CN110990443A - Mobile phone signaling-based professional and living population characteristic estimation method - Google Patents
Mobile phone signaling-based professional and living population characteristic estimation method Download PDFInfo
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Abstract
The invention discloses a mobile phone signaling-based occupational population characteristic estimation method, which comprises the following steps: step S1), acquiring target city signaling data, calculating a Geohash character string corresponding to the signaling record, and sorting according to time; step S2), eliminating ping-pong and drifting signaling records, and combining adjacent identical Geohash grids to obtain resident model data; step S3), selecting data of working time and night sleep period, and judging whether the working time and the night sleep period meet working and living conditions to obtain working and living model data; step S4) processing the mobile service user data sheet and the real-name system wide sheet provided by the operator to obtain a user data sheet; step S5) matching the user data sheet with the position model data to obtain position user basic information model data; step S6) statistically analyzing corresponding characteristics of the occupational population; the method comprises the steps that the position of a user is accurately identified by utilizing mobile phone signaling data and a user data sheet provided by an operator, and the position population characteristics are obtained; compared with the traditional investigation approach, the method saves time cost and human resources.
Description
Technical Field
The invention relates to the field of traffic planning, in particular to a mobile signaling-based estimation method for the occupational population characteristics.
Background
A common survey mode for acquiring basic data of resident travel, age, sex and the like in the traditional traffic planning is questionnaire survey, and the problems of long period, low sampling rate, time and labor waste and the like generally exist. Many years of questionnaires cannot meet long-term and continuous traffic planning requirements and cannot support rapid infrastructure construction in modern cities. The problems of unreasonable sampling rate, low sampling rate and the like easily occur under the condition of unbalanced population distribution, the accuracy and objectivity of data cannot be ensured, and the trend of traffic facility construction may be misled even under certain conditions. The preparation of questionnaire content, the manual delivery and retrieval of questionnaires, to a large extent, results in time-consuming and labor-intensive data acquisition, and lags behind the subsequent development of traffic planning.
By 2018, three operators in China have reached 3.03 hundred million households, 9.25 hundred million households and 3.15 hundred million households respectively for telecommunication, mobile and Unicom mobile users, and the mobile users in the whole country reach 15.43 hundred million households and almost spread all over the country. The users can generate a great amount of mobile phone data every day, and relevant data of the urban macro level, such as population density, resident trip OD and the like, can be quickly and accurately acquired by reasonably and effectively utilizing the data.
With the rapid progress of communication equipment technology and the rapid increase of the number of users, the daily output data also increases exponentially. The conventional data analysis means gradually reveals a bottleneck, the urgent need for a technical means for processing a large amount of data greatly promotes the development of the big data industry, and a plurality of excellent frameworks for big data acquisition, storage, calculation and analysis appear along with the development of the big data industry. Hadoop is one of the Hadoop, and due to the excellent design mode, low deployment cost and a comprehensive ecosystem, the Hadoop becomes a mainstream standard for processing big data in the existing industry, and the omnibearing requirement of enterprises on analysis and processing of the big data is met.
Disclosure of Invention
The invention aims to provide a mobile phone signaling-based professional and living population characteristic estimation method.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a mobile phone signaling-based professional and living population characteristic estimation method is characterized by comprising the following steps:
step S1) obtaining signaling data of a target city within one month from an operator, calculating a Geohash character string corresponding to each signaling record, and sequencing each user signaling record according to time;
step S2), after ping-pong and drifting signaling records are eliminated, adjacent identical Geohash grids are combined to obtain resident model data;
step S3), selecting data of working time and night sleep period, and judging whether the working time and the night sleep period meet working and living conditions to obtain working and living model data;
step S4), processing the mobile service user data sheet and the real name system wide sheet provided by the operator to obtain a user data sheet which can be used for matching with the occupational model user;
step S5) matching the obtained user data sheet with the position model data to obtain the basic information model data of the position user, such as age, sex and the like;
step S6), based on the basic information model data of the occupational users, the corresponding characteristics of the occupational population are analyzed statistically according to needs.
Further, the step S1 includes:
the Geohash algorithm obtains the length of the character corresponding to the latitude in each signaling record as LgeoAnd each of the Geohash character stringsThe user signaling records are ordered by time.
Further, in step S2, the removing the ping-pong signaling record specifically includes:
step S21), setting the total number of the records of the signaling as N, and counting all the records from the second record to the N-1 records;
step S22), setting the current record as the nth record, if the Geohash grids of the nth-1 record and the (n + 1) th record are the same, assigning a pingpang field of the record as 1, and if the Geohash grids are different, assigning a value of 0;
step S23) if the number of records with continuously 0 pingpang fields is larger than the set threshold NmaxIf so, the piece is continuously recorded as ping-pong signaling record;
step S24) calculating the total residence time of the two grids in all the records which continuously and alternately appear, and replacing all the grids of the record with the grids with the longest residence total time.
Further, in step S2, the elimination of the drift signaling record includes the following steps:
acquiring the time difference T of two adjacent signaling recordsdiff；
By calculating an angle radian formula:
rad＝α×π÷180
spherical distance formula:
S＝R×cos-1[cosβ1×cosβ2×cos(α1-α2)+sin(β1)×sinβ2]
velocity formula:
V＝S÷T
wherein α is an angle, R is the radius of the earth, α 1, α 2 respectively represent the longitude and latitude of one point, β 1, β 2 respectively represent the longitude and latitude of the other point, and the speed V is obtained by calculation;
the removing speed is more than VmaxIs recorded.
In step S2, merging neighboring identical Geohash grids includes:
merging and recording adjacent signaling data with the same Geohash grid, wherein the starting time of the first piece of signaling data in the same grid is taken as the starting time T in the resident gridstartEnd time of last piece of signaling dataAs an end time T at the resident gridend。
Further, the step S3 includes:
analyzing the data of the continuous residence model for one month according to the average working time of the city and the night sleeping time, and taking whether the accumulated days of the residence time exceeding the set duration in the same grid in the month are more than the set days as the conditions for judging the place of employment. Selecting the resident model record in the time interval to satisfy the condition that the resident time length in the same grid in one month is longer than TminIf the accumulated days of the grid is more than N, the grid is a residential place or a working place, and the user is a residential population or a working population of the place, so that the user occupation model data is obtained.
Further, the step S4 includes: the number in the mobile service user data table is removed, the most recently used one is selected, and the user information fields such as age, gender and the like in the wide table are obtained by matching the field of the subscription instance identifier (user _ id) in the table with the fields of the user id (subs _ instance _ id) in the wide table with the 2G, 3G and 4G real-name system.
Further, the step S5 includes: matching an access number (device _ number) field in a mobile service user data sheet with a mobile phone number (msisdn) field in a user occupation model, acquiring user basic information of the occupation model, such as information of age, gender and the like, and acquiring basic information model data of the occupation user.
The invention has the advantages that: only by using the mobile phone signaling data and a user data table provided by an operator, the place where the user is occupied is accurately identified, and the population characteristics of the user are obtained; compared with the traditional investigation approach, the method saves time cost and human resources. By means of the large sample size and the wide coverage of the signaling data, the finally obtained result is more accurate, the reliability is higher, and data support is provided for rapidly developing traffic planning.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a flowchart illustrating step S2 according to the present invention;
FIG. 3 is a diagram of Geohash character length correspondence accuracy.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment discloses a mobile signaling-based method for estimating occupational population characteristics, as shown in fig. 1, comprising the following steps:
step S1) obtaining signaling data of a target city within one month from Chinese communication, calculating a Geohash character string corresponding to each signaling record, and sequencing each user signaling record according to time;
step S2), after ping-pong and drifting signaling records are eliminated, adjacent identical Geohash grids are combined to obtain resident model data;
step S3), selecting data of working time and night sleep period, and judging whether the working time and the night sleep period meet working and living conditions to obtain working and living model data;
step S4), processing a mobile service user data sheet and a real-name system wide sheet provided by China Union to obtain a user data sheet which can be used for matching with a position model user;
step S5) matching the obtained user data sheet with the position model data to obtain the basic information model data of the position user, such as age, sex and the like;
step S6), based on the basic information model data of the occupational users, the corresponding characteristics of the occupational population are analyzed statistically according to needs.
The step S1 includes:
the Geohash algorithm obtains the length of the character corresponding to the latitude in each signaling record as LgeoAnd sorting each user signalling record by time, see fig. 3.
As shown in fig. 2, in the step S2, the removing the ping-pong signaling record specifically includes:
step S21), setting the total number of the records of the signaling as N, and counting all the records from the second record to the N-1 records;
step S22), setting the current record as the nth record, judging whether the Geohash grids of the nth-1 record and the nth +1 record are the same, if so, assigning the pingpang field of the record as 1, and if not, assigning 0;
step S23) determines whether the number of records satisfying the pingpang field being continuously 0 is greater than a set threshold NmaxIf yes, the piece is continuously recorded as ping-pong signaling record, and if not, no ping-pong signaling record exists;
step S24) calculating the total residence time of the two grids in all the records which continuously and alternately appear, and replacing all the grids of the record with the grids with the longest residence total time.
In step S2, the elimination of the drift signaling record includes the following steps:
acquiring the time difference T of two adjacent signaling recordsdiff；
By calculating an angle radian formula:
rad＝α×π÷180
spherical distance formula:
S＝R×cos-1[cosβ1×cosβ2×cos(α1-α2)+sin(β1)×sinβ2]
velocity formula:
V＝S÷T
wherein α is an angle, R is the radius of the earth, α 1, α 2 respectively represent the longitude and latitude of one point, β 1, β 2 respectively represent the longitude and latitude of the other point, and the speed V is obtained by calculation;
and rejecting records with the speed of more than 900 km/h.
In step S2, merging neighboring identical Geohash grids includes:
merging and recording adjacent signaling data with the same Geohash grid, wherein the starting time of the first piece of signaling data in the same grid is taken as the starting time T in the resident gridstartThe end time of the last piece of signaling data is taken as the end time T in the resident gridend。
The step S3 includes:
according to the cityAnalyzing the data of the continuous residence model for one month according to the average working time and the night sleeping time, and taking the condition that whether the accumulated days of the residence time exceeding the set duration in the same grid in the month are more than the set days as the conditions for judging the place of employment; selecting the resident model record in the time interval to satisfy the condition that the resident time length in the same grid in one month is longer than TminIf the accumulated days of the grid is more than N, the grid is a residential place or a working place, and the user is a residential population or a working population of the place, so that the user occupation model data is obtained.
In specific implementation, continuous one-month residence model data are analyzed according to the average working time [09:00,18:00] of the city and the night sleeping time [00:00,8:00 ]; and selecting the residence model record in the time interval, wherein the residence time of the residence model record in the same grid in one month is more than 2 hours, and the cumulative number of days is more than 20, the grid is a residence or a workplace, and the user is a residence or a workplace of the residence, so that the user occupation model data is obtained.
The step S4 includes: the number in the mobile service user data table is removed, the most recently used one is selected, and the user information fields such as age, gender and the like in the wide table are obtained by matching the field of the subscription instance identifier (user _ id) in the table with the fields of the user id (subs _ instance _ id) in the wide table with the 2G, 3G and 4G real-name system.
The step S5 includes: matching an access number (device _ number) field in a mobile service user data sheet with a mobile phone number (msisdn) field in a user occupation model, acquiring user basic information of the occupation model, such as information of age, gender and the like, and acquiring basic information model data of the occupation user.
The step S6 includes: and counting the distribution and proportion of the ages, the sexes and the like of the users in the position model based on the basic information model data of the users in the position, so as to obtain the corresponding position population characteristics.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. A mobile phone signaling-based professional and living population characteristic estimation method is characterized by comprising the following steps:
step S1) obtaining signaling data of a target city within one month from an operator, calculating a Geohash character string corresponding to each signaling record, and sequencing each user signaling record according to time;
step S2), after ping-pong and drifting signaling records are eliminated, adjacent identical Geohash grids are combined to obtain resident model data;
step S3), selecting data of working time and night sleep period, and judging whether the working time and the night sleep period meet working and living conditions to obtain working and living model data;
step S4), processing the mobile service user data sheet and the real name system wide sheet provided by the operator to obtain a user data sheet which can be used for matching with the occupational model user;
step S5) matching the obtained user data sheet with the position model data to obtain the basic information model data of the position user, such as age, sex and the like;
step S6), based on the basic information model data of the occupational users, the corresponding characteristics of the occupational population are analyzed statistically according to needs.
2. The estimation method according to claim 1, wherein the step S1 includes:
the Geohash algorithm obtains the length of the character corresponding to the latitude in each signaling record as LgeoAnd sequencing each user signaling record according to time.
3. The estimation method according to claim 1, wherein the eliminating the ping-pong signaling record in step S2 specifically comprises:
step S21), setting the total number of the records of the signaling as N, and counting all the records from the second record to the N-1 records;
step S22), setting the current record as the nth record, if the Geohash grids of the nth-1 record and the (n + 1) th record are the same, assigning a pingpang field of the record as 1, and if the Geohash grids are different, assigning a value of 0;
step S23) if the number of records with continuously 0 pingpang fields is larger than the set threshold NmaxIf so, the piece is continuously recorded as ping-pong signaling record;
step S24) calculating the total residence time of the two grids in all the records which continuously and alternately appear, and replacing all the grids of the record with the grids with the longest residence total time.
4. The estimation method according to claim 1, wherein in the step S2, the elimination of the drift signaling record comprises the steps of:
acquiring the time difference T of two adjacent signaling recordsdiff；
By calculating an angle radian formula:
rad＝α×π÷180
spherical distance formula:
S＝R×cos-1[cosβ1×cosβ2×cos(α1-α2)+sin(β1)×sinβ2]
velocity formula:
V＝S÷T
wherein α is an angle, R is the radius of the earth, α 1, α 2 respectively represent the longitude and latitude of one point, β 1, β 2 respectively represent the longitude and latitude of the other point, and the speed V is obtained by calculation;
the removing speed is more than VmaxIs recorded.
5. The estimation method according to claim 1, wherein the step S2, merging neighboring identical Geohash grids comprises:
merging and recording adjacent signaling data with the same Geohash grid, wherein the starting time of the first piece of signaling data in the same grid is taken as the starting time T in the resident gridstartThe end time of the last piece of signaling data is taken as the node in the resident gridTime of beam Tend。
6. The estimation method according to claim 1, wherein the step S3 includes:
analyzing the data of the continuous residence model for one month according to the average working time of the city and the night sleeping time, and taking whether the accumulated days of the residence time exceeding the set duration in the same grid in the month are more than the set days as the conditions for judging the place of employment. Selecting the resident model record in the time interval to satisfy the condition that the resident time length in the same grid in one month is longer than TminIf the accumulated days of the grid is more than N, the grid is a residential place or a working place, and the user is a residential population or a working population of the place, so that the user occupation model data is obtained.
7. The estimation method according to claim 1, wherein the step S4 includes: the number in the mobile service user data table is removed, the most recently used one is selected, and the user information fields such as age, gender and the like in the wide table are obtained by matching the field of the subscription instance identifier (user _ id) in the table with the fields of the user id (subs _ instance _ id) in the wide table with the 2G, 3G and 4G real-name system.
8. The estimation method according to claim 1, wherein the step S5 includes: matching an access number (device _ number) field in a mobile service user data sheet with a mobile phone number (msisdn) field in a user occupation model, acquiring user basic information of the occupation model, such as information of age, gender and the like, and acquiring basic information model data of the occupation user.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112001829A (en) * | 2020-08-14 | 2020-11-27 | 青岛市城市规划设计研究院 | Population distribution judgment method based on mobile phone signaling data |
| CN112312303A (en) * | 2020-09-29 | 2021-02-02 | 南京瑞栖智能交通技术产业研究院有限公司 | Mobile phone signaling data fine preprocessing method based on space-time characteristics |
| CN112711576A (en) * | 2020-12-11 | 2021-04-27 | 上海城市交通设计院有限公司 | Method for identifying inter-city travel modes of high-speed rail and airplane with mobile phone signaling data |
| CN113535785A (en) * | 2021-09-15 | 2021-10-22 | 北京交研智慧科技有限公司 | Job space analysis method and device, electronic equipment and readable storage medium |
| CN113613174A (en) * | 2021-07-09 | 2021-11-05 | 中山大学 | Method, device and storage medium for identifying occupational sites based on mobile phone signaling data |
| CN115034524A (en) * | 2022-08-11 | 2022-09-09 | 北京融信数联科技有限公司 | Method, system and storage medium for predicting working population based on mobile phone signaling |
| CN115062244A (en) * | 2022-08-18 | 2022-09-16 | 深圳市城市交通规划设计研究中心股份有限公司 | Space-time accompanying person and co-worker resident searching method based on multi-source data |
| CN116992267A (en) * | 2023-09-28 | 2023-11-03 | 北京融信数联科技有限公司 | Regional population gender identification method and system based on signaling data |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140301218A1 (en) * | 2011-12-29 | 2014-10-09 | Beijing Netqin Technology Co., Ltd. | Statistical analysis and prompting method and system for mobile terminal internet traffic |
| CN108650632A (en) * | 2018-04-28 | 2018-10-12 | 广州市交通规划研究院 | It is a kind of based on duty live correspondence and when space kernel clustering stationary point judgment method |
-
2019
- 2019-10-28 CN CN201911032331.2A patent/CN110990443A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140301218A1 (en) * | 2011-12-29 | 2014-10-09 | Beijing Netqin Technology Co., Ltd. | Statistical analysis and prompting method and system for mobile terminal internet traffic |
| CN108650632A (en) * | 2018-04-28 | 2018-10-12 | 广州市交通规划研究院 | It is a kind of based on duty live correspondence and when space kernel clustering stationary point judgment method |
Non-Patent Citations (2)
| Title |
|---|
| 刘耀林等: "基于手机数据的城市内部就业人口流动特征及形成机制分析――以武汉市为例" * |
| 许洪波;许金辉;: "基于手机信令的空间位置数据计算职住地" * |
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| CN112001829A (en) * | 2020-08-14 | 2020-11-27 | 青岛市城市规划设计研究院 | Population distribution judgment method based on mobile phone signaling data |
| CN112001829B (en) * | 2020-08-14 | 2024-03-12 | 青岛市城市规划设计研究院 | Population distribution judging method based on mobile phone signaling data |
| CN112312303A (en) * | 2020-09-29 | 2021-02-02 | 南京瑞栖智能交通技术产业研究院有限公司 | Mobile phone signaling data fine preprocessing method based on space-time characteristics |
| CN112312303B (en) * | 2020-09-29 | 2022-07-22 | 南京瑞栖智能交通技术产业研究院有限公司 | Mobile phone signaling data fine preprocessing method based on space-time characteristics |
| CN112711576A (en) * | 2020-12-11 | 2021-04-27 | 上海城市交通设计院有限公司 | Method for identifying inter-city travel modes of high-speed rail and airplane with mobile phone signaling data |
| CN112711576B (en) * | 2020-12-11 | 2023-03-10 | 上海城市交通设计院有限公司 | Method for identifying inter-city travel modes of high-speed rail and airplane with mobile phone signaling data |
| CN113613174A (en) * | 2021-07-09 | 2021-11-05 | 中山大学 | Method, device and storage medium for identifying occupational sites based on mobile phone signaling data |
| CN113535785A (en) * | 2021-09-15 | 2021-10-22 | 北京交研智慧科技有限公司 | Job space analysis method and device, electronic equipment and readable storage medium |
| CN115034524A (en) * | 2022-08-11 | 2022-09-09 | 北京融信数联科技有限公司 | Method, system and storage medium for predicting working population based on mobile phone signaling |
| CN115062244A (en) * | 2022-08-18 | 2022-09-16 | 深圳市城市交通规划设计研究中心股份有限公司 | Space-time accompanying person and co-worker resident searching method based on multi-source data |
| CN116992267A (en) * | 2023-09-28 | 2023-11-03 | 北京融信数联科技有限公司 | Regional population gender identification method and system based on signaling data |
| CN116992267B (en) * | 2023-09-28 | 2024-01-23 | 北京融信数联科技有限公司 | Regional population gender identification method and system based on signaling data |
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