WO2021143090A1 - 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 living circle space identification method, system, computer equipment and storage medium Technical field

The invention relates to a method, system, equipment and medium for identifying the space of a community life circle, and belongs to the field of quantitative measurement of the community life circle.

Background technique

The concept of "living circle" originated from the basic settlements 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 space or behavior space formed by residents taking their homes as the center to carry out shopping, leisure, commuting (learning), social communication and medical services (Aitken, 1988; Algers, 2005; Tian) , 2018). Compared with the economic connection of the general regional network, the living circle reflects the interactive relationship between the living space unit of the residents and the actual life of the residents, and the dynamic relationship between the supply of facilities and the needs of the residents from the perspective of residents' lives. As far as planning practice is concerned, the significance of the identification of community living circle space is to optimize the allocation of public service facilities (Zhang Neng, Wu Tinghai, Lin Wenqi, 2011; Geng Hong, Xu Jinhua, Zhang Yi, 2013) to improve the efficiency of public service rationing and the people’s public The standard of living can truly match the supply and demand of public services. At present, due to the lack of a scientific and effective method of dividing life circles, life circle planning is difficult to directly participate in the practice of urban and rural planning, and it is more like a way of post-planning evaluation.

The identification of community life circle is the study of urban and rural spatial structure from the perspective of crowd connection. It is based on the characteristics of residents’ travel and temporal and spatial distribution. Areas with similar characteristics are divided into the same planning unit, so as to ensure that people in the same planning unit have similarities. The habit of using public service facilities has effectively improved the use efficiency of public service facilities. Although the new version of "Urban Residential District Planning and Design Standards (GB50180-2018)" does not emphasize the division of spatial boundaries, it still replaces the original hierarchical model with living circle residential areas with different travel time distances, and gives the population and number of sets standards for reference. ; Shanghai, Guangzhou and other places are also currently implementing urban living circle residential area planning, and exploring the improvement of community life quality based on this (Li Meng, 2017; Cheng Rong, 2018; Yu Yifan, 2019); Chengdu also develops according to planning needs Qualitative division of multiple types of community life circles.

At present, in principle, the division of living circles is usually a bottom-up spatial organization based on the wishes of residents. Therefore, most of the classification methods in the research are from the perspective of the demand side, according to the principle of dividing the living circle, adopting the method of residents’ willingness survey, randomly issuing questionnaires to survey the time that urban and rural residents are willing to spend in order to obtain public service facilities. Determine the radius of the living circle at all levels. Sun Defang (2012) and others took Pizhou City, Jiangsu Province as an example. Through a willingness survey, the time cost that residents are willing to pay for public services such as education, medical care, culture and entertainment, etc., was used to determine the best time interval and construct the county. Life circle system. Part of the research divides the life circle based on the characteristic structure of the population. For example, Zhu Chasong (2010) and others took the Xiantao city area as an example, and divided the living circle into different levels and types according to residents' travel distance, travel mode, demand frequency and service radius; Chai Yanwei (2015) et al. introduced in detail Planning the living circle of different regional spatial scales, and based on GPS travel trajectory, using time-space behavior analysis method to explore the scope of urban community living circle. Regarding the identification technology of the living circle, Jiang Ming (2015) divides the county living circle level based on the topography, hydrogeology, resource conditions, and economic development level, taking the best distance for villagers to obtain public service facilities as the radius; Wang Shaobo (2015), Zhou Xinxin, Wang Peizhen, Yang Fan and others (2016), Tian (2018) also use GIS technology to explore the spatial division of living circles; but the above methods of identifying and dividing living circles rely more on the disciplines of urban planners. Quality, the empirical identification and judgment based on comprehensive consideration of various information, does not have a considerable degree of operability, and it is difficult to be reproducible like quantitative analysis.

Starting from the behavior and activities of residents to study the urban spatial structure, traditional techniques are either based on a small number of sample surveys, or based on statistical data such as the census. China's mobile phone users have reached 1.42 billion in 2018, and the huge mobile phone user base provides a large number of data sources for all kinds of data collection. The mobile phone signaling data is a 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 of using urban space, it can be used to study the urban spatial structure and planning practice by integrating the temporal and spatial laws 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 can be used as an important supplement to the existing planning data collection technology, and it provides good data support for the extraction of the characteristics of residents' travel time and space distribution.

The emergence of all kinds of new data in recent years has brought new opportunities for urban and rural planning technology, and new technologies such as the use of mobile phone signaling data have gradually received attention in the planning field. For example, Niu Xinyi (2019) and others used cell phone signaling data to conduct research on Shanghai's public service consumption behavior, Liu Qiang (2018) used cell phone signaling to conduct spatial analysis of urban living circles; Gong Yongxi (2018), Yang Junyan (2019), etc. People have developed a patented technology for dividing living circles based on positioning data. Since mobile phone signaling data has the advantages of convenient and low-cost acquisition, and can generate aggregated data of different time periods and different spatial scales, the OD modeling of non-commuting contacts based on base station data realizes the spatial division of community living circles, which can not only support the city The layout planning of residential areas and villages can also realize the targeted allocation of public services, but it lacks 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 method, system, computer equipment and storage medium for identifying the space of the community living circle, which realizes the spatial division of the community living circle according to the mobile phone signaling data and the construction land data of each community. Identifying the life circle based on daily travel can be closer to the concept of the life circle.

The first object of the present invention is to provide a method for identifying the space of a community living circle

The second object of the present invention is to provide a space identification system for community living circles.

The third object of the present invention is to provide a computer device.

The fourth object of the present invention is to provide a storage medium.

The first objective of the present invention can be achieved by adopting the following technical solutions:

A method for identifying the space of a community living circle, the method comprising:

Extract mobile phone signaling data and construction land data;

Organize the construction land data into the construction land data of each community;

Calculate the arrival population density of each community based on the mobile phone signaling data and the construction land data of each community;

According to the construction land data of each community, obtain the construction geological center of each community, and generate the distance matrix between the centroids as the community distance matrix;

According to the arrival population density of each community, find the community with the highest arrival population density as the current central community;

According to the community distance matrix, select the service radius and identify the living circle of the current central community;

Outside the service radius of the current central community, look for a new central community, take the new central community as the current central community, return to the community distance matrix, select the service radius, and identify the life circle of the current central community until all communities are included The corresponding life circle.

Further, the sorting of construction land data into construction land data of each community specifically includes:

Load the CAD graphics of the construction land, and generate the construction ground line graphics according to the CAD graphics of the construction land;

Use the line-to-surface tool to generate the ground line graphics for construction into ground graphics for construction;

Use fusion tools to combine scattered construction land elements in construction ground graphics into one element to generate construction land fusion graphics;

Use the intersection tool to intersect the construction land fusion graphics with the administrative community division graphics, divide the construction land by communities, and assign the construction land community fields to generate the construction land graphics for each community;

Calculate the construction land area of each community according to the construction land graph of each community.

Further, the calculation of the arrival population density of each community based on the mobile phone signaling data and the construction land data of each community specifically includes:

According to the mobile phone signaling data, count the arrival population of each community, and obtain the arrival population table of each community;

Connect the arrival population number table of each community to the construction land graph of each community to obtain the arrival population density graph of each community;

According to the arrival population density graph of each community, divide the OD number by the construction land area to obtain the arrival population density of each community.

Further, the statistics of the arriving population of each community based on the mobile phone signaling data to obtain a table of the arriving population of each community specifically includes:

Generate base station graphics according to mobile phone signaling data;

Use the intersection tool to intersect the base station graphics with the administrative community division graphics, divide the base stations into communities, give the base station community fields, and generate community base station graphics with community tags;

Convert crowd OD data between base stations into crowd OD data between communities;

According to the population OD data between communities, the arrival population of each community is counted, and the arrival population number table of each community is obtained.

Further, according to the construction land data of each community, obtaining the geological center for construction of each community, and generating a distance matrix between centroids, as a community distance matrix, specifically includes:

Use the surface-to-point tool to generate the geological core graphics for the construction of each community from the construction land graphics of each community;

Calculate the distance between the centroids according to the geological center graphics used in the construction of each community;

Using the pivot table, the starting point is the row, the ending point is the column, and the distance average value is the value to generate the distance matrix between centroids, which is used as the community distance matrix.

Further, the method further includes:

According to the mobile phone signaling data, use the pivot table to generate the non-commuter OD contact matrix between communities with the starting community as the row, the ending community as the column, and the sum of the number of people coming and going;

According to the inter-community non-commuter OD contact matrix, compare the number of non-commuter OD contacts between each community and different central communities, select the central community with the strongest non-commuter OD contact as the central community of each community, and set the central community of each community Be classified into the life circle of each community and complete the second identification of the life circle.

The second objective of the present invention can be achieved by adopting the following technical solutions:

A space identification system for a community living circle, the system comprising:

Extraction module, used to extract mobile phone signaling data and construction land data;

The sorting module is used to sort the construction land data into the construction land data of each community;

The calculation module is used to calculate the arrival population density of each community based on the mobile phone signaling data and the construction land data of each community;

The first generation module is used to obtain the geological center for construction of each community according to the construction land data of each community, and generate the distance matrix between centroids as the community distance matrix;

The search module is used to find the community with the highest arrival population density according to the arrival population density of each community as the current central community;

The first identification module is used to select the service radius according to the community distance matrix and identify the life circle of the current central community;

The second identification module is used to find a new central community outside the service radius of the current central community, regard the new central community as the current central community, and return to select the service radius according to the community distance matrix to identify the life circle of the current central community , Until all communities are classified into the corresponding life circle.

Further, the system further includes:

The second generation module is used to generate a non-commuting OD contact matrix between communities by using a pivot table based on the mobile phone signaling data, taking the starting community as the row, the ending community as the column, and the sum of the number of people in and out of the community;

The third identification module is used to compare the number of non-commuter OD contacts between communities and different central communities based on the non-commuter OD contact matrix between communities, and select the central community with the strongest non-commuter OD contact strength as the central community of each community. And the central community of each community is included in the life circle of each community.

The third objective of the present invention can be achieved by adopting the following technical solutions:

A computer device includes a processor and a memory for storing an executable program for the processor, wherein the processor executes the program stored in the memory to realize the above-mentioned method for identifying the space of a community living circle.

The fourth objective of the present invention can be achieved by adopting the following technical solutions:

A storage medium storing a program, and when the program is executed by a processor, the above-mentioned method for identifying the space of a community living circle is realized.

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

The invention is based on the mobile phone signaling data information processing technology, and uses the crowd trajectory data generated by the mobile phone signaling at the technical level to carry out the identification of the vitality center of the community, and identify the community life circle according to the reachability range and the actual characteristics, which is suitable for urban planning Personnel use; Compared with the prior art, the present invention emphasizes the practicality of residents' daily life, and thus adopts the largest non-commuting connection to divide the space range of the living circle community.

Description of the drawings

In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only These are some embodiments of the present invention. For those of ordinary skill in the art, without creative work, other drawings can be obtained based on the structure shown in these drawings.

Fig. 1 is a flowchart of a method for identifying a space in a community living circle according to Embodiment 1 of the present invention.

Fig. 2 is a schematic diagram of finding a central community in Embodiment 1 of the present invention.

FIG. 3 is a schematic diagram of the first identification of the community living circle in Embodiment 1 of the present invention.

4 is a schematic diagram of the second identification of the community living circle in Embodiment 1 of the present invention.

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

Fig. 6 is a structural block diagram of a sorting module according to Embodiment 3 of the present invention.

FIG. 7 is a structural block diagram of a calculation module in Embodiment 3 of the present invention.

FIG. 8 is a structural block diagram of an acquisition module according to Embodiment 3 of the present invention.

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

Detailed ways

In order to make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of the embodiments of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention. .

Example 1:

This embodiment provides a method for identifying the space of a community life circle. The method uses the administrative community boundary map and the construction map of the studied area as the working base map. After calculating the distance between the communities in the ArcGIS software, the method uses the China Mobile company’s Cell phone signaling data is used as the main data source. Excel is used to clean up the data, and the area under study is used in Excel software to complete the spatial identification of the community life circle through two superimposed analysis of the community distance matrix and the non-commuting OD contact matrix. As shown in Figure 1, the method includes the following steps:

S1. Extract mobile phone signaling data and construction land data.

In this embodiment, before extracting mobile phone signaling data and construction land data, the research city and research scope can be determined first, that is, it is limited to a certain urban area, town area, etc., in principle, the living circle is not identified across regions, and for each region , Determine the basic unit boundaries of the area, such as base station coverage boundaries, community boundaries, village boundaries, etc., this embodiment uses communities as basic units.

The cell phone signaling data extracted in this embodiment is cell phone signaling data provided by China Mobile, and the construction land data is a CAD drawing of construction land.

S2. Sort the construction land data into the construction land data of each community.

In this embodiment, administrative community division and construction land data are used as the main data sources, and the two are integrated in ArcGIS to form construction land data classified by communities, that is, construction land data of each community, and prepare data for subsequent analysis.

Further, this step S2 specifically includes:

S21. Load the CAD graphics of the construction land, and generate the construction ground line graphics according to the CAD graphics of the construction land.

Specifically, load the polyline in "Construction Land CAD.dwg" into ArcGIS to generate construction ground line graphics, and save the shapefile as "Construction Land_Line.shp".

S22. Use the line-to-surface tool to generate the construction-use ground graphics from the construction-use ground line graphics.

Specifically, the line-to-surface tool in ArcGIS is used to generate "construction land_line.shp" from "construction land_line.shp", and its graphics are ground graphics for construction.

S23. Use a fusion tool to combine scattered construction land elements in the construction ground graphics into one element to generate a construction land fusion graphic.

Specifically, the fusion tool in ArcGIS is used to synthesize scattered construction land elements in "construction land_surface.shp" into one element to generate "construction land_fusion.shp", and its graphic is a construction land fusion graphic.

S24. Use the intersection tool to intersect the construction land fusion graphics with the administrative community division graphics, divide the construction land by communities, and assign the construction land community fields to generate the construction land graphics for each community.

Specifically, use the intersection tool in ArcGIS to intersect "Construction Land_Integration.shp" and "Administrative Community Division.shp", divide the construction land into communities, and give the construction land a "community" field to generate "communities" "Construction land.shp" file, whose graphics are the construction land graphics of each community.

S25. Calculate the construction land area of each community based on the construction land graph of each community.

Specifically, open the attribute table of "Construction land for each community.shp", create a new "Area" field, and use "Calculated Geometry" to calculate the area of construction land for each community.

S3. Calculate the arrival population density of each community based on the mobile phone signaling data and the construction land data of each community.

Further, this step S3 specifically includes:

S31. According to the mobile phone signaling data, count the arriving population of each community, and obtain a table of the arriving population of each community.

1) Generate base station graphics according to mobile phone signaling data.

Through cell phone signaling data, you can organize the base station table sheet in Excel, import the base station table sheet in ArcGIS, right-click the layer to display the xy data, and export the "base station.shp" file. The graph is the base station graph.

2) Use the intersection tool to intersect the base station graphics with the administrative community division graphics, divide the base stations by communities, and assign the base station community fields to generate community base station graphics with community tags.

Specifically, use the intersection tool in ArcGIS to intersect "base station.shp" and "administrative community division.shp", divide the base stations by communities, and assign the base station "community" field to generate "community base stations with community tags." "shp" file, graphics of each community base station with community tags, and export the Excel table "community-base station comparison table.xlsx", which is used to convert crowd OD data between base stations into crowd OD data between communities.

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

Specifically, import the crowd OD data between the base stations in Excel to obtain the "inter-base station OD contact.xlsx"; open the "community-base station comparison table.xlsx" and use the vlookup function to convert the crowd OD data between the base stations into communities The OD data of the population between the communities is saved as "Intercommunity OD Contact.xlsx".

4) According to the population OD data between communities, count the arrival population of each community, and obtain the arrival population number table of each community.

Specifically, according to the population OD data between communities, using a pivot table, taking the "destination community" as the row and the "OD population" as the value, count the arrival population of each community, and obtain the arrival population number table of each community. And save it as "the number of arrivals in each community.xlsx".

S32. Connect the arrival population number table of each community to the construction land graph of each community to obtain the arrival population density graph of each community.

Specifically, import "the number of arrivals in each community.xlsx" in ArcGIS, connect to "the construction land of each community.shp" based on the "community" field, and export "the arrival density of each community.shp", and the graph is for each community Reach the population density graph.

S33. According to the arrival population density graph of each community, divide the number of OD by the area of construction land to obtain the arrival population density of each community.

Specifically, in the attribute table of "arrival population density of each community.shp", add a new field "arrival population density", use the "field calculator" and enter "=[OD number]/[construction land area]" to obtain For each arrival population density, export "each community arrival population density.xlsx", and transpose the table so that the field names become rows, as shown in Table 1.

Table 1 The arrival population density of each community

Serial number	A	B	C	D	E	...
1	Community	Chencun	Zhangcun	Liu Cun	Li Cun	To
2	Arrival population density	B2	C2	D2	E2	To

S4. According to the construction land data of each community, obtain the geological center for construction of each community, and generate a distance matrix between centroids as the community distance matrix.

In this embodiment, the centroid of the construction land of each community is created through ArcGIS, and the distance between the centroids is calculated with the aid of a neighboring analysis tool, and the repeated and meaningless data is cleaned up using Excel data, and finally the distance matrix between communities is generated through a pivot table.

Further, this step S4 specifically includes:

S41. Use the surface-to-point tool to generate the geological core graphics for the construction of each community from the construction land graphics of each community.

Specifically, using the surface-to-point tool in Arcgis, "each community construction land.shp" is generated into "each community construction use geological center.shp", and its graphics are the geological center graphics used for the construction of each community.

S42. Calculate the distance between the centroids according to the geological center graphics used for the construction of each community.

Specifically, using the "Analysis Tools-Neighborhood Analysis-Nearby Analysis" tool in ArcGIS, the input elements and neighboring elements are all set to "geological centers for community construction.shp", and the distance between the centroids is calculated to generate "each "Geological center distance.shp" file for community construction.

S43. Use the pivot table to generate a distance matrix between centroids, using the starting point as the row, the ending point as the column, and the distance average value as the value, as the community distance matrix.

Insert a "Pivot Table" in Excel, with the starting point as the row, the ending point as the column, and the distance average value as the value, to generate the distance matrix between centroids, which is used as the community distance matrix, as shown in Table 2.

Table 2 Community distance matrix

S5. Identify the community life circle for the first time.

S51. According to the arrival population density of each community, find the community with the highest arrival population density as the 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; among them, use the LARGE and 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: third Only the first central community in the row shows the true arrival population density, and the remaining communities show the arrival population density as 0, as shown in Table 3.

Table 3 Finding the first central community

S52. According to the community distance matrix, select the service radius and identify the life circle of the first central community.

Identify other communities served by the central community and classify them as a living circle, which is the living circle of the first central community, as follows:

1) Calculate the distance between the central community and other communities

When the number of central communities is small, it can be copied directly from the community distance matrix table. .

When there is a certain number, the following methods are adopted: ①Combine the result of the fourth row to identify the community in the first row of the column with a value of 1; ②Combine the community distance matrix in Table 2 and use the community name identified in ① to compare Table 2. Column A of, locate the corresponding row n; ③B5 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 vb programming can be considered.

2) Identify the community within the service radius R1 of the first central community

According to the arrival population density of the central community, the service radius R1 is selected, and other communities within the service radius R1 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 R1 of the first central community, and extract the communities whose distance is less than the service radius R1, that is, the life belonging to the first central community The communities in the circle are shown in Table 4.

Table 4 Identify the communities within the service radius of the first central community

S53. Eliminate the identified communities, and repeat steps S51 to S52 in other communities in the area, until all the communities are classified into the corresponding life circle.

Find the second central community outside the service radius R1 of the first central community, as shown in Table 5;

Table 5 Finding the second central community

According to the community distance matrix, select the service radius R2 to identify the living circle of the second central community;

Find a third central community outside the service radius R2 of the second central community;

According to the community distance matrix, select the service radius R3 to identify the living circle of the third central community;

...

Find the nth central community outside the service radius Rn-1 of the n-1th central community;

According to the community distance matrix, select the service radius Rn to identify the life circle of the nth central community.

The schematic diagram of the central community found through the above is shown in Figure 2.

So far, the first identification of the community life circle is completed. The schematic diagram is shown in Figure 3, and the identification results are shown in Table 6. The first central community of A01: Combining the results of the fourth row, identify the first in the column with a value of 1. The community that is OK, B01-E01 judges the community covered by the first central community service...B0n-E0n judges the community covered by the nth central community service.

Table 6 Results of the first life circle recognition

S6. Identify the community life circle for the second time.

The following two types of problems will arise when identifying a living circle at one time: 1) A considerable proportion of communities are contained by two or more living circles at the same time; 2) Individual communities cannot be classified into any adjacent living circle because of the distance. Therefore, the identification of the secondary life circle is also needed. This part mainly uses the inter-community population connection matrix to classify the communities contained in two or more life circles into the life circle with more closely connected people. In addition, if the distance is really too far, it will be classified into the nearest living circle based on the principle of proximity.

Further, this step S6 specifically includes:

S61. According to the mobile phone signaling data, a pivot table is used to generate a non-commuter OD contact matrix between communities, taking the starting community as a row, the ending community as a column, and the sum of the number of contacts as a value.

Inter-community non-commuting OD connection refers to the estimated life travel and the number of people in the non-commuting state between communities. The mobile phone signaling data provided by China Mobile records the spatio-temporal trajectory and OD connections of users between communities. The study of commuting OD connections can help discover the temporal and spatial patterns of crowd activities, group closely connected communities into the same life circle, and improve the scientific nature of life circle planning.

The trip data whose starting point and ending point are both within a town area are screened out, and "inter-community OD contact.xlsx" is created. Using the pivot table, taking the starting community as the row, the ending community as the column, and the sum of the number of contacts as the value, a "non-community OD contact matrix between communities" is formed, as shown in Table 7.

Table 7 Non-commuter OD contact matrix between communities

S62. According to the non-commuter OD contact matrix between communities, compare the number of non-commuter OD contacts between each community and different central communities, select the central community with the strongest non-commuter OD contact as the central community of each community, and compare the The central community is classified into the life circle of each community to complete the second identification of the life circle.

According to the method of step S5, some communities may be covered by the hinterland of multiple central communities. Therefore, the unique attribution of the community and different centers are used to determine its unique ownership, as shown in Table 8.

Table 8 The second life circle identification

The specific operations are as follows:

1) A001 first central community: equal to A01.

2) B001-E001 calculates the non-commuting 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 contact value is displayed, and the numerical calculation adopts the following methods: ①Combined with the result of the fourth row, identify the community in the first row of the column with the value of 1; ②Combine the community OD contact matrix in Table 2 , Use the community name identified in ① to filter column A of Table 2 and locate the corresponding row n; ③ 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 service range of the central community, OD contact is displayed as 0.

...

3) B00n-E00n calculates the non-commuting OD connection between the nth central community and other communities.

Compare the number of non-commuter OD contacts between each community and different central communities, select the central community with the strongest non-commuter OD contact as the central community of each community, and classify the central community of each community into the life circle of each community. The schematic diagram of the second identification of the living circle is shown in Figure 4.

It should be noted that although the method operations of the above embodiments are described in a specific order, this does not require or imply that these operations must be performed in the specific order, or that all the operations shown must be performed to achieve the desired result. Conversely, the depicted steps can change the order of execution. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and/or one step may be decomposed into multiple steps for execution.

Example 2:

This embodiment is a specific application example, taking Guzhen Town, Zhongshan City, Guangdong Province as the research object, based on the implementation of the community living circle space identification method of the above-mentioned embodiment 1, using mobile phone signaling big data method to propose from the perspective of crowd movement OD relationship The new living circle identification method forms a complementary relationship with the traditional questionnaire interview survey, and it also improves the urban planning survey system.

1) Organize basic data on the community living circle of Guzhen Town

1.1) Sorting out the construction land data of various communities in Guzhen Town

Load the polyline in "Guzhen Town Construction Land CAD.dwg" into ArcGIS and save it as the shapefile "Guzhen Town Construction Land_Line.shp".

Use the line-to-surface tool on "Guzhen Town Construction Land_Line.shp" to generate "Guzhen Town Construction Land_Plane.shp".

Use the fusion tool on "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_Integration.shp" and "Guzhen Town Administrative Community Division.shp", divide the construction land into communities, and give the construction land a "community" field to generate "Guzhen Town communities Construction land.shp" file.

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

1.2) Sorting out cell phone signaling data of various communities in Guzhen Town

Import the ancient 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 tool to intersect "Guzhen town base station.shp" and "Guzhen town administrative community division.shp", divide the base stations by communities, and assign the base station "community" field to generate "Guzhen town community base stations with community tags." shp" file, and export the Excel table "Guzhen Town Community-Base Station Comparison Table.xlsx", which is used to convert the OD data of the population between the base stations into the OD data of the population between the communities.

Import the population OD data between the base stations in the ancient town into Excel, and get the "OD contact between the base stations in the ancient town.xlsx"; open the "Guzhen town community-base station comparison table.xlsx" and use the vlookup function to convert the information between the base stations Crowd OD data is converted into inter-community crowd OD data and saved as "Guzhen town inter-community OD contact.xlsx".

Using the pivot table, take "Destination Community" as the row and "OD Number" as the value, count the arrival population of each community, and save it as "Guzhen Town, each community arrival population.xlsx".

1.3) Sorting out the arrival population density of each community in Guzhen Town

Import "Arrival population number of communities in ancient towns.xlsx" in ArcGIS, connect to "Construction land of communities in ancient towns.shp" based on the "community" field, and export "Arrival population density of communities in ancient towns.shp".

In the attribute table of "arrival population density of each community in Guzhen town.shp", add a new field "arrival population density", use the "field calculator", enter "=[OD number]/[construction land area]" to find each The arrival population density is shown in Table 9.

Table 9 The arrival population density of each community in Guzhen Town

Derive "the arrival population density of each community in Guzhen Town.xlsx", and transpose the table so that the field names become rows, as shown in Table 10.

Table 10 The arrival population density of each village (community) in Guzhen Town

1.4) Create a distance matrix between communities in Guzhen Town

Use the surface-to-point tool in ArcGIS to generate "geological heart for the construction of communities in ancient towns.shp" by using "the construction land of various communities in ancient towns and towns.shp"

Use the "Analysis Tools-Neighborhood Analysis-Nearby Neighbor Analysis" tool, and set the input elements and neighboring elements to "Geological center.shp for the construction of communities in ancient towns", calculate the distance between the two centroids, and generate "Construction of communities in ancient towns" Use geocentric distance.shp" file.

Insert the "Pivot Table" in Excel, with the starting point as the row, the ending point as the column, and the distance average value as the value, to generate the distance matrix between centroids, which is used as the community distance matrix, as shown in Table 11.

Table 11 Distance matrix of Guzhen town community

1.5) Create a non-commuting OD contact matrix between communities in Guzhen Town

Open "Guzhen Town Community OD Contact.xlsx", use the pivot table, take the starting community as the row, the end community as the column, and the OD population as the value to form the "Guzhen Town Community Non-Commuter OD Contact Matrix", as shown in Table 12. Shown.

Table 12 Non-Commuter OD Contact Matrix of Guzhen Town Community

2) First life circle recognition

2.1) First find the community with the highest population density as the central community "Liufang Village 2", that is, the first central community (town center), and then select the density of 29918 people/km2 in "Liufang Village 2" from Table 13. 2km is taken as the service radius, and Gangdong Village, Gangnan Village, Guer Village, Gusan Village, Gusi Village, Guyi Village, and Liufang Village 2 are divided into other communities covered by the central community.

2.2) The remaining communities in Liufang Village 2 located at a distance of 2.0km find the second central community "Cao Yi Village". According to its density of 146,958 people/km2, 2km in Table 13 is selected as the service radius to divide Cao Er Village and Cao Village. The general communities covered by one village and one ancient village.

2.3) The third central community "Cao San Village" was found in the remaining communities of Cao Yi Village 2.0km away. Based on its density of 7052 people/km2, 3km in Table 13 was selected as the service radius to divide Cao Er Village and Cao Village. Sancun, Caoyi Village, Gangdong Village, Guyi Village, and Qifang Village are the general communities covered by their services.

2.4) The fourth central community "Haizhou Village" was found in the remaining community 3.0km away from Caosan Village. According to its density of 3813 people/km2, 4km in Table 13 was selected as the service radius to divide Caoer Village and Cao Village. The general communities covered by Sancun, Caoyi Village, Gangdong Village, Gangnan Village, Guercun, Gusancun, Gusi Village, Guyi Village, Liufang Village 2, and Qifang Village are the general communities covered by their services, thus completing the first community life circle Recognition, Table 14 shows the actual operation process of finding the central community, and Table 15 is the recognition result of the first living circle.

Table 13 Value standard of service radius of living circle

Table 14 Finding the first central community community in Guzhen

Table 15 The identification results of the first living circle in Guzhen Town

3) Second life circle recognition

According to the non-commuting OD contact matrix of the Guzhen town community in Table 12, a judgment table for the second life circle identification is made, as shown in Table 16.

Table 16 Identification of the second living circle in Guzhen Town

The communities covered by multiple life circles are screened out. According to the number of non-commuters between each community and different central communities, the central community with the largest contact is selected as the central community of each community, and the central community with the largest contact is classified into each community. The community’s living circle has completed the second identification of the living circle, as shown in Table 17.

Table 17 Recognition results of the second living circle in Guzhen Town

Life circle	Central community	Community name
Liufang Life Circle	Liufang Village	Liufang Village
Liufang Life Circle	Liufang Village	Gangdong Village
Liufang Life Circle	Liufang Village	Gangnan Village
Liufang Life Circle	Liufang Village	Guercun
Liufang Life Circle	Liufang Village	Gusancun
Liufang Life Circle	Liufang Village	Ancient four villages
Liufang Life Circle	Liufang Village	Guyi Village
Cao Yi Life Circle	Cao Yicun	Caoyicun
Cao Yi Life Circle	Cao Yicun	Cao Ercun
Cao San Life Circle	Cao Sancun	Cao Sancun
Cao San Life Circle	Cao Sancun	Qifang Village
Haizhou Life Circle	Haizhou Village	Haizhou Village

In this embodiment, the mobile phone signaling data is used to analyze the population travel situation in each community, combined with the traditional life circle analysis theory, find the central community based on the population concentration of the life circle, and set the service radius, and then divide the ancient town into four Big life circle. Compared with the traditional life circle analysis, it greatly improves the efficiency of analysis, and provides a basis for delimiting the life circle from another angle, enriching the research methods of the life circle.

Example 3:

As shown in Figure 5, this embodiment provides a community living circle space identification system. The system includes an extraction module 501, a sorting module 502, a calculation module 503, an acquisition module 504, a search module 505, a first identification module 506, and a first identification module 506. Second identification module 507, the specific functions of each module are as follows:

The extraction module 501 is used to extract mobile phone signaling data and construction land data.

The sorting module 502 is used to sort the construction land data into the construction land data of each community.

The calculation module 503 is used to calculate the arrival population density of each community based on the mobile phone signaling data and the construction land data of each community.

The first generating module 504 is configured to obtain the geological center for construction of each community according to the construction land data of each community, and generate a distance matrix between centroids as a community distance matrix.

The finding module 505 is used to find the community with the highest arriving population density according to the arriving population density of each community as the current central community.

The first identification module 506 is used to select the service radius according to the community distance matrix and identify the living circle of the current central community.

The second identification module 507 is used to find a new central community outside the service radius of the current central community, regard the new central community as the current central community, and return to select the service radius according to the community distance matrix to identify the current central community Life circle until all communities are included in the corresponding life circle.

Further, the community living circle space identification system of this embodiment may further include:

The second generation module 508 is used to generate a non-commuting OD contact matrix between communities by using a pivot table based on the mobile phone signaling data, taking the starting community as the row, the ending community as the column, and the sum of the number of people coming and going as the value.

The third identification module 509 is used to compare the number of non-commuter OD contacts between each community and different central communities according to the non-commuter OD contact matrix between communities, and select the central community with the strongest non-commuter OD contact as the central community of each community , And put the central community of each community into the life circle of each community.

Further, the sorting module 502 is shown in FIG. 6, and specifically includes:

The first generating unit 5021 is used to load the CAD graphics of the construction land, and generate the construction ground line graphics according to the CAD graphics of the construction land.

The second generating unit 5022 is used to generate a construction ground graphic from a construction ground graphic using a line-to-surface tool.

The third generating unit 5023 is used to use a fusion tool to synthesize scattered construction land elements in the construction ground graphics into one element to generate a construction land fusion graphic;

The fourth generating unit 5024 is used to use an intersection tool to intersect the construction land fusion graphic with the administrative community division graphic, divide the construction land by communities, and assign the construction land community fields to generate construction land graphics for each community.

The first calculation unit 5025 is used to calculate the construction land area of each community according to the construction land graph of each community.

Further, the calculation module 503 is shown in FIG. 7, and specifically includes:

The statistics unit 5031 is used to count the arrival population of each community according to the mobile phone signaling data, and obtain a table of the arrival population of each community.

The input unit 5032 is used to connect the arrival population number table of each community to the construction land graph of each community to obtain the arrival population density graph of each community.

The second calculation unit 5033 is used to calculate the arrival population density of each community by dividing the OD number by the area of construction land according to the arrival population density graph of each community

Further, the acquiring module 504 is shown in FIG. 8 and specifically includes:

The fifth generating unit 5041 is used to generate the construction land graphics of each community into the geological core graphics of each community by using the surface-to-point tool.

The third calculation unit 5042 is used to calculate the distance between the two centroids according to the geological center graphics used for the construction of each community.

The sixth generating unit 5043 is used to generate a distance matrix between centroids as a community distance matrix by using a pivot table, taking the starting point as the row, the ending point as the column, and the distance average value as the value.

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

It can be understood that the terms "first", "second", etc. used in the above devices can 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 module. For example, without departing from the scope of the present invention, the first identification module may be referred to as the second identification module, and similarly, the second identification module may be referred to as the first identification module, the first identification module and The second identification modules are both identification modules, but not the same identification module.

Example 4:

This embodiment provides a computer device, which may be a computer, as shown in FIG. 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 processing The storage device 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 an operating system, a computer program, and a database. The internal memory 907 is non-volatile. The operating system and computer program in the storage medium provide an environment for running. When the processor 902 executes the computer program stored in the memory, it implements the community living circle space identification method of Embodiment 1 as follows:

Extract mobile phone signaling data and construction land data;

Organize the construction land data into the construction land data of each community;

Calculate the arrival population density of each community based on the mobile phone signaling data and the construction land data of each community;

According to the construction land data of each community, obtain the construction geological center of each community, and generate the distance matrix between the centroids as the community distance matrix;

According to the arrival population density of each community, find the community with the highest arrival population density as the current central community;

According to the community distance matrix, select the service radius and identify the living circle of the current central community;

Outside the service radius of the current central community, look for a new central community, take the new central community as the current central community, return to the community distance matrix, select the service radius, and identify the life circle of the current central community until all communities are included The corresponding life circle.

Further, the method may further include:

According to the mobile phone signaling data, use the pivot table to generate the non-commuter OD contact matrix between communities with the starting community as the row, the ending community as the column, and the sum of the number of people coming and going;

According to the inter-community non-commuter OD contact matrix, compare the number of non-commuter OD contacts between each community and different central communities, select the central community with the strongest non-commuter OD contact as the central community of each community, and set the central community of each community Be classified into the life circle of each community and complete the second identification of the life circle.

Example 5:

This embodiment provides a storage medium, which is a computer-readable storage medium that stores a computer program. When the computer program is executed by a processor, the method for identifying the community living circle space in Embodiment 1 is implemented as follows:

Extract mobile phone signaling data and construction land data;

Organize the construction land data into the construction land data of each community;

Calculate the arrival population density of each community based on the mobile phone signaling data and the construction land data of each community;

According to the construction land data of each community, obtain the construction geological center of each community, and generate the distance matrix between the centroids as the community distance matrix;

According to the arrival population density of each community, find the community with the highest arrival population density as the current central community;

According to the community distance matrix, select the service radius and identify the living circle of the current central community;

Outside the service radius of the current central community, look for a new central community, take the new central community as the current central community, return to the community distance matrix, select the service radius, and identify the life circle of the current central community until all communities are included The corresponding life circle.

Further, the method may further include:

According to the mobile phone signaling data, use the pivot table to generate the non-commuter OD contact matrix between communities with the starting community as the row, the ending community as the column, and the sum of the number of people coming and going;

According to the inter-community non-commuter OD contact matrix, compare the number of non-commuter OD contacts between each community and different central communities, select the central community with the strongest non-commuter OD contact as the central community of each community, and set the central community of each community Be classified into the life circle of each community and complete the second identification of the life circle.

The storage medium in this embodiment may be a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a USB flash drive, a mobile hard disk, and other media.

In summary, the present invention is based on the mobile phone signaling data information processing technology, and uses the crowd trajectory data generated by the mobile phone signaling at the technical level to carry out the identification of the vitality center of the community, and identify the community life according to the reachability range and the actual characteristics. The circle is suitable for use by urban planners; compared with the prior art, the present invention emphasizes the practicality of residents' daily life, so the largest non-commuting connection is adopted to divide the space range of the living circle community.

The above are only the preferred embodiments of the patent of the present invention, but the scope of protection of the patent of the present invention is not limited to this. Anyone familiar with the technical field within the scope of the patent of the present invention, according to the patent of the present invention The technical scheme and its invention patent concept are equivalently replaced or changed, all belong to the protection scope of the invention patent.

Claims (10)

1. A method for identifying the space of a community living circle, characterized in that the method includes:

   Extract mobile phone signaling data and construction land data;

   Organize the construction land data into the construction land data of each community;

   Calculate the arrival population density of each community based on the mobile phone signaling data and the construction land data of each community;

   According to the construction land data of each community, obtain the construction geological center of each community, and generate the distance matrix between the centroids as the community distance matrix;

   According to the arrival population density of each community, find the community with the highest arrival population density as the current central community;

   According to the community distance matrix, select the service radius and identify the living circle of the current central community;

   Outside the service radius of the current central community, look for a new central community, take the new central community as the current central community, return to the community distance matrix, select the service radius, and identify the life circle of the current central community until all communities are included The corresponding life circle.
2. The method for identifying the space of a community living circle according to claim 1, wherein the sorting construction land data into construction land data of each community specifically includes:

   Load the CAD graphics of the construction land, and generate the construction ground line graphics according to the CAD graphics of the construction land;

   Use the line-to-surface tool to generate the ground line graphics for construction into ground graphics for construction;

   Use fusion tools to combine scattered construction land elements in construction ground graphics into one element to generate construction land fusion graphics;

   Use the intersection tool to intersect the construction land fusion graphics with the administrative community division graphics, divide the construction land by communities, and assign the construction land community fields to generate the construction land graphics for each community;

   Calculate the construction land area of each community according to the construction land graph of each community.
3. The method for identifying the space of a community living circle according to claim 1, wherein the calculating the arrival population density of each community according to the mobile phone signaling data and the construction land data of each community specifically includes:

   According to the mobile phone signaling data, count the arrival population of each community, and obtain the arrival population table of each community;

   Connect the arrival population number table of each community to the construction land graph of each community to obtain the arrival population density graph of each community;

   According to the arrival population density graph of each community, divide the OD number by the construction land area to obtain the arrival population density of each community.
4. The method for recognizing the space of a community living circle according to claim 3, characterized in that, according to mobile phone signaling data, counting the number of arriving population in each community to obtain a table of the number of arriving population in each community, which specifically includes:

   Generate base station graphics according to mobile phone signaling data;

   Use the intersection tool to intersect the base station graphics with the administrative community division graphics, divide the base stations into communities, give the base station community fields, and generate community base station graphics with community tags;

   Convert crowd OD data between base stations into crowd OD data between communities;

   According to the population OD data between communities, the arrival population of each community is counted, and the arrival population number table of each community is obtained.
5. The method for recognizing the space of a community living circle according to claim 1, characterized in that, according to the construction land data of each community, the geological center for construction of each community is obtained, and the distance matrix between centroids is generated as a community distance matrix, which specifically includes :

   Use the surface-to-point tool to generate the geological core graphics for the construction of each community from the construction land graphics of each community;

   Calculate the distance between the centroids according to the geological center graphics used in the construction of each community;

   Using the pivot table, the starting point is the row, the ending point is the column, and the distance average value is the value to generate the distance matrix between centroids, which is used as the community distance matrix.
6. The method for identifying the space of a community living circle according to any one of claims 1-5, wherein the method further comprises:

   According to the mobile phone signaling data, use the pivot table to generate the non-commuter OD contact matrix between communities with the starting community as the row, the ending community as the column, and the sum of the number of people coming and going;

   According to the inter-community non-commuter OD contact matrix, compare the number of non-commuter OD contacts between each community and different central communities, select the central community with the strongest non-commuter OD contact as the central community of each community, and set the central community of each community Be classified into the life circle of each community and complete the second identification of the life circle.
7. A system for identifying the space of a community living circle, which is characterized in that the system includes:

   Extraction module, used to extract mobile phone signaling data and construction land data;

   The sorting module is used to sort the construction land data into the construction land data of each community;

   The calculation module is used to calculate the arrival population density of each community based on the mobile phone signaling data and the construction land data of each community;

   The first generation module is used to obtain the geological center for construction of each community according to the construction land data of each community, and generate the distance matrix between centroids as the community distance matrix;

   The search module is used to find the community with the highest arrival population density according to the arrival population density of each community as the current central community;

   The first identification module is used to select the service radius according to the community distance matrix and identify the life circle of the current central community;

   The second identification module is used to find a new central community outside the service radius of the current central community, regard the new central community as the current central community, and return to select the service radius according to the community distance matrix to identify the life circle of the current central community , Until all communities are classified into the corresponding life circle.
8. The community living circle space identification system according to claim 7, wherein the system further comprises:

   The second generation module is used to generate a non-commuting OD contact matrix between communities by using a pivot table based on the mobile phone signaling data, taking the starting community as the row, the ending community as the column, and the sum of the number of people in and out of the community;

   The third identification module is used to compare the number of non-commuter OD contacts between communities and different central communities based on the non-commuter OD contact matrix between communities, and select the central community with the strongest non-commuter OD contact strength as the central community of each community. And the central community of each community is included in the life circle of each community.
9. A computer device comprising a processor and a memory for storing an executable program of the processor, wherein the processor executes the program stored in the memory to implement the community living circle of any one of claims 1 to 6 Spatial identification method.
10. A storage medium storing a program, wherein when the program is executed by a processor, the method for identifying the space of a community living circle according to any one of claims 1 to 6 is realized.

Images