Land attribute classification method based on rental and return data of shared object

Abstract

The invention discloses a land attribute classification method based on rental and return data of a shared object, and the method comprises the steps: carrying out the clustering analysis of the rental and return position information of the shared object, so as to form a virtual rental point of the shared object; taking the virtual rental point location as a unit, collecting the rental and returndata of the shared object to construct a time rental and return curve corresponding to the virtual rental point location; normalizing the rental and return curves at each time; based on Fourier transform, deconstructing each normalized rental and return curve is deconstructed into a rental and return curve function expression formed by superposing a plurality of sine wave functions; and constructing a rental and return curve parameter set of the virtual rental points, and performing clustering analysis based on parameters in the rental and return curve parameter set to form classification of each virtual rental point. According to the method provided by the invention, the land attribute can be identified by means of the data, especially the rental and return data, such as shared bicycles in the sharing economy, and the land attribute can be analyzed and classified by utilizing the existing data.

Description

technical field [0001] The invention relates to a land use attribute classification method based on lease-return data of shared objects. Background technique [0002] In the traditional field of urban planning, such as traffic travel OD (traffic trip volume) characteristics are highly correlated with the nature of land use, and it is generally believed that the accuracy of OD travel characteristics is higher based on different types of land use distribution. But on the contrary, it is difficult to use the conventional means of inferring the nature of land use from traffic OD features, because the characteristic difference of conventional traffic OD features is very low, and there are relatively many types of land use properties, and the accuracy of inferring high-dimensional features by using low-dimensional features Difficult to guarantee. [0003] Various shared economic products that have been greatly developed and widely used in recent years, such as shared bicycles, us...

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is to overcome the lack of prior art that can analyze and classify land use attributes based on the rent-return data of shared objects in the sharing economy, thereby effectively assisting urban land use and space planning, and proposes a new Land use attribute classification method based on rent-return data of shared objects

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