1. Assessing High Exposure Population to Traffic Pollution with a Multidisciplinary Database for Greater Taipei Area, Taiwan Chung-Rui Lee*, Shih-Chun Candice Lung*, Jane W. S. Liu**&, Chih-Da Wu* *Research Center for Environmental Changes, Academia Sinica, R.O.C. **Institute of Information Science, Academia Sinica, R.O.C. Open Data & Information for a Changing Planet A4 Planet Under Pressure- Pollution Chair: Prof. Ron Abler

2. A Framework for Open and Sustainable DMIS Jan-Ming Ho (IIS), Ching-Teng Hsiao (CITI), Der-Tsai Lee (IIS) and Jane W. S. Liu (IIS & CITI) Application ComponentIT Component Flow Control and Fusion of Symbiotic Information John K. Zao (NCTU), Sheng-Wei Chen (IIS) Jane W. S. Liu (IIS) & Edward T.-H. Chu (NYUST) Disaster Information System for Resilient Communities Feng-Tyan Lin (NCKU) Hsueh-Cheng Chou (NCDR) Han-Liang Lin (NCKU) Climate Extremes and Weather Disasters Data Repository Shaw-Chen Liu (RCEC); Chia Chou (RCEC) Shih-Chun Lung (RCEC) & Mong-Ming Lu (CWB) Crustal Deformation and Faulting Behavior Databases Jian-Cheng Lee (IES), Long-Chen Kuo (IES) Wen-Tzong Liang (IES) Virtual Repository Ching-Teng Hsiao (CITI), Pen-Chung Yew (U of Minn) David Hung-Chang Du (U. of Minn) Open Information Gateway Chi-Sheng Shih (NTU), Ling-Jyl Chen (IIS) Phone Lin (NTU), Kwei-Jay Lin (UCI) Ching-Ju Lin (CITI) & Wei-Ho Chung (CITI)

3. Survey data GIS data Data Sources Population & Vulnerability Database (PV DB) Climate Extreme & Weather Disaster Database (CEWD DB) Weather Disaster Risk DB Filter, merging and visualization tools for weather disaster risk assessment & other scientific studies Interface services Meteorological and pollution data Risk Management for Mitigation & Adaptation Disaster Risk Assessment for Weather Disasters Access control services CEWD Data Repository-WDR DB 3

4. Pollution under Hot Weather  Pollution emission and production may be high in hot weather.  Traffic-related air pollution is the primary emission source in urban areas (Aguilera et al. 2008)  Traffic pollution cause health problems - Exposure to traffic-related emission would increase the risks of cardiorespiratory diseases (Barraza-Villarreal et al.2008; HEI 2010; McCreanor et al. 2007) 4

5. Intra-urban Variability of Air Pollution  Residents who live along busy roads have higher risk to have asthma and chronic respiratory diseases (Edwards et al. 2001). Furthermore, people who living within 50 m from a major road have a 63% excess risk of developing high coronary artery calcification compared with those living 200 m or more away from it (Hoffmann et al. 2007) * Thus, exploring the distribution of high exposure population to air pollution is a critical issue in a metropolitan area. 5

6. Research Aims  Use advanced IT tools setting up a multidisciplinary database to facilitate data exchange for scientific research  Assess high exposure population to traffic-related air pollution with data from multiple sources  Explore data interpolation with Open Source geospatial software on air pollution and Socio-economic Status (SES) data * Locating the true hot spots of high exposure population offers a scientific foundation for traffic-related air pollution mitigation policy. 6

7. Estimate Vertical Population Distribution Access High Exposure Ratio, HER Population Exanimate Correlation between HER & SES Detecting Spatial Autocorrelation* * A measure of the degree to a set of spatial features and their associated data values tend to be clustered together in space (ESRI,2006) 7 (Wu & Lung, 2012) Methodology w/ 3DIG

8. Spatial Issue 8  The First Law of Geography "Everything is related to everything else, but near things are more related than distant things." - Waldo Tobler, 1970  Local Indicator of Spatial Association, LISA - measures and analyzes type of spatial autocorrelation (i.e. clusters & outliers) whether is occurring around specific location, provides information on the degree of dependency among observations. - There are four categories, i.e. high-high, low-low, high-low, and low-high correspond to the local Moran’s I value. 1 represents clustered, 0 represents random, and -1 represents dispersed. Based on Anselin (1995, 2005)

9. Data Collection  Digital Terrain Model (DTM) - High Resolution and High Accuracy DTM (DEM and DSM) Satellite Survey Center, Dept. of Land Administration, MOI  Land Use Survey - 2008 data by National Land Surveying and Map Center, MOI  Population and Vulnerability Database - Virtual Repository, VR - Weather Disaster Risk Database, WDR DB testbed * Population Indices from MOI * Survey of Personal Income Distribution in Taiwan from DGBAS * Urban Land Price Indexes from CAPMI 9

10. Greater Taipei Area, Taiwan Taipei City and New Taipei City. Total area: 2,334.5 sq. km. 41 districts, and 1,467 “Lis” (equivalent to US census tracts) Population density of Taipei City and New Taipei City was 9,650 and 1,868 persons per sq. km of 2008 (DGBAS, 2011). Motor vehicle density was 4,040 and 1,536 vehicles per sq. km (MOTC, 2009). 10 Greater Taipei Area DEM source: NASA EOSDIS

11. High Exposure Population  There are 2,669,475 people (42.3%) live on the 1 st and 2 nd floor in Greater Taipei area.  There are approximately 12% of Taipei residents (0.8 million), who live within 5 m of municipal roads live on the 1 st and 2 nd floor, the high exposure areas.  Our related research articles Wu, C. D. and S. C. Lung. 2012. Applying GIS and Fine-Resolution Digital Terrain Models to Assess Three-Dimensional Population Distribution Under Traffic Impacts. Journal of Exposure Science and Environmental Epidemiology 22(2): 126-34. Lee, C.-R., S. C. Lung and C. D. Wu. Upcoming. Spatial Distribution and Socio-demographic Characteristics of High Traffic-related Exposure Population in Taipei. Journal of Population Studies. In Chinese. 11

12. HER, HER & SES Cluster Analysis (a) HER LISA analysis at Li Level (clustered) (b) HER LISA analysis at District Level (clustered) 12 * The granularity of data influences geospatial analysis outcomes.

13. HER and SES Coefficient Note: ***: p < 0.01, **: 0.01< p < 0.05. Local Moran's I is statistically significant (0.05 level) 13 SES Indicatorsrho Clustering Local Moran's I Sex Ratio (males to females)0.13589 clustered 0.07157 Aging Index (aged >65 to aged <14)0.51951***clustered0.29171 Young Dependency (aged <14 to aged 15-64)-0.32101**dispersed-0.01881 Elder Dependency (aged >65 to aged 15-64)0.55192***clustered0.30113 Advanced Education Pct. (college & above to total)-0.08868 dispersed -0.02747 Population Density (population to area)-0.08711 clustered 0.01437 Total Household Income (in thousand NTD)0.04669 dispersed -0.01800 Low Income Household Percentage0.37822**clustered0.21726 Disabled Percentage0.51239***clustered0.27070 Urban Land Price Index (time periods comparison)0.11412 clustered 0.07910 Residential Urban Land Price (land value per sq m )-0.08685 clustered 0.03720

14. (a) Bivariate LISA: HER vs. Sex Ratio (Local Moran’s I: 0.07157) (b) Bivariate LISA: HER vs. Young Dependency (Local Moran’s I: -0.01881) (c) Bivariate LISA: HER vs. Aging Index (Local Moran’s I: 0.29171) (d) Bivariate LISA: HER vs. Elder Dependency (Local Moran’s I: 0.30113) 14

15. (a) Bivariate LISA: HER vs. Advanced Education (Local Moran’s I: -0.02747) (b) Bivariate LISA: HER vs. Low Household Income (Local Moran’s I: 0.21726) (c) Bivariate LISA: HER vs. Total Household Inc. (Local Moran’s I: -0.0180) (d) Bivariate LISA: HER vs. Disabled (Local Moran’s I: 0.2707) 15

16. (a) Bivariate LISA: HER vs. Res. Urban Land Price (Local Moran’s I: 0.0372) (b) Bivariate LISA: HER vs. Urban Land Price Index (Local Moran’s I: 0.0791) (c) Bivariate LISA: HER vs. Population Density (Local Moran’s I: 0.01437) 16

17. Findings & Discussions  Social and environmental inequality exist for the disadvantaged SES population, i.e. disabled, low income household, and elderly, are positively correlated to high exposure to air pollution. Both statistically (correlation) and spatially (LISA).  Double jeopardy of lower SES population - Where you live affects how you will live. What would be a good method to monitor air pollution in urban areas?  Actual traffic busy degree of the major roads would vary; thus, air pollution concentrations would be different 17

18. Conclusions  Virtual Repository is a good approach for data management in a multidisciplinary database  Geospatial analysis shows that high traffic-related exposure population is clustered, i.e. spatial autocorrelation exist in Greater Taipei Area  3DIG is a versatile methodology which can be used in any research focusing on three-dimensional population distribution 18

19. Acknowledgements  We thank the Research Center of Environmental Changes, Institute of Information Science, and Academia Sinica Thematic Project OpenISDM (http://openisdm.iis.sinica.edu.tw) for their partial funding supports. We also thank Satellite Survey Center, Department of Land Administration, Minister of Interior Affairs, R.O.C. for providing the high resolution DTM modules.http://openisdm.iis.sinica.edu.tw  The contents of this paper are solely the responsibility of the authors and do not represent the official views of the aforementioned institutes and funding agencies. 19

20. Question? E-mail: crlee@gate.sinica.edu.tw 20 Thank you for your attention!

21. References Aguilera, I., J. Sunyer, R. Fernadez-Patier, G. Hoek, A. Aguirre-Alfaro, K. Meliefste, M. T. Bomboi-Mingarro, M. J. Nieuwenhuijsen, D. Herce-Garraleta and B. Brunekreef. 2008. Estimation of Outdoor NOx, NO2, and BTEX Exposure in a Cohort of Pregnant Women Using Land Use Regression Modeling. Environmental Science & Technology 42 (3): 815–821. Anselin, L. (1995). "Local indicators of spatial association – LISA". Geographical Analysis, 27, 93-115. - (2005). Exploring Spatial Data with GeoDa. Center for Spatially Integrated Social Science, UIUC: Urban-Champagn, IL. http://geodacenter.asu.edu/system/files/geodaworkbook.pdf (Date visited: January 4, 2012) Ashby, D. I., and P. A. Longley (2005) Geocomputation, Geodemographics and Resource Allocation for location Policing. Transactions in GIS, 9(1), pp. 53-72 Barraza-Villarreal, A., J. Sunyer, L. Hernandez-Cadena, M. C. Escamilla-Nuñez, J. J. Sienra-Monge, M. Ramírez-Aguilar, M. Cortez-Lugo, F. Holguin, D. Diaz-Sánchez, A. C. Olin and I. Romieu. 2008. Air pollution, Airway Inflammation, and Lung Function in a Cohort Study of Mexico City Schoolchildren. Environmental Health Perspectives 116: 832–838. Havard, S., S. Deguen, D. Zmirou-Navier, C. Schillinger and D. Bard. 2009. Traffic-Related Air Pollution and Socioeconomic Status: A Spatial Autocorrelation Study to Assess Environmental Equity on a Small-Area Scale. Epidemiology 20(2) 223-230. Health Effect Institute (HEI). 2010..Health Effects of Traffic-related Air Pollution: A Critical Review of the Literature on Emissions, Exposure, and Health Effects. Health Effect Institute, Special Report 17: Boston, Massachusetts. Jerrett, M., A. Arain, P. Kanaroglou, B. Beckerman, D. Potoglou, T. Sahsuvarogiu, J. Morrison and C. Giovis. 2005. A Review and Evaluation of Intraurban Air Pollution Exposure Models. Journal of Exposure Analysis and Environmental Epidemiology 15(2): 185–204. Laden F, J. Schwartz, F. E. Speizer, D. W. Dockery. 2006. Reduction in Fine Particulate Air Pollution and Mortality: Extended Follow-up of the Harvard Six Cities Study. American Journal of Respiratory and Critical Care Medicine 173: 667-672. McCreanor, J., P. Cullinan, M. J. Nieuwenhuijsen, J. Stewart-Evans, E. Malliarou, L. Jarup, R. Harrington, M. Svartengren, I.-K. Han, P. Ohman-Strickland, K. F. Chung and J. Zhang. 2007. MOTC, Minister of Transportation, R.O.C. (2009) Statistics. Available at: http://www.motc.gov.tw/mocwebGIP/wSite/dp?mp=2 (Accessed January 13 th, 2012) NASA, Earth Observing System Data and Information System. Available at: http://earthdata.nasa.gov/data (Accessed September 23 rd, 2012) National Land Surveying and Map Center., MOI, R.O.C. (2009) Land Use Survey Data 2008. Available at: http://lui.nlsc.gov.tw/LUWeb/eng/AboutLU.aspx (Accessed December 3rd, 2010) Population Vulnerability Database Data - Survey of Personal Income Distribution in Taiwan from DGBAS. Available at: http://www.stat.gov.tw/point.asp?index=4 - General Statistics from MOI and DGBAS. Available at: http://www.moi.gov.tw/index.asp - NGIS. Available at: http://segis.moi.gov.tw/ - Survey of Personal Income Distribution from Taipei City Government. Available at: http://www.dbas.taipei.gov.tw/ct.asp?xItem=1310095&CtNode=6154&mp=120001 - Survey of Personal Income Distribution from New Taipei City Government. Available at: http://www.ntpc.gov.tw/_file/2890/SG/21862/D.html - Urban Land Price Indexes from CAPMI. Available at: http://whi.capmi.gov.tw/Upload/sys/cityprice/98H1_market.pdf Respiratory Effects of Exposure to Diesel Traffic in Persons with Asthma. The New England Journal of Medicine 357: 2348–2358. Satellite Survey Ctr., Dept. of Land Administration, MOI, R.O.C (n.d.) High Resolution and High Accuracy DTM Available at: https://www.gps.moi.gov.tw/satellite/DTM/DTM_03.htm (Accessed December 3rd, 2010)https://www.gps.moi.gov.tw/satellite/DTM/DTM_03.htm Tobler, W. 1970. A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography 46(2): 234-240. Wu, C. D. and S. C. Lung. 2012. Applying GIS and Fine-Resolution Digital Terrain Models to Assess Three-Dimensional Population Distribution Under Traffic Impacts. Journal of Exposure Science and Environmental Epidemiology 22(2): 126-34. 21

22. Local Moran’s I of Advanced Education 0.630909 Local Moran’s I of Low Income Household 0.138509 Local Moran’s I of Aging Index 0.55395 Local Moran’s I of Elder Dependency 0.587057. Local Moran’s I of Young Dependency 0.402276 Local Moran’s I of Population Density 0.598667 Local Moran’s I of Sex Ratio 0.555932 Local Moran’s I of Disabled 0.5297 Local Moran’s I of Household Income 0.6134 Local Moran’s I of Urban Land Price (Res. Area) 0.6525 Local Moran’s I of Urban Land Price Index 0.1157 22 Appendix

23. WDR Testbed Functions WDR HardwareCooperationSoftware Collaborati on Middleware Collective action Human resources Sharing 23

24. HEPD, HEPD & SES Cluster Analysis 24 (a) HER LISA analysis at Li Level (clustered)(b) HER LISA analysis at District Level (clustered) * High dense areas are more vulnerable to air pollution.

25. HEPD and SES Coefficient Note: ***: p < 0.01, **: 0.01< p < 0.05. Local Moran's I is statistically significant (0.05 level) 25 SES Indicatorsrho Clustering Local Moran's I Sex Ratio (males to females)-0.81132***dispersed-0.44723 Aging Index (aged >65 to aged <14)-0.18920dispersed-0.14238 Young Dependency (aged <14 to aged 15-64)0.11534clustered0.01165 Elder Dependency (aged >65 to aged 15-64)-0.23467dispersed-0.11724 Advanced Education Pct. (college & above to total)0.80035***clustered0.49369 Total Household Income (in thousand NTD)0.78025***clustered0.47450 Low Income Household Percentage-0.18449dispersed-0.05167 Disabled Percentage-0.34705**dispersed-0.17470 Urban Land Price Index (time periods comparison)0.89473***dispersed-0.09950 Residential Urban Land Price(land value per sq m )-0.28443*clustered0.53430

26. (a) Bivariate LISA: HEPD vs. Sex Ratio (Local Moran’s I : -0.44723) (b) Bivariate LISA: HEPD vs. Young Dependency (Local Moran’s I: 0.01165) (c) Bivariate LISA: HEPD vs. Aging Index (Local Moran’s I: -0.14238) (d) Bivariate LISA: HEPD vs. Elder Dependency (Local Moran’s I: -0.11724) 26

27. (a) Bivariate LISA: HEPD vs. Advanced Education (Local Moran’s I: 0.49369) (b) Bivariate LISA: HEPD vs. Low Income Household (Local Moran’s I: -0.05167) (c) Bivariate LISA: HEPD vs. Household Income (Local Moran’s I: 0.4745) (d) Bivariate LISA: HEPD vs. Disabled (Local Moran’s I: -0.1747) 27

28. (a) Bivariate LISA: HEPD vs. Res. Urban Land Price (Local Moran’s I: 0.5343) (b) Bivariate LISA: HEPD vs. Urban Land Price Index (Local Moran’s I: -0.0995) 28