Urban center system identification method

Abstract

The invention relates to the field of big data analysis, adopts the thought of positioning, delimiting, grading and classifying, and discloses a city center system identification method which comprises the following steps: acquiring analysis data, performing grid division on a center city area, constructing a centrality index calculation model based on a crowd vitality index and a facility gathering index to acquire a centrality index, and calculating the centrality index according to the centrality index. Identifying the grid position of the city center; based on a public activity facility height index and a public activity facility density index, preliminarily delimiting an urban center boundary by adopting a Murphy index defining method, and finally delimiting a boundary range of an urban center through element analysis in combination with data such as land utilization; constructing a central level index calculation model based on the passenger flow visitors and the passenger flow radiation range dimensions to obtain a central level index, and dividing urban central levels; and respectively constructing a crowd activity function index and a location entropy from the angles of crowd travel activities and facility function business forms to judge the function category of the city center.

Description

Technical Field

[0001] The present invention relates to the field of big data analysis, and in particular to a method for identifying a city center system. Background Art

[0002] The city center is the spatial carrier of the city's public service function at the city level. Due to differences in function, level, location, and scale, there are differences in the levels of the city center. The so-called "city center system" refers to a collection of central areas with different leading functions, different levels and scales, and different service scopes in a city. It is an organic whole that is closely connected and interdependent. Establishing the city center system is of great significance for clarifying the urban spatial structure, planning the layout of urban land use, and guiding regional development.

[0003] At present, the research on the city center system mainly focuses on the concept connotation, analytical framework and development momentum of the city center system. In terms of empirical research, scholars in the past mainly conducted cognitive research on the city center system based on traditional static data such as field investigation, literature search, land use census and historical data. Traditional measurement methods require a lot of time to conduct census surveys, and these data are static and updated infrequently, which cannot immediately represent the development status of the city center, resulting in deviations in research conclusions. With the rapid development of Internet information and communication technology, urban big data has the characteristics of high timeliness, high spatial accuracy and massive samples, providing a new data source for the study of the city center system. For example, spatial big data from a single data source such as mobile phone signaling data, Dianping.com POI data, Baidu map LBS data, etc. are used to identify the city center system. In recent years, some scholars have also conducted identification and exploration of city centers based on multi-source data fusion. However, the existing technology focuses on the identification and qualitative evaluation of the location of the city center, lacking a comprehensive and complete research method and application practice from location identification, boundary demarcation, level division to function determination. Summary of the invention

[0004] The present invention aims to provide a method for identifying a city center system. From the perspective of "activity-facility", the method adopts the idea of ​​"positioning, delimitation, grading and classification" to conduct urban function category analysis, so as to completely identify the city center system in terms of location, scope, energy level and type.

[0005] In order to achieve the above object, the present invention adopts the following technical scheme:

[0006] A method for identifying a city center system, comprising:

[0007] S1, obtaining analysis data, the analysis data including mobile phone signaling data, map POI data, land use data and building census data, and preprocessing the analysis data;

[0008] S2, the central urban area is divided into grids, and the crowd vitality index is obtained in the grid based on the mobile phone signaling data, and the facility aggregation index is obtained based on the map POI data. A centrality index calculation model is created based on the crowd vitality index and the facility aggregation index, and the centrality index is obtained according to the centrality index calculation model, and the grid is determined as the city center through the centrality index. The centrality index calculation model is:

[0009]

[0010] Among them, P i is the population vitality index of grid space unit i; F i is the facility agglomeration index of grid space unit i;

[0011] S3, based on the public activity facilities index, the Murphy index definition method is used to preliminarily define the boundaries of the city center, and then the land use data and building census data are superimposed to finally define the boundaries of the city center through factor analysis;

[0012] S4, constructing a center level index calculation model based on the dimensions of passenger flow visitor number and passenger flow radiation range, and obtaining a center level index based on the center level index calculation model; the center level index calculation model is:

[0013] L i =V i ×0.5+S i ×0.5

[0014] Among them, V i is the passenger flow attraction index of city center i; S i is the passenger flow attraction range index of city center i;

[0015] S5, construct the crowd activity function index and location entropy from the perspectives of crowd travel activities and facility functional formats, and determine the functional category of each urban center based on the crowd activity function index and location entropy.

[0016] The principles and advantages of this solution are: in actual application, mobile phone signaling data is selected to represent crowd activities, and POI data is selected to represent facility aggregation. Based on multi-source data, from the perspective of "activity-facility", the city center system is explored from the aspects of location identification, boundary demarcation, level division, and function determination, providing support for planning implementation evaluation and special planning areas such as commercial centers and public services. The characteristics of urban centers of different levels are analyzed from the dimensions of service population, radiation range, and construction intensity, and the implementation evaluation of the planned urban center system is carried out, and planning guidelines are proposed from the aspects of planning control, business guidance, and planning implementation.

[0017] Preferably, as an improvement, the calculation model of the crowd vitality index is:

[0018]

[0019] Among them, A i is the average daily flow of people in two recreational periods in grid space unit i, A max , A min They are the maximum and minimum values ​​of the average daily flow of people in all grid space units during the two recreational periods;

[0020]

[0021] Among them, X i , Y i are the average daily flow of people in the preset time periods of working days and rest days for grid space unit i; X min , Y min are the minimum values ​​of the average daily flow of people in all grid space units during the preset time periods on working days and holidays; X max , Y max They are the maximum values ​​of the average daily flow of people in all grid space units during the preset time periods on weekdays and weekends.

[0022] Technical effect: The crowd vitality index can reflect the degree of concentration of crowd vitality.

[0023] Preferably, as an improvement, the calculation model of the facility agglomeration index is:

[0024]

[0025] Among them, N i N is the total number of POIs of all functional categories in grid space unit i; max 、N min are the maximum and minimum values ​​of the “total number” of POIs of all functional categories in all grid space cells, respectively;

[0026]

[0027] Where n is the number of functional categories of POI; w j is the weight of POI of functional category j; is the number of POIs of functional category j in spatial unit i.

[0028] Technical effect: Based on POI data of maps of different functional categories, the facility agglomeration index of grid space units is constructed to reflect the spatial agglomeration degree of public facilities.

[0029] Preferably, as an improvement, the public activity facility index includes a public activity facility height index PAFHI and a public activity facility density index PAFII, and the calculation model is as follows:

[0030]

[0031] Among them, P j P is the building area of ​​the public activity facilities of the evaluation unit. y is the land area of ​​the evaluation unit, P z is the total building area of ​​the evaluation unit.

[0032] Technical effect: It is easy to fully reflect the capacity characteristics of the city center.

[0033] Preferably, as an improvement, the calculation model of the passenger flow attraction index is:

[0034]

[0035] Among them, X i represents the average daily passenger flow to city center i at the target time, X max , X min They are the maximum and minimum values ​​of the average daily passenger visits in all city centers respectively.

[0036] Technical effect: The passenger flow in the city center can reflect its population vitality and commercial value.

[0037] Preferably, as an improvement, the calculation model of the passenger flow attraction range index is:

[0038]

[0039] Among them, Sc i is the radiation range index, Sc max Sc min are the maximum and minimum values ​​of the radiation range index of all urban centers respectively;

[0040]

[0041] Among them, R i is the radiation radius of city center i, R max , R min are the maximum and minimum values ​​of the radiation radius of all city centers respectively; A i is the radiation area of ​​city center i, R max , R min They are respectively the maximum and minimum values ​​of the radiation areas of all city centers.

[0042] Technical effect: The passenger flow attraction range index characterizes the comprehensive influence area of ​​the city center.

[0043] Preferably, as an improvement, the radiation radius is the radius of a circle drawn based on the number of visitors from each grid to a city center, with each city center as the center, and the grid that includes 80% of the visitors to the city center is selected; the radiation area is the sum of the grid areas covered by the residences of 80% of the passengers visiting the city center based on the radiation radius.

[0044] Technical effect: It is easy to accurately obtain the passenger flow attraction range index.

[0045] Preferably, as an improvement, the calculation model of the crowd activity function index is:

[0046]

[0047] Among them, w i is the average number of visitors to city center i on weekdays; w max 、w min are the maximum and minimum average daily visitor numbers of all city centers on weekdays; r i is the average number of visitors per day on weekends in the city center; max 、r min They are respectively the maximum and minimum average daily number of visitors to all city centers on weekends.

[0048] Technical effect: The crowd activity function index can reflect the actual function of urban land.

[0049] Preferably, as an improvement, the calculation model of the location entropy is:

[0050]

[0051] in, is the number of POIs of functional category j in area i of the city center; m is the number of identified city centers; n is the number of POI functional categories; Express the number of POIs of all functional categories in the city center i area; It represents the number of POIs of functional category j in all city center areas; Represents the number of POIs of all functional categories in all city center areas.

[0052] Technical effect: Location entropy can be used to measure the spatial distribution of elements in a certain region and analyze the status of the region's dominant specialized departments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A schematic diagram of the technical route of a city center system identification method.

[0054] Figure 2 This is an example diagram of the spatial distribution of centrality index;

[0055] Figure 3 This is an example diagram of the spatial distribution of the centrality index Z value;

[0056] Figure 4 This is an example diagram of the spatial distribution of the crowd vitality index;

[0057] Figure 5 This is an example diagram of the spatial distribution of the facility agglomeration index;

[0058] Figure 6 This is an example of the spatial distribution of urban centers;

[0059] Figure 7 This is an example diagram of the spatial distribution of urban centers of different levels;

[0060] Figure 8 This is an example chart of the passenger flow attraction index of the city center;

[0061] Fig. 9 This is an example chart of the passenger flow attraction range index of each city center;

[0062] Fig.10 An example map of spatial distribution of the functional positioning of crowd activities in the city center;

[0063] Fig.11 This is an example diagram of the location entropy results of the central system;

[0064] Fig.12 This is an example diagram of the spatial distribution of functional positioning. DETAILED DESCRIPTION

[0065] The following is further described in detail through specific implementation methods:

[0066] The embodiment is basically as shown in the attached Figure 1 As shown, a method for identifying a city center system includes:

[0067] S1, obtain analysis data, the analysis data includes mobile phone signaling data, map POI data, land use data and building census data, and pre-process the analysis data, the pre-processing includes de-duplication processing; in this embodiment, considering the regional differences in market share of different operators, it is necessary to select the mobile phone signaling data of the operator according to the regional market share. In order to more accurately analyze the population distribution characteristics, the population size is expanded on the basis of the number of users of the selected operator. The expansion coefficient is the ratio of the number of Unicom resident users in the district or county in that month to the number of permanent residents in the statistical caliber of the district or county.

[0068] S2, the central urban area is divided into grids. In this embodiment, the grid size is preferably divided into 250m×250m according to the accuracy of mobile phone signaling data, and the grid is used as the basic unit of identification and evaluation; in the grid, the crowd vitality index is obtained based on the mobile phone signaling data, the facility aggregation index is obtained based on the map POI data, and a centrality index calculation model is created based on the crowd vitality index and the facility aggregation index. The centrality index is obtained according to the centrality index calculation model, and the grid is determined as the city center through the centrality index; the centrality index calculation model is:

[0069]

[0070] Among them, P i is the population vitality index of grid space unit i; F i is the facility agglomeration index of grid space unit i.

[0071] The crowd vitality index and facility agglomeration index are constructed from the two dimensions of crowd activities and functional formats, thereby constructing the centrality index. The location of the city center is identified by the size of the centrality index of the grid. The larger the centrality index value, the stronger its centrality relative to other areas. The centrality index of each grid (C i The centrality index is divided into five levels according to the natural break point classification method (Z value ≥ 12.18), and the grids with centrality index in the first two levels (C value ≥ 0.18) and in the high-value clustering area (Z value ≥ 12.18) are determined as the location of the city center. The example diagrams of the spatial distribution of the centrality index and the example diagrams of the spatial distribution of the centrality index Z value are shown in Figure 2. Figure 2 and Figure 3 shown.

[0072] In this embodiment, based on the average daily flow of people during the period of 7:00-9:00 pm on weekdays and 3:00-5:00 pm on weekends, the crowd vitality index P of the spatial unit i is constructed with a weight ratio of 0.5. i , to reflect the concentration degree of population vitality, the calculation model of the population vitality index is:

[0073]

[0074] Among them, A i is the average daily flow of people in two recreational periods in grid space unit i, A max , A minThey are respectively the maximum and minimum values ​​of the average daily flow of people in all grid space units during the two recreational periods.

[0075]

[0076] Among them, X i , Y i are the average daily flow of people in the preset time periods of working days and rest days for grid space unit i; X min , Y min are the minimum values ​​of the average daily flow of people in all grid space units during the preset time periods on working days and holidays; X max , Y max They are the maximum values ​​of the average daily flow of people in all grid space units during the preset time periods on weekdays and weekends. Figure 4 This is an example diagram of the spatial distribution of the crowd vitality index.

[0077] Based on the POI data of different functional categories, the facility agglomeration index F of spatial unit i is constructed. i , to reflect the spatial agglomeration degree of public facilities. In this embodiment, the map POI data is reclassified to calculate the facility agglomeration index as shown in Table 1:

[0078] Table 1 Reclassification table of POI data on the central urban area map (used to calculate the facility agglomeration index)

[0079]

[0080] The calculation model of the facility agglomeration index is:

[0081]

[0082] Among them, N i N is the total number of POIs of all functional categories in grid space unit i; max 、N min are the maximum and minimum values ​​of the “total number” of POIs of all functional categories in all grid space cells, respectively;

[0083]

[0084] Where n is the number of functional categories of POI; w j is the weight of POI of functional category j; is the number of POIs of functional category j in spatial unit i; the spatial distribution example of facility agglomeration index is shown in Figure 5 shown.

[0085] S3, based on the public activity facilities index, the Murphy index definition method is used to preliminarily define the boundaries of the city center, and then the land use data and building census data are superimposed and factor analysis is used to finally define the boundaries of the city center.

[0086] The key attributes of urban centers include: (1) public activities such as commerce and public services are the functional essence of urban centers; (2) public facilities such as commerce and public services are the functional essence of urban centers. Based on the key attributes of urban centers, the public activity facilities index is proposed to fully reflect the capacity characteristics of urban centers. The public activity facilities index includes the public activity facilities height index PAFHI (Public Activity Facilities Height Index) and the public activity facilities density index PAFII (Public Activity Facilities Intensity Index). The calculation model is as follows:

[0087]

[0088] Among them, P j To evaluate the building area of ​​public activity facilities such as commercial and public services, P y is the land area of ​​the evaluation unit, P z is the total building area of ​​the evaluation unit.

[0089] Taking the plot as the evaluation unit, the continuous blocks with PAFHI≥2 and PAFII≥50% are the preliminary scope of the urban center. Then the current land use data and satellite image data are superimposed. Through the analysis of factors such as the relevance of land use properties (i.e., selecting plots with land use properties related to the urban center, including commercial service facilities land and public management and public service facilities land), element segmentation (i.e., the segmentation effect of water systems, urban roads, green spaces and other elements on space), and spatial texture heterogeneity (i.e., the characteristics of the urban texture of the target area are obviously different from those of the surrounding areas), the boundary range of each urban center is finally delineated. The spatial distribution example of the urban center is shown in the figure below. Figure 6 shown.

[0090] S4, taking the delineated city center boundary range as the analysis unit, constructing a center level index calculation model based on the dimensions of passenger flow visitor number and passenger flow radiation range, and obtaining the center level index based on the center level index calculation model. The larger the center level index L is, the higher the level of the city center is; the center level index calculation model is:

[0091] L i =V i ×0.5+S i ×0.5

[0092] Among them, V i is the passenger flow attraction index of city center i; S i is the passenger flow attraction range index of city center i.

[0093] By calculating the central level index of each city center, the city centers are divided into four levels: city level, district level, group level and typical community center according to the natural break point classification method. The results show that Figure 7 shown.

[0094] The amount of passenger flow in a city center determines its population vitality and commercial value, and is a key factor in the long-term development of the city center. The passenger flow attraction index V is constructed based on the number of passenger visits. i , the calculation model of the passenger flow attraction index is:

[0095]

[0096] Among them, X i represents the average daily passenger flow to city center i at the target time, X max , X min They are the maximum and minimum values ​​of the daily average passenger flow visits of all city centers, and the example of the passenger flow attraction index of the city center is shown in the figure below. Figure 8 shown.

[0097] The radiation range of the city center is the geographical range where the city center effectively attracts people, which represents the comprehensive influence area of ​​the city center. The radiation ranges of city centers of different levels are different. In this embodiment, the radiation radius and the radiation area are comprehensively considered, and the passenger flow attraction range index S is constructed with a weight ratio of 0.5 respectively. i , the calculation model of the passenger flow attraction range index is:

[0098]

[0099] Among them, Sc i is the radiation range index, Sc max Sc min are the maximum and minimum values ​​of the radiation range index of all urban centers respectively;

[0100]

[0101] Among them, R i is the radiation radius of city center i, R max , R min are the maximum and minimum values ​​of the radiation radius of all city centers respectively; A i is the radiation area of ​​city center i, R max , R minThe radiation radius is the radius of a circle drawn based on the number of visitors from each grid to a certain city center, with each city center as the center, and the grid containing 80% of the number of visitors to the city center is selected; the radiation area is the sum of the grid areas covered by the residences of 80% of the passengers visiting a certain city center based on the radiation radius; the example of the passenger flow attraction range index of each city center is as follows Fig. 9 shown.

[0102] S5, construct the crowd activity function index and location entropy from the perspectives of crowd travel activities and facility functional formats, and determine the functional category of each urban center from the dynamic and static dimensions based on the crowd activity function index and location entropy.

[0103] Crowd activity reflects the actual function of urban land. The crowd activity function index (G i ), used to identify the actual functional structure of the city center, the calculation model of the crowd activity function index is:

[0104]

[0105] Among them, w i is the average number of visitors to city center i on weekdays; w max 、w min are the maximum and minimum average daily visitor numbers of all city centers on weekdays; r i is the average number of visitors per day on weekends in the city center; max 、r min are the maximum and minimum values ​​of the average number of visitors per day on weekends in all city centers. i The range of 10% above and below the threshold 1 is taken as the critical value range, that is, G i The city center with a G<0.9 is considered as a leisure and entertainment center; i The city center of G∈[0.9, 1.1] is determined as a comprehensive center; i The city center with a value of >1.1 is considered a business office center. Fig.10 shown.

[0106] Location entropy is used to measure the spatial distribution of elements in a region and analyze the status of the dominant specialized departments in the region. In this embodiment, the location entropy is used to determine the dominant functional category of the urban center in the facility format dimension, and the functional category location entropy is constructed based on POI data. Characterizes the degree of agglomeration of city center i relative to all city centers in the central urban area in functional category j. In this embodiment, the map POI data is reclassified to calculate the location entropy as shown in Table 2.

[0107] Table 2 Reclassification table of POI data in Gaode map of central urban area (used to calculate location entropy)

[0108]

[0109]

[0110] The calculation model of the location entropy is:

[0111]

[0112] in, is the number of POIs of functional category j in area i of the city center; m is the number of identified city centers; n is the number of POI functional categories; Express the number of POIs of all functional categories in the city center i area; It represents the number of POIs of functional category j in all city center areas; It indicates the number of POIs of all functional categories in all city centers. The average value of the location entropy of all city centers is used as the critical point to determine the dominant function of each city center. When the location entropy value is higher than the average value, it is a professional center; otherwise, it is a comprehensive center. The example of the location entropy result of the center system is shown in the figure below. Fig.11 As shown in the figure, the example of functional positioning spatial distribution is as follows Fig.12 As shown, finally the level division and function determination analysis results are combined.

[0113] The above is only an embodiment of the present invention, and the common knowledge such as the known specific technical solutions and / or characteristics in the solution is not described in detail here. It should be pointed out that for those skilled in the art, without departing from the technical solution of the present invention, several modifications and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A method for identifying a city center system, characterized in that: include: S1, obtaining analysis data, the analysis data including mobile phone signaling data, map POI data, land use data and building census data, and preprocessing the analysis data; S2, the central urban area is divided into grids, and the crowd vitality index is obtained in the grid based on the mobile phone signaling data, and the facility aggregation index is obtained based on the map POI data. A centrality index calculation model is created based on the crowd vitality index and the facility aggregation index, and the centrality index is obtained according to the centrality index calculation model, and the grid is determined as the city center through the centrality index. The centrality index calculation model is: Among them, P i is the population vitality index of grid space unit i; F i is the facility agglomeration index of grid space unit i; S3, based on the public activity facilities index, the Murphy index definition method is used to preliminarily define the boundaries of the city center, and then the land use data and building census data are superimposed to finally define the boundaries of the city center through factor analysis; S4, constructing a center level index calculation model based on the dimensions of passenger flow visitor number and passenger flow radiation range, and obtaining a center level index based on the center level index calculation model; the center level index calculation model is: L i =V i ×0.5+S i ×0.5 Among them, V i is the passenger flow attraction index of city center i; S i is the passenger flow attraction range index of city center i; S5, construct the crowd activity function index and location entropy from the perspectives of crowd travel activities and facility functional formats, and determine the functional category of each urban center based on the crowd activity function index and location entropy.

2. A city center system identification method according to claim 1, characterized in that: The calculation model of the population vitality index is: Among them, A i is the average daily flow of people in two recreational periods in grid space unit i, A max , A min They are the maximum and minimum values ​​of the average daily flow of people in all grid space units during the two recreational periods; Among them, X i , Y i are the average daily flow of people in the preset time periods of working days and rest days for grid space unit i; X min , Y min are the minimum values ​​of the average daily flow of people in all grid space units during the preset time periods on working days and holidays; X max , Y max They are the maximum values ​​of the average daily flow of people in all grid space units during the preset time periods on weekdays and weekends.

3. A city center system identification method according to claim 1, characterized in that: The calculation model of the facility agglomeration index is: Among them, N i N is the total number of POIs of all functional categories in grid space unit i; max 、N min are the maximum and minimum values ​​of the "total number" of POIs of all functional categories in all grid space units, respectively; Where n is the number of functional categories of POI; w j is the weight of POI of functional category j; is the number of POIs of functional category j in spatial unit i.

4. A city center system identification method according to claim 1, characterized in that: The public activity facility index includes the public activity facility height index PAFHI and the public activity facility density index PAFII, and the calculation model is as follows: Among them, P j P is the building area of ​​the public activity facilities of the evaluation unit. y is the land area of ​​the evaluation unit, P z is the total building area of ​​the evaluation unit.

5. A city center system identification method according to claim 1, characterized in that: The calculation model of the passenger flow attraction index is: Among them, X i represents the average daily passenger flow to city center i at the target time, X max , X min They are the maximum and minimum values ​​of the average daily passenger visits in all city centers respectively.

6. A city center system identification method according to claim 1, characterized in that: The calculation model of the passenger flow attraction range index is: Among them, Sc i is the radiation range index, Sc max Sc min are the maximum and minimum values ​​of the radiation range index of all urban centers respectively; Among them, R i is the radiation radius of city center i, R max , R min are the maximum and minimum values ​​of the radiation radius of all city centers respectively; A i is the radiation area of ​​city center i, R max , R min They are respectively the maximum and minimum values ​​of the radiation areas of all city centers.

7. A city center system identification method according to claim 6, characterized in that: The radiation radius is the radius of a circle drawn based on the number of visitors from each grid to a city center, with each city center as the center, and the grid that includes 80% of the visitors to the city center is selected; the radiation area is the sum of the grid areas covered by the residences of 80% of the passengers visiting a city center based on the radiation radius.

8. A city center system identification method according to claim 1, characterized in that: The calculation model of the population activity function index is: Among them, w i is the average number of visitors to city center i on weekdays; w max 、w min are the maximum and minimum average daily visitor numbers of all city centers on weekdays; r i is the average number of visitors per day on weekends in the city center; max 、r min They are respectively the maximum and minimum average daily number of visitors to all city centers on weekends.

9. A city center system identification method according to claim 1, characterized in that: The calculation model of the location entropy is: in, is the number of POIs of functional category j in area i of the city center; m is the number of identified city centers; n is the number of POI functional categories; Express the number of POIs of all functional categories in the city center i area; It represents the number of POIs of functional category j in all city center areas; Represents the number of POIs of all functional categories in all city center areas.

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