{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T04:23:08Z","timestamp":1745986988378,"version":"3.40.4"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030461324"},{"type":"electronic","value":"9783030461331"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-46133-1_40","type":"book-chapter","created":{"date-parts":[[2020,4,30]],"date-time":"2020-04-30T20:02:49Z","timestamp":1588276969000},"page":"672-687","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An Aggregate Learning Approach for Interpretable Semi-supervised Population Prediction and Disaggregation Using Ancillary Data"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6700-3519","authenticated-orcid":false,"given":"Guillaume","family":"Derval","sequence":"first","affiliation":[]},{"given":"Fr\u00e9d\u00e9ric","family":"Docquier","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3153-8941","authenticated-orcid":false,"given":"Pierre","family":"Schaus","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,4,30]]},"reference":[{"issue":"1","key":"40_CR1","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001). https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach. Learn."},{"issue":"4","key":"40_CR2","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1016\/j.rse.2006.11.020","volume":"108","author":"DJ Briggs","year":"2007","unstructured":"Briggs, D.J., Gulliver, J., Fecht, D., Vienneau, D.M.: Dasymetric modelling of small-area population distribution using land cover and light emissions data. Remote Sens. Environ. 108(4), 451\u2013466 (2007). https:\/\/doi.org\/10.1016\/j.rse.2006.11.020","journal-title":"Remote Sens. Environ."},{"doi-asserted-by":"publisher","unstructured":"Center for International Earth Science Information Network - CIESIN - Columbia University: Gridded population of the world, Version 4 (GPWv4): Population density, Revision 10, 11 July 2018 (2017). https:\/\/doi.org\/10.7927\/H4DZ068D","key":"40_CR3","DOI":"10.7927\/H4DZ068D"},{"doi-asserted-by":"publisher","unstructured":"Center for International Earth Science Information Network - CIESIN - Columbia University: U.S. census grids 2010 (Summary file 1), 19 July 2018 (2017). https:\/\/doi.org\/10.7927\/H40Z716C","key":"40_CR4","DOI":"10.7927\/H40Z716C"},{"key":"40_CR5","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/j.apgeog.2014.07.003","volume":"53","author":"A Dmowska","year":"2014","unstructured":"Dmowska, A., Stepinski, T.F.: High resolution dasymetric model of U.S. demographics with application to spatial distribution of racial diversity. Appl. Geogr. 53, 417\u2013426 (2014). https:\/\/doi.org\/10.1016\/j.apgeog.2014.07.003","journal-title":"Appl. Geogr."},{"doi-asserted-by":"publisher","unstructured":"Doupe, P., Bruzelius, E., Faghmous, J., Ruchman, S.G.: Equitable development through deep learning: the case of sub-national population density estimation. In: Proceedings of the 7th Annual Symposium on Computing for Development, DEV 2016, pp. 6:1\u20136:10. ACM, New York (2016). https:\/\/doi.org\/10.1145\/3001913.3001921","key":"40_CR6","DOI":"10.1145\/3001913.3001921"},{"issue":"2","key":"40_CR7","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1559\/152304001782173727","volume":"28","author":"CL Eicher","year":"2001","unstructured":"Eicher, C.L., Brewer, C.A.: Dasymetric mapping and areal interpolation: implementation and evaluation. Cartogr. Geogr. Inf. Sci. 28(2), 125\u2013138 (2001)","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"40_CR8","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/978-3-642-77500-0_5","volume-title":"Geographic Information Systems, Spatial Modelling and Policy Evaluation","author":"R Flowerdew","year":"1993","unstructured":"Flowerdew, R., Green, M.: Developments in areal interpolation methods and GIS. In: Fischer, M.M., Nijkamp, P. (eds.) Geographic Information Systems, Spatial Modelling and Policy Evaluation, pp. 73\u201384. Springer, Heidelberg (1993). https:\/\/doi.org\/10.1007\/978-3-642-77500-0_5"},{"issue":"6","key":"40_CR9","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1007\/s11111-010-0108-y","volume":"31","author":"FJ Gallego","year":"2010","unstructured":"Gallego, F.J.: A population density grid of the European union. Popul. Environ. 31(6), 460\u2013473 (2010). https:\/\/doi.org\/10.1007\/s11111-010-0108-y","journal-title":"Popul. Environ."},{"issue":"3","key":"40_CR10","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1068\/a250383","volume":"25","author":"MF Goodchild","year":"1993","unstructured":"Goodchild, M.F., Anselin, L., Deichmann, U.: A framework for the areal interpolation of socioeconomic data. Environ. Plan. A 25(3), 383\u2013397 (1993)","journal-title":"Environ. Plan. A"},{"issue":"6789","key":"40_CR11","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1038\/35016072","volume":"405","author":"RH Hahnloser","year":"2000","unstructured":"Hahnloser, R.H., Sarpeshkar, R., Mahowald, M.A., Douglas, R.J., Seung, H.S.: Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature 405(6789), 947 (2000)","journal-title":"Nature"},{"unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs\/1412.6980 (2014). http:\/\/arxiv.org\/abs\/1412.6980","key":"40_CR12"},{"issue":"11","key":"40_CR13","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"issue":"1","key":"40_CR14","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1111\/0033-0124.10042","volume":"55","author":"J Mennis","year":"2003","unstructured":"Mennis, J.: Generating surface models of population using dasymetric mapping. Prof. Geogr. 55(1), 31\u201342 (2003)","journal-title":"Prof. Geogr."},{"issue":"115","key":"40_CR15","first-page":"e121","volume":"24","author":"MS Monmonier","year":"1984","unstructured":"Monmonier, M.S., Schnell, G.A.: Land use and land cover data and the mapping of population density. Int. Yearb. Cartogr. 24(115), e121 (1984)","journal-title":"Int. Yearb. Cartogr."},{"doi-asserted-by":"crossref","unstructured":"Musicant, D.R., Christensen, J.M., Olson, J.F.: Supervised learning by training on aggregate outputs. In: Seventh IEEE International Conference on Data Mining (ICDM 2007), pp. 252\u2013261. IEEE (2007)","key":"40_CR16","DOI":"10.1109\/ICDM.2007.50"},{"unstructured":"Paszke, A., et al.: Automatic differentiation in PyTorch (2017)","key":"40_CR17"},{"doi-asserted-by":"crossref","unstructured":"Robinson, C., Hohman, F., Dilkina, B.: A deep learning approach for population estimation from satellite imagery. In: Proceedings of the 1st ACM SIGSPATIAL Workshop on Geospatial Humanities, pp. 47\u201354. ACM (2017)","key":"40_CR18","DOI":"10.1145\/3149858.3149863"},{"unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)","key":"40_CR19"},{"issue":"2","key":"40_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0107042","volume":"10","author":"FR Stevens","year":"2015","unstructured":"Stevens, F.R., Gaughan, A.E., Linard, C., Tatem, A.J.: Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. Plos One 10(2), 1\u201322 (2015). https:\/\/doi.org\/10.1371\/journal.pone.0107042","journal-title":"Plos One"},{"issue":"1\u20132","key":"40_CR21","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.ecolmodel.2005.03.012","volume":"189","author":"Y Tian","year":"2005","unstructured":"Tian, Y., Yue, T., Zhu, L., Clinton, N.: Modeling population density using land cover data. Ecol. Model. 189(1\u20132), 72\u201388 (2005)","journal-title":"Ecol. Model."},{"issue":"367","key":"40_CR22","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1080\/01621459.1979.10481647","volume":"74","author":"WR Tobler","year":"1979","unstructured":"Tobler, W.R.: Smooth pycnophylactic interpolation for geographical regions. J. Am. Stat. Assoc. 74(367), 519\u2013530 (1979)","journal-title":"J. Am. Stat. Assoc."},{"unstructured":"UN Economic and Social Council: Resolution adopted by the economic and social council on 10 June 2015 (2020 world population and housing census programme), August 2015. http:\/\/www.un.org\/ga\/search\/view_doc.asp?symbol=E\/RES\/2015\/10","key":"40_CR23"},{"issue":"1","key":"40_CR24","doi-asserted-by":"publisher","first-page":"103","DOI":"10.2307\/209467","volume":"26","author":"JK Wright","year":"1936","unstructured":"Wright, J.K.: A method of mapping densities of population: with cape cod as an example. Geogr. Rev. 26(1), 103\u2013110 (1936)","journal-title":"Geogr. Rev."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-46133-1_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T22:05:38Z","timestamp":1745964338000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-46133-1_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030461324","9783030461331"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-46133-1_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"30 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"W\u00fcrzburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ecmlpkdd2019.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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"733","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":"130","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":"0","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":"18% - 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":"3.04","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":"5.3","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)"}},{"value":"ECML PKDD Workshops Information: single-blind review, submissions: 200, full papers accepted: 70, short papers accepted: 46","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}