CN114037239A - Potential model employment reachability analysis method based on multi-source big data - Google Patents
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Abstract
The invention discloses a potential model employment reachability analysis method based on multi-source data, which comprises the following steps: (1) determining a research unit according to the service range of the mobile phone base station and the traffic network data; (2) constructing a employment reachability database capable of reflecting living information, employment information and traffic infrastructure information according to multi-source data such as mobile phone signaling big data and path planning API big data; (3) constructing a potential model according to the travel cost and the mobile phone signaling data; (4) and analyzing employment accessibility by using a potential model. The method can be used for comparing employment reachability differences of different traffic modes and correlation analysis of multiple elements and employment reachability so as to guide planning and construction of urban traffic infrastructure and residential district groups.
Description
Technical Field
The invention relates to a potential model employment reachability analysis method based on multi-source big data, and belongs to the technical field of reachability.
Background
Past reachability has been based on location analysis of location reachability on a macro level, or infrastructure analysis of traffic infrastructure performance or service levels, or cost analysis of travel based on cost. However, as modern cities continue to grow, the content of reachability is also continuously enriched and expanded, and reachability can also be understood as the ease with which more available opportunities are obtained. In addition to the location reachability, infrastructure reachability, cost reachability, and the like, a branch of employment reachability is derived. Employment accessibility is defined as the ease of arrival from a residential location to a place of employment and the spatial potential to obtain employment opportunities, and can be used to analyze problems such as job-space mismatch, job-balance, excessive commute, and the like. The employment accessibility is closely related to the distribution of the road network and the employment posts, and when the city form changes, the employment accessibility is changed. Employment accessibility spatial patterns under different transportation modes also have differences. In turn, employment accessibility has a certain adverse effect on urban space architecture and facility layout. In the process of rapid urbanization in China, the employment accessibility space is unbalanced due to uneven resource distribution. Therefore, the improvement of the accessibility of the employment of residents is significant for building a city with fair traffic centering on people. This puts higher demands on the analysis of employment accessibility.
In the initial stage of employment accessibility research, researchers directly regard travel time from a place of residence to a place of employment as employment accessibility, neglect attributes of the place of residence and the place of employment, and do not consider competitive relations among people in employment. As technology advances, employment supply and demand relationships, land types, and transportation facilities are gradually taken into account in the development of potential models of employment accessibility. However, due to the problems of difficult data acquisition, technical operation deficiency, difference in transportation modes and the like, a potential model of employment accessibility needs to be further deepened and perfected.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: the problems of overhigh data research cost, low traffic impedance precision and wide model application range in the employment accessibility calculation process of the potential model.
In order to solve the technical problems, the invention provides a potential model employment reachability evaluation method based on multi-source big data, which considers employment requirements, post supply and traffic impedance distance attenuation, can reflect the actual employment and commuting conditions of a city by using the existing big data, and obtains a more real employment reachability result.
The technical scheme adopted by the invention for solving the technical problems is as follows:
the invention provides a potential model employment reachability analysis method based on multi-source big data, which comprises the following steps:
(1) acquiring traffic cell data of a research city, and determining a research unit as a traffic cell;
(2) mobile phone signaling data, road network data, bus stops and line network data and travel distance data in different transportation modes of a researched city are obtained, and a multi-source big data construction employment accessibility base database is formed;
(3) acquiring mobile phone signaling positioning data in a research range, setting judgment rules of the number of the residential and employment population, and counting to obtain the number of the employment and residential population of each base station;
(4) carrying out statistics again on the employment and residence data of the base station by the boundaries of the traffic districts to obtain the number of residences and employment people of each traffic district;
(5) introducing three factors of employment demand, post supply and traffic impedance distance attenuation to construct a potential model;
(6) and (5) obtaining a potential model suitable for the researched city according to the step (5), calculating employment accessibility index values of all traffic cells, and analyzing the employment accessibility of the researched city in different traffic modes.
Further, the potential model employment accessibility analysis method based on the multi-source big data provided by the invention comprises the following steps of (2):
calling a God map API through programming to obtain travel distance data; the population numbers of the living and employment are put in a list form, and the impedance of the public transportation and the car are put in an OD form.
Further, the potential model employment accessibility analysis method based on the multi-source big data provided by the invention comprises the following steps of (3):
firstly, determining the specific time period of the morning and evening peak of a city according to the result of urban traffic comprehensive survey, and respectively counting the departure and attraction traffic of the morning and evening peak in the service range of each base station; sample expansion is carried out according to a certain principle, and the real travel situation is approached as much as possible;
secondly, determining the one-to-one corresponding relation between the base station and the mobile phone signaling data according to the codes to determine the working and rest time: specifically selecting a conventional working time period of a working day in a working period, identifying a base station with average daily stay time more than two hours, and simultaneously removing mobile phone users with the activity ranges of daytime and night fixed within a certain range, wherein the position of the finally identified base station is the working place of the MSID; the residence place is the residence place of the mobile phone, the residence time of the mobile phone signal in the base station is the maximum during the early morning, and the daily residence time is longer than a certain time length, and the base station is identified as the residence place.
Further, the potential model employment accessibility analysis method based on the multi-source big data provided by the invention comprises the following steps of (4):
firstly, creating a Thiessen polygon in ArcGIS according to the service range of a base station, endowing the trip data of the base station to the Thiessen polygon, establishing spatial correlation, and calculating the trip population density;
then, overlapping and analyzing INTERSECT between the traffic cell and the Thiessen polygon of the base station, and calculating the number of people going out;
and finally, carrying out space statistics to obtain the number of residences and employment people in each traffic community.
Further, in the potential model employment accessibility analysis method based on multi-source big data provided by the invention, in the step (5), the potential model calculation formula is as follows:
whereinA i Refers to living in a traffic districtiThe employment accessibility of residents;
O j finger in traffic districtjThe number of employment posts present in (a);
T ij finger traffic districtiTojGo out ofImpedance, expressed in trip distance;
F(T ij ) Finger slave traffic districtiTojTraffic impedance function of traffic cellsiTojA square representation of the distance of (d);
P k means living in traffic districtsjBut in traffic districtskThe number of persons in the middle employment;
T kj for subordinate traffic districtskTojThe trip impedance, expressed as a trip distance;
F(T kj ) For subordinate traffic districtsiTokTraffic impedance function of, using traffic cellsiTokIs expressed as the square of the distance of (a).
Compared with the prior art, the invention has the beneficial technical effects that:
(1) the invention adopts the mobile phone signaling data to identify the residential areas and employment areas and judge the quantity, can fully utilize the existing data, construct the employment accessibility research basic database, reduce the investigation cost, obtain more accurate and new population numbers of the residential areas and the employment, and solve the problem that the residential and employment data of China are difficult to obtain.
(2) The invention takes the traffic district as a basic research unit, and the analysis result can directly provide guidance and reference for the specific design, evaluation and implementation of the city and traffic planning.
(3) The method has wide application range, can be used for calculating employment accessibility of different traffic modes in cities of different scale grades, so as to explore the weak areas of public traffic, analyze the overall structural layout of urban residential areas and the like.
Drawings
FIG. 1 is a flowchart of a job accessibility analysis method of the present invention.
Fig. 2 is a employment reachability database construction diagram in the present invention.
Fig. 3 is a technical route for job site determination in the present invention.
Fig. 4 is a schematic diagram of car employment accessibility.
Fig. 5 is a schematic diagram of public transportation employment accessibility.
Fig. 6 is a schematic diagram of a traffic cell in an embodiment of the invention.
Fig. 7 is a distribution diagram of the number of residents in the urban area of the kunshan city based on the base station.
Fig. 8 is a distribution diagram of the number of residents in the central urban area of the kunshan city based on the traffic zone.
Fig. 9 is a distribution diagram of the number of employment people in the urban area of the kunshan city based on the base station.
Fig. 10 is a distribution diagram of the number of employment people in the urban area of the kunshan city based on the base station.
Fig. 11 is a view of availability of urban cars in the city of Kun shan.
Fig. 12 is a map of public transportation employment accessibility in urban areas in the city of Qunshan.
Fig. 13 is a comparison graph of employment accessibility of different travel modes in urban areas of the city of Qunshan.
Detailed Description
The following description of the embodiments of the present invention will be described in detail with reference to the accompanying drawings and examples.
The invention designs a potential model employment reachability analysis method based on multi-source big data, and an implementation flow chart is shown in figure 1. Taking the analysis of Kunshan city, Jiangsu province as an example, the method specifically comprises the following steps:
(1) and acquiring traffic cell data of the Kun mountain city, and determining a research unit as a traffic cell. As shown in fig. 6.
(2) The database required for the study was constructed with reference to fig. 2. Using mobile phone signaling data with large sample size and fast data updating to represent residence and employment population data required in the research, and warehousing the data in a list form; and (3) calling a Gauss map API through programming to acquire travel time of various travel modes as impedance data, and storing the impedance data in an OD table form. And in other basic data, the boundary of the traffic cell and the position data of the base station are provided by a planning office. And the Qunshan city road network, the bus stop and the network are obtained by crawling by using a high-grade map API.
(4) Referring to fig. 3, the position of the base station and the signaling data of the mobile phone are obtained to determine the place of employment according to the set determination rule. Firstly, determining the one-to-one corresponding relation between a base station and mobile phone signaling data according to codes; and secondly, determining the working and rest time. Specifically selecting 9:00-11:00 and 14:00-16:30 of a working day in the working period, identifying base stations with average daily stay time longer than two hours, simultaneously eliminating mobile phone users with the activity ranges of daytime and night fixed within the range of 400m, and finally identifying the position of the base station, namely the working place of the MSID. The residence is the residence time when the stay time of the mobile phone signal is maximum in the base station in the period of 1:00 to 7:00 and the average daily stay time is more than 4 hours, the base station is identified as the residence. And rejecting unconventional occupational data with the same place and unfixed work place of the occupational and the work place, and disregarding the unconventional occupational data. In the research, data of 14 days including 156 ten thousand MSIDs (MSID data) of a certain mobile communication operator 2017, 6 months, 4-17 is adopted, and the positions of 135 ten thousand people are identified.
(5) And carrying out statistics again on employment and residence data of 2548 base stations in the urban area of the Kun-shan city by using the boundary of the traffic cell. Creating a Thiessen polygon in the ArcGIS according to the service range of the base station, giving the trip data of the base station to the Thiessen polygon, establishing spatial correlation, and calculating the trip population density; then, overlapping and analyzing INTERSECT (intersection) are carried out on the traffic cell and the Thiessen polygon of the base station, and the number of people going out is calculated; and finally, carrying out space statistics to obtain the number of residences and employment people in 497 traffic districts in the central urban area of the Kun mountain city. The results are shown in fig. 7 to 10, in which: fig. 7 is a distribution diagram of the number of persons living in the central urban area of the kun-shan city based on the base station, fig. 8 is a distribution diagram of the number of persons living in the central urban area of the kun-shan city based on the traffic cell, fig. 9 is a distribution diagram of the number of persons working in the central urban area of the kun-shan city based on the base station, and fig. 10 is a distribution diagram of the number of persons working in the central urban area of the kun-shan city based on the base station.
Land utilization, traffic facilities and population distribution characteristics are gradually considered in the career accessibility research, and three factors of career demand, post supply and traffic impedance distance attenuation are specifically introduced to construct a potential model:
wherein A isiThe employment accessibility of residents living in a traffic cell i is referred to;
Ojthe number of employment posts existing in the traffic cell j is referred to;
Tijthe travel impedance of the traffic cell j is expressed by the travel distance;
F(Tij) Refers to the traffic impedance function from traffic cell i to j, expressed as the square of the distance from traffic cell i to j;
Pkthe number of people who live in the traffic cell j but have employment in the traffic cell k;
Tkjis the travel impedance from traffic cell k to j, expressed as the travel distance;
F(Tkj) Is a traffic impedance function from traffic cell i to k expressed as the square of the distance of the traffic cell i to k.
And determining the employment reachability of each traffic cell according to the finally obtained potential model. Fig. 11 is a plot of employment accessibility of cars in urban areas in the center of the kunshan city, fig. 12 is a plot of employment accessibility of public transportation in urban areas in the center of the kunshan city, and fig. 13 is a comparison plot of employment accessibility of different travel modes in urban areas in the center of the kunshan city.
The spatial characteristic of car employment accessibility in urban areas in the center of Kunshan city is a ring layer structure which is deviated to the east, and areas with high car employment accessibility are concentrated in old urban areas, eastern New cities and bridgework business areas. The car employment accessibility of the city in the center of the Kun mountain city presents a spatial characteristic from the center to the periphery, the distribution characteristic of the car employment accessibility has a certain relation with the car traveling impedance and the distribution of employment posts, and the flower bridge business city with a large number of posts can be provided with high employment accessibility. Influenced by the traffic convenience of the traffic trunk, the accessibility of cars near Shanhun lines and Kunshan south stations is high. As the new employment center in Kun mountain city, not only a large number of employment posts are provided, but also the distance from the old city is made to be far away from the living density, so that the employment competition pressure among people is small, and the employment accessibility is improved.
The public transportation employment accessibility of the city region of the Kun mountain city center presents a spatial characteristic of expanding from the center to four city auxiliary centers along the public transportation axis, and the regions with high public transportation employment accessibility are concentrated in the old city region, the middle part of the eastern New City, the south part of the southern New City and the eastern part of the bridgework business region. The spatial pattern of the public transportation employment accessibility has certain similarity with the public transportation impedance, and presents certain axial characteristics, but due to the influence of the spatial distribution of the employment opportunities, the gathering center of the employment accessibility is an old city area, a south Queen mountain station and a flower bridge business area, and the parts of the south and north new cities, which are positioned in a bus corridor, have better employment accessibility level due to smaller public transportation travel impedance. It is noted that there are areas with higher levels of accessibility in parts of the southern new city because the population of residences is relatively harmonious with the population of employment, and away from the old city. Thus, the employment competition pressure is less, and the employment accessibility level is greatly improved.
The public transportation employment reachability is arranged from big to small, then the data of the car employment reachability is displayed in a statistical mode according to the sequence of traffic districts, and the fact that the employment reachability of the cars in the urban area of the city of the Qunshan is stronger than that of the public transportation can be known through the analysis of the different travel modes of the car employment reachability. On the basis, other evaluation indexes can be introduced to specifically analyze the accessibility difference of different traffic modes. Fig. 4 is a schematic view of car employment accessibility, and fig. 5 is a schematic view of public transportation employment accessibility.
The foregoing is a more detailed description of the invention in connection with specific urban areas and the specific nature of the invention is not to be considered as limited to the foregoing description. It will be apparent to those skilled in the art that various modifications, additions, substitutions, and the like can be made without departing from the spirit of the invention and are therefore considered to be within the scope of the invention.
Claims (5)
1. A potential model employment reachability analysis method based on multi-source big data is characterized by comprising the following steps:
(1) acquiring traffic cell data of a research city, and determining a research unit as a traffic cell;
(2) mobile phone signaling data, road network data, bus stops and line network data and travel distance data in different transportation modes of a researched city are obtained, and a multi-source big data construction employment accessibility base database is formed;
(3) acquiring mobile phone signaling positioning data in a research range, setting judgment rules of the number of the residential and employment population, and counting to obtain the number of the employment and residential population of each base station;
(4) carrying out statistics again on the employment and residence data of the base station by the boundaries of the traffic districts to obtain the number of residences and employment people of each traffic district;
(5) introducing three factors of employment demand, post supply and traffic impedance distance attenuation to construct a potential model;
(6) and (5) obtaining a potential model suitable for the researched city according to the step (5), calculating employment accessibility index values of all traffic cells, and analyzing the employment accessibility of the researched city in different traffic modes.
2. The potential model employment reachability analysis method based on multi-source big data, according to claim 1, is characterized in that in step (2):
calling a God map API through programming to obtain travel distance data; the population numbers of the living and employment are put in a list form, and the impedance of the public transportation and the car are put in an OD form.
3. The potential model employment reachability analysis method based on multi-source big data, according to claim 1, is characterized in that in step (3):
firstly, determining the specific time period of the morning and evening peak of a city according to the result of urban traffic comprehensive survey, and respectively counting the departure and attraction traffic of the morning and evening peak in the service range of each base station; sample expansion is carried out according to a certain principle, and the real travel situation is approached as much as possible;
secondly, determining the one-to-one corresponding relation between the base station and the mobile phone signaling data according to the codes to determine the working and rest time: specifically selecting a conventional working time period of a working day in a working period, identifying a base station with average daily stay time more than two hours, and simultaneously removing mobile phone users with the activity ranges of daytime and night fixed within a certain range, wherein the position of the finally identified base station is the working place of the MSID; the residence place is the residence place of the mobile phone, the residence time of the mobile phone signal in the base station is the maximum during the early morning, and the daily residence time is longer than a certain time length, and the base station is identified as the residence place.
4. The potential model employment reachability analysis method based on multi-source big data, according to claim 1, is characterized in that in step (4):
firstly, creating a Thiessen polygon in ArcGIS according to the service range of a base station, endowing the trip data of the base station to the Thiessen polygon, establishing spatial correlation, and calculating the trip population density;
then, overlapping and analyzing INTERSECT between the traffic cell and the Thiessen polygon of the base station, and calculating the number of people going out;
and finally, carrying out space statistics to obtain the number of residences and employment people in each traffic community.
5. The multi-source big data-based potential model employment reachability analysis method of claim 1, wherein in the step (5), the potential model calculation formula is as follows:
whereinA i Refers to living in a traffic districtiThe employment accessibility of residents;
O j finger in traffic districtjThe number of employment posts present in (a);
T ij finger traffic districtiTojThe impedance of the row of (a) is,expressed in trip distance;
F(T ij ) Finger slave traffic districtiTojTraffic impedance function of traffic cellsiTojA square representation of the distance of (d);
P k means living in traffic districtsjBut in traffic districtskThe number of persons in the middle employment;
T kj for subordinate traffic districtskTojThe trip impedance, expressed as a trip distance;
F(T kj ) For subordinate traffic districtsiTokTraffic impedance function of, using traffic cellsiTokIs expressed as the square of the distance of (a).
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