AU2020356806A1 - Historical city protection and development cooperative control scheme aided design system - Google Patents
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Abstract
A historical city protection and development cooperative control scheme aided design
system is disclosed. The system includes a server and a user terminal. The server
establishes a connection with a related department server. City factor data is acquired based
5 on user requirements, and data processing methods for various city factor data and related
analysis methods for population change monitoring are packaged in advance. Requirements
of a user for areas and data to be researched are received, related city factor data is acquired,
and a corresponding data processing method is acquired for processing. The city factor data
is matched according to spatial position parameters. Environmental population capacity
10 estimation is performed on an old urban district and a new urban district, respectively. A
population overrun time is determined based on an environmental population capacity and
population prediction of the old urban district, and a development intensity of the new
urban district is estimated based on an environmental population capacity of the new urban
district. A protection scheme of the old urban district is designated accordingly, and data
15 obtained in an analysis process may be visualized. According to the present disclosure, the
early-stage data preparation and preprocessing workload of a project can be reduced.
Description
Technical Field
The present disclosure belongs to the technical field of data visualization, and particularly relates to a historical city protection and development cooperative control scheme aided design system.
Related Art
The description in this section merely provides background information related to the present disclosure and does not necessarily constitute the prior art.
At present, China's 108 national historical cities are mostly political, economic and cultural centers. There are many serious city problems such as high population density, traffic congestion, long-term building disrepair, inadequate living services and municipal facilities, etc. In the measures of "protecting old cities and developing new cities", the site selection and planning of new urban districts have still lacked quantitative bases, and "man-made decision" has led to the lack of scientific and forward-looking city planning,
even into the waste dilemma of repeated construction.
At present, there are the following problems in related projects about protection of historical old urban districts:
Although the concept of an environmental capacity has emerged in the 1990s (Michael Jacobs, 1997), the researches on the environmental capacity both at home and abroad remain at the macroscopic research level, and specific methods and steps for determining an environmental threshold quantitative index are not proposed.
In the city protection planning logic of "population--land--facility", the accuracy of population size prediction greatly affects the scientificity and rationality of protection planning. However, most of the current research methods are based on the discussion of the rationality of a prediction model itself and a mathematical logic relationship, and lack a tailored model screening and combination process according to current city and population situation and changing characteristics.
However, for the optimal density and spatial distribution of city population, most of the existing research models are based on the mathematical derivation of a single factor in city composition, and do not consider the comprehensive effect of various city factors.
In summary, in the protection research of historical cities, there has not been a precedent of quantitative researches based on a new city development control model.
At present, a large amount of data preparation and preprocessing work is needed in the early stage of planning and design projects related to planning of new urban districts. For example, basic geographic data, socioeconomic data, etc. need to be inquired and downloaded from a public website, and in order to acquire basic geographic data such as remote sensing images and digital elevation data, it is also necessary to manually set a data range to be acquired in the inquiry process (by longitude and latitude input or box selection). After the socioeconomic data is acquired, it is also necessary to manually assign the socioeconomic data to digitized district division data or land use data, and the data assignment process is very complicated due to various data types. A large number of superposition operations need to be performed when a weight layer is acquired in a subsequent GIS platform, and the superposition operations involve a minimizing superposition method and a maximizing superposition method, so that errors are easy to occur.
In order to overcome the defects of the prior art, the present disclosure provides a historical city protection and development cooperative control scheme aided design system. Related data, data preprocessing and analysis methods can be automatically acquired according to the requirements of users, and a large amount of early-stage data preparation and preprocessing workload of a project can be effectively reduced.
To achieve the foregoing objective, one or more embodiments of the present disclosure provide the following technical solutions:
A server includes:
a data storage subsystem, configured to acquire and store various city factor data;
a method management subsystem, configured to package data processing methods for the various city factor data, related analysis methods, and calculation formulas in advance; and
a data analysis subsystem, including:
a data acquisition module, configured to receive a designation of a user about a data area range and a data requirement, acquire related city factor data from the data storage subsystem, and acquire the corresponding data processing methods from the method management subsystem to process the city factor data;
an old urban district population prediction and environmental population carrying capacity estimation module, configured to acquire city factor data of corresponding areas of an old urban district, perform matching according to spatial position parameters, receive a designation of the user about a basic statistical unit, and acquire the related analysis methods to perform population prediction and environmental population carrying capacity estimation, respectively;
a new urban district development intensity prediction module, configured to acquire city factor data of corresponding areas of a suburb, perform matching according to spatial position parameters, perform site selection according to an acquired total population pre-accommodated in a new urban district and a development suitability evaluation result, receive, for suburban districts within a site selection range, a designation of the user about a basic statistical unit, acquire the related analysis methods to perform environmental population carrying capacity estimation, respectively, and predict a development intensity according to population carrying capacity distribution; and
an old urban district protection scheme making module, including: obtaining a population prediction overrun time according to a population prediction result and an environmental population carrying capacity of the old urban district, predicting a development and construction period according to a development intensity prediction value of the new urban district, and calculating a new district construction starting time according to the population prediction overrun time of the old urban district and the development and construction period of the new urban district.
Further, the new urban district development intensity prediction module specifically includes:
a new urban district aided site selection unit, configured to receive the total population pre-accommodated in the new urban district, calculate an area of a new district to be developed, acquire city factor data of corresponding areas of a suburb, perform matching according to spatial position parameters, receive a designation of the user about development limiting factors and non-limiting factors, perform development suitability evaluation, generate new urban district candidate sites according to the area of the new district to be developed in combination with a development suitability evaluation result map, receive adjustment of the user for the candidate sites, and determine a new urban district construction area; and
a new urban district development intensity prediction unit, configured to estimate an environmental resource capacity corresponding to each basic statistical unit of the new urban district, estimate an environmental population carrying capacity of the new urban district according to an environmental resource capacity estimation result, and estimate a total amount of city development and a construction scale of various facilities according to a distribution map of the environmental population carrying capacity of the new urban district.
Further, the old urban district protection scheme making module specifically includes:
a population regulation index calculation unit, configured to predict a population overrun time value and a population dispersal control index based on the population prediction result and the environmental population carrying capacity of the old urban district of each basic statistical unit; a new urban district development and construction period prediction unit, configured to predict a development period in combination with the total amount of city development and the construction scale of various facilities; and a new urban district development time prediction unit, a difference between the population overrun time value and the development and construction period of the new urban district being a time starting point for starting new urban district construction from a current time as a reference.
Further, the city factor data includes: basic geographic data, social population data, socioeconomic data, natural resource environment data, infrastructure data, and spatial
system data.
Further, the server further includes: a permission management subsystem, configured to manage user information and corresponding permissions.
Further, the data acquisition module further receives a weight assignment file for assigning weights to a series of city factor layers to obtain a plurality of raster layers having weight values as pixel values for population prediction analysis and development suitability analysis.
Further, the population prediction analysis method includes a population prediction method, an environmental capacity estimation method, and an environmental population
carrying capacity estimation method.
One or more embodiments provide a user terminal, which is communicatively connected with the server and includes:
a city factor data editing module, configured to upload local city factor data or city factor data in the server subjected to secondary processing to the server;
a basic statistical unit designating module, configured to designate basic statistical units for an old urban district and a new urban district respectively and send the basic statistical units to the server;
a method selection module, configured to select an analysis method and a calculation formula; and a visualization module, configured to acquire and visualize data generated in an analysis process.
Further, the user terminal further includes: a weight editing module, configured to receive a designation of a user for various city factor influence weight values, limiting factors and non-limiting factors, and generate a weight assignment file.
One or more embodiments provide a historical city protection and development cooperative control scheme aided design system, which includes the server and the user terminal.
The foregoing one or more technical solutions have the following beneficial effects:
In the present disclosure, old urban district population prediction, system inherent data and related analysis algorithms are all packaged in advance at a server side and cannot be downloaded at will, so that the safety of the data is ensured. After data edited and created by a user through a user terminal and the related analysis algorithms are uploaded to the server, only the user can use the data without authorizing other people, and personal intellectual property is protected on the premise of ensuring data safety.
In the present disclosure, the server establishes a communication connection with a related department server, so that data required by planning and design can be conveniently and fully acquired, and a large amount of early-stage data preparation workload of a planning project is avoided. Moreover, common algorithms of data preprocessing and layer superposition stages are configured in advance, and the algorithms can be modified in a self-defined manner so as to meet personalized requirements of the user and avoid a large amount of early-stage data preparation and preprocessing workload of a project.
In the present disclosure, data analysis can only be carried out at the server side, so that data leakage is effectively prevented, and hardware configuration requirements of the user terminal are reduced.
In the present disclosure, the server provides various population prediction algorithms for a population prediction stage, the user can select and modify according to specific conditions of a city. Meanwhile, in order to reduce the negative influence caused by city dynamics, a series of methods such as law analysis and result verification are also provided for assisting the user in modifying a prediction model so as to obtain a model capable of objectively and accurately predicting the population.
In the present disclosure, on the basis of defining basic statistical units (neighborhood committees), population prediction, environmental capacity estimation and environmental population carrying capacity estimation are performed on each basic statistical unit, influence factors of the city are comprehensively considered in this method, and the spatial distribution of the indexes is also implemented while microscopically quantitative population prediction, environmental capacity estimation and environmental population carrying capacity estimation are implemented.
In the present disclosure, environmental population capacity estimation is performed on an old urban district and a new urban district, respectively. A population overrun time is determined based on an environmental population capacity and population prediction of the old urban district, and a development intensity of the new urban district is estimated based on an environmental population capacity of the new urban district. A development and construction starting time of the new urban district is estimated accordingly to implement protection of the old urban district.
In the present disclosure, a city environmental population carrying capacity is taken as a quantitative basis of famous historical city protection planning cooperative control, a famous historical city protection planning cooperative control system is constructed, a city population capacity is logically deduced by adopting a simulation model construction method as a quantitative basis of old urban district protection and new urban district development cooperative control. It is scientific and operable to use the environmental carrying capacity to estimate and deduce overall protection and development timing and countermeasures of historical cities.
The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The exemplary examples of the present disclosure and descriptions thereof are used to explain the present disclosure, and do not constitute an improper limitation of the present disclosure.
FIG. 1 is a schematic diagram of a system framework in one or more embodiments of the present disclosure.
FIG. 2 is a schematic diagram of a population prediction process in one or more embodiments of the present disclosure.
FIG. 3 is a schematic diagram of hierarchical district division of an old urban district with a "subdistrict office/neighborhood committee" as a basic statistical unit in one or more embodiments of the present disclosure.
FIG. 4 is a population density heat map of a new city expansion area with a "geographic grid residential area section" as a basic statistical unit.
It should be noted that, the following detailed descriptions are all exemplary, and are intended to provide further descriptions of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those usually understood by a person of ordinary skill in the art to which the present disclosure belongs.
It should be noted that the terms used herein are merely used for describing specific implementations, and are not intended to limit exemplary implementations of the present disclosure. As used herein, the singular form is intended to include the plural form, unless the context clearly indicates otherwise. In addition, it should further be understood that terms "comprise" and/or "include" used in this specification indicate that there are features, steps, operations, devices, components, and/or combinations thereof.
The embodiments in the present disclosure and features in the embodiments may be mutually combined in case that no conflict occurs.
Embodiment 1
The present embodiment provides a historical city protection and development cooperative control scheme aided design system. The system includes a server and a user terminal.
The server includes:
a data storage subsystem for storing six city factor data. The city factor data includes: basic geographic data, social population data, socioeconomic data, natural resource environment data, infrastructure data, and spatial system data. The details are as follows:
The basic geographic data includes administrative district division data, digital elevation data, high-resolution remote sensing images, and land use data. Vector graphic data is divided into layers according to land types, including, in the present embodiment: water, roads, vegetation coverage areas, residential land, park land, etc. The above layers are one or more of point, line and surface element forms. Each layer of the vector graphic data corresponds to an attribute table and is used for recording all attributes of each graphic unit on the layer.
The social population data includes: a target year-end total city population size, a population size of each urban district, an annual average population growth rate, an urban district population density, a population spatial distribution situation, a population age structure, a population gender structure, a population nation structure, a labor population composition, a family population composition, an industrial population composition, a population culture composition, and a city floating population size. For all kinds of population in a planning range including total population and city population, as well as related basic data including current situation and historical series data, officially-published statistical data dominate. The data mainly includes "Statistical Yearbook", Statistical Bulletin, Population Census Bulletin, Population Sample Survey Bulletin, etc. Other related data such as public security and family planning departments may be used as the basis and reference for checking.
The socioeconomic data includes: an annual city GDP gross, an annual per capita GDP target value, a total city labor demand, and industrial structure system data.
The natural resource environment data includes: a total area of urban ecological land, a standard area value of annual per capita ecological land, a total amount of urban available water resources, a standard amount of per capita water use, topographic landforms, roads, rivers, lakes, nature reserves, basic farmland, geology, plants, mineral resources, climate, and other data. The data may be specifically divided into: land resource data, including agricultural land quality rating, a soil database, etc.; water resource data, including water resource distribution of the old urban district, etc.; environmental data, including environmental pollutant statistical data, atmospheric and water environmental quality monitoring data, etc.; ecological data, including spatial distribution data of various vegetation covers, spatial distribution data of parks, spatial distribution data of nature reserves, spatial distribution data of scenic spots, etc.; and weather and meteorological data, including coordinates of the urban district and surrounding weather station sites, and data such as an annual average wind speed, the number of strong wind days, the number of calm wind days, precipitation, and air temperature.
The infrastructure data includes: a total area of urban roads, a per capita road area target value, a primary and secondary school degree total number, a per capita primary and secondary school degree target value, a medical facility sickbed total number, a per capita sickbed number target value, an urban annual power supply total amount, and an annual per capita power consumption standard amount.
The spatial system data includes: a total area of urban construction land, an urban land classification and planning construction land standard, an urban individual construction land standard, a per capita urban construction land area, an urban construction land structure, a per capita urban construction total land quota index, an urban per capita land classification index, and an urban per capita housing area.
The server establishes a communication connection with servers of related departments such as a resource department, an agricultural department, a water conservancy department, an ecological environment department, and a weather department, and periodically acquires the latest data from the servers of the corresponding departments.
A permission management subsystem is configured to manage an access permission of the user terminal. The system may be used for aid decision-making of government departments, and scientific research project analysis of planning and designing organizations and universities or research institutes. Therefore, the permission management subsystem receives and stores registration information of the user terminal. The registration information includes information such as organizations, names and certificate numbers.
An analysis method management subsystem is configured to store related analysis and calculation formulas which aim at preprocessing (missing data processing, normalization, spatial interpolation algorithm, etc.) of six city factor data and are related to spatial data, layer superposition, population prediction, environmental carrying capacity, environmental population limit carrying capacity, development suitability analysis, city construction scale, etc. Those skilled in the art would understand that these methods may be stored in the form of code files, and a certain method aiming at a certain type of data is represented by a file name.
A population prediction method specifically includes: two mathematical predictions, including a comprehensive growth rate method and a regression model method, a socioeconomic prediction, i.e. an economic related analysis method, and a BP neural network model method.
Mathematical Predictions:
(1) Comprehensive Growth Rate Method:
Pt 0x (+r)
where Pt is a predicted target annual basic statistical unit population size; Po is a reference annual basic statistical unit population size; r is a basic statistical unit population annual average comprehensive growth rate; and n is the number of predicted years (when n>5, 5 years is a timing section).
(2) Regression Model Method:
Pt a+ b-T (2)
Pt= A -e7 +b (3)
Pt = a+ b- In(T) (4)
where Pt is a Tth year population prediction size; and a and b are parameters and are generally fitted by a least square method.
Socioeconomic Prediction, i.e. Economic Related Analysis Method:
Pt = a + b ln(Yt) (5)
where Pt is a target tth year population prediction size; Yt is a predicted target annual GDP gross; and a and b are parameters.
BP Neural Network Prediction Model Method:
Six census data of a research area is taken as original data, and a population size prediction model is established according to a change relationship between various data sets. Repeated iteration is performed to obtain a prediction model which accords with an actual condition of a research city.
A method for calculating an environmental carrying capacity includes: providing six models of environmental capacity estimation for multi-angle combination based on social environmental situations of an old urban district. The method includes: three capacity researches: a water resource carrying capacity method, a land resource carrying capacity method and an environmental capacity method; and three infrastructure carrying capacity researches: a road carrying capacity method, an education facility carrying capacity method and a medical facility carrying capacity method. The method specifically includes the following details:
1. Capacity Researches
Water Resource Carrying Capacity Method:
Pt =Wt/wt (6)
Land Resource Carrying Capacity Method:
P= (7) where p is a population capacity; L is the largest scale of construction land; and La is an area standard of per capita construction land.
Environmental Capacity Method:
Pt =St St (8)
where Pt is a predicted target year-end population size; St is a predicted target annual ecological land area; and st is a predicted target annual per capita ecological land area.
2. Infrastructure Carrying Capacity Researches
Road Carrying Capacity Method:
Pt = dt (9)
where Pt is a predicted target year-end population size; Di is a predicted target annual total road area; and di is a predicted target annual per capita road land area.
Education Facility Carrying Capacity Method:
Pt = St/st (10)
where Pt is a predicted target year-end population size; St is a predicted target year-end primary and secondary school degree total number; and st is a predicted target year-end per capita primary and secondary school degree number.
Medical Facility Carrying Capacity Method:
Pt = tbt(11)
where Pt is a predicted target year-end population size; Bt is a predicted target year-end sickbed total number; and bt is a predicted target annual per capita sickbed number.
A result verification method includes: providing mutual checking of multiple prediction models, and judging the accuracy of a result. Specifically, two result verification methods are provided: a comparison verification method and a water resource capacity method.
Comparison Verification Method
A mathematical model is constructed by utilizing a relationship between fourth and fifth population census data, an operation result is compared with fifth and sixth population census result data, and the correctness is verified.
Water Resource Capacity Method: like formula (6).
A related method of a law analysis model includes: providing two nonlinear geometric mathematical analyses and one spatial statistical analysis.
1) The two nonlinear geometric fractal mathematical analyses include an order-scale index analysis method and a population-area allometric analysis method.
Order-Scale Index Analysis Method:
Pk - P1K-q (12)
where K is a sample serial number (K=1, 2, ... , N, N is a total number of samples in the system); PK is a sample element of serial number K; Pi is a first sample element, also called a first city element; and q is an order scale index.
The logarithm of formula 7 is taken:
InP(k) - InP1 - qink (13)
q is a constant related to area conditions and development stages
Population-Area Allometric Analysis Method:
= b1dx (14) y dt x dt
where y is a determination of a local or subsystem; x is a measure of the whole system; and b is an allometric factor.
According to formula 9, city population-area allometric relationship may be expressed as:
A = aPb (15)
where A is the area of an urban district; P is the population of the urban district; and b is a scaling index.
2) The one spatial statistical analysis is a population spatial autocorrelation analysis method (including a global spatial correlation index Moran I and local spatial correlation indexes Local Moran's I and Getis's Gi).
(1) Global Spatial Correlation Index Moran I:
_i_ (16)
where n is a total number of area units participating in the analysis; xi and xj are respectively expressed as observation values of a certain phenomenon x or a certain attribute feature x on spatial regional units i and j; X is an average value of a research object x; and Wij is a spatial weight matrix.
(2) Local Spatial Correlation Indexes Local Moran's I and Getis's Gi:
=n-( Wi-(x - X) (17)
.j:3-i W ;-x; Gijd) = n_ Yj --i Xi (18)
where n is a total number of area units participating in the analysis; xi and xj are respectively expressed as observation values of a certain phenomenon x or a certain attribute feature x on spatial regional units i and j; X is an average value of a research object x; and Wij is a spatial weight matrix.
Method for Calculating Population Overrun Time Value and Population Dispersal Control Index of Each Unit:
(1) A population overrun time value t1 is calculated according to the population situation of each basic unit in an old urban district
An actual population of each basic statistical unit in the old urban district is compared with an environmental population ultimate carrying capacity of this unit, and the carrying population allowance of this unit may be calculated. According to an annual average city population growth rate of this unit provided by population census, the time when the population of this unit reaches the environmental ultimate carrying capacity may be calculated.
An actual population So of each basic statistical unit in the old urban district is compared with an environmental population ultimate carrying capacity Sti of this unit. When Sti>So, the carrying population allowance of this statistical unit is:
SU = St 1 - So (19)
where Su is a carrying population allowance of a basic statistical unit; Sti is an environmental population ultimate carrying capacity of a basic statistical unit; and So is an actual population of a basic statistical unit.
The annual average city population growth rate of this unit provided by population census is expressed as:
so (20)
where 6 is an annual average population growth rate; tI is time; Sti is a total population of year tl; and So is an initial population.
A time value tI when the population of this statistical unit reaches the environmental ultimate carrying capacity may be calculated:
t1 Sti = So(1+ 6) (21)
where Sti is a total population of year tl; So is an initial population; 6 is an annual average population growth rate; and t Iis time.
(2) A population dispersal quantity index Se value of each basic statistical unit may be determined
Through the logical deduction of four stages and five steps, quantitative data may be provided for the population dispersal of the old urban district, and a city protection strategy may be formulated according to local conditions.
Se = So - St1 (22)
where Se is a population dispersal quantity of a basic statistical unit; So is an actual population of a basic statistical unit; and S is an environmental population ultimate carrying capacity of a basic statistical unit.
Based on the lack of human-developed natural environment situations in suburbs, two capacity researches are selected, i.e. a water resource carrying capacity method and an environmental capacity method.
Water Resource Capacity Method: like formula (6).
Environmental Capacity Method: like formula (8).
Formula for evaluating development suitability of new urban districts:
According to the influence of natural and socioeconomic factors on site selection, evaluation factors of new city development suitability are divided into limiting factors and non-limiting factors. A Delphi scoring method is used for comprehensive summarization:
TF = (23)
where TF is a comprehensive evaluation value of all nonlinear factors; Wi is a weight of a single non-limiting factor; and Fi is a specific rating assignment of a single factor.
A new urban district development implementation measure formula set aiming at old urban district protection is as follows:
A difference between a population overrun time value tI and a new city development and construction period value t2 is a time starting point t for starting new urban district construction from a current time as a reference, and the construction timing of a new urban district and an old urban district may be coordinated integrally. The formula is expressed as:
(24)
where ti is a population overrun time value; t2 is a new city development and construction period time value; and t is a time starting point t for new urban district construction.
A data analysis subsystem receives a data analysis request of the user terminal, creates an analysis task for the user terminal, and performs correspondingly analysis. The data analysis subsystem specifically includes:
a city factor data retrieval module, configured to retrieve related city factor data in a designated area according to the selection of the user terminal, and associate the related city factor data to the analysis task of the user terminal after receiving a confirmation message of the user terminal;
a city factor data preprocessing module, configured to pre-process the retrieved city factor data, and specifically, retrieve corresponding preprocessing methods in the analysis algorithm management subsystem for preprocessing population data and socioeconomic data, mainly including filling of missing data, data normalization, etc, wherein for environmental (air pollution, etc.) and meteorological data with only a point value, the preprocessing methods mainly include data normalization, spatial interpolation, etc. since the data has space continuity;
a spatial data preparation module, configured to associate socioeconomic data into attribute data of administrative district division data according to the district division based on the received high-precision land use data and/or the administrative district division data or based on the land use data and/or the administrative district division data carried by the system, and associate natural resource environment data into attribute data of corresponding layers of the land use data according to geographic coordinate information;
a weight assignment module, configured to receive a weight assignment file sent by the user terminal and generate a plurality of raster layers with weight values as pixel values based on a weight assignment rule, the weight assignment file including the weight assignment rule for each layer, and the weight assignment rule including a corresponding relationship between conditions to be met and weight values;
an old urban district population prediction module, including: an old urban district population prediction unit, receiving a basic statistical unit designated by the user through the user terminal and years for performing population prediction, associating population data of the corresponding years into the corresponding basic statistical unit, calling one or more population prediction methods designated by the user, predicting the population size of the designated years, and receiving model modification of the user terminal according to a verification result; and a result verification unit, receiving reference demographic data sent by the user terminal, specifying a verification method, calculating the verification result, and feeding the verification result back to the user terminal; an environmental population carrying capacity estimation module, including: an old urban district environmental capacity estimation unit, configured to estimate an environmental resource capacity corresponding to each basic statistical unit of the old urban district; and an environmental population carrying capacity estimation unit, configured to estimate an environmental population carrying capacity of the old urban district according to an environmental resource capacity estimation result; a new urban district development intensity prediction module, including: a new district area estimation unit, configured to receive a total new district accommodated population sent by the user terminal, and calculate the area of a new district to be developed; a city resource population capacity estimation unit, configured to estimate a population capacity of administrative districts around the old urban district by using a city resource population capacity estimation model, and perform grading and sorting, wherein comprehensive evaluation on the environmental population carrying capacity is performed by using a city resource population capacity estimation theoretical model and a GIS application model according to city natural basic geographic information data; a new urban district development suitability evaluation unit, configured to receive designations of the user about a limiting factor and a non-limiting factor, and perform development suitability evaluation to obtain a development suitability evaluation map taking a development suitability score as a pixel value; a new urban district candidate site generation unit, configured to, using quantitative result analysis of "environmental population carrying capacity estimation + environmental development suitability evaluation" as a new urban district site selection basis, perform comprehensive grading and sorting according to an environmental carrying capacity condition evaluation result of each large area, acquire a candidate overall area suitable for new city development, associate graphic data with a model result according to the comprehensive grading and sorting of environmental carrying capacity conditions of each basic statistical unit, perform visual expression by adopting a three-dimensional visualization means, acquire an expansion area development suitability evaluation grading schematic diagram in units of basic statistical units, receive the selection of the user, defining a new urban region construction red line, and calculating an environmental population ultimate carrying capacity of each basic statistical unit; a city axis distribution calculation unit, configured to send an environmental population ultimate carrying capacity distribution map to the user terminal and perform three-dimensional visual simulation, so as to present a fuzzy city axis distribution relationship (a primary and secondary axis relationship), wherein the fuzzy evaluation result provides a design quantitative basis suitable for the city population survival and development requirements for new district planning; after obtaining a predicted value of a total population accommodated in the expansion area, a city construction and development gross of a new district to be developed is calculated according to a per capita city construction land area (i.e. city land standard, m/person), the per capita occupied area of various land types, such as residence, public facilities, industry, road squares, foreign transportation, warehousing, municipal public facilities, green space, and special land is calculated according to a city per capita land classification index (m/person), and a construction scale of various functional areas is calculated by using the product of the per capita occupied area and the population quantity; and a new district development intensity index measuring and calculating unit, configured to determine a total amount of city development and a construction scale of various facilities by means of a city construction index according to a city population capacity distribution situation of each basic statistical unit of a new city expansion area, and forming a planning logic of "population calculation + suitability evaluation" --* "total amount of city development" --* "facility development", wherein the environmental population ultimate carrying capacity is positively correlated with land and space development intensive quantity; an old urban district protection scheme making module, including: a population regulation index calculation unit, configured to estimate an environmental population ultimate carrying capacity in each basic statistical unit of the old urban district, and calculate a population overrun time value and a population dispersal control index of each unit according to a population situation of each basic unit; and a new district development and construction period prediction unit, configured to predict a development and construction period, a construction investment and a total amount of materials on the basis of general rules and sub-rules according to a project construction scheme, scheduling construction procedures, and providing data support for new city development; and a new district development time prediction module, configured to integrally coordinate the construction timing of the new and old urban districts, wherein a difference between the population overrun time value and the development and construction period value of the new urban district is a time starting point for starting new urban district construction from a current time as a reference.
In the present embodiment, the server employs a cloud server. The data of the related departments is uploaded and retrieved through an encryption and decryption mechanism, the data is only used at a server side, and any user cannot download the data at will, so that the safety of the original data is protected. Moreover, for the personalized analysis of each user terminal, an independent storage space is developed for each user, data uploaded by the user or processed by the user, an analysis method and data obtained in the analysis process are stored, so that the user can trace back the analysis process. Moreover, the storage space of each user is only accessed by the user, and cannot be accessed by other users without authorization.
The user terminal includes:
a city factor data visualization module, configured to retrieve city factor data from the server according to a user request and perform visualization;
a city factor data editing module, configured to perform secondary processing on city factor data and upload the data to the server, for example, retrieve the data processed by the server to be audited and revised, and perform digitalization and visual interpretation based on the retrieved high-resolution remote sensing image data to obtain high-precision land use data, wherein those skilled in the art would understand that the land use data may also be pre-prepared and uploaded directly to the server via the module for related subsequent analysis;
a weight editing module, configured to calculate an influence weight value of each city factor and a designation of limiting factors and non-limiting factors by using an evidence weight method model, and generate a weight assignment file;
Table 1
Type Evaluation factor
Basic farmland
Constructed area
Limiting Water area factors Natural reserve
Slope >20°
Altitude >300 m
Distance from water source Non-limiting Distancefromexistingroad factors Distancefromexistingroad Slope <20
a basic statistical unit designating module, configured to designate a basic unit for statistics, wherein the present embodiment defines an administrative management unit "subdistrict office/neighborhood committee" as a basic statistical unit;
a method selection module, configured to select a calculation method used in the analysis process;
a model editing module, configured to modify model parameters according to a verification result of population prediction; and
a visualization module, configured to visualize a population prediction result, a law analysis result, a verification result, an environmental resource capacity estimation result, an environmental population carrying capacity estimation result, and a population regulation index obtained in the analysis process.
Different visualization forms may be set for the visualization according to content to be visualized, respectively.
By visualizing the data generated in the analysis process, the user can conveniently compare the differences between different analysis methods and different parameters, and understand or learn the influence of different analysis methods and different parameters on population prediction or environmental resource capacity estimation, thereby contributing to selection and modification to obtain a more accurate model.
As an example, the present embodiment uses the sixth population census data as reference data to predict a population size of the future years. More than two different prediction methods are selected according to an old urban district to be analyzed to perform prediction, respectively, and a plurality of prediction schemes are obtained by adjusting parameter assignment in a formula. Since there are many population prediction methods and each prediction method has its adaptive conditions, advantages and limitations, it is necessary to select the prediction method according with the characteristics of city population and environmental resources according to the principle of easy operation and popularization. Two models are generally selected: 1) universality model; and 2) specificity model. The comparison verification method is a universality method, has strong adaptability, is suitable for all population census areas, and belongs to the universality model. The specificity model such as the water resource capacity method is suitable for cities that are constrained by water resource conditions (Kashi in Xinjiang will be a sample of this research). The land resource capacity method is not suitable for a scarcely populated city environment of Xinjiang. Therefore, it is necessary to adjust according to different city environments and select an appropriate model.
In order to ensure the comprehensiveness and correctness of a model deduction conclusion, the population distribution change law is also analyzed after adopting a variety of models for population prediction. The order-scale index analysis method is used for searching for a correlation between elements and sequences in a certain area, reflecting the distribution features of city elements in different levels, also reflecting the concentration or equilibrium degree of the elements, understanding whether the structural distribution of city population size is loose, ideal or concentrated, and evaluating whether the population distribution is suitable for city development or not. The population-area allometric analysis method is used for predicting the city population and area, calculating a city population-area allometric scaling factor year by year according to prediction data, and acquiring the change situation of the city population and area on a time axis according to a scaling factor change value. The spatial autocorrelation analysis method is used for revealing a spatial distribution law and internal correlation of population static distribution between the whole research area and the internal areas. The global index is used for verifying a spatial mode of the whole research area, and the calculation results show the overall features of population spatial distribution (i.e. an adjacent trend of high population distribution and low density area) in this area. The local index is used for reflecting the correlation degree of a certain geographical phenomenon or a certain attribute value on one area unit and the same phenomenon or attribute value on a neighboring area unit, and the calculation results show the population distribution features (i.e. high and low density situations of specific blocks of population distribution) of each local area.
After a variety of models are used for population prediction, the prediction results are verified by the comparison verification method or the water resource capacity method. Specifically, the comparison verification method performs model comparison and correction according to an actual deviation, and the water resource capacity method performs calculation by using the model of the "water resource capacity method" (formula 6) and introducing related numerical values of a target unit. The correctness of a multi-dimensional construction cooperative control model system is judged according to whether the calculation result is the same as or similar to the model derivation results of the other modules or not.
A multi-angle city population prediction model (PCPM model) is obtained through three processes of quantitative prediction (extrinsic analysis), rule analysis (intrinsic analysis) and result verification.
Based on the social environment situation of the old urban district, one or more of the six models are selected to calculate an environmental capacity respectively for multi-angle combination to construct an environmental resource capacity estimation theoretical model of the old urban district (UECE model).
According to the influence of natural and socioeconomic factors on site selection, development suitability evaluation factors are divided into two classes: limiting factors and non-limiting factors. Road, river, lake, nature reserve, DEM, and land use data are extracted from a basic geographic database, converted into raster data, and then subjected to development suitability evaluation according to the technical process. For the non-limiting factors, areas close to the road and water source (river or lake) and areas with gentle slopes are mainly considered, then values are assigned by grading according to the conditions, and the Delphi scoring method is used for comprehensive summarization. New city site selection candidates are found through a spatial inquiry mode. Finally, overlay analysis is performed according to a distribution map of limiting factors and non-limiting factors, a development suitability evaluation map is acquired, and a reference basis (DAEM model) is provided for new city site selection.
In the present disclosure, old urban district population prediction, system inherent data and related analysis algorithms are all packaged in advance at a server side and cannot be downloaded at will, so that the safety of the data is ensured. After data edited and created by a user through a user terminal and the related analysis algorithms are uploaded to the server, only the user can use the data without authorizing other people, and personal intellectual property is protected on the premise of ensuring data safety.
In the present disclosure, the server establishes a communication connection with a related department server, so that data required by planning and design can be conveniently and fully acquired, and a large amount of early-stage data preparation workload of a planning project is avoided. Moreover, common algorithms of data preprocessing and layer superposition stages are configured in advance, and the algorithms can be modified in a self-defined manner so as to meet personalized requirements of the user and avoid a large amount of early-stage data preparation and preprocessing workload of the planning project.
In the present disclosure, data analysis can only be carried out at the server side, so that data leakage is effectively prevented, and hardware configuration requirements of the user terminal are reduced.
In the present disclosure, the server provides various population prediction algorithms for a population prediction stage, the user can select and modify according to specific conditions of a city. Meanwhile, in order to reduce the negative influence caused by city dynamics, a series of methods such as law analysis and result verification are also provided for assisting the user in modifying a prediction model so as to obtain a model capable of objectively and accurately predicting the population.
A person skilled in the art should understand that the modules or steps in the present disclosure may be implemented by using a general-purpose computer apparatus. Optionally, they may be implemented by using program code executable by a computing apparatus, so that they may be stored in a storage apparatus and executed by the computing apparatus. Alternatively, the modules or steps are respectively manufactured into various integrated circuit modules, or a plurality of modules or steps thereof are manufactured into a single integrated circuit module. The present disclosure is not limited to any specific combination of hardware and software.
The foregoing descriptions are merely preferred embodiments of the present disclosure, but are not intended to limit the present disclosure. A person skilled in the art may make various alterations and variations to the present disclosure. Any modification, equivalent replacement, or improvement made and the like within the spirit and principle of the present disclosure shall fall within the protection scope of the present disclosure.
The specific implementations of the present disclosure are described above with reference to the accompanying drawings, but are not intended to limit the protection scope of the present disclosure. A person skilled in the art should understand that various modifications or deformations may be made without creative efforts based on the technical solutions of the present disclosure, and such modifications or deformations shall fall within the protection scope of the present disclosure.
Claims (10)
1. A server, comprising:
a data storage subsystem, configured to acquire and store various city factor data;
a method management subsystem, configured to package data processing methods for the various city factor data, related analysis methods, and calculation formulas in advance; and
a data analysis subsystem, comprising:
a data acquisition module, configured to receive a designation of a user about a data area range and a data requirement, acquire related city factor data from the data storage subsystem, and acquire the corresponding data processing methods from the method management subsystem to process the city factor data;
an old urban district population prediction and environmental population carrying capacity estimation module, configured to acquire city factor data of corresponding areas of an old urban district, perform matching according to spatial position parameters, receive a designation of the user about a basic statistical unit, and acquire the related analysis methods to perform population prediction and environmental population carrying capacity estimation, respectively;
a new urban district development intensity prediction module, configured to acquire city factor data of corresponding areas of a suburb, perform matching according to spatial position parameters, perform site selection according to an acquired total population pre-accommodated in a new urban district and a development suitability evaluation result, receive, for suburban districts within a site selection range, a designation of the user about a basic statistical unit, acquire the related analysis methods to perform environmental population carrying capacity estimation, respectively, and predict a development intensity according to population carrying capacity distribution; and
an old urban district protection scheme making module, in order to obtaining a population prediction overrun time according to a population prediction result and an environmental population carrying capacity of the old urban district, predicting a development and construction period according to a development intensity prediction value of the new urban district, and calculating a new district construction starting time according to the population prediction overrun time of the old urban district and the development and construction period of the new urban district.
2. The server according to claim 1, wherein the new urban district development intensity prediction module specifically comprises:
a new urban district aided site selection unit, configured to receive the total population pre-accommodated in the new urban district, calculate an area of a new district to be developed, acquire city factor data of corresponding areas of a suburb, perform matching according to spatial position parameters, receive a designation of the user about development limiting factors and non-limiting factors, perform development suitability evaluation, generate new urban district candidate sites according to the area of the new district to be developed in combination with a development suitability evaluation result map, receive adjustment of the user for the candidate sites, and determine a new urban district construction area; and
a new urban district development intensity prediction unit, configured to estimate an environmental resource capacity corresponding to each basic statistical unit of the new urban district, estimate an environmental population carrying capacity of the new urban district according to an environmental resource capacity estimation result, and estimate a total amount of city development and a construction scale of various facilities according to a distribution map of the environmental population carrying capacity of the new urban district.
3. The server according to claim 1, wherein the old urban district protection scheme making module specifically comprises:
a population regulation index calculation unit, configured to predict a population overrun time value and a population dispersal control index based on the population prediction result and the environmental population carrying capacity of the old urban district of each basic statistical unit; a new urban district development and construction period prediction unit, configured to predict a development period in combination with the total amount of city development and the construction scale of various facilities; and a new urban district development time prediction unit, a difference between the population overrun time value and the development and construction period of the new urban district being a time starting point for starting new urban district construction from a current time as a reference.
4. The server according to claim 1, wherein the city factor data comprises: basic geographic data, social population data, socioeconomic data, natural resource environment data, infrastructure data, and spatial system data.
5. The server according to claim 1, further comprising: a permission management subsystem, configured to manage user information and corresponding permissions.
6. The server according to claim 1, wherein the data acquisition module further receives a weight assignment file for assigning weights to a series of city factor layers to obtain a plurality of raster layers having weight values as pixel values for population prediction analysis and development suitability analysis.
7. The server according to claim 1, wherein the population prediction analysis method comprises a population prediction method, an environmental capacity estimation method, and an environmental population carrying capacity estimation method.
8. A user terminal, communicatively connected with the server according to any one of claims 1 to 7, and comprising:
a city factor data editing module, configured to upload local city factor data or city factor data in the server subjected to secondary processing to the server;
a basic statistical unit designating module, configured to designate basic statistical units for an old urban district and a new urban district respectively and send the basic statistical units to the server; a method selection module, configured to select an analysis method and a calculation formula; and a visualization module, configured to acquire and visualize data generated in an analysis process.
9. The user terminal according to claim 6, further comprising: a weight editing module, configured to receive a designation of a user for various city factor influence weight values, limiting factors and non-limiting factors, and generate a weight assignment file.
10. A historical city protection and development cooperative control scheme aided design system, comprising the server according to any one of claims 1 to 7 and the user terminal according to any one of claims 8 and 9.
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| CN2019112037989 | 2019-11-29 | ||
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| CN113240344A (en) * | 2021-06-16 | 2021-08-10 | 中国林业科学研究院资源昆虫研究所 | National park function partition zoning method based on ecological protection weighting |
| CN113404226B (en) * | 2021-06-23 | 2022-05-10 | 中科青城(天津)科技有限公司 | Urban green roof construction scale decision-making method for maximizing cooling effect |
| CN113421010A (en) * | 2021-07-05 | 2021-09-21 | 深圳市蕾奥规划设计咨询股份有限公司 | Green road route selection planning design method based on big data |
| CN113674410B (en) * | 2021-07-28 | 2024-05-24 | 杭州鲁尔物联科技有限公司 | Description method and device for grade of slope unit, computer equipment and storage medium |
| CN113704978B (en) * | 2021-08-05 | 2023-11-07 | 武汉大学 | Urban ring man-water coupling simulation method and system based on social hydrology |
| CN114095390B (en) * | 2021-11-11 | 2023-10-13 | 北京百度网讯科技有限公司 | Method, device, equipment and storage medium for predicting flow of objects in area |
| CN114048910B (en) * | 2021-11-17 | 2024-03-29 | 东南大学 | Community public green land group layout optimization method based on big data |
| CN115062897B (en) * | 2022-05-05 | 2023-03-24 | 中国测绘科学研究院 | Urban ecological space equilibrium measurement and urban ecological pattern comprehensive evaluation method |
| CN114862276B (en) * | 2022-06-21 | 2023-01-24 | 华南理工大学 | Method and system for collaborative analysis and application of large data of producing city |
| CN115014399A (en) * | 2022-08-04 | 2022-09-06 | 中国船舶重工集团公司第七0七研究所 | Scale factor error analysis method of fiber-optic gyroscope |
| CN114997344B (en) * | 2022-08-04 | 2022-10-25 | 中关村科学城城市大脑股份有限公司 | Multi-source data planning method and system based on urban brain |
| CN116703122B (en) * | 2022-11-16 | 2024-01-19 | 上海张江智荟科技有限公司 | Smart city information resource display system and method |
| CN116308437A (en) * | 2022-12-27 | 2023-06-23 | 中国人民解放军陆军工程大学 | Method for evaluating peripheral house price and land price of underground urban sewage treatment plant |
| CN116340698B (en) * | 2023-03-01 | 2024-04-19 | 中农创达(北京)环保科技有限公司 | Method for measuring and calculating bearing capacity of livestock and poultry manure soil |
| CN116384829A (en) * | 2023-04-04 | 2023-07-04 | 国家基础地理信息中心 | Ecological analysis method, ecological analysis device, electronic equipment and computer readable storage medium |
| CN116451963B (en) * | 2023-04-21 | 2023-11-07 | 中国测绘科学研究院 | Multi-factor pension service facility optimal configuration method based on ensemble learning |
| CN116485143B (en) * | 2023-04-27 | 2024-06-07 | 华北水利水电大学 | Space planning processing method based on population density big data |
| CN116628525B (en) * | 2023-06-01 | 2024-06-14 | 华中科技大学 | Spatial feature extraction method and system for new energy automobile industry chain supply chain |
| CN117313981B (en) * | 2023-07-11 | 2024-05-14 | 厦门身份宝网络科技有限公司 | Dynamic urban village population analysis method, device, equipment and readable storage medium |
| CN117077005B (en) * | 2023-08-21 | 2024-05-10 | 广东国地规划科技股份有限公司 | Optimization method and system for urban micro-update potential |
| CN117610707B (en) * | 2023-10-19 | 2024-05-24 | 华中科技大学 | Urban mass production space utilization prediction method and system |
| CN117252404B (en) * | 2023-11-20 | 2024-02-23 | 武汉市规划研究院 | Method and system for measuring and calculating population and post scale of city updating unit |
| CN117521908A (en) * | 2023-11-20 | 2024-02-06 | 深圳技术大学 | Urban space region suitability evaluation method, system and terminal |
| CN117688120B (en) * | 2024-02-02 | 2024-04-19 | 中国测绘科学研究院 | Method and system for finely dividing public population space data set based on multi-source data |
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| CN104794350A (en) * | 2015-04-23 | 2015-07-22 | 中国科学院地理科学与资源研究所 | System and method for evaluating comprehensive carrying capacity of region |
| CN106845749A (en) * | 2016-11-03 | 2017-06-13 | 深圳市建筑科学研究院股份有限公司 | A kind of city development level computational methods |
| CN107609748A (en) * | 2017-08-18 | 2018-01-19 | 深圳航天智慧城市系统技术研究院有限公司 | A kind of method and system that Development Assessment is carried out to urban facilities |
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