CN114091140B - Network construction method for urban space density data - Google Patents

Network construction method for urban space density data Download PDF

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CN114091140B
CN114091140B CN202111190642.9A CN202111190642A CN114091140B CN 114091140 B CN114091140 B CN 114091140B CN 202111190642 A CN202111190642 A CN 202111190642A CN 114091140 B CN114091140 B CN 114091140B
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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 space density data, which comprises the following steps: 1: acquiring a group of urban building data; 2: abstracting all building monomers of the collected sample group into space capacity particles; 3: the function differentiation particles are divided into two main categories according to space usage: public space capacity dots and private space capacity dots; 4: calculating Euclidean distances of all influence particles with the target particle as the center and the radius within the range R; 5: calculating the influence of the target particles; 6: constructing a distance network structure by using a form of an 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 invention can identify the characteristics of the urban density evolution process, thereby displaying the state that the space morphology is driven by the internal cause of the space demand and develops and changes according to a certain rule principle.

Description

Network construction method for 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 space density data.
Background
Cities can be understood as an open, constantly changing complex macro system with many networks, some regular and some random. The natural growth state of the city refers to a state that the space morphology is driven by the internal motion of space requirements and is developed and changed according to a certain rule principle. This principle is a complex fusion of socioeconomic performance level, urban planning design method and urban management regulation policy over a period of time. A typical network consists of a number of particles and edges connecting the particles, which edges are understood to be the influencing relationship between the particles. The three-dimensional form of the entity space in the city does not belong to the category of networks, but the aggregation degree of the entity space capacity within a certain range shows a plurality of characteristics conforming to the characteristics of the complex network. Because the urban density and the building area are related to the distance, the structural characteristics of the interrelated network are researched, and the method has very important significance for the development of cities.
At present, china research mainly calculates from bottom to top through energy consumption intensity, and establishes a building energy consumption model based on energy consumption data and judging the building area of an area through energy consumption. 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 calculate building density only from building energy consumption without considering the influence of surrounding buildings on it. Therefore, how to combine the complex network theory with the urban planning, and analyze the urban development process by adopting the complex network theory, so as to find the relation among the elements of urban density, provide basis and guarantee for the future urban planning, and become the technical problem to be solved urgently.
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. Correlation links are established between different buildings, and building areas and distances are proposed as impact frameworks. The method can identify the characteristics of the urban density evolution process, so that the state that the space morphology is driven by the internal cause of space demand and changes is developed according to a certain rule principle is displayed.
Based on the above purpose, 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 urban building data;
step 2: abstracting all building monomers of the collected sample group into space capacity particles;
step 3: the function differentiation particles are divided into two main categories according to space usage: public space capacity dots and private space capacity dots;
step 4: calculating Euclidean distances of all influence particles with the target particle as the center and the radius within the range R;
step 5: calculating the influence of the target particles;
step 6: constructing a distance network structure by using a form of an incidence matrix;
step 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 spatial usage function classification specifically includes:
s01: the common space capacity particle comprises: building with business, administrative service, medical treatment, education and culture functions;
s02: the private space capacity dots include: residential, business, hotel, industrial, municipal settings, and the like.
Further, in step 4, the calculation formula for calculating the euclidean distance between particles is:
wherein, (x) 1 ,y 1 ) And (x) 2 ,y 2 ) The coordinates of the two particles respectively.
Further, in step 5, the influence calculation formula of the target particle is:
wherein n and m are the quality points of the public space and the private space respectively; w (w) i And w j Building areas of public space and private space respectively; d, d i And d j The Euclidean distance between the public space and the private space and the target particle is respectively; r is a radius; ΔW (delta W) i And DeltaW j The public and private space influence forces, respectively.
Further, in step 6, the construction of the association matrix specifically includes: the calculation formula of the power law distribution is p (k) =ck -γ Wherein, C is a proportionality constant and gamma is a power exponent; calculation of poisson distributionFormula (VI)Where λ is the expected 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 reference indexes for predicting urban development, and provides important basis for urban planning more reasonably and orderly.
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.
Drawings
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 impact.
Detailed Description
Specific embodiments of the present invention will be further described below with reference to the accompanying drawings.
As shown in fig. 1, a network construction method for urban space density data includes the following steps:
step 1: acquiring a group of urban building data;
step 2: abstracting all building monomers of the collected sample group into space capacity particles;
step 3: the function differentiation particles are divided into two main categories according to space usage: public space capacity dots and private space capacity dots;
step 4: calculating Euclidean distances of all influence particles with the target particle as the center and the radius within the range R;
step 5: calculating the influence of the target particles;
step 6: constructing a distance network structure by using a form of an incidence matrix;
step 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 2015 inspirational ring data set used in the embodiment selects 198 buildings to be analyzed, and the longitude and latitude information and the building area of all the buildings are listed in detail.
Firstly, 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:
1. urban space density network construction:
the research of the correlation of urban space density plays a very important role in optimizing urban construction and predicting the future trend of cities, and the basic principle is to construct a network by utilizing building correlation attributes. Describing particle location information using longitude and latitude information using Euclidean distance formula
The distance between all particles is calculated. Wherein, (x) 1 ,y 1 ) And (x) 2 ,y 2 ) The coordinates of the two particles respectively. Thus, the processed dataset contains positional information of 198 buildings. The particles are used as different buildings in the network, the range of the particles takes a circle with a radius of 5 km, and all the particles are conveniently used as target particles. An assumption is made for the model that public space attracts space capacity, and private space repels space capacity. The following formula is adopted to respectively calculate the influence data of the target particles:
wherein n and m are a public space and a private space respectivelyNumber of intermediate points; w (w) i And w j Building areas of public space and private space respectively; d, d i And d j The Euclidean distance between the public space and the private space and the target particle is respectively; r is a radius, here 5 km; ΔW (delta W) i And DeltaW j The public and private space influence forces, respectively.
In fig. 2 it can be seen that two influencing particles N and M from the target particle P, N being public space buildings and M being private space buildings, the magnitude of their influence on the target particle is:
wherein the distances of N and M are d respectively n And d m The building surface is w n And w m . And calculating the total influence of all particles with the target particle P as the center radius of 5 km to obtain the influence of the point P.
2. And (3) analyzing the network structure of the building influence by the distance:
this section mainly demonstrates the relationship of distance from a target particle to building influence, and the correlation of particles between networks can be affected by attribute changes.
In fig. 3, the building area of all affected particles is fixed, and it can be observed that the distance and the magnitude of the influence represent a power law distribution, which indicates that a relative change in one quantity will result in a corresponding power-to-scale change in another quantity, independent of the initial value.
When the distance is very close, the public space influence is very positive and the private space influence is very negative, since the influence is inversely proportional to the distance. When the distance is in the middle, the influence is reduced as the distance increases. When the distance is far away, the influence of the building on the target particles is reduced to a very small extent due to the overlarge distance.
3. And (3) analyzing the network structure of the influence of the distance and the building area on the building:
this section mainly demonstrates the relationship of distance from target particles and building area to building influence, and the correlation of particles between networks can be affected by property changes.
As can be seen from fig. 4, the distance and building area and impact size exhibit poisson distribution. When the distance is short, the public space is relatively small, the building area is large, the private space is large, and the building area is small, so that the whole body presents an ascending situation. When the distance is in the middle, the public space is more and the distance is moderate, so the influence is strongest. When the distance is large, the distance is dominant, so the influence value is slowly reduced.

Claims (2)

1. The network construction method of the urban space density data is characterized by comprising the following steps of:
step 1: acquiring a group of urban building data;
step 2: abstracting all building monomers of the collected sample group into space capacity particles;
step 3: the function differentiation particles are divided into two main categories according to space usage: public space capacity dots and private space capacity dots; the space-consuming functional classification specifically includes:
s01: the common space capacity particle comprises: building with business, administrative service, medical treatment, education and culture functions;
s02: the private space capacity dots include: residential, business, hotel, industrial, municipal setting building;
step 4: calculating Euclidean distances of all influence particles with the target particle as the center and the radius within the range R;
step 5: calculating the influence of the target particles; calculating the influence of the target particles according to the public space attraction space capacity and the private space exclusion space capacity; the influence calculation formula of the target particle is as follows:
wherein n and m are the mass of public space and private space respectivelyCounting points; w (w) i And w j Building areas of public space and private space respectively; d, d i And d j The Euclidean distance between the public space and the private space and the target particle is respectively; r is a radius; ΔW (delta W) i And DeltaW j The influence of public space and private space respectively;
step 6: constructing a distance network structure by using a form of an incidence matrix; the construction of the incidence matrix specifically comprises the following steps: the calculation formula of the power law distribution is p (k) =ck -γ Wherein, C is a proportionality constant and gamma is a power exponent; calculation formula of poisson distributionWhere λ is the expected and variance of the poisson distribution;
step 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 of claim 1, wherein in step 4, the calculation formula for calculating the euclidean distance between particles is:
wherein, (x) 1 ,y 1 ) And (x) 2 ,y 2 ) The coordinates of the two particles respectively.
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Citations (4)

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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
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