GB2606114A - Community life circle space identification method and system, computer device and storage medium - Google Patents

Abstract

Disclosed are a community life circle space identification method and system, a computer device and a storage medium. The method comprises: extracting mobile phone signaling data and construction land data; organizing the construction land data into construction land data of each community; calculating the arrival population density of each community; obtaining the center of mass of the construction land of each community, and generating a center-of-mass distance matrix as a community distance matrix; finding the community with the highest arrival population density to be the current central community; identifying the life circle of the current central community; looking for a new central community outside the service radius of the current central community, regarding the new central community as the current central community, and returning to identify the life circle of the current central community until all of the communities are included into a corresponding life circle. The present invention achieves spatial division of the community's life circles on the basis of the mobile phone signaling data and the construction land data of each community. By identifying life circles according to the daily travels of the residents, the concept of life circles is more closely reflected.

Description

Community Life Circle Space Identification Method, System, Computer Device and Storage Medium

Technical Field

The invention relates to a community life circle space identification method, system, computer device and storage medium, and belongs to the field of quantitative measurement of a community life circle.

Technical Background

The concept of "life circle" originated from the basic residential areas in the comprehensive development plan of Japan in the 1970s. It is the geographical distribution of daily production and life activities within a specific geographical and social village. More generally, the life circle is the spatial scope or behavioral space formed by residents taking home as the center to carry out various travels such as shopping, leisure, work commuting (school commuting), social interaction and medical treatment (Aitken, 1988; Algers, 2005; Tian, 2018). Compared with thc economic connections of general regional networks, the life circle reflects the interactive relationship between the life space unit of die residents and the actual life of the residents, as well as the dynamic relationship between the supply of facilities and the demand of the residents, from the perspective of the residents' life. As far as planning practice is concerned, the significance of community life circle space identification is to optimize the allocation of public service facilities (Zhang Neng, Wu Tinghai, Lin Wenqi, 2011; Gong Hong, Xu Jinhua, Zhang Yi, 2013) to improve the efficiency of public service distribution and the public standard of life, to truly realize the matching of supply and demand of public services. At present, due to the lack of a scientific and effective method of dividing the life circle, it is difficult for life circle planning to directly participate in the practice of urban and rural planning, and it is more like a method of post-planning evaluation.

The identification of the community life circle is a study of the urban and rural spatial structure from the perspective of population connection. Based on the characteristics of residents' travel and temporal and spatial distribution, areas with similar characteristics arc divided into the same planning unit. This ensures that people in the same planning unit have similar usage habits of public service facilities, and effectively improves the use efficiency of public service facilities. Although the new version of "Standard for Urban Residential Area Planning and Design (GB50180-2018)" does not emphasize the division of spatial boundaries, it still replaces the original hierarchical model with life circle residential areas with different travel time distances, and provides population and number of units for reference; Shanghai, Guangzhou and other places are currently implementing the planning of urban life circle residential areas, and based on this, they are exploring the improvement of community life quality (Li Meng, 2017; Chong Rong, 2018; Yu Yifan, 2019); Chengdu also carries out qualitative division of various types of community life circles according to planning needs.

At present, in principle, the division of life circles is usually a bottom-up spatial organization method based on the wishes of residents. Therefore, most of the division methods in the study are from the perspective of the demand side. According to the principle of dividing the life circle, the survey of residents' wishes is adopted. Questionnaires were randomly distributed to investigate the time that urban and rural residents were willing to spend in order to obtain public service facilities to determine the radius of the life circle at all levels. Sun Defang et al. (2012) took Pizhou City, Jiangsu Province as an example, and obtained the time costs that residents are willing to pay to obtain public services such as education, medical care, cultural entertainment through willingness survey. In this way, the best time interval is determined and the county life circle system is constructed. Some studies divide the life circle based on the characteristic structures of the population. For example, Zhu Chasong et al. (2010) took Xiantao City as an example, and divided life circles into different levels and types according to residents' travel distance, travel mode, demand frequency and service radius; Chai Yanwei et al. (2015) introduced the planning of life circles at different regional spatial scales in detail, and based on GPS travel trajectories, they explored the scope of urban community life circles by using spatial temporal behavior analysis methods. In terms of the identification technology of the life circle, Jiang Ming (2015) divided the life circle level of county based on the topography, hydrogeology, resource conditions, and economic development level, using the best distance for villagers to obtain public service facilities as the radius; Wang Shaobo (2015), Zhou Xinxin, Wang Peizhen, Yang Fan et al. (2016), Tian (2018) also used CS technology to explore the spatial division of life circles; however, the above-mentioned methods of identifying and dividing life circles rely more on the disciplinary quality of urban planners themselves. On the basis of comprehensive consideration of various types of information, empirically scrutinize and judge, it does not have a considerable degree of operability, and it is difficult to be reproducible like quantitative analysis.

To study the urban spatial structure from the behavioral activities of residents, traditional techniques are either based on a small number of sample surveys, or based on statistical data such as population censuses. In 2018, the number of mobile phone users in China has reached 1.42 billion, and the huge mobile phone user base provides a large number of data sources for the collection of various data. Mobile phone signaling data is the record of information exchanged between the mobile phone and the base station when the mobile phone user is active in the mobile communication network. Since the mobile phone signaling data records the daily behavior of each user and the way they use the urban space, it may be used to study the urban spatial structure and planning practice by synthesizing the spatial temporal regular patterns of all user activities. In addition, the mobile phone signaling data collection technology also has the advantages of low cost and wide coverage. Therefore, mobile phone data may be used as an important supplement to the existing planning data collection technology, which provides a good data support for the extraction of the temporal and spatial distribution characteristics of residents' travel.

The emergence of various new data in recent years has brought new opportunities for urban and rural planning technology. The adoption of new technologies such as cell phone signaling data is gradually gaining attention in the planning field. For example, Niu Xinyi et al. (2019) used mobile phone signaling data to study the consumption behavior of public services in Shanghai; Liu Qiang (2018) used mobile phone signaling to conduct spatial analysis of urban life circles; Gong Yongxi (2018), Yang Junyan (2019) and others developed a patented technology for dividing life circles based on positioning data. Due to the advantages of convenient and low-cost acquisition of mobile phone signaling data, and the ability to generate aggregated data in different time periods and different spatial scales. OD modeling of non-commuter connections based on base station data may realize spatial division of community life circles, which can, not only support the layout planning of urban residential areas and villages, but also realize the targeted configuration of public services, but it lacks the consideration of residents daily travel, and its technology is difficult to apply to actual planning.

Summary of the Invention

In view of this, the present invention provides a community life circle space identification method, system, computer device and storage medium, which realizes a spatial division of a community life circle according to mobile phone signaling data and construction land data of each community. Identifying the life circle from the daily travel of residents may be closer to the concept of life circle.

A first objective of the present invention is to provide a method for identifying a community life circle space.

A second objective of the present invention is to provide a system for identifying a community life circle space.

A third objective of the present invention is to provide a computer device.

A fourth objective of the present invention is to provide a storage medium.

The first objective of the present invention may be achieved by taking the following technical solutions: A method for identifying a community life circle space, the method includes: extracting mobile phone signaling data and construction land data; arranging the construction land data into construction land data of each community: according to the mobile phone signaling data and the construction land data of each community, calculating an arrival population density of each community: according to the construction land data of each community, obtaining a construction land center of each community, generating a distance matrix between the centers as a community distance matrix; according to the arrival population density of each community, finding a community with a highest arrival population density as a current central community; according to the community distance matrix, selecting a service radius, identifying a life circle of the current central community: outside the service radius of the current central community, finding a new central community, using the new central community as the current central community, returning to the community distance matrix, selecting a service radius, identifying the life circle of the current central community until all communities are classified into corresponding life circles.

Further, the step of arranging the construction land data into construction land data of each community specifically includes: loading construction land CAD graphics, generating construction land line graphics according to the construction land CAD graphics; using line-to-surface tools to generate construction land surface graphics from the construction land line graphics; using fusion tools, combining scattered construction land elements in the construction land surface graphics into one element to generate construction land fusion graphics; using intersection tools, intersecting the construction land fusion graphics with administrative community division graphics, dividing a construction land according to communities, assigning construction land community text fields, generating construction land graphics of each community; according to the construction land graphics of each community, calculating a construction land area of each community.

Further, the step of, according to the mobile phone signaling data and the construction land data of each community, calculating an arrival population density of each community, specifically includes: according to the mobile phone signaling data, counting a number of arrival population in each community, and obtaining a table of the number of arrival population in each community; linking the table of the number of arrival population in each community to construction land graphics of each community to obtain a arrival population density graphics of each community; according to the arrival population density graphics of each community, dividing an OD population by a construction land area to obtain the arrival population density of each community.

Further, the step of, according to the mobile phone signaling data, counting a number of arrival population in each community, and obtaining a table of the number of arrival population in each community, specifically includes: according to the mobile phone signaling data, generating base station graphics: using intersection tools, intersecting the base station graphics with administrative community division graphics, dividing base stations by communities, assigning base station community text fields, generating base station graphics of each community with community labels; converting crowd OD data between base stations into crowd OD data between communities; according to the crowd OD data between communities, counting die number of arriving population in each community, obtaining the table of the number of arriving population in each community.

Further, the step of, according to the construction land data of each community, obtaining a construction land center of each community, generating a distance matrix between the centers as a community distance matrix, specifically includes: using surface-to-point tools, generating construction land center graphics of each community from construction land graphics of each community; according to the construction land center graphics of each community, calculating distance between two centers; using a pivot table, with a starting point as a row, an end point as a column, an average distance as a value, generating the distance matrix between the centers as the community distance matrix.

Further, the method further includes: according to the mobile phone signaling data, using a pivot table, with a starting community as a row, a destination community as a column, a sum of a number of population coming and going as a value, generating a non-commuter OD connection matrix between communities; according to the non-commuter OD connection matrix between communities, comparing a number of people in each community with non-commuter OD connections to different central communities, selecting a central community with a strongest non-commuter OD connection strength as the central community of each community, putting the central community of each community into a life circle of each community, completing a secondary identification of die life circle.

The second objective of the present invention may be achieved by taking the following technical solutions: A community life circle space identification system, the system includes: an extraction module used to extract mobile phone signaling data and construction land data; an arranging module used to arrange the construction land data into construction land data of each community; a calculation module used to, according to the mobile phone signaling data and the construction land data of each community, calculate an arrival population density of each community; a first generation module used to, according to the construction land data of each community, obtain a construction land center of each community, generate a distance matrix between the centers as a community distance matrix; a search module used to, according to the arrival population density of each community, find a community with a highest arrival population density as a current central community; a first identification module used to, according to the community distance matrix, select a service radius, identify a life circle of the current central community; a second identification module used outside the service radius of the current central community, to find a new central community, use the new central community as the current central community, return to the community distance matrix, select a service radius, identify the life circle of the current central community until all communities arc classified into corresponding life circles.

Further, the system further includes: a second generation module used to, according to the mobile phone signaling data, use a pivot table, with a starting community as a row, a destination community as a column, a sum of a number of population coming and going as a value, generate a non-commuter OD connection matrix between communities; a third identification module used to, according to the non-commuter OD connection matrix between communities, compare a number of people in each community with non-commuter OD connections to different central communities, select a central community with a strongest non-commuter OD connection strength as the central community of each community, put the central community of each community into a life circle of each community, complete a secondary identification of the life circle.

The third objective of the present invention may be achieved by taking the following technical solutions: A computer device comprising a processor and a memory for storing a program executable by the processor, characterized in that, when the processor executes the program stored in the memory, the above-mentioned method for identifying a community life circle space is realised.

The fourth objective of the present invention may be achieved by taking the following technical solutions: A storage medium storing a program, when the program is executed by a processor, the above-mentioned method for identifying a community life circle space is realised.

The present invention has the following beneficial effects with respect to the existing technology: The present invention is based on mobile phone signaling data information processing technology, and uses the crowd trajectory data generated by mobile phone signaling at the technical level to identify the vitality center of the community, and identify the community life according to the accessibility range and accessibility characteristics. It is suitable for urban planners to use; compared with the prior art, the present invention emphasizes the accessibility of residents' daily life, so the most oriented non-commuting connection is used to divide die space range of the life circle community.

Description of Figures

In order to explain the embodiments of the present invention or the technical solutions in the existing technology more clearly, the Following briefly introduces the accompanying figures that need to be used in the description of the embodiments or the existing technology. Obviously, the figures in the following description are only some embodiments of die present invention. For those of ordinary skill in die art, other figures may also be obtained according to the structures shown in these figures without inventive efforts.

Figure 1 is a flowchart of a method for identifying a community life circle space according to Embodiment 1 of the present invention.

Figure 2 is an illustrative diagram of finding a central community according to Embodiment 1 of the present invention.

Figure 3 is an illustrative diagram of die first identification of a community life circle according to Embodiment 1 of the present invention.

Figure 4 is an illustrative diagram of the second identification of a community life circle according to Embodiment I of the present invention.

Figure 5 is a structural block diagram of a community life circle space identification system according to Embodiment 3 of the present invention.

Figure 6 is a structural block diagram of an arranging module according to Embodiment 3 of the present invention.

Figure 7 is a structural block diagram of a calculation module according to Embodiment 3 of the present invention Figure 8 is a structural block diagram of a first generation module according to Embodiment 3 of the present invention.

Figure 9 is a structural block diagram of a computer device according to Embodiment 4 of the present invention.

Description

In order to make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying figures of the embodiments of the present invention. Obviously, the described embodiments are a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without inventive efforts shall fall within the protection scope of the present invention.

Embodiment 1: This embodiment provides a method for identifying a community life circle space. The method uses the administrative community boundary map and the construction land map of the study area as the working base map. After calculating the distance between communities in ArcGIS software, the mobile phone signaling data of China Mobile is used as the main data source.

Using Excel to clean the data, and in die Excel software, the spatial identification of the community life circle is completed through two overlay analysis of the community distance matrix and the non-commuter OD connection matrix for the study area, as shown in Figure 1, the method includes the following steps: Sl, extracting mobile phone signaling data and construction land data.

In this embodiment, before extracting the mobile phone signaling data and construction land data, the study city and the study scope may be determined first, that is, it is limited to a certain urban area, township and other areas. In principle, life circles are not identified across regions. For each region, determine the basic unit boundaries of the region, such as base station coverage boundaries, community boundaries, and village boundaries. In this embodiment, each community is used as the basic unit.

The mobile phone signaling data extracted in this embodiment is the mobile phone signaling data provided by China Mobile, and the construction land data is the construction land CAD graphics.

S2. arranging die construction land data into construction land data of each community.

This embodiment uses administrative community zoning and construction land data as the main data sources, and integrates the two in ArcGIS to form construction land data classified by community, that is, construction land data for each community, and prepares data for analysis in subsequent steps.

Further, this step S2 specifically includes: S21, loading construction land CAD graphics, generating construction land line graphics according to the construction land CAD graphics.

Specifically, load the polylthe in "Construction Land CAD.dwg" into ArcGIS, generate the construction land line graphics, and save the shapefile file as "Construction Land_Line.shp".

S22, using line-to-surface tools to generate construction land surface graphics from the construction land line graphics.

Specifically, using the line-to-surface tools in ArcGIS, "Construction Land Line.shp" is generated into "Construction Land_Surface.shp", and its graphics are construction land surface graphics.

S23, using fusion tools, combining scattered construction land elements in the construction land surface graphics into one element to generate construction land fusion graphics.

Specifically, using the fusion tools in AreGIS, the scattered construction land elements in "Construction Land_Surface.shp" are combined into one element to generate "Construction Land Jusion.shp", and the graphics are the construction land fusion graphics.

S24. using intersection tools, intersecting the construction land fusion graphics with administrative community division graphics, dividing a construction land according to communities, assigning construction land community text fields, generating construction land graphics of each community.

Specifically, using the intersection tools in AreGIS, intersect "Construction Land Fusion.shp" with "Administrative Community Division.shp", divide the construction land by community, and assign the construction land "community" text field to generate Each Community Construction Land.shp" file, and its graphics are the construction land graphics of each community.

S25, according to the construction land graphics of each community, calculating a construction land area of each community.

Specifically, open the attribute table of "Each Community Construction Land.shp", create a new "area" text field, and use "computational geometry" to calculate the area of construction land in each community.

S3. according to the mobile phone signaling data and the construction land data of each community, calculating an arrival population density of each community.

Further, this step 53 specifically includes: S31. according to the mobile phone signaling data, counting a number of arrival population in each community, and obtaining a table of the number of arrival population in each community.

1) according to the mobile phone signaling data, generating base station graphics.

The mobile phone signaling data may be sorted out in Excel to get the base station table sheet, import the base station table sheet in ArcGIS, right-click the layer to display the xy data, and export the "Base Station.shp" file, its graphics are the base station graphics.

2) using intersection tools, intersecting the base station graphics with administrative community division graphics, dividing base stations by communities, assigning base station community text fields, generating base station graphics of each community with community labels.

Specifically, use the intersection tools in ArcGIS to intersect "Base Station.shp" and "Administrative Community Division.shp", divide the base stations by community, assign the base station "Community" text field, and generate "Each Community Base Station.shp" file, each community base station graphics with community labels, and export the Excel table "Community-Base Station Comparison Table.xlsx" to convert the crowd OD data between base stations into crowd OD data between communities.

3) converting crowd OD data between base stations into crowd OD data between communities.

Specifically, import the crowd OD data between base stations in Excel to obtain "OD Connection Between Base Stations.xlsx"; open the "Community-Base Station Comparison Table.xlsx", and use the vlookup function to covert crowd OD data between base stations into crowd OD data between communities, and saved as "Inter-Community OD Connections.xlsx".

4) according to the crowd OD data between communities, counting the number of arriving population in each community, obtaining the table of the number of arriving population in each community.

Specifically, according to the crowd OD data between communities, using the pivot table, take "Destination Community" as the row and "OD number" as the value, count the number of arrival population in each community, and obtain the table of the number of arrival population in each community, and save it as "Each Community Arrival Population.xlsx".

532. linking the table of the number of arrival population in each community to construction land graphics of each community to obtain a arrival population density graphics of each community.

Specifically, import "Each Community Arrival Population.xlsx" in ArcGIS, connect to Each Community Construction Land.shp" based on the "Community" text field, and export "Each Community Arrival Population Density.shp", its graphics is the arrival population density graphics of each community.

533. according to the arrival population density graphics of each community, dividing an OD population by a construction land area to obtain the arrival population density of each community.

Specifically, in the attribute table "Arrival Population Density.shp", add a new text field "Arrival Population Density", use the "Text Field Calculator", and enter "=10D populationV[Construction land area]" to obtain each arrival population density, export "Each Community Arrival Population Density.xlsx", transpose the table so that the text field name becomes a row, as shown in Table 1.

Tab e 1: Arrival nonulation density in each community Serial A B C D E 1 Community Chen Village Zhang Village Liu Village Li Village 2 Arrival population density' B2 C2 D2 E2 S4. according to the construction land data of each community, obtaining a construction land center of each community, generating a distance matrix between the centers as a community distance matrix.

In this embodiment, ArcGIS is used to create the construction land center of each community, and proximity analysis tools arc used to calculate the distance between the centers, use Excel data to clean up duplicate and meaningless data, and finally generate a matrix of inter-community distances through a pivot table.

Further, this step S4 specifically includes: 541. using surface-to-point tools, generating construction land center graphics of each community from construction land graphics of each community Specifically, using the surface-to-point tools in ArcGIS, "Each Commtmity Construction Land.shp" is used to generate "Each Community Construction Land Centers.shp", and its graphics are the construction land center graphics of each community.

S42. according to the construction land center graphics of each community, calculating distance between two centers.

Specifically, using the "Analysis Tools-Neighborhood Analysis-Nearby Analysis" tools in ArcGIS, the input elements and adjacent elements are set to "Each Community Construction Land Centers.shp", calculate the distance between the centers, and generate the "Each Community Construction Land Center Distance.shp" file.

543. using a pivot table, with a starting point as a row, an end point as a column, an average distance as a value, generating the distance matrix between the centers as the community distance matrix.

Insert a "pivot table" in Excel, the starting point as the row, the end point as the column, mid the average distance as the value, and the distance matrix between centers is generated, which is used as the community distance matrix, as shown in Table 2, Table 2: Community distance matrix Community distance matrix Serial A B C D E 1 Ch Zhang Village Liu Village Li Village Chen Village 2 Chen Village 3 Zhang Village 4 Liu Village Li Village S5 identifyii g the community life circle for the first time.

S51, according to the arrival population density of each community, finding a commun th a highest arrival population density as a first central community.

Specifically, use the arrival population density table of each community to find the community with the highest arrival population density and mark it as the first central community; wherein use the LARGE. IF functions to find the community with the highest arrival population density, which is the first central community, the specific function is: B3=IF(B2=LARGE(B2:E2,1),B2,0), when B2 is equal to the maximum value of the second row. B3=B2, otherwise, B3=0, the result is: in row 3, only the first central community shows the true arrival population density, and the rest of the communities show die arrival population density as 0, as shown in Table 3.

Table 3: Finding the first central community Serial A B C D E 1 Arrival community Chen Village Zhang. Liu Village Li Village Village 2 Arrival population density B2 C2 02 E2 3 Find the first central =IF(B2=LAR GE(B2:E2,1) ,B2,0) =IF(C2=LAR GE(B2:E2,1), C2,0) =IF(02=LA RGE(B2:E2, 1),D2,0) =IF(E2=LAR GE(B2:E2,1), E2,0) community 4 Tag central =IF(B3=B2, B1,0) =IF(C3=C2, C1,0) =1F(03=02, 01,0) =IF(E3=E2,E 1,0) community 552. accordii g to the community distance matrix, selecting a service radius, identifying a life circle of the first central community.

Identify other communities served by the central community and classify them as a life circle, which is the life circle of the first central community, as follows: 1) Calculate the distance between the central commtmity and other communities When the number of central communities is small, it may be directly copied from the community distance matrix table.

When there is a certain number, the following methods are adopted: Combine the results of the row 4, identify thc community in the row 1 of the column whose value is 1; (1'4 Combine the community distance matrix of Table 2, use the community name identified in 0) to select column A of Table 2 and locate the corresponding row n; cj) B5 of Table 4 = Bn of Table 2, C5 of Table 4 = Cn of Table 2, and so on When the number is large, the means of using vb programming may be considered.

2) Identify the community with n the service radius RI of the first central community According to the population density of the central community, the service radius RI is selected, and other communities within the service radius 12.1 of the first central community are regarded as the service scope of the first central community.

Use the IF function in Excel to determine the relationship between the distance L between the first central community and other communities and the service radius RI of the first central community, and extract the community whose distance is less than the service radius RI, that is, the community that belongs to the life circle of the first central community:, as shown in Table 4.

Table 4: Identification of communities within the service radius of the first central community Seria A B C D E

I

1 Arrival Chen Village ZI.tang Village Liu Village Li Village m comunity 2 Arrival population density B2 C2 D2 E2 3 Find the first central -IF(B2=LAR GE(B2:E2,1) ,B2,0) -IF(C2=LAR GE(B2:E2,I), C2,0) -IF(D2=LA RGE(B2:E2, 1),D2,0) =I F(E2=LAR GE(B2:E2,I), E2,0) community 4 Tag central =I F(B3=B2,1,0) =I F(C3=C 2,1,0) =I F(D3=D2,1,0) =I F(E3=E2,1,0) community Calculate the L(Chen) L(Zhang) L(Liu) L(Li) distance between the central community and other communities 6 The first =IF(B5<RI, =IF(C5<RI, =IF(D5<RI, =IF(E5<RI, central community serves communities the central the central the central the central within radius community is community a, community is community a, community is community a, community is community a, R1 the central the central the central the central village is not community a) village is not community a) village is not community a) village is not community a) S53. elimina ing the identified communities, and repeating steps S51 to S52 for other communities it the area until all communities are classified into corresponding life circles.

Look for the second central community outside the sentce radius RI of the first central community, as shown in Table 5; Table 5: Finding a second central community Serial A B C D E 1 Arrival community Chen Village Zhang Village Liu Village Li Village 2 Arrival population density B2 C2 02 E2 3 Find the first -IF(B2=LAR -IF(C2=LAR -IF(D2=LA =I F(E2=LAR central community GE(B2:E2,1) ,B2,0) GE(B2:E2,1), C2,0) RGE(B2-E2, 1),D2,0) GE(B2:E2,1), E2,0) 4 Tag central AF(B3=B2,1,0) -IF(C3=C2,1,O) -IF(D3=D2,1,0) -IF(E3=E2,1,0) community Calculate the distance L(Chen) L(Zhang) L(Liu) L(Li) between the central community and other comm unities 6 The first =1F(B5<R1, =1F(C5<R1, =1F(D5<R1, =1F(E5<R1, central community serves the central the central the central the central comm unities within radius RI community is community a, community is community a, community is community a, community is community a, the central the central the central the central village is not community a) village is not community a) village is not community a) village is not community a) 7 Population =IF(B5<R,O, =IF(C5<R,O, C2) =IF(D5<R,O, D2) =IF(E5<R,O, E2) density of B2) comm unities outside.. rl from the first central community 8 Find a second central community According to the community distance matrix, select the service radius R2 to identify the life circle of the second central community; Look for a third central community outside the service radius R2 of thc second central community; According to the community distance matrix, select the service radius R3 to identify the life circle of the third central community; Look for the n-th central community outside the service radius Rn-1 of the n-1 -th central community; According to the community distance matrix, select the service radius Rn to identify the life circle of the nth central community.

An illustrative diagram of the central community found through the above search is shown in Figure 2 At this point, the first identification of the community life circle has been completed. An illustrative diagram is shown in Figure 3, and the identification results are shown in Table 6. The first central community in A(11: Combined with the results of the row 4, identify the community in row 1 of the column with the value 1. B01-E01 determines die community covered by the first central community service... BOn-E0n determines the community covered by the nth central community service.

Table 6: The results of the first life circle identification Serial A B C D E 1 Arrival Chen Village Zhang Liu Village Li Village community Village 2 Arrival population density B2 C2 D2 E2 3 Find the first central =IF(B2=LAR GE(B2:E2,1) ,B2,0) =IF(C2=LAR GE(32:E2,1), C2,0) =IF(D2=LA RGE(B2:E2, 1),D2,0) =IF(E2=LAR GE(B2:E2,1), E2,0) community 4 Tag central =IF(B3=B2,1,0) =IF(C3=C2,1,0) =IF(D3=D2,1,0) =IF(E3=E2,1,0) community Calculate the distance L(Chen) L(Zhang) L(Liu) L(Li) between the central community and other communities 6 The first =11(B5-cR1, =1F(C5,--R1, =_LF(D5<R1, =LF(E5-(R1, central community serves communities within radius RI the central the central the central the central community is community a, community is community a, community is community a, community is community a, the central the central the central the central village is not village is not village is not village is not community a) community a) community a) community a) 7 Population =IF(B5<R,0, 32) =IF(C5<R,0, C2) =IF(D5<R,0, D2) =IF(E5<R,0, E2) density of communities outside rl from the first central community 8 Find a second central community 00 Central Community Judging the general commimi service y covered by the central comn unity 01 Combined -IF(B5<R1, B1,0) -1E(C5<R1, C1,0) -IF(D5<R1, D1,()) -1F(E5<R1,E 1,0) with the results of row 4, identify the community in row 1 of the column with a value of 1, and fill in the community name in this position 02 Second Central Community =IF(B10<R2, 31,0) =IF(C10<R2, C1,0) =IF(D10<R2, D1,0) =IF(E10<R2, E1,0) O n nth central =I F(B(5n)<R n,B 1,0) =I E(C(5n)<R n,C1,0) =I F(D(5n)<R n,D1,0) =I F(E(5n)<R n,E1,0) community 56. the second identification of the community life circle.

There are two types of problems in one-time identification of life circles: 1) A considerable proportion of communities are included in two or more life circles at the same time; 2) Individual communities cannot be classified into any adjacent life circles because they are too far away. Therefore, it is necessary to carry out a second identification of life circles. This part mainly uses the inter-community population connection matrix to classify the communities included in two or more life circles into the life circles with closer connection between the people. In addition, if the distance is really too far, it is classified into the nearest life circle according to the principle of proximity.

Further, this step 56 specifically includes: 561. according to the mobile phone signaling data, using a pivot table, with a starting community as a row, a destination community as a column, a sum of a number of population coming and going as a value, generating a non-commuter OD connection matrix between communities.

Inter-community non-commuter OD connection refers to the estimated life travel and the number of people in each community in the non-commuting state. The mobile phone signaling data provided by China Mobile records the time and space trajectories and OD connections of users between communities. The study of non-commuter OD connections among communities is helpful to discover the temporal and spatial patterns of crowd activities, and to classify communities with close crowd connections into the same life circle, and to improve scientific life circle planning.

Select the trip data whose origin and destination are in the same town, and create "OD Connection Between Communities.xlsx". Using the pivot table, with the starting community as the row, the destination community as the column, and the sum of the number of people coming and going as the value, form the "Inter-community non-commuter OD connection matrix", as shown in Table 7.

Table 7: Inter-community non-commuter OD connection matrix Inter-community non-commuter OD connection matrix Serial A B C D E 1 Chen Village Zhang Village Liu Village Li Village 2 Chen Village 3 Zhang Village 4 Liu Village Li Village 562. according to the non-commuter OD connection matrix between communities, comparing a number of people in each community with non-commuter OD connections to different central communities, selecting a central community with a strongest non-commuter OD connection strength as the central community of each community, putting the central community of each community into a life circle of each community, completing a secondary identification of the life circle.

According to the method of step S5, some communities may be covered by the central area of multiple central communities, so the OD connection between the community and different centers is used to determine its unique belonging, as shown in Table 8.

Table 8: The second life circle identification Serial A B C D E 1 Arrival comm unity Chen Village Zhang Village Liu Village Li Village 000 Centra Second judging the general community covered by die central community

I

Comm unity service 001 -A01 =IF(B5<R1 Bn of Table 2,0') =I F(C5<R1' =IF(D5<R1, Dn of Table 2,0) =IF(E5<R1, En of Table 2,0)

Cii of Table 2,0)

002 =A02 =IF(B1O<R2, =IF(C1O<R2, IF(D1O<R2, Dn of Table 2,0) =IF(E10<R2, Bn of Table Ca of Table En of Table 2,0) 2,0) 2,0) 00n -A0n =IF(B(5n)<Rn, =IF(C(5n)<Rn =IF( D(5 n)<Rn, Dn of Table 2,0) -1 F(E(5n)<Rn, En of Table 2,0)

Bn of Table '

2,0) Ca of Table 2,0)

The specific operations are as follows: 1) A001 first central community: equal to A01.

2) B001-E001 calculate the non-commuter OD connection between the first central community and other communities: When the distance is less than the service range of the central community, the normal OD connection value is displayed, and the numerical calculation adopts the following methods: El) Combine the results of the row 4, identify the community in the row 1 of the column with a value of 1, Cig) Combine the community OD connection matrix in Table 2, use the community name identified in (i), select column A of Table 2 and locate the corresponding row n; (14) B001 of Table 8 = Bn of Table 2, C001 of Table 8 = Cn of Table 2, and so on When the distance is greater than the central community service range the OD connection is displayed as 0.

3) BOOn-E0On calculates the non-commuter OD connection between the n-th central community and other communities.

Compare die number of people with non-commuter OD connections between each community and different central communities, select the central community with the highest non-commuter OD connection strength as the central community of each community, and classify the central community of each community into the life circle of each community. The illustrative diagram of the second identification of the community life circle is shown in Figure 4.

It should be noted that although the method operations of the above-described embodiments are depicted in a particular order, this does not require or imply that the operations must be performed in that particular order, or that all illustrated operations must be performed to achieve the desired results. Conversely, the depicted steps may change the order of execution. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined to be performed as one step, and/or one step may be decomposed into multiple steps to be performed.

Embodiment 2: This embodiment is a specific application example, taking Guzhen Town, Zhongshan, Guangdong Province as the study object, based on the realization of the method for identifying a community life circle space in the above-mentioned Embodiment 1, and using the method of mobile phone signaling big data, a new method of identifying life circle is proposed from the perspective of the crowd movement OD relationship, which forms a complementary relationship with the traditional questionnaire and interview survey, and also improves the urban planning survey system.

1) Arrange the basic data of the community life circle in Guzlien Town 1.1) Arrange the construction land data of each community in Guzhen Town Load the polylinc in "Guzhen Town Construction Land CA D.dwg" into ArcGIS and save it as the shapefile "Guzhen Town Construction Land Line.shp".

Use the line-to-surface tool for "Guzhen Town Construction Land Line.shp" to generate "Guzhen Town Construction Land Surface.shp".

Use the fusion tool for "Guzhen Town Construction Land Surface.shp" to combine scattered construction land elements into one element to generate "Guzhen Town Construction Land_Fusion.shp".

Use the intersection tool to intersect "Guzhen Town Construction Land Fusion.shp" with "Guzhen Town Administrative Community Division.shp", divide the construction land by community, and assign the construction land "Community" text field to generate "Guzhen Town Each Community Construction Land.shp" file.

Open the attribute table "Guzhen Town Each Community Construction Land.shp", create a new "Area" text field, and use "Calculation Geometry" to calculate the construction land area of each community in Guzhen Town.

1.2) Arrange the mobile phone signaling data of each community in Guzhen Town Import the Guzhen Town base station table sheet in ArcGIS, right-click the layer to display the xy data, and export the "Guzhen Town Base Station.shp" file.

Use the intersection tools to intersect "Guzhcn Town Base Station.shp" and "Guzhen Town Administrative Community Division.shp", divide the base stations by community, assign the base station "Community" text field, and generate "Guzhen Town Community Base Stations.shp" file, and export the Excel table "Guthen Town Community-Base Station Comparison Table.xlsx" to convert the crowd OD data between base stations into crowd OD data between communities.

Import the crowd OD data between the base stations in Guzhen Town in Excel to obtain "Guzhen Town OD Connection Between Base Stations.xlsx"; open the "Guzhen Town Community-Base Station Comparison Table.xlsx", and use the vlookup function to convert crowd OD data between base stations into crowd OD data between communities, and saved as "Guzhen Town Inter-Community OD Connection s.xlsx".

Using the pivot table, take "Destination Community" as the row and "OD Number" as the value, count the number of arrival population in each community, and save it as "Guzhen Town Each Community Arrival Population.xl sx".

1.3) Arrange the population density of each community in Guzhen Town Import "Guzhen Town Each Community Arrival Population.xlsx" in ArcGIS, connect to "Guzhen Town Each Commtmity Construction Land.shp" based on the "Community" text field, and export "Guzhen Town Each Community Arrival Population Density.shp".

In the attribute table "Guzhen Town Each Community Arrival Population Density.shp", add a new text Field "Arrival Population Density", use the "Text Field Calculator", enter "=I OD population I/ Construction land area]", and obtain each arrival population density, as shown in Table 9.

Table 9: Arrival population density of each community in Guzhen Town Community Number of arrivals Community area (square kilometers) Arrival population density (person/square kilometer) Gusi Village 112 1.462619187 77 Qifang Village 597 0.990296628 603 Gnu Village 19939 2.194514558 9086 Gusan Village 23115 3.200981494 7221 Caosan Village 32511 4.610087253 7052 Caoyi Village 32808 2.232523862 14695 Gangnan Village 35518 4.545823223 7813 Guyi Village 44523 3.000579217 14838 Caoer Village 62937 5.394596595 11667 Haizhou Village 63747 16.71596229 3814 Liufang Village 65623 2.193381814 29919 Gangdong Village 66511 4.188872281 15878 Export "Guzhen Town Each Community Arrival Population Densitv.xlsx", transpose the table, and make the text field name a row, as shown in Table 10.

Table 10: The arrival nonulation density of each villa e community in Guzhen Town Communit Gus Qifa Gue Gus Cao Cao Gan Guy Cao Hai Liuf Gan Y i ng r an san yi gna i er zho ang gdo Vill Vill Vill Vill Vill Vill n Vill Vill u Vill ng age age age age age age Vill age age age Vill age age Vill age Arrival population density 77 603 908 722 705 146 781 148 116 381 299 158 (person/sq uarc 6- 1 2 95 3 38 67 4 19 78 kilometer) 1.4)Create a distance matrix between communities in Guzhen TOWII Use the polygon-to-point tool in AreGIS to use the "Guzhen Town Each Community Construction Land.shp" to generate "Guzhen Town Each Community Construction Land Centers.shp".

Use the "Analysis Tools-Neighborhood Analysis-Nearby Analysis" tools, the input elements and adjacent elements are set to "Guzhen Town Each Community Construction Land Centers.shp", calculate the distance between the centers, and generate the "Guzhen Town Each Community Construction Land Center Distance.shp" file.

Insert a "pivot table" in Excel, the starting point as the row, the end point as the column, and the average distance as the value, and the distance matrix between centers is generated, which is used as the community distance matrix, as shown in Table 11.

Table 11: Guzhen Town Community D stance Matrix Cao er Vill age Cao san Vill age Cao yi Vill age Gan Gan gnan Vill age Gue r VIII age Gus an Vill age. Guy Hail'. Littl ang Vill age Qifa rig Vill age gdo __ Gust. . - hou Vill age "g Vill VI" i age age Vill age Cao 0 1.97 957 124 3 1.45 3.47 ' 4-51 2.99 3'65 5.06 ' 2.35 ' 491 3 55 432 er ' '' 39 930 545 787 963 102 -- '_:' '' PP6 -- - 5 9 1 307 6 960 Vill 2 3 7 age Cao san Vill age 1.97 957 1243 0 2.69 2.32 3.44 3.67 4.11 4. 90 _. _ 6.64 - 3.17 2.61 073 5 544 6 087 5 491 4 597 3 155 P9P 542 083 8 823 9 3 8 Cao yi 1.45 2.69 0 3.08 3.91 1.58 2.27 3.85 1.19 3.99 2.61 4.30 073 589 241 153 453 656 904 106 135 831 Vill age 152 496 1 2 6 8 8 5 2 6 3 Gan gdo ng Viii age 3 47 ' 232 544 624 8)89 _1.12 339 2 2.93 2.95 2.93 2.16 6.76 1.38 1.42 390 1 3.08 038 3 462 273 3 435 3 098 873 2 950 8 028 0 5 5 Gan gnan Viii age 4'51 930 481 7 3.44 3.91 1.12 0 3.32 3.09 2.39 2.81 7.24 1.51 1.76 087 241 339 179 692 191 006 152 673 862 452 2 2 7 3 9 1 4 7 8 Gue r Vill age 2.99 3 67 1.58 2.93 3.32 0 0'70 2 44 _. 0 96 ' 3 91 1 80 ' 4.35 _. _ - ' 153 038 3 179 907 _)99 204 - *- 562 230 )4.) 491 6 7) 986 2 2 909 44 6 Gus an Vill age 3.65 4-11. 2.95 3.09 0.70 0 1.79 1.42 4-25 1.63 4-37 787 597 2.27 462 _ 692 907 404 2 442 459 279 768 311 453) 3 5 5 1 7 9 4 8 Gusi 0 4 90 3._85 2-93 2.39 0 0 0 2.72 0 1.73 0 Viii 1,5-5 6)6 273 191 764 113 age 90--8 8 3 9 7 Guy 2' 35 2,72 ' 1.19 2.16 2'81 0 96 1.42 2.72 0 4.59 1.42 3.53 - 102 595 904 _ 435 3 006 1 - 442 _ 764 977 217 906 Viii age 033 L--) - - ) 204) 9 5 3 klaiz 4.91 6'64 542 282 1 3 99 6.76 7.24 3.91 4.25 5.87 4.59 0 5.72 8.13 hou 307 - ' 098 - 152 986 459 1 412 2 977 495 301 1 Viii age 334 106 5 4 6 9 7 8 2 Liuf ang Viii age 3.55 556 568 2 3-17 2.61 1.38 1.51 1,80 1.63 1.73 1.42 5.72 0 2.77 083 135 873 2 673 56'2 279 1'13 217 _ 495 7 652 7 764 6 7 2 - ) 8 7 Qifa ng Viii age 4.32 2.61 4.30 1.42 1.76 4.35 4.37 4.10 3.53 8.13 2.77 0 960 823 831 950 8-- 862 230 768 665 906 301 1 652 658 826 3- 8 2 7 9 3 7 9 8 1.5)Create a non-commuter OD connection matrix between communities in Guzhen Town Open the "Guzhen Town Inter-Community OD Comiections.xlsx"" use the pivot table, take the starting community as the row, the destination community as the column, and the sum of the OD population as the value to form the "Guzhen Town Community Non-Commuter OD Connection Matrix", as shown in Table 12 shown. ;Table 12: Guzhen town community non-commuter OD connection matrix Cao cr Vill age Cao san Viii age Cao yi Vill age Gan gdo Gan grum Viii age Gue r Viii age Gus an Vill age. Guy Haiz hou Viii age Liuf ang Viii age Qifa ng Viii age ng Gust -Viii VIII Viii age age age Cao cr Viii age 256 321 314 618 441 271 1 131 254 5 185 37 0 2 8 2 8 Cao 311 123 140 287 250 227 14 921 379 177 11 sari 8 4 5 6 Viii age Cao yi Viii age 395 152 2 148 281 518 492 4 218 0 125 166 20 7 9 5 7 Gan gdo rig Vill age 304 126 118 299 3 9 69 - 110 24 268 4 163 3 602 5 5 7 6 1 4 1 ' Gan gnan Viii age 109 615 449 488 0 366 390 12 126 4 767 377 1 51 7 -Gue r Vill age i41 - 257 504 104 284 193 0 1 231 0 382 125 4 6 Gus an Vill age 523 273 415 158 341 186 4 3 132 2 101 5 201 4 24 Gusi 0 20 A) 16 2 0 0 6 0 3 0 Vill age Guy i Vill age 189 1 106 4 209 428 115 268 9 1380 _ 977 478 28 4 4 5 A, 6 Haiz hou Vill age 260 399 - 135 _ 237 276 376 447 3 714 835 108 7 5 6 Liuf ang Viii age 195 4 176 9 151 647 251 1 985 160 6 361 2 123 42 2 - - 6 8 Qifa ng Viii age 22 1 3 18 29 22 2 3 1 38 11 2) The first life circle identification 2.1) First find the community with the highest population density as the central community "Liufang Village", that is, the first central community (town center), and then, according to the density of 29,918 persons/square kilometer in "Liufang Village", 2km in Table 13 is selected as the service radius to divide Gangdong Village, Gangnan Village, Guer Village, Gusan Village, Gusi Village, Guyi Village, Liufang Village as other communities covered by the central community. ;2.2) In the remaining communities 2.0km away from Liufang Village, the second central community "Caoyi Village" was found. According to its density of 146,958 persons/square kilometer, 2km in Table 13 is selected as the service radius to divide Caoer Village, Caoyi Village and Guyi Village as general commtmities covered by its services. ;2.3) In the remaining communities 2.0km away from Caoyi Village, the third central community "Caosan Village" was found. According to its density of 7,052 persons/square kilometer, 3km in Table 13 is selected as the service radius to divide Caoer Village, Caosan Village, Caoyi Village, Gangdong Village, Guyi Village and Qi fang Village as general communities covered by its services. ;2.4) In the remaining communities 3.0km away from Caosan Village, the fourth central community "Haizhou Village" was found. According to its density of 3,813 persons/square kilometer, 4km in Table 13 is selected as the service radius to divide Caoer Village, Caosan Village, Caoyi Village, Gangdong Gangnan Village, Guer Village, Gusan Village, Gusi Village, Guyi Village, Liufang Village, Qilang Village as general communities covered by its services. So far, the first community' life circle has been completed. Identification, Table 14 shows the actual operation process of finding the central community, and Table 15 is the identification results of the first life circle. ;Table 13 The value standard of the service radius of life circles standard Arrival population density Life circle radius Life circle identification radius R (km) (person/km') (kin) ) 1 12000 1.0 2.0 2 6000 1.5 3.0 3 3000 2.0 4.0 Remarks: Standard 1 is based on the average value of the community in the central urban area of Zhongshan, with an average community area of 4.2 square kilometers and a travel destination population density of 11,800 people per square kilometer; Standard 2 is the average value of villages and towns in Zhongshan, with an average area of 7.0 square kilometers mid a travel destination population density of 5,880 people per square kilometer; Standard 3 is based on the average value of the eastern and western groups of villages mid towns with low urbanization in Zhongshan, with an average area of 9.6 square kilometers and a travel destination population density of 2,792 persons per square kilometer. ;Table 14: Looking for the first ce Ural community Guzhen Arrivalcommunity Cao er Vill age Cao san Vill age Cao yi Vill age Gan gdo lig Vill age Gan gna n Vill age Guc r Vill age Gus an Vill age Gus i Vill age Guy Hai zho uVill age Lit& ang Vill age Qir ang Vill age ;- ;Vill age Arrival population density 116 705 146 158 781 908 722 76.5 148 381 299 602. ;66.7 2.14 95.5 78 3.33 5.84 1.22 75 38.1 3.54 18.6 85 1. Find the 0 0 0 0 0 0 0 0 0 0 299 0 first central 18.6 community 4 2. Mark the 0 0 0 0 0 0 0 0 0 0 1 0 first central comm unity 4. Calculate the 3.55 3.17 2,61 1.38 1,51 1.80 163. 1.73 1.42 5.72 0 2.77 coverage of 556 083 8 135 6 873 2 673 7 562 2 279 113 7 217 495 7 652 7 the first 6 5 central community 5. Calculate nodes 116 705 146 0 0 0 0 0 0 381 0 602. ;outside the 66.6 2.14 95.4 3.54 849 7 coverage area 7 4 8 1 6. Find a 0 0 146 0 0 0 0 0 0 0 0 0 second central 95.4 8 community 7. Tag the 0 0 1 0 0 0 0 0 0 0 0 0 second central community 9. Calculate 1.45 2.69 0 3.08 3.91 1.58 2.27 3.85 1.19 3.99 2.61 4.30 the second 030 073 589 1 241 2 153 453 656 904 5 106 135 831 central community coverage 2 5 6 8 8 2 6 3 10. 0 705 0 0 0 0 0 0 0 381 0 602. ;Compute nodes 2.14 3.54 1 849 outside of 4 7 coverage 11. Find a 0 705 0 0 0 0 0 0 0 0 0 0 third central community 2.14 12. Mark 0 1 0 0 0 0 0 0 0 0 0 0 the third central community 14. 1.97 0 2.69 2.32 3.44 3.67 4.11 4,90 *j_. 272. 6,64 ' 3.17 2.61 Calculate 957 1 073 544 087 491 4 597 3 1:.0 595 542 7 ' 823 the 5 6 5 9 3 083 8 coverage of 8 the third central community 15. Compute nodes 0 0 0 0 0 0 0 0 0 381 0 0 outside of 3.54 1 coverage 16. Find a 0 0 0 0 0 0 0 0 0 381 0 0 fourth central 3.54 1 comm unity 17. Mark 0 0 0 0 0 0 0 0 0 1 0 0 the fourth central community 18. Calculate 4.91 6.64 3.99 6.76 7.24 3.91 4.25 5.87 4.59 0 5.72 8.13 the 307 542 3 106 098 5 152 986 459 1 412 2 977 495 301 1 coverage of 3 2 4 6 9 7 the fourth central community Table 15. Identification results of the first life circle in Guzhen Town Cent ral cam Community within the life circle mun it Liuf ang Viiiage 0.0 0 Gan Gan gnan Viii age Viii Gus. Guy 0 Liuf ang Viii age 0 gdo ng Gue r an Gusi. . -age Viii age. VIII i VI11 age Viii age age Cao yi Viii age Cao 0 Cao yi Viii age 0 0 Gue 0 0 Guy 0 0 0 " Viii r i Viii age VI.11 age age Cao Cao Cao Cao Gan gdo ng Vill age 0 0 0 0 Guy 0 0 Qin ng Viii age san er san yi i \Till vitt yin yin yin age age age age age Haiz hou vill Cao yi Viii age Gue r Viii age 0 0 0 Haiz hou Viii age 0 0 age 3) The second hfe c cle identification According to the Guzhen Town community non-commuter OD connection matrix in Table 12, a judgment table for the second identification of life circles is made, as shown in Table 16.

Table 16: Second identification of the life circles in Guzhen Town Cao er Vill age Cao san Viii age Cao yi Vill age Gan gdo ng Viii age Gan gnan Vill age Guc r Vill age Gus an Vill age. Guy Hait hou Vill age Liu!' ang Vill age Qifa ng Vill age Gust. . -VIII i age Vill age Liuf ang Viii age 0 0 0 6025 3771 1254 2014 3 4786 0 0 0 Cao 321 2 0 0 0 0 504 0 0 2094 0 0 0 yi Viii age Cao san Viii age 256 0 0 1522 1261 0 0 0 0 1064 0 0 1 Haiz hou Vill age 0 0 1255 0 0 382 0 0 0 0 0 0 Select the communities covered by mul iple life circles. According to the number of non-commuters between each community and different cc ural communities, select the central community with the greatest connection as the central community of each community, and classify the central community with the largest connection into the life circle of each community, and the second identification of the life circle is completed, as shown in Table 17.

Table 17: Identification results of the second life circle in Guzhen Town life circle Central Community community name Liufang Life Circie Liufang Village Liufang Village Liufang Life Circle Liufang Village Gangdong V Ilage Liufang Life Circle Liufang Village Gangnan Village Liufang Life Circle Liufang Village Guer Village Liufang Life Circle Liufang Village Gusan Village Liufang Life Circle Liufang Village Gusi villages Liufang Life Circle Liufang Village Guyi village Caoyi life circle Caoyi Village Caoyi Village Caoyi life circle Caoyi Village Caoer Village Caosan life circle Caosan Village Caosan Village Caosan life circle Caosan Village Qifang Village Haizhou Life Circle Flaiihou Village Haizhou Village In this embodiment, by using the mobile phone signaling data to analyze the population travel situation among the communities, combined with the traditional life circle analysis theory, the central community is Found according to the population aggregation degree of lire circle, and the service radius is set, and then the Guzhen Town is divided into four big life circles. Compared with the traditional life circle analysis, the analysis efficiency is greatly improved, and the basis for the delineation of the life circle is provided from another perspective, which enriches the study methods of the life circle.

Embodiment 3: As shown in Figure 5, this embodiment provides a community life circle space identification system. The system includes an extraction module 501, an arranging module 502, a calculation module 503, a first generation module 504, a search module 505, a first identification module 506, and a second identification module 507. The specific functions of each module are as follows: The extraction module 501 used to extract mobile phone signaling data and construction land data.

The arranging module 502 used to arrange the construction land data into construction land data of each communi iv, The calculation module 503 used to, according to the mobile phone signaling data and the construction land data of each community, calculate an arrival population density of each community.

The first generation module 504 used to, according to the construction land data of each community, obtain a construction land center of each community, generate a distance matrix between the centers as a community distance matrix.

The search module 505 used to, according to the arrival population density of each community, find a community with a highest arrival population density as a current central community.

The first identification module 506 used to, according to the community distance matrix, select a service radius, identify a life circle of the current central community.

The second identification module 507 used outside the senrice radius of the current central community, to find a new central community, use the new central community as the current central community, return to the community distance matrix, select a service radius, identify the life circle of the current central community until all communities are classified into corresponding life circles.

Further, the community lire circle space identification system of this embodiment may further include: A second generation module 508 used to, according to the mobile phone signaling data, use a pivot table, with a starting community as a row, a destination community as a column, a sum of a number of population coming and going as a value, generate a non-commuter OD connection matrix between communities.

A third identification module 509 used to, according to the non-commuter OD connection matrix between communities, compare a number of people in each community with non-commuter OD connections to different central communities, select a central community with a strongest non-commuter OD connection strength as the central community of each community, put the central community of each community into a life circle of each community, complete a secondary identification of the life circle.

Further, the arranging module 502, as shown in Figure 6, specifically includes: A first generating unit 5021 used to load construction land CAD graphics, generate construction land line graphics according to the construction land CAD graphics; A second generating unit 5022 used to use line-to-surface tools to generate construction land surface graphics from the construction land line graphics; A third generating unit 5023 used to use fusion tools, combine scattered construction land elements in the construction land surface graphics into one element to generate construction land fusion graphics; A fourth generating unit 5024 used to use intersection tools, intersect the construction land fusion graphics with administrative community division graphics, divide a construction land according to communities, assign construction land community text fields, generate construction land graphics of each community; A first calculation unit 5025 used to, according to the construction land graphics of each community, calculate a construction land area of each community.

Further, the calculation module 503, as shown in Figure 7, specifically includes: A counting unit 5031 used to, according to the mobile phone signaling data, count a number of arrival population in each community, and obtain a table of the number of arrival population in each community.

An input unit 5032 used to link the table of the number of arrival population in each community to construction land graphics of each community to obtain a arrival population density graphics of each comm uni ty.

A second calculation unit 5033 used to, according to the arrival population density graphics of each community, dividing an OD population by a construction land area to obtain the arrival population density of each community.

Further, the lirst generation module 504, as shown in Figure 8, specifically includes: A fifth generating unit 5041 used to use surface-to-point tools, generate construction land center graphics of each community from construction land graphics of each community.

A third calculation unit 5042 used to, according to the construction land center graphics of each community, calculate distance beftveen two centers.

A sixth generating unit 5043 used to use a pivot table, with a starting point as a row, an end point as a column, an average distance as a value, generate the distance matrix between the centers as the community distance matrix.

It should be noted that, the system provided in this embodiment only takes the division of the above-mentioned functional modules as an example. In practical applications, the above-mentioned function allocation may be completed by different functional modules according to requirements, that is, the internal structure is divided into different functional modules to complete all or part of the functions described above.

It may be understood that the terms "first", "second", etc. used in the above devices may be used to describe various modules, but these modules are not limited by these terms. These terms are only used to distinguish the first module from another. For example, without departing from the scope of the present invention, the first identification module may be referred to as a second identification module, and similarly, the second identification module may be referred to as a first identification module. Both the first identification module and the second identification module are identification modules, but not the same identification module.

Embodiment 4: This embodiment provides a computer device, which may be a computer, as shown in Figure 9, which includes a processor 902, a memory, an input device 903, a display 904 and a network interface 905 connected through a system bus 901. The processor is used to provide computing and control capabilities. The memory includes a non-volatile storage medium 906 and an internal memory 907. The non-volatile storage medium 906 stores operating systems, computer programs and databases. The internal memory 907 provides an environment for the operation of the operating system and computer programs in the nonvolatile storage medium. When the processor 902 executes the computer program stored in the memory, the method for identifying the community life circle space of the above-mentioned Embodiment 1 is implemented as follows: extracting mobile phone signaling data and construction land data; arranging the construction land data into construction land data of each community; according to die mobile phone signaling data and the construction land data of each community, calculating an arrival population density of each community; according to the construction land data of each community, obtaining a construction land center of each community, generating a distance matrix between the centers as a community distance matrix; according to the arrival population density of each community, finding a community with a highest arrival population density as a current central community; according to the community distance matrix, selecting a service radius, identifying a life circle of the current central community; outside the service radius of the current central community, finding a new central community, using the new central community as the current central community, returning to the community distance matrix, selecting a service radius, identifying the life circle of the current central community until all communities arc classified into corresponding life circles.

Further, the method may also include: according to the mobile phone signaling data, using a pivot table, with a starting community as a row, a destination community as a column, a stun of a number of population coining and going as a value, generating a non-commuter OD connection matrix between communities; according to the non-commuter OD connection matrix between communities, comparing a number of people in each community with non-commuter OD connections to different central communities, selecting a central community with a strongest non-commuter OD connection strength as the central community of each community, putting the central community of each community into a life circle of each community, completing a secondary identification of the life circle.

Embodiment 5: This embodiment provides a storage medium. The storage medium is a computer-readable storage medium and stores a computer program. When the computer program is executed by a processor, the method for identifying a community life circle space of the above-mentioned Embodiment I is implemented, as follows: extracting mobile phone signaling data and construction land data; arranging the construction land data into construction land data of each community: according to the mobile phone signaling data mid the construction land data of each community, calculating an arrival population density of each community; according to the construction land data of each community, obtaining a construction land center of each community, generating a distance matrix between the centers as a community distance matrix; according to the arrival population density of each community, finding a community with a highest arrival population density as a current central community; according to the community distance matrix, selecting a service radius, identifying a life circle of the current central community: outside the service radius of the current central community, finding a new central community, using the new central community as the current central community, returning to the community distance matrix, selecting a service radius, identifying the life circle of the current central community until all communities are classified into corresponding life circles.

Further, the method may also include: according to the mobile phone signaling data, using a pivot table, with a starting community as a row, a destination community as a column, a sum of a number of population coming and going as a value, generating a non-commuter OD connection matrix between communities; according to the non-commuter OD connection matrix between communities, comparing a number of people in each community with non-commuter OD connections to different central communities, selecting a central community with a strongest non-commuter OD connection strength as the central community of each community, putting the central community of each community into a life circle of each community, completing a secondary identification of the life circle.

The storage medium in the above embodiment may be media such as a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), a U disk, a mobile hard disk etc. In summary, the present invention is based on mobile phone signaling data information processing technology, and uses the crowd trajectory data generated by mobile phone signaling at the technical level to identify the vitality center of the community, and identify the community life according to the accessibility range and accessibility characteristics. It is suitable for urban planners to use; compared with the prior art, the present invention emphasizes the accessibility of residents' daily life, so the most oriented non-commuting connection is used to divide the space range of die life circle community.

The above is only preferred embodiments of the present invention patent, but the protection scope of the present invention patent is not limited to those. Any equivalent replacement or change made by a person skilled in the art who is familiar with the technical field and within the scope disclosed by the present invention patent, according to the technical solution and the inventive patent concept of the present invention patent, all belong to the protection scope of the patent of the present invention.

Claims (10)

1. Claims 1. A method for identifying a community life circle space, characterized in that, the method includes: extracting mobile phone signaling data and construction land data; arranging the construction land data into construction land data of each community; according to the mobile phone signaling data and the construction land data of each community, calculating an arrival population density of each community; according to the construction land data of each community, obtaining a construction land center of each community, generating a distance matrix between the centers as a community distancc matrix; according to the arrival population density of each community, finding a community with a highest arrival population density as a current central community; according to the community distance matrix, selecting a service radius, identifying a life circle of the current central community; outside the service radius of the current central community, finding a new central community, using the new central community as the current central community, returning to thc community distance matrix, selecting a service radius, identifying the life circle of the current central community until all communities are classified into corresponding life circles.
2. 2. The method for identifying a community life circle space according to claim 1, characterized in that, the step of arranging the construction land data into construction land data of each community specifically includes: loading construction land CAD graphics, generating construction land line graphics according to the construction land CAD graphics; using line-to-surface tools to generate construction land surface graphics from the construction land line graphics; using fusion tools, combining scattered construction land elements in the construction land surface graphics into one clement to generate construction land fusion graphics; using intersection tools, intersecting the construction land fusion graphics with administrative community division graphics, dividing a construction land according to communities, assigning construction land community text fields, generating construction land graphics or each community; according to the construction land graphics of each community, calculating a construction land area of each community.
3. 3. The method for identifying a community life circle space according to claim 1, characterized in that, the step of, according to the mobile phone signaling data and the construction land data of each community, calculating an arrival population density of each community, specifically includes: according to the mobile phone signaling data, counting a number of arrival population in each community, and obtaining a table of the limber of arrival population in each community; Finking the table of the number of arrival population in each community to construction land graphics of each community to obtain a arrival population density graphics of each community; according to the arrival population density graphics of each community, dividing an OD population by a construction land arca to obtain the arrival population density of each community.
4. 4. The method for identifying a community life circle space according to claim 3, characterized in that, the step of, according to the mobile phone signaling data, counting a number of arrival population in each community, and obtaining a table of the number of arrival population in each community, specifically includes: according to thc mobile phone signaling data, generating base station graphics; using intersection tools, intersecting the base station graphics with administrative community division graphics, dividing base stations by communities, assigning base station community text fields, generating base station graphics of each community with community labels; converting crowd OD data between base stations into crowd OD data between communities; according to the crowd OD data between communities, counting the number of arriving population in each community, obtaining the table of the number of arriving population in each community.
5. 5. The method for identifying a community life circle space according to claim 1, characterized in that, the step of, according to the construction land data of each community, obtaining a construction land center of each community, generating a distance matrix between the centers as a community distance matrix, specifically includes: using surface-to-point tools, generating construction land center graphics of each community from construction land graphics of each comm unity; according to the construction land center graphics of each community, calculating distance between two centers; using a pivot table, with a starting point as a row, an end point as a column, an average distance as a value, generating the distance matrix between the centers as the community distance matrix.
6. 6. The method for identifying a community life circle space according to any one of claims 1-5, characterized in that, the method further includes: according to the mobile phone signaling data, using a pivot table, with a starting community as a row, a destination community as a column, a sum of a number of population coming and going as a value, generating a non-commuter OD connection matrix between communities; according to the non-commuter OD connection matrix between communities, comparing a number of people in each community with non-commuter OD connections to different central communities, selecting a central commtmity with a strongest non-commuter OD connection strength as the central community of each community, putting the central community of each community into a life circle of each community, completing a secondary identification of the life circle.
7. 7. A community life circle space identification system, characterized in that, the system includes: an extraction module used to extract mobile phone signaling data and construction land data; an arranging module used to arrange the construction land data into construction land data of each community; a calculation module used to, according to the mobile phone signaling data and the construction land data of each community, calculate an arrival population density of each community; a first generation module used to, according to the construction land data of each community, obtain a construction land center of each community, generate a distance matrix between the centers as a community distance matrix; a search module used to, according to the arrival population density of each community, find a commtmity with a highest arrival population density as a current central community; a first identification module used to, according to the community distance matrix, select a service radius, identify a life circle of the cumin central community; a second identification module used outside the service radius of the current central community, to find a new central community, use the new central community as the current central community, return to the community distance matrix, select a service radius, identify the life circle of the current central community until all communities are classified into corresponding life circles.
8. 8. The community life circle space identification system according to claim 7, characterized in that, the system further includes: a second generation module used to, according to the mobile phone signaling data, use a pivot table, with a starting community as a row, a destination community as a column, a sum of a number of population coming and going as a value, generate a non-commuter OD connection matrix between communities; a third identification module used to, according to the non-commuter OD connection matrix between communities, compare a number of people in each community with non-commuter OD connections to different central communities, select a central community with a strongest non-commuter OD connection strength as the central community of each community, put the central community of each community into a life circle of each community, complete a secondary identification of the life circle.
9. 9. A computer device comprising a processor and a memory for storing a program executable by the processor, characterized in that, when the processor executes the program stored in the memory, the method for identifying a community life circle space of any one of claims 1-6 is implemented.
10. 10. A storage medium storing a program, characterized in that, when the program is executed by a processor, the method for identifying a community life circle space according to any one of claims 1-6 is implemented.