Магистратура
2023/2024
Программирование для анализа городских данных
Статус:
Курс обязательный (Цифровая урбанистика и аналитика города)
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
Охват аудитории:
для своего кампуса
Преподаватели:
Кульчицкий Юрий Викторович
Прогр. обучения:
Цифровая урбанистика и аналитика города
Язык:
русский
Кредиты:
6
Контактные часы:
32
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.