Urban characteristic block population density estimation method and system based on spatial big data

Abstract

The invention belongs to the technical field of population density estimation, and discloses an urban characteristic block population density estimation method and system based on spatial big data. The urban characteristic block population density estimation system based on the spatial big data comprises a remote sensing image acquisition module, a population information acquisition module, a main control module, an image recognition module, a population statistics module, a population density calculation module, a simulation module, a cloud storage module and a display module. According to the invention, through the population information acquisition module, the staff management work required for floating population information acquisition by a floating population management department is saved, and no special population information acquisition equipment needs to be purchased, so the cost is saved; meanwhile, the population density condition of the prefecture-level city-level cities can be effectively simulated through the simulation module, manual statistics is replaced with an automatic means, and a large amount of manpower and material resources can be saved.

Description

technical field [0001] The invention belongs to the technical field of population density estimation, and in particular relates to a population density estimation method and system method of urban characteristic blocks based on spatial big data. Background technique [0002] Population density is the number of people per unit of land area. There are two commonly used units of measurement: person / square kilometer; person / ha. It is an important indicator to measure the population distribution of a country or region. The land area for calculating population density refers to the land area and inland waters within the territory, excluding the territorial sea. Since the population density indicator assumes that the population is evenly distributed in a certain area it involves, the smaller the population density calculation range, the more faithfully it can reflect the population distribution; the larger the range, it can only reveal the population distribution in a general way...

AI Technical Summary

Problems solved by technology

[0003] To sum up, the problems existing in the existing technology are: the existing population information collection method requires a lot of manpower management and equipment procurement costs, which brings difficulties to the collection of floating population information; at the same time, the population density statistics are time-consuming and laborious

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