CN114091140A - Network construction method of urban space density data - Google Patents

Network construction method of urban space density data Download PDF

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CN114091140A
CN114091140A CN202111190642.9A CN202111190642A CN114091140A CN 114091140 A CN114091140 A CN 114091140A CN 202111190642 A CN202111190642 A CN 202111190642A CN 114091140 A CN114091140 A CN 114091140A
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王健嘉
朱浩然
刘坤
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University of Shanghai for Science and Technology
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Abstract

The invention relates to a network construction method of urban spatial density data, which comprises the following steps: 1: acquiring a group of city building data; 2: abstracting all building monomers of the collected sample group into space capacity particles; 3: particles are classified into two main categories according to space usage functions: public space capacity particles and private space capacity particles; 4: calculating Euclidean distances of all affected particles with the target particle as the center and the radius of R; 5: calculating the influence of target particles; 6: constructing a distance network structure by using the form of the incidence matrix; 7: and displaying the incidence matrix by using a visualization method, and analyzing the relationship between the building area and the distance and the influence. The method can identify the characteristics of the urban density evolution process, thereby showing the state of the space form driven by the internal cause of the space requirement and developing the change according to a certain rule principle.

Description

Network construction method of urban space density data
Technical Field
The invention belongs to the field of urban density processing methods, and particularly relates to a network construction method of urban spatial density data.
Background
A city can be understood as an open, ever-changing complex macrosystem with many networks, some regular networks and some random networks. The "natural growth" state of a city refers to a state in which the spatial form is driven by the internal factors of the spatial requirements and develops and changes according to a certain rule and principle. The principle is a complex integration of the socio-economic development level, the urban planning and designing method and the urban management regulation policy in a certain period. A typical network is composed of many nodes and edges connecting the nodes, and the edges can be understood as influence relationships between the nodes. The three-dimensional shape of the physical space in a city does not seem to belong to the network category, but the aggregation degree of the physical space capacity within a certain range shows a plurality of characteristics conforming to the characteristics of a complex network. Since city density is related to building area and distance, the research on the structural characteristics of the correlated network has very important significance for the development of cities.
At present, Chinese research mainly carries out calculation from bottom to top through energy consumption intensity, and establishes a building energy consumption model which judges the size of a building area through energy consumption based on energy consumption data. The building area calculation method based on energy consumption is combined with building related factor modeling to predict future building area and building energy consumption development trend. But such studies only calculate building density through building energy consumption without considering the influence of surrounding buildings on it. Therefore, how to combine the complex network theory with the city planning and analyze the city development process by adopting the complex network theory to find the relation among all elements of the city density becomes a technical problem which needs to be solved urgently, so that the basis and the guarantee are provided for the future city planning.
Disclosure of Invention
The invention aims to provide a network construction method of urban space density data, which analyzes the development of urban space density through a new technology of a network structural expression mode. And establishing a correlation relation among different buildings, and proposing building area and distance to influence the framework. The method can identify the characteristics of the urban density evolution process, thereby showing the state of the space form driven by the internal cause of the space requirement and developing the change according to a certain rule principle.
Based on the above purposes, the invention adopts the following technical scheme:
a network construction method of urban space density data comprises the following steps:
step 1: acquiring a group of city building data;
step 2: abstracting all building monomers of the collected sample group into space capacity particles;
and step 3: particles are classified into two main categories according to space usage functions: public space capacity particles and private space capacity particles;
and 4, step 4: calculating Euclidean distances of all affected particles with the target particle as the center and the radius of R;
and 5: calculating the influence of target particles;
step 6: constructing a distance network structure by using the form of the incidence matrix;
and 7: and displaying the incidence matrix by using a visualization method, and analyzing the relationship between the building area and the distance and the influence.
Further, in step 3, the space usage function classification specifically includes:
s01: the common space capacity dots include: buildings with functions of business, administrative service, medical treatment, education and culture;
s02: the private space capacity dots include: buildings for residential, commercial, hotel, industrial, municipal settings, etc.
Further, in step 4, the calculation formula for calculating the euclidean distance between the particles is as follows:
Figure BDA0003300858610000021
wherein (x)1,y1) And (x)2,y2) The coordinates of two particles, respectively.
Further, in step 5, the influence of the target particles is calculated by the following formula:
Figure BDA0003300858610000022
wherein n and m are the number of public space and private space interstitial points respectively; w is aiAnd wjRespectively public space and private space building areas; diAnd djRespectively the Euclidean distances between the public space and the private space and a target node; r is a radius; Δ WiAnd Δ WjPublic space and private space influences, respectively.
Further, in step 6, the constructing of the incidence matrix specifically includes: the formula for the power law distribution is p (k) ═ Ck-γWhere C is a proportionality constant and γ is a power exponent; formula for calculating poisson distribution
Figure BDA0003300858610000023
Where λ is the expectation and variance of the poisson distribution.
Compared with the prior art, the invention has the beneficial effects that:
1) the invention provides the relation between the building influence and the building area and distance, and provides an important basis for judging the size of the building influence.
2) The method for judging the urban density related attributes not only provides support for urban planning, but also provides a referential index for predicting urban development, and provides an important basis for more reasonable and orderly urban planning.
3) The invention not only provides a new network construction method for the urban space density data set, but also can be expanded to other related fields of urban planning, such as railway planning, urban overhead planning and the like.
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FIG. 1 is a flow chart of urban spatial density network construction;
FIG. 2 is a complex network computing model;
FIG. 3 distance versus influence;
FIG. 4 distance and building area versus influence.
Detailed Description
The following further describes a specific embodiment of the present invention with reference to the drawings.
As shown in fig. 1, a method for constructing a network of urban spatial density data includes the following steps:
step 1: acquiring a group of city building data;
step 2: abstracting all building monomers of the collected sample group into space capacity particles;
and step 3: particles are classified into two main categories according to space usage functions: public space capacity particles and private space capacity particles;
and 4, step 4: calculating Euclidean distances of all affected particles with the target particle as the center and the radius of R;
and 5: calculating the influence of target particles;
step 6: constructing a distance network structure by using the form of the incidence matrix;
and 7: and displaying the incidence matrix by using a visualization method, and analyzing the relationship between the building area and the distance and the influence.
In 2015, the Shanghai inner-ring data set used in this embodiment selects 198 analyzed buildings, and lists longitude and latitude information and building areas of all the buildings in detail.
First, the original data set needs to be preprocessed to remove abnormal data in each building. On the basis of data preprocessing, the present embodiment will complete the following steps:
firstly, constructing an urban space density network:
the research on the correlation of the urban space density plays an important role in optimizing urban construction and predicting the future trend of the city, and the basic principle is to construct a network by utilizing relevant building attributes. Describing node position information by using longitude and latitude information and using Euclidean distance formula
Figure BDA0003300858610000031
The distances between all nodes are calculated. Wherein (x)1,y1) And (x)2,y2) Are respectively twoThe coordinates of the particle. Thus, the processed data set contains 198 location information of the building. Nodes are used as different buildings in the network, and the node range is a circle with the radius of 5 kilometers, so that all the nodes are convenient to serve as target nodes. An assumption is made for the model that the public space attracts spatial capacity and the private space repels spatial capacity. Respectively calculating the influence data of the target nodes by adopting the following formula:
Figure BDA0003300858610000032
wherein n and m are the number of public space and private space interstitial points respectively; w is aiAnd wjRespectively public space and private space building areas; diAnd djRespectively the Euclidean distances between the public space and the private space and a target node; r is the radius, here 5 km; Δ WiAnd Δ WjPublic space and private space influences, respectively.
In fig. 2, two influencing nodes N and M from the target node P can be seen, N being public space buildings and M being private space buildings, so that their influence on the target node is:
Figure BDA0003300858610000041
wherein the distances of N and M are each dnAnd dmThe building area is wnAnd wm. And calculating the influence of the point P by calculating the sum of the influences of all nodes with the target node P as the center and the radius of 5 kilometers.
Secondly, analyzing the network structure of the influence of the distance on the building:
the section mainly proves the relationship between the distance from a target node and the influence of a building, and the correlation of nodes among networks is influenced by the change of attributes.
In fig. 3, by fixing the building areas of all the influencing nodes, it can be observed that the distances and the influencing power levels exhibit a power law distribution, which means that relative changes in one quantity cause corresponding power proportions of the other quantity to change, independently of the initial values.
When the distance is very close, the public space influence is very positive and the private space influence is very negative because the influence is inversely proportional to the distance. When the distance is in the middle, the influence decreases with increasing distance. When the distance is far away, the influence of the building on the target node is reduced and is almost negligible due to the excessive distance.
Thirdly, analyzing the network structure of the influence of the distance and the building area on the building:
the section mainly proves the relationship between the distance from a target node and the building area and the building influence, and the correlation of the nodes among networks is influenced due to the change of the attributes.
As can be seen from fig. 4, the distance and the building area and the magnitude of the influence exhibit a poisson distribution. When the distance is close, the public space is relatively less but the building area is large, and the private space is more but the building area is small, so that the whole body presents an ascending situation. When the distance is in the middle, there is more public space and the distance is moderate, so the influence is strongest. When the distance is larger, the distance is the dominant factor, so the influence value is slowly reduced.

Claims (5)

1. A network construction method of urban space density data is characterized by comprising the following steps:
step 1: acquiring a group of city building data;
step 2: abstracting all building monomers of the collected sample group into space capacity particles;
and step 3: particles are classified into two main categories according to space usage functions: public space capacity particles and private space capacity particles;
and 4, step 4: calculating Euclidean distances of all affected particles with the target particle as the center and the radius of R;
and 5: calculating the influence of target particles;
step 6: constructing a distance network structure by using the form of the incidence matrix;
and 7: and displaying the incidence matrix by using a visualization method, and analyzing the relationship between the building area and the distance and the influence.
2. The method for constructing a network of urban spatial density data according to claim 1, wherein in step 3, the spatial use function classification specifically includes:
s01: the common space capacity dots include: buildings with functions of business, administrative service, medical treatment, education and culture;
s02: the private space capacity dots include: buildings for residential, commercial, hotel, industrial, municipal settings, etc.
3. The method for constructing a network of urban spatial density data according to claim 1, wherein in step 4, the calculation formula for calculating the euclidean distance between particles is as follows:
Figure FDA0003300858600000011
wherein (x)1,y1) And (x)2,y2) The coordinates of two particles, respectively.
4. The method as claimed in claim 1, wherein in step 5, the calculation formula of the influence of the target particles is:
Figure FDA0003300858600000012
wherein n and m are the number of public space and private space interstitial points respectively; w is aiAnd wjRespectively public space and private space building areas; diAnd djRespectively the Euclidean distances between the public space and the private space and a target node; r is a radius; Δ WiAnd Δ WjRespectively public space and private spaceAnd (4) influence.
5. The method for constructing a network of urban spatial density data according to claim 1, wherein in step 6, the association matrix construction specifically comprises: the formula for the power law distribution is p (k) ═ Ck-γWhere C is a proportionality constant and γ is a power exponent; formula for calculating poisson distribution
Figure FDA0003300858600000013
Where λ is the expectation and variance of the poisson distribution.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115859765A (en) * 2022-09-29 2023-03-28 中山大学 Method, device, equipment and storage medium for predicting city expansion

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109284918A (en) * 2018-09-11 2019-01-29 中国科学院城市环境研究所 A kind of urban dimensional compactness Measurement Method and system
CN109977600A (en) * 2019-04-10 2019-07-05 中国科学院城市环境研究所 A kind of standardized urban spatial shape compactedness Measurement Method and system
CN111047217A (en) * 2019-12-27 2020-04-21 中国科学院城市环境研究所 Urban functional space compactness measuring method combined with electronic map interest points
CN111339492A (en) * 2020-02-29 2020-06-26 河南大学 Regional city system evolution and space action quantification method and system

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109284918A (en) * 2018-09-11 2019-01-29 中国科学院城市环境研究所 A kind of urban dimensional compactness Measurement Method and system
CN109977600A (en) * 2019-04-10 2019-07-05 中国科学院城市环境研究所 A kind of standardized urban spatial shape compactedness Measurement Method and system
CN111047217A (en) * 2019-12-27 2020-04-21 中国科学院城市环境研究所 Urban functional space compactness measuring method combined with electronic map interest points
CN111339492A (en) * 2020-02-29 2020-06-26 河南大学 Regional city system evolution and space action quantification method and system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
丁亮;钮心毅;宋小冬;: "基于个体移动轨迹的多中心城市引力模型验证", 地理学报 *
苏浩然;陈文凯;王紫荆;孙艳萍;马小平;: "基于改进引力模型的城市应急避难场所空间布局合理性评价", 地震工程学报 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115859765A (en) * 2022-09-29 2023-03-28 中山大学 Method, device, equipment and storage medium for predicting city expansion
CN115859765B (en) * 2022-09-29 2023-12-08 中山大学 Urban expansion prediction method, device, equipment and storage medium

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