Geographic space abnormal accumulation area scanning statistical method based on interaction force

An interaction force and geospatial technology, applied in the field of scanning statistics of geospatial anomaly gathering areas based on interaction force, can solve the problem of not considering the interaction effect of geospatial objects, and achieve the effect of accurate detection and strong detection ability

Active Publication Date: 2016-11-16
CHINA UNIV OF PETROLEUM (EAST CHINA)
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Problems solved by technology

[0006] In reality, geospatial anomaly clusters have different shapes and most of them are irregular. Therefore, based on the Kulldorff circular and elliptical scanning statistical methods, existing research focuses on improving the detection ability of irregular areas, that is, scanning Optimizing methods, window shapes, etc., so that various irregular shapes of spatial anomaly clusters can be detected, but these studies have not considered the inherent interaction between geospatial objects

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  • Geographic space abnormal accumulation area scanning statistical method based on interaction force
  • Geographic space abnormal accumulation area scanning statistical method based on interaction force
  • Geographic space abnormal accumulation area scanning statistical method based on interaction force

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0129] Example 1: Simulation data set I

[0130] The simulated data set I includes 4 data sets, and each data set contains an MLC cluster of different shapes, such as band, S-shape, O-shape, and cross-shape, such as Figure 4(a) ~ 4(d) shown.

[0131] The total number of spatial units per data set N = 400, the overall attribute b of each unit i = 40, the instance attribute c of the unit in the cluster i = 20, the instance attribute c of the unit outside the cluster i = 10. The number of cluster space units in the four data sets are 40, 40, 80, 80 respectively, and the MLC cluster size ratio (referring to the ratio of the number of MLC cluster units to the total number of data set units N) is 0.1, 0.1, 0.2, 0.2.

[0132] Spatial adjacency takes the form of queens whose common boundaries and vertices are directly adjacent. The parameters k and b of the spatial interaction SIM model take the value of 1, a takes the value of 2, and d adopts the ordinary Euclidean distance bet...

Embodiment 2

[0146] Example 2: Simulation Dataset II

[0147] The simulated data set II includes two data sets, each of which contains an MLC cluster with depressed units, and the shapes of the MLC clusters are bands and crosses, as shown in Figure 5(a) and Figure 6(a) , a depression unit refers to a cluster unit whose event rate is slightly higher than that of the units outside the cluster, but significantly lower than that of other units in the cluster. The existence of the depression unit increases the detection difficulty of the spatial scanning method , when the detection ability of the scanning method is weak, the clustering results may be interrupted here because the sunken units cannot be detected. The banded cluster contains 1 sunken unit, and the cross-shaped cluster contains 3 adjacent sunken units on the left and right sides. If no sunken unit is detected, a small number of cluster units isolated by the sunken unit (the banded cluster is isolated Open 1 unit, the left and righ...

Embodiment 3

[0154] Example 3: Simulation Dataset III

[0155] The simulated dataset III includes two datasets III(a) and III(b), each of which contains two MLC clusters of different shapes, such as Figure 7(a) , 7(b) shown. The total number of spatial units per data set N = 400, the overall attribute b of each unit i = 40, the instance attribute c of the unit in the cluster i =20, instance attribute c of the unit outside the cluster i =10.

[0156] Ⅲ(a) contains two clusters of O-shape and I-shape, the numbers of cluster space units are 80 and 40 respectively, the total number of cluster units is 120, and the ratio of the total size of clusters is 0.3. Ⅲ(b) contains two clusters of L-shape and S-shape, the numbers of cluster space units are 40 and 40 respectively, the total number of cluster units is 80, and the ratio of the total size of clusters is 0.2. These two data sets are used to compare and test the three methods of SIM depth scanning, SIM breadth scanning and Kulldorff cir...

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Abstract

The invention discloses a geographic space abnormal accumulation area scanning statistical method based on interaction force. The method comprises the following steps: based on a selected space adjacency type, establishing a spatial neighbor relation matrix; using a spatial interaction model to measure interaction strength between adjacent objects; based on a deep scan method or a breadth scan method, continuously selecting the adjacent objects whose interaction strength is maximum, and adding the adjacent objects into a candidate accumulation area, until the value of likelihood ratio LR/log-likelihood ratio LLR corresponding to high value anomaly accumulation no longer increases or the value of likelihood ratio LR/log-likelihood ratio LLR corresponding to low value anomaly accumulation no longer reduces or the candidate accumulation area reaches maximum specified dimensions, stopping to add the adding the adjacent objects into the candidate accumulation area; performing Monte Carlo simulation on the plurality of formed candidate accumulation areas, so as to detect an abnormal accumulation area which passes non-stochastic hypothesis testing. The method has stronger detection ability on abnormal accumulation areas which are in irregular shapes, and can easily detect the abnormal accumulation area which includes weak links, and non-abnormal geographic objects would not be included in the detected abnormal accumulation area.

Description

technical field [0001] The invention relates to the technical field of geospatial information processing, in particular to a scanning statistics method for geospatial anomaly gathering areas based on interaction force. Background technique [0002] Geospatial scan statistics are methods of geospatial clustering. Spatial clustering refers to grouping geographical objects into several categories according to spatial characteristics and attribute characteristics, so that the similarity between objects of the same type is the largest, the difference between objects of the same type is the largest, and objects of different types have obvious distinctions in spatial distribution. The purpose of spatial clustering is to discover geospatial distribution patterns, as well as potential interrelationships between geographic objects. Traditional spatial clustering methods can be divided into partition clustering, hierarchical clustering, density clustering, grid clustering and other ty...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00G06K9/62
CPCG16Z99/00G06F18/23
Inventor 王海起董倩楠桂丽彭佳琦车磊陈冉刘玉曾喆翟文龙费涛闫滨
Owner CHINA UNIV OF PETROLEUM (EAST CHINA)
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