WO2020172954A1

WO2020172954A1 - Living circle identification method based on positioning data

Info

Publication number: WO2020172954A1
Application number: PCT/CN2019/081820
Authority: WO
Inventors: 杨俊宴; 金探花; 史北祥; 王桥; 魏晋; 史宜
Original assignee: 东南大学
Priority date: 2019-02-28
Filing date: 2019-04-08
Publication date: 2020-09-03
Also published as: CN110032609B; CN110032609A

Abstract

Disclosed in the present invention is a living circle identification method based on positioning data: first pre-processing a positioning data file; then performing behaviour point identification on the positioning data and, on the basis of the continuous duration of the behaviour points in a specific time period, dividing into residential behaviour point, work behaviour point, and living behaviour point sets; selecting a specific city or area, and measuring or assigning an experience value to acquire a living circle radius D value; extracting living behaviour points that are respectively less than the D value from residential behaviour points M and work behaviour points N to form a residential living behaviour point set Q and a work living behaviour point set S; respectively extracting points M and point set Q and points N and point set S to form a minimum boundary geometric area and forming a residential living circle area and a work living circle area. The present invention makes full use of positioning data to accurately demarcate the real residential or work living circles of a population, being more scientific and objective than the traditional method of demarcating a circle with the place of residence as the centre point, and providing a scientific basis for residential or work living circle planning analysis and construction.

Description

A life circle identification method based on positioning data

Technical field

The invention belongs to the technical field of urban planning, and particularly relates to a method for identifying a living circle based on positioning data.

Background technique

With the continuous upsurge of urban renewal, people's demand for intensive spatial layout and exquisite quality of life continues to increase. As the basic unit of the urban human settlement environment, the living circle determines the urban facility space allocation and the shaping of the quality of life. The existing methods of defining the scope of the living circle at home and abroad mainly define the living circle in terms of time distance, spatial distance or function.

Among the methods of defining the scope of this kind of living circle, the analysis method of defining the living circle by time distance mainly delimits the boundary of the urban living circle by calculating "15-minute walking distance", "one-hour commuting circle", etc.; defined by spatial distance The living circle of Shanghai mainly uses the "800m walking distance" to determine the reachable space boundary to form the range of the living circle; while the living circle defined by function determines the boundary of the living circle by calculating the distance of major facilities. The above three types of life circle delineation methods all have certain limitations, and more people use subjective feelings and experience to determine the life circle. First of all, in the purely dividing method by traffic time, the continuous movement behavior does not conform to the characteristics of the actual crowd activities. This method lacks consideration of the real behavior of people in life; through the pure spatial distance dividing method, by judging the short-term walking The farthest distance ignores the behavioral characteristics of specific individuals and blurs the differences between crowd activities. Through the classification method of major facilities, the analysis of the level of the living circle only at the level of the city range does not incorporate the actual use of the population.

In addition, there are some experts and scholars who use GPS positioning data to truly reproduce the range of crowd activities, but the analysis methods and techniques are more limited. A small area, a small number of people, and a short-term measurement method are used to delimit a small area. The boundary of the living circle of the lot makes it difficult to define the scope of the overall urban living circle. Therefore, the current life circle identification methods are more subjectively defined methods or small-scale real representation methods, lacking the true restoration of the urban-scale population's living area, and failing to integrate with the current multi-source big data application.

Summary of the invention

Purpose of the invention: In view of the above problems, the present invention proposes a living circle identification method based on positioning data, aiming at the problem of simply making a circle with the place of residence or work in the existing living circle, ignoring the real situation of the crowd, and identifying the crowd with positioning data The behavior of people is integrated with the behavior points within the daily walking range to get the real living and working life circle of the crowd.

Technical solution: In order to achieve the purpose of the present invention, the technical solution adopted by the present invention is: a method for identifying a living circle based on positioning data. The method includes the following steps:

(1) Obtain the user's positioning data file, and use EXCEL software to preprocess the positioning data file.

(2) Identify the behavior points of the effective positioning data file obtained in step 1, and divide the different behavior characteristics of the behavior points according to the duration of the behavior points in a specific time period: including residential behavior point M, work behavior point N, and life Behavior point set P.

(3) Select a specific city or region, measure or assign an empirical value to obtain the walking distance of the crowd of a specific time period, and obtain the value of the radius D of the life circle for a specific time period. Extract the life behavior points whose distance from the residence behavior point M is less than the value of D to form the residence life behavior point set Q; and/or extract the life behavior points whose distance from the work behavior point N is less than the value D to form the work behavior point set S.

(4) Extract point M and point set Q to form the minimum boundary geometric area, obtain the user’s living area area Y, and/or extract points N and point set S to form the minimum boundary geometric area, obtain the user’s work and life area Area L.

Further, the step (1) includes:

(1.1) Obtain desensitization positioning data. The data collection time is every 5 minutes. The data content includes the user number, the user's location update date and time, and the user's real-time latitude and longitude coordinates. Positioning data can be obtained through mobile phone operators, or through GPS data collection. The anonymous location data number is the user identification number after desensitization, and does not involve the user's private information. The data collection time interval can be set freely in actual work. The standard format of positioning data is as follows:

Table 1 Example of positioning data format

(1.2) Store the desensitized positioning data on a workstation with no lower configuration than Intel Xeon Processor E5-2620V4 processor, 512G SSD, 128G DDR4 memory, and perform specific operations on this workstation.

(1.3) In the Excel program, use the ‘find and select’ command, check the ‘empty value’ to get all the blank value cells, right click on the “delete” command and select to delete the “whole row”. Use the "Delete Duplicate Items" under the "Data" command column, check the "User ID" column to delete all user duplicate data. Use the "Filter" command under the "Sort and Filter" command bar, check "Digital Filter", select and delete data whose date or latitude and longitude are not within the normal range.

Further, the step (2) includes:

(2.1) Define the distance threshold and the shortest time interval threshold, the distance threshold is S meters, and the shortest time interval threshold is T ₁ minute;

(2.2) points to sort all tracks in time series trajectory point O _n, determines which path point O _{n + 1} from the track point O _n is less than S m, such as less than S m, is as O _n a Adjacent points; continue the above operations, continue to judge, to the track point O _n+m , if the distance from the track point O _n is greater than or equal to the threshold S meters, the search stops; calculate the track point O _n+m and the track point O _n time intervals, such as greater than or equal to the duration T ₁ minute, the recording track of all points to O _n O _{n + m-1;} such duration is less than T ₁ minute, to O _n O _{n + m-1} is not satisfied Request, the result is invalid; go to the next track point, that is, start at track point O _n+1 , repeat the above steps;

(2.3) Cluster all trajectory points O _n to On _+m-1 obtained in step 2.1.2 to generate behavior points, where the latitude and longitude of the behavior point is the mean value of all trajectory points O _n to O _n+m-1 , The time t _n when the track point O _n is located is the start time of the action point, and the time t _n+m-1 when the track point O _n+m-1 is located is the end time of the action point;

(2.4) Starting from the track point On _+m , repeat steps 2.2 and 2.3 to traverse all track points and identify all behavior points;

(2.5) Determine in turn whether the interval between the start time and end time of the action points obtained in step (2.4) has an intersection with the interval 23:00-05:00, record the above-mentioned intersection action points to form a set A1, and calculate the A1 The behavior point with the maximum time difference between the end time and the start time of the behavior point is regarded as the residence behavior point M;

(2.6) Determine in turn whether the start time and end time of the behavior points obtained in step (2.4) have an intersection with the 08:00-17:00 interval, record the above-mentioned intersection behavior points to form a set A2, and calculate the behavior points in A2 The action point with the maximum time difference between the end time and the start time of is regarded as the work action point N;

(2.7) In all the behavior point files, remove the residence behavior point M and the work behavior point N, and get the life behavior point set P={p1, p2, p3,..., pn}.

Further, the step (3) includes:

(3.1) Determine the radius of the living circle through actual measurement: select the city/region to be delimited for the living circle, and choose people who travel independently, including citizens aged 0-20, citizens 21-40, citizens 41-60, and citizens older than 60 , There are L men and women in all age groups, the total number of people in the experiment is 8*L, and they walked on the road for T ₂ minutes continuously, and recorded all their walking distances with Jiaming GPSMAP 62scGPS recording instrument, and took the average of all distances as the radius of the life circle D; or by assigning an empirical value, the walking distance per minute is d, D=d*T ₂ , where the duration T _{2 is} based on the difference between cities and regions, usually taking the value 15, 30 or 60.

(3.2) Load the behavior point file obtained in step (2) into ArcGIS software in layers to form three layers: residential behavior point M, work behavior point N, and life behavior point set P.

(3.3) In the ArcGIS software, draw a circle with a radius of D with point M and point N as the center of the circle, which is a circle M layer and a circle N layer.

(3.4) Use the "Select file according to location" command under "Select" in ArcGIS software, check "Select features from the following layers", the target layer is the life behavior point set P layer, and the source layers are circles. M layer and source N layer, the spatial selection method of the target layer feature is "completely within the range of the source layer feature", after selection, the exported layer is "living life behavior point set Q" and "working life behavior point Set S" SHP file.

Further, the step (4) includes:

(4.1) In ArcGIS software, use the "merge" command to merge point M and point set Q to get point set X;

(4.2) In ArcGIS software, use the "minimum boundary geometry" command, in the input features, input the point set X, in the geographic options, select "CONVEX HULL", output area Y, the formed surface The domain scope is the living circle scope of the target object. As shown in Fig. 3, it is a schematic diagram of the space of the present invention, in which the area MP ₁ P _{2 is} the range of the living circle.

(4.3) In ArcGIS software, use the "merge" command to merge point N and point set S to get point set K;

(4.4) In ArcGIS software, use the "minimum boundary geometry" command, in the input elements, input point set K, in the geographic options, select "CONVEX HULL", output area L, the formed area The domain scope is the working life circle scope of the target object. As shown in Fig. 4, it is a schematic diagram of the space of the present invention, where the area NP ₃ P _n P _{4 is} the range of the working and living circle.

Furthermore, the living area of each object in the residential area can be combined to obtain the living area of all objects in the residential area; the working and living area of each object in the office area can be combined to obtain a specific The working life circle area of all objects in the office area.

Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

The present invention uses positioning data to identify effective behaviors of the crowd through algorithms and rules, and obtains the actual residential behavior points, work behavior points and life behavior points of the crowd. Taking the walking distance in a specific area within a specific time as the radius of the life circle, extract the life behavior points within the radius of the life circle from the residence behavior point and the work behavior point respectively, as the residence life behavior point and work life behavior point. The minimum boundary geometric area for generating living behavior points and living life behavior points is the living life circle, and the minimum boundary geometric area for generating working behavior points and work life behavior points is the working life circle. The present invention makes full use of positioning data to accurately locate the crowd The real living or working life circle is more scientific and objective than the circle in the traditional sense, providing a scientific basis for the analysis and construction of the living or working life circle planning.

Description of the drawings

Figure 1 Flow chart of the present invention;

Figure 2 Schematic diagram of behavior point recognition method;

Fig. 3 is a schematic diagram of the living space of the present invention;

Fig. 4 is a schematic diagram of the working and living circle space of the present invention;

Figure 5 The application of the present invention in the living circle of a user in the Zhima Village residential area in Xinjiekou area, Nanjing;

Fig. 6 The application of the present invention in the residential living circle of Zhima Village, Xinjiekou, Nanjing.

detailed description

The technical scheme of the present invention will be further described below in conjunction with the drawings and embodiments.

The technical scheme of the present invention will be described in detail below based on the positioning data to identify the 15-minute residential life circle of the Zhima Village residential area in Xinjiekou, Nanjing and the accompanying drawings, such as:

(1) Use EXCEL software to preprocess the anonymous mobile phone location data CSV file, where the anonymous mobile phone location data CSV file is obtained from the mobile phone information push service provider, including the user number, the date, time, and latitude and longitude coordinate information of the user's location update; and , Or use GPS positioning equipment to collect positioning data files;

(1.1) The specific steps for obtaining the mobile phone location data of anonymous users through the mobile phone information push service provider are as follows: Obtain the mobile phone location data of all users in Nanjing on November 11, 2017 through the mobile phone information push service provider. The data collection time is every 5 minutes. The collection time interval can be preset according to actual needs. The data content includes user ID, user location update date and time, user real-time latitude and longitude coordinates, a total of 2.321 billion pieces of collected data. The specific data format requirements are shown in Table 1, including desensitized data. User ID, user gender, age, location update date, time, longitude, latitude information. The user's mobile phone location data number is the desensitized mobile phone identification number, which does not involve the user's private information. Store anonymous mobile phone location data on a workstation with no lower configuration than Intel Xeon Processor E5-2620V4 processor, 512G SSD, 128G DDR4 memory, and perform specific operations on this workstation.

Table 2 Data format example

(1.2) Use EXCEL software to preprocess the CSV file of all mobile phone user positioning data in Nanjing obtained in step 1, delete missing data, duplicate data and abnormal data, and obtain effective positioning data CSV files for all users.

For example, in the Excel program, use the ‘find and select’ command, check the ‘empty value’ to get all the blank value cells, right click on the “delete” command, and select to delete the “whole row”. Use the "Delete Duplicate Items" under the "Data" command column, check the "User ID" column to delete all user duplicate data. Use the "Filter" command under the "Sort and Filter" command bar, check "Digital Filter", select and delete data whose date or latitude and longitude are not within the normal range.

(2) Perform behavior point recognition on the CSV file of effective positioning data for all mobile phone users in Nanjing obtained in step 2. According to the time period when the behavior point is located, divide the different behavior characteristics of all mobile phone users in Nanjing: including residential behavior points and work behaviors Points and life behavior points.

(2.1) Define the distance threshold and the shortest time interval threshold. The distance threshold S is 50 meters, and the shortest time interval T ₁ threshold is 15 minutes. The 50 meters and 15 minutes in this example are just examples, and different values can be set according to actual needs;

(2.2) points to sort all tracks in time series trajectory point O _n, determines which path point O _{n + 1} from the track point O _n is less than 50 m, such as less than 50 meters, then as O _n a Adjacent points; continue the above operations, continue to judge, to the track point O _n+m , if the distance from the track point O _n is greater than or equal to the threshold 50 meters, the search stops; calculate the track point O _n+m and the track point O _n time intervals, as a duration greater than or equal to 15 minutes, all the recording track point to O _n O _{n + m-1;} such a duration less than 15 minutes, to O _n O _{n + m-1} does not meet the requirements, The result is invalid; go to the next trajectory point, starting from the trajectory point O _n+1 , repeat the above steps until a point that meets the conditions is found, go to step (2.3).

(2.3) Cluster all track points O _n to O _n+m-1 obtained in step 2.2 to generate behavior points. Among them, the latitude and longitude of the behavior point is the mean value of the latitude and longitude of all the trajectory points O _n to On _+m-1 , the time t _n when the trajectory point O _n is located is the start time of the behavior point, and the trajectory point O _n+m-1 The time t _n+m-1 when it is located is the end time of the action point;

(2.5) Determine in turn whether the interval between the start time and the end time of the action point obtained in step (2.4) has an intersection with the interval 23:00-05:00, record all the above-mentioned intersection action points to form a set A1, and calculate A1 The behavior point with the maximum time difference between the end time and the start time of the behavior point is taken as the residence behavior point M;

(2.6) Determine in turn whether the interval between the start time and end time of the action point obtained in step (2.4) has an intersection with the interval 08:00-17:00, record all the above-mentioned intersection action points to form a set A2, and calculate A2 The behavior point with the maximum time difference between the end time and the start time of the behavior point is regarded as the work behavior point N;

(2.7) In all the behavior point files, remove the residence behavior point M and the work behavior point N, and get the life behavior point set P file={p1, p2, p3,..., pn}.

(3) Obtain the 15-minute walking distance D value through actual measurement. In this embodiment, the measured D value is 1.5 km, and the life behavior point set Q within 1.5 km from the residential behavior point is extracted.

(3.1) Select Nanjing Xinjiekou area and the characteristics of the crowd, and the specific steps to obtain a 15-minute walking distance of 1.5km are as follows: Actual measurement determines the radius of the living circle: select a specific Nanjing Xinjiekou area and choose independent travellers, among which Citizens aged 0-20, citizens 21-40, citizens 41-60, citizens older than 60, 10 men and women of all ages, and 80 test subjects each, walked for 15 minutes on Xinjiekou city roads. Then use Garmin GPSMAP 62sc recording instrument to record the distance between the start point and the end point, and take the average of all distances as the radius D of the living circle. The distance value in this embodiment is 1.5km.

(3.2) Taking the residential area of Zhima Village in Nanjing as an example, take the user 00069f1927ee5cef9919335d75170d3 whose residence is located in the residential area as an example to extract the data of the trajectory points of their behavior in one day.

(3.3) Load the user 00069f1927ee5cef9919335d75170d3 behavior point CSV file obtained in step 3.2 into ArcGIS software in layers to form three layers of user 00069f1927ee5cef9919335d75170d3 residence behavior point M, work behavior point N, and life behavior point set P.

(3.4) In the ArcGIS software, take the residence M of the user 00069f1927ee5cef9919335d75170d3 as the center of the circle, and draw a circle with a radius of 1.5km, which is the circle M layer.

(3.5) Extract the life behavior point set P located in the circle M to obtain the residential life behavior point set Q, the method is as follows:

Use the "Select file by location" command under "Select" in ArcGIS software, check "Select features from the following layers", the target layer is the life behavior point set P layer of user 00069f1927ee5cef9919335d75170d3, the source layer is circle M The spatial selection method of the layer and target layer features is "completely located within the range of the source layer features". After selection, the exported layer is an SHP file of the "living life behavior point set Q".

(4) Extract the point M and the point set Q to form the minimum boundary geometric area, and obtain the area that forms the living circle of the mobile phone user.

(4.2) In ArcGIS software, use the "minimum boundary geometry" command, in the input features, input the point set X, in the geographic options, select "CONVEX HULL", output area Y, the formed surface The domain range is the living area of the mobile phone user 00069f1927ee5cef9919335d75170d3. As shown in Figure 5, it is the actual living circle of the mobile phone user 00069f1927ee5cef9919335d75170d3, and the specific range is the area P ₁ P ₂ P ₃ P ₄ P ₅ P ₆ P ₇ .

(4.3) Extract the behavioral activity point data of all users in the Ma Village residential area up to the residential area, repeat the above steps 3.2 to 4.2 to obtain the geometric area set {Y ₁ ,Y ₂ ,…… , Y _n }, take the union of all areas, and output area Y _zmc , which is the area of the living circle of the residential area. As shown in Figure 6, the area of the residential life circle of Zhima Village Residential Area:

P _M2 1-H ₁ -P _M3 2-P _M3 4-P _M1 2-H ₂ -P _M3 7-H ₃ -P _M2 2-P _M2 3-P _M1 5-P _M3 6-H ₄ -P _M1 6-P _M1 7-P _M2 6-P _M2 7, where P _M2 1 is the life behavior point of the mobile phone user whose residential behavior point is M2, and so on; H is the intersection of the residential area of the user in the residential area point.

Claims

A method for identifying a living circle based on positioning data, characterized in that the method includes the following steps:

(1) Obtain the user's location data file and preprocess the location data file;

(2) Perform behavior point recognition on the effective positioning data obtained in step (1), and divide the behavior points into residential behavior point M, work behavior point N, and life behavior point set P;

(3) Set the radius of the life circle D value, extract the life behavior points whose distance from the residence behavior point M is less than the value of D, and form the residence life behavior point set Q; and/or, extract the life behaviors whose distance from the work behavior point N is less than the value D Point, forming a point set S of work and life behavior;

(4) Extract point M and point set Q to form the minimum boundary geometric area, obtain the user’s living area area Y, and/or extract points N and point set S to form the minimum boundary geometric area, obtain the user’s work and life area Area L.
A method for identifying a living circle based on positioning data according to claim 1 or 2, wherein the positioning data file includes a user number, date, time, and latitude and longitude coordinate information of the user's location update.
The method for identifying a living circle based on positioning data according to claim 1 or 2, characterized in that the method of obtaining the user's positioning data file in step (1) is as follows: the positioning data file can be obtained from a mobile phone information push service provider. Sensitive positioning data, or GPS positioning data collected by GPS positioning equipment.
The method for identifying a living circle based on positioning data according to claim 1 or 2, characterized in that, the method for preprocessing the positioning data file in the step (1) is as follows: In EXCEL software, use search and select The positioning option in the function, select and delete the missing user number and latitude and longitude information; use the delete duplicate command to select and delete the data of the repeated user number; use the digital filter command to select and delete the date or the longitude and latitude are not in the preset normal value range Abnormal data within.
The method for identifying a living circle based on positioning data according to claim 1 or 2, characterized in that the positioning data file obtained in step (2) is identified by behavior points, and the behavior points are divided into residential behavior points M, The methods of work behavior point N and life behavior point set P are as follows:

(2.1) Define the distance threshold and the shortest time interval threshold, set the distance threshold to S meters, and the shortest time interval threshold to T 1 minute;

(2.2) Sort all the trajectory points in the positioning data according to the time sequence. For the trajectory point O n , determine whether the distance between the next trajectory point O n+1 and the trajectory point O n is less than S meters. If it is less than S meters, it is taken as The adjacent point of O n ; continue the above operation, continue to judge, to the trajectory point O n+m , if the distance from the trajectory point O n is greater than or equal to the threshold S meters, the search stops; calculate the trajectory point O n+m and O n locus of points of time intervals, as a duration equal to or greater than the predetermined time T 1, the recording track of all the points O n O n + m-1; such a duration less than T 1, to the O n O n + m -1 does not meet the requirements, the result is invalid; go to the next trajectory point, starting from the trajectory point O n+1 , repeat the above steps until a point that meets the conditions is found, go to step (2.3);

(2.3) Cluster all trajectory points O n to O n+m-1 obtained in step (2.2) to generate behavior points, where the latitude and longitude of the behavior point is the mean value of all trajectory points O n to O n+m-1 , The time t n when the track point O n is located is the start time of the action point, and the time t n+m-1 when the track point O n+m-1 is located is the end time of the action point;

(2.4) Starting from the track point On +m , repeat steps 2.2 and 2.3 to traverse all track points and identify all behavior points;

(2.5) Determine in turn whether the interval between the start time and end time of the action point obtained in step (2.4) has an intersection with the interval 23:00-05:00, record all the above-mentioned intersection action points to form a set A1, and select A1 The behavior point with the maximum time difference between the end time and the start time of the behavior point is taken as the residence behavior point M;

(2.6) Determine in turn whether the start time and end time of the action points obtained in step (2.4) have an intersection with the 08:00-17:00 interval, record all the above-mentioned intersection action points to form a set A2, and select the behavior in A2 The action point with the maximum time difference between the end time and the start time of the point is regarded as the work action point N;

(2.7) In all the behavior point files, remove the residence behavior point M and the work behavior point N, and get the life behavior point set P={p1, p2, p3,..., pn}.
The method for identifying a living circle based on positioning data according to claim 5, characterized in that, in step (3), the living behavior point whose distance from the living behavior point M and the working behavior point N is less than the value of D is extracted, and forming The specific methods of living life behavior point set Q and work life behavior point set S are as follows:

(3.1) Load the behavior point files obtained in steps (2.5)-(2.7) into ArcGIS software in layers to form three layers of residential behavior point M, work behavior point N, and life behavior point set P;

(3.2) In ArcGIS software, draw a circle with a radius of D with point M and point N as the center of the circle, which is a circle M layer and a circle N layer;

(3.3) In ArcGIS software, extract the points in the life behavior point set P that fall within the circle M layer, and export the layer as the SHP file of the life behavior point set Q;

(3.4) In the ArcGIS software, extract the points in the life behavior point set P falling within the circle N layer, and export the layer as an SHP file of the work life behavior point set S.
A method for identifying a living circle based on positioning data according to claim 6, characterized in that, in step (4), the point M and the point set Q are extracted to form a minimum boundary geometric area, and the user's living circle area Y is obtained The specific steps are as follows:

(4.1) In ArcGIS software, merge point M and point set Q to get point set X;

(4.2) In ArcGIS software, use the "minimum boundary geometry" command, in the input elements, input the point set X, in the geographic options, select "CONVEX HULL", output area Y, the formed area The domain scope is the living circle scope of the target object.
A method for identifying a living circle based on positioning data according to claim 6, characterized in that, in step (4), the point N and the point set S are extracted to form a minimum boundary geometric area, and the user's work and life circle area L is obtained The specific steps are as follows:

(4.3) In ArcGIS software, merge point N and point set S to get point set K;

(4.4) In the ArcGIS software, use the "minimum boundary geometry" command, in the input element, input the point set K, in the geographic options, select "CONVEX HULL", output the area L, the formed area The domain scope is the working life circle scope of the target object.
The method for identifying the living circle based on positioning data according to claim 6 or 7, characterized in that the living circle area of each object in the residential area is merged to obtain the living circle area of all objects in the residential area Domain: Combine the work and life circle areas of each object in the office area to obtain the work and life circle areas of all objects in the office area.