CN104123305B - Geoprocessing method and its system - Google Patents
Geoprocessing method and its system Download PDFInfo
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- CN104123305B CN104123305B CN201310154394.1A CN201310154394A CN104123305B CN 104123305 B CN104123305 B CN 104123305B CN 201310154394 A CN201310154394 A CN 201310154394A CN 104123305 B CN104123305 B CN 104123305B
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Abstract
Embodiments of the present invention are related to Geoprocessing method and its system.The present inventor's creativeness has invented a kind of Geoprocessing scheme.The program can utilize dynamic traffic data, including initial address and destination address coordinate, to determine the correlation between geographical location, to classify to geographical location, the classification results being deployed in map can provide more rich resource and information to people, to execute more analysis tasks, realize more analytic functions.Specifically, the present invention provides a kind of Geoprocessing methods, including：Read traffic data；Classified to geographical location according to the traffic data；And classification results are deployed in map.
Description
Technical field
Embodiments of the present invention relate generally to data processing, and further embodiments of the present invention are related to geodata
Processing method and its system.
Background technology
Data analysis refer to statistical analysis technique appropriate to collect come mass data data analyze, with maximum
The function of changing ground exploitation data information, plays the effect of data.The purpose of data analysis is large quantities of apparently mixed and disorderly being hidden in
Information in the data of no chapter is concentrated, is extract, the inherent law of research object to find out.Geodata be it is direct or
Connect the data being associated with relative to some place, including geographic position data and with some relevant factors of the geographical location
Data, including natural cause and social factor.Often there is prodigious correlation in the geodata in multiple areas, how to find
These geographic position datas with correlation are that many scientists are attempt to solve the problems, such as.The prior art mainly passes through
Intrinsic geodata relevance determines relevant geographical location, for example when the weather condition of user querying regional A1, is
System can provide a user the weather conditions of regional A1 and area adjacent regional A2, A3, A4 of A1 automatically.
Invention content
The present inventor's creativeness has invented a kind of Geoprocessing scheme.The program can utilize dynamic
Traffic data, including starting point coordinate and destination coordinate, to determine the correlation between geographical location, to geographical location
Classify, the classification results being deployed in map can provide more rich resource and information to people, more to execute
Analysis task, realize more analytic functions.
Specifically, the present invention provides a kind of Geoprocessing methods, including：Traffic data is read, wherein described
Traffic data includes the first end point coordinate and the second extreme coordinates of traffic route, wherein the first end point is traffic route
One in starting point and destination, and the second endpoint be traffic route starting point and destination in another；According to institute
Traffic data is stated to classify to geographical location；And classification results are deployed in map.
The present invention also provides a kind of Geoprocessing systems, including：First reading device is configured as reading traffic
Data, wherein the traffic data includes the first end point coordinate and the second extreme coordinates of traffic route, wherein the first end
Point be traffic route starting point and destination in one, and the second endpoint be traffic route starting point and destination in
Another；Sorter is configured as classifying to geographical location according to the traffic data；And deployment device, by with
It is set to and classification results is deployed in map.
The Geoprocessing method or Geoprocessing system of one embodiment through the invention, can be according to
Initial address and destination address classify to geographical location, to which classification results be used.
Description of the drawings
Disclosure illustrative embodiments are described in more detail in conjunction with the accompanying drawings, the disclosure above-mentioned and its
Its purpose, feature and advantage will be apparent, wherein in disclosure illustrative embodiments, identical reference label
Typically represent same parts.
Fig. 1 shows the block diagram of the exemplary computing system 100 suitable for being used for realizing embodiment of the present invention.
Fig. 2 shows the method flow diagrams of the progress Geoprocessing of one embodiment according to the invention.
Fig. 3 A show classifying to geographical location according to the traffic data for one embodiment according to the invention
Method flow diagram.
Fig. 3 B show dividing geographical location according to the traffic data for another embodiment according to the invention
The method flow diagram of class.
Fig. 4 shows the method flow diagram being deployed in classification results in map of one embodiment according to the invention.
Fig. 5 shows the traffic data schematic diagram of one embodiment according to the invention.
Fig. 6 A show the longitude coordinate schematic diagram of first raster data of one embodiment according to the invention.
Fig. 6 B show the latitude coordinate schematic diagram of first raster data of one embodiment according to the invention.
Fig. 7 A show the interpolating unit schematic diagram of one embodiment according to the invention.
Fig. 7 B show the interpolating unit schematic diagram of another embodiment according to the invention.
Fig. 8 shows the schematic diagram classified to map grid of one embodiment according to the invention.
Fig. 9 shows the scatter plot of first raster data of one embodiment according to the invention.
Figure 10 shows the point diagram schematic diagram of the interest point data of one embodiment according to the invention.
Figure 11 A show the original point of interest value schematic diagram of one embodiment according to the invention.
Figure 11 B show the synthesis point of interest value schematic diagram of one embodiment according to the invention.
Figure 12 A show the schematic diagram classified to map grid in Fig. 8 after further being marked.
Figure 12 B show the road net data schematic diagram of one embodiment according to the invention.
Figure 12 C show one embodiment according to the invention classification results are reconstructed according to road net data
Process schematic.
Figure 12 D show the classification results schematic diagram after the reconstruct of one embodiment according to the invention.
Figure 13 A show schematic diagram of the classification results in map before the reconstruct of one embodiment according to the invention.
Figure 13 B show the road net data schematic diagram of another embodiment according to the invention.
Figure 13 C show effect signal of the classification results after the reconstruct of one embodiment according to the invention in map
Figure.
Figure 13 D show the attribute information schematic diagram in the display same class region of one embodiment according to the invention.
Figure 13 E show the attribute information schematic diagram in the display same class region of another embodiment according to the invention.
Figure 14 shows the system block diagram of the progress Geoprocessing of one embodiment according to the invention.
Figure 15 A show the sorter block diagram of one embodiment according to the invention.
Figure 15 B show the sorter block diagram of another embodiment according to the invention.
Figure 16 shows the deployment device block diagram of one embodiment according to the invention.
Specific implementation mode
The preferred embodiment of the disclosure is more fully described below with reference to accompanying drawings.Although showing the disclosure in attached drawing
Preferred embodiment, however, it is to be appreciated that may be realized in various forms the disclosure without the embodiment party that should be illustrated here
Formula is limited.On the contrary, these embodiments are provided so that the disclosure is more thorough and complete, and can be by the disclosure
Range is completely communicated to those skilled in the art.Disclosure illustrative embodiments are carried out in conjunction with the accompanying drawings more detailed
Description, above-mentioned and other purpose, the feature and advantage of the disclosure will be apparent, wherein exemplary in the disclosure
In embodiment, identical reference label typically represents same parts.
Fig. 1 shows the block diagram of the exemplary computer system/server 12 suitable for being used for realizing embodiment of the present invention.
The preferred embodiment of the disclosure is more fully described below with reference to accompanying drawings.Although showing the disclosure in attached drawing
Preferred embodiment, however, it is to be appreciated that may be realized in various forms the disclosure without the embodiment party that should be illustrated here
Formula is limited.On the contrary, these embodiments are provided so that the disclosure is more thorough and complete, and can be by the disclosure
Range is completely communicated to those skilled in the art.
Those skilled in the art will appreciate that the present invention can be implemented as system, method or computer program product.
Therefore, the disclosure can be with specific implementation is as follows, i.e.,：It can be complete hardware, can also be complete software（Including
Firmware, resident software, microcode etc.）, can also be the form that hardware and software combines, referred to generally herein as " circuit ", " mould
Block " or " system ".In addition, in some embodiments, the present invention is also implemented as in one or more computer-readable mediums
In computer program product form, include computer-readable program code in the computer-readable medium.
The arbitrary combination of one or more computer-readable media may be used.Computer-readable medium can be calculated
Machine readable signal medium or computer readable storage medium.Computer readable storage medium for example can be --- but it is unlimited
In --- electricity, system, device or the device of magnetic, optical, electromagnetic, infrared ray or semiconductor, or the arbitrary above combination.It calculates
The more specific example of machine readable storage medium storing program for executing（Non exhaustive list）Including：Electrical connection with one or more conducting wires, just
Take formula computer disk, hard disk, random access memory（RAM）, read-only memory (ROM), erasable type may be programmed read-only storage
Device (DPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), light storage device, magnetic memory device,
Or above-mentioned any appropriate combination.In this document, can be any include computer readable storage medium or storage journey
The tangible medium of sequence, the program can be commanded the either device use or in connection of execution system, device.
Computer-readable signal media may include in a base band or as the data-signal that a carrier wave part is propagated,
Wherein carry computer-readable program code.Diversified forms may be used in the data-signal of this propagation, including --- but
It is not limited to --- electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be
Any computer-readable medium other than computer readable storage medium, which can send, propagate or
Transmission for by instruction execution system, device either device use or program in connection.
The program code for including on computer-readable medium can transmit with any suitable medium, including --- but it is unlimited
In --- wireless, electric wire, optical cable, RF etc. or above-mentioned any appropriate combination.
It can be write with one or more programming languages or combinations thereof for executing the computer that operates of the present invention
Program code, described program design language include object oriented program language-such as Java, Smalltalk, C++,
Further include conventional procedural programming language-such as " C " language or similar programming language.Program code can be with
It fully executes, partly execute on the user computer on the user computer, being executed as an independent software package, portion
Divide and partly executes or executed on a remote computer or server completely on the remote computer on the user computer.
Be related in the situation of remote computer, remote computer can pass through the network of any kind --- including LAN (LAN) or
Wide area network (WAN)-be connected to subscriber computer, or, it may be connected to outer computer（Such as it is carried using Internet service
It is connected by internet for quotient）.
Below with reference to the method, apparatus of the embodiment of the present invention（System）With the flow chart of computer program product and/or
The block diagram description present invention.It should be appreciated that each box in each box and flowchart and or block diagram of flowchart and or block diagram
Combination, can be realized by computer program instructions.These computer program instructions can be supplied to all-purpose computer, special
The processor of computer or other programmable data processing units, to produce a kind of machine, these computer program instructions
It is executed by computer or other programmable data processing units, produces and advised in the box in implementation flow chart and/or block diagram
The device of fixed function/operation.
These computer program instructions can also be stored in can be so that computer or other programmable data processing units
In computer-readable medium operate in a specific manner, in this way, the instruction of storage in computer-readable medium just produces one
Command device (the instruction of function/operation specified in a box including in implementation flow chart and/or block diagram
Means manufacture)（manufacture）.
Computer program instructions can also be loaded into computer, other programmable data processing units or miscellaneous equipment
On so that series of operation steps are executed on computer, other programmable data processing units or miscellaneous equipment, in terms of generating
The process that calculation machine is realized, so that the instruction executed on a computer or other programmable device is capable of providing implementation flow chart
And/or the process of function/operation specified in the box in block diagram.
Fig. 1 shows the block diagram of the exemplary computer system/server 12 suitable for being used for realizing embodiment of the present invention.
The computer system/server 12 that Fig. 1 is shown is only an example, should not be to the function and use scope of the embodiment of the present invention
Bring any restrictions.
As shown in Figure 1, computer system/server 12 is showed in the form of universal computing device.Computer system/service
The component of device 12 can include but is not limited to：One or more processor or processing unit 16, system storage 28, connection
Different system component（Including system storage 28 and processing unit 16）Bus 18.
Bus 18 indicates one or more in a few class bus structures, including memory bus or Memory Controller,
Peripheral bus, graphics acceleration port, processor or the local bus using the arbitrary bus structures in a variety of bus structures.It lifts
For example, these architectures include but not limited to industry standard architecture（ISA）Bus, microchannel architecture（MAC）
Bus, enhanced isa bus, Video Electronics Standards Association（VDSA）Local bus and peripheral component interconnection（PCI）Bus.
Computer system/server 12 typically comprises a variety of computer system readable media.These media can be appointed
What usable medium that can be accessed by computer system/server 12, including volatile and non-volatile media, it is moveable and
Immovable medium.
System storage 28 may include the computer system readable media of form of volatile memory, such as arbitrary access
Memory（RAM）30 and/or cache memory 32.Computer system/server 12 may further include other removable
Dynamic/immovable, volatile/non-volatile computer system storage medium.Only as an example, storage system 34 can be used for
Read and write immovable, non-volatile magnetic media（Fig. 1 do not show, commonly referred to as " hard disk drive "）.Although not showing in Fig. 1
Go out, can provide for moving non-volatile magnetic disk（Such as " floppy disk "）The disc driver of read-write, and to removable
Anonvolatile optical disk（Such as CD-ROM, DVD-ROM or other optical mediums）The CD drive of read-write.In these cases,
Each driver can be connected by one or more data media interfaces with bus 18.Memory 28 may include at least one
A program product, the program product have one group（For example, at least one）Program module, these program modules are configured to perform
The function of various embodiments of the present invention.
With one group（It is at least one）Program/utility 40 of program module 42 can be stored in such as memory 28
In, such program module 42 includes --- but being not limited to --- operating system, one or more application program, other programs
Module and program data may include the realization of network environment in each or certain combination in these examples.Program mould
Block 42 usually executes function and/or method in embodiment described in the invention.
Computer system/server 12 can also be with one or more external equipments 14（Such as it is keyboard, sensing equipment, aobvious
Show device 24 etc.）It is logical can also to enable a user to the equipment interacted with the computer system/server 12 with one or more for communication
Letter, and/or any set with so that the computer system/server 12 communicated with one or more of the other computing device
It is standby（Such as network interface card, modem etc.）Communication.This communication can pass through input/output（I/O）Interface 22 carries out.And
And computer system/server 12 can also pass through network adapter 20 and one or more network（Such as LAN
（LAN）, wide area network（WAN）And/or public network, such as internet）Communication.As shown, network adapter 20 passes through bus
18 communicate with other modules of computer system/server 12.It should be understood that although not shown in the drawings, computer can be combined
Systems/servers 12 use other hardware and/or software module, including but not limited to：Microcode, device driver, at redundancy
Manage unit, external disk drive array, RAID system, tape drive and data backup storage system etc..
Flow chart and block diagram in attached drawing show the system, method and computer journey of multiple embodiments according to the present invention
The architecture, function and operation in the cards of sequence product.In this regard, each box in flowchart or block diagram can generation
A part for a part for one module, section or code of table, the module, section or code includes one or more uses
The executable instruction of the logic function as defined in realization.It should also be noted that in some implementations as replacements, being marked in box
The function of note can also occur in a different order than that indicated in the drawings.For example, two continuous boxes can essentially base
Originally it is performed in parallel, they can also be executed in the opposite order sometimes, this is depended on the functions involved.It is also noted that
It is the combination of each box in block diagram and or flow chart and the box in block diagram and or flow chart, can uses and execute rule
The dedicated hardware based systems of fixed functions or operations is realized, or can use the group of specialized hardware and computer instruction
It closes to realize.
Fig. 2 shows the method flow diagrams of the progress Geoprocessing of one embodiment according to the invention.In step
Traffic data is read in 21（travel data）, wherein the traffic data includes the first end point coordinate and of traffic route
Two extreme coordinates, wherein the first end point be traffic route starting point and destination in one, and the second endpoint be hand over
Lead to another in the starting point and destination of circuit.
Fig. 5 shows that the traffic data schematic diagram of one embodiment according to the invention, wherein Subject indicate that record is compiled
Number, O-Time indicate that initial time, O-Latitude indicate that the latitude coordinate of origin, O-Longitude indicate starting point
Longitude coordinate, the D-Time of point indicate that the time arrived at, D-Latitude indicate latitude coordinate, the D- of destination
Longitude indicates the longitude coordinate of destination.The traffic data can be obtained from the following：The operation of taxi
The driving recording of record, private vehicle of swiping the card of getting on or off the bus of mileometer, bus or subway.For bus or subway up and down
Vehicle is swiped the card for record, due to many buses or subway require passenger when getting on the bus and when getting off respectively will brush mass transit card,
Thus can from swipe the card record in obtain passenger starting point time and coordinate and destination time and coordinate to
Record above-mentioned traffic data.For the driving recording of private vehicle, some regional requirements or recommendation private vehicle installation RFID know
Not Ka etc. identification equipments so as to recording the enforcement record of private vehicle, including the time of starting point and coordinate and destination
Time and coordinate.The traffic data can also be obtained by other channels, for example the other manners such as questionnaire obtain by inquiry
.
One embodiment according to the invention may only include that starting point coordinate and destination are sat in the traffic data
Mark；Another embodiment according to the invention can be in the traffic data other than starting point coordinate and destination coordinate
Including other contents such as other contents, such as the departure time of starting point, the arrival time of destination, record number.
One embodiment according to the invention, the starting point coordinate and destination coordinate are indicated with longitude and latitude（Such as Fig. 5
It is shown）；It is according to the invention another, the starting point coordinate and destination coordinate can use the coordinate value of X-direction and Y-direction
It indicates, needs the direction and the origin that determine coordinate first on map with Y-coordinate value using X-coordinate value certainly；According to this hair
Bright other embodiments can indicate starting point coordinate and destination coordinate with other modes.
Fig. 2 is returned to, in step 23, is classified to geographical location according to the traffic data.That is this implementation
Example considers starting point in classification（Origin）And destination（Destination）.Such as starting point and destination
Two all same or similar areas will be divided into same class, cite a plain example, and city A is divided into five areas, point
Be not that the resident of A1, A2, A3, A4, A5, wherein A1 and A2 mainly goes to work place in the areas A3, and the resident of A3, A4, A5 mainly on
Class place is respectively in city B, C and D.Therefore A1 and A2 is divided into identical one kind, and tri- cells of A3, A4, A5 are respectively
As one kind.For another example, starting point is same or similar, and the identical area of the direction of advance to destination will be divided into it is same
Class.Still the example for using city A, the resident's working for staying in cell A1 and A2 is required for walking toward east, and stays in the residence of cell A3, A4
People's working head north, the resident's working for staying in cell A5 goes in a westward direction, therefore A1, A2 cell are divided into one kind, A3, A4 cell quilt
It is divided into one kind, A5 cells are divided into one kind.
In step 25, classification results are deployed in map.For example, in the above example, can use identical
Color indicates area A1 and A2, and other different colors is used to indicate area A3, A4, A5.Certainly, using different colours to different
Classification is identified a kind of only implementation, can also be distinguished using other identification methods, for example uses different figures
Case distinguishes different classification, distinguishes different classification using different coding or word, the request in response to user is highlighted
Similar area etc..In short, as long as the deployment enables identical or different classification to be able to display in map i.e.
It can.
Geographical location is subjected to classification according to traffic data, multiple use may be implemented, for example government department utilizes geographical position
The classification results set may determine where need to build new means of transportation, for example builds new road, builds new parking
New bus routes etc. are set up in field.For another example chain store can carry out addressing using above-mentioned classification so as to some chain store
Have no matter the user group of preference can enjoy identical chain store's service near working place or near place of abode；
For another example for user when inquiring the traffic congestion situation from the areas A1 to the areas A3, system can inform the congestion feelings from the areas A1 to the areas A3
Also jam situation of the user from the areas A2 to the areas A3 is informed while condition as reference；In addition result of the invention can be used for fixed
To advertisement, for example the restaurant newly opened in the areas A3 can launch its advertisement to the resident in the areas A1 and the areas A2, because of the areas A1 and A2
The resident in area mainly works in the areas A3.Certainly the data processed result of the present invention is not limited to be applied to above-mentioned scene,
It can be used to realize other functions, since length is limited, this is no longer going to repeat them.
Fig. 3 A show classifying to geographical location according to the traffic data for one embodiment according to the invention
Method flow diagram.The first raster data is formed in step 2311（raster data）, the first grid data packet includes ground
The grid coordinate (raster coordinate) of figure grid (map raster), the grid coordinate are basis and map grid
The second extreme coordinates corresponding to matched first end point and obtain.For example, it can first determine and some in map
The first end point of the matched traffic data of grid, then using the coordinate value of the second endpoint corresponding with the first end point as this
The coordinate value of grid.
Fig. 6 A show the longitude coordinate (longitude of first raster data of one embodiment according to the invention
Coordinate) schematic diagram.Fig. 6 B show the latitude coordinate of first raster data of one embodiment according to the invention
(latitude coordinate) schematic diagram.In the example of Fig. 6 A and 6B, it is assumed that a certain geographic area is divided into 5 × 5 grid
Lattice, it is assumed that just there are one the starting points of traffic data in each grid, then Z11The coordinate value recorded in this grid be from
Z11The latitude and longitude coordinates for the destination corresponding to starting point set out in this grid, i.e. [32.0211,118.7639], Z12This
The coordinate value recorded in a grid is from Z12The latitude and longitude coordinates for the destination corresponding to starting point set out in this grid, i.e.,
[32.0201,118.7643], and so on.Grid coordinate in this is the mesh reached by starting point using the point in grid
Ground coordinate, those skilled in the art understand that the coordinate value in grid can also be for the purpose of the point in grid
Ground relevant starting point coordinate.Since length is limited, hereafter mainly illustrated for the former.
If not one and only one starting point in each grid, for example there are multiple starting points to fall into a certain grid
Lattice then need to polymerize multiple coordinates in grid.In one embodiment, the correspondence mesh of multiple starting points can be used
Ground coordinate value average value as grid coordinate.For example a certain grid is fallen into there are two starting point, the two starting points pair
The destination coordinate answered is respectively [32.0211,118.7639] and [32.0213,118.7641], then the coordinate value of the grid is
The two is averaged, i.e. [32.0212,118.7640].In another embodiment, a representativeness in a grid can be taken
Grid coordinate of the traffic data as the grid, for example a certain grid is fallen into there are three starting point, and in these three starting points
In, there are two the southeastern directions that the destination corresponding to starting point is located at corresponding starting point, and only there are one starting point is corresponding
Destination is located at the direction northwest of starting point, then one can be chosen in the traffic route that destination is starting point southeastern direction
It is a, using its destination coordinate as this grid coordinate.It is of course also possible to use other mode computation grid coordinates.
In some cases, since the traffic data of acquisition is limited, it can not ensure that starting point is fallen into each grid, therefore
Some grid coordinates are sky.And to be sky be not offered as starting point of the nobody from the grid to grid coordinate, and it is merely meant that
The data of this grid are not collected.This phenomenon is especially susceptible to when the area of grid is smaller.Therefore it needs
It will be to raster data into row interpolation（interpolation）, to make up the missing of data acquisition.An implementation according to the present invention
Example, step 2311 form the first raster data and further comprise utilizing spatial interpolation technology（spatial interpolation
technology）Calculate the grid coordinate of map grid.Spatial interpolation technology can to the data of series of discrete into row interpolation,
To form continuous data.It is flat can also to carry out data other than it can realize the purpose of data interpolating for spatial interpolation technology
It is sliding（data smoothing）.That is, using spatial interpolation technology, the grid for shortage of data is not present originally may be used also
To be smoothed to its data, to avoid sorted data excessively broken (broken), and change violent, it is difficult to
It is used in subsequent step.
One embodiment according to the invention, the grid coordinate that map grid is calculated using spatial interpolation technology are further wrapped
It includes：According to falling on interpolating unit（interpolation unite）In the coordinate of point, the point in interpolating unit to the grid
Distance and the smoothing factor (smoothing factor) of interpolating unit calculate the grid coordinate, wherein falling on interpolation
Point in unit includes the first end point fallen in interpolating unit, and the coordinate of the point fallen in interpolating unit is slotting including falling on
The second extreme coordinates corresponding to first end point in value cell.Specifically, can be sat according to 1 computation grid of following formula
Mark:
Wherein ZpThe coordinate for indicating point p in interpolating unit, that is, the corresponding destination for falling into the starting point in interpolating unit are sat
Mark（Longitude coordinate or latitude coordinate）.R indicates the sum of the point of interpolating unit, the i.e. number of starting point in interpolating unit.d
Indicate interpolating unit in point p to grid distance, such as arrive grid central point distance.N indicates smoothing factor, and n is bigger,
The grid coordinate being calculated is more smooth.In one example, n can take 1.ZijIndicate that the grid on the i-th row jth row is sat
Mark.
Fig. 7 A show the interpolating unit schematic diagram of one embodiment according to the invention.In the present embodiment, grid Zij
Interior no any point.Interpolating unit in the present embodiment（It is indicated with circle）In have 12 points（Therefore R=12）.It can preset
One interpolating unit radius, then determines the range of interpolating unit by the center of circle of grid center by radius of interpolating unit radius.
Although, can be with computation grid coordinate using the value of the point in grid extrapolation value cell without any point in grid.
Fig. 7 B show the interpolating unit schematic diagram of another embodiment according to the invention.In the example of Fig. 7 B, no
Interpolating unit is indicated with circle but indicates interpolating unit with an irregular pattern, by choosing 12 nearest from grid center
A point（R=12）So that it is determined that the range of interpolating unit.That is it is not necessarily to predefine interpolating unit half in this example
Diameter, it is only necessary to predefine R values.
Although in the example of Fig. 7 A and Fig. 7 B, grid ZijIt is interior there is no any point, it will be appreciated that even if grid Zij
In a little, still can pass through 1 computation grid Z of above-mentioned formulaijCoordinate value.Can be each raster symbol-base by formula 1
One unique grid coordinate, to form the grid latitude coordinate in grid longitude coordinate and Fig. 6 B in Fig. 6 A.
It is appreciated that other than above-named spatial interpolation technology, the present invention can also use other interpolation techniques
Computation grid coordinate, such as can be to falling into grid ZijIn several grids of surrounding（8 grids around such as）Starting point
The coordinate of corresponding destination average, to computation grid ZijGrid coordinate.Since length is limited, herein to other skies
Interpolation method no longer describes one by one.
In addition, it should be understood that although the size of grid is illustratively indicated in Fig. 6 A and 6B with rectangle, and actually grid
Lattice can also use square, triangle or any other shape representation.
Although being also understood that for illustrative purposes, Fig. 6 A and 6B indicates grid coordinate in the form of table, in reality
Other data formats may be used when storage to store grid coordinate, for example record each grid by way of data vector
The coordinate and grid coordinate that each of lattice pinpoint.
Classified to map grid based on predtermined category rule and first raster data in step 2313.
In a simply example, the grid centered on [32.0180,118.7650] in pair graph 6A, 6B carries out
Classification.As shown in table 1 below, when grid coordinate be longitude be more than 32.0180 and latitude be more than 118.7650 when, types value 0；
When grid coordinate be longitude be less than 32.0180 and latitude be more than 118.7650 when, types value 1;When grid coordinate is longitude
More than 32.0180 and when latitude is less than 118.7650, types value 2;When grid coordinate is that longitude is less than 32.0180 and latitude
When less than 118.7650, types value 3.
| Types value | 0 | 1 | 2 | 3 |
| Grid is sat | Longitude>32.0180 | Longitude<32.0180 | Longitude>32.0180 | Longitude<32.0180 |
| Mark | Latitude>118.7650 | Latitude>118.7650 | Latitude<118.7650 | Latitude<118.7650 |
Table 1
After classifying to the grid coordinate in Fig. 6 A and 6B according to above-mentioned judgment rule, classification results can be such as Fig. 8 institutes
Show.Containing type value is to describe the classification of the grid in wherein each grid.Wherein grid Z11Types value be 2, grid Z14's
Types value is 0, grid Z25Types value be 1, grid Z26Types value be 3, and so on.
A kind of embodiment according to the invention, the predtermined category rule include at least one in the following：It is predetermined
Criteria for classification, it is scheduled classification number, first raster data distribution.Wherein according to scheduled criteria for classification（As divided
The interval threshold of class）Grid classify and belongs to supervised classification (supervised classification), in example as above
Point splits data into four quadrants centered on [32.0180,118.7650], to which the data for falling into same quadrant are divided into one
Class.When user is familiar with data characteristics, threshold value can be rule of thumb provided.This when, supervised classification was suitable.
It should be appreciated that other supervised classifications can also be used to carry out classification to grid to form the in other embodiments of the invention
Two raster datas, such as can also be using from starting point to the direction that destination is exercised as object is judged, with the southeast, southwest, west
North, southwestern four direction are classifying rules, to classify to the first raster data.In this way, destination is in the starting point southeast
Direction as type 0, destination starting point southwestward as Class1, destination starting point direction northwest work
For type 2, destination starting point southwestward conduct type 3.For another example, destination can be divided into several cells, B1,
B2 ... BN, to classify to the grid where starting point, one is divided into N classes, and destination falls into the starting point of cell B1
The type of the grid at place is 1, and the type that destination falls into the grid where the starting point of cell B2 is 2, and so on；Certainly
The present invention can also use other supervised classification methods.
Unsupervised classification method (unsupervised classification) is used when user is unfamiliar with data
Sorting technique.Unsupervised classification method can look for optimal interval threshold automatically according to the characteristics of data oneself.Ordinary circumstance
Under, optimal criterion is whether the difference between class and class is maximum.One embodiment according to the invention, it is described scheduled
Classifying rules includes scheduled classification number.That is user need to only provide the quantity of classification and can divide data
Class.Such common non-supervised classification such as iteration self-organizing data analysis technique（iterative self
organizing data analysis technique）.This sorting technique only requires that user specifies categorical measure and iteration time
Number.This sorting technique is initially with the threshold value generated at random by data preliminary classification.Then it is carried out step by step to having classification
Cancel, division, the processing such as merging.The classification results of each step are all compared with previous step.It is determined in next step by comparing value
Processing mode.When operation be more than user provide iterations will stop, complete assorting process.
According to another embodiment of the invention, the predtermined category rule can also include point of the first raster data
Cloth.In this embodiment, user need not specify the class number or iterations of classification, completely according to the characteristic of data itself
Classify, such as CURE, BIRCH, Chameleon, the sorting techniques such as DBSCAN, OPTICS may be used to realize above-mentioned point
Class process.Fig. 9 shows the scatter plot of first raster data of one embodiment according to the invention.Abscissa indicates the first grid
Longitude, ordinate in lattice data indicate the latitude in the first raster data.User, which need not specify, to be needed the first raster data
It is divided into several classes, the point in Fig. 9 can be only divided into three classes by the cutting of two dotted lines by above-mentioned non-supervised classification,
As shown in 0,1 and 2 in Fig. 9.
Above-described embodiment classifies geographical location according to the traffic data to form different types of geographic region
Domain.Another embodiment according to the invention can also consider traffic data and interest point data to geographical position
It sets and classifies.Specifically, further reading point of interest (POI, Point of in Geoprocessing method
Interest) data include distribution of the point of interest on map（distribution）, interest point data described further also wraps
Include the value of point of interest（It is not shown in Fig. 2）, and the step 23 in Fig. 2 divides geographical location according to the traffic data
Class further comprises classifying to geographical location according to the traffic data and interest point data.
The example of interest point data has very much, such as hospital, school, company, shop may all constitute interest point data.It will
Interest point data is combined with traffic data can carry out geodata more complicated classification, to realize more complicated data
Analytic function, for example will be seen that the resident for living in the areas A1 goes to the main purpose in the areas A3 by data processing is whatIt is
In order to go to work, go to school, see a doctor or carry out other activities.
The point diagram schematic diagram of the interest point data of one embodiment according to the invention is shown in Figure 10.Point in Figure 10
Indicate that there are a points of interest on corresponding geographical location（Such as hospital, school, company, shop etc.）.Rectangle table in Figure 10
Show a map grid, for simplicity other grids are not shown in Figure 10.It can be indicated by the form of point diagram
Distribution of the point of interest on map.In one embodiment, it is believed that the value each put is identical（For example it is all 1）, such as
One hospital then corresponds to a point in point diagram, and Different hospital both corresponds to the difference in point diagram regardless of size.Another
In kind of embodiment, it is believed that the value each put is different, such as sweeping hospital point value than small scale hospital
The value of point is high, therefore the value corresponding to the different points being distributed in point diagram may be different.
Classification is carried out according to the traffic data and interest point data to geographical location to indicate during classification both
Consider traffic data it is further contemplated that interest point data, only traffic data（Including starting point and destination）All belong to interest point data
It can just be divided into same class in of a sort area.It is A1, A2, A3, A4, A5 respectively for example, city A is divided into five areas,
And have a large amount of traffic data show many people from A1 cells reach cell A3 and A4, and A3 cells geographically with
A4 is adjacent.According to embodiment shown in Fig. 3 A, A3 and A4 cells will be divided into same class.But in the present embodiment due to
Traffic data and interest point data are considered, classification results may be different.For example assume to have a large amount of public affairs stationed in A3 cells
It takes charge of and has a large amount of hospitals in A4 cells stationed, then while adjacent A3 and A4 cells are reached from A1 cells, but due to
The main interest point that A3 cells are stayed with A4 cells is different, therefore still can A3 cells and A4 cells be divided into different classes
Not.
Fig. 3 B show dividing geographical location according to the traffic data for another embodiment according to the invention
The method flow diagram of class.In step 2321, the first raster data is formed, the first grid data packet includes the grid of map grid
Lattice coordinate, the grid coordinate be according to the second extreme coordinates corresponding to the matched first end point of map grid and obtain
's.Step 2321 and step 2311 content in Fig. 3 A are essentially identical, therefore no longer repeat herein it.
The second raster data is formed in step 2323, the second grid data packet includes the synthesis point of interest of map grid
Value, the comprehensive point of interest value are obtained according to the matched point of interest number of map grid.Optionally, described comprehensive
Close point of interest value according to the matched point of interest number of map grid and its value and obtain.In one embodiment,
Assuming that the value of each point of interest is identical, then 9 points of interest included in the grid in Figure 10, therefore the grid in Figure 10 is comprehensive
It is 9 to close point of interest value.In another embodiment, it is assumed that the value of each point of interest is different, includes 9 in the grid in Figure 10
A point of interest, one of point of interest value are 3, remaining eight point of interest value is 1, therefore the synthesis of the grid in Figure 10 is emerging
Interest point value is 11.As it can be seen that comprehensive point of interest value reflects the number of point of interest in the grid, the optional comprehensive interest
Point value also reflects the value of point of interest.
Optionally, in order to obtain the second smoother raster data, and prevent from being formed the second excessively broken grid number
According to can be further smoothed to the second raster data.That is, the step 2323 in Fig. 3 B can be wrapped further
It includes based on the original point of interest value with the matched point of interest number computation grid of map grid（original POI value）；
And the original point of interest value of grid is smoothed to obtain the synthesis point of interest value of grid（general POI
value）.
Figure 11 A show the original point of interest value schematic diagram of one embodiment according to the invention.The original interest
Point value is the interest point data without smoothing processing.Such as, it is assumed that the value of each point of interest is identical, then the grid in Figure 10
Included in 9 points of interest, therefore the original point of interest value of the grid in Figure 10 be 9.Original point of interest in this example
Value is multiplied by the value of point of interest equal to the number of point of interest in grid.It should be appreciated that original point of interest value can be based on
With the function of the matched point of interest number of map grid and its value.
As shown in formula 2 above, wherein N indicates that the total number of the point of interest of all grids on map, V indicate the face of grid
Product, l indicate that first of point of interest in grid, K indicate the point of interest number in grid, QlIndicate first point of interest in grid
Value, P（Zij）Indicate grid ZijOriginal point of interest value.
Certainly, formula 2 is only to calculate an example of original point of interest value to adopt in other embodiments
Original point of interest value is calculated with other functions.
Figure 11 B show the synthesis point of interest value schematic diagram of one embodiment according to the invention.Comprehensive point of interest takes
Value obtains after being smoothed for original point of interest value.Compared with the data in Figure 11 A, it is clear that in Figure 11 B
Data are more smooth, and the difference between the original point of interest value of adjacent cells is weakened.Data can be carried out smoothly
Method has very much, for example Gaussian Kernel Density equation, Bayes's density equation formula may be used etc..Based on given original grid
Data（That is original point of interest value shown in Figure 11 A）, nuclear radius（kernel radius）And and equation, so that it may with calculate
Raster data after going out smoothly（That is comprehensive point of interest value shown in Figure 11 B）.The wherein described nuclear radius is Gaussian Kernel Density side
The parameter needed in formula and Bayes's density equation formula scheduling algorithm, which determine smooth degree, after the bigger processing of nuclear radius
Data it is more smooth.The wherein described equation refers to just the equations such as Gaussian Kernel Density equation, Bayes's density equation formula.Through
What the synthesis point of interest value crossed in each grid of smoothing processing represented is the smooth dot density near the grid（smoothed
point density）.
In step 2325 over the ground based on predtermined category rule, first raster data and second raster data
Figure grid is classified.That is the present embodiment is in classification, while considering starting point, destination and interest point data.
For example, grid coordinate（Including latitude coordinate value and latitude coordinate values）And the grid that comprehensive point of interest value is substantially the same will
It is divided into same class.Above have been described above several sorting technique, including supervised classification method and unsupervised classification side
Method, these sorting techniques stand good in this example.
Optionally, can be classified to the grid according to scheduled criteria for classification.Such as in the criteria for classification
One is if the longitude coordinate of two grids is both less than 32.0180, and latitude coordinate is both greater than 118.7650, and the two grids
Synthesis point of interest value all 5 or more, then the two grids will be divided into one kind.By taking Fig. 6 A, 6B, 11B as an example, grid Z44、
Z45Meet above-mentioned criteria for classification, therefore grid Z44、Z45Belong to same class.
Optionally, can be classified to the grid according to scheduled classification number.For example iteration can be used from group
Organization data analytical technology, the classification number specified according to user（For example it is divided into 10 classes）, to Fig. 6 A, 6B, 11B（Or 11A）In
Three-dimensional data is classified.
Optionally, can also be classified to grid according to the distribution of the first raster data and the second raster data.Than
CURE can be such as used, BIRCH, Chameleon, the sorting techniques such as DBSCAN, OPTICS are according to the first raster data and second
The characteristics of raster data itself is distributed classifies to grid, without specified specific classification number.
It should be noted that in figure 3b, step 2321 can be executed sequentially with step 2323 and can also be executed parallel.
Fig. 4 shows the method flow diagram being deployed in classification results in map of one embodiment according to the invention.
Wherein according to road net data in step 251（real road boundary）The classification results are reconstructed
(calibrate).Since map grid is according to geographical coordinate divide to map being formed by grid graph, map grid
In do not consider road net data, in order to further such that above-mentioned classification results meet practical road network divides, therefore in step 251
The classification results are reconstructed according to road net data.
Figure 12 A show the schematic diagram classified to map grid in Fig. 8 after further being marked.Point of Figure 12 A
Class result is identical with the classification results in Fig. 8, is only identified not using different colours for the sake of eye-catching in fig. 12
Same classification, white indicate that type 0, grey indicate that Class1, speckle patterns indicate type 2, black expression type 3.Figure 12 B show
The road net data schematic diagram of one embodiment according to the invention is gone out.Dotted line in Figure 12 B indicates road, non-dashed part
Indicate non-rice habitats region.In order to allow sorted geographic area to be more in line with the distribution of road network, by road net data to the classification
As a result it is reconstructed.Figure 12 C show one embodiment according to the invention according to road net data to the classification results into
The process schematic of row reconstruct.Geographical grid in Figure 12 A is combined with the road net data in Figure 12 B and just constitutes Figure 12 C
In schematic diagram.As can be seen that grid Z from Figure 12 C42With grid Z below52(Z52Label be not shown in figure) it is same
Belong to Class1, but the two is separated by a road, and grid Z42With the grid Z above it32(Z32Label do not show in figure
Go out) belong to different type, but the two is in adjacent area and is not separated by road, therefore can be by grid Z42Type
(calibrate) is reconstructed into type 0.It similarly, can be by grid Z34With grid Z35Type and distribution at Class1.In short, can be with
The classification results are reconstructed using scheduled reconfiguration rule.The reconfiguration rule include according to current grid it is adjacent and
There is no the type of the grid of road separation（Abbreviation adjacent type）Determine the type of the current grid.Optionally, if there is
Multiple and different adjacent types, can therefrom choose around main adjacent type, such as certain grid that there are three without road point
Every adjacent cells, the types of one of adjacent cells is 1, and the type of two adjacent cells is 2, then selects 2 as the grid
The adjacent type of lattice.Optionally, if the quantity of the adjacent cells belonging to different lattice types is identical such as adjacent there are two
The type of grid, an adjacent cells is 1, another type is 2, then can further expand the quantity of adjacent cells, such as
Also as reference the grid of indirect neighbor, so that it is determined that the type of current grid.Optionally, if different adjacent types are wrapped
The quantity of the adjacent cells included is identical, can not also change the type of current grid.Therefore, the invention is not limited in must make
After obtaining classification results reconstruct, the type of adjacent cells is identical, and the type of adjacent cells can also be different under predetermined circumstances.
The content of reconfiguration rule can be formulated specifically according to actual needs in a word, be not necessarily constrained to content cited hereinabove.
In addition, as can be seen that the road net data is just coincide with map grid edge from Figure 12 C.But in fact,
It is also likely to be present the case where road net data is misfitted with map grid edge, for example certain road leads to from the center of some grid
It crosses, it is therefore desirable to road net data is first adjusted to the shape coincideing with map grid edge, then again to according to shown in Figure 12 C
The classification results of map grid are reconstructed in process.
Figure 12 D show the classification results schematic diagram after the reconstruct of one embodiment according to the invention.It can be seen that figure
The classification results after reconstruct in 12D are more in line with the distribution situation of road net data so that the type in same panel region is most
Amount is unified, unicom also has more scale effect so that the classification results after reconstruct are more complete.
The classification results after reconstruct are deployed in map in step 253（As shown in fig. 13 c）.
Figure 12 A- Figure 12 D describe the process that classification results are reconstructed from microcosmic point.Figure 13 A-13C are from macroscopic view
Level describes the effect that classification results are reconstructed.Before Figure 13 A show the reconstruct of one embodiment according to the invention
Schematic diagram of the classification results in map.It can be seen that the geographical location in Figure 13 A is divided into 10 classes, respectively with 10 kinds of differences
Color be identified.Figure 13 B show the road net data schematic diagram of another embodiment according to the invention.According to Figure 13 B
Shown in road net data the classification results in Figure 13 A are reconstructed after classification results may refer to Figure 13 C.In Figure 13 C
Geographical location is still divided into 10 classes, but it is clear that the classification results in Figure 13 C meet the distribution of road network, and more connectivity
（connectivity）, it is more complete, smooth, have scale effect.
Optionally, wherein classification results, which are deployed in map, further comprises the category for showing same class region in map
Property information.The attribute information include based on classification for information about, such as common destination region etc..Figure 13 D are shown
The attribute information schematic diagram in the display same class region of one embodiment according to the invention.In the example shown in Figure 13 D,
The attribute information in certain class region is A3 cells, indicates by the destination region of starting point of such region to be A3 cells.Figure 13 E show
The attribute information schematic diagram in the display same class region of another embodiment according to the invention is gone out.In the example described in Figure 13 E
In son, the region at the main place in starting point expression starting point of arrow, and the terminal of arrow indicates the region at the main place in destination.
In conclusion one embodiment according to the invention, it can be by the geographical location in map according in traffic data
Starting point classify with destination, so as to realize the analysis of further geodata and excavate.
Described above is the geodata methods in the present invention, below in conjunction with Figure 14-Figure 16 descriptions in same invention structure
Geoprocessing system under thinking, wherein identical or corresponding realization details is detailed and complete due to hereinbefore having been carried out
Whole description, therefore hereinafter will no longer repeat.
Figure 14 shows the system block diagram of the progress Geoprocessing of one embodiment according to the invention.The geography
Data processing system includes the first reading device, sorter and deployment device.Wherein described first reading device is configured
To read traffic data, wherein the traffic data includes the first end point coordinate and the second extreme coordinates of traffic route, wherein
The first end point is one in the starting point and destination of traffic route, and the second endpoint be traffic route starting point and
Another in destination.The wherein described sorter is configured as classifying to geographical location according to the traffic data.
The wherein described deployment device is configured as classification results being deployed in map.
Figure 15 A show the sorter block diagram of one embodiment according to the invention.In the present embodiment, the classification dress
It sets and further comprises the first forming apparatus and the first classification sub-device.Wherein described first forming apparatus is configured to form
One raster data, the first grid data packet include the grid coordinate of map grid, and the grid coordinate is basis and map grid
The second extreme coordinates corresponding to the matched first end point of lattice and obtain.The first classification sub-device is configured as based on pre-
Determine classifying rules and first raster data classifies to map grid.
One embodiment according to the invention, first forming apparatus are configured to utilize spatial interpolation technology
Calculate the grid coordinate of map grid.
One embodiment according to the invention, it is described to calculate the grid coordinate of map grid into one using spatial interpolation technology
Step includes according to the distance and interpolation list for falling on the point in the coordinate of the point in interpolating unit, interpolating unit to the grid
The smoothing factor of member calculates the grid coordinate, wherein the point fallen in interpolating unit includes the first end fallen in interpolating unit
Point, and the coordinate of the point fallen in interpolating unit includes that the second endpoint corresponding to the first end point that falls in interpolating unit is sat
Mark.
One embodiment according to the invention, the predtermined category rule include at least one in the following：It is predetermined
Criteria for classification, it is scheduled classification number and first raster data distribution.
One embodiment according to the invention, the Geoprocessing system further comprise the second reading device, institute
It states the second reading device to be configured as reading interest point data, includes distribution of the point of interest on map.The sorter into
One step is configured as classifying to geographical location according to the traffic data and interest point data.
Figure 15 B show the sorter block diagram of another embodiment according to the invention.Classify described in the present embodiment
Device further comprises the first forming apparatus, the second forming apparatus and the second classification sub-device.Wherein described first forms dress
It sets and is configured to form the first raster data, the first grid data packet includes the grid coordinate of map grid, and the grid is sat
Mark be according to the second extreme coordinates corresponding to the matched first end point of map grid and obtain.Second forming apparatus
It is configured to form the second raster data, the second grid data packet includes the synthesis point of interest value of map grid, described comprehensive
Close point of interest value according to the matched point of interest number of map grid and obtain.The second classification sub-device is configured
Classify to map grid to be based on predtermined category rule, first raster data and second raster data.
One embodiment according to the invention, second forming apparatus are configured to be based on and map grid
The original point of interest value for the point of interest number computation grid matched；And the original point of interest value of grid is smoothed
To obtain the synthesis point of interest value of grid.
Figure 16 shows the deployment device block diagram of one embodiment according to the invention.The deployment device further comprises
Reconstruct device and the first deployment sub-device.The wherein described reconstruct device is configured as according to road net data to the classification results
It is reconstructed.And the first deployment sub-device is configured as the classification results after reconstruct being deployed in map.
One embodiment according to the invention, the deployment device are configured to show same class area in map
The attribute information in domain.
Various embodiments of the present invention are described above, above description is exemplary, and non-exclusive, and
It is not limited to disclosed each embodiment.Without departing from the scope and spirit of illustrated each embodiment, for this skill
Many modifications and changes will be apparent from for the those of ordinary skill in art field.The selection of term used herein, purport
In the principle, practical application or technological improvement to the technology in market for best explaining each embodiment, or this technology is made to lead
Other those of ordinary skill in domain can understand each embodiment disclosed herein.
Claims (18)
1. a kind of Geoprocessing method, including：
Traffic data is read, wherein the traffic data includes the first end point coordinate and the second extreme coordinates of traffic route,
Described in first end point be the traffic route starting point and destination in one, and second endpoint is the traffic
Another in the starting point and destination of circuit；
According in the traffic data starting point and destination classify jointly to geographical location；And
Classification results are deployed in map,
It is wherein described according to the traffic data to geographical location carry out classification further comprise：
The first raster data is formed, the first grid data packet includes the grid coordinate of map grid, and the grid coordinate is root
It is obtained according to the second extreme coordinates corresponding to the matched first end point of map grid；And
Classified to the map grid based on predtermined category rule and first raster data.
2. the method as described in claim 1, wherein the first raster data of the formation further comprises：
The grid coordinate of the map grid is calculated using spatial interpolation technology.
3. method as claimed in claim 2, wherein the grid for being calculated the map grid using spatial interpolation technology is sat
Mark further comprises：
According to falling on distance and interpolating unit of the point in the coordinate of the point in interpolating unit, interpolating unit to the grid
Smoothing factor calculate the grid coordinate, wherein the point fallen in interpolating unit includes the first end fallen in interpolating unit
Point, and the coordinate of the point fallen in interpolating unit includes that the second endpoint corresponding to the first end point that falls in interpolating unit is sat
Mark.
4. the method as described in claim 1 further comprises：
Interest point data is read, distribution of the point of interest on map is included,
It is wherein described according to the traffic data to geographical location carry out classification further comprise：
Classified to geographical location according to the traffic data and interest point data.
5. method as claimed in claim 4, wherein being carried out to geographical location according to the traffic data and interest point data
Classification further comprises：
The first raster data is formed, the first grid data packet includes the grid coordinate of map grid, and the grid coordinate is root
It is obtained according to the second extreme coordinates corresponding to the matched first end point of map grid；
The second raster data is formed, the second grid data packet includes the synthesis point of interest value of the map grid, described comprehensive
Close point of interest value according to the matched point of interest number of the map grid and obtain；And
The map grid is divided based on predtermined category rule, first raster data and second raster data
Class.
6. method as claimed in claim 5, wherein the second raster data of the formation further comprises：
Based on the original point of interest value with the matched point of interest number computation grid of the map grid；And
The original point of interest value of the grid is smoothed to obtain the synthesis point of interest value of grid.
7. the method as described in claim 1,2,3,5, any one of 6, wherein the predtermined category rule includes following each
At least one of in：The distribution of scheduled criteria for classification, scheduled classification number and first raster data.
8. the method as described in claim 1, classification results are deployed in map further comprise wherein described：
The classification results are reconstructed according to road net data；And
Classification results after reconstruct are deployed in map.
9. the method as described in claim 1, classification results are deployed in map further comprise wherein described：
The attribute information in same class region is shown in map.
10. a kind of Geoprocessing system, including：
First reading device is configured as reading traffic data, wherein the traffic data includes the first end point of traffic route
Coordinate and the second extreme coordinates, wherein the first end point be the traffic route starting point and destination in one, and
Second endpoint is another in the starting point and destination of the traffic route；
Sorter, be configured as according in the traffic data starting point and destination geographical location is divided jointly
Class；And
Device is disposed, is configured as classification results being deployed in map,
The wherein described sorter further comprises：
First forming apparatus, is configured to form the first raster data, and the first grid data packet includes the grid of map grid
Coordinate, the grid coordinate be according to the second extreme coordinates corresponding to the matched first end point of map grid and obtain
's；And
First classification sub-device, is configured as based on predtermined category rule and first raster data to the map grid
Classify.
11. system as claimed in claim 10, wherein first forming apparatus is configured to：
The grid coordinate of the map grid is calculated using spatial interpolation technology.
12. system as claimed in claim 11, wherein the grid for calculating the map grid using spatial interpolation technology
Coordinate further comprises：
According to falling on distance and interpolating unit of the point in the coordinate of the point in interpolating unit, interpolating unit to the grid
Smoothing factor calculate the grid coordinate, wherein the point fallen in interpolating unit includes the first end fallen in interpolating unit
Point, and the coordinate of the point fallen in interpolating unit includes that the second endpoint corresponding to the first end point that falls in interpolating unit is sat
Mark.
13. system as claimed in claim 10, further comprising：
Second reading device is configured as reading interest point data, includes distribution of the point of interest on map,
The wherein described sorter is configured to：
Classified to geographical location according to the traffic data and interest point data.
14. system as claimed in claim 13, wherein the sorter further comprises：
First forming apparatus, is configured to form the first raster data, and the first grid data packet includes the grid of map grid
Coordinate, the grid coordinate be according to the second extreme coordinates corresponding to the matched first end point of map grid and obtain
's；
Second forming apparatus, is configured to form the second raster data, and the second grid data packet includes the map grid
Comprehensive point of interest value, the comprehensive point of interest value be according to the matched point of interest number of the map grid and obtain
's；And
Second classification sub-device, is configured as based on predtermined category rule, first raster data and second grid
Data classify to the map grid.
15. system as claimed in claim 14, wherein second forming apparatus is configured to：
Based on the original point of interest value with the matched point of interest number computation grid of the map grid；And
The original point of interest value of grid is smoothed to obtain the synthesis point of interest value of grid.
16. such as system any one of in claim 10,11,12,14 or 15, wherein the predtermined category rule includes
At least one of in the following：Point of scheduled criteria for classification, scheduled classification number and first raster data
Cloth.
17. system as claimed in claim 10, wherein the deployment device further comprises：
Device is reconstructed, is configured as that the classification results are reconstructed according to road net data；And
First deployment sub-device, is configured as the classification results after reconstruct being deployed in map.
18. system as claimed in claim 10, wherein the deployment device is configured to：
The attribute information in same class region is shown in map.
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| CN105912598A (en) * | 2016-04-05 | 2016-08-31 | 中国农业大学 | Method and system for determining high-frequency regions for roadside stall business in urban streets |
| CN107451673B (en) * | 2017-06-14 | 2019-05-14 | 北京小度信息科技有限公司 | Dispense region partitioning method and device |
| CN107977427B (en) * | 2017-11-28 | 2021-08-24 | 鲁东大学 | Storage and reading method of classified raster data |
| CN108875013B (en) * | 2018-06-19 | 2022-05-27 | 百度在线网络技术（北京）有限公司 | Method and device for processing map data |
| CN109345445B (en) * | 2018-09-03 | 2022-11-25 | 中国电子科技集团公司第二十八研究所 | Regional migration method and system for geographic data |
| CN109493119B (en) * | 2018-10-19 | 2020-06-23 | 南京图申图信息科技有限公司 | POI data-based urban business center identification method and system |
| CN111047130B (en) * | 2019-06-11 | 2021-03-02 | 北京嘀嘀无限科技发展有限公司 | Method and system for traffic analysis and management |
| CN110503485B (en) * | 2019-08-27 | 2020-09-01 | 北京京东智能城市大数据研究院 | Geographical region classification method and device, electronic equipment and storage medium |
| CN117202106B (en) * | 2023-10-19 | 2024-05-14 | 北京融信数联科技有限公司 | Regional space place attribute labeling method, system and medium based on signaling data |
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