CN112541012A - City position balance demographic method based on mobile communication big data and entropy value - Google Patents
City position balance demographic method based on mobile communication big data and entropy value Download PDFInfo
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Abstract
The invention provides a city occupation balance population statistical method based on mobile communication big data and entropy, which utilizes telecommunication operator signaling data to identify the position of a base station where each IMSI number in a certain city is located in the working place in the day and the residence place in the night by entropy calculation, and calculate the distance between the working place and the residence place, wherein the distance is the working commuting distance of the IMSI, and determines whether the commuting distance of the IMSI is in a reasonable range according to the distance information, and finally, the IMSI with all the commuting distances in the reasonable range is used as a numerator, the number of all the ordinary population in the month of the city is used as a denominator, namely, the percentage of the city occupation balance population in the month is counted.
Description
Technical Field
The invention relates to the technical field of mobile communication, in particular to a statistical method for urban position and occupation balance population by utilizing mobile communication big data and entropy calculation.
Background
The problem of job-hold balance directly affects the benign operation and social harmony of traffic functions. The term of occupation balance is a term in the field of urban planning, and the basic connotation means that most residents can work nearby in a given regional range; commuting traffic may be by foot, bicycle, or other non-motorized means; even if the motor vehicle is used, the traveling distance and the traveling time are short and limited within a reasonable range, so that the use of the motor vehicle, particularly a car, is favorably reduced, and the traffic jam and the air pollution are reduced.
In view of the current urban population regulation and control requirements, a statistical department needs to use big data to carry out dynamic monitoring on population data, improve the existing population monitoring system, explore the relationship among industrial regulation and control, functional layout and population development, realize regular tracking and mastering of the flow direction of the dismissed population and timely warn the change trend of the regional population. With the continuous expansion of mobile communication scale and the continuous development of technology, the storage of large-scale communication data, particularly trajectory data, is realized, so that the estimation of urban population scale and flow situation by taking mobile communication big data analysis as a research means becomes possible. Compared with the traditional research method, the method has higher reliability and accuracy based on big data statistics and population monitoring.
Disclosure of Invention
The invention aims to provide a city job-live balance population statistical method based on mobile communication big data and entropy calculation.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for city occupation balance demographics based on mobile communication big data and entropy, characterized by:
(1) data acquisition: acquiring and storing the position of a base station where each IMSI identification number is located in a designated city and the time information of entering and exiting the base station by using signaling data of a telecom operator to obtain the movement track data of each IMSI;
(2) data preprocessing: carrying out interpolation compensation on missing signaling entering and exiting a base station, and if a certain IMSI only enters a certain base station time and does not leave the base station time or only leaves the certain base station time and does not enter the base station time in a statistical time period, carrying out interpolation on missing data, wherein interpolation time points are the starting time and the ending time of the statistical time period;
(3) and (3) working place data screening: reading one IMSI and all base stations visited between nine points early and four points late each time, listing the base stations visited in all working time periods corresponding to one IMSI into a statistical table according to the track data obtained in the data acquisition step, further counting the stay time of each base station, and then calculating the entropy value of the IMSI in each base station;
(4) residential area data screening: reading one IMSI and all base stations visited between eight late points and five early points each time, listing the base stations visited in all residence time periods corresponding to the IMSI into a statistical table according to the track data obtained in the data acquisition step, further counting the stay time of each base station, and then calculating the entropy value of the IMSI in each base station;
(5) and (3) distance calculation: selecting a base station with the largest IMSI entropy value between nine points in the morning and four points in the evening as a working place; selecting one base station with the maximum IMSI entropy value from eight late points to five early points as a residence, and calculating the distance between the residence and a working place according to the longitude and latitude corresponding to the specific base station; continuously calculating the distance between the residence and the working place within one month and averaging, and if the distance average is smaller than the average commuting distance of the city, considering the IMSI as the commuting distance within a reasonable range, namely, the working balance population;
(6) balance of employment demographics: and taking the IMSI of all the commuting distances within a reasonable range as a numerator, and taking the number of all the regular lives in the current month of the city as a denominator, namely counting the percentage of the working and living balance population of the city.
The invention counts the resident time of the mobile user in the accessed base station through the mobile track data of the mobile user, predicts the working place and the residence place of the user through the time of the accessed base station, has reliable data source, simple judgment method and high accuracy of the prediction result, and provides favorable support for the practice of carrying out population statistics and monitoring by utilizing the big communication data.
Detailed Description
The process of the invention is illustrated below by means of a specific example.
The invention can be used for counting the balanced occupational population of any city. The following example is a statistical analysis of the balanced occupational population in Beijing. Currently, the academic world generally considers that the average or medium commute distance in a city should be used as the equilibrium distance of the job, so in this embodiment, the average commute distance of 17.4 km in beijing is used as the reasonable commute distance, and the population smaller than the average commute distance in beijing is used as the equilibrium population of the job.
The specific implementation mode of the invention is as follows:
(1) data acquisition: the data adopted by the embodiment is the mobile phone signaling data between 7 months and 1 day to 31 days in 2019 of Beijing City. And acquiring and storing the position of the base station where each IMSI identification number is located and the time information of entering and exiting the base station by utilizing the signaling data of the telecom operator to obtain the moving track data of each IMSI. The data adopted by the invention comes from signaling data of a mobile operator, and comprises the following steps: subscriber's Mobile phone Number-IMSI (International Mobile Subscriber identity Number); position region identifier lac: for identifying different location areas; base station number ci: combined with a location area identity (lac) for identifying a cell covered in the network; the time the IMSI enters the base station, the time it leaves the base station.
(2) Data preprocessing: and carrying out interpolation compensation on the missing signaling of the in-out base station. To ensure the integrity of data, if a user only enters a sector time and does not leave the sector time or leaves the sector time and does not enter the sector time within a statistical time period, the missing data needs to be interpolated, and the interpolation time points are the starting time and the ending time of the statistical time period.
For example, a user a enters sector X on day 1 of 7 month 23:00:00, leaves sector X on day 2 of 7 month 7:00:00, and enters sector Y at time 23:00: 00:00 on day 2 of 7 month, and leaves at time 7:00:00 on day 3 of 7 month, and when the information of the user a on day 2 of 7 month is collected, the time point of entering sector X and the time point of leaving sector Y are missing, so it is necessary to interpolate the time point of entering sector X at 00:00: 00:00 on day 2 of 7 month, and the time point of leaving sector Y at 23:59:59 on day 2 of 7 month.
(3) And (3) working place data screening: reading one IMSI and all loc-ci accessed between nine points early and four points late each time; the selection from the early nine points to the late four points is to simulate working hours on duty; and according to the track data obtained in the data acquisition step, listing the loc-ci accessed in all the working time periods corresponding to one IMSI into a statistical table, further counting the stay time of each loc-ci, and then calculating the entropy value of the IMSI in each base station. The specific calculation method comprises the following steps:
the entropy value of IMSI i at base station j is:
(4) Residential area data screening: reading one IMSI and all loc-ci accessed between eight late points and five early points each time; the selection from eight night to five morning is to simulate off-duty rest time; and according to the track data obtained in the data acquisition step, listing the loc-ci accessed in all the working time periods corresponding to one IMSI into a statistical table, further counting the stay time of each loc-ci, and then calculating the entropy value of the IMSI in each base station. The specific calculation method comprises the following steps:
the entropy value of IMSI i at base station j is:
(5) And (3) distance calculation: selecting a base station with the largest IMSI entropy value between nine points in the morning and four points in the evening as a working place; selecting one base station with the maximum IMSI entropy value from eight late points to five early points as a residence, and calculating the distance between the residence and a working place according to the longitude and latitude corresponding to the specific base station; the distance between the residence and the workplace within one month is continuously calculated and averaged, and if the average distance is less than the average commuting distance in Beijing, the IMSI is considered to be within a reasonable range-the working balance population.
The distance Dis is calculated as follows:
Dis＝R*acos(sinpi(y1/180)*sinpi(y2/180)+cospi(y1/180)*cospi(y2/180)*cospi((x1-x2)/180))；
wherein R represents the radius of the earth; x1, x2 represent the longitude of two loc-ci, respectively, and y1, y2 represent the latitude of two loc-ci, respectively.
(6) Balance of employment demographics: and taking the IMSI with the commuting distance within a reasonable range as a numerator and taking the number of all the regular living population in the month of Beijing as a denominator, namely counting the percentage of the balanced population of the working and living in the Beijing.
Claims (2)
1. A city occupation balance demographic method based on mobile communication big data and entropy is characterized in that:
(1) data acquisition: acquiring and storing the position of a base station where each IMSI identification number is located in a designated city and the time information of entering and exiting the base station by using signaling data of a telecom operator to obtain the movement track data of each IMSI;
(2) data preprocessing: carrying out interpolation compensation on missing signaling entering and exiting a base station, and if a certain IMSI only enters a certain base station time and does not leave the base station time or only leaves the certain base station time and does not enter the base station time in a statistical time period, carrying out interpolation on missing data, wherein interpolation time points are the starting time and the ending time of the statistical time period;
(3) and (3) working place data screening: reading one IMSI and all base stations visited between nine points early and four points late each time, listing the base stations visited in all working time periods corresponding to one IMSI into a statistical table according to the track data obtained in the data acquisition step, further counting the stay time of each base station, and then calculating the entropy value of the IMSI in each base station;
(4) residential area data screening: reading one IMSI and all base stations visited between eight late points and five early points each time, listing the base stations visited in all residence time periods corresponding to the IMSI into a statistical table according to the track data obtained in the data acquisition step, further counting the stay time of each base station, and then calculating the entropy value of the IMSI in each base station;
(5) and (3) distance calculation: selecting a base station with the largest IMSI entropy value between nine points in the morning and four points in the evening as a working place; selecting one base station with the maximum IMSI entropy value from eight late points to five early points as a residence, and calculating the distance between the residence and a working place according to the longitude and latitude corresponding to the specific base station; continuously calculating the distance between the residence and the working place within one month and averaging, and if the distance average is smaller than the average commuting distance of the city, considering the IMSI as the commuting distance within a reasonable range, namely, the working balance population;
(6) balance of employment demographics: and taking the IMSI of all the commuting distances within a reasonable range as a numerator, and taking the number of all the regular lives in the current month of the city as a denominator, namely counting the percentage of the working and living balance population of the city.
2. The mobile communication big data and entropy based city-occupational balanced demographic method of claim 1, wherein: the distance Dis between two base stations is calculated according to the following formula:
Dis＝R*acos(sinpi(y1/180)*sinpi(y2/180)+cospi(y1/180)*cospi(y2/180)*cospi((x1-x2)/180))，
in the formula, R represents the radius of the earth; x1 and x2 respectively represent the longitude of the positions of the two base stations, and y1 and y2 respectively represent the latitude of the positions of the two base stations.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN116128128A (en) * | 2023-01-17 | 2023-05-16 | 北京融信数联科技有限公司 | Urban job-living balance prediction method, system and medium based on intelligent agent map |
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| WO2017133627A1 (en) * | 2016-02-03 | 2017-08-10 | 中兴通讯股份有限公司 | User commuter track management method, device and system |
| CN110472775A (en) * | 2019-07-26 | 2019-11-19 | 广州大学 | A kind of series case suspect's foothold prediction technique |
| CN110473132A (en) * | 2019-08-27 | 2019-11-19 | 上海云砥信息科技有限公司 | Balance evaluation method is lived in a kind of region duty based on mobile data |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017133627A1 (en) * | 2016-02-03 | 2017-08-10 | 中兴通讯股份有限公司 | User commuter track management method, device and system |
| CN110472775A (en) * | 2019-07-26 | 2019-11-19 | 广州大学 | A kind of series case suspect's foothold prediction technique |
| CN110473132A (en) * | 2019-08-27 | 2019-11-19 | 上海云砥信息科技有限公司 | Balance evaluation method is lived in a kind of region duty based on mobile data |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN116128128A (en) * | 2023-01-17 | 2023-05-16 | 北京融信数联科技有限公司 | Urban job-living balance prediction method, system and medium based on intelligent agent map |
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Application publication date: 20210323 |