analyzing building damages of a major earthquake using grass and r

Free and Open Source Software for Geospatial 24-27 September 2007 Victoria, Canada

Analyzing Building Damages of a Major Earthquake Using GRASS and R

Analyzing Building Damages of a Major Earthquake Using GRASS and R

WANG, Wen-Chieh (Jeffrey)

Chaoyang University of TechnologyTaichung, Taiwan

FOSS4G: 26 September 2007 Analyzing Building Damages of a Major Earthquake Using GRASS and R 2

OutlineOutline

• Introduction• Chi-Chi Earthquake• Spatial Data

Analysis• The Analysis • Conclusion

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Outline - 1Outline - 1

• Introduction

• Chi-Chi Earthquake

• Spatial Data Analysis

• The Analysis

• Conclusion

1.1 Motivation

1.2 Rationale

1.3 Objectives

1.1 Motivation

1.2 Rationale

1.3 Objectives

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1.1 Motivation1.1 Motivation

A magnitude 7.3 earthquake hit Taiwan on September 21, 1999.

A valuable dataset recording over 7000 damaged buildings is available as an ArcView GIS 3.x shape file.

Can a sensible analysis of the dataset be conducted using FOSS4G?

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1.2 Rationale1.2 Rationale

GRASS stands for Geographic Resources Analysis Support System.

R is a free software environment for statistical computing and graphics, that provides many “packages” to handle spatial data.

Both tight and loose coupling of GRASS and R in conducting spatial data analysis are popular.

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1.3 Objectives1.3 Objectives

Using GRASS with R as spatial data analysis tools to

visualize,

explore, and

model the spatial aspect of building damages in the Chi-Chi earthquake.

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Outline - 2Outline - 2

• Introduction

• Chi-Chi Earthquake

• Spatial Data Analysis

• The Analysis

• Conclusion

2.1 Earthquake

2.2 Non-spatial info

2.3 Spatial info

2.1 Earthquake

2.2 Non-spatial info

2.3 Spatial info

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2.1 Earthquake (1)2.1 Earthquake (1)

The tectonic plates of the world mapped in the second half of the 20th century. #2

“An earthquake is caused by tectonic plates getting stuck and putting a strain on the ground. The strain becomes so great that rocks give way by breaking and sliding along fault planes.” #1

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2.1 Earthquake (2)2.1 Earthquake (2)

Plate motion based on Global Positioning System (GPS) satellite data from NASA JPL. Vectors show direction and magnitude of motion. #2

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2.1 Earthquake (3)2.1 Earthquake (3)

3 Fault types:

1. Strike-slip

2. Normal

3. Thrust #1

Preliminary determination of epicenters of 358,214 earthquakes, 1963-1998. #1

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2.2 Non-spatial Info of the Earthquake #32.2 Non-spatial Info of the Earthquake #3

Chi-Chi earthquake is one of the 40 major earthquakes listed on Wikipedia.

Also called the 921 earthquake.

Struck Taiwan on September 21 1999.

Over 2,000 people killed, destroyed or damaged over ten thousand buildings.

Caused world computer prices to rise sharply.

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2.3 Spatial Info of the Earthquake (1) #4 ,#52.3 Spatial Info of the Earthquake (1) #4 ,#5

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2.3 Spatial Info of the Earthquake (2) #52.3 Spatial Info of the Earthquake (2) #5

Chi-Chi earthquake measured 7.3 on the Richter scale.

Its epicentre was at 23.87° N latitude, 120.75° E longitude, in Chi-Chi Township in Nantou County.

The depth was 7.0 km. #3

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Outline - 3Outline - 3

• Introduction

• Chi-Chi Earthquake

• Spatial Data Analysis

• The Analysis

• Conclusion

3.1 Definition

3.2 Types of spatial data

3.3 Characteristics of spatial

data

3.4 Methods

3.1 Definition

3.2 Types of spatial data

3.3 Characteristics of spatial

data

3.4 Methods

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3.1 Definition3.1 Definition

A map

A process

Spatial data analysis focuses on the statistical description and modeling of spatial data.

“Spatial data analysis is involved when data are spatially located and explicit consideration is given to the possible importance of their spatial arrangement in the analysis or in the interpretation of results.” #6

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3.2 Types of Spatial Data (1)Point patterns : 3.2 Types of Spatial Data (1) : Point patterns

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3.2 Types of Spatial Data (2)Spatially continuous data : 3.2 Types of Spatial Data (2) : Spatially continuous data

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3.2 Types of Spatial Data (3)Area data : 3.2 Types of Spatial Data (3) : Area data

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3.2 Types of Spatial Data (4)Spatial interaction data : 3.2 Types of Spatial Data (4) : Spatial interaction data

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3.3 Characteristics of Spatial Data (1)3.3 Characteristics of Spatial Data (1)

Linear regression #7

Y = β1 + β2X2 + . . . + βpXp + ε

Time Series #8, #9

Yt = α0 + α1Yt-1 + α2Yt-2 + . . . + αpYt-p + εt

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3.3 Characteristics of Spatial Data (2)3.3 Characteristics of Spatial Data (2)

Spatial dependency or auto-correlation #6

Y (s)= xT(s)β + U(s)

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3.4 Methods of Spatial Data Analysis #103.4 Methods of Spatial Data Analysis #10

Point Patterns Spatially continuous data

Visualization

GRASS display commands GRASS display commands and nviz

R spatstat, splancs, graphics R geoR, gstat, graphics

Exploration

GRASS vector commands GRASS vector and raster commands

R spatstat, splancs, spatial R geoR, gstat, spatial

Modeling R spatstat, splancs, spatial R geoR, gstat, spatial,

geoRglm

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Outline - 4Outline - 4

• Introduction

• Chi-Chi Earthquake

• Spatial Data Analysis

• The Analysis

• Conclusion

4.1 Visualizing …

4.2 Exploring …

4.3 Modelling …

4.4 Discussion …

4.1 Visualizing …

4.2 Exploring …

4.3 Modelling …

4.4 Discussion …

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4.1 Visualizing the Spatial Data (1)4.1 Visualizing the Spatial Data (1)

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4.1 Visualizing the Spatial Data (2)4.1 Visualizing the Spatial Data (2)

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4.1 Visualizing the Spatial Data (3)4.1 Visualizing the Spatial Data (3)

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4.1 Visualizing the Spatial Data (4)4.1 Visualizing the Spatial Data (4)

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4.1 Visualizing the Spatial Data (5)4.1 Visualizing the Spatial Data (5)

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4.2 Exploring the Spatial Data (1)4.2 Exploring the Spatial Data (1)

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4.2 Exploring the Spatial Data (2)4.2 Exploring the Spatial Data (2)

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4.2 Exploring the Spatial Data (3)4.2 Exploring the Spatial Data (3)

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4.2 Exploring the Spatial Data (4)4.2 Exploring the Spatial Data (4)

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4.2 Exploring the Spatial Data (5)4.2 Exploring the Spatial Data (5)

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4.2 Exploring the Spatial Data (6)4.2 Exploring the Spatial Data (6)

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4.2 Exploring the Spatial Data (7)4.2 Exploring the Spatial Data (7)

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4.2 Exploring the Spatial Data (8)4.2 Exploring the Spatial Data (8)

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4.3 Modelling the Spatial Data (1)4.3 Modelling the Spatial Data (1)

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4.3 Modelling the Spatial Data (2)4.3 Modelling the Spatial Data (2)

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4.3 Modelling the Spatial Data (3)4.3 Modelling the Spatial Data (3)

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4.3 Modelling the Spatial Data (4)4.3 Modelling the Spatial Data (4)

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4.3 Modelling the Spatial Data (5)4.3 Modelling the Spatial Data (5)

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4.3 Modelling the Spatial Data (6)4.3 Modelling the Spatial Data (6)

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4.4 Discussion4.4 Discussion

1. Learning spatial data analysis in R:

Many duplicated functionalities are provided in various packages.

It’s very confusing for newcomers to choose a suitable package.

2. Comparing GRASS and R for spatial data analysis:

Some capabilities appear in both GRASS and R, too.

For those equivalent functions, R seems running faster than GRASS.

3. Regarding data exchange between GRASS and R:

For point vector data, it is straightforward and the “spgrass6” doesn’t seem necessary.

Additional efforts are necessary to exchange image/raster data due to R treats images as mathematical matrices.

FOSS4G: 26 September 2007 Analyzing Building Damages of a Major Earthquake Using GRASS and R 44

Outline - 5Outline - 5

• Introduction

• Chi-Chi Earthquake

• Spatial Data Analysis

• The Analysis

• Conclusion 5.1 Summary

5.2 Further works

5.1 Summary

5.2 Further works

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5.1 Summary5.1 Summary

GRASS and R provide researchers a powerful tool set for spatial data analysis.

The analysis in this study shows that PGA is a good predictor variable in explaining the intensity of building damages in a major earthquake.

This case study doesn’t find the “spgrass6” necessary in using GRASS and R for spatial data anaylsis.

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5.2 Further Works5.2 Further Works

Conducting related spatial analyses on area data and spatial interaction data using GRASS and R.

Conducting logistic regression to explain the severity variation in the Chi-Chi earthquake building damages dataset using GRASS and R.

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ReferencesReferences1. Earthquake. (2007, September 12). In Wikipedia, The Free Encyclopedia. Retrieved 02:19, September

13, 2007, from http://en.wikipedia.org/w/index.php?title=Earthquake&oldid=157361106

2. Plate tectonics. (2007, September 12). In Wikipedia, The Free Encyclopedia. Retrieved 02:49, September 13, 2007, from http://en.wikipedia.org/w/index.php?title=Plate_tectonics&oldid=157471038

3. Chi-Chi earthquake. (2007, July 31). In Wikipedia, The Free Encyclopedia. Retrieved 09:38, September 13, 2007, from http://en.wikipedia.org/w/index.php?title=Chi-Chi_earthquake&oldid=148197918

4. Active Faults in Taiwan. (2000). Central Geological Survey, Ministry of Ecnoomic Affairs, Taiwan. http://cgsweb.moeacgs.gov.tw/result/Fault/web/index-1.htm

5. National Center for Research on Earthquake Engineering, 921 Chi-Chi Earthquake Investigation Reports, http://gisdb.ncree.gov.tw/ncree/doc/

6. Bailey, T.C. and Gatrell, A. C. (1995). Interactive Spatial Data Analysis. John Wiley and Sons, New York, NY.

7. Linear regression. (2007, August 28). In Wikipedia, The Free Encyclopedia. Retrieved 03:05, September 14, 2007, from http://en.wikipedia.org/w/index.php?title=Linear_regression&oldid=154163772

8. Time series. (2007, September 1). In Wikipedia, The Free Encyclopedia. Retrieved 03:29, September 14, 2007, from http://en.wikipedia.org/w/index.php?title=Time_series&oldid=155004349

9. Trend estimation. (2007, June 21). In Wikipedia, The Free Encyclopedia. Retrieved 03:24, September 14, 2007, from http://en.wikipedia.org/w/index.php?title=Trend_estimation&oldid=139770931

10. Roger Bivand. (unknown). CRAN Task View: Analysis of Spatial Data. Retrieved August, 2007, from http://cran.r-project.org/src/contrib/Views/Spatial.html

11. Baddeley, A. and Turner, R. (2005). Spatstat: an R package for analyzing spatial point patterns. Journal of Statistical Software 12:6 1-42. www.jstatsoft.org ISSN: 1548-7660.

12. Ribeiro Jr., P.J. and Diggle, P.J. (2006) geoR : Package for Geostatistical Data Analysis, An illustrative session. geoR: A package for geostatistical data analysis using the R software www.leg.ufpr.br/geoR.

Free and Open Source Software for Geospatial 24-27 September 2007 Victoria, Canada

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