City updating effect evaluation method and system based on real estate big data

Abstract

The invention discloses a city updating effect evaluation method based on real estate big data, and the method comprises the steps: firstly respectively constructing an IP agent pool, a collection channel pool and a flow load balance according to corresponding construction principles; conducting cleaning, merging, updating and data collection on the same object data from different channels throughthe IP agent pool, the collection channel pool and the flow load balance; using house surveying and mapping information as core basic information, using a name method and an address method for processing the collected data, conducting data mining, and obtaining metadata used by a city updating performance evaluation model; and by the constructed city updating performance analysis and evaluation model taking real estate as a driving force, conducting statistical analysis by utilizing the obtained metadata to obtain an index system suitable for city updating performance evaluation and analysis,and displaying index data on a mobile terminal through data visualization. According to the method, the urban form and the gradual evolution and effect of governance are reflected spatio-temporally,and a decision basis can be provided for long-term sustainable development of cities.

Description

technical field [0001] The invention relates to the technical field of urban management, in particular to a method and system for evaluating urban renewal effects based on real estate big data. Background technique [0002] China's large-scale urbanization process has gone through nearly 30 years, bringing the modern urban life that the Chinese people yearn for. With the continuous evolution of this process and the continuous development of human civilization, the original ignorance of the natural environment and history The transformation of the environment has begun to receive people's constant reflection. When the urbanization that will last for a long time no longer continues at the original speed and scale, small-scale gradual transformation is more in line with the needs of urban development and social development. . Therefore, in recent years, the central cities in my country's urbanization process have begun to gradually transition from the "urban reconstruction" of...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problem of evaluating and analyzing models, many consulting agencies and universities in the industry currently provide professional consulting services for urban managers. On the whole, their work is more of a traditional consulting service. For urban renewal performance The research work of the evaluation is more qualitative research, which has a certain degree of subjectivity, and the focus is more on the planning and design, project positioning and strategic planning of the traditional inner part of the city (with projects and communities as units). On the one hand, it failed to conduct follow-up evaluation of the renewal work from the perspective of the social public sector, and failed to conduct continuous and comprehensive observation and evaluation of the renewal effect of the city as a whole (by region or city)

[0006] 2. Lack of continuous and long-term high-quality basic data

[0007] Although traditional consulting and analysis institutions have scientific and reasonable evaluation and analysis models, they are limited by the degree of domestic information disclosure, and the channels for obtaining data are effective. Most of them obtain data through traditional Internet data collection technology or manual collection. To a certain extent, it meets the timeliness needs of analysis, but compared with government affairs data, it is difficult to match the accuracy of data

Even if the government develops government data from consulting agencies, the data from different channels, due to the weak correlation between them, makes it difficult to integrate data from various channels, which further restricts the depth of evaluation and analysis work, and it is difficult to systematically meet the needs of data mining.

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