Geoinformatica最新文献_第2页

A spatial dependency based reinforcement learning model for selecting features in spatial classification 基于空间依赖性的强化学习模型，用于在空间分类中选择特征

IF 2 4区计算机科学

Geoinformatica Pub Date : 2024-05-17 DOI: 10.1007/s10707-024-00523-x

Cheng Wei, Wenhao Yu

引用次数: 0

How opportunistic mobile monitoring can enhance air quality assessment? 机会性移动监测如何加强空气质量评估？

IF 2 4区计算机科学

Geoinformatica Pub Date : 2024-04-29 DOI: 10.1007/s10707-024-00516-w

Mohammad Abboud, Yehia Taher, Karine Zeitouni, Ana-Maria Olteanu-Raimond

{"title":"How opportunistic mobile monitoring can enhance air quality assessment?","authors":"Mohammad Abboud, Yehia Taher, Karine Zeitouni, Ana-Maria Olteanu-Raimond","doi":"10.1007/s10707-024-00516-w","DOIUrl":"https://doi.org/10.1007/s10707-024-00516-w","url":null,"abstract":"The deteriorating air quality in urban areas, particularly in developing countries, has led to increased attention being paid to the issue. Daily reports of air pollution are essential to effectively manage public health risks. Pollution estimation has become crucial to expanding spatial and temporal coverage and estimating pollution levels at different locations. The emergence of low-cost sensors has enabled high-resolution data collection, either in fixed or mobile settings, and various approaches have been proposed to estimate air pollution using this technology. The objective of this study is to enhance the data from fixed stations by incorporating opportunistic mobile monitoring (OMM) data. The main research question we are dealing with is: How can we augment fixed station data through OMM? In order to address the challenge of limited OMM data availability, we leverage existing data collected during periods when the pollution maps align with those observed by the fixed stations. By combining the fixed and mobile data, we apply interpolation techniques to produce more accurate pollution maps. The efficacy of our approach is validated through experiments conducted on a real-life dataset.","PeriodicalId":55109,"journal":{"name":"Geoinformatica","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140840735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Foresight plus: serverless spatio-temporal traffic forecasting 前瞻加：无服务器时空流量预测

IF 2 4区计算机科学

Geoinformatica Pub Date : 2024-04-26 DOI: 10.1007/s10707-024-00517-9

Joe Oakley, Chris Conlan, Gunduz Vehbi Demirci, Alexandros Sfyridis, Hakan Ferhatosmanoglu

{"title":"Foresight plus: serverless spatio-temporal traffic forecasting","authors":"Joe Oakley, Chris Conlan, Gunduz Vehbi Demirci, Alexandros Sfyridis, Hakan Ferhatosmanoglu","doi":"10.1007/s10707-024-00517-9","DOIUrl":"https://doi.org/10.1007/s10707-024-00517-9","url":null,"abstract":"Building a real-time spatio-temporal forecasting system is a challenging problem with many practical applications such as traffic and road network management. Most forecasting research focuses on achieving (often marginal) improvements in evaluation metrics such as MAE/MAPE on static benchmark datasets, with less attention paid to building practical pipelines which achieve timely and accurate forecasts when the network is under heavy load. Transport authorities also need to leverage dynamic data sources such as roadworks and vehicle-level flow data, while also supporting ad-hoc inference workloads at low cost. Our cloud-based forecasting solution Foresight, developed in collaboration with Transport for the West Midlands (TfWM), is able to ingest, aggregate and process streamed traffic data, enhanced with dynamic vehicle-level flow and urban event information, to produce regularly scheduled forecasts with high accuracy. In this work, we extend Foresight with several novel enhancements, into a new system which we term Foresight Plus. New features include an efficient method for extending the forecasting scale, enabling predictions further into the future. We also augment the inference architecture with a new, fully serverless design which offers a more cost-effective solution and which seamlessly handles sporadic inference workloads over multiple forecasting scales. We observe that Graph Neural Network (GNN) forecasting models are robust to extensions of the forecasting scale, achieving consistent performance up to 48 hours ahead. This is in contrast to the 1 hour forecasting periods popularly considered in this context. Further, our serverless inference solution is shown to be more cost-effective than provisioned alternatives in corresponding use-cases. We identify the optimal memory configuration of serverless resources to achieve an attractive cost-to-performance ratio.","PeriodicalId":55109,"journal":{"name":"Geoinformatica","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140799495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transfer-learning-based representation learning for trajectory similarity search 基于迁移学习的轨迹相似性搜索表征学习

IF 2 4区计算机科学

Geoinformatica Pub Date : 2024-04-13 DOI: 10.1007/s10707-024-00515-x

Danling Lai, Jianfeng Qu, Yu Sang, Xi Chen

{"title":"Transfer-learning-based representation learning for trajectory similarity search","authors":"Danling Lai, Jianfeng Qu, Yu Sang, Xi Chen","doi":"10.1007/s10707-024-00515-x","DOIUrl":"https://doi.org/10.1007/s10707-024-00515-x","url":null,"abstract":"Trajectory similarity search is one of the most fundamental tasks in spatial-temporal data analysis. Classical methods are based on predefined trajectory similarity measures, consuming high time and space costs. To accelerate similarity computation, some deep metric learning methods have recently been proposed to approximate predefined measures based on the learned representation of trajectories. However, instead of predefined measures, real applications may require personalized measures, which cannot be effectively learned by existing models due to insufficient labels. Thus, this paper proposes a transfer-learning-based model FTL-Traj, which addresses this problem by effectively transferring knowledge from several existing measures as source measures. Particularly, a ProbSparse self-attention-based GRU unit is designed to extract the spatial and structural information of each trajectory. Confronted with diverse source measures, the priority modeling assists the model for the rational ensemble. Then, sparse labels are enriched with rank knowledge and collaboration knowledge via transfer learning. Extensive experiments on two real-world datasets demonstrate the superiority of our model.","PeriodicalId":55109,"journal":{"name":"Geoinformatica","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140565932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A survey on the computation of representative trajectories 代表性轨迹计算调查

IF 2 4区计算机科学

Geoinformatica Pub Date : 2024-04-02 DOI: 10.1007/s10707-024-00514-y

引用次数: 0

SkyEye: continuous processing of moving spatial-keyword queries over moving objects 天眼：连续处理移动物体上的移动空间关键词查询

IF 2 4区计算机科学

Geoinformatica Pub Date : 2024-03-20 DOI: 10.1007/s10707-024-00512-0

Mariam Orabi, Zaher Al Aghbari, Ibrahim Kamel

引用次数: 0

Cross-domain NER in the data-poor scenarios for human mobility knowledge 在数据匮乏的情况下进行跨域 NER，以获取人类流动知识

IF 2 4区计算机科学

Geoinformatica Pub Date : 2024-03-05 DOI: 10.1007/s10707-024-00513-z

Yutong Jiang, Fusheng Jin, Mengnan Chen, Guoming Liu, He Pang, Ye Yuan

{"title":"Cross-domain NER in the data-poor scenarios for human mobility knowledge","authors":"Yutong Jiang, Fusheng Jin, Mengnan Chen, Guoming Liu, He Pang, Ye Yuan","doi":"10.1007/s10707-024-00513-z","DOIUrl":"https://doi.org/10.1007/s10707-024-00513-z","url":null,"abstract":"In recent years, the exploration of knowledge in large-scale human mobility has gained significant attention. In order to achieve a semantic understanding of human behavior and uncover patterns in large-scale human mobility, Named Entity Recognition (NER) is a crucial technology. The rapid advancements in IoT and CPS technologies have led to the collection of massive human mobility data from various sources. Therefore, there’s a need for Cross-domain NER which can transfer entity information from the source domain to automatically identify and classify entities in different target domain texts. In the situation of the data-poor, how could we transfer human mobility knowledge over time and space is particularly significant, therefore this paper proposes an Adaptive Text Sequence Enhancement Module (at-SAM) to help the model enhance the association between entities in sentences in the data-poor target domains. This paper also proposes a Predicted Label-Guided Dual Sequence Aware Information Module (Dual-SAM) to improve the transferability of label information. Experiments were conducted in domains that contain hidden knowledge about human mobility, the results show that this method can transfer task knowledge between multiple different domains in the data-poor scenarios and achieve SOTA performance.","PeriodicalId":55109,"journal":{"name":"Geoinformatica","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140032631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DGFormer: a physics-guided station level weather forecasting model with dynamic spatial-temporal graph neural network DGFormer：采用动态时空图神经网络的物理学指导台站级天气预报模型

IF 2 4区计算机科学

Geoinformatica Pub Date : 2024-02-16 DOI: 10.1007/s10707-024-00511-1

Zhewen Xu, Xiaohui Wei, Jieyun Hao, Junze Han, Hongliang Li, Changzheng Liu, Zijian Li, Dongyuan Tian, Nong Zhang

{"title":"DGFormer: a physics-guided station level weather forecasting model with dynamic spatial-temporal graph neural network","authors":"Zhewen Xu, Xiaohui Wei, Jieyun Hao, Junze Han, Hongliang Li, Changzheng Liu, Zijian Li, Dongyuan Tian, Nong Zhang","doi":"10.1007/s10707-024-00511-1","DOIUrl":"https://doi.org/10.1007/s10707-024-00511-1","url":null,"abstract":"In recent years, there has been an increased interest in understanding and predicting the weather using weather station data with Spatial-Temporal Graph Neural Networks (STGNN). However, it has large prediction errors as a result of the inherent non-linearities and the influence of dynamic spatio-temporal auto-correlation. Using a continuously-varying graph topology chronologically, while embedding domain knowledge to enforce validity, can effectively resolve the issue, but the implementation of such concept constitutes an interdisciplinary challenge for researchers. A Dynamic Graph Former (DGFormer) model is proposed to address this challenge. It combines a topology learner through a deep generative layer with domain knowledge enhancement inserted into the STGNN structure, where the derived physics-guided method allows for an efficient integration with the earth system. For capture of the optimal topology, we merge a node-embedding-based similarity metric learning and the superposition principle as physical assistants into the dynamic graph module. We evaluate our model with a real-world weather dataset on short-term (12 hours) and medium-range (360 hours) prediction tasks. DGFormer achieves outstanding performance with obvious improvements by up to 34.84% at short-term prediction and by up to 23.25% at medium-range prediction compared with the state-of-the-art methods. We also conducted detailed analyses for cities in three regions and visualized the dynamic graph, revealing the characteristics, advantages, and graph visualization of our model.","PeriodicalId":55109,"journal":{"name":"Geoinformatica","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139753342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient spatial queries over complex polygons with hybrid representations 使用混合表示法对复杂多边形进行高效空间查询

IF 2 4区计算机科学

Geoinformatica Pub Date : 2023-12-27 DOI: 10.1007/s10707-023-00508-2

Dejun Teng, Furqan Baig, Zhaohui Peng, Jun Kong, Fusheng Wang

引用次数: 0

Meta-learning based passenger flow prediction for newly-operated stations 基于元学习的新站客流预测

IF 2 4区计算机科学

Geoinformatica Pub Date : 2023-11-29 DOI: 10.1007/s10707-023-00510-8

Kuo Han, Jinlei Zhang, Xiaopeng Tian, Songsong Li, Chunqi Zhu

{"title":"Meta-learning based passenger flow prediction for newly-operated stations","authors":"Kuo Han, Jinlei Zhang, Xiaopeng Tian, Songsong Li, Chunqi Zhu","doi":"10.1007/s10707-023-00510-8","DOIUrl":"https://doi.org/10.1007/s10707-023-00510-8","url":null,"abstract":"By tapping into the human mobility of the urban rail transit (URT) network to understand the travel demands and characteristics of passengers in the urban space, URT managers are able to obtain more support for decision-making to improve the effectiveness of operation and management, the travel experience of passengers, as well as public safety. However, not all URT networks have sufficient human mobility data (e.g., newly-operated URT networks). It is necessary to provide data support for mining human mobility in data-poor URT networks. Therefore, we propose a method called Meta Long Short-Term Memory Network (Meta-LSTM) for passenger flow prediction at URT stations to provide data support for networks that lack data. The Meta-LSTM is to construct a framework that increases the generalization ability of a long short-term memory network (LSTM) to various passenger flow characteristics by learning passenger flow characteristics from multiple data-rich stations and then applying the learned parameter to data-scarce stations by parameter initialization. The Meta-LSTM is applied to the URT network of Nanning, Hangzhou, and Beijing, China. The experiments on three real-world URT networks demonstrate the effectiveness of our proposed Meta-LSTM over several competitive baseline models. Results also show that our proposed Meta-LSTM has a good generalization ability to various passenger flow characteristics, which can provide a reference for passenger flow prediction in the stations with limited data.","PeriodicalId":55109,"journal":{"name":"Geoinformatica","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138516628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0