{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T06:43:48Z","timestamp":1655102628088},"reference-count":0,"publisher":"Copernicus GmbH","license":[{"start":{"date-parts":[[2022,6,11]],"date-time":"2022-06-11T00:00:00Z","timestamp":1654905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AGILE GIScience Ser."],"abstract":"<jats:p>Abstract. Regionalization is the process of aggregating contiguous spatial units to form areas that are homogeneous with respect to one or a set of variables. It is useful when studying spatial phenomena or when designing region-based policies, as it allows to unravel the latent spatial structure of a dataset. However, this task is challenging when small-scale fluctuations in the data interfere with the phenomenon of interest. In such circumstances, regionalization techniques are prone to overfitting small-scale fluctuations, and producing erratic regions. This paper presents a regionalization method robust to small-scale variations that is particularly relevant when handling demographic data. Fluctuations are filtered out using a weighted spatial average before applying agglomerative clustering. The method is tested against a conventional agglomerative clustering approach on a fine-resolution demographic dataset, for a set of indicators quantifying: the ability to identify large-scale spatial patterns, the homogeneity of the regions produced, and the spatial regularity of these regions. These indicators have been computed for the two methods for a number of clusters ranging from 2 to 101, and results show that the proposed approach performs better than conventional agglomerative clustering more than 90% of the time at identifying large-scale patterns, and produces more regular regions 96% of the time.\n                    <\/jats:p>","DOI":"10.5194\/agile-giss-3-61-2022","type":"journal-article","created":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T06:01:31Z","timestamp":1655100091000},"page":"1-6","source":"Crossref","is-referenced-by-count":0,"title":["A regionalization method filtering out small-scale spatial fluctuations"],"prefix":"10.5194","volume":"3","author":[{"given":"Lucas","family":"Spierenburg","sequence":"first","affiliation":[]},{"given":"Sander","family":"van Cranenburgh","sequence":"additional","affiliation":[]},{"given":"Oded","family":"Cats","sequence":"additional","affiliation":[]}],"member":"3145","published-online":{"date-parts":[[2022,6,11]]},"container-title":["AGILE: GIScience Series"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/agile-giss.copernicus.org\/articles\/3\/61\/2022\/agile-giss-3-61-2022.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T06:01:32Z","timestamp":1655100092000},"score":1,"resource":{"primary":{"URL":"https:\/\/agile-giss.copernicus.org\/articles\/3\/61\/2022\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,11]]},"references-count":0,"URL":"https:\/\/doi.org\/10.5194\/agile-giss-3-61-2022","relation":{},"ISSN":["2700-8150"],"issn-type":[{"value":"2700-8150","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,11]]}}}