{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T16:02:17Z","timestamp":1743004937620,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819729654"},{"type":"electronic","value":"9789819729661"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-97-2966-1_10","type":"book-chapter","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T12:01:57Z","timestamp":1714392117000},"page":"120-131","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["RCPM_RLM: A Regional Co-location Pattern Mining Method Based on Representation Learning Model"],"prefix":"10.1007","author":[{"given":"Yi","family":"Cai","sequence":"first","affiliation":[]},{"given":"Lizhen","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Lihua","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,30]]},"reference":[{"key":"10_CR1","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1111\/gean.12274","volume":"54","author":"H Zhang","year":"2022","unstructured":"Zhang, H., Zhou, X., et al.: Detecting colocation flow patterns in the geographical interaction data. Geogr. Anal. 54, 84\u2013103 (2022)","journal-title":"Geogr. Anal."},{"key":"10_CR2","first-page":"1","volume":"31","author":"S Baride","year":"2022","unstructured":"Baride, S., Saxena, A.S., Goyal, V.: Efficiently mining colocation patterns for range query. Big Data Res. 31, 1\u201313 (2022)","journal-title":"Big Data Res."},{"unstructured":"Roya, H., Ali, A., Sayeh, B.: An event-based model and a map visualization approach for spatiotemporal association relations discovery of diseases diffusion. Sustain. Cities Soc. (2022)","key":"10_CR3"},{"key":"10_CR4","doi-asserted-by":"publisher","first-page":"102149","DOI":"10.1016\/j.inffus.2023.102149","volume":"104","author":"Y Wang","year":"2024","unstructured":"Wang, Y., Zhu, D.: A hypergraph-based hybrid graph convolutional network for intracity human activity intensity prediction and geographic relationship interpretation. Inf. Fusion 104, 102149 (2024)","journal-title":"Inf. Fusion"},{"issue":"11","key":"10_CR5","doi-asserted-by":"publisher","first-page":"205","DOI":"10.21105\/joss.00205","volume":"2","author":"L McInnes","year":"2017","unstructured":"McInnes, L., et al.: HDBSCAN: hierarchical density based clustering. J. Open Source Softw. 2(11), 205 (2017)","journal-title":"J. Open Source Softw."},{"key":"10_CR6","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.ins.2017.02.040","volume":"396","author":"X Yao","year":"2017","unstructured":"Yao, X., Chen, L., Peng, L., Chi, T.: A co-location pattern-mining algorithm with a density-weighted distance thresholding consideration. Inf. Sci. 396, 144\u2013161 (2017)","journal-title":"Inf. Sci."},{"issue":"1","key":"10_CR7","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1111\/gean.12155","volume":"51","author":"J Cai","year":"2019","unstructured":"Cai, J., Deng, M., et al.: Nonparametric significance test for discovery of network-constrained spatial co-location patterns. Geogr. Anal. 51(1), 3\u201322 (2019)","journal-title":"Geogr. Anal."},{"issue":"3","key":"10_CR8","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1080\/13658816.2018.1550784","volume":"33","author":"M Zhou","year":"2019","unstructured":"Zhou, M., Ai, T., et al.: A visualization approach for discovering colocation patterns. Int. J. Geogr. Inf. Sci. 33(3), 567\u2013592 (2019)","journal-title":"Int. J. Geogr. Inf. Sci."},{"doi-asserted-by":"crossref","unstructured":"\u848b\u5e0c\u6587, \u738b\u4e3d\u73cd, \u5468\u4e3d\u534e. \u57fa\u4e8e\u6a21\u7cca\u5bc6\u5ea6\u5cf0\u503c\u805a\u7c7b\u7684\u533a\u57df\u540c\u4f4d\u6a21\u5f0f\u5e76\u884c\u6316\u6398\u7b97\u6cd5. \u4e2d\u56fd\u79d1\u5b66: \u4fe1\u606f\u79d1\u5b66 53(7), 1281\u20131298 (2023)","key":"10_CR9","DOI":"10.37155\/2717-5170-0504-18"},{"key":"10_CR10","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.patcog.2018.06.016","volume":"84","author":"X Dong","year":"2018","unstructured":"Dong, X., Gong, Y., Cao, L.: F-NSP+: a fast negative sequential patterns mining method with self-adaptive data storage. Pattern Recogn. 84, 13\u201327 (2018)","journal-title":"Pattern Recogn."},{"issue":"2","key":"10_CR11","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1109\/TNNLS.2020.3041732","volume":"34","author":"X Gao","year":"2023","unstructured":"Gao, X., Gong, Y., Xu, T., Lu, J., Zhao, Y., Dong, X.: Towards to better structure and constraint to mine negative sequential patterns. IEEE Trans. Neural Netw. Learn. Syst. 34(2), 571\u2013585 (2023)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"4","key":"10_CR12","doi-asserted-by":"publisher","first-page":"1864","DOI":"10.1109\/TNNLS.2021.3063162","volume":"34","author":"P Qiu","year":"2023","unstructured":"Qiu, P., Gong, Y., Zhao, Y., Cao, L., Zhang, C., Dong, X.: An efficient method for modeling non-occurring behaviors by negative sequential patterns with loose constraints. IEEE Trans. Neural Netw. Learn. Syst. 34(4), 1864\u20131878 (2023)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"5","key":"10_CR13","doi-asserted-by":"publisher","first-page":"2084","DOI":"10.1109\/TCYB.2018.2869907","volume":"50","author":"X Dong","year":"2020","unstructured":"Dong, X., Gong, Y., Cao, L.: E-RNSP: an efficient method for mining repetition negative sequential patterns. IEEE Trans. Cybern. 50(5), 2084\u20132096 (2020)","journal-title":"IEEE Trans. Cybern."},{"issue":"9","key":"10_CR14","doi-asserted-by":"publisher","first-page":"2764","DOI":"10.1109\/TNNLS.2018.2886199","volume":"30","author":"X Dong","year":"2019","unstructured":"Dong, X., Qiu, P., Lv, J., Cao, L., Xu, T.: Mining top-k useful negative sequential patterns via learning. IEEE Trans. Neural Netw. Learn. Syst. 30(9), 2764\u20132778 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"doi-asserted-by":"crossref","unstructured":"Dong, X., Zheng, Z., Cao, L., et al.: e-NSP: efficient negative sequential pattern mining based on identified positive patterns without database rescanning. In: CIKM, pp. 825\u2013830 (2011)","key":"10_CR15","DOI":"10.1145\/2063576.2063695"},{"doi-asserted-by":"crossref","unstructured":"Wang, D., Wang, L., Jiang, X., Yang, P.: RCPM_CFI: a regional core pattern mining method based on core feature influence. Inf. Sci. 119895 (2023)","key":"10_CR16","DOI":"10.1016\/j.ins.2023.119895"},{"unstructured":"\u5218\u65b0\u658c, \u738b\u4e3d\u73cd, \u5468\u4e3d\u534e. MLCPM-UC: \u4e00\u79cd\u57fa\u4e8e\u6a21\u5f0f\u5b9e\u4f8b\u5206\u5e03\u5747\u5300\u7cfb\u6570\u7684\u591a\u7ea7co-location\u6a21\u5f0f\u6316\u6398\u7b97\u6cd5. \u8ba1\u7b97\u673a\u79d1\u5b66  48(11), 208\u2013218 (2021)","key":"10_CR17"},{"doi-asserted-by":"crossref","unstructured":"Celik, M., Kang, J., Shekhar, S.: Zonal co-location pattern discovery with dynamic parameters. In: Proceedings of the 7th IEEE International Conference on Data Mining, pp. 28\u201331 (2007)","key":"10_CR18","DOI":"10.1109\/ICDM.2007.102"},{"unstructured":"Mikolov, T., Chen, K., Corrado, G.S., Dean, J.A.: Computing numeric representations of words in a high-dimensional space. Google Patents (2015)","key":"10_CR19"},{"unstructured":"Rong, X.: word2vec parameter learning explained. arXiv preprint arXiv:1411.2783 (2014)","key":"10_CR20"},{"key":"10_CR21","doi-asserted-by":"publisher","first-page":"101442","DOI":"10.1016\/j.compenvurbsys.2019.101442","volume":"80","author":"H Sheng","year":"2020","unstructured":"Sheng, H., Zhan, J., Liang, W., et al.: A framework for extracting urban functional regions based on multiprototype word embeddings using points-of-interest data. Comput. Environ. Urban Syst. 80, 101442 (2020)","journal-title":"Comput. Environ. Urban Syst."},{"issue":"10","key":"10_CR22","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1109\/TKDE.2006.150","volume":"18","author":"J Yoo","year":"2006","unstructured":"Yoo, J., Shekhar, S.: A joinless approach for mining spatial colocation patterns. IEEE Trans. Knowl. Data Eng. 18(10), 1323\u20131337 (2006)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"doi-asserted-by":"crossref","unstructured":"Yang, J., Cao, J., He, R., et al.: A unified clustering approach for identifying functional zones in suburban and urban areas. In: IEEE INFCOM 2018-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 94\u201399 (2018)","key":"10_CR23","DOI":"10.1109\/INFCOMW.2018.8406847"},{"issue":"4","key":"10_CR24","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1080\/13658816.2016.1244608","volume":"31","author":"Y Yao","year":"2017","unstructured":"Yao, Y., Li, X., Liu, X., et al.: Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec mode. Int. J. Geogr. Inf. Sci. 31(4), 825\u2013848 (2017)","journal-title":"Int. J. Geogr. Inf. Sci."},{"doi-asserted-by":"crossref","unstructured":"Yan, B., Janowicz, K., Mai, G., et al.: From ITDL to Place2vec: reasoning about place type similarity and relatedness by learning embeddings from augmented spatial contexts. In: Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, vol. 35, pp. 1\u201310. ACM (2017)","key":"10_CR25","DOI":"10.1145\/3139958.3140054"}],"container-title":["Lecture Notes in Computer Science","Spatial Data and Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-2966-1_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T12:03:57Z","timestamp":1714392237000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-2966-1_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819729654","9789819729661"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-2966-1_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"30 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"SpatialDI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Spatial Data and Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanjing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 April 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 April 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"spatialdi2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/spatialdi2024.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}