Object-orientated city entity geocoding integration method

Abstract

The invention relates to an object-orientated city entity geocoding integration method. The method comprises the following steps of: step 1, generating a type code of an entity object; step 2, generating a space code of the entity object; step 3, judging the code similarity, including two sub-steps of judging the space code similarity and judging the type code similarity; and step 4, for a new entity object, performing connection by "-" according to an 8-bit time code which is accurate to the day when the new entity object enters a central encoding library, along with the space codes and the type codes for warehousing, and for a suspected new entity object, transferring the suspected object to "a repeated entity candidate area" for further processing. The invention provides a special entity comprehensive encoding construction method for city vector data; efficient indexing and integration of rapidly expanded and disordered city running data in an ordered and infinitely expanding manner on a geographic framework are realized by the encoding; and the integration method is a supplement of an existing integration technology.

Description

technical field [0001] The invention belongs to the technical field of urban geographic coding, and in particular relates to an object-oriented urban entity geographic coding integration method. Background technique [0002] Big data has triggered a new round of smart revolution. Various information carriers, transmission media and technologies involved in urban operation have given birth to the rapid rise of smart cities in the era of big data. In 2008, enterprise pioneers represented by IBM promoted the construction of smart cities in full swing in countries around the world, forming a series of urban geographic information public platforms based on data integration and sharing. The public platform can be compared to a data secondary production machine. "Raw materials" (structured or unstructured data of various explosions generated by urban operations) are input in various forms, and after data extraction, cleaning, integration and other operations, a basic The geographi...

AI Technical Summary

Problems solved by technology

[0006] However, the shortcomings of the above two commonly used technical solutions are: the integration method based on data mapping focuses on the secondary mapping of data by using transformation tools, which realizes subject-oriented data reorganization and high application efficiency, but requires manual data processing. The technical threshold of integration is high, time-consuming and labor-intensive, which is not conducive to the rapid incorporation of new data; the integration method based on spatial location can make up for the deficiency of the former to a certain extent, and the use of effective coding indexes can also It can achieve efficient retrieval, but ignores the core of geographical entities participating in the operation of the city, and only realizes the correspondence of different data layers purely through location coding. lead to a certain degree of data redundancy and ambiguity

In summary, the above two types of methods lack consideration of integration from the perspective of urban data composition system and logical relationship, resulting in content ambiguity and inefficient retrieval

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