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Data Driven Cities

2019/2020
Учебный год
ENG
Обучение ведется на английском языке
4
Кредиты
Статус:
Курс обязательный
Когда читается:
1-й курс, 1 семестр

Course Syllabus

Abstract

Theory and practice at domain of data-driving city including sharing economy, time use, lean master planning, smart city. How these concepts cover and incorporate into the resettlement system to produce self-organization of cities, urban agglomerations, network city, inter-municipal cooperation, moreover urban communities and spatio-temporal segregation and regeneration of built-up territories. How to do economic assessment of master-planning decisions based on the data. Features of the data analysis for urban planning. Capabilities and limitations for big data and spontaneous data including types, sources, structure, licensing, processing and storage. Proprietary and open toolsets and programming frameworks for analyzing urban data. Data processing and scientific visualization of geographical and time-series data, specific algorithms and methods for data analysis. Making workflow and managing research and project are based on urban data and technologies and data analysis including resources, organization, team, stages, budget. Seeking and definition of a problem. Preparing the data maps and diagrams during the research. Doing research using data-driven techniques and data and preparing conclusions that can be scaled forward into an app or service.
Learning Objectives

Learning Objectives

  • Give broad spectrum of the contemporary theories and trends at urban planning related to commercial functions, residential areas and transport infrastructure based on data-driven technology.
  • Give a number of skills needs for building up of full workflow and managing of data-driven urban analysis projects from scratch.
  • Give a toolbox of soft and hard skills for data gathering, preparing and analysis, scientific visualization and cartography.
Expected Learning Outcomes

Expected Learning Outcomes

  • As a result of mastering the discipline student have: to know: key point of urban planning theory; key point of sharing economy concepts; key point of time-sharing concepts; key point of smart city concepts; terms and methods with help of which the spatial-temporal is being analyzed; toolset with help of which the data is being gathered, prepared and analyzed; individual research project including proposal, data analysis and report.
  • To be able to map, to measure and analyze urban spatial-temporal data to apply project-based approach in urban development to build up analytical research project
  • To possess the following skills: to lead the urban spatial analysis projects in research institutions, private companies and city municipalities; to present the results of spatial analysis research in a format of science reports and presentations.
Course Contents

Course Contents

  • Topic 1. Theory and practice at domain of data-driving city
    Theory and practice at domain of data-driving city including sharing economy, time use, lean master planning, smart city. How these concepts cover and incorporate into the resettlement system to produce self-organization of cities, urban agglomerations, network city, inter-municipal cooperation, moreover urban communities and spatio-temporal segregation and regeneration of built-up territories. How to do economic assessment of master-planning decisions based on the data.
  • Topic 2. Features of the data analysis for urban planning.
    Capabilities and limitations for big data and spontaneous data including types, sources, structure, licensing, processing and storage. Proprietary and open toolsets and programming frameworks for analyzing urban data. Data processing and scientific visualization of geographical and time-series data, specific algorithms and methods for data analysis.
  • Topic 3. Making workflow and managing research and project
    Making workflow and managing research and project are based on urban data and technologies and data analysis including resources, organization, team, stages, budget. Seeking and definition of a problem. Preparing the data maps and diagrams during the research. Doing research using data-driven techniques and data and preparing conclusions that can be scaled forward into an app or service.
Assessment Elements

Assessment Elements

  • non-blocking Homework 1
  • non-blocking Homework 2
  • non-blocking Homework 3
  • non-blocking Final Project
Interim Assessment

Interim Assessment

  • Interim assessment (1 semester)
    0.6 * Final Project + 0.135 * Homework 1 + 0.135 * Homework 2 + 0.13 * Homework 3
Bibliography

Bibliography

Recommended Core Bibliography

  • Robert G. Hollands. (2008). Will the real smart city please stand up? City, (3), 303. https://doi.org/10.1080/13604810802479126

Recommended Additional Bibliography

  • Boeing, G. (2019). Spatial information and the legibility of urban form: Big data in urban morphology. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edssch&AN=edssch.oai%3aescholarship.org%2fark%3a%2f13030%2fqt6mn025sb
  • Dameri, R. P., & Rosenthal-Sabroux, C. (2014). Smart City : How to Create Public and Economic Value with High Technology in Urban Space. Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=806612
  • Lau, B. P. L., Marakkalage, S. H., Zhou, Y., Hassan, N. U., Yuen, C., Zhang, M., & Tan, U.-X. (2019). A Survey of Data Fusion in Smart City Applications. https://doi.org/10.1016/j.inffus.2019.05.004