CN112543412A - Crowd classification method based on mobile phone signaling track point convex hull - Google Patents
Crowd classification method based on mobile phone signaling track point convex hull Download PDFInfo
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- H04W4/02—Services making use of location information
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- H04W4/02—Services making use of location information
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04W8/18—Processing of user or subscriber data, e.g. subscribed services, user preferences or user profiles; Transfer of user or subscriber data
- H04W8/183—Processing at user equipment or user record carrier
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
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- H04W8/18—Processing of user or subscriber data, e.g. subscribed services, user preferences or user profiles; Transfer of user or subscriber data
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- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention provides a crowd classification method based on mobile phone signaling track point convex hulls, which is characterized in that a track point data set of mobile phone users in a certain period of time in a certain area is obtained by utilizing mobile phone signaling data, the convex hulls of the track points are obtained through calculation according to the distribution of the track point positions of the mobile phone users, the users are classified according to the shapes and the areas of the convex hulls, the users are divided into unipolar large-range active crowds, unipolar small-range active crowds, bipolar large-range active crowds and bipolar small-range active crowds, and the crowd classification method can be used for providing reference basis for the construction of urban and community population supporting facilities.
Description
Technical Field
The invention relates to the technical field of mobile big data application, in particular to a crowd classification method based on a mobile phone signaling track point convex hull.
Background
Currently, mobile phone users are classified based on mobile phone signaling data, and there are mainly two directions: the method comprises the following steps of firstly, identifying population based on demographics, for example, analyzing the residence law of a mobile phone user by using signaling data to identify a standing population, a floating population and a working population of a certain place; the second is the identification of the transportation mode, for example, the mobile phone user is identified whether to take a certain transportation means by intercepting the signaling data of the mobile phone user in different time periods and calculating the displacement distance and the used time. The two crowd division methods can provide a reference for city managers to carry out city planning macroscopically.
Along with the development of society, traffic is more and more convenient, the range of activities of mobile phone users in a short time is larger and larger, the accessible spatial positions in unit time are wider and wider, the activity rules of the mobile phone users show the characteristics of complexity and diversity, the service objects of city managers are also shifted from the traditional constant population to the population with more complex structure, the service management measures are not only macroscopic city planning, but also the corresponding management and service schemes need to be formulated according to the characteristics of the population activities. Taking the construction of the supporting facilities of the community as an example, a community manager can identify the resident population in the community according to the existing population classification method at present and then estimate the scale of the requirements of the supporting facilities. However, the activity laws of the regular population are not exactly the same, and generally, the local support utilization rate of the unipolar active population is higher than that of the bipolar active population, and the support utilization rate of the small-range active population is higher than that of the large-range active population. If the groups can be classified more carefully according to the activity rules of the groups, a more reasonable community matching setting construction scheme can be made.
Disclosure of Invention
The invention aims to provide a crowd classification method based on a mobile phone signaling track point convex hull.
The technical scheme of the invention is as follows:
a crowd classification method based on a mobile phone signaling tracing point convex hull is characterized by comprising the following steps:
(1) acquiring signaling data: acquiring track information of each mobile phone user in a certain area range within a certain time period according to signaling data of a mobile operator, wherein the track information comprises longitude and latitude of a base station through which the mobile phone user passes, and acquiring a track point set of each mobile phone user;
(2) calculating a convex hull of a track point of a mobile phone user: calculating the track point convex hull of each user by using a Kudzuvine constant scanning method, wherein the specific method comprises the following steps:
(21) defining the direction of a latitude line from west to east as the positive direction of an x axis;
(22) selecting a point with the smallest latitude from the track points, and if a plurality of points exist, selecting the point with the smallest longitude and recording as A1;
(23) calculating the included angle between the connecting line of each track point and A1 and the positive direction of the x axis, sequencing the track points from small to large according to the included angle, and marking the obtained track point sequences as A1, A2, A3, … and An;
(24) a1 must be on the convex hull; considering points a1, a2, A3, if the line a2 to A3 is turned right relative to the line a1 to a2, then a2 is not on the convex hull, otherwise a2 is a point on the convex hull; if A2 is not a point on the convex hull, continuously considering three points A1, A3 and A4, if A2 is a point on the convex hull, continuously considering three points A2, A3 and A4 … …, and sequentially scanning each track point in this way until returning to A1, and obtaining the convex hull of the track point;
(3) determining the shape of the convex hull: selecting vertexes of the convex hull one by one, calculating the length of a connecting line between each vertex and the midpoint of each edge of the convex hull, and selecting the connecting line with the largest length as a long axis of the convex hull; making a vertical line of the long axis passing through the middle point of the long axis, wherein the vertical line and the convex hull are intersected at two points, and a connecting line between the two points is used as a short axis of the convex hull to calculate the length of the short axis; setting a shape threshold, wherein if the length difference between the long axis and the short axis of the convex hull is smaller than the set threshold, the shape of the convex hull is a unipolar convex hull, otherwise, the convex hull is a bipolar convex hull;
(4) calculating the area of the convex hull: selecting one vertex of the convex hull as a fixed point, gradually selecting two continuous vertexes on the convex hull clockwise or anticlockwise to form a triangle with the fixed point, and calculating the area of each triangle, wherein the area of the convex hull is the sum of the areas of the triangles; setting an area threshold, wherein if the area of the convex hull is smaller than the set area threshold, the convex hull is a small-area convex hull, otherwise, the convex hull is a large-area convex hull;
(5) classifying users according to the shape and area of the convex hull: according to the shape of the convex hull, users are divided into a unipolar activity crowd and a bipolar activity crowd; according to the convex hull area, dividing users into a large-range activity crowd and a small-range activity crowd; further, the users are finally divided into unipolar large-range active people, unipolar small-range active people, bipolar large-range active people and bipolar small-range active people.
The invention obtains the shape and the range of the activity track of the mobile phone user based on the track point distribution of the mobile phone user, classifies the user in the research area according to the shape and the range, can be used for providing reference for the construction of commercial supporting facilities, sports fitness facilities, traffic supporting facilities and the like in the area, and can assist in finishing more precise and reasonable city planning.
Drawings
FIG. 1 is a diagram of a user's trace point distribution;
FIG. 2 is a schematic diagram of a method for calculating a convex hull of track points of a user by using a Kudzuvine constant scanning method;
FIG. 3 is a schematic diagram of the determination of the major axis and the minor axis of a convex hull;
fig. 4 is a schematic diagram of the manner in which the area of the convex hull is determined.
Detailed Description
The method of the present invention is illustrated by a specific embodiment in the following with reference to the accompanying drawings.
(1) Acquiring signaling data: according to the signaling data of a mobile operator, acquiring the track information of each mobile phone user in a certain area range within a certain time period, including the longitude and latitude of a base station through which the mobile phone user passes, and obtaining the track point set of each mobile phone user.
The information acquisition scope of this embodiment selects certain large-scale community, gathers the position of every mobile user through the basic station in this community in a week and stores, obtains every mobile user's track point set, includes: the user IMSI (International Mobile Subscriber identity Number), the base station location longitude and the base station location latitude, wherein the base station passed by one user is shown in the following table:
| IMSI | base station location longitude | Base station location latitude |
| facb12baa6***********a1303f956a | 116.606246 | 40.112773 |
| facb12baa6***********a1303f956a | 116.64203 | 40.113672 |
| facb12baa6***********a1303f956a | 116.653958 | 40.126262 |
| facb12baa6***********a1303f956a | 116.621752 | 40.119967 |
| facb12baa6***********a1303f956a | 116.628909 | 40.132558 |
| facb12baa6***********a1303f956a | 116.624138 | 40.12986 |
| facb12baa6***********a1303f956a | 116.615788 | 40.131658 |
| facb12baa6***********a1303f956a | 116.60386 | 40.124464 |
The user's moving track points are shown in fig. 1.
(2) Calculating a convex hull of a track point of a mobile phone user: as shown in fig. 2, the trajectory point convex hull of each user is calculated by using the pueraria constant scanning method, and the specific method is as follows:
(21) defining the direction of a latitude line from west to east as the positive direction of an x axis;
(22) selecting a point with the smallest latitude from the track points, and if a plurality of points exist, selecting the point with the smallest longitude and recording as A1;
(23) calculating the included angle between the connecting line of the A1 and each track point (including the A1) and the positive direction of the x axis, sequencing the track points from small to large according to the included angle, and marking the obtained track point sequences as A1, A2, A3, A4, A5, A6, A7 and A8;
(24) a1 must be on the convex hull; considering points a1, a2, A3, if the line from a2 to A3 is turned right relative to the line from a1 to a2, then a2 is not on the convex hull, otherwise a2 is a point on the convex hull. If A2 is not a point on the convex hull, continuously considering three points A1, A3 and A4, if A2 is a point on the convex hull, continuously considering three points A2, A3 and A4 … …, and sequentially scanning each track point in this way until returning to A1, and obtaining the convex hull of the track point;
in fig. 2, a2 is a point on the convex hull because the line from a2 to A3 is turned left relative to the line from a1 to a 2; considering the three points a2, A3, a4 continuously, since the line A3 to a4 is turned left with respect to the line a2 to A3, A3 is a point on the convex hull, considering the three points A3, a4, a5 continuously, since the line a4 to a5 is turned right with respect to the line A3 to a4, a4 is not a point on the convex hull; continuing to consider the three points A3, a5, a6, a5 is a point on the convex hull because the line connecting a5 to a6 is rotated to the left relative to the line connecting A3 to a 5; continuing to consider the three points a5, a6, a7, a6 is not a point on the convex hull because the line connecting a6 to a7 is turned right relative to the line connecting a5 to a 6; continuing to consider the three points a5, a7, A8, a7 is a point on the convex hull because the line connecting a7 to A8 is rotated to the left relative to the line connecting a5 to a 7; continuing with the three points a7, A8, a1, A8 is a point on the convex hull because the line connecting A8 to a1 is rotated to the left relative to the line connecting a7 to A8.
And connecting the points on the convex hulls in sequence, wherein the convex hulls of the track points are formed by connecting lines A1-A2-A3-A5-A7-A8-A1 in FIG. 2.
(3) Determining the shape of the convex hull: as shown in fig. 3, selecting the vertexes of the convex hull one by one, calculating the length of a connecting line between each vertex and the midpoint of each edge of the convex hull, and selecting the connecting line with the largest length as the long axis of the convex hull; making a vertical line of the long axis passing through the middle point of the long axis, wherein the vertical line and the convex hull are intersected at two points, and a connecting line between the two points is used as a short axis of the convex hull to calculate the length of the short axis; setting a shape threshold, wherein if the length difference between the long axis and the short axis of the convex hull is smaller than the set threshold, the shape of the convex hull is a unipolar convex hull, otherwise, the convex hull is a bipolar convex hull;
the shape threshold of the invention can be set according to the project of specific application, the embodiment is used for the crowd division in a community, and the shape threshold is set to be 1000 meters. The user's trajectory convex hull has a major axis length of 4325 meters and a minor axis length of 1735 meters, the difference between these two being 2590 meters, which is greater than a given threshold of 1000 meters, and thus the convex hull is a bi-polar convex hull.
(4) Calculating the area of the convex hull: selecting one vertex of the convex hull as a fixed point, gradually selecting two continuous vertexes on the convex hull clockwise or anticlockwise to form a triangle with the fixed point, and calculating the area of each triangle, wherein the area of the convex hull is the sum of the areas of the triangles; and setting an area threshold, wherein if the area of the convex hull is smaller than the set area threshold, the convex hull is a small-area convex hull, and otherwise, the convex hull is a large-area convex hull.
As shown in fig. 4, a1 is selected as a fixed point, A8A7, A7A5, A5A3, A3a2 and a1 are selected clockwise to form a triangle, and the area of the convex hull is the sum of the areas of all the triangles formed in this way.
The area threshold value can be set according to specific application projects, the embodiment is used for crowd division in a community, and the area threshold value is set to be 5 square kilometers; the coverage area of the convex hull of the mobile phone user track point is calculated to be 6.97 square kilometers and is greater than a given threshold value by 5 square kilometers, so that the convex hull is a large-area convex hull.
(5) Classifying users according to the shape and area of the convex hull: according to the shape of the convex hull, users are divided into a unipolar activity crowd and a bipolar activity crowd; according to the convex hull area, dividing users into a large-range activity crowd and a small-range activity crowd; further, the users are finally divided into unipolar large-range active people, unipolar small-range active people, bipolar large-range active people and bipolar small-range active people.
In this embodiment, the convex hull of the tracing point of the user is bipolar, so the user of the mobile phone belongs to a bipolar active crowd; the track point convex hull belongs to a large-area convex hull, so that the mobile phone user belongs to a large-range active crowd; taken together, the user belongs to a bipolar wide range of active population.
In the above manner, all the residential population of the large community is classified in the above manner, and four types of population numbers shown in the following table are obtained:
| categories | Number of people |
| Monopole small range | 7481 |
| Monopole large area | 6334 |
| Dipole minimum range | 3534 |
| Bipolar large area | 2381 |
The classification of the invention provides reference for the matched construction of the service facilities of the community. Assuming that the community needs to newly start a supermarket, the size of the supermarket needs to be planned. Setting the demand coefficients of four groups of people for the supermarket respectively: the population coefficient of the monopole small-range activities is 0.8, the population coefficient of the monopole large-range activities is 0.5, the population coefficient of the bipolar small-range activities is 0.6, and the population coefficient of the bipolar large-range activities is 0.3, so that the supermarket needs to serve the following population scales:
7581 0.8+6334 0.5+3534 0.6+2381 0.3-12066.
Claims (1)
1. A crowd classification method based on a mobile phone signaling tracing point convex hull is characterized by comprising the following steps:
(1) acquiring signaling data: acquiring track information of each mobile phone user in a certain area range within a certain time period according to signaling data of a mobile operator, wherein the track information comprises longitude and latitude of a base station through which the mobile phone user passes, and acquiring a track point set of each mobile phone user;
(2) calculating a convex hull of a track point of a mobile phone user: calculating the track point convex hull of each user by using a Kudzuvine constant scanning method, wherein the specific method comprises the following steps:
(21) defining the direction of a latitude line from west to east as the positive direction of an x axis;
(22) selecting a point with the smallest latitude from the track points, and if a plurality of points exist, selecting the point with the smallest longitude and recording as A1;
(23) calculating the included angle between the connecting line of each track point and A1 and the positive direction of the x axis, sequencing the track points from small to large according to the included angle, and marking the obtained track point sequences as A1, A2, A3, … and An;
(24) a1 must be on the convex hull; considering points a1, a2, A3, if the line a2 to A3 is turned right relative to the line a1 to a2, then a2 is not on the convex hull, otherwise a2 is a point on the convex hull; if A2 is not a point on the convex hull, continuously considering three points A1, A3 and A4, if A2 is a point on the convex hull, continuously considering three points A2, A3 and A4 … …, and sequentially scanning each track point in this way until returning to A1, and obtaining the convex hull of the track point;
(3) determining the shape of the convex hull: selecting vertexes of the convex hull one by one, calculating the length of a connecting line between each vertex and the midpoint of each edge of the convex hull, and selecting the connecting line with the largest length as a long axis of the convex hull; making a vertical line of the long axis passing through the middle point of the long axis, wherein the vertical line and the convex hull are intersected at two points, and a connecting line between the two points is used as a short axis of the convex hull to calculate the length of the short axis; setting a shape threshold, wherein if the length difference between the long axis and the short axis of the convex hull is smaller than the set threshold, the shape of the convex hull is a unipolar convex hull, otherwise, the convex hull is a bipolar convex hull;
(4) calculating the area of the convex hull: selecting one vertex of the convex hull as a fixed point, gradually selecting two continuous vertexes on the convex hull clockwise or anticlockwise to form a triangle with the fixed point, and calculating the area of each triangle, wherein the area of the convex hull is the sum of the areas of the triangles; setting an area threshold, wherein if the area of the convex hull is smaller than the set area threshold, the convex hull is a small-area convex hull, otherwise, the convex hull is a large-area convex hull;
(5) classifying users according to the shape and area of the convex hull: according to the shape of the convex hull, users are divided into a unipolar activity crowd and a bipolar activity crowd; according to the convex hull area, dividing users into a large-range activity crowd and a small-range activity crowd; further, the users are finally divided into unipolar large-range active people, unipolar small-range active people, bipolar large-range active people and bipolar small-range active people.
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