User menu

Bayesian maximum entropy and data fusion for processing qualitative data: theory and application for crowdsourced cropland occurrences in Ethiopia

Bibliographic reference Bogaert, Patrick ; Gengler, Sarah. Bayesian maximum entropy and data fusion for processing qualitative data: theory and application for crowdsourced cropland occurrences in Ethiopia. In: Stochastic Environmental Research and Risk Assessment, Vol. 31, p. 1-17 (16 June 2017)
Permanent URL
  1. Abramov Rafail, A practical computational framework for the multidimensional moment-constrained maximum entropy principle, 10.1016/
  2. Abramov Rafail V., The multidimensional maximum entropy moment problem: a review of numerical methods, 10.4310/cms.2010.v8.n2.a5
  3. Agresti A (2013) Categorical data analysis, 3rd edn. Wiley, Hoboken
  4. Ali Ahmed Loai, Schmid Falko, Al-Salman Rami, Kauppinen Tomi, Ambiguity and plausibility : managing classification quality in volunteered geographic information, 10.1145/2666310.2666392
  5. Allard D., D'Or D., Froidevaux R., An efficient maximum entropy approach for categorical variable prediction, 10.1111/j.1365-2389.2011.01362.x
  6. Andersen EB (1980) Discrete statistical models with social science applications. North Holland, Amsterdam
  7. Bandyopadhyay K., Bhattacharya A. K., Biswas Parthapratim, Drabold D. A., Maximum entropy and the problem of moments: A stable algorithm, 10.1103/physreve.71.057701
  8. Bayat Bardia, Nasseri Mohsen, Zahraie Banafsheh, Identification of long-term annual pattern of meteorological drought based on spatiotemporal methods: evaluation of different geostatistical approaches, 10.1007/s11069-014-1499-3
  9. BIERKENS M. F. P., BURROUGH P. A., The indicator approach to categorical soil data : I. Theory, 10.1111/j.1365-2389.1993.tb00458.x
  10. Bishop YMM, Fienberg SE, Holland PW (2007) Discrete multivariate analysis: theory and practice. Springer, Berlin
  11. BMELib : a MATLAB numerical toolbox of modern spatiotemporal geostatistics implementing the Bayesian maximum entropy theory.
  12. Bogaert P., Spatial prediction of categorical variables: the Bayesian maximum entropy approach, 10.1007/s00477-002-0114-4
  13. Bogaert P, Gengler S (2014) MinNorm approximation of MaxEnt/MinDiv problems for probability tables. In MaxEnt 2014—Bayesian inference and maximum entropy methods in science and engineering, Amboise, France, 21–26 September 2014, pp 287–296
  14. Brus D. J., Bogaert P., Heuvelink G. B. M., Bayesian Maximum Entropy prediction of soil categories using a traditional soil map as soft information, 10.1111/j.1365-2389.2007.00981.x
  15. Canosa N, Miller H.G, Plastino A, Rossignoli R, Maximum Entropy-Minimum Norm method for the determination of level densities, 10.1016/0378-4371(95)00212-p
  16. Cao C, Kyriakidis PC, Goodchild MF (2011) A multinomial logistic mixed model for the prediction of categorical spatial data. Int J Geogr Inf Sci 25(12):2017–2086
  17. Cao Guofeng, Yoo Eun-hye, Wang Shaowen, A statistical framework of data fusion for spatial prediction of categorical variables, 10.1007/s00477-013-0842-7
  18. Cardille Jeffrey A., Clayton Murray K., A regression tree-based method for integrating land-cover and land-use data collected at multiple scales, 10.1007/s10651-007-0012-5
  19. Christakos G (2000) Modern spatiotemporal geostatistics. Oxford University Press, Oxford
  20. Christakos G, Bogaert P, Serre M (2002) Temporal geographical information systems: advanced functions for field-based applications. Springer, Berlin
  21. Christensen R (1997) Log-linear models and logistic regression, 2nd edn. Springer, Berlin
  22. Comber Alexis, See Linda, Fritz Steffen, Van der Velde Marijn, Perger Christoph, Foody Giles, Using control data to determine the reliability of volunteered geographic information about land cover, 10.1016/j.jag.2012.11.002
  23. Comber A., Mooney P., Purves R. S., Rocchini D., Walz A., COMPARING NATIONAL DIFFERENCES IN WHAT PEOPLE PERCEIVE TO BE THERE: MAPPING VARIATIONS IN CROWD SOURCED LAND COVER, 10.5194/isprsarchives-xl-3-w3-71-2015
  24. Comber Alexis, Fonte Cidália, Foody Giles, Fritz Steffen, Harris Paul, Olteanu-Raimond Ana-Maria, See Linda, Geographically weighted evidence combination approaches for combining discordant and inconsistent volunteered geographical information, 10.1007/s10707-016-0248-z
  25. Cressie N (2015) Statistics for spatial data, 2nd edn. Wiley-Interscience, Hoboken
  26. Cressie N, Wikle CK (2011) Statistics for spatial-temporal Data. Wiley, Hoboken
  27. D'Or D., Bogaert P., Spatial prediction of categorical variables with the Bayesian Maximum Entropy approach: the Ooypolder case study, 10.1111/j.1365-2389.2004.00628.x
  28. Fienberg Stephen E., An Iterative Procedure for Estimation in Contingency Tables, 10.1214/aoms/1177696968
  29. Fienberg Stephen E., Rinaldo Alessandro, Maximum likelihood estimation in log-linear models, 10.1214/12-aos986
  30. Foody GM, See L, Fritz S, Van der Velde M, Perger C, Schill C, Boyd DS, Comber A (2015) Accurate attribute mapping from volunteered geographic information: issues of volunteer quantity and quality. Cartogr J 52:336–344
  31. Fritz Steffen, McCallum Ian, Schill Christian, Perger Christoph, Grillmayer Roland, Achard Frédéric, Kraxner Florian, Obersteiner Michael, Geo-Wiki.Org: The Use of Crowdsourcing to Improve Global Land Cover, 10.3390/rs1030345
  32. Fritz S, See LM, Rembold F (2010) Comparison of global and regional land cover maps with statistical information for the agricultural domain in Africa. Int J Remote Sens 25:1527–1532
  33. Fritz Steffen, You Liangzhi, Bun Andriy, See Linda, McCallum Ian, Schill Christian, Perger Christoph, Liu Junguo, Hansen Matt, Obersteiner Michael, Cropland for sub-Saharan Africa: A synergistic approach using five land cover data sets : A NEW CALIBRATED CROPLAND DATA SET, 10.1029/2010gl046213
  34. Gengler Sarah, Bogaert Patrick, Bayesian Data Fusion Applied to Soil Drainage Classes Spatial Mapping, 10.1007/s11004-015-9585-y
  35. Gengler Sarah, Bogaert Patrick, Integrating Crowdsourced Data with a Land Cover Product: A Bayesian Data Fusion Approach, 10.3390/rs8070545
  36. Goodchild Michael F., Li Linna, Assuring the quality of volunteered geographic information, 10.1016/j.spasta.2012.03.002
  37. Goovaerts P (1997) Geostatistics for natural resources evaluation (applied geostatistics). Oxford University Press, Oxford
  38. Huang Xiang, Li Jie, Liang Yuru, Wang Zhizhong, Guo Jianhua, Jiao Peng, Spatial hidden Markov chain models for estimation of petroleum reservoir categorical variables, 10.1007/s13202-016-0251-9
  39. Hunter Jane, Alabri Abdulmonem, van Ingen Catharine, Assessing the quality and trustworthiness of citizen science data : ASSESSING THE QUALITY AND TRUSTWORTHINESS OF CITIZEN SCIENCE DATA, 10.1002/cpe.2923
  40. Hurtt George C., Rosentrater Lynn, Frolking Steve, Moore Berrien, Linking remote-sensing estimates of land cover and census statistics on land use to produce maps of land use of the conterminous United States, 10.1029/2000gb001299
  41. Jafari Azam, Khademi Hossein, Finke Peter A., Van de Wauw Johan, Ayoubi Shamsollah, Spatial prediction of soil great groups by boosted regression trees using a limited point dataset in an arid region, southeastern Iran, 10.1016/j.geoderma.2014.04.029
  42. Jaynes E. T., Probability Theory : The Logic of Science, ISBN:9780511790423, 10.1017/cbo9780511790423
  43. Jin Chongyang, Zhu Jun, Steen-Adams Michelle M., Sain Stephan R., Gangnon Ronald E., Spatial multinomial regression models for nominal categorical data: a study of land cover in Northern Wisconsin, USA : SPATIAL MULTINOMIAL REGRESSION FOR NOMINAL CATEGORICAL DATA, 10.1002/env.2189
  44. Johnson Brian A., Iizuka Kotaro, Integrating OpenStreetMap crowdsourced data and Landsat time-series imagery for rapid land use/land cover (LULC) mapping: Case study of the Laguna de Bay area of the Philippines, 10.1016/j.apgeog.2015.12.006
  45. Kapur JN (2009) Maximum entropy models in science and engineering. New Age, New Delhi
  46. Kou Xiaokang, Jiang Lingmei, Bo Yanchen, Yan Shuang, Chai Linna, Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method, 10.3390/rs8020105
  47. Messier Kyle P., Campbell Ted, Bradley Philip J., Serre Marc L., Estimation of Groundwater Radon in North Carolina Using Land Use Regression and Bayesian Maximum Entropy, 10.1021/acs.est.5b01503
  48. Muller C.L., Chapman L., Johnston S., Kidd C., Illingworth S., Foody G., Overeem A., Leigh R.R., Crowdsourcing for climate and atmospheric sciences: current status and future potential : CROWDSOURCING FOR CLIMATE AND ATMOSPHERIC SCIENCES, 10.1002/joc.4210
  49. Pérez-Hoyos A., García-Haro F.J., San-Miguel-Ayanz J., A methodology to generate a synergetic land-cover map by fusion of different land-cover products, 10.1016/j.jag.2012.04.011
  50. Poser K, Dransch D (2010) Volunteered geographic information for disaster management with application to rapid flood damage estimation. Geomatica 64:89–98
  51. See Linda, McCallum Ian, Fritz Steffen, Perger Christoph, Kraxner Florian, Obersteiner Michael, Baruah Ujjal Deka, Mili Nitashree, Kalita Nripen Ram, Mapping Cropland in Ethiopia Using Crowdsourcing, 10.4236/ijg.2013.46a1002
  52. See Linda, Fritz Steffen, You Liangzhi, Ramankutty Navin, Herrero Mario, Justice Chris, Becker-Reshef Inbal, Thornton Philip, Erb Karlheinz, Gong Peng, Tang Huajun, van der Velde Marijn, Ericksen Polly, McCallum Ian, Kraxner Florian, Obersteiner Michael, Improved global cropland data as an essential ingredient for food security, 10.1016/j.gfs.2014.10.004
  53. See Linda, Mooney Peter, Foody Giles, Bastin Lucy, Comber Alexis, Estima Jacinto, Fritz Steffen, Kerle Norman, Jiang Bin, Laakso Mari, Liu Hai-Ying, Milčinski Grega, Nikšič Matej, Painho Marco, Pődör Andrea, Olteanu-Raimond Ana-Maria, Rutzinger Martin, Crowdsourcing, Citizen Science or Volunteered Geographic Information? The Current State of Crowdsourced Geographic Information, 10.3390/ijgi5050055
  54. Thenkabail PS (ed) (2015) Remotely sensed data characterization, classification, and accuracies (remote sensing handbook). CRC Press, Boca Raton
  55. Wahyudi Agung, Bartzke Mariana, Küster Eberhard, Bogaert Patrick, Maximum entropy estimation of a Benzene contaminated plume using ecotoxicological assays, 10.1016/j.envpol.2012.08.018
  56. Waller Lance A., Spatial Models for Categorical Data, 10.1002/0470011815.b2a10056
  57. Werner H, Hanke M, Neubauer A (2000) Regularization of inverse problems. Kluwer, Berlin
  58. Whittaker Joshua, McLennan Blythe, Handmer John, A review of informal volunteerism in emergencies and disasters: Definition, opportunities and challenges, 10.1016/j.ijdrr.2015.07.010
  59. Wrigley N (2002) Categorical data analysis for geographers and environmental scientists. Blackburn Press, Caldwell
  60. Wu Ximing, Calculation of maximum entropy densities with application to income distribution, 10.1016/s0304-4076(03)00114-3
  61. Xu Yadong, Serre Marc L., Reyes Jeanette, Vizuete William, Bayesian Maximum Entropy Integration of Ozone Observations and Model Predictions: A National Application, 10.1021/acs.est.6b00096
  62. Zook Matthew, Graham Mark, Shelton Taylor, Gorman Sean, Volunteered Geographic Information and Crowdsourcing Disaster Relief: A Case Study of the Haitian Earthquake, 10.2202/1948-4682.1069