CN103593361B - Movement space-time trajectory analysis method in sense network environment - Google Patents

Movement space-time trajectory analysis method in sense network environment Download PDF

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CN103593361B
CN103593361B CN201210290571.4A CN201210290571A CN103593361B CN 103593361 B CN103593361 B CN 103593361B CN 201210290571 A CN201210290571 A CN 201210290571A CN 103593361 B CN103593361 B CN 103593361B
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data
space
time
mobile
analysis
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CN201210290571.4A
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CN103593361A (en
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库涛
朱云龙
王亮
吴俊伟
吕赐兴
陈瀚宁
张丁
张丁一
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中国科学院沈阳自动化研究所
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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90Details of database functions independent of the retrieved data types
    • G06F16/95Retrieval from the web
    • G06F16/953Querying, e.g. by the use of web search engines
    • G06F16/9537Spatial or temporal dependent retrieval, e.g. spatiotemporal queries

Abstract

The invention relates to the technical field of movement behavioral analysis and prediction in a sense network environment, and specifically to a movement space-time trajectory analysis method in the sense network environment. The movement space-time trajectory analysis method in the sense network environment comprises data reception of receiving trajectory movement position data generated by a positioning device and resolving the data format into a data format applicable to data treatment; semantic treatment of performing clustering operation on the semantic trajectory data; space-time correlation of performing characteristic analysis and statistics on clustered semantic trajectory data in a time domain and a space domain, and performing time-space correlation analysis in combination with the time domain and the space domain; correlation similarity analysis of calculating space-time correlation similarity of the semantic trajectory and performing analysis and calculation on the correlation among different space domains and different movement objects; outputting a result. The movement space-time trajectory analysis method in the sense network environment solves the problem of continuous treatment and mutual correlation of time and space dimensions in a traditional transactional database, and meets the need from a sense network application service to real-time analysis of trajectory movement data.

Description

Mobile Space-time trajectory analysis method under sensing network environment

Technical field

The present invention relates to the mobile behavior analysis under sensing network environment and electric powder prediction, specifically a kind of sense Answer Mobile Space-time trajectory analysis method under network environment.

Background technology

At present, the popularization with the shift position such as GPS harvester and be wirelessly transferred, the development of ubiquitous computation technology, The space-time trajectory analysis necks such as the behavior patterns mining based on moving object position information, real time position service, shift position prediction The research in domain increasingly causes the concern of academia and industrial circle, and the application of its correlation is also increasingly extensive.At present in intelligent transportation Management and the aspect such as scheduling, the monitoring process of rapid onset flood, the change perception of ecological environment, mobile network's value-added service Existing related base application.Because space-time trajectory data is respectively provided with continuation property on time dimension and Spatial Dimension, with When space time correlation universally present in trajectory analysis during, therefore carry out trajectory analysis ten with reference to Spatial dimensionality distribution character Divide necessity.But, the mobile analysis method in existing track or the distribution characteristicss only considering Spatial Dimension at present, or only consider Shift position, according to the spatial distribution characteristic of time order and function order, there is no the analysis side of maturation for space time correlation track aspect Method.It is simultaneously based on background knowledge(As geo-demographics' distribution, shift position point semantic expressiveness etc.), time and space mutually closes Connection similarity cluster, the mobility model of semantic space represents etc. that aspect lacks the analysis and research method of correlation, and this moves for track Dynamic analysis brings a huge difficult problem with the application of Information Mobile Service.Therefore, in the urgent need to one kind trajectory analysis method pair effectively Carry out feature analysiss and the pattern extraction of profound level in mobile location information.

Content of the invention

For the above-mentioned problems in the prior art, the invention provides a kind of based on shift position trajectory analysis it is System property method, carries out the analysis of track and the discovery of pattern by space time correlation analysis with locus semantic background, effectively solves The deficiency that the association analysiss aspect of the moving object position trace information determined under LBS exists, meets Information Mobile Service application right Needs in aspects such as real-time, complexity, integration, actualities.

The present invention be employed technical scheme comprise that for achieving the above object:Mobile Space-time trajectory analysis under sensing network environment Method, comprises the following steps:

Data receiver and parsing:Track mobile position data produced by receiving positioner, including shift position points According to corresponding time data;Noise data therein, redundant data, wrong data and deficiency of data are filtered Cleaning;Linear interpolation operation is carried out to the data after cleaning, adjacent position data time spacing value is exceeded with the data of threshold value, Linearisation point of addition point data between adjacent position, this threshold value is combined with specific application background and is given by user;

Semantic processes:Track mobile position data after parsing is converted into the semantic track data on abstract sense, that is, The mobile position data that space-time three-dimensional coordinate represents is carried out with the semantic conversion under two-dimensional coordinate, specially will be by GPS longitude, dimension The space two-dimensional element that degree represents is converted to the region semantic one-dimensional element under geography information, and corresponding time dimension element is not Become;Proximate region position data in semantic track data is sorted out on basis by here;

Space time correlation:In time domain and spatial domain, distribution characteristicss and density feature are pressed to the data after semantic processes respectively Carry out similarity analysis statistics, binding time domain and spatial domain carry out space time correlation degree analysis;Described association similarity analysis be: Calculate the space time correlation similarity of semantic track, for the degree of association between different spaces domain, between different mobile object respectively Calculated;

Output:Probabilistic model is set up according to above-mentioned association similarity analysis result, the trajectory model being found is entered Row semantic intergrationization is processed, and produces readable output result;Calculate the individual space-time track probability with colony of mobile object, prediction Its following space-time track position.

The object of described parsing includes various criterion acquired in the collecting device of different shift positions, the rail of different-format Mark mobile position data.

Described locus semantic knowledge information represents social satellite information, including demographics distributed intelligence, economic society Can information and mechanism's setting, region division.

Described semantic track data after semantic processes is stored in semantic knowledge-base.

Track mobile position data produced by described positioner is sent to corresponding movement in the way of prompting message and sets Standby upper.

Described prompting message includes data is activation, transmission message and abnormal conditions prompting.

Described space time correlation is specially:

Position between produce similarity measure matrix:1)The trajectory range that motion track is covered is with network interconnection Mode carries out location network calculating, trajectory range is divided into n mutually disjunct regional ensemble, to any two in this set Individual region is based on its track of topological relationship calculation and connects number;

2)Calculate its region interest measure corresponding to above-mentioned divided region:

Value=f (Nin,Nout,ΔT)

Wherein NinIndicate entry into the track number in this region, NoutRepresent the track number leaving this region, Δ T represents that track is stopped The accumulated time staying, Value represents the interest measure of the area of space being drawn based on above three parameter;

3)Carry out cluster between analogous location with interregional track on the basis of region interestingness measure is connected, thus setting up Region and association in time quantitative relationship, and draw that there is the region that same trajectories access feature;

0≤lI, j≤1

Wherein, lI, jRepresent the similarity value between ith zone and j-th region, and matrix diagonals line element is identical In 1;

4)Position under utilization space semanteme clusters to locus to similar matrix, makes the sky with similar semantic Between position be classified as a class;

Similarity measure matrix is set up between mobile object:Based on mobile object trajectory range network topology, to not Set up metric matrix with the mobile object similarity relation on region, this metric matrix is in order to represent n mobile object in a certain area The similar incidence relation of movement on domain, the such as time of staying, travel frequency, initial and final position,

0≤mI, j≤1

Wherein, mI, jRepresent the similarity value between i-th mobile object and j-th mobile object, matrix diagonals line element Plain mI, iIt is constantly equal to 1.

Described data receiver, semantic processes, space time correlation, association similarity analysis and result output are all in monitor state Under carry out, alarm is carried out for exception and error situation.

Track data after described parsing carries out semantic conversion processing, the semantic flow data one side after conversion and history rail Mark data compares, and carries out individual Track association analysis and colony's Track association analysis respectively;On the other hand using presetting Known event condition carry out condition coupling, if meeting certain event structure condition set in advance, that is, be considered some Determine the generation of event, thus producing the operation such as abnormal alarm.

The anomalous event that described monitoring obtains is saved in historical data base to carry out historical events renewal.

The present invention has advantages below:

By shift position perceive the mancarried device or equipment acquisition and recording to mobile object itself mobile message, can between The relevant information of the relevant information of external environment condition and mobile colony residing for ground connection reflection mobile object.Track mobile conduct sensing Directly perceived under network environment, dynamic message form not only can provide location Based service, mobile message to hand over mobile object Mutually wait application service, and the inherent law of mobile colony can be perceived with variation tendency and predict, simultaneously can be right Environmental information is reflected construction and maintenance in order to managing, optimizing public infrastructure in real time.

The analysis method of track mobile location information weakening with disappearance to the phase under sensing network in space time correlation dimension Close application and create larger inhibition, become the bottleneck that sensing network related application is widely popularized to a certain extent, Apply the quick inspection in real time service offer, abnormal conditions especially for complicated integrated service on real-time position information basis Survey and define obvious restriction with the aspect such as tracking, comprehensive extraction of mobile behavior pattern.

Provided by the present invention it is mutually related trajectory analysis method based on time, Spatial Dimension, in conjunction with trajectory location points Semantic expressiveness and relevant position demographics distributed intelligence, in track historical time record, real-time time record and future The different aspects such as finite time interval between single individual, same community, be analyzed between different groups comparing, solve Between position semantic background knowledge, demographics distributed intelligence and locus point combine defective tightness, time dimension information with Spatial positional information cannot cannot interact etc. related between efficient association, space-time track mobile behavior pattern and real knowledge excavation Problem.

Brief description

Fig. 1 is method of the present invention flow chart;

Fig. 2 is that space time correlation of the present invention processes schematic diagram;

Fig. 3 is track of the present invention flow data real-time processing engine principles structure chart;

Fig. 4 is the integrated system structure chart of present invention application.

Specific embodiment

Below in conjunction with the accompanying drawings and embodiment the present invention is described in further detail.

As shown in figure 1, being method of the present invention flow chart.Space time correlation is analyzed(Spatial-Temporal Correlation Analysis, STCA)Based on semantic knowledge-base track data is carried out semantic conversion on Spatial dimensionality and Represent, compared by the association analyzer of time domain, spatial domain calculate diverse location between, the pass between different mobile object Connection similarity, using the track data in historical data base as comparison other, ultimately generates semantic track behavioral pattern.Space-time closes Connection analysis is mainly made up of following assembly:

1. track data receives:For track mobile position data produced by receiving positioner.

2. parse:Track for various criterion acquired in the collecting device of different shift positions for the parsing, different-format Position data.

3. semantic knowledge-base:The locus semantic knowledge information of storage track mobile institute overlay area, including population system Meter distributed intelligence, economic society information and the society such as mechanism's setting, region division satellite information, and the shifting on time dimension Dynamic statistical distribution knowledge etc., semantic knowledge-base provides for the operation such as the pretreatment of space-time track, division, cluster as background knowledge and props up Hold.

4. semantic processes:Interact with semantic knowledge-base, for parsing produced by track data carry out semantic expressiveness, Semantic coordinate transformation, Semantic Clustering etc. operate;Semantic expressiveness is the semantic rail being converted into initial trace data on abstract sense Mark data, semantic coordinate transformation is the two-dimensional coordinate table being converted into the space-time three-dimensional coordinate of initial trace data under semantic coordinate Show, Semantic Clustering is the cluster operation on here basis, semantic track being carried out.On the one hand reject redundancy track data, another Aspect notes abnormalities track.

5. message produces:Produce and be sent to data is activation on associated mobile device and transmission message and abnormal conditions Prompting message.

6. space time correlation analysis:In time domain and spatial domain, feature analysiss and statistics are carried out to semantic track data respectively, Carry out space time correlation degree analysis in combination with time domain and spatial domain.

7. similarity analysis are associated:For calculating the space time correlation similarity of semantic track, for different spaces region it Between, the degree of association between different mobile object is analyzed calculating.

8. flow data processes engine:The real time knowledge providing oriented locus flow data finds to support work(with application service Can, and carry out data renewal, transmission with portable running fix equipment, interact.

9. monitoring management:It is responsible for specially to track data reception, parsing, semanteme converts and process, space time correlation are analyzed, phase It is monitored like operations such as degree calculating.

10. export:It is responsible for setting up probabilistic model according to Similarity Measure statistical law, the trajectory model being found is entered Row semantic intergrationization is processed to produce more succinct readability, more abstract complicated semantic intergration output result, base simultaneously In probabilistic model, for mobile object, the individual Future Trajectory movement with colony and behavior are analyzed and predict.

Space-time trajectory data flow chart of data processing is as shown in Figure 2:After being sent to trajectory analysis system receiving terminal, analysis System is by the identification operation that by historical data base, it is carried out with space-time mobile behavior and track data, semantic knowledge-base afterwards Track data will be carried out with the association identification process of semantic knowledge information, thereafter will respectively position between and mobile object it Between produce similarity measure matrix.For position between similarity measure matrix, space time correlation analysis first by track move institute The trajectory range covering carries out location network calculating in the way of network interconnection, and utilization space semantic information is to locus afterwards Carry out Semantic Clustering.In cluster process, the locus with similar semantic will be classified as a class, the semantic locations point of exception Semantic association analysis part will be sent to and detect abnormal track behavior.By semantic coordinate transformation process, by semantic locations rail Mark extracts as semantic space probabilistic model, the region in built vertical statistical significance and association in time quantitative relationship.Mobile object it Between similar matrix calculate first calculate mobile object network topology, and then take mobile object semanteme conversion process draw The Semantic Clustering relation of mobile object.By position between similarity measure can be found that having same trajectories accesses feature Region, and then combine semantic knowledge, track data can be analyzed, explain and predict.By the phase between mobile object Likelihood metric can carry out cluster analyses to the individuality with similar mobile behavior and colony, sets up the mobile power in colony's meaning Pattern knowledge, provides more targeted, more selectively service support for Mobile solution service.

Space time correlation analysis in the present invention(STAC)Affiliated track flow data processes the principle assumption diagram of engine modules such as Shown in Fig. 3:Individual flow data is activation collected by running fix station acquisition device to while trajectory analysis system preliminary Pass to flow data after pretreatment and process engine, flow data processes engine and utilizes its internal semantic knowledge management plug-in unit convection current Data carries out semantic conversion processing.Semantic flow data one side after conversion, compared with historical trajectory data, carries out individual respectively The analysis of body Track association and colony's Track association analysis;On the other hand semantic flow data passes through event monitor, using setting before Fixed known event condition carries out condition coupling, once meeting certain set event structure condition, that is, is considered some Determine the generation of event, thus producing the operation such as abnormal alarm, to realize the purpose of real-time monitoring mobile object behavior.Institute simultaneously Monitor the anomalous event obtaining will be saved in historical data base to carry out historical events renewal.

The mobile flow data space time correlation analysis of track designed by the present invention is as shown in Figure 4 with integrated morphology:Mobile portable Formula terminal and various application platform are connected with track BMAT server by cloud network, are on the one hand gathered itself Track mobile data pass through wireless network transmissions to cloud network platform, another aspect background server is that it provides real-time Related Mobile solution calculates service support.Space-time track flow data passes to space-time trajectory analysis system in real time by transmission platform System, system it is carried out preliminary pretreatment and backup preserve operation after, by the space language of space-time Track association analyzer Behavioral pattern data storehouse carries out space time correlation analysis to real-time streaming data, by comparing language for adopted data base, location database After the feature clustering relation on time dimension and Spatial Dimension, comprehensive spatial and temporal association enters every trade to it to adopted track data For analyzing and researching, integrating engine and space time correlation engine carry out higher level semantic collection to the mobile semanteme behavior being drawn into Become and operation associated, final track knowledge is saved in be updated to rule base in knowledge base, export available simultaneously Integrated serve the representation of knowledge.

Claims (8)

1. under a kind of sensing network environment Mobile Space-time trajectory analysis method it is characterised in that comprising the following steps:
Data receiver and parsing:Track mobile position data produced by receiving positioner, including shift position point data and Corresponding time data;Noise data therein, redundant data, wrong data and deficiency of data are carried out filter clearly Wash;Linear interpolation operation is carried out to the data after cleaning, adjacent position data time spacing value is exceeded with the data of threshold value, Linearisation point of addition point data between adjacent position, this threshold value is combined with specific application background and is given by user;
Semantic processes:That is, pair track mobile position data after parsing is converted into semantic track data on abstract sense, when The mobile position data that empty three-dimensional coordinate represents carries out the semantic conversion under two-dimensional coordinate, specially will be by GPS longitude, dimension table The space two-dimensional element showing is converted to the region semantic one-dimensional element under geography information, and corresponding time dimension element is constant;? On this basis, the proximate region position data in semantic track data is sorted out;
Space time correlation:In time domain and spatial domain, the data after semantic processes is carried out with density feature by distribution characteristicss respectively Similarity analysis count, and binding time domain and spatial domain carry out space time correlation degree analysis;Described association similarity analysis be:Calculate The space time correlation similarity of semantic track, is carried out respectively for the degree of association between different spaces domain, between different mobile object Calculate;
Described space time correlation is specially:
Position between produce similarity measure matrix:1) trajectory range that motion track is covered is in the way of network interconnection Carry out location network calculating, trajectory range is divided into n mutually disjunct regional ensemble, to any two area in this set Domain is based on its track of topological relationship calculation and connects number;
2) calculate its region interest measure corresponding to above-mentioned divided region:
Value=f (Nin,Nout,ΔT)
Wherein NinIndicate entry into the track number in this region, NoutRepresent the track number leaving this region, Δ T represents what track stopped Accumulated time, Value represents the interest measure of the area of space being drawn based on above three parameter;
3) carry out cluster between analogous location with interregional track on the basis of region interestingness measure is connected, thus setting up region With association in time quantitative relationship, and draw have same trajectories access feature region;
L = l 1 , 1 ... l 1 , n ... l i , i ... l n , 1 ... l n , n , 0 ≤ l i , j ≤ 1
Wherein, li,jRepresent the similarity value between ith zone and j-th region, and matrix diagonals line element is constantly equal to 1;
4) position under utilization space semanteme clusters to locus to similar matrix, makes the space bit with similar semantic Put and be classified as a class;
Similarity measure matrix is set up between mobile object:Based on mobile object trajectory range network topology, to not same district Mobile object similarity relation on domain sets up metric matrix, and this metric matrix is in order to represent n mobile object on a certain area The similar incidence relation of movement,
M = m 1 , 1 ... m 1 , n ... m i , i ... m n , 1 ... m n , n , 0 ≤ m i , j ≤ 1
Wherein, mi,jRepresent the similarity value between i-th mobile object and j-th mobile object, matrix diagonals line element mi,iIt is constantly equal to 1;
Output:Probabilistic model is set up according to above-mentioned association similarity analysis result, language is carried out for the trajectory model being found Adopted integrated approach, produces readable output result;Calculate the individual space-time track probability with colony of mobile object, predict it not The space-time track position come;
Described data storage after semantic processes is in semantic knowledge-base.
2. under sensing network environment according to claim 1 Mobile Space-time trajectory analysis method it is characterised in that described solution The object of analysis includes various criterion acquired in the collecting device of different shift positions, the track shift position number of different-format According to.
3. under sensing network environment according to claim 1 Mobile Space-time trajectory analysis method it is characterised in that institute's predicate Memory space position semantic knowledge information in adopted knowledge base, represents social satellite information, including demographics distributed intelligence, economy Social information and mechanism's setting, region division.
4. under sensing network environment according to claim 1 Mobile Space-time trajectory analysis method it is characterised in that described fixed Track mobile position data produced by the device of position is sent on corresponding mobile device in the way of prompting message.
5. according to claim 4 sensing network environment under Mobile Space-time trajectory analysis method it is characterised in that described carry Show that message includes data is activation, transmission message and abnormal conditions prompting.
6. under sensing network environment according to claim 1 Mobile Space-time trajectory analysis method it is characterised in that described number It is all to carry out under monitor state according to reception, semantic processes, space time correlation, association similarity analysis and result output, for different Normal and error situation carries out alarm.
7. under sensing network environment according to claim 1 Mobile Space-time trajectory analysis method it is characterised in that described solution On the one hand and historical trajectory data track mobile position data after analysis carries out semantic conversion processing, and the semantic flow data after conversion Compare, carry out individual Track association analysis and colony's Track association analysis respectively;On the other hand utilize set in advance known Event condition carries out condition coupling, if meeting certain event structure condition set in advance, that is, is considered that some determines thing The generation of part, thus produce abnormal alarm operation.
8. under sensing network environment according to claim 7 Mobile Space-time trajectory analysis method it is characterised in that described product The event of raw abnormal alarm is saved in historical data base to carry out historical events renewal.
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Families Citing this family (21)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103914563A (en) * 2014-04-18 2014-07-09 中国科学院上海微系统与信息技术研究所 Pattern mining method for spatio-temporal track
CN104036139B (en) * 2014-06-12 2017-07-07 中国科学院软件研究所 A kind of mobile object track monitoring method
CN104252527B (en) * 2014-09-02 2018-04-20 百度在线网络技术(北京)有限公司 A kind of method and apparatus of the resident information of definite mobile subscriber
CN104679911B (en) * 2015-03-25 2018-03-27 武汉理工大学 It is a kind of based on discrete weak related cloud platform decision forest sorting technique
CN104765873B (en) * 2015-04-24 2019-03-26 百度在线网络技术(北京)有限公司 User's similarity determines method and apparatus
US10073908B2 (en) 2015-06-15 2018-09-11 International Business Machines Corporation Functional space-time trajectory clustering
CN105652304B (en) * 2016-01-06 2017-10-20 成都小步创想畅联科技有限公司 Merge the place recognition methods of multisensor
CN105426553B (en) * 2016-01-15 2018-09-11 四川农业大学 A kind of object real-time tracking method for early warning and system based on smart machine
CN105719191B (en) * 2016-01-20 2019-10-11 东北大学 The social groups that behavior semanteme is not known under multiscale space find method
CN107229940A (en) * 2016-03-25 2017-10-03 阿里巴巴集团控股有限公司 Data adjoint analysis method and device
CN106649656B (en) * 2016-12-13 2020-03-17 中国科学院软件研究所 Database-oriented space-time trajectory big data storage method
CN106951455A (en) * 2017-02-24 2017-07-14 河海大学 A kind of similar track analysis system and its analysis method
CN108537241A (en) * 2017-03-02 2018-09-14 镇江雅迅软件有限责任公司 A kind of building moving object track method for measuring similarity
CN107133646B (en) * 2017-05-03 2019-09-17 山东合天智汇信息技术有限公司 A kind of method and system by human behavior track identification emphasis personnel
CN107180378A (en) * 2017-05-11 2017-09-19 北京旷视科技有限公司 Commodity attention rate preparation method and device
CN108268597A (en) * 2017-12-18 2018-07-10 中国电子科技集团公司第二十八研究所 A kind of moving-target activity probability map construction and behavior intension recognizing method
CN108170793A (en) * 2017-12-27 2018-06-15 厦门市美亚柏科信息股份有限公司 Dwell point analysis method and its system based on vehicle semanteme track data
CN108052924A (en) * 2017-12-28 2018-05-18 武汉大学深圳研究院 The discrimination method of spatial movement behavior semantic pattern
CN108629000A (en) * 2018-05-02 2018-10-09 深圳市数字城市工程研究中心 A kind of the group behavior feature extracting method and system of mobile phone track data cluster
CN110519324B (en) * 2019-06-06 2020-08-25 特斯联(北京)科技有限公司 Person tracking method and system based on network track big data
CN110134097B (en) * 2019-07-10 2019-09-20 江苏金恒信息科技股份有限公司 A kind of control method and device acquiring equipment

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101231642A (en) * 2007-08-27 2008-07-30 中国测绘科学研究院 Space-time database administration method and system
US8224766B2 (en) * 2008-09-30 2012-07-17 Sense Networks, Inc. Comparing spatial-temporal trails in location analytics

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Finding Similar Users Using Category-Based Location History;Xiangye Xiao 等;《international conference on advances in geographic information systems》;20101231;442-445 *
基于时空约束的轨迹聚类方法研究与应用;张旭;《中国优秀硕士学位论文全文数据库 信息科技辑》;20101115(第11期);I140-34 *
用于交通出行调查的GPS时空轨迹数据简化与语义增强研究;张波;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20110915(第09期);C034-14 *

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