Proceedings of the 28th International Conference on Advances in Geographic Information Systems最新文献_第8页

A Proposal of Latent Interest Analysis by Geo-tagged SNS for Advertisement Recommendation 基于地理标记的社交网络潜在兴趣分析在广告推荐中的应用

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI: 10.1145/3397536.3428351

Takanobu Omura, Yukiko Kawai, Shinsuke Nakajima, Kenta Suzuki

引用次数: 0

Supervised-CityProphet: Towards Accurate Anomalous Crowd Prediction 监督城市先知:走向准确的异常人群预测

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI: 10.1145/3397536.3422219

Soto Anno, K. Tsubouchi, M. Shimosaka

引用次数: 3

Incorporating domain knowledge into Memetic Algorithms for solving Spatial Optimization problems 将领域知识融入模因算法求解空间优化问题

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI: 10.1145/3397536.3422265

Subhodip Biswas, Fanglan Chen, Zhiqian Chen, Chang-Tien Lu, Naren Ramakrishnan

{"title":"Incorporating domain knowledge into Memetic Algorithms for solving Spatial Optimization problems","authors":"Subhodip Biswas, Fanglan Chen, Zhiqian Chen, Chang-Tien Lu, Naren Ramakrishnan","doi":"10.1145/3397536.3422265","DOIUrl":"https://doi.org/10.1145/3397536.3422265","url":null,"abstract":"Spatial optimization problems (SOPs) are characterized by spatial relationships governing the decision variables, objectives and/or constraint functions. These are mostly combinatorial problems (NP-hard) due to the presence of discrete spatial units. Hence, exact optimization methods cannot solve them optimally under practical time constraints, especially for large-sized instances. Motivated by this challenge, we explore the use of population-based metaheuristics for solving SOPs. To this end, we observe that the search moves employed by these methods are suited to real-parameter continuous search space rather. To adapt them to the SOPs, we explore the role of domain knowledge in designing spatially-aware search operators that can efficiently search for an optimal solution in discrete search space while respecting the spatial constraints. These modifications result in a simple yet highly effective spatial hybrid metaheuristic called SPATIAL, which is applied to the problem of school boundary formation (also called school redistricting). Experimental findings on real-world datasets reveal the efficacy of our algorithm in obtaining superior quality solutions in comparison to traditional baseline methods. Additionally, we perform an in-depth study of the individual components of our framework and highlight the flexibility of our method in assimilating other search operators as well as in adapting it to related SOPs.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131122919","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: 10

An Effective Fleet Management Strategy for Collaborative Spatio-Temporal Searching: GIS Cup 一种有效的协同时空搜索车队管理策略:GIS杯

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI: 10.1145/3397536.3427187

Lingfeng Ming, Q. Hu, Ming Dong, Bolong Zheng

引用次数: 7

A Geocoding Framework Powered by Delivery Data 一个由交付数据驱动的地理编码框架

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI: 10.1145/3397536.3422254

Vishal Srivastava, Priyam Tejaswin, Lucky Dhakad, Mohit Kumar, Amar Dani

{"title":"A Geocoding Framework Powered by Delivery Data","authors":"Vishal Srivastava, Priyam Tejaswin, Lucky Dhakad, Mohit Kumar, Amar Dani","doi":"10.1145/3397536.3422254","DOIUrl":"https://doi.org/10.1145/3397536.3422254","url":null,"abstract":"Over the last decade, India has witnessed an explosion in the ecommerce industry. There is increasing adoption of e-commerce in smaller towns and cities over and above the densely populated urban centers. In this paper, we discuss the practical challenges involved with developing high-precision geocoding engines for these geographical regions in India. These challenges motivate the next iteration of our geocoding framework. In particular, we focus on addressing three core areas of improvement: 1) leveraging customer delivery data for geocoding, 2) understanding and solving for the diversity and variations in addresses for these new regions, and 3) overcoming the limited coverage of our reference corpus. To this end, we present GeoCloud. Key contributions of GeoCloud are 1) a training algorithm for learning reference-representations from delivery coordinates and 2) a retrieval algorithm for geocoding new addresses. We perform extensive testing of GeoCloud across India to capture the regional, socio-economical and linguistic diversity of our country. Our evaluation data is sampled from 72 cities and 21 states from the delivery addresses of a large e-commerce platform in India. The results show a significant improvement in precision and recall over the state-of-the-art geocoding system for India, and demonstrate the effectiveness of our intuitive, robust and generic approach. While we have shown the effectiveness of the framework for Indian addresses, we believe the framework can be applied to other countries as well, particularly where addresses are unstructured. To the best of our knowledge, this is the first instance of geocoding by learning reference-representations from large-scale delivery data.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125167946","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: 13

Leveraging Geospatial Data Gateways to Support the Operational Application of Deep Learning Models: Vision Paper 利用地理空间数据网关支持深度学习模型的操作应用:远景论文

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI: 10.1145/3397536.3422232

A. Soliman, J. Terstriep

{"title":"Leveraging Geospatial Data Gateways to Support the Operational Application of Deep Learning Models: Vision Paper","authors":"A. Soliman, J. Terstriep","doi":"10.1145/3397536.3422232","DOIUrl":"https://doi.org/10.1145/3397536.3422232","url":null,"abstract":"Geospatial data providers have adopted a variety of science gateways as the primary method for accessing remote geospatial data. Early systems provided little more than a simple file transfer mechanism but over the past decade, advanced features were incorporated to allow users to retrieve data seamlessly without concern for native file formats, data resolution, or even spatial projections. However, the recent growth in Deep Learning models in the geospatial domains has exposed additional requirements for accessing geospatial repositories. In this paper we discussed the major data accessibility challenges faced by the Deep Learning community namely: (1) reproducibility of data preprocessing workflows, (2) optimizing data transfer between gateways and computational environments, and (3) minimizing local storage requirements using on-the-fly augmentation. In this paper, we present our vision of spatial data generators to act as middleware between geospatial data gateways and Deep Learning models. We propose advanced features for spatial data generators and describe how they could satisfy the data accessibility requirements of the geospatial Deep Learning community. Lastly, we argue that satisfying these data accessibility requirements will not only enhance the reproducibility of Deep Learning workflows and speed their development but will also improve the quality of training and prediction of operational Deep Learning models.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127611517","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

Online In-Route Task Selection in Spatial Crowdsourcing 空间众包中的在线路由任务选择

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI: 10.1145/3397536.3422215

Camila F. Costa, M. Nascimento

{"title":"Online In-Route Task Selection in Spatial Crowdsourcing","authors":"Camila F. Costa, M. Nascimento","doi":"10.1145/3397536.3422215","DOIUrl":"https://doi.org/10.1145/3397536.3422215","url":null,"abstract":"Consider the following scenario: (a) a worker traveling on the shortest path between two locations in a city's road network, (b) he/she is willing to deviate from such path in order to complete tasks in the network, (c) tasks are associated with rewards and appear and disappear dynamically, i.e., they are not known in advance, and (d) the worker specifies a time budget which limits the total time he/she is willing to spend on his/her trip. Now assume the worker wants to minimize the detour from the original path while, at the same time, maximizing the rewards collected by completing tasks; clearly two competing criteria. We call this problem the Online In-Route Task Selection (Online-IRTS) query, and we investigate it using the paradigm of skyline queries in order to systematically explore different trade-offs between earned rewards and path deviation. Because of the online nature of the problem, i.e., irrevocable decisions about which task to perform have to be made without knowledge of future tasks, it is not possible to guarantee optimal solutions for the Online-IRTS query. Therefore, we propose two heuristic approaches, one is based on local optimizations, and the other one is based on incremental solutions, along with a method to evaluate the quality of their solutions w.r.t. the optimal offline solution. Our experiments using city-scale realistic datasets show that the first approach is more effective whereas the second is more efficient, allowing one to choose which approach to use according to his/her priorities.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115384368","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: 5

Poet 诗人

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI: 10.1145/3397536.3422230

Johns Paul, Jieliang Ang, Tianyuan Fu, Bingsheng He, Shengliang Lu, S. Tan, Feng Cheng

{"title":"Poet","authors":"Johns Paul, Jieliang Ang, Tianyuan Fu, Bingsheng He, Shengliang Lu, S. Tan, Feng Cheng","doi":"10.1145/3397536.3422230","DOIUrl":"https://doi.org/10.1145/3397536.3422230","url":null,"abstract":"Interaction-based systems have been widely used in many enterprises like Grab to enable quick and easy analysis of large-scale spatial data. Unlike traditional instruction-based query processing systems, modern interaction-based systems allow users to issue complex queries through simple interactions with a Graphical User Interface (GUI). While such systems have significantly transformed the process of spatial query processing, they still rely on a process-after-query approach for executing the queries. Even though the user is continuously interacting with the GUI, the actual processing is only initiated after the user completes their interactions, thus wasting the opportunities to reduce the response time of query processing. Inside Grab, we develop Poet, a progressive execution framework to continuously analyze user interactions and to perform progressive execution as soon as the system gains reasonable confidence regarding the user intentions. By integrating Poet, the interaction-based system can begin processing before the query is expressed in its whole by the user. The user interactions are captured and modelled in Markov chains, which guide the probability of progressive execution. For handling large-scale trajectory data in Grab, the progressive execution engine of Poet has been designed on top of Apache Flink. Our experiments show that Poet is able to reduce the latency in generating the output, providing a more interactive experience. Our experiments find that Poet helps reduce the query execution latency by up to 25x.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114219090","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: 1

Platooning Graph for Safer Traffic Management 基于更安全交通管理的队列图

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI: 10.1145/3397536.3422272

Lakmal Muthugama, S. Karunasekera, E. Tanin

引用次数: 3

Enabling Finer Grained Place Embeddings using Spatial Hierarchy from Human Mobility Trajectories 利用人类移动轨迹的空间层次实现更细粒度的位置嵌入

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI: 10.1145/3397536.3422229

Toru Shimizu, T. Yabe, K. Tsubouchi

引用次数: 4