Spatio-Temporal Outlier Detection in Precipitation Data

Name: Spatio-Temporal Outlier Detection in Precipitation Data
Uploaded: 2017-08-16T06:30:57+00:00
Duration: PTM27S57
Description: Spatio-Temporal Outlier Detection in Precipitation Data

Spatio-Temporal Outlier Detection in Precipitation Data
SensorKDD 2008 Sunday, 24th August, 2008 Spatio-Temporal Outlier Detection in Precipitation Data Elizabeth Wu, Wei Liu, Sanjay Chawla The University of Sydney, Australia

Outline What is a spatio-temporal outlier? Motivation Previous Work Contributions Our Approach Future Work

Aims Find moving spatial outlier paths in South American precipitation data. Show how the paths can be compared to weather phenomenon, such as the El Niño Southern Oscillation (ENSO).

What is a Spatio-Temporal Outlier?
“A spatio-temporal object whose thematic attribute values are significantly different from those of other spatially and temporally referenced objects in its spatial and/or temporal neighborhoods.” – Cheng and Li (2006) 5 4 3 2 1 5 4 3 2 1 5 4 3 2 1 5 4 3 2 1 5 4 3 2 1 Insert a map of your country. t=1 t=2 t=3 t=4 t=5

What is a spatio-temporal object?
“A time-evolving spatial object whose evolution or ‘history’ is represented by a set of instances (o_id, si, ti) where the spacestamp si is the location of object o_id at timestamp ti.” - Theodoris et. al. (1999) Simply put, A point becomes a line A 2D region becomes a 3D region time time y co-ordinate y co-ordinate x co-ordinate x co-ordinate

Figure: Stations used to produce gridded precipitation fields
Data Figure: Stations used to produce gridded precipitation fields South American precipitation data (NOAA) 10 years ( ) 2.5 x 2.5° grids 31 latitude x 23 longitude divisions 713 grids total 2,609,580 possible data values Missing data – spatially and temporally El Niño Southern Oscillation Data (NOAA) Southern Oscillation Index (SOI) Measures the difference in Sea Surface Temperature (SST) between Tahiti and Darwin The lower the score, the more intense an El Niño event

Motivation Why would we be interested in moving outlier regions in precipitation data? Knowing the location, time and duration of past extreme precipitation events helps to understand and prepare for future events. We can analyse how different phenomenon interact. E.g. ENSO and precipitation.

Previous Work Spatial Scan Statistics
Used to find spatial outliers Cluster detection using the spatial scan statistic in spatio-temporal point data (Iyengar, 2004) Exact-Grid and Approx-Grid (Agarwal et. al., 2006) Uses the Kulldorff Spatial Scan Statistic Finds the highest discrepancy region (by location and size) in a spatial grid dataset. Spatio-temporal outlier detection (Birant and Kut, 2006) Limited to finding outliers over a single time period. time y co-ordinate x co-ordinate

Contributions Extended Exact-Grid and Approx-Grid to find the top-k outliers in a single time period. Developed the Outstretch & RecurseNodes algorithm to find outliers that repeatedly appear over several time periods. Apply to South American Precipitation data. Analyse the behaviour of the outliers against the El Niño Southern Oscilation (ENSO).

Our Approach Find the top-k outliers in a spatial grid for each time period Extend Exact-Grid and Approx-Grid algorithms Use Oustretch to find spatial outliers which extend over several time periods. Use RecurseNodes to extract the sequences from the Outstretch tree.

Finding the top-k outliers
Find every possible region size and shape in the grid. Get each region’s discrepancy value to determine which is a more significant outlier. Our extension keeps track of the top-k regions rather than just the top-1. left right top bottom

Kulldorff Scan Statistic
Uses two values: Measurement – Number of incidences of an event E.g. In how many cells is precipitation extreme? M – for the whole dataset m(p) - for the cell p mR = ΣpєR m(p) / M Baseline – Total population at risk I.e. How many cells have we recorded values for? B – for the whole dataset b(p) - for the cell p bR = ΣpєR b(p) / B We find the discrepancy for local region R by subsitution into: When mR > bR d(mR, bR) = mRlog(mR/bR) + (1-mR)log((1-mR)/(1-bR)) Otherwise d(mR, bR) = 0

Kulldorff Scan Statistic: Example
M = 6 = total # cells with “1” in entire grid ΣpєR m(p) = 4 = total # cells with “1” in R mR = ΣpєR m(p)/M = 0.67 B = 16 = total # cells in entire grid ΣpєR b(p) = Sum of b’s in region = 4 = total # cells in R bR = ΣpєR b(p)/B = 0.25 Result: d(mR, bR) = 1 4 3 2 1 1 4 3 2 1

Finding the top-k outliers: Exact-Grid
left right top bottom

left right top Keeps moving top and bottom lines until all regions have been examined between the left and right lines… bottom

left right Keeps moving top and bottom lines until all regions have been examined… top bottom

left right Same again… Top and bottom lines define all possible areas between the left and right lines… top bottom

Continue until all regions have been examined… left right top bottom

Finding the top-k outliers: Approx-Grid
Reduces the time complexity of the algorithm by using only two sweep lines and finding the interval that maximises the discrepancy function (See Agarwal et al. (2006) paper). m(I,j) stores the sum of the m(p)’s for each column top For each move of a sweep line, run the Linear1D algorithm to find the interval that maximises the discrepancy function bottom

Finding the top-k outliers: Considerations
Overlapping Regions

Overlapping Regions – Overlap types

Chain effect One option: Union Solution d=0.45 d=0.54 d=0.51

Chosen Option: Allow a percentage of overlap If this overlap is less than allowable_overlap % then, keep both regions in the top-k list. d=0.45 d=0.51

Outstretch Outstretch – find the paths of the outliers over time. t=1
5 4 3 2 1 5 4 3 2 1 5 4 3 2 1 5 4 3 2 1 5 4 3 2 1 t=1 t=2 t=3 t=4 t=5

This region (dark green) has been stretched by r=2 grid cells…
Outstretch Use Outstretch to find spatial outliers which extend over several time periods. Check the same region (slightly stretched to cover more area) in the next time period, to see if another outlier lies in the region. If it is, then it is considered to be part of the spatio-temporal outlier, which is now extended over an additional time period. Store in a tree data structure. This region (dark green) has been stretched by r=2 grid cells… In the next time period, we will check if any outliers fall in that area. r

Outstretch Store outliers found over subsequent time periods in a tree data structure. Node Num Children Children 1,1 1 {2,2} 1,2 3 {2,2}, {2,3}, {2,4} 1,3 {2,1} 1,4 {2,4} 2,1 {3,2} 2,2 {3,1}, {3,4} 2,3 2 {3,3} 2,4 - 3,1 3,2 3,3 3,4 1,1 1,2 1,3 1,4 2,1 2,4 2,2 2,3 3,1 3,2 3,3 3,4

Outstretch Stretch the top-k outliers from t=1 by r (their spatial neighbourhood). 1,1 1,2 1,3 1,4 1 2 4 3

Outstretch From the top-k in t=2, find those which fall inside the stretched region from the previous period, t=1. 1,1 1,2 1,3 1,4 1 2 2 3 2,1 2,4 2,2 2,3 4 4 1 3

Outstretch Stretch the new outliers from t=2 and find the outliers from t=3, that fall in the newly stretched regions. 1,1 1,2 1,3 1,4 1 4 2 2 1 3 3 2,1 2,4 2,2 2,3 4 4 2 1 3 3,1 3,2 3,3 3,4

RecurseNodes Now that we’ve stored all the sequences in the tree, how do we get them out? Use RecurseNodes to extract the sequences from the Outstretch tree. Node Num Children Children 1,1 1 {2,2} 1,2 3 {2,2}, {2,3}, {2,4} 1,3 {2,1} 1,4 {2,4} 2,1 {3,2} 2,2 {3,1}, {3,4} 2,3 2 {3,3} 2,4 - 3,1 3,2 3,3 3,4

RecurseNodes Adds the full sequence to the sequence_list. Does not add subsequences. Looks at every item in the list. If the item has children, append it to the current sequence. The grandchildren will then be examined, if any. If it doesn’t, sequence is complete, so we move onto the next child. Keep track of which children have already been seen.

RecurseNodes Start at {1,1} We notice it has a child {2,2} Check {2,2}
We notice {2,2} has two children {3,1} and {3,4}. Check {3,1} first. {3,1} has no children. Stop and store sequence: [ {1,1}, {2,2}, {3,1} ] Now check {3,4}. {3,4} has no children. Stop and store sequence: [ {1,1}, {2,2}, {3,4} ] And so on… Node Num Children Children 1,1 1 {2,2} 1,2 3 {2,2}, {2,3}, {2,4} 1,3 {2,1} 1,4 {2,4} 2,1 {3,2} 2,2 {3,1}, {3,4} 2,3 2 {3,3} 2,4 - 3,1 3,2 3,3 3,4

Results: Exact vs. Approx-Grid Top-k
Length and number of outliers found Exact-Grid Top-k: finds longer sequences than Approx-Grid Top-k Approx-Grid Top-k Is faster than Exact- Grid Top-k Outlier Discovery – Time Taken Exact-Grid Top-k O(n4k) 229s Approx-Grid Top-k O(n3k) 35s

Results: Mean discrepancy of Exact-Grid Top-k sequences and the mean SOI
Notice that some of the discrepancies at the centre time period are higher during the more intense El Niño event This is showing that there are more extreme extremes during an El Niño event.

Results: Mean discrepancy of Approx-Grid Top-k sequences and the mean SOI
We also find extreme extremes in the Approx-Grid Top-k sequences

Future Work Evaluate against Other metrics (besides SOI), such as Sea Surface Temperature (SST) Point data Other data e.g. other precipitation data.

Conclusion Our contributions: Results showed:
Top-k extension to Exact and Approx-Grid algorithms Outlier sequence discovery over time Evaluate using precipitation data Compared results to the El Niño Southern Oscillation Index (SOI) Results showed: More extreme extreme values during El Niño periods Able to find these with both Exact and Approx-Grid algorithms

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Spatio-Temporal Outlier Detection in Precipitation Data

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