Big Data Intelligent Analysis Technology for the Study of Spatial and Time Trends in the Development of Large Cities

Abstract

The purpose of this research is to study modern problems and prospects for solving the processing of big data containing information about real estate, as well as the possibility of practical implementation of the methodology for processing such data arrays by designing and filling a special graphic abstraction «metahouse» on a practical example.Materials and methods. The study includes a review of bibliographic sources on the problems of big data analysis and their application in the modern field of construction of large cities. During the study, a technique for presenting data in a graphical form – abstraction was used. The mathematical basis of the technique is the use of multidimensional spaces, where measurements are the characteristics of individual objects. Computer simulation of a practical problem was applied using the C# programming language. Big data storage is based on the MongoDB server. To visualize data, a Web interface based on HTML and CSS is used.Results. In the course of the work, the main characteristics of big data were identified, and the specifics of data arrays consisting of information about real estate objects in a large city were described. When processing data consisting of information about real estate objects of a large city, certain difficulties arise. Thereby, methods for effectively solving the set practical task of processing and searching for patterns in a large data array were proposed: «metahouse» abstraction, data aggregator.Tabular data were obtained for a large city by analyzing three million records containing more than 10 data groups, with a basic set of parameters: floor, number of floors, price, area, living area, kitchen area, type, operation. A MongoDB cluster was created on several computers, each of which was working with its own data set without intermediate results.The results of the computational experiment showed that when using the graphical form (vector) of big data representation, the costs and time for interpreting mining data were reduced.Combining big data processing methods and their presentation through graphical abstraction allows getting new results from existing data sets.Conclusion. During the study, it was found that the presentation of groups of the received data in a graphic image has a number of advantages over a tabular presentation of data (a vector image is easy to scale, the ability to compare without plotting).The proposed way for visualizing big data by constructing abstract vector images is an alternative to traditional tables, allowing you to take a different look at data arrays and the results of their processing. The results obtained 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 floors of urban development, dynamics and distribution of supply and demand, etc.