CN108960624A - Grid similarity determination method, device and system based on user's visiting information - Google Patents
Grid similarity determination method, device and system based on user's visiting information Download PDFInfo
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Abstract
A kind of to be visited the grid similarity determination method of information, device and system based on user, method includes: to obtain region to be determined, carries out grid dividing to the region to be determined；According to the frequency that the point of interest in the grid is visited, structural matrix；Dimensionality reduction is carried out to the matrix, and grid similarity is calculated according to dimensionality reduction result, it can be judged by the similarity to grid, to evaluate the similarity of functional module in city, the accuracy for effectively promoting city function evaluation, improves the efficiency of city function evaluation.
Description
Technical field
The present invention relates to the judgements of grid similarity, and in particular to a kind of grid similarity judgement based on user's visiting information
Methods, devices and systems.
Background technique
With the development of the city, the accurate positionin of city function, it is significant for the development in city, it is urban development
Direction sign and power source.For urban economy development, first it should be understood that the foothold of competitive advantage, gives priority to advantage production
Industry carries out the effect of highly-specialised, scale, this is the important means for promoting Urban Competitiveness.Urban inner function is refined,
The characteristic and advantage of the prominent each component part in city, it appears particularly important.
In order to evaluate each function of urban inner, in the related technology, qualitative functional approach and quantitative is generallyd use
Index method evaluates city function.Wherein, functional approach refers to according to studied and evaluation city various functions one by one
Carry out analysis comparison；Index method is first specified a set of standards system, and determines the ideal index of each index, then with being evaluated city
The actual index value comparison in city, calculates comprehensive evaluation value.Therefore, because functional approach is qualitative method, subjective impact is larger,
And index method introduces quantitative factor, needs a large amount of index desired quantity and the real index value for being evaluated city, prepares
It is more to expend resource for larger workload.
Summary of the invention
The application provides a kind of grid similarity determination method based on user's visiting information, can be realized to city internal strength
Similarity is evaluated between energy module.
According in a first aspect, providing a kind of grid similarity judgement side based on user's visiting information in a kind of embodiment
Method, comprising the following steps: obtain region to be determined, grid dividing is carried out to the region to be determined；According in the grid
The frequency that point of interest is visited, structural matrix；Dimensionality reduction is carried out to the matrix, and grid similarity is calculated according to dimensionality reduction result.
Further, the frequency that the point of interest according in the grid is visited, structural matrix, comprising: to described
Point of interest in grid is classified and is numbered；Obtain each point of interest in the grid, the frequency visited within a preset time；
According to the frequency that the number of the point of interest and the point of interest are visited, the vector of a grid is constructed；According to each grid
Vector, construct the matrix in region to be determined.
Further, according to the location-based service of mobile terminal, the frequency that the point of interest is visited is obtained.
Further, before the progress dimensionality reduction to the matrix, further includes: carry out TF-IDF to the matrix and turn
It changes；Wherein, the matrix is the matrix in region to be determined.
Further, before the progress dimensionality reduction to the matrix, further includes: carry out TF-IDF in the matrix and convert it
Afterwards, singular value decomposition is carried out to the matrix after conversion.
Further, when carrying out dimensionality reduction to the matrix, retain the maximum singular value of preset quantity.
Further, described and according to dimensionality reduction result calculate grid similarity, comprising: using the included angle cosine meter between vector
Calculate grid similarity.
According to second aspect, a kind of computer readable storage medium, including program, described program are provided in a kind of embodiment
It can be executed by processor to realize the grid similarity determination method above-mentioned based on user's visiting information.
According to the third aspect, is provided in a kind of embodiment and a kind of system is determined based on the visit grid similarity of information of user
System, comprising: memory, processor and be stored in the computer journey that can be run on the memory and on the processor
Sequence when the processor executes the computer program, realizes that the grid similarity above-mentioned based on user's visiting information determines
Method.
According to fourth aspect, is provided in a kind of embodiment and a kind of dress is determined based on the visit grid similarity of information of user
It sets characterized by comprising obtain module, for obtaining region to be determined, grid dividing is carried out to the region to be determined；
Constructing module, the frequency for being visited according to the point of interest in the grid, structural matrix；Computing module, for described
Matrix carries out dimensionality reduction, and calculates grid similarity according to dimensionality reduction result.
According to the grid similarity determination method based on user's visiting information of above-described embodiment, passes through and obtain area to be determined
Domain, and treat determinating area and carry out grid dividing, the frequency then visited according to point of interest in grid, structural matrix, to square
Battle array carries out dimensionality reduction, and calculates grid similarity according to dimensionality reduction result.The determination method of the embodiment of the present invention can be by right as a result,
The similarity of grid is judged, to evaluate the similarity of functional module in city, is effectively promoted city function and is commented
The accuracy of valence improves the efficiency of city function evaluation.
Detailed description of the invention
Fig. 1 is the flow chart of the grid similarity determination method based on user's visiting information of the embodiment of the present invention；
Fig. 2 is the schematic diagram for carrying out unusual decomposition in one embodiment of the invention to matrix；
Fig. 3 is the result schematic diagram of a specific embodiment of the invention；
Fig. 4 is the box signal of the grid similarity decision maker based on user's visiting information of the embodiment of the present invention.
Specific embodiment
Below by specific embodiment combination attached drawing, invention is further described in detail.Wherein different embodiments
Middle similar component uses associated similar element numbers.In the following embodiments, many datail descriptions be in order to
The application is better understood.However, those skilled in the art can recognize without lifting an eyebrow, part of feature
It is dispensed, or can be substituted by other elements, material, method in varied situations.In some cases, this Shen
Please it is relevant it is some operation there is no in the description show or describe, this is the core in order to avoid the application by mistake
More descriptions are flooded, and to those skilled in the art, these relevant operations, which are described in detail, not to be necessary, they
Relevant operation can be completely understood according to the general technology knowledge of description and this field in specification.
It is formed respectively in addition, feature described in this description, operation or feature can combine in any suitable way
Kind embodiment.Meanwhile each step in method description or movement can also can be aobvious and easy according to those skilled in the art institute
The mode carry out sequence exchange or adjustment seen.Therefore, the various sequences in the description and the appended drawings are intended merely to clearly describe a certain
A embodiment is not meant to be necessary sequence, and wherein some sequentially must comply with unless otherwise indicated.
It is herein component institute serialization number itself, such as " first ", " second " etc., is only used for distinguishing described object,
Without any sequence or art-recognized meanings.And " connection ", " connection " described in the application, unless otherwise instructed, include directly and
It is indirectly connected with (connection).
As the continuous development in China mobile Internet market expands, by June, 2017, China mobile netizen scale is
Up to 7.24 hundred million, 96.3% is also up to using the ratio on mobile phone in netizen.Mobile device be constantly be generated magnanimity, in real time, it is accurate,
There are the data of spatial distribution characteristic, these data can accurately portray the visiting track of user.It therefore, can be mobile by telecommunications
The radio communication network of operator or external positioning method (GPS) obtain the location information of customer mobile terminal, i.e. position takes
It is engaged in LBS (Location Based Service).
Latent Semantic analyzes LSA (Latent SemanticAnalysis), and also known as Latent Semantic indexes (Latent
Semantic Indexing, LSI), be a kind of common method of natural language processing, this method indicated using vector word and
Document, word-document matrix of construction are a sparse matrixes, and row represents document, and column represent word, and then pass through the pass between vector
System carrys out the relationship of grammatical term for the character and document.Basic idea is exactly, by sparse high-dimensional vector space be mapped to a low-dimensional to
Quantity space.
Based on this, applicant thinking left using customer mobile terminal magnanimity, in real time, crowd space can accurately be described
The big data resource of distribution, in conjunction with city POI (Point ofInterest, point of interest) information, to realize low cost, high efficiency
Purpose extract similar/different region of regional advantages function, looking up function.It is a kind of based on user's visiting information to propose
Grid similarity determination method.
Below with reference to the accompanying drawings come describe the embodiment of the present invention based on user visit information grid similarity determination method
And device.
Fig. 1 is the flow chart of the grid similarity determination method based on user's visiting information of the embodiment of the present invention.Such as Fig. 1
It is shown, the grid similarity determination method based on user's visiting information of the embodiment of the present invention, comprising the following steps:
S101: obtaining region to be determined, treats determinating area and carries out grid dividing.
It wherein, can be according to the size in region to be determined, the length and height of customized grid, to treat determinating area progress
Grid dividing.
Further, it is also necessary to which grid coding is carried out to the grid after division.
Specifically, coordinate origin is chosen in region to be determined, determines the length l and height h of grid.Wherein, the length of grid
The unit of degree and height is rice.To any grid according to XiYjFormat be numbered, wherein i, j number formula can are as follows:
Wherein, lng1Longitude, lat for coordinate origin1Latitude, lng for coordinate originiFor selected point coordinate longitude,
latiFor the latitude of selected point coordinate.
S102: the frequency visited according to the point of interest in grid, structural matrix.
According to one embodiment of present invention, the frequency visited according to the point of interest in grid, structural matrix, comprising:
S201: the point of interest in grid is classified and is encoded.
It should be noted that the interest point information table in grid can be obtained first, wherein interest point information table includes point of interest
The information such as title, point of interest category, address and WGS1984 latitude and longitude coordinates, according to interest point information table to the interest in grid
Point carries out the merging and/or reorganization operation between major class, so that the point of interest in grid is divided into N major class.Wherein, the N of point of interest
Major class may include food and drink lodging, government organs, medical institutions etc., i.e. city incity functional module.
Further, it is encoded after classifying to the point of interest in grid, for example, the numerical value that classification food and drink is stayed
Coding can be 1, and the numeric coding of classification government organs can be 2, and the numeric coding of classification medical institutions can be 3.
S202: each point of interest in grid, the frequency visited within a preset time are obtained.
It should be understood that due to the visiting data that can not obtain region to be determined completely, for zonule
Be also required to many human and material resources etc., for city or it is wider for, obtain the full dose data for all POI of visiting almost
It is unlikely that.Therefore, in present example, each point of interest in grid, the frequency visited within a preset time are only chosen
It is secondary to be used as research object.
Wherein, preset time can be day, the moon, season, year etc..
It should be noted that the frequency visited can be obtained by location-based service LBS above-mentioned.Specifically, according to position
It services LBS to obtain within a preset time, the location information that customer mobile terminal reports appears in the number of point of interest.Wherein, it uses
Family mobile terminal can be the mobile terminal of multiple users, that is, get any user mobile terminal in the point of interest and carry out position
It reports, then the frequency that the point of interest is visited within a preset time adds 1.
It should also be noted that, the latitude and longitude coordinates range of point of interest can be set.Specifically, by customer mobile terminal
The location information of report extracts latitude and longitude coordinates locating for user, judges whether latitude and longitude coordinates value locating for user is in interest
The latitude and longitude coordinates range of point, if latitude and longitude coordinates value locating for user is in the latitude and longitude coordinates range of point of interest, really
Determine user to visit the point of interest, if latitude and longitude coordinates value locating for user is not in the latitude and longitude coordinates range of point of interest,
Determine that user does not visit the point of interest.
S203: the frequency visited according to point of interest number and point of interest constructs the vector of a grid.
Specifically, the frequency point of interest in the class number of point of interest and grid visited, one as grid
Vector.
S204: according to the vector of each grid, the matrix in region to be determined is constructed.
As an example it is assumed that being grid a according to first grid of above-mentioned grid coding Rule, second grid is
Grid b, wherein the government organs for being 2 are stayed and number in the food and drink for being 1 with point of interest number in grid a, have in grid b
The medical institutions that the food and drink that point of interest number is 1 is stayed and number is 3.Within a preset time, the meal in visiting grid a is collected
The frequency that drink is stayed is 1000, and the frequency of the government organs to visit in grid a is 100, the frequency that the food and drink visited in grid b is stayed
Secondary is 1500, and the frequency of the medical institutions to visit in grid b is 800.Then have, the corresponding vector of grid a be [(1,1000), (2,
100)], the corresponding vector of grid b is [(1,1500), (3,800)].
Based on this, according to stealthy semantic analysis LSA method, row is represented into grid, column represent point of interest number, construction such as table
Matrix shown in 1.
Table 1
It should be noted that the matrix in table 1 is the Term-Document (word-in stealthy semantic analysis LSA method
Document) matrix, that is, grid can be regarded as " document ", each point of interest in grid regards as " word ", each emerging by visiting in grid
The frequency of interest point regards as " word " and appears in the frequency in " document ".
TF-IDF conversion can be carried out to above-mentioned Term-Document matrix as a result,.
It should be noted that TF-IDF (Term Frequency-Inverse Document Frequency) is a kind of
For the common weighting technique that information retrieval and information are prospected, to assess a words for a file set or a corpus
In a copy of it file significance level.If the number that a word occurs in certain document is more, and in other documents
In number it is fewer, weight is bigger, and discrimination is higher.That is, the frequency that point of interest is visited in some grid is higher, and
The frequency visited in other grids is lower, and weight is bigger, and discrimination is higher.
TF word frequency (Term Frequency) indicates the frequency that entry occurs in document d, IDF inverse document frequency
(Inverse Document Frequency)。
Wherein, TF word frequency, IDF inverse document frequency and TF-IDF, pass through following fair computation respectively:
TF-IDF=TF × IDF
Wherein, k=1,2 ... N, N are the sum of word；nI, jFor word tiIn part djIn frequency of occurrence；| D | it is corpus
In total number of files.
Therefore, the Term-Document matrix further progress TF-IDF in above-mentioned example is calculated, can be had:
Table 2
| Document | Term | TF | IDF | TF-IDF |
| Grid a | Food and drink is stayed | 0.91 | 0.00 | 0.00 |
| Grid a | Government organs | 0.09 | 0.30 | 0.03 |
| Grid b | Food and drink is stayed | 0.65 | 0.00 | 0.00 |
| Grid b | Medical institutions | 0.35 | 0.30 | 0.10 |
In other words, matrix can be obtained after carrying out TF-IDF conversion in the Term-Document matrix in above-mentioned example:
Table 3
From geometric meaning, feature vector be matrix by specified transformation rear direction those of do not change to
Amount.Therefore, singular value decomposition can be carried out to the matrix in table 3.
Wherein, the formula of singular value decomposition are as follows:
A=U × ∑ × VT
Wherein Σ is diagonal matrix, and element is by greatly to the singular value of the matrix A of minispread, the non-zero surprise of A on diagonal line
Different value is numerically equal to ATThe square root of the characteristic value of A；U is AATFeature vector composition matrix；V is ATThe feature vector of A
The matrix of composition.Wherein, in this law embodiment, A is the matrix in table 3.
S103: dimensionality reduction is carried out to matrix, and network similarity is calculated according to dimensionality reduction result.
Specifically, dimensionality reduction is carried out to matrix can include: setting needs the information content retained, the total information in calculating matrix
Amount, i.e., the quadratic sum of all singular values, enabling r is to be added to the unusual of presupposed information amount by the singular value quadratic sum greatly to minispread
The quantity of value extracts the preceding r column of U matrix, and r is arranged before the preceding r row of ∑ matrix, the preceding r column of V matrix, obtain U ' as shown in Figure 2,
∑ ', V ' matrix.Wherein, the information content for needing to retain can be the percentage of the gross information content in matrix, for example, 90%.
Wherein, similarity calculation may include calculating the various ways such as distance, related coefficient, angle.In the embodiment of the present invention
In, it is assumed that think that the similarity degree between two samples is only related with the angle between them, that is, two grids can be used
The included angle cosine of vector is corresponded to measure similarity.
Specifically, after dimensionality reduction, U ' ∑ ' be space in each grid vectorial coordinate.Utilize the cosine angle between vector
Formula can be obtained the similarity between any two grids, wherein similarity numerical value is between [- 1,1], when similarity value is got over
Close to 1, then grid is more similar.
Wherein, the calculation formula of similarity are as follows:
Wherein, r is the quantity of the singular value retained, (a1,a2,…,ar) it is grid a corresponding vector in U' Σ ',
(b1,b2,…,br) grid b corresponding vector in U' Σ '.
Fig. 3 is the result schematic diagram of a specific embodiment of the invention, in this specific embodiment, grid A, B, C, D, E,
F, similarity is higher between G, H, and similarity is higher between grid I, J, K, L.
In conclusion above-described embodiment based on user visit information grid similarity determination method, by obtain to
Determinating area, and treat determinating area and carry out grid dividing, the frequency then visited according to point of interest in grid constructs square
Battle array carries out dimensionality reduction to matrix, and calculates grid similarity according to dimensionality reduction result.The determination method energy of the embodiment of the present invention as a result,
It is enough to be judged by the similarity to grid, to evaluate the similarity of functional module in city, effectively promote city
The accuracy of city's functional evaluation improves the efficiency of city function evaluation.
The embodiment of the present invention also proposed a kind of non-transitory readable storage medium storing program for executing, including program, which can be located
Reason device is executed to realize the grid similarity determination method above-mentioned based on user's visiting information.
Non-transitory readable storage medium storing program for executing according to an embodiment of the present invention can be sentenced by the similarity to grid
It is disconnected, to evaluate the similarity of functional module in city, the accuracy of city function evaluation is effectively promoted, improves city
The efficiency of functional evaluation.
The embodiment of the present invention also proposed a kind of grid similarity decision-making system based on user's visiting information, including storage
Device, processor and storage on a memory and the computer program that can run on a processor, processor execution computer journey
When sequence, the grid similarity determination method above-mentioned based on user's visiting information is realized.
Grid similarity decision-making system according to an embodiment of the present invention based on user's visiting information, can be by grid
Similarity judged, to evaluate the similarity of functional module in city, effectively promote city function evaluation
Accuracy improves the efficiency of city function evaluation.
Corresponding, this hair based on user's visiting grid similarity determination method of information provided with above-mentioned several embodiments
A kind of bright embodiment additionally provides the grid similarity decision maker based on user's visiting information, since the embodiment of the present invention mentions
The grid similarity decision maker based on user's visiting information supplied is believed with what above-mentioned several embodiments provided based on user's visiting
The grid similarity determination method of breath is corresponding, therefore in the aforementioned grid similarity determination method based on user's visiting information
Embodiment is also applied for the grid similarity decision maker provided in this embodiment based on user's visiting information, in the present embodiment
In no longer describe.
Fig. 4 is the block diagram of the grid similarity decision maker based on user's visiting information of the embodiment of the present invention.
As shown in figure 4, the grid similarity decision maker 100 based on user's visiting information of the embodiment of the present invention, comprising: obtain module
10, constructing module 20 and computing module 30.
Wherein, module 10 is obtained for obtaining region to be determined, is treated determinating area and is carried out grid dividing；Constructing module 20
The frequency for being visited according to the point of interest in grid, structural matrix；Computing module 30 is used to carry out matrix dimensionality reduction, and root
Grid similarity is calculated according to dimensionality reduction result.
Further, constructing module 20 is also used to: the point of interest in grid is classified and numbered；It obtains each in grid
Point of interest, the frequency visited within a preset time；According to the frequency that the number of point of interest and point of interest are visited, one is constructed
The vector of grid；According to the vector of each grid, the matrix in region to be determined is constructed.
Further, it obtains module 10 to be also used to: according to the location-based service of mobile terminal, obtaining the frequency that point of interest is visited
It is secondary.
Further, computing module 30 is also used to: carrying out TF-IDF conversion to matrix；Wherein, matrix is region to be determined
Matrix.
Further, computing module 30 is also used to: after matrix carries out TF-IDF conversion, being carried out to the matrix after conversion
Singular value decomposition.
Further, computing module 30 is also used to: when carrying out dimensionality reduction to matrix, the maximum for retaining preset quantity is unusual
Value.
Further, computing module 30 is also used to: calculating grid similarity using the included angle cosine between vector.
It will be understood by those skilled in the art that all or part of function of various methods can pass through in above embodiment
The mode of hardware is realized, can also be realized by way of computer program.When function all or part of in above embodiment
When being realized by way of computer program, which be can be stored in a computer readable storage medium, and storage medium can
To include: read-only memory, random access memory, disk, CD, hard disk etc., it is above-mentioned to realize which is executed by computer
Function.For example, program is stored in the memory of equipment, when executing program in memory by processor, can be realized
State all or part of function.In addition, when function all or part of in above embodiment is realized by way of computer program
When, which also can store in storage mediums such as server, another computer, disk, CD, flash disk or mobile hard disks
In, through downloading or copying and saving into the memory of local device, or version updating is carried out to the system of local device, when logical
When crossing the program in processor execution memory, all or part of function in above embodiment can be realized.
Use above specific case is illustrated the present invention, is merely used to help understand the present invention, not to limit
The system present invention.For those skilled in the art, according to the thought of the present invention, can also make several simple
It deduces, deform or replaces.
Claims (10)
1. a kind of grid similarity determination method based on user's visiting information, which comprises the following steps:
Region to be determined is obtained, grid dividing is carried out to the region to be determined；
According to the frequency that the point of interest in the grid is visited, structural matrix；
Dimensionality reduction is carried out to the matrix, and grid similarity is calculated according to dimensionality reduction result.
2. the grid similarity determination method according to claim 1 based on user's visiting information, which is characterized in that described
According to the frequency that the point of interest in the grid is visited, structural matrix, comprising:
Point of interest in the grid is classified and numbered；
Obtain each point of interest in the grid, the frequency visited within a preset time；
According to the frequency that the number of the point of interest and the point of interest are visited, the vector of a grid is constructed；
According to the vector of each grid, the matrix in region to be determined is constructed.
3. the grid similarity determination method according to claim 2 based on user's visiting information, which is characterized in that
According to the location-based service of mobile terminal, the frequency that the point of interest is visited is obtained.
4. the grid similarity determination method according to claim 1 or 3 based on user's visiting information, which is characterized in that
Before the progress dimensionality reduction to the matrix, further includes:
TF-IDF conversion is carried out to the matrix；Wherein, the matrix is the matrix in region to be determined.
5. the grid similarity determination method according to claim 4 based on user's visiting information, which is characterized in that described
Before matrix progress dimensionality reduction, further includes:
After the matrix carries out TF-IDF conversion, singular value decomposition is carried out to the matrix after conversion.
6. the grid similarity determination method according to claim 5 based on user's visiting information, which is characterized in that right
When the matrix carries out dimensionality reduction, retain the maximum singular value of preset quantity.
7. the grid similarity determination method according to claim 5 based on user's visiting information, which is characterized in that described
And grid similarity is calculated according to dimensionality reduction result, comprising:
Grid similarity is calculated using the included angle cosine between vector.
8. a kind of computer readable storage medium, which is characterized in that including program, described program can be executed by processor with reality
Now such as the grid similarity determination method of any of claims 1-7 based on user's visiting information.
9. a kind of grid similarity decision-making system based on user's visiting information characterized by comprising memory, processor
And it is stored in the computer program that can be run on the memory and on the processor, the processor executes the meter
When calculation machine program, realize such as the grid similarity judgement side of any of claims 1-7 based on user's visiting information
Method.
10. a kind of grid similarity decision maker based on user's visiting information characterized by comprising
Module is obtained, for obtaining region to be determined, grid dividing is carried out to the region to be determined；
Constructing module, the frequency for being visited according to the point of interest in the grid, structural matrix；
Computing module for carrying out dimensionality reduction to the matrix, and calculates grid similarity according to dimensionality reduction result.
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| CN110009175A (en) * | 2018-12-25 | 2019-07-12 | 阿里巴巴集团控股有限公司 | The performance estimating method and device of OD demand analysis algorithm |
| CN111274345A (en) * | 2020-01-21 | 2020-06-12 | 成都智库二八六一信息技术有限公司 | Similar area retrieval method and system based on grid division and value taking |
| CN112200269A (en) * | 2020-11-17 | 2021-01-08 | 北京津发科技股份有限公司 | Similarity analysis method and system |
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