BIG DATA RANKING SYSTEM AS AN EFFECTIVE METHOD OF VISUALIZING THE QUALITY OF URBAN STRUCTURAL UNITS
URI (for links/citations):http://elib.sfu-kras.ru/handle/2311/111719
(Engelgardt, A.E.: Siberian Federal University, School of Architecture and Design, Russian Federation, Krasnoyarsk, Svobodny 79, 660041 e-mail: email@example.com)
Proceedings of the XXV ISUF International Conference “Urban Form and Social Context: from Traditions to Newest Demands” (Krasnoyarsk, July 5–9, 2018)
Big data is the basis for new technological changes. Constantly growing volumes of arrays greatly complicate data processing and understanding. Big data analysis extracts knowledge and meaningful information from large and complex data sets. The extraction of information displays regularities hidden in the data. Modern cities use the latest technologies to support sustainable development and a high standard of living. The indicator of a high standard of living of the urban population and, consequently, an indicator of a quality city is the quality of the urban environment. To evaluate the structural units of a city, the most common method is ranking. Ranking systems based on big data are the most effective method of visualizing the quality of structural elements of a city. Innovative ways of collecting and analyzing data are gradually replacing obsolete mechanisms of city management. Unlike statistical data, which are out of date by the time of their analysis, big data can be processed in real time that increases the quality and speed of decision making. The complexity of big data methods implementing in ranking systems is caused by problems of staff shortages, technical equipment, legal rights, security problems and openness of data. Ranking quality systems of the urban environment can be used by the city administration, designers, civil communities to assess the current state and management of the urban environment. The creation of such ranking systems is the first step towards the formation of smart open data-driven cities. The introduction of big data into cities can be divided into three levels as the influence of data on urban governance increases: applied (open data city); semi-autonomous (data-driven city); autonomous (smart city).
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