Grid land hot area display method

Abstract

The invention relates to a grid land hot area display method, belongs to the technical field of big data application, and solves the problem of imperfect land hot area algorithm in the prior art. Thegrid land hot area display method includes: establishing a perfect index system in advance; carrying out de-duplication on each index; assigning the weight of each index by adopting an entropy weightmethod; calculating a grid entropy weight score; further calculating the island chain score through the entropy weight score; obtaining a comprehensive score by using the entropy weight score and theisland chain score; using the obtained comprehensive score rank to divide the plot into a high-energy-level hot zone, a medium-energy-level hot zone and a low-energy-level hot zone; dividing the plotinto a low-potential area, a high-potential area and a risk area by calculating the potential value of each grid, forming nine definition labels through intersection and displaying the nine definitionon the map in different colors. A user can conveniently check the score of each grid, and the position of each hot area on the map can be visually and clearly known.

Description

technical field [0001] The invention belongs to the technical field of big data applications, and relates to a land value evaluation method, in particular to a method for evaluating and displaying land hot spots in gridded regions. Background technique [0002] In the development process of the real estate market, there are a lot of preliminary land evaluation work. Land price evaluation is one of the important contents of land market management. Conventional land price evaluation methods mainly include feature selection, feature quantification, feature extraction, subject modeling, land price evaluation, etc. step. Among them, there are the following shortcomings in the process of land price feature quantification: [0003] 1: Massive data, artificial bottleneck [0004] With the more detailed and in-depth research on cities, plates, and land in the early stage, the demand for data is also increasing. Compared with the increasing data volume, the operation cycle is const...

AI Technical Summary

Problems solved by technology

Compared with the continuous growth of data volume, the operation cycle has been continuously improved, and manual execution is overtime, it is difficult to realize the collection and statistics of such a large amount of data

[0005] Two: information lags, affecting decision-making

[0006] Manually respond to land market fluctuations, premiums, failed auctions, etc., and there is a time period for cause investigation, and real-time feedback cannot be achieved

For the surrounding market, new houses, second-hand houses, and even surrounding supporting planning, it takes a longer period of time to collect, and there is a lag in market response

[0007] Three: static data, lack of timeliness

[0008] Manually executed reports have research value in the short to medium term, but long-term static data will lose their reference value, and the report cannot be continuously updated

[0009] Four: Rough particles, biased conclusions

If the granularity of data is required to break through the boundaries of sectors and general administrative divisions, and to be more detailed, manual labor can no longer meet the requirements

[0011] Five: Marginal plots, lack of data

If the granularity of data is required to break through the boundaries of sectors and general administrative divisions, and to be more detailed, manual labor can no longer meet the requirements

[0013] Six: The algorithm is simple and has no growth potential

[0014] The manual report is a static value, long-term use, need to do data query work again

And the algorithm is generally a traditional algorithm, which cannot actively respond to the ever-changing market environment

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