CN109325523A - A spatiotemporal scan clustering method for irregular shapes based on maximum association and risk bias - Google Patents
A spatiotemporal scan clustering method for irregular shapes based on maximum association and risk bias Download PDFInfo
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- 230000002776 aggregation Effects 0.000 claims abstract description 37
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- 238000012502 risk assessment Methods 0.000 claims description 4
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Abstract
The invention relates to an irregular-shaped space-time scanning clustering method based on maximum correlation and risk deviation, and belongs to the field of space-time event clustering analysis methods. Firstly, obtaining a graph structure of a research area according to the disease incidence place and the traffic route of a case in the area; then according to a maximum correlation (mlink) algorithm, determining the shape of a scanning window and searching a potential aggregation area; and then, whether the relative risk of the aggregation is accurate or not is judged by calculating the relative risk and the risk deviation of the aggregation. Compared with the prior art, the invention changes the scanning window of the conventional cylindrical space-time scanning method, and the space base can be in an irregular shape, thereby being beneficial to finding out the space aggregation in the irregular shape; and relative risk deviation evaluation is introduced to evaluate the risk of aggregation, if the deviation is overlarge, the area can be further observed, unnecessary early warning loss is avoided, and the reliability of regional disease prevention and early warning is improved.
Description
Technical field
The present invention relates to a kind of irregular shape space scanning clustering method based on most relevance and risk deviation, belongs to
Spatio-temporal event clustering method field.
Background technique
In disease detection and prevention, the early stage that space scanning clustering method is normally used for illness outbreak is explored, many
Scientist is explored and is assessed geographic disease aggregation using space scanning clustering method.However, in space scanning clustering method
The middle scanning window for defining disease aggregation is often cylindrical, and its scanning window is often that dynamic does not change at any time,
For it is certain it is related to road traffic, be possible to change the diseases aggregation of shape, space scanning clustering method can not it is accurate and
Timely detect the aggregation of the disease；And in previous research, not in view of being carried out to the aggregation having been detected by
Relative risk assessments occur the possibility of mistake although the accuracy rate of space scanning model inspection to Spatiotemporal Aggregation is higher
Property, if detecting the clustering phenomena of mistake, unnecessary Disease Warning Mechanism can be caused to lose, cause this area people unnecessary
Fear, reduce the confidence level of Disease Warning Mechanism.
Summary of the invention
The technical problem to be solved in the present invention is to provide when a kind of irregular shape based on most relevance and risk deviation
Sky scanning clustering method with the space clustering for solving unconventional shape present in discovery survey region, and introduces opposite
Risk Bias assesses the risk of aggregation, if deviation is excessive, can further look to the region, avoid drawing
Unnecessary early warning loss is played, the reliability of regional disease prevention early warning is increased.
The technical scheme is that a kind of clustered based on most relevance and the irregular shape space scanning of risk deviation
Method, specific steps are as follows:
Step1, survey region S is determined；
Morbidity place and the population P of Step2, the case load C in acquisition monitoring area S and each case；
Step3, according to the position coordinates and traffic roadmap of all subregion in the S of region, construct the graphic structure of region S；
Step4, according to most relevance algorithm, detected from the S of region and determine scanning window shape and search for potential accumulation regions
Domain M；
Step5, the true Relative risk value R and relative risk estimated value for calculating aggregation zone MTo obtain opposite wind
The inclined B in danger；Relative risk estimated valueCloser to 1, then evidence show in survey region A without clustering phenomena；
Step6, according to the value of relative risk deviation B, judge whether estimated relative risk reliable, and the value of B more approaches
In 1, risk assessment confidence level is higher.
Further, in the Step3, the position coordinates of all subregion i are in the space of village i or area i in survey region S
Heart point longitude and latitude (xi, yi)；Subregion i is at least connected with other any subregion j and k according to traffic roadmap, that is, there is ground
Manage adjacent structure；To construct the graphic structure in the area according to geographical adjacent structure.
Further, in the Step4, scanning window shape is determined using most relevance algorithm and searches for potential accumulation regions
Domain, wherein the specific implementation step of most relevance algorithm be:
Step S1, any one subregion i is chosen as current aggregator region, i ∈ s；
Step S2, the graphic structure constructed using Step3 arranges current cluster i's according to the connection number of current cluster i
Neighbours；
Step S3, the adjacent subarea domain j of maximum number of connections or k will integrate current aggregator area i with current aggregator region i；
Step S4, step S2 to S3 is repeated until aggregation zone MiReach pre-set maximum parameter size；
Step S5, step S1 to S4 is repeated, until sub-regions each in survey region S are all by as initial aggregation；
Step S6, by step S1 to S5, according to potential aggregation zone MiThe shape for determining scanning window utilizes scanning window
Practical morbidity number and theoretical morbidity number inside and outside mouthful construct test statistics: likelihood ratio Likelihood Ratio, abbreviation LR, benefit
The intensity of anomaly that number of falling ill in scanning window is evaluated with LR, therefrom determines aggregation zone M.
Further, in the step S6, specific modeling process is as follows:
Enable CMFor scanning window MiIn practical morbidity number, PMFor scanning window MiMiddle population, enables EMFor according to invalid false
If obtaining scanning window MiMiddle expected morbidity number, C is total case load in the S of region, total number of people P, it is contemplated that morbidity number ESAre as follows:
ES=∑ EM (2)
Wherein, LMFor scanning window MiLikelihood function value, L0For based on obtaining likelihood function value under null hypothesis.
Further, in the step S6, space scanning statistic T is defined as all possible scanning window MiMiddle maximum
Likelihood ratio:
The morbidity highest window M of number intensity of anomaly is found out, determines aggregation zone M.
Further, in the Step5, true Relative risk value R and relative risk estimated valueFormula are as follows:
Wherein, WMFor the value-at-risk and W of region MS/MFor the value-at-risk in survey region S in addition to the area M；C is survey region S
Interior total case load, CMFor the case load in the M of region, EMFor the expection case load in the M of region, ESFor case load in survey region S
Desired value.
Further, in the Step5, in region, the event number of M obeys Poisson distribution, so WMFormula are as follows:
Wherein, FMIt is the Poisson stochastic variable of event number in the M of region, E (FM) it is desired value, define WS/MFormula are as follows:
Further, in the Step6, the formula of relative risk deviation B is calculated are as follows:
As relative deviation B=1, no deviation, relative risk estimated value are indicatedAccurately；
As relative deviation B > 1, indicate that there are overgauge, relative risk estimated valuesIt is relatively large；
As relative deviation B < 1, indicate that there are minus deviation, relative risk estimated valuesIt is relatively small.
The beneficial effects of the present invention are: the present invention is compared with the prior art, the present invention changes conventional cylindrical space-time and sweeps
The scanning window of method is retouched, space pedestal can be irregular shape, be conducive to the space clustering for finding unconventional shape；And
Relative risk Bias is introduced, the risk of aggregation is assessed, if deviation is excessive, which can further be seen
It examines, avoids that unnecessary early warning is caused to be lost, increase the reliability of regional disease prevention early warning.
Detailed description of the invention
Fig. 1 is flow chart of steps of the present invention；
Fig. 2 is the simple graph structural schematic diagram of region S of the present invention；
Fig. 3 is the potential aggregation schematic diagram of the unconventional shape of the present invention.
Specific embodiment
With reference to the accompanying drawings and detailed description, the invention will be further described.
Embodiment 1: as shown in Figure 1, a kind of clustered based on most relevance and the irregular shape space scanning of risk deviation
Method introduces graphic structure and most relevance (mlink) algorithm, finds there is unconventional shape aggregation zone in the region；Again
By calculating the relative risk and risk deviation of the aggregation, whether the relative risk of judgement this time aggregation is accurate.
Specific steps are as follows:
Step1, survey region S is determined；
Morbidity place and the population P of Step2, the case load C in acquisition monitoring area S and each case；
Step3, according to the position coordinates and traffic roadmap of all subregion in the S of region, construct the graphic structure of region S；
Step4, according to most relevance algorithm, detected from the S of region and determine scanning window shape and search for potential accumulation regions
Domain M；
Step5, the true Relative risk value R and relative risk estimated value for calculating aggregation zone MTo obtain opposite wind
The inclined B in danger；Relative risk estimated valueCloser to 1, then evidence show in survey region A without clustering phenomena；
Step6, according to the value of relative risk deviation B, judge whether estimated relative risk reliable, and the value of B more approaches
In 1, risk assessment confidence level is higher.
Further, in the Step3, the position coordinates of all subregion i are in the space of village i or area i in survey region S
Heart point longitude and latitude (xi, yi)；Subregion i is at least connected with other any subregion j and k according to traffic roadmap, that is, there is ground
Manage adjacent structure；To construct the graphic structure in the area according to geographical adjacent structure.
Further, in the Step4, scanning window shape is determined using most relevance algorithm and searches for potential accumulation regions
Domain, wherein the specific implementation step of most relevance algorithm be:
Step S1, any one subregion i is chosen as current aggregator region, i ∈ S；
Step S2, the graphic structure constructed using Step3 arranges current cluster i's according to the connection number of current cluster i
Neighbours；
Step S3, the adjacent subarea domain i of maximum number of connections or k will integrate current aggregator area i with current aggregator region i；
Step S4, step S2 to S3 is repeated until aggregation zone MiReach pre-set maximum parameter size；
Step S5, step S1 to S4 is repeated, until sub-regions each in survey region S are all by as initial aggregation；
Step S6, by step S1 to S5, according to potential aggregation zone MiThe shape for determining scanning window utilizes scanning window
Practical morbidity number and theoretical morbidity number inside and outside mouthful construct test statistics: likelihood ratio Likelihood Ratio, abbreviation LR, benefit
The intensity of anomaly that number of falling ill in scanning window is evaluated with LR, therefrom determines aggregation zone M.
Further, in the step S6, specific modeling process is as follows:
Enable CMFor scanning window MiIn practical morbidity number, PMFor scanning window MiMiddle population, enables EMFor according to invalid false
If obtaining scanning window MiMiddle expected morbidity number, C is total case load in the S of region, total number of people P, it is contemplated that morbidity number ESAre as follows:
ES=∑ EM (2)
Wherein, LMFor scanning window MiLikelihood function value, L0For based on obtaining likelihood function value under null hypothesis.
Further, in the step S6, space scanning statistic T is defined as all possible scanning window MiMiddle maximum
Likelihood ratio:
The morbidity highest window M of number intensity of anomaly is found out, determines aggregation zone M.
Further, in the Step5, true Relative risk value R and relative risk estimated valueFormula are as follows:
Wherein, WMFor the value-at-risk and W of region MS/MFor the value-at-risk in survey region S in addition to the area M；C is survey region S
Interior total case load, CMFor the case load in the M of region, EMFor the expection case load in the M of region, ESFor case load in survey region S
Desired value.
Further, in the Step5, in region, the event number of M obeys Poisson distribution, so WMFormula are as follows:
Wherein, FMIt is the Poisson stochastic variable of event number in the M of region, E (FM) it is desired value, define WS/MFormula are as follows:
Further, in the Step6, the formula of relative risk deviation B is calculated are as follows:
As relative deviation B=1, no deviation, relative risk estimated value are indicatedAccurately；
As relative deviation B > 1, indicate that there are overgauge, relative risk estimated valuesIt is relatively large；
As relative deviation B < 1, indicate that there are minus deviation, relative risk estimated valuesIt is relatively small.
In conjunction with attached drawing, the embodiment of the present invention is explained in detail above, but the present invention is not limited to above-mentioned
Embodiment within the knowledge of a person skilled in the art can also be before not departing from present inventive concept
Put that various changes can be made.
Claims (8)
1. a kind of irregular shape space scanning clustering method based on most relevance and risk deviation, it is characterised in that:
Step1, survey region S is determined；
Morbidity place and the population P of Step2, the case load C in acquisition monitoring area S and each case；
Step3, according to the position coordinates and traffic roadmap of all subregion in the S of region, construct the graphic structure of region S；
Step4, according to most relevance algorithm, detected from the S of region and determine scanning window shape and search for potential aggregation zone M；
Step5, the true Relative risk value R and relative risk estimated value for calculating aggregation zone MIt is inclined to obtain relative risk
B；Relative risk estimated valueCloser to 1, then evidence show in survey region A without clustering phenomena；
Step6, according to the value of relative risk deviation B, judge whether estimated relative risk reliable, the value of B more levels off to 1,
Risk assessment confidence level is higher.
2. the irregular shape space scanning clustering method according to claim 1 based on most relevance and risk deviation,
It is characterized by: space center's point that the position coordinates of all subregion i are village i or area i in survey region S passes through in the Step3
Latitude (xi,yi)；Subregion i is at least connected with other any subregion j and k according to traffic roadmap, that is, exists geographical adjacent
Structure；To construct the graphic structure in the area according to geographical adjacent structure.
3. the irregular shape space scanning clustering method according to claim 1 based on most relevance and risk deviation,
It is characterized by: determine scanning window shape in the Step4 using most relevance algorithm and search for potential aggregation zone,
The specific implementation step of middle most relevance algorithm is:
Step S1, any one subregion i is chosen as current aggregator region, i ∈ S；
Step S2, the graphic structure constructed using Step3, according to the neighbour for arranging current cluster i with the connection number of current cluster i
It occupies；
Step S3, the adjacent subarea domain j of maximum number of connections or k will integrate current aggregator area i with current aggregator region i；
Step S4, step S2 to S3 is repeated until aggregation zone MiReach pre-set maximum parameter size；
Step S5, step S1 to S4 is repeated, until sub-regions each in survey region S are all by as initial aggregation；
Step S6, by step S1 to S5, according to potential aggregation zone MiThe shape for determining scanning window, using in scanning window
Outer practical morbidity number and theoretical morbidity number construct test statistics: likelihood ratio Likelihood Ratio, abbreviation LR utilize LR
It evaluates the intensity of anomaly of morbidity number in scanning window, therefrom determines aggregation zone M.
4. the irregular shape space scanning clustering method according to claim 3 based on most relevance and risk deviation,
It is characterized by: specific modeling process is as follows in the step S6:
Enable CMFor scanning window MiIn practical morbidity number, PMFor scanning window MiMiddle population, enables EMTo be obtained according to null hypothesis
To scanning window MiMiddle expected morbidity number, C is total case load in the S of region, total number of people P, it is contemplated that morbidity number ESAre as follows:
ES=∑ EM (2)
Wherein, LMFor scanning window MiLikelihood function value, L0For based on obtaining likelihood function value under null hypothesis.
5. the irregular shape space scanning clustering method according to claim 3 based on most relevance and risk deviation,
It is characterized by: space scanning statistic T is defined as all possible scanning window M in the step S6iIn maximum likelihood
Than:
The morbidity highest window M of number intensity of anomaly is found out, determines aggregation zone M.
6. the irregular shape space scanning clustering method according to claim 1 based on most relevance and risk deviation,
It is characterized by: in the Step5, true Relative risk value R and relative risk estimated valueFormula are as follows:
Wherein, WMFor the value-at-risk and W of region MS/MFor the value-at-risk in survey region S in addition to the area M；C is total in survey region S
Case load, CMFor the case load in the M of region, EMFor the expection case load in the M of region, ESIt is expected for case load in survey region S
Value.
7. the irregular shape space scanning clustering method according to claim 1 based on most relevance and risk deviation,
It is characterized by: in region, the event number of M obeys Poisson distribution, so W in the Step5MFormula are as follows:
Wherein, FMIt is the Poisson stochastic variable of event number in the M of region, E (FM) it is desired value, define WS/MFormula are as follows:
8. the irregular shape space scanning clustering method according to claim 1 based on most relevance and risk deviation,
It is characterized by: calculating the formula of relative risk deviation B in the Step6 are as follows:
As relative deviation B=1, no deviation, relative risk estimated value are indicatedAccurately；
As relative deviation B > 1, indicate that there are overgauge, relative risk estimated valuesIt is relatively large；
As relative deviation B < 1, indicate that there are minus deviation, relative risk estimated valuesIt is relatively small.
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| CN115862888A (en) * | 2023-02-17 | 2023-03-28 | 之江实验室 | Method, system, equipment and storage medium for predicting infection of infectious diseases |
| CN117807811A (en) * | 2024-02-28 | 2024-04-02 | 济南轨道交通集团有限公司 | Method and system for determining limit side resistance of pile-soil interface |
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