关于延期举办SpatialDI 2022的通知
关于延期举办SpatialDI 2022的通知 致尊敬的各位参会代表: 近期全国多地出现新冠疫情本土确诊病例,诸多省市(区)陆续出台了最新的疫情防控政策和严格的旅行禁令。为切实保障参会代表人身安全,配合武汉相关防疫部门贯彻落实疫情防控工作部署,经大会组委会和分会主席团审慎共同研究决定,将原定于2022年4月14-16日在武汉举办的“第三届中国空间数据智能学术会议(SpatialDI 2022)” ,延期至2022年6月8-10日举行,相关参会形式和内容将另行通知。 已经完成注册缴费的代表,请您保留好注册及缴费信息,相关信息对延期后会议依然有效。组委会将按照相关规定和财务管理流程妥善处理相关变更事宜,因会议延期给参会代表带来不便,我们深表歉意,敬请谅解! 我们将充分做好本次大会延期召开的各项工作,期待在疫情得到有效控制之后,各位专家、学者继续支持本次会议的举办。 会务组联系人: 范明 15927333030 李应争 13349838503 SpatialDI 2022会务组 2022年4月6日
Estimating urban functional distributions with semantics preserved POI embedding
We present a novel approach for estimating the proportional distributions of function types (i.e. functional distributions) in an urban area through learning semantics preserved embeddings of points-of-interest (POIs). Specifically, we represent POIs as low-dimensional vectors to capture (1) the spatial co-occurrence patterns of POIs and (2) the semantics conveyed by the POI hierarchical categories (i.e. categorical semantics). The proposed approach utilizes spatially explicit random walks in a POI network to learn spatial co-occurrence patterns, and a manifold learning algorithm to capture categorical semantics. The learned POI vector embeddings are then aggregated to generate regional embeddings with long short-term memory (LSTM) and attention mechanisms, to take account of the different levels of importance among the POIs in a region. Finally, a multilayer perceptron (MLP) maps regional embeddings to functional distributions. A case study in Xiamen Island, China implements and evaluates the proposed approach. The results indicate that our approach outperforms several competitive baseline models in all evaluation measures, and yields a relatively high consistency between the estimation and ground truth. In addition, a comprehensive error analysis unveils several intrinsic limitations of POI data for this task, e.g. ambiguous linkage between POIs and functions.