Scraping

Элементы контроля

• Exam
  The exam is carried out using proctoring (asynchronous type) / Экзамен проводится с применением прокторинга (асинхронного типа)
• Lab 04 - Regression Models and EDA
• Lab 06 - Spatial Regression
• Lab 01 - Introduction to Scripted Data Analysis and Reproducible Research
• Lab 03 - Tidy Data
• Lab 07 - Working with APIs and Web Scraping
• Lab 05 - Spatial Data Analysis, Processing and Visualisation
• Lab 02 - Data Vis and EDA

Промежуточная аттестация

• 2022/2023 учебный год 2 модуль
  0.08 * Lab 03 - Tidy Data + 0.12 * Lab 07 - Working with APIs and Web Scraping + 0.08 * Lab 04 - Regression Models and EDA + 0.05 * Lab 01 - Introduction to Scripted Data Analysis and Reproducible Research + 0.12 * Lab 06 - Spatial Regression + 0.4 * Exam + 0.05 * Lab 02 - Data Vis and EDA + 0.1 * Lab 05 - Spatial Data Analysis, Processing and Visualisation

Список литературы

Рекомендуемая основная литература

• Arbia G. A Primer for Spatial Econometrics: With Applications in R. Basingstoke: Palgrave Macmillan, 2014.
• Knaflic C.N. Storytelling with data: a data visualization guide for business professionals. New Jersey: Wiley, 2015.
• Munzert S. Automated data collection with R: a practical guide to Web scraping and text mining. Chichester, West Sussex, United Kingdom: Wiley, 2014. 1 p.
• Offenhuber D., Ratti C. Decoding the city: Urbanism in the age of big data. Birkhäuser, 2014.
• Pace L., Hlynka M. Beginning R an introduction to statistical programming. New York: Apress, 2012.
• Peng R.D., Dominici F. Statistical methods for environmental epidemiology with R: a case study in air pollution and health. New York ; London: Springer, 2008. 144 p.
• Wickham H. ggplot2: elegant graphics for data analysis. Second edition. Cham: Springer, 2016. 260 p.

Рекомендуемая дополнительная литература

• Arbia G. Spatial Econometrics: Statistical Foundations and Applications to Regional Convergence. Springer Science & Business Media, 2006. 220 p.

Авторы

• Котов Егор Андреевич
• Кульчицкий Юрий Викторович