E3S Web Conf.
Volume 258, 2021Ural Environmental Science Forum “Sustainable Development of Industrial Region” (UESF-2021)
|Number of page(s)||11|
|Section||IT in Environmental Science|
|Published online||20 May 2021|
Big data analysis for studying spatiotemporal trends in the sustainable development of large cities
1 Institutes of Academy for Engineering and Technologies of the Southern Federal University, 44, Nekrasovsky lane, 347922, Taganrog, Russia
Corresponding author: email@example.com
The article covers the analysis of big data in urban planning. The purpose of this work is to study modern problems of processing big data containing information about real estate objects and prospects for solving these problems, as well as the possibility of practical implementation of the methodology for processing such data sets by designing and filling a special graphic abstraction “metahouse” using a practical example. The relevance of the study lies in identifying a number of advantages in the presentation of data in graphical form. The mathematical basis of the technique is the use of multidimensional spaces, where measurements are the characteristics of individual objects. In the course of the work, the specifics of big data sets, consisting of information about real estate in a large city, were described. methods of effective solution of the set practical problem of processing and searching for patterns in a large data array were proposed: abstraction “metahouse”, data aggregator. In the course of the study, it was revealed that the presentation of groups of the obtained data in a graphical form has a number of advantages over the tabular presentation of data. The obtained results can be used both for the primary study of big data processing technologies, and as a basis for the development of real applications in the following areas: analysis of changes in the area of houses over time, analysis of changes in the number of storeys in urban development, etc.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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