, Volume 7, Issue 1, pp 55–68 | Cite as

Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway)

  • Arzu Erener
  • H. Sebnem B. Düzgün
Original Paper


Statistical models are one of the most preferred methods among many landslide susceptibility assessment methods. As landslide occurrences and influencing factors have spatial variations, global models like neural network or logistic regression (LR) ignore spatial dependence or autocorrelation characteristics of data between the observations in susceptibility assessment. However, to assess the probability of landslide within a specified period of time and within a given area, it is important to understand the spatial correlation between landslide occurrences and influencing factors. By including these relations, the predictive ability of the developed model increases. In this respect, spatial regression (SR) and geographically weighted regression (GWR) techniques, which consider spatial variability in the parameters, are proposed in this study for landslide hazard assessment to provide better realistic representations of landslide susceptibility. The proposed model was implemented to a case study area from More and Romsdal region of Norway. Topographic (morphometric) parameters (slope angle, slope aspect, curvature, plan, and profile curvatures), geological parameters (geological formations, tectonic uplift, and lineaments), land cover parameter (vegetation coverage), and triggering factor (precipitation) were considered as landslide influencing factors. These influencing factors together with past rock avalanche inventory in the study region were considered to obtain landslide susceptibility maps by using SR and LR models. The comparisons of susceptibility maps obtained from SR and LR show that SR models have higher predictive performance. In addition, the performances of SR and LR models at the local scale were investigated by finding the differences between GWR and SR and GWR and LR maps. These maps which can be named as comparison maps help to understand how the models estimate the coefficients at local scale. In this way, the regions where SR and LR models over or under estimate the landslide hazard potential were identified.


Logistic regression Spatial regression Geographically weighted regression Susceptibility mapping 



This study was conducted during the first author’s stay at ICG (International Centre for Geohazards) in Norway. The authors thank to ICG and NGI (Norwegian Geotechnical Institute) for providing financial support during the course of this research. The authors also acknowledge NGU (Geological Survey of Norway), Meteorological Institute of Norway for supporting this work with relevant background information and data, as well as Marc-Henri Derron for his comments on this work.


  1. Ahlberg P, Stigler B, Viberg L (1988) Experiences of landslide risk considerations in land use planning in Sweden. 5th International Symposium on Landslides. Lausanne 2:1091–1096Google Scholar
  2. Akgün A, Bulut F (2007) GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region. Environ Geol 51:1377–1387CrossRefGoogle Scholar
  3. Anagnosti P, Lesevic Z (1991) Probabilistic versus deterministic approach in hazard assessment of landslides along man made reservoirs. Landslides 2:1221–1227Google Scholar
  4. Anselin L (1988) Spatial econometrics: methods and models. Kluwer, Dordrecht, pp 32–33Google Scholar
  5. Anselin L (1995) Local indicators of spatial association—LISA. Geogr Anal 27:93–115Google Scholar
  6. Atkinson PM, Massari R (1998) Generalised linear modeling of susceptibility to landsliding in the Central Apennines, Italy. Comput Geosci 24(4):373–385CrossRefGoogle Scholar
  7. Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65(1–2):15–31CrossRefGoogle Scholar
  8. Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides 1:73–81CrossRefGoogle Scholar
  9. Baeza C, Corominas J (2001) Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surf Process Landforms 26:251–1263CrossRefGoogle Scholar
  10. Bailey TA (1994) A review of statistical spatial analysis in geographical information systems. In: Fotheringham S, Rogerson P (eds) Spatial analysis and GIS. Taylor & Francis, London, pp 13–44Google Scholar
  11. Bailey TC, Gatrell AC (1995) Interactive spatial data analysis. Longman Scientific & Technical, Harlow, p 413Google Scholar
  12. Begueria S, Lorente A (1999) Hazard mapping by multivariate statistics: comparison of methods and case study in the Spanish pyrenees. Debrisfall assessment in mountain catchments for local end-users. Instituto Pirenaico de Ecologia, CSIC, 202, 50080-Zaragoza, SpainGoogle Scholar
  13. Bernknopf RL, Campbell RH, Brookshire DS, Shapiro CD (1988) A probabilitic approach to landslide hazard mapping in Cincinnati, Ohio, with applications for economic evaluation. Bull Am Assoc Engineering Geologists 25(1):39–56Google Scholar
  14. Bhasin R, Kaynia AM (2004) Static and dynamic simulation of a 700-m high rock slope in western Norway. Eng Geol 71(3–4):213–226CrossRefGoogle Scholar
  15. Braathen A, Blikra LH, Berg SS, Karlsen F (2004) Rock-slope failures in Norway; type, geometry, deformation mechanisms and stability. Norwegian J Geology 84:67–88 Trondheim. ISSN 029-196XGoogle Scholar
  16. Brand EW (1988) Special lecture: landslide risk assessment in Hong Kong. 5th International Symposium on Landslides. Lausanne 2:1059–1074Google Scholar
  17. Brunsdon C, Fotheringham AS, Charlton M (1996) Geographically weighted regression: a method for exploring spatial nonstationarity. Geogr Anal 28(4):281–298Google Scholar
  18. Brunsdon C, Fotheringham AS, Charlton M (1998) Spatial nonstationarity and autoregressive models. Environ Plann A 30:957–973CrossRefGoogle Scholar
  19. Carson MA, Kirby MJ (1972) Hillslope form and process. Cambridge University Press, CambridgeGoogle Scholar
  20. Carrara A, Cardinali M, Detti R, Guzzetti F, Pasqui V, Reichenbach P (1991) GIS Techniques and statistical models in evaluating landslide hazard. Earth Surf Processes Landf 16:427–445CrossRefGoogle Scholar
  21. Carrara A, Cardinalli M, Guzetti F, Reichenbach P (1995) In: Carrara A, Guzzetti F (eds) GIS technology in mapping landslide hazard. . Geographical information systems in assessing natural hazards. Kluwer Academic, Dordrecht, pp 135–175Google Scholar
  22. Chau KT, Chan JE (2005) Regional bias of landslide data in generating susceptibility maps using logistic regression: case of Hong Kong Island. Landslides 2(4):280–290CrossRefGoogle Scholar
  23. Chung CJF, Fabbri AG (1999) Probabilistic models for landslide hazard mapping. Photogramm Eng Remote Sensing 65(12):1389–1399Google Scholar
  24. Cruden DM, Fell R (1997) Landslide risk assessment. Proceedings International Workshop on Landslide Risk Assessment, Honolulu. Balkema, Rotterdam, p 371Google Scholar
  25. Dai FC, Lee CF (2001) Terrain-based mapping of landslide susceptibility using a geographical information system: a case study. Can Geotech J 38:911–923CrossRefGoogle Scholar
  26. Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42:213–228CrossRefGoogle Scholar
  27. Donati L, Turrini MC (2002) An objective method to rank the importance of the factors predisposing to landslides with the GIS methodology: application to an area of the Apennines (Valnerina; Perugia, Italy). Eng Geol 63:277–289CrossRefGoogle Scholar
  28. Dubin RA (1988) Estimation of regression coefficients in the presence of spatially autocorrelated error terms. Rev Econ Statist 70:466–474CrossRefGoogle Scholar
  29. Dubin RA (1992) Spatial autocorrelation and neighborhood quality. Reg Sci Urban Econ 22:433–452CrossRefGoogle Scholar
  30. Düzgün HŞB, Kemeç S (2008) "Spatial regression and geographically weighted regression for spatial prediction" in the Encyclopedia of Geographical Information Science, S. Shekhar and H. Xiong (Eds.) New York: SpringerGoogle Scholar
  31. Ercanoglu M, Gokceoglu C, Van Asch TWJ (2004) Landslide susceptibility zoning North of Yenice (NW Turkey) by multivariate statistical techniques. Nat Hazards 32:1–23CrossRefGoogle Scholar
  32. Fell R (1994) Landslide risk assessment and acceptable risk. Can Geotech J 31(2):261–272CrossRefGoogle Scholar
  33. Fotheringham AS (1997) Trends in quantitative methods I: stressing the local. Progress in Human Geography 21:88–96CrossRefGoogle Scholar
  34. Fotheringham AS, Charlton M, Brunsdon C (1996) The geography of parameter space: an investigation of spatial nonstationarity. Int J Geogr Inf Syst 10(5):605–627CrossRefGoogle Scholar
  35. Fotheringham AS, Brunsdon C, Charlton M (2000) Quantitative geography perspectives on spatial data analysis. Sage, New York, pp 15–46Google Scholar
  36. Fotheringham AS, Charlton ME, Brunsdon C (2001) Spatial variations in school performance: a local analysis using geographically weighted regression. Geogr Environ Model 5:43–66CrossRefGoogle Scholar
  37. Gamerman D, Moreira ARB (2004) Multivariate spatial regression models. J Multivar Anal 91:262–281CrossRefGoogle Scholar
  38. Guzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F (2005) Landslide hazard assessment in the Staffora basin, Northern Italian Apennines. Geomorphology 72:272–299CrossRefGoogle Scholar
  39. Guzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81:166–184CrossRefGoogle Scholar
  40. Henderson IHC, Saintot A, Derron MH (2006) Structural mapping of potential rockslide sites in the Storfjorden area, western Norway: the influence of bedrock geology on hazard analysis, NGU open-report 2006.052Google Scholar
  41. Isaaks EH, Srivastava RM (1989) Applied geostatistics. Oxford University Press, New YorkGoogle Scholar
  42. Kazar BM, Shekhar S, Boley D, Lilja DJ, Pace RK, LeSage JP (2005) Parameter estimation for the spatial autoregression model: a rigorous approach. IEEE Transactions On Knowledge And Data Engineering. Technical Report. Revisited date 13.01.2010 from
  43. Keller EA (1992) Environmental geology. Macmillan Publishing Company, New YorkGoogle Scholar
  44. Lee S (2004) Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS. Environ Manage 34:223–232CrossRefGoogle Scholar
  45. Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int. J. Remote Sensing preview article. Taylor & Francis, UKGoogle Scholar
  46. Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40:1095–1113CrossRefGoogle Scholar
  47. Lee S, Dan N (2005) Probabilistic landslide susceptibility mapping in the Lai Chau province of Vietnam: focus on the relationship between tectonic fractures and landslides. Environ Geol 48:778–787CrossRefGoogle Scholar
  48. Lee S, Pradhan B (2006) Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. J Earth Syst Sci 115(6):661–672CrossRefGoogle Scholar
  49. Lee S, Chi J, Min K (2004) Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun, Korea. Int J Remote Sensing 25(11):2037–2052CrossRefGoogle Scholar
  50. Mark RK, Ellen SD (1995) In: Carrara A, Guzzetti F (eds) Statistical and simulation models for mapping Debris-flow hazard. Geographical Information Systems in assessing Natural Hazards. Kluwer Academic, Dordrecht, pp 93–106Google Scholar
  51. Marquínez J, Menéndezduarte R, Farias P, Jiménez Sánchez M (2003) Predictive GIS-based model of rockfall activity in mountain cliffs. Nat Hazards 30:341–360CrossRefGoogle Scholar
  52. Ohlmacher GC, Davis CJ (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng Geol 69:331–343CrossRefGoogle Scholar
  53. Pace K (1997) Performing large spatial regressions and autoregressions. Econ Lett 54:283–291CrossRefGoogle Scholar
  54. Pace K, Barry R, Clapp JM, Rodriguez M (1998) Spatiotemporal autoregressive models of neighborhood effects. J Real Estate Finance Econ 17:15–34CrossRefGoogle Scholar
  55. Pistocchi A, Luzi L, Napolitano P (2002) The use of predictive modelling techniques for optimal exploitation of spatial databases: a case study in landslide hazard mapping with expert system-like methods. Environ Geol 41:765–775CrossRefGoogle Scholar
  56. Remondo J, Bonachea J, Cendrero A (2005) A statistical approach to landslide risk modelling at basin scale: from landslide susceptibility to quantitative risk assessment. Landslides 2:321–328CrossRefGoogle Scholar
  57. Sabins FF (1996) Remote sensing: principles and interpretation, 494 pages, 3rd edn. W. H. Freeman and Company, New YorkGoogle Scholar
  58. Saha AK, Gupta RP, Sarkar I, Arora MK, Csaplovics E (2005) An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas. Landslides 2:61–69CrossRefGoogle Scholar
  59. Sandersen F, Bakkehoi S, Hestnes E, Lied K (1996) The influence of meteorological factors on the initiation of debris flows, rockfalls, rockslides and rockmass stability. In: Senneset K (ed) Landslides. Proceedings of the 7th symposium on landslides, Trondheim, 17–21 June 1996, pp 97–114Google Scholar
  60. Santiago B (2006) Validation and evaluation of predictive models in hazard assessment and risk management. Nat Hazards 37:315–329CrossRefGoogle Scholar
  61. Suzen LM, Doyuran V (2004) Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey. Eng Geol 71(3):303–321CrossRefGoogle Scholar
  62. Terzaghi K (1950) Mechanisms of landslides. Application of geology to engineering practice, Berken volume, Geological Society of America Bulletin, 83–123Google Scholar
  63. Tveten E, Lutro O, Thorsnes T (1998) Bedrock map Alesund, M 1:250 000. Geological Survey of NorwayGoogle Scholar
  64. USGS (1993) Digital elevation models data users guide 5: U.S. Geological Survey, Reston, p 48Google Scholar
  65. USGS (2002) National mapping program technical instructions. Quality Control Standards for Digital Elevation Models.
  66. Varnes DJ (1984) with IAEG Commission on landsildes and other Mass movements: landslide hazard zonation: a rewiev of principles and practices. UNESCO, Paris, p 63Google Scholar
  67. Van Westen CJ (1997) Statistical landslide hazard analysis. ILWIS 2.1 for Windows application guide. ITC Publication, Enschede, pp 73–84Google Scholar
  68. Van Westen CJ, van Asch TWJ, Soeters R (2006) Landslide hazard and risk zonation—why is it still so difficult? Bull Eng Geol Env 65:167–184CrossRefGoogle Scholar
  69. Yesilnacara E, Topal T (2005) Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol 79:251–266CrossRefGoogle Scholar
  70. Yong RN, Alonso E, Tabba MM, Fransham PB (1977) Application of risk analysis to the prediction of slope instability. Can Geotech J 14(3):540–543CrossRefGoogle Scholar
  71. Yoshimatsu H, Abe S (2006) A review of landslide hazards in Japan and assessment of their susceptibility using an analytical hierarchic process (AHP) method. Landslides 3:149–158CrossRefGoogle Scholar
  72. Web 1 (2009) Spectral Signatures and Multi-spectral Image Interpretation Note #8.
  73. Zezere JL, Oliveira SC, Garcia RAC, Reis E (2006) Landslide risk analysis in the area North of Lisbon (Portugal): evaluation of direct and indirect costs resulting from a motorway disruption by slope movements, Landslides, Original article. doi: 10.1007/s10346-006-0070-z
  74. Zhu L, Huang J (2006) GIS-based logistic regression method for landslide susceptibility mapping in regional scale. J Zhejiang University Science A 7(12):2007–2017CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Geodetic and Geographic Information TechnologiesMiddle East Technical UniversityAnkaraTurkey

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