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Method for selecting maximum scanning window in space scanning statistics

A scanning window and spatial scanning technology, which is applied in computing, structured data retrieval, resources, etc., can solve problems such as high false alarm rate and the difference in detection results of the largest scanning window, so as to ensure speed and accuracy and improve practical applicability , the effect of broad real-world applicability

Pending Publication Date: 2020-03-06
马越
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But the first two methods have their own disadvantages: the default 50% window will have a higher false positive rate
In the actual data set, the detection results of different maximum scan windows also have significant differences

Method used

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  • Method for selecting maximum scanning window in space scanning statistics
  • Method for selecting maximum scanning window in space scanning statistics
  • Method for selecting maximum scanning window in space scanning statistics

Examples

Experimental program
Comparison scheme
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Embodiment 1

[0036] This embodiment discloses a method for selecting the largest scanning window in spatial scanning statistics, including the following steps:

[0037] Step 1: Given the maximum scanning window, scan the research area to obtain a series of scanning windows.

[0038] Step 2: Use the obtained scan window as an alternative hypothesis, and use the scan window without clustered areas in the study area as the null hypothesis, then construct the log-likelihood ratio of each scan window based on the Poisson distribution, and finally select the logarithm The scanning window whose likelihood ratio is greater than the critical value of Monte Carlo simulation is used as the detected aggregation area.

[0039] In this step, the scanning window as the alternative hypothesis is set to be z, then the calculation method of the logarithmic likelihood ratio of the scanning window z is:

[0040]

[0041]

[0042] C and N in formulas (1) and (2) represent the total number of occurrences...

Embodiment 2

[0054] This embodiment is further described on the basis of embodiment 1 in conjunction with specific actual data, specifically as follows:

[0055] Detect the clustering areas of high female breast cancer deaths in 245 counties in the northeastern United States. The data include 44,182 deaths from 2011 to 2015, that is, C=44,182, and the average annual female population is 3,258,7167. The maximum scanning window parameters to be selected are respectively (here the maximum scanning window is defined as the maximum population proportion) 50%, 49.9%, ..., 0.2%, 0.1%, 500 in total. The inspection level was set at 0.05.

[0056] Under the above conditions, the method of selecting the maximum scanning window includes the following steps:

[0057] 1. First, select the maximum scanning window of 5% as a given scanning parameter, and use SaTScan software for detection. A total of 8 aggregation areas without spatial overlap were detected, and a joint aggregation area was obtained afte...

Embodiment 3

[0070] This embodiment discloses a method for selecting the largest scanning window in spatial scanning statistics, including the following steps:

[0071] Step 1: Given the maximum scanning window, scan the research area to obtain a series of scanning windows.

[0072] Step 2: Use the obtained scan window as an alternative hypothesis, and use the scan window without clustered areas in the study area as the null hypothesis, then construct the log-likelihood ratio of each scan window based on the Poisson distribution, and finally select the logarithm The scanning window whose likelihood ratio is greater than the critical value of Monte Carlo simulation is used as the detected aggregation area.

[0073] In this step, the scanning window as the alternative hypothesis is set to be z, then the calculation method of the logarithmic likelihood ratio of the scanning window z is:

[0074]

[0075]

[0076] C and N in formulas (1) and (2) represent the total number of occurrences...

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Abstract

The invention discloses a method for selecting a maximum scanning window in space scanning statistics. The method comprises the following steps: 1, giving the maximum scanning window to scan a research area to obtain a series of scanning windows; 2, taking the obtained scanning window as a standby hypothesis, taking the scanning window without the aggregation region in the research region as a zero hypothesis, constructing a log-likelihood ratio of each scanning window based on Poisson distribution, and selecting the scanning window of which the log-likelihood ratio is greater than a Monte Carlo simulation critical value as a detected aggregation region; 3, extracting an aggregation region without spatial overlapping, and calculating an evaluation index under the given parameter; 4, replacing different maximum scanning windows, and calculating evaluation indexes under various given parameter conditions; and 5, comparing the evaluation indexes, wherein the given parameter condition corresponding to the maximum evaluation index value is the maximum scanning window. According to the method, the maximum scanning window can be calculated based on actual data without knowing the exact scanning aggregation situation before analysis.

Description

technical field [0001] The invention belongs to the technical field of spatio-temporal event cluster analysis, in particular to a method for selecting the largest scanning window in spatial scanning statistics. Background technique [0002] With the development of geographic information systems, global positioning systems, and remote sensing technologies, a large number of health-related datasets with geographic locations have emerged. Accurately identifying spatial variability between regions, such as differences in disease incidence, plays an important role in finding potential causes of diseases, allocating limited health resources, formulating reasonable public health policies, and exploring the characteristics of health-related problems, etc. . One of the most common methods for identifying such differences is Kulldorff's spatial scanning statistic, which detects regions that are significantly different from other regions, ie clustered regions. [0003] Kulldorff's sp...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06F16/29
CPCG06Q10/06393G06F16/29
Inventor 马越张韬殷菲肖雄王维程磊蒋小辉
Owner 马越