{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T15:53:53Z","timestamp":1778860433582,"version":"3.51.4"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031188398","type":"print"},{"value":"9783031188404","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-18840-4_10","type":"book-chapter","created":{"date-parts":[[2022,11,5]],"date-time":"2022-11-05T07:04:51Z","timestamp":1667631891000},"page":"127-140","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Spatial Cross-Validation for\u00a0Globally Distributed Data"],"prefix":"10.1007","author":[{"given":"Rita","family":"Beigait\u0117","sequence":"first","affiliation":[]},{"given":"Michael","family":"Mechenich","sequence":"additional","affiliation":[]},{"given":"Indr\u0117","family":"\u017dliobait\u0117","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,6]]},"reference":[{"key":"10_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2020.117479","volume":"230","author":"MD Adams","year":"2020","unstructured":"Adams, M.D., Massey, F., Chastko, K., Cupini, C.: Spatial modelling of particulate matter air pollution sensor measurements collected by community scientists while cycling, land use regression with spatial cross-validation, and applications of machine learning for data correction. Atmos. Environ. 230, 117479 (2020)","journal-title":"Atmos. Environ."},{"issue":"3","key":"10_CR2","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1007\/s10618-018-00607-x","volume":"33","author":"A Airola","year":"2019","unstructured":"Airola, A., et al.: The spatial leave-pair-out cross-validation method for reliable auc estimation of spatial classifiers. Data Min. Knowl. Disc. 33(3), 730\u2013747 (2019)","journal-title":"Data Min. Knowl. Disc."},{"key":"10_CR3","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1214\/09-SS054","volume":"4","author":"S Arlot","year":"2010","unstructured":"Arlot, S., Celisse, A.: A survey of cross-validation procedures for model selection. Stat. Surv. 4, 40\u201379 (2010)","journal-title":"Stat. Surv."},{"issue":"3","key":"10_CR4","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1111\/j.1600-0706.2012.00299.x","volume":"122","author":"V Bahn","year":"2013","unstructured":"Bahn, V., McGill, B.J.: Testing the predictive performance of distribution models. Oikos 122(3), 321\u2013331 (2013)","journal-title":"Oikos"},{"issue":"2","key":"10_CR5","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1111\/j.1475-2743.2009.00202.x","volume":"25","author":"N Batjes","year":"2009","unstructured":"Batjes, N.: Harmonized soil profile data for applications at global and continental scales: updates to the wise database. Soil Use Manag. 25(2), 124\u2013127 (2009)","journal-title":"Soil Use Manag."},{"key":"10_CR6","unstructured":"Channan, S., Collins, K., Emanuel, W.: Global mosaics of the standard modis land cover type data. University of Maryland and the Pacific Northwest National Laboratory, College Park, Maryland, USA 30 (2014)"},{"issue":"2","key":"10_CR7","first-page":"298","volume":"1","author":"NR Chopde","year":"2013","unstructured":"Chopde, N.R., Nichat, M.: Landmark based shortest path detection by using a* and haversine formula. Int. J. Innov. Res. Comput. Commun. Eng. 1(2), 298\u2013302 (2013)","journal-title":"Int. J. Innov. Res. Comput. Commun. Eng."},{"issue":"2","key":"10_CR8","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/0167-9473(92)90002-W","volume":"13","author":"W Feluch","year":"1992","unstructured":"Feluch, W., Koronacki, J.: A note on modified cross-validation in density estimation. Comput. Stat. Data Analysis 13(2), 143\u2013151 (1992)","journal-title":"Comput. Stat. Data Analysis"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Galbrun, E., Tang, H., Fortelius, M., \u017dliobait\u0117, I.: Computational biomes: The ecometrics of large mammal teeth. Palaeontol. Electron. 21(21.1. 3A), 1\u201331 (2018)","DOI":"10.26879\/786"},{"issue":"3","key":"10_CR10","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1111\/j.1538-4632.2008.00727.x","volume":"40","author":"A Getis","year":"2008","unstructured":"Getis, A.: A history of the concept of spatial autocorrelation: a geographer\u2019s perspective. Geogr. Anal. 40(3), 297\u2013309 (2008)","journal-title":"Geogr. Anal."},{"issue":"3","key":"10_CR11","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1890\/11-0826.1","volume":"93","author":"RJ Hijmans","year":"2012","unstructured":"Hijmans, R.J.: Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model. Ecology 93(3), 679\u2013688 (2012)","journal-title":"Ecology"},{"key":"10_CR12","doi-asserted-by":"publisher","unstructured":"Karasiak, N., Dejoux, J.-F., Monteil, C., Sheeren, D.: Spatial dependence between training and test sets: another pitfall of classification accuracy assessment in remote sensing. Mach. Learn. 111 1\u201326 (2021). https:\/\/doi.org\/10.1007\/s10994-021-05972-1","DOI":"10.1007\/s10994-021-05972-1"},{"key":"10_CR13","series-title":"ISSI Scientific Report Series","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/978-3-319-65633-5_8","volume-title":"Earth Observation Open Science and Innovation","author":"D Lary","year":"2018","unstructured":"Lary, D., et al.: Machine learning applications for earth observation. In: Mathieu, P.-P., Aubrecht, C. (eds.) Earth Observation Open Science and Innovation. ISRS, vol. 15, pp. 165\u2013218. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-65633-5_8"},{"issue":"7","key":"10_CR14","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1111\/geb.12161","volume":"23","author":"K Le Rest","year":"2014","unstructured":"Le Rest, K., Pinaud, D., Monestiez, P., Chadoeuf, J., Bretagnolle, V.: Spatial leave-one-out cross-validation for variable selection in the presence of spatial autocorrelation. Glob. Ecol. Biogeogr. 23(7), 811\u2013820 (2014)","journal-title":"Glob. Ecol. Biogeogr."},{"issue":"1","key":"10_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-022-29838-9","volume":"13","author":"H Meyer","year":"2022","unstructured":"Meyer, H., Pebesma, E.: Machine learning-based global maps of ecological variables and the challenge of assessing them. Nat. Commun. 13(1), 1\u20134 (2022)","journal-title":"Nat. Commun."},{"issue":"2","key":"10_CR16","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1111\/j.1467-8306.2004.09402005.x","volume":"94","author":"HJ Miller","year":"2004","unstructured":"Miller, H.J.: Tobler\u2019s first law and spatial analysis. Ann. Assoc. Am. Geogr. 94(2), 284\u2013289 (2004)","journal-title":"Ann. Assoc. Am. Geogr."},{"issue":"1","key":"10_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-18321-y","volume":"11","author":"P Ploton","year":"2020","unstructured":"Ploton, P., et al.: Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nat. Commun. 11(1), 1\u201311 (2020)","journal-title":"Nat. Commun."},{"issue":"10","key":"10_CR18","doi-asserted-by":"publisher","first-page":"2001","DOI":"10.1080\/13658816.2017.1346255","volume":"31","author":"J Pohjankukka","year":"2017","unstructured":"Pohjankukka, J., Pahikkala, T., Nevalainen, P., Heikkonen, J.: Estimating the prediction performance of spatial models via spatial k-fold cross validation. Int. J. Geogr. Inf. Sci. 31(10), 2001\u20132019 (2017)","journal-title":"Int. J. Geogr. Inf. Sci."},{"issue":"8","key":"10_CR19","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1111\/ecog.02881","volume":"40","author":"DR Roberts","year":"2017","unstructured":"Roberts, D.R., et al.: Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40(8), 913\u2013929 (2017)","journal-title":"Ecography"},{"key":"10_CR20","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.ecolmodel.2019.06.002","volume":"406","author":"P Schratz","year":"2019","unstructured":"Schratz, P., Muenchow, J., Iturritxa, E., Richter, J., Brenning, A.: Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data. Ecol. Model. 406, 109\u2013120 (2019)","journal-title":"Ecol. Model."},{"issue":"5","key":"10_CR21","doi-asserted-by":"publisher","first-page":"1215","DOI":"10.5194\/cp-12-1215-2016","volume":"12","author":"M Trachsel","year":"2016","unstructured":"Trachsel, M., Telford, R.J.: Estimating unbiased transfer-function performances in spatially structured environments. Climate of the Past 12(5), 1215\u20131223 (2016)","journal-title":"Climate of the Past"},{"issue":"2","key":"10_CR22","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1111\/2041-210X.13107","volume":"10","author":"R Valavi","year":"2019","unstructured":"Valavi, R., Elith, J., Lahoz-Monfort, J.J., Guillera-Arroita, G.: blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models. Methods Ecol. Evol. 10(2), 225\u2013232 (2019)","journal-title":"Methods Ecol. Evol."},{"key":"10_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolmodel.2021.109692","volume":"457","author":"AMC Wadoux","year":"2021","unstructured":"Wadoux, A.M.C., Heuvelink, G.B., De Bruin, S., Brus, D.J.: Spatial cross-validation is not the right way to evaluate map accuracy. Ecol. Model. 457, 109692 (2021)","journal-title":"Ecol. Model."},{"issue":"45","key":"10_CR24","doi-asserted-by":"publisher","first-page":"12751","DOI":"10.1073\/pnas.1609409113","volume":"113","author":"I \u017dliobait\u0117","year":"2016","unstructured":"\u017dliobait\u0117, I., et al.: Herbivore teeth predict climatic limits in kenyan ecosystems. Proc. Natl. Acad. Sci. 113(45), 12751\u201312756 (2016)","journal-title":"Proc. Natl. Acad. Sci."}],"container-title":["Lecture Notes in Computer Science","Discovery Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-18840-4_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,5]],"date-time":"2022-11-05T07:05:59Z","timestamp":1667631959000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-18840-4_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031188398","9783031188404"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-18840-4_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"6 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Discovery Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Montpellier","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dis2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ds2022.sciencesconf.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"59","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"12","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"46% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}