A GIS tool for spatiotemporal modeling under a knowledge synthesis framework

详细信息    查看全文

• 作者：Hwa-Lung Yu ; Shang-Chen Ku…
• 关键词：Spatiotemporal analysis ; Stochastic processes ; Prediction ; BME ; QGIS
• 刊名：Stochastic Environmental Research and Risk Assessment (SERRA)
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：30
• 期：2
• 页码：665-679
• 全文大小：5,807 KB
• 参考文献：Akita Y, Serre M (2009) BMEGUI user manual. Department of Environmental Sciences and Enginnering, School of Public Health, University of North Carolina
  Baz I, Geymen A, Er SN (2009) Development and application of GIS-based analysis/synthesis modeling techniques for urban planning of Istanbul Metropolitan Area. Adv Eng Softw 40(2):128–140CrossRef
  Bennett ND, Croke BFW, Guariso G, Guillaume JHA, Hamilton SH, Jakeman AJ, Marsili-Libelli S, Newham LTH, Norton JP, Perrin C, Pierce SA, Robson B, Seppelt R, Voinov AA, Fath BD, Andreassian V (2013) Characterising performance of environmental models. Environ Model Softw 40:1–20CrossRef
  Bivand RS, Pebesma EJ, Gómez-Rubio V (2008) Applied spatial data analysis with R. Springer, New York
  Bogaert P, Christakos G, Jerrett M, Yu HL (2009) Spatiotemporal modelling of ozone distribution in the State of California. Atmos Environ 43(15):2471–2480CrossRef
  Câmara G, Vinhas L, Ferreira KR, deQueiroz GR, Souza RCMD, Monteiro AMV, Carvalho MTD, Casanova MA, deFreitas UM (2008) TerraLib: an open source gis library for large-scale environmental and socio-economic applications. In: Hall B, Leahy MG (eds) Open source approaches in spatial data handling. Springer, Berlin
  Chiles J-P, Delfiner P (1999) Geostatistics: modeling spatial uncertainty. Wiley, New YorkCrossRef
  Choi KM, Yu HL, Wilson ML (2008) Spatiotemporal statistical analysis of influenza mortality risk in the State of California during the period 1997–2001. Stoch Env Res Risk Assess 22:S15–S25CrossRef
  Christakos G (1990) A Bayesian/maximum-entropy view to the spatial estimation problem. Math Geol 22(7):763–776CrossRef
  Christakos G (1992) Random field models in earth sciences. Academic Press Inc, San Diego
  Christakos G (2000) Modern spatiotemporal geostatistics. Oxford Univ. Press, New York
  Christakos G (2002) On the assimilation of uncertain physical knowledge bases: Bayesian and non-Bayesian techniques. Adv Water Resour 25(8–12):1257–1274CrossRef
  Christakos G (2010) Integrative problem-solving in a time of decadence. Springer, New York
  Christakos G, Hristopulos DT (1998) Spatiotemporal environmental health modelling: a tractatus stochasticus. Kluwer Academic, BostonCrossRef
  Christakos G, Serre ML (2000a) BME analysis of spatiotemporal particulate matter distributions in North Carolina. Atmos Environ 34(20):3393–3406CrossRef
  Christakos G, Serre ML (2000b) Spatiotemporal analysis of environmental exposure-health effect associations. J Expo Anal Environ Epidemiol 10(2):168–187CrossRef
  Christakos G, Bogaert P, Serre ML (2002) Temporal GIS: advanced functions for field-based applications. Springer-Verlag, New York
  Christakos G, Olea RA, Serre ML, Yu H-L, Wang L (2005) Interdisciplinary public health reasoning and epidemic modelling: the case of black death. Springer-Verlag, New York
  Chuang M-T, Chiang P-C, Chan C-C, Wang C-F, Chang EE, Lee C-T (2008) The effects of synoptical weather pattern and complex terrain on the formation of aerosol events in the Greater Taipei area. Sci Total Environ 399(1–3):128–146CrossRef
  Clark V (2004) SAS/STAT 9.1 user’s guide, In: Institute, S. (Ed.). SAS Pub: Cary, N.C., p. 7 v. ill. 28 cm
  Cleveland RB, Cleveland WS, McRae JE, Terpenning I (1990) STL: a seasonal-trend decomposition procedure based on Loess. J Off Stat 6(1):3–73
  Cressie N (1985) Fitting variogram models by weighted least-squares. J Int Assoc Math Geol 17(5):563–586CrossRef
  Cressie N, Huang HC (1999) Classes of nonseparable, spatio-temporal stationary covariance functions. J Am Stat Assoc 94(448):1330–1340CrossRef
  Dahal KR, Chow TE (2014) A GIS toolset for automated partitioning of urban lands. Environ Model Softw 55:222–234CrossRef
  Deutsch CV, Journel AG (1998) GSLIB geostatistical software library and user’s guide, 2.0, 2nd ed. Oxford University Press, New York, p 1 (computer laser optical disc)
  Diggle P (1990) Time series: a biostatistical introduction. Oxford University Press, Oxford
  Diggle PJ, Menezes R, Su TL (2010) Geostatistical inference under preferential sampling. J R Stat Soc Ser C 59:191–232CrossRef
  Gebberta S, Pebesma E (2014) A temporal GIS for field based environmental modeling. Environ Model Softw 53:1–12CrossRef
  Goodchild ME (2003) Geographic information science and systems for environmental management. Annu Rev Environ Resour 28:493–519CrossRef
  Goodchild MF, Haining RP (2004) GIS and spatial data analysis: converging perspectives. Papers in Regional Science 83(1):363–385CrossRef
  Goodchild MF, Yuan M, Cova TJ (2007) Towards a general theory of geographic representation in GIS. Int J Geogr Inf Sci 21(3):239–260CrossRef
  Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, New York
  Hardisty F, Peuquet DJ, Robinson AC, Xu S, Stehle S (2012) STempo: finding patterns in space-time events. In: The 108th annual meeting of the Association of American Geographers, New York
  Hunter JD (2007) Matplotlib: a 2D graphics environment. Comput Sci Eng 9(3):90–95CrossRef
  Jian XD, Olea RA, Yu YS (1996) Semivariogram modeling by weighted least squares. Comput Geosci 22(4):387–397CrossRef
  Johnston K (2004) ArcGIS 9. Using ArcGIS geostatistical analyst. ESRI, Redlands
  Journel AG, Huijbregts CJ (1978) Mining geostatistics. Academic Press, London
  Johnson SG (2015) The NLopt nonlinear-optimization package. http://​ab-initio.​mit.​edu/​nlopt . Accessed 23 May 2015
  Kitanidis PK, Shen KF (1996) Geostatistical interpolation of chemical concentration. Adv Water Resour 19(6):369–378CrossRef
  Kolovos A, Christakos G, Hristopulos DT, Serre ML (2004) On certain classes of non-separable spatiotemporal covariance models. Adv Water Resour 27:815–830
  Kolovos A, Skupin A, Jerrett M, Christakos G (2010) Multi-perspective analysis and spatiotemporal mapping of air pollution monitoring data. Environ Sci Technol 44(17):6738–6744. doi:10.​1021/​es1013328
  Kolovos A, Angulo JM, Modis K, Papantonopoulos G, Wang J-F, Christakos G (2013) Model-driven development of covariances for spatiotemporal environmental health assessment. Environ Monit Assess 185(1):815–831. doi:10.​1007/​s10661-012-2593-1
  Kyriakidis PC, Journel AG (1999) Geostatistical space-time models: a review. Math Geol 31(6):651–684CrossRef
  Li L, Revesz P (2004) Interpolation methods for spatio-temporal geographic data. Comput Environ Urban Syst 28:201–227CrossRef
  Liu SC, Shiu C-J (2001) Asian dust storms and their impact on the air quality of Taiwan. Aerosol Air Qual Res 1(1):1–8
  Maceachren AM, Wachowicz M, Edsall R, Haug D, Masters R (1999) Constructing knowledge from multivariate spatiotemporal data: integrating geographical visualization with knowledge discovery in database methods. Int J Geogr Inf Sci 13(4):311–334CrossRef
  Maguire DJ, Batty M, Goodchild MF (2005) GIS, spatial analysis, and modeling, 1st edn. ESRI Press, Redlands
  Matejicek L, Engst P, Janour Z (2006) A GIS-based approach to spatio-temporal analysis of environmental pollution in urban areas: a case study of Prague’s environment extended by LIDAR data. Ecol Model 199(3):261–277CrossRef
  Neteler M, Bowman MH, Landa M, Metz M (2012) GRASS GIS: a multi-purpose open source GIS. Environ Model Softw 31:124–130CrossRef
  Obe R, Hsu L (2011) PostGIS in action. Manning, Greenwich
  Olea RA (2007) Declustering of clustered preferential sampling for histogram and semivariogram inference. Math Geol 39(5):453–467CrossRef
  Pardo-Iguzquiza E (1999) VARFIT: a fortran-77 program for fitting variogram models by weighted least squares. Comput Geosci 25(3):251–261CrossRef
  Pebesma E (2012) Spacetime: spatio-temporal data in R. J Stat Softw 51(7):1–30CrossRef
  Peuquet DJ (2002) Representations of space and time. Guilford Press, New York
  Pierce FJ, Clay D (2007) GIS applications in agriculture. CRC Press, Boca Raton
  Powell MJD (2009) The BOBYQA algorithm for bound constrained optimization without derivatives, technical report. Department of Applied Mathematics and Theoretical Physics, Cambridge
  Pultar E, Cova TJ, Yuan M, Goodchild MF (2010) EDGIS: a dynamic GIS based on space time points. Int J Geogr Inf Sci 24(3):329–346CrossRef
  QGIS Development Team (2012) Quantum GIS 1.8 geographic information system API documentation. Open Source Geospatial Foundation Project
  Reed SE, Boggs JL, Mann JP (2012) A GIS tool for modeling anthropogenic noise propagation in natural ecosystems. Environ Model Softw 37:1–5CrossRef
  Rempt B (2001) Python’s PyQt toolkit. Dr Dobbs J 26(1):88
  Rey SJ, Janikas MV (2006) STARS: space-time analysis of regional systems. Geographical Analysis 38(1):67–86CrossRef
  Schmitz O, Karssenberg D, van Deursen WPA, Wesseling CG (2009) Linking external components to a spatio-temporal modelling framework: coupling MODFLOW and PCRaster. Environ Model Softw 24(9):1088–1099CrossRef
  Sherman M (2011) Spatial statistics and spatio-temporal data: covariance functions and directional properties. Wiley, Chichester
  Taylor J, Lai KM, Davies M, Clifton D, Ridley I, Biddulph P (2011) Flood management: prediction of microbial contamination in large-scale floods in urban environments. Environ Int 37(5):1019–1029CrossRef
  Vallejos RO, Fabre NN, Batista VD, Acosta J (2013) The application of a general time series model to floodplain fisheries in the Amazon. Environ Model Softw 48:202–212CrossRef
  Van Der Knijff JM, Younis J, De Roo APJ (2010) LISFLOOD: a GIS-based distributed model for river basin scale water balance and flood simulation. Int J Geogr Inf Sci 24(2):189–212CrossRef
  van der Walt S, Colbert SC, Varoquaux G (2011) The NumPy Array: a structure for efficient numerical computation. Comput Sci Eng 13(2):22–30CrossRef
  Venigalla MM, Baik BH (2007) GIS-based engineering management service functions: taking GIS beyond mapping for municipal governments. Journal of Computing in Civil Engineering 21(5):331–342CrossRef
  Vijay R, Satapathy DR, Nimje B, Nema S, Dhurve S, Gupta A (2009) Development of GIS-based environmental information system: an Indian scenario. International Journal of Digital Earth 2(4):382–392CrossRef
  Wackernagel H (2003) Multivariate geostatistics: an introduction with applications, 3rd edn. Springer, BerlinCrossRef
  Wang XD, Zhong XH, Gao P (2010) A GIS-based decision support system for regional eco-security assessment and its application on the Tibetan Plateau. J Environ Manag 91(10):1981–1990CrossRef
  Yanosk JD, Paciorek CJ, Schwartz J, Laden F, Puett R, Suh HH (2008) Spatio-temporal modeling of chronic PM10 exposure for the nurses’ health study. Atmos Environ 42(18):4047–4062CrossRef
  Yin L, Shaw SL, Yu HB (2011) Potential effects of ICT on face-to-face meeting opportunities: a GIS-based time-geographic approach. J Transp Geogr 19(3):422–433CrossRef
  Yu HL, Wang CH (2010) Retrospective prediction of intraurban spatiotemporal distribution of PM2.5 in Taipei. Atmos Environ 44(25):3053–3065CrossRef
  Yu H-L, Kolovos A, Christakos G, Chen J-C, Warmerdam S, Dev B (2007) Interactive spatiotemporal modelling of health systems: the SEKS–GUI framework. Stoch Env Res Risk Assess 21(5):555–572CrossRef
  Yu H-L, Wang C-H, Wu Y-Z (2009a) An automatic approach to the mean and covariance estimations of spatiotemporal nonstationary processes, In: Pilz, J. (ed) The StatGIS Conference, Milos
  Yu HL, Chen JC, Christakos G, Jerrett M (2009b) BME estimation of residential exposure to ambient PM10 and ozone at multiple time scales. Environ Health Perspect 117(4):537–544CrossRef
  Yu H-L, Ku S-C, Yang C-H, Cheng T-J, Chen L (2011a) Assessment of areal average air quality level over irregular areas: a case study of PM10 exposure estimation in Taipei (Taiwan). In: Nejadkoorki F (ed) Advanced air pollution. InTech, Rijeka
  Yu HL, Wang CH, Liu MC, Kuo YM (2011b) Estimation of fine particulate matter in Taipei using landuse regression and Bayesian maximum entropy methods. Int J Environ Res Public Health 8(6):2153–2169CrossRef
  Yu HL, Yang SJ, Yen HJ, Christakos G (2011c) A spatio-temporal climate-based model of early dengue fever warning in southern Taiwan. Stoch Env Res Risk Assess 25(4):485–494CrossRef
  Yu H-L, Ku S-C, Kolovos A (2012) Advanced space-time predictive analysis with STBME. In: SIGSPATIAL 2012 international conference on advances in geographic information systems proceedings, Redondo Beach
  Yuan M (2001) Representing complex geographic phenomena in GIS. Cartogr Geogr Inf Sci 28:83–96CrossRef
  Yuan M (2009) Challenges and critical issues for temporal GIS research and technologies. In: Karimi HA (ed) Handbook of Research on Geoinformatics. IGI Global, Hershey, pp 144–153CrossRef
  Zagouras A, Kolovos A, Coimbra CFM (2015) Objective framework for optimal distribution of solar irradiance monitoring networks. Renew Energy 80:153–165. doi:10.​1016/​j.​renene.​2015.​01.​046 CrossRef
  
• 作者单位：Hwa-Lung Yu (1)
  Shang-Chen Ku (1)
  Alexander Kolovos (2)
  
  1. Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, 10617, Taiwan
  2. SpaceTimeWorks LLC, San Diego, CA, USA
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Environment
  Mathematical Applications in Environmental Science
  Mathematical Applications in Geosciences
  Probability Theory and Stochastic Processes
  Statistics for Engineering, Physics, Computer Science, Chemistry and Geosciences
  Numerical and Computational Methods in Engineering
  Waste Water Technology, Water Pollution Control, Water Management and Aquatic Pollution
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1436-3259

文摘

In recent years, there has been a fast growing interest in the space–time data processing capacity of Geographic Information Systems (GIS). In this paper we present a new GIS-based tool for advanced geostatistical analysis of space–time data; it combines stochastic analysis, prediction, and GIS visualization technology. The proposed toolbox is based on the Bayesian Maximum Entropy theory that formulates its approach under a mature knowledge synthesis framework. We exhibit the toolbox features and use it for particulate matter spatiotemporal mapping in Taipei, in a proof-of-concept study where the serious preferential sampling issue is present. The proposed toolbox enables tight coupling of advanced spatiotemporal analysis functions with a GIS environment, i.e. QGIS. As a result, our contribution leads to a more seamless interaction between spatiotemporal analysis tools and GIS built-in functions; and utterly enhances the functionality of GIS software as a comprehensive knowledge processing and dissemination platform. Keywords Spatiotemporal analysis Stochastic processes Prediction BME QGIS