A method for predicting urban spatial density under self-influence mechanism

Abstract

The present invention discloses a method for predicting urban spatial density under a self-influence mechanism. First, effective spatial information is extracted from the current spatial distribution status of urban buildings, spatial units are divided, and buildings are abstracted as spatial capacity particles. Then, the small-world model in complex network theory is used to calculate the interaction between capacity particles to obtain the capacity change trend of the capacity particles. The spatial filtering model is then used to calculate the overall capacity change of the spatial unit, thereby obtaining a prediction of the change in urban spatial density. The present invention constructs a mathematical model based on relevant theories of architecture, urban morphology, and complex network research, and can effectively predict the spatial density of cities that have been completed and entered a renewal and development stage.

Description

Technical Field

[0001] The present invention belongs to the field of urban space density measurement and relates to an urban space density prediction method under a self-influence mechanism. The mutual influence of building space capacity in a city within a certain range can be calculated, and the influence can be converted into a prediction of the change trend of urban space density. Background Art

[0002] The change of urban space density is affected by many factors, and the influencing mechanism is complex and diverse. For the built urban areas, they will enter a relatively slow renewal and development stage in the future. The construction and changes of urban space in this stage are closer to the state of natural growth, and are affected by the complex integration of the level of social and economic development, urban planning and design methods, and urban management laws and policies. Under the natural growth state, the density characteristics of urban space form are not only a result-oriented representation, but also a motivation for capacity changes. According to the different use functions of buildings, public spaces will attract more spatial capacity aggregation, while private spaces will exclude further increases in spatial capacity. At the same time, this influence does not directly act on the building itself, but is reflected in the change of spatial density within a certain adjacent area. Based on the above principles, the present invention calculates the mutual influence between the distribution of existing urban building space capacity by establishing a mathematical model, and predicts the changing trend of the spatial density of the region in a certain period of time in the future. The prediction results can provide a scientific basis for the formulation of relevant planning policies for the future spatial development of the city. Summary of the invention

[0003] The purpose of this invention is to propose a method for predicting urban space density under a self-influence mechanism, calculate the mutual influence of building space capacity with different functions within a certain range, and convert the influence into a prediction of the changing trend of urban space density.

[0004] Based on the above objectives, the present invention adopts the following technical solutions:

[0005] A method for predicting urban spatial density under a self-influence mechanism comprises the following steps:

[0006] Step 1: Determine the urban space scope involved in density calculation, and obtain spatial information from the building distribution status map of the scope, including the spatial location and total building area of ​​each independent building unit;

[0007] Step 2: Divide the urban space into square space units with a side length of L using an orthogonal grid along the urban space coordinate system;

[0008] Step 3: Abstract each building unit in the target space unit (x, y) as a capacity particle, the particle coordinates are the centroid coordinates (u, v) of the outer contour of the original building unit base, and define the variable w of the particle as the total building area of ​​the original building unit;

[0009] Step 4: Use the small world model to calculate the building area change Δw of the capacity particle;

[0010] Step 5: Sum the Δw values ​​of all capacity particles in the target spatial unit to obtain Δw(x,y), and then use the spatial filtering method to obtain the spatial capacity change value F(x,y) of the target unit;

[0011] Step 6: Calculate the floor area ratio change value R(x,y) of each spatial unit within the predetermined urban space range.

[0012] Furthermore, in step 3, when the building units are abstracted as capacity points, they are divided into the following two categories according to the use functions of the building space:

[0013] S01, attracting capacity particles, including: buildings with commercial, administrative services, medical, educational and cultural functions;

[0014] S02, repulsive capacity particles, including: buildings with residential, commercial office, hotel, industrial, municipal and other functions;

[0015] For buildings with multiple functions, they are abstracted into multiple independent capacity particles according to their different functions.

[0016] Furthermore, in step 4, the calculation method of the building area change value Δw of a single capacity particle in the target space unit is:

[0017]

[0018] Where a is the sum of the target particle's horizontal coordinate u and the influence range, b is the sum of the target particle's vertical coordinate v and the influence range, A(u,v) is the attraction, R(u,v) is the repulsion, and k(u,v) is the number of all buildings that have an impact on a single particle.

[0019] Furthermore, in step 5, the calculation method of the space capacity change value F(x, y) is:

[0020]

[0021] Where s is the sum of the target particle's horizontal coordinate x and the influence range, t is the sum of the target particle's vertical coordinate y and the influence range, and f(x+s,y+t) is the average distribution function with (x,y) as the center and (x+s,y+t) as the distance range.

[0022] Furthermore, in step 6, the calculation method of the change value of the floor area ratio of each space unit is:

[0023]

[0024] Where L is the side length of the square space unit.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] (1) Existing spatial density measurement technologies often need to first establish a new spatial structure or spatial classification, and then obtain new density results through intensity zoning or analogy research. Such technical methods are effective in the rapid growth (new construction) stage of cities. However, cities entering the slow development (renewal) stage will not undergo significant changes in spatial structure or spatial classification in the short term, so existing measurement technologies are not applicable. The present invention is based on the self-influence mechanism of spatial density of cities under natural growth conditions, and does not rely on changes in spatial structure or spatial classification at the macro level. It can effectively measure the spatial density of cities entering the slow renewal stage. At present, my country has entered the stage of refined urban development, and the present invention has broad application prospects.

[0027] (2) The present invention abstracts the building unit into two different spatial capacity particles according to different usage functions, and uses the small world model to calculate the mutual influence between the capacity particles. That is, the minimum calculation factor of the present invention is controlled at the building unit level, and the map scale used by the original spatial data is about 1:3000. Compared with the existing spatial density measurement technology, it has higher measurement accuracy.

[0028] (3) The present invention introduces the small-world model into the field of urban spatial morphology research and constructs a self-influence mechanism of urban spatial density, which has certain theoretical innovations. The research ideas of the invention technology can also be extended to other related fields of urban research, such as the network layout of certain commercial facilities in the city, the reasonable distribution calculation of urban public service space, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the process of the present invention.

[0030] Figure 2 Schematic diagram of spatial unit grid construction and abstract capacity particles.

[0031] Figure 3 Schematic diagram for measuring the change in building area of ​​a single capacity particle within a spatial unit.

[0032] Figure 4 Schematic diagram of using spatial filtering method to measure spatial density changes.

[0033] Figure 5 This is the influence distribution map of each building when the influence range is 500m.

[0034] Figure 6This is the unit grid influence distribution diagram when the influence range is 500m. DETAILED DESCRIPTION

[0035] The specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0036] like Figure 1 As shown, the present invention aims to propose a method for predicting urban spatial density under a self-influence mechanism. First, effective spatial information is extracted from the current status of urban building spatial distribution, spatial units are divided, and buildings are abstracted as spatial capacity particles. Then, the small world model in complex network theory is used to calculate the interaction between capacity particles to obtain the capacity change trend of capacity particles. Then, the spatial filtering model is used to calculate the overall capacity change of the spatial unit, thereby obtaining a prediction of the change in urban spatial density.

[0037] The urban space density prediction method under the self-influence mechanism of the present invention includes the following implementation steps:

[0038] Step 1: Determine the urban space scope involved in density calculation, and obtain spatial information from the building distribution status map of the scope, including the spatial location and total building area of ​​each independent building unit;

[0039] Step 2: Divide the urban space into square space units with a side length of L using an orthogonal grid along the urban space coordinate system;

[0040] Step 3: Abstract each building unit in the target space unit (x, y) as a capacity particle, the particle coordinates are the centroid coordinates (u, v) of the outer contour of the original building unit base, and define the variable w of the particle as the total building area of ​​the original building unit;

[0041] Step 4: Use the small world model to calculate the building area change Δw of the capacity particle;

[0042] Step 5: Sum the Δw values ​​of all capacity particles in the target spatial unit to obtain Δw(x,y), and then use the spatial filtering method to obtain the spatial capacity change value F(x,y) of the target unit;

[0043] Step 6: Calculate the floor area ratio change value R(x,y) of each spatial unit within the predetermined urban space range.

[0044] Example:

[0045] The dataset of the vicinity of Jing'an Temple in Shanghai in 2015 used in this embodiment selects 1500 building data for analysis, and lists the latitude and longitude information, building area, and space identification of all buildings in detail.

[0046] First, the original data set was preprocessed. Because the data in this project involved practical significance, it was impossible to fill the data with the average value, so the abnormal and missing data were directly removed. After data preprocessing, 1435 valid data were screened out.

[0047] Then, if Figure 2 As shown, the urban space is divided into square space units with a side length of L using an orthogonal grid along the urban space coordinate system.

[0048] Then, if Figure 3 As shown, according to the spatial characteristics of the building, it is divided into attractive capacity particles and repulsive capacity particles.

[0049] In step 4, if Figure 4 As shown in the figure, the calculation method of the building area change value Δw of a single capacity particle in the target space unit is:

[0050]

[0051] Among them, a is the sum of the target particle's horizontal coordinate u and the influence range, b is the sum of the target particle's vertical coordinate v and the influence range, A(u,v) is the attraction, R(u,v) is the repulsion, and k(u,v) is the number of all buildings that have an impact on a single particle. The experiment obtained data at different distances by changing the influence range to 100 meters, 200 meters, 500 meters, and 1000 meters.

[0052] In step 5, the calculation method of the space capacity change value F(x,y) is:

[0053]

[0054] Where s is the sum of the target particle's horizontal coordinate x and the influence range, t is the sum of the target particle's vertical coordinate y and the influence range, and f(x+s,y+t) is the average distribution function with (x,y) as the center and (x+s,y+t) as the distance range.

[0055] In step 6, the calculation method of the change value R(x,y) of the volume ratio of each space unit is:

[0056]

[0057] Wherein, L is the side length of the square space unit, L=500, in meters.

[0058] pass Figure 5 and Figure 6The experimental results show that there are more data repulsion units than attraction units near Jing'an Temple, which generally repels urban density. Combined with reality, it shows that based on the current functional business distribution in the Jing'an Temple area, the probability of continued increase in urban space density in the future is small. If you want to increase the spatial density of this area, you need to increase the proportion of buildings that attract spatial capacity, such as buildings with commercial, administrative services, medical, educational, cultural and other functions.

[0059] The above-mentioned embodiment is a method for predicting urban spatial density under the self-influence mechanism. First, effective spatial information is extracted from the current distribution status of urban building space, the spatial units are divided and the buildings are abstracted as spatial capacity particles. Then, the small-world model in the complex network theory is used to calculate the interaction between the capacity particles to obtain the capacity change trend of the capacity particles. The spatial filtering model is then used to calculate the overall capacity change of the spatial unit, thereby obtaining a prediction of the change in urban spatial density. The above-mentioned embodiment constructs a mathematical model based on the relevant theories of architecture, urban morphology, and complex network research, which can effectively predict the spatial density of cities that have been completed and entered the renewal and development stage.

[0060] The above describes the embodiments of the present invention in conjunction with the accompanying drawings, but the present invention is not limited to the above embodiments. Various changes can be made according to the purpose of the invention. Any changes, modifications, substitutions, combinations or simplifications made according to the spirit and principle of the technical solution of the present invention should be equivalent replacement methods. As long as they meet the purpose of the invention and do not deviate from the technical principles and inventive concepts of the present invention, they belong to the protection scope of the present invention.

Claims

1. A method for predicting urban spatial density under a self-influence mechanism, It is characterized in that The following steps are involved: Step 1: Determine the urban space scope involved in density calculation, and obtain spatial information from the building distribution status map of the scope, including the spatial location and total building area of ​​each independent building unit; Step 2: Divide the urban space into square space units with a side length of L using an orthogonal grid along the urban space coordinate system; Step 3: Abstract each building unit in the target space unit (x, y) as a capacity particle, the particle coordinates are the centroid coordinates (u, v) of the outer contour of the original building unit base, and define the variable w of the particle as the total building area of ​​the original building unit; Step 4: Use the small world model to calculate the building area change Δw of the capacity particle; Step 5: Sum the Δw values ​​of all capacity particles in the target spatial unit to obtain Δw(x, y), and then use the spatial filtering method to obtain the spatial capacity change value F(x, y) of the target unit; Step 6: Calculate the floor area ratio change value R(x, y) of each space unit within the predetermined urban space range.

2. The urban space density prediction method under the self-influence mechanism according to claim 1, It is characterized in that In step 3, when the building units are abstracted as volumetric particles, they are divided into the following two categories according to the use functions of the building space: S01, attracting capacity particles, including: buildings with commercial, administrative services, medical, educational and cultural functions; S02, exclusion capacity particles, including: buildings used for residence, commercial office, hotel, industry, and municipal settings; For buildings with multiple functions, they are abstracted into multiple independent capacity particles according to their different functions.

3. The urban space density prediction method under the self-influence mechanism according to claim 1, It is characterized in that In step 4, the calculation method of the building area change value Δw of a single capacity particle in the target space unit is: Where a is the sum of the target particle's horizontal coordinate u and the influence range, b is the sum of the target particle's vertical coordinate v and the influence range, A(u, v) is the attraction, R(u, v) is the repulsion, and k(u, v) is the number of all buildings that have an impact on a single particle.

4. The urban space density prediction method under the self-influence mechanism according to claim 1, It is characterized in that In step 5, the calculation method of the space capacity change value F(x, y) is: Where s is the sum of the target particle's horizontal coordinate x and the influence range, t is the sum of the target particle's vertical coordinate y and the influence range, and f(x+s, y+t) is the average distribution function with (x, y) as the center and (x+s, y+t) as the distance range.

5. The urban space density prediction method under the self-influence mechanism according to claim 1, It is characterized in that In step 6, the calculation method of the volume ratio change value R(x, y) of each space unit is: Where L is the side length of the square space unit.

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