{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T10:04:55Z","timestamp":1779357895934,"version":"3.51.4"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T00:00:00Z","timestamp":1634688000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T00:00:00Z","timestamp":1634688000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Hochschule f\u00fcr Angewandte Wissenschaften Hamburg (HAW Hamburg)"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Datenbank Spektrum"],"published-print":{"date-parts":[[2021,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>More and more applications nowadays use spatio-temporal data for different purposes. In order to be processed and used efficiently, this unique type of data requires special handling. This paper summarizes methods and approaches for feature selection of spatio-temporal data and machine learning algorithms for spatio-temporal data engineering. Furthermore, it highlights relevant work in specific domains. The range of possible approaches for data processing is quite wide. However, in order to use these approaches with the spatio-temporal data in a\u00a0meaningful and practical way, individual data processing steps need to be adapted. One of the most important steps is feature engineering.<\/jats:p>","DOI":"10.1007\/s13222-021-00391-x","type":"journal-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T17:53:02Z","timestamp":1634752382000},"page":"237-244","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Feature Engineering Techniques and Spatio-Temporal Data Processing"],"prefix":"10.1007","volume":"21","author":[{"given":"Chris-Marian","family":"Forke","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1623-5309","authenticated-orcid":false,"given":"Marina","family":"Tropmann-Frick","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,20]]},"reference":[{"issue":"3","key":"391_CR1","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.tjem.2018.08.001","volume":"18","author":"H Akoglu","year":"2018","unstructured":"Akoglu H (2018) User\u2019s guide to correlation coefficients. Turk J Emerg Med 18(3):91\u201393","journal-title":"Turk J Emerg Med"},{"key":"391_CR2","doi-asserted-by":"publisher","DOI":"10.1145\/3161602","author":"G Atluri","year":"2018","unstructured":"Atluri G, Karpatne A, Kumar V (2018) Spatio-temporal data mining: a\u00a0survey of problems and methods. ACM Comput Surv. https:\/\/doi.org\/10.1145\/3161602","journal-title":"ACM Comput Surv"},{"issue":"1","key":"391_CR3","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.compeleceng.2013.11.024","volume":"40","author":"G Chandrashekar","year":"2014","unstructured":"Chandrashekar G, Sahin F (2014) A\u00a0survey on feature selection methods. Comput Electr Eng 40(1):16\u201328 (40th-year commemorative issue)","journal-title":"Comput Electr Eng"},{"issue":"8","key":"391_CR4","doi-asserted-by":"publisher","first-page":"1958","DOI":"10.3390\/en11081958","volume":"11","author":"L Cheng","year":"2018","unstructured":"Cheng L, Zang H, Ding T, Sun R, Wang M, Wei Z, Sun G (2018) Ensemble recurrent neural network based probabilistic wind speed forecasting approach. Energies 11(8):1958","journal-title":"Energies"},{"key":"391_CR5","first-page":"2151","volume-title":"The Thirty-second AAAI Conference on Artificial Intelligence","author":"W Cheng","year":"2018","unstructured":"Cheng W, Shen Y, Zhu Y, Huang L (2018) A\u00a0neural attention model for urban air quality inference: learning the weights of monitoring stations. In: The Thirty-second AAAI Conference on Artificial Intelligence, pp 2151\u20132158"},{"key":"391_CR6","series-title":"arXiv preprint arXiv:1801.02143","volume-title":"Deep bidirectional and unidirectional lstm recurrent neural network for network-wide traffic speed prediction","author":"Z Cui","year":"2018","unstructured":"Cui Z, Ke R, Pu Z, Wang Y (2018) Deep bidirectional and unidirectional lstm recurrent neural network for network-wide traffic speed prediction. arXiv preprint arXiv:1801.02143"},{"key":"391_CR7","first-page":"1","volume-title":"2016 International Conference on Engineering MIS (ICEMIS)","author":"N El Aboudi","year":"2016","unstructured":"El Aboudi N, Benhlima L (2016) Review on wrapper feature selection approaches. In: 2016 International Conference on Engineering MIS (ICEMIS), pp 1\u20135"},{"key":"391_CR8","first-page":"17","volume-title":"International conference on machine learning, electrical and mechanical engineering","author":"M-R Feizi-Derakhshi","year":"2014","unstructured":"Feizi-Derakhshi M-R, Ghaemi M (2014) Classifying different feature selection algorithms based on the search strategies. In: International conference on machine learning, electrical and mechanical engineering, pp 17\u201321"},{"issue":"14","key":"391_CR9","doi-asserted-by":"publisher","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","volume":"31","author":"R Genuer","year":"2010","unstructured":"Genuer R, Poggi J-M, Tuleau-Malot C (2010) Variable selection using random forests. Pattern Recognit Lett 31(14):2225\u20132236","journal-title":"Pattern Recognit Lett"},{"key":"391_CR10","doi-asserted-by":"publisher","DOI":"10.1515\/9781547401567","volume-title":"Big data analytics methods","author":"P Ghavami","year":"2019","unstructured":"Ghavami P (2019) Big data analytics methods. De Gruyter, Boston"},{"issue":"7","key":"391_CR11","doi-asserted-by":"publisher","first-page":"1551","DOI":"10.1109\/TMI.2017.2715285","volume":"37","author":"H Huang","year":"2018","unstructured":"Huang H, Hu X, Zhao Y, Makkie M, Dong Q, Zhao S, Guo L, Liu T (2018) Modeling task fmri data via deep convolutional autoencoder. IEEE Trans Med Imaging 37(7):1551\u20131561","journal-title":"IEEE Trans Med Imaging"},{"key":"391_CR12","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1109\/MIPRO.2015.7160458","volume-title":"2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)","author":"A Jovi\u0107","year":"2015","unstructured":"Jovi\u0107 A, Brki\u0107 K, Bogunovi\u0107 N (2015) A\u00a0review of feature selection methods with applications. In: 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp 1200\u20131205"},{"key":"391_CR13","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1145\/1390156.1390224","volume-title":"Proceedings of the 25th International Conference on Machine Learning","author":"H Larochelle","year":"2008","unstructured":"Larochelle H, Bengio Y (2008) Classification using discriminative restricted boltzmann machines. In: Proceedings of the 25th International Conference on Machine Learning ICML \u201908. Association for Computing Machinery, New York, NY, USA, pp 536\u2013543"},{"key":"391_CR14","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1109\/ICDE.2018.00062","volume-title":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","author":"X Li","year":"2018","unstructured":"Li X, Zhao K, Cong G, Jensen CS, Wei W (2018) Deep representation learning for trajectory similarity computation. In: 2018 IEEE 34th International Conference on Data Engineering (ICDE), pp 617\u2013628"},{"key":"391_CR15","doi-asserted-by":"publisher","first-page":"1883","DOI":"10.1145\/3240508.3240656","volume-title":"Proceedings of the 26th ACM international conference on Multimedia","author":"B Liao","year":"2018","unstructured":"Liao B, Zhang J, Cai M, Tang S, Gao Y, Wu C, Yang S, Zhu W, Guo Y, Wu F (2018) Dest-resnet: a deep spatiotemporal residual network for hotspot traffic speed prediction. In: Proceedings of the 26th ACM international conference on Multimedia, pp 1883\u20131891"},{"issue":"1","key":"391_CR16","doi-asserted-by":"publisher","first-page":"119","DOI":"10.2298\/YJOR1101119N","volume":"21","author":"J Novakovi\u0107","year":"2016","unstructured":"Novakovi\u0107 J (2016) Toward optimal feature selection using ranking methods and classification algorithms. Yugosl J Oper Res 21(1):119\u2013135","journal-title":"Yugosl J Oper Res"},{"key":"391_CR17","series-title":"arXiv preprint arXiv:1612.02095","volume-title":"Extremeweather: a\u00a0large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events","author":"E Racah","year":"2016","unstructured":"Racah E, Beckham C, Maharaj T, Ebrahimi Kahou S, Pal C et al (2016) Extremeweather: a\u00a0large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events. arXiv preprint arXiv:1612.02095"},{"key":"391_CR18","first-page":"178","volume-title":"Proceedings of the 8th International Conference on Intelligent Data Engineering and Automated Learning","author":"N S\u00e1nchez-Maro\u00f1o","year":"2007","unstructured":"S\u00e1nchez-Maro\u00f1o N, Alonso-Betanzos A, Tombilla-Sanrom\u00e1n M (2007) Filter methods for feature selection: a\u00a0comparative study. In: Proceedings of the 8th International Conference on Intelligent Data Engineering and Automated Learning IDEAL\u201907. Springer, Berlin, Heidelberg, pp 178\u2013187"},{"key":"391_CR19","doi-asserted-by":"publisher","first-page":"816","DOI":"10.1109\/FTC.2016.7821697","volume-title":"2016 Future Technologies Conference (FTC)","author":"S Sarraf","year":"2016","unstructured":"Sarraf S, Tofighi G (2016) Deep learning-based pipeline to recognize alzheimer\u2019s disease using fmri data. In: 2016 Future Technologies Conference (FTC), pp 816\u2013820"},{"key":"391_CR20","series-title":"arXiv:1802.00002","volume-title":"Dxnat \u2013 deep neural networks for explaining non-recurring traffic congestion","author":"F Sun","year":"2018","unstructured":"Sun F, Dubey A, White J (2018) Dxnat \u2013 deep neural networks for explaining non-recurring traffic congestion. arXiv:1802.00002"},{"key":"391_CR21","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1007\/978-1-4612-4974-0_43","volume-title":"Manual of pharmacologic calculations","author":"RJ Tallarida","year":"1987","unstructured":"Tallarida RJ, Murray RB (1987) Chi-square test. In: Manual of pharmacologic calculations. Springer, New York, pp 140\u2013142"},{"issue":"1","key":"391_CR22","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/s00521-013-1368-0","volume":"24","author":"JR Vergara","year":"2014","unstructured":"Vergara JR, Est\u00e9vez PA (2014) A\u00a0review of feature selection methods based on mutual information. Neural Comput Appl 24(1):175\u2013186","journal-title":"Neural Comput Appl"},{"key":"391_CR23","series-title":"CoRR, abs\/1906.04928","volume-title":"Deep learning for spatio-temporal data mining: a survey","author":"S Wang","year":"2019","unstructured":"Wang S, Jiannong C, Yu PS (2019) Deep learning for spatio-temporal data mining: a survey. CoRR, abs\/1906.04928"},{"key":"391_CR24","first-page":"984","volume-title":"Hetero-ConvLSTM: a\u00a0deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data","author":"Z Yuan","year":"2018","unstructured":"Yuan Z, Zhou X, Yang T (2018) Hetero-ConvLSTM: a\u00a0deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data. Association for Computing Machinery, New York, NY, USA, pp 984\u2013992"},{"key":"391_CR25","first-page":"7","volume":"143","author":"A Zaytar","year":"2016","unstructured":"Zaytar A, El Amrani C (2016) Sequence to sequence weather forecasting with long short-term memory recurrent neural networks. Int J Comput Appl 143:7\u201311","journal-title":"Int J Comput Appl"},{"key":"391_CR26","first-page":"3655","volume-title":"Deeptravel: a\u00a0neural network based travel time estimation model with auxiliary supervision","author":"H Zhang","year":"2018","unstructured":"Zhang H, Hao W, Sun W, Baihua Z (2018) Deeptravel: a\u00a0neural network based travel time estimation model with auxiliary supervision, pp 3655\u20133661"},{"issue":"2","key":"391_CR27","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J\u00a0Royal Stat Soc Ser\u00a0B 67(2):301\u2013320","journal-title":"J Royal Stat Soc Ser B"}],"container-title":["Datenbank-Spektrum"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13222-021-00391-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13222-021-00391-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13222-021-00391-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,19]],"date-time":"2021-11-19T08:30:01Z","timestamp":1637310601000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13222-021-00391-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,20]]},"references-count":27,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["391"],"URL":"https:\/\/doi.org\/10.1007\/s13222-021-00391-x","relation":{},"ISSN":["1618-2162","1610-1995"],"issn-type":[{"value":"1618-2162","type":"print"},{"value":"1610-1995","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,20]]},"assertion":[{"value":"31 May 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 September 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 October 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}