CN106980644A - A kind of individual interpersonal relationships visual inference method of isomery Urban Data - Google Patents
A kind of individual interpersonal relationships visual inference method of isomery Urban Data Download PDFInfo
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Abstract
The invention discloses a kind of visual inference method of individual interpersonal relationships of isomery Urban Data, comprise the following steps：(1) data are obtained；(2) urban area of data in step (1) is encoded to quaternary tree region, by geographical location information according to its longitude and latitude position in city with quadtree coding；(3) encoded data are subjected to classification storage for feature；(4) unified space-time and path matching is carried out to geographical location information；(5) selection analysis target；(6) determined to analyze the Move Mode of target according to trace time line chart；(7) according to the Move Mode of analysis target, determine to analyze the different types of interpersonal relationships of target by path matching；(8) to obtained best match object, both interpersonal relationships is verified by analyzing all path matching situations of best match object and target；The present invention can be used in terms of the analysis of target life pattern, interpersonal relationships analysis and interpersonal relationships checking.
Description
Technical field
The present invention relates to visual analysis technical field, more particularly to a kind of individual interpersonal relationships of isomery Urban Data are visual
Inference method.
Background technology
Urban Data is in traffic planninng and prediction, the analysis of crowd's Move Mode, air pollution monitoring, location-based
It is widely used in terms of service recommendation, the reasoning application of few individual interpersonal relationshipss, because individual movement often has
There is uncertainty, individual interpersonal relationships is just more difficult to obtain by automation algorithm.The master of existing interpersonal relationships related work
To be extracted by the relation of determination, relation of such as making a phone call, social networks friend relation, microblogging forwarding relation.Individual and other
Colleague's relation of people is also the important clue that social networks are understood by Fiel that can express, and but the data in default of support are difficult to dig
Pick.
Data support is there has been, the length only according to colleague's time goes to judge two individual interpersonal relationships is close and distant is also a lack of
Foundation.Therefore the manual intervention of user's intelligence is needed to judge the type and significance level of track, such as judgement for household
It is accomplished by the Dual Matching of festivals or holidays track and night track.The interpersonal relationships of data-driven judges that machine intelligence must be combined
(match query) and user intelligence (decision-making judgement).Therefore a kind of side of the individual interpersonal relationships of reasoning from Urban Data is needed
Method.
The content of the invention
The invention provides a kind of visual inference method of individual interpersonal relationships of isomery Urban Data, by people in Urban Data
Move Mode analysis and matching expressed and guided with method for visualizing and interactive mode, assistant analysis teacher infer and
Analyze the related interpersonal relationships of target.
A kind of visual inference method of individual interpersonal relationships of isomery Urban Data, comprises the following steps：
(1) data with geographical location information of separate sources and form are obtained；
(2) urban area of data in step (1) is encoded to quaternary tree region, by geographical location information according to it in city
Longitude and latitude position in city is with quadtree coding；Target can be expressed as r (t in the position in either segment periods；te；sr), represent
To end time t between from the outsets-te, the target is in position sr。
(3) data that will be encoded by step (2) carry out classification storage for feature；
(4) unified space-time and path matching is carried out to the geographical location information in step (3)；
(5) the selection analysis target from the data of step (1)；
(6) determined to analyze the Move Mode of target according to trace time line chart；
(7) according to the Move Mode of analysis target, determine to analyze the different types of interpersonal pass of target by path matching
System；
(8) for the best match object obtained in step (7), by the institute's rail for analyzing best match object and target
Mark match condition verifies both interpersonal relationships.
Number is stored for data characteristicses (Deta sparseness, sample frequency, data volume) respectively in forms such as database, files
According to (including geographical location information and other information after quadtree coding), it is preferred that in step (3), car data warp is hired out
Cross after quadtree coding by taxi index with time sequencing deposit local file；
In microblogging, POI and data in mobile phone deposit database, microblogging, POI and data in mobile phone are larger due to general data amount
(the even upper T of tens G), these data are stored into database, and the fields such as time, space are indexed, it is possible to increase data
Search efficiency.
It is preferred that, in step (4), unified space-time and path matching are carried out to the geographical location information in step (3)
Specific method is as follows：
For time-space registration c (t, sc), including an a time variable t and geospatial location scIf, some target
In the location of time t and scIt is close, then goal satisfaction time-space registration；
For the track R={ r being made up of n orbit segment1,r2,...,rnMatching, track R and some target trajectory
The total duration that is matched by orbit segment two-by-two of matching degree determine.Remember tsiEnter the time of certain geospatial location, t for targetei
The time of the geospatial location is left for the target, then by corresponding on the premise of location matches can be matched in position two-by-two
Time, which occurs simultaneously, to be determined, i.e. leave the time min (t of the geospatial location in target i and j earliestei,tej) subtract target i and j
In the latest enter the geospatial location time min (tsi,tsj), it is defined as min (tei,tej)-max(tsi,tsj)。
It is preferred that, in step (6), determined to analyze concretely comprising the following steps for the Move Mode of target according to trace time line chart：
For type of cell phone target, the trace time line chart visual encoding rule of target movement, including often stop
Place and stay time；
For from taxi type target, trace time line chart visual encoding target carrying situation and duration, rather than ground
Point is stopped, and the coding can avoid excessive place color from using, and show the carrying behavior of taxi.
It is preferred that, in step (7), determine to analyze the specific step of the different types of interpersonal relationships of target by path matching
Suddenly it is：
7-1, the path matching correspondence household for being in neighbouring and neighbours, the path matching correspondence near job site are worked together
With the people near job site, the track cascade matching weekend trip near the result of multiple path matching, such as matching man is superimposed
Track；
7-2, weight, order and the mark for adjusting matched rule, are ranked up to matching result；
7-3, the result for deleting some matched rule；
7-4, the result of new matched rule and existing result be replaced；
7-5, the best match object for obtaining meeting multiple matched rules.
User it can be found that best match object, and be visually observed that each matching object be directed to each matched rule
Matching degree, facilitate its to target and match object behavior analyze.
It is preferred that, in step (1), the data include microblog data, hire out car data, mobile phone location data and POI numbers
At least two in.
The data such as news, map can be increased afterwards in step (4) and be used as contextual information.
The data such as news and map facilitate user to explain and analyze behavior often with abundant semantic information.
Beneficial effects of the present invention：
The visual inference method of individual interpersonal relationships of the isomery Urban Data of the present invention can be used as system by visually interaction
System input adds decision-making and analysis process, to being risen in terms of the analysis of target life pattern, interpersonal relationships analysis, interpersonal relationships checking
Decisive role.
Brief description of the drawings
Fig. 1 is the schematic diagram of step (4) location matches and path matching.
Fig. 2 is the schematic diagram of step (6) positioning analysis target.
Fig. 3 is the schematic diagram of the visual reasoning of step (6).
Fig. 4 is the schematic diagram for the trace time line chart that step (7) expresses type of cell phone target on map.
Fig. 5 is the schematic diagram for the trace time line chart that step (7) expresses taxi type target on map.
Fig. 6 is the movement schematic diagram that is marked out on map of target in step (7).
Fig. 7 is the schematic diagram that step (8) analyzes the different types of interpersonal relationships of target by path matching.
Fig. 8 is the schematic diagram that step (8) analyzes the different types of interpersonal relationships of target by path matching.
Fig. 9 is the synoptic chart of target interpersonal relationships in step (9).
Figure 10 is the detail view of target interpersonal relationships in step (9).
Embodiment
Below by the case of Urban Data collection, the present invention is described in detail with reference to accompanying drawing, the purpose of the present invention and effect will
Become readily apparent from.
The visual inference method of individual interpersonal relationships of the isomery Urban Data of the present embodiment includes data processing section and can
Depending on reasoning part.
Data processing section：
(1) data (microblog data, taxi car data, the hand with geographical location information of separate sources different-format are obtained
Machine position data, POI data etc.)；
(2) urban area is encoded to quaternary tree region, by geographical location information data according to its longitude and latitude in city
Spend position same with quadtree coding.Target can be expressed as r (t in the position in either segment periods；te；sr) represent from the outset
Between to end time ts-te, the target is in position sr。
(3) deposited respectively in forms such as database, files for data characteristicses (Deta sparseness, sample frequency, data volume)
Store up data (including geographical location information and other information after quadtree coding).Taxi car data in present case passes through four
Local file, microblogging, POI and data in mobile phone deposit database are stored in time sequencing by taxi index after fork tree-encoding.
(4) unified space-time, path matching method are designed to the geographical location information data in step (3), such as Fig. 1 institutes
Show, wherein t is the time, and L is geospatial location, and c is time-space registration, for time-space registration c (t, sc), including anaplasia at one
Measure t and geospatial location scIf some target is in the location of time t and scIt is close, then the goal satisfaction this when
Sky matching；
For one section of track R={ r being made up of n orbit segment1,r2,...,rnMatching, track R and some target
The total duration that the matching degree of track is matched by orbit segment two-by-two is determined.Remember tsiFor target enter certain geospatial location when
Between, teiThe time of the geospatial location is left for the target, then on the premise of location matches can be matched in position two-by-two by
The corresponding time, which occurs simultaneously, to be determined, i.e. leave the time (min (t of the geospatial location in target i and j earliestei,tej)) subtract
Enter the time (min (t of the geospatial location in target i and j the latestsi,tsj)), it is defined as min (tei,tej)-max(tsi,
tsj)；That is, for two sections of tracks of two objects, in the case of having coincidence in time, space, matching degree by when
The duration that bare weight is closed determines.
(5) data such as increase news, map are used as contextual information.
Visual reasoning part：
(6) the selection reasoning starting point first from data (microblog data, taxi car data, mobile phone location data)：Select point
Target is analysed, as shown in Figure 2：Some microblog account, a certain taxi can be selected, or is looked for according to given time-space registration
To analysis target；Can with the motion track of preview target, as shown in figure 3, come verify some mobile phone whether with some microblog account
Or the matching of some space-time condition；
(7) after determination analysis target, according to the Move Mode of trace time chart analysis target：For type of cell phone mesh
Mark, as shown in figure 4, the rule of figure visual encoding target movement, including the place often stopped and stay time, A, B and E
It is 3 places therein, the expression Zonal expression of the annulus target is in these local residence times and duration；And for from going out
Hire a car type target, as shown in figure 5, the figure visual encoding target carrying situation and duration., can by the analysis of movement law
To position the scope of activities of target, such as family and job site.The movement of target can be marked out on map simultaneously, such as
Shown in Fig. 6, conveniently check and analyze；
(8) according to the Move Mode of target, the different types of interpersonal relationships of target can be analyzed by path matching, be in
Neighbouring path matching has often corresponded to household and neighbours, and the path matching near job site has generally corresponded to colleague and work
Make the people near place.The result of multiple path matching can be superimposed, the track cascade matching weekend trip near such as matching man
Track；The weight, order and mark of matched rule can be adjusted, matching result is ranked up；Some matching rule can be deleted
Result then；The result (as shown in Figure 7) of new matched rule or existing result (as shown in Figure 8) can also be replaced.
Final purpose is to find the best match object for meeting multiple matched rules.
(9) for obtained best match object, can by analyze all path matching situations of they and target come
Verify their interpersonal relationships.For the interpersonal relationships confirmed after analysis, their relation, Fig. 9 can be marked in figures 7 and 8
Express the synoptic chart and detail view of target interpersonal relationships respectively with Figure 10, Fig. 9 color depth expresses each period and target
The number that track matches, then whether expression matching object and target match Figure 10 color in the correspondence period.
The present embodiment method, with reference to machine intelligence and user's intelligence, solves analysis by method for visualizing and exchange method
The analysis of target life pattern, interpersonal relationships analysis, interpersonal relationships checking three major issues.For this Urban Data collection case, the party
Method positioning analysis target, analyze target life track, extract track of interest find Corresponding matching party, checking target with
The relation of party.The important relationship people of target and its journey in close relations can be analyzed by a series of this step this method
Degree.
Claims (6)
1. the visual inference method of individual interpersonal relationships of a kind of isomery Urban Data, it is characterised in that comprise the following steps：
(1) data with geographical location information of separate sources and form are obtained；
(2) urban area of data in step (1) is encoded to quaternary tree region, by geographical location information according to it in city
Longitude and latitude position with quadtree coding；
(3) data that will be encoded by step (2) carry out classification storage for feature；
(4) unified space-time and path matching is carried out to the geographical location information in step (3)；
(5) the selection analysis target from the data of step (1)；
(6) determined to analyze the Move Mode of target according to trace time line chart；
(7) according to the Move Mode of analysis target, determine to analyze the different types of interpersonal relationships of target by path matching；
(8) for the best match object obtained in step (7), by all tracks for analyzing best match object and target
Both interpersonal relationships is verified with situation.
2. the visual inference method of individual interpersonal relationships of isomery Urban Data as claimed in claim 1, it is characterised in that step
(3) in, hire out car data and pass through after quadtree coding by taxi index with time sequencing deposit local file；
In microblogging, POI and data in mobile phone deposit database.
3. the visual inference method of individual interpersonal relationships of isomery Urban Data as claimed in claim 1, it is characterised in that step
(4) in, the space-time unified to the geographical location information progress in step (3) and the specific method of path matching are as follows：
For time-space registration c (t, sc), including an a time variable t and geospatial location scIf, some target when
Between the location of t and scIt is close, then goal satisfaction time-space registration；
For the track R={ r being made up of n orbit segment1,r2,...,rnMatching, of track R and some target trajectory
The total duration matched with degree by orbit segment two-by-two is determined, remembers tsiEnter the time of certain geospatial location, t for targeteiFor this
Target leaves the time of the geospatial location, then by the corresponding time on the premise of location matches can be matched in position two-by-two
Occur simultaneously and determine, be i.e. leave the time min (t of the geospatial location in target i and j earliestei,tej) subtract in target i and j most
Evening enters the time min (t of the geospatial locationsi,tsj), it is defined as min (tei,tej)-max(tsi,tsj)。
4. the visual inference method of individual interpersonal relationships of isomery Urban Data as claimed in claim 1, it is characterised in that step
(6) in, determined to analyze concretely comprising the following steps for the Move Mode of target according to trace time line chart：
For type of cell phone target, the trace time line chart visual encoding rule of target movement, including the place often stopped
And stay time；
For from taxi type target, trace time line chart visual encoding target carrying situation and duration.
5. the visual inference method of individual interpersonal relationships of isomery Urban Data as claimed in claim 1, it is characterised in that step
(7) in, determine to analyze concretely comprising the following steps for the different types of interpersonal relationships of target by path matching：
7-1, the path matching correspondence household for being in neighbouring and neighbours, path matching correspondence colleague and work near job site
Make the people near place, be superimposed the track cascade matching weekend trip track near the result of multiple path matching, such as matching man；
7-2, weight, order and the mark for adjusting matched rule, are ranked up to matching result；
7-3, the result for deleting some matched rule；
7-4, the result of new matched rule and existing result be replaced；
7-5, the best match object for obtaining meeting multiple matched rules.
6. the visual inference method of individual interpersonal relationships of isomery Urban Data as claimed in claim 1, it is characterised in that step
(1) in, the data include microblog data, hire out in car data, mobile phone location data and POI data at least two.
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| CN111291243A (en) * | 2019-12-30 | 2020-06-16 | 浙江大学 | Visual reasoning method for uncertainty of spatiotemporal information of character event |
| CN111581320A (en) * | 2020-05-09 | 2020-08-25 | 海南中智信信息技术有限公司 | Behavior track-based method and system for identifying head-collision place in time |
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