Proceedings of the 18th International Symposium on Spatial and Temporal Data最新文献_第2页

A Design of Activity-Based Mobility Intervention 基于活动的流动性干预设计

Proceedings of the 18th International Symposium on Spatial and Temporal Data Pub Date : 2023-08-23 DOI: 10.1145/3609956.3609970

Joon-Seok Kim, Gautam Thakur, S. C. Christopher

{"title":"A Design of Activity-Based Mobility Intervention","authors":"Joon-Seok Kim, Gautam Thakur, S. C. Christopher","doi":"10.1145/3609956.3609970","DOIUrl":"https://doi.org/10.1145/3609956.3609970","url":null,"abstract":"Human mobility influences our society and vice versa. During the COVID-19 pandemic, non-pharmaceutical intervention that alters activity-based mobility such as work-from-home greatly impacted human mobility patterns. Many studies on developing mitigation strategies have employed or implemented their own mobility intervention within their model assumption. For fair evaluation between intervention strategies across models, it is significant to set up compatible experimental environments. However, it is difficult to apply the identical intervention to different kinds of models and compare their effectiveness because each model might have different assumptions, capabilities, and implementations. Even if one can apply intervention to heterogeneous models, it may produce undesirable artifacts due to difference of models and integration with intervention. Therefore, minimizing undesirable artifacts and facilitating intervention experiments across heterogeneous models are substantial. Taking this into account, this paper investigates a design of activity-based mobility intervention (ABMI). We define ABMI together with related concepts and develop an extensible data model and schema of ABMI based on the 5W1H method that can be used in different models. As a case study, we apply the ABMI model to a micro-simulation to demonstrate the usability of the proposed model. We expect that standardized ABMI and interfaces may help to streamline development and experiments of intervention strategies across heterogeneous models.","PeriodicalId":274777,"journal":{"name":"Proceedings of the 18th International Symposium on Spatial and Temporal Data","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124589430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards a fixed-gear AIS trajectory differentiation 向定齿轮AIS轨迹分化

Proceedings of the 18th International Symposium on Spatial and Temporal Data Pub Date : 2023-08-23 DOI: 10.1145/3609956.3609972

Mirjam Bayer, Daniyal Kazempour, Peer Kröger

{"title":"Towards a fixed-gear AIS trajectory differentiation","authors":"Mirjam Bayer, Daniyal Kazempour, Peer Kröger","doi":"10.1145/3609956.3609972","DOIUrl":"https://doi.org/10.1145/3609956.3609972","url":null,"abstract":"The increasing digital traces of fishing fleets nowadays available allow for automatized observation of the oceans, a vulnerable space which could hardly be monitored or governed previously. Data streams from satellite base communication systems are being used for a variety of applications such as collision avoidance, route optimization, and monitoring of illegal activities. Classification of fishing vessels according to the specific fishing method is a developing branch towards assessing the compliance of fishing regulations. It is not feasible to verify reported fishing efforts, fishing quota or even fishing methods at sea via manual inspections. Classification of the fishing trajectories into trawlers, long-liners, pure-seine, and fixed-gear are well researched. However, the distinction between different fixed-gear fishing methods has not been studied so far. Therefore, this work proposes the vision to distinguish the previously undifferentiated fixed-gear fishing trajectories. We outline our vision, discuss the challenges of exploiting the small differences in the trajectories, and potential approaches towards realizing this vision.","PeriodicalId":274777,"journal":{"name":"Proceedings of the 18th International Symposium on Spatial and Temporal Data","volume":"213 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131725415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scalable Overlay Operations over DCEL Polygon Layers 可扩展的覆盖操作在DCEL多边形层

Proceedings of the 18th International Symposium on Spatial and Temporal Data Pub Date : 2023-08-23 DOI: 10.1145/3609956.3609964

Andres Calderon-Romero, V. Tsotras, A. Magdy

引用次数: 0

Highway Systems: How Good are They, Really? 高速公路系统到底有多好?

Proceedings of the 18th International Symposium on Spatial and Temporal Data Pub Date : 2023-08-23 DOI: 10.1145/3609956.3609963

Theodoros Chondrogiannis, Michael Grossniklaus

引用次数: 0

RouteDOC: Routing with Distance, Origin and Category Constraints (Demonstration Paper) RouteDOC:具有距离、原产地和类别约束的路由(演示文件)

Proceedings of the 18th International Symposium on Spatial and Temporal Data Pub Date : 2023-08-23 DOI: 10.1145/3609956.3609977

Thomas Frohwein, Zachary Garwood, Dylan Hampton, Kevin Knack, Nate Schenck, Britney Yu, Joe Zuber, Goce Trajcevski, Xu Teng, Andreas Züfle

{"title":"RouteDOC: Routing with Distance, Origin and Category Constraints (Demonstration Paper)","authors":"Thomas Frohwein, Zachary Garwood, Dylan Hampton, Kevin Knack, Nate Schenck, Britney Yu, Joe Zuber, Goce Trajcevski, Xu Teng, Andreas Züfle","doi":"10.1145/3609956.3609977","DOIUrl":"https://doi.org/10.1145/3609956.3609977","url":null,"abstract":"Route planning based on user’s preferences and Points of Interests (POIs) is one of the most popular applications of Location-Based Services (LBS). Variants of route planning consider distance constraints (e.g., the maximum length of the route), origin constraints (e.g., a set of possible starting locations of the route), and category constraints (e.g., a multiset of POI categories that the route must visit). However, the problem of deciding whether a route exists that visits all required POI categories under the distance constraint is known to be NP-hard. Assuming P ≠ NP, this means that there is no efficient (polynomial time) solution to find such paths. Recently, approximate algorithms have been proposed for searching for such a path. This demonstration leverages several of these algorithms to provide a web-based system with a graphical user interface (UI) which allows the users to find a path that: (a) satisfies a distance limit; (b) generates a route to visit a list of POIs, based on the user’s preferred categories; (c) provides a set of hotels (as possible starting locations of the path). If the approximate search algorithms are able to find such a path, it will be displayed on a Mapbox-based map interface that shows: (1) all POIs on a path and (2) alternative paths if any were found. The system then allows a user to explore the returned paths, select a path, or refine their constraints. Moreover, the system allows the users to select which approximate algorithm they would prefer to execute.","PeriodicalId":274777,"journal":{"name":"Proceedings of the 18th International Symposium on Spatial and Temporal Data","volume":"14 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132737759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unveiling the Dynamic Interactions between Spatial Objects: A Graph Learning Approach with Evolving Constraints 揭示空间对象之间的动态相互作用:一种具有演化约束的图学习方法

Proceedings of the 18th International Symposium on Spatial and Temporal Data Pub Date : 2023-08-23 DOI: 10.1145/3609956.3609965

Daniel Glake, Ulfia A. Lenfers, T. Clemen, N. Ritter

{"title":"Unveiling the Dynamic Interactions between Spatial Objects: A Graph Learning Approach with Evolving Constraints","authors":"Daniel Glake, Ulfia A. Lenfers, T. Clemen, N. Ritter","doi":"10.1145/3609956.3609965","DOIUrl":"https://doi.org/10.1145/3609956.3609965","url":null,"abstract":"Discovering time-aware interactions among spatial objects is essential for various urban applications, such as offline advertising and public transport planning. Although previous studies have focused on identifying static relationships among spatial objects, little attention has been given to investigating dynamic location interactions. However, the availability of urban data through human activity creates new opportunities to understand the evolving relationships between connected objects. Therefore, we introduce a new problem of determining multiple interactions among spatial objects to address the challenge of integrating dynamic and spatial impact under interacting sparsity constraints. To tackle this problem, we propose a graph learning solution that leverages an Evolving Graph Neural Network (EGNN) consisting of two collaborative components: a Cross Spatial-Interaction Propagation (CSIP) and an Evolving Self-Supervised Learning (ESSL) module. CSIP enables aggregation within- and propagation across time segments to capture evolving context within a spatial scope from the perspective of message passing between placements. ESSL employs time-aware learning with global and local loss reduction and introduces an additional evolving constraint to consider sparsity of interactions in spatial representation learning. Experiments on two real-world datasets demonstrate the superiority of our approach over several state-of-the-art methods.","PeriodicalId":274777,"journal":{"name":"Proceedings of the 18th International Symposium on Spatial and Temporal Data","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128562176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scalable Spatial Analytics and In Situ Query Processing in DaskDB DaskDB中的可扩展空间分析和原位查询处理

Proceedings of the 18th International Symposium on Spatial and Temporal Data Pub Date : 2023-08-23 DOI: 10.1145/3609956.3609978

Suvam Kumar Das, Ronnit Peter, S. Ray

{"title":"Scalable Spatial Analytics and In Situ Query Processing in DaskDB","authors":"Suvam Kumar Das, Ronnit Peter, S. Ray","doi":"10.1145/3609956.3609978","DOIUrl":"https://doi.org/10.1145/3609956.3609978","url":null,"abstract":"Vast amounts of data are stored in raw data files. Data scientists and practitioners typically use data science frameworks for data analysis on raw data. Among them, Python Pandas library is one of the most popular language-based frameworks. On the other hand, relational databases (RDBMSs) are still widely used for SQL query execution. Before querying, raw data must be loaded into RDBMSs through an ETL process. Conversely, data stored in RDBMSs may need to be exported out or moved into a suitable format to perform complex data analysis. This movement of data adversely affects the time-to-insight. Recently a scalable system, called DaskDB, was introduced, which supports unified data analytics and in situ SQL query processing without requiring any data movement. It supports invoking existing Python API’s as User-Defined Functions (UDF) as a part of SQL queries, so they can be easily integrated with most of the existing Python applications. Due to the importance of supporting spatial analytics and spatial SQL queries, we have extended DaskDB to support spatial functionalities. In this paper, we present our enhanced DaskDB system. With two real-world spatial datasets, we demonstrate the scalability of DaskDB’s spatial features.","PeriodicalId":274777,"journal":{"name":"Proceedings of the 18th International Symposium on Spatial and Temporal Data","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116990360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

NALSD: A Natural Language Interface for Spatial Databases NALSD:空间数据库的自然语言接口

Proceedings of the 18th International Symposium on Spatial and Temporal Data Pub Date : 2023-08-23 DOI: 10.1145/3609956.3609974

Mengyi Liu, Xieyang Wang, Jianqiu Xu

引用次数: 0

Interactive Detection and Visualization of Ocean Carbon Regimes 海洋碳状态的交互检测与可视化

Proceedings of the 18th International Symposium on Spatial and Temporal Data Pub Date : 2023-08-23 DOI: 10.1145/3609956.3609973

Sweety Mohanty, Daniyal Kazempour, L. Patara, Peer Kröger

引用次数: 0

Social Community Recommendation based on Large-scale Semantic Trajectory Analysis Using Deep Learning 基于深度学习的大规模语义轨迹分析的社会社区推荐

Proceedings of the 18th International Symposium on Spatial and Temporal Data Pub Date : 2023-08-23 DOI: 10.1145/3609956.3609957

Chao Cai, Wei Jiang, Dan Lin

{"title":"Social Community Recommendation based on Large-scale Semantic Trajectory Analysis Using Deep Learning","authors":"Chao Cai, Wei Jiang, Dan Lin","doi":"10.1145/3609956.3609957","DOIUrl":"https://doi.org/10.1145/3609956.3609957","url":null,"abstract":"The widespread use of smart mobile devices has resulted in a massive accumulation of trajectory data by service providers. The analysis of human trajectories, particularly semantic location information, has opened up avenues for discovering common social behavior and enhancing social connections, leading to a range of applications such as friend recommendations and product suggestions. However, the exponential growth of trajectory information generated every day presents significant challenges for existing trajectory analysis algorithms, which are no longer capable of delivering timely analysis results. To address this issue, we propose a highly efficient algorithm that can recommend social communities for new users in real time by leveraging knowledge gained from large-scale semantic trajectories. Specifically, we develop a novel two-branch deep neural network model that extracts semantic meanings at different levels of granularity from human trajectories and uncovers the hidden relationship between trajectories and social communities. We then utilize this model to perform instant social community recommendations. Our experimental results have demonstrated that our approach is not only significantly faster than traditional trajectory analysis algorithms in terms of social community recommendation, but also preserves high prediction accuracy with F1-score above 97%.","PeriodicalId":274777,"journal":{"name":"Proceedings of the 18th International Symposium on Spatial and Temporal Data","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123480078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0