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Geo-propagation from Incomplete Spatial Distribution Data: A Case Study of House Price Estimation

Zhu, D; Cheng, T; Liu, Y; (2019) Geo-propagation from Incomplete Spatial Distribution Data: A Case Study of House Price Estimation. In: Robson, C and Fairbairn, D, (eds.) Proceedings of the 27th Geographical Information Science Research International Conference UK and the Republic of Ireland (GISRUK 2019). GISRUK: Newcastle, UK. Green open access

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Abstract

Despite the emergence of big data that contains plentiful spatiotemporal information, researchers are still facing the challenge that the acquired data after time slicing are insufficient to characterize the real heterogeneous spatial pattern. The problem becomes especially important in areas such as epidemiology and economy where the fine-resolution data are under intense demand. This paper introduces a novel approach named geo-propagation to propagate sampled data to unsampled locations incorporating semi-supervised learning under the graph context. A case study of estimating property price is shown to demonstrate the effectiveness of our proposed method.

Type: Proceedings paper
Title: Geo-propagation from Incomplete Spatial Distribution Data: A Case Study of House Price Estimation
Event: GISRUK 2019, 23-26 April 2019, Newcastle, UK
Location: Newcastle
Open access status: An open access version is available from UCL Discovery
Publisher version: file://ad.ucl.ac.uk/homeo/ucyllno/Downloads/GISRUK...
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Spatial estimation, semi-supervised learning, graph, propagation, house price
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10085157
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