Proceedings of the 29th International Conference on Advances in Geographic Information Systems最新文献

{"title":"Online Adaptation of Parameters using GRU-based Neural Network with BO for Accurate Driving Model","authors":"Zhanhong Yang, Satoshi Masuda, Michiaki Tatsubori","doi":"10.1145/3474717.3483632","DOIUrl":"https://doi.org/10.1145/3474717.3483632","url":null,"abstract":"Testing self-driving cars in different areas requires surrounding cars with accordingly different driving styles such as aggressive or conservative styles. Calibrating a driving model (DM) makes the simulated driving behavior closer to human-driving behavior, and enable the simulation of human-driving cars. Conventional DM-calibrating methods do not take into account that the parameters in a DM vary while driving. These \"fixed\" calibrating methods cannot reflect an actual interactive driving scenario. In this paper, we propose a DM-calibration method for measuring human driving styles to reproduce real car-following behavior more accurately. The method includes 1) an objective entropy weight method for measuring and clustering human driving styles, and 2) online adaption of DM parameters based on deep learning by combining Bayesian optimization and a gated recurrent unit neural network. We conducted experiments to evaluate the proposed method, and the results indicate that it can be easily used to measure human driver styles. The experiments also showed that we can calibrate a corresponding DM in a virtual testing environment with up to 26% more accuracy than with fixed calibration methods.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":" 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132159195","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}

{"title":"Automated Feature-Topic Pairing: Aligning Semantic and Embedding Spaces in Spatial Representation Learning","authors":"Dongjie Wang, Kunpeng Liu, David A. Mohaisen, Pengyang Wang, Chang-Tien Lu, Yanjie Fu","doi":"10.1145/3474717.3484212","DOIUrl":"https://doi.org/10.1145/3474717.3484212","url":null,"abstract":"Automated characterization of spatial data is a kind of critical geographical intelligence. As an emerging technique for characterization, Spatial Representation Learning (SRL) uses deep neural networks (DNNs) to learn non-linear embedded features of spatial data for characterization. However, SRL extracts features by internal layers of DNNs, and thus suffers from lacking semantic labels. Texts of spatial entities, on the other hand, provide semantic understanding of latent feature labels, but is insensible to deep SRL models. How can we teach a SRL model to discover appropriate topic labels in texts and pair learned features with the labels? This paper formulates a new problem: feature-topic pairing, and proposes a novel Particle Swarm Optimization (PSO) based deep learning framework. Specifically, we formulate the feature-topic pairing problem into an automated alignment task between 1) a latent embedding feature space and 2) a textual semantic topic space. We decompose the alignment of the two spaces into: 1) point-wise alignment, denoting the correlation between a topic distribution and an embedding vector; 2) pair-wise alignment, denoting the consistency between a feature-feature similarity matrix and a topic-topic similarity matrix. We design a PSO based solver to simultaneously select an optimal set of topics and learn corresponding features based on the selected topics. We develop a closed loop algorithm to iterate between 1) minimizing losses of representation reconstruction and feature-topic alignment and 2) searching the best topics. Finally, we present extensive experiments to demonstrate the enhanced performance of our method.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127923344","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}

{"title":"POI Alias Discovery in Delivery Addresses using User Locations","authors":"Tianfu He, Guochun Chen, Chuishi Meng, Huajun He, Zheyi Pan, Yexin Li, Sijie Ruan, Huimin Ren, Ye Yuan, Ruiyuan Li, Junbo Zhang, Jie Bao, Hui He, Yu Zheng","doi":"10.1145/3474717.3483950","DOIUrl":"https://doi.org/10.1145/3474717.3483950","url":null,"abstract":"People often refer to a place of interest (POI) by an alias. In ecommerce scenarios, the POI alias problem affects the quality of the delivery address of online orders, bringing substantial challenges to intelligent logistics systems and market decision-making. Labeling the aliases of POIs involves heavy human labor, which is inefficient and expensive. Inspired by the observation that the users' GPS locations are highly related to their delivery address, we propose a ubiquitous alias discovery framework. Firstly, for each POI name in delivery addresses, the location data of its associated users, namely Mobility Profile are extracted. Then, we identify the alias relationship by modeling the similarity of mobility profiles. Comprehensive experiments on the large-scale location data and delivery address data from JD logistics validate the effectiveness.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116367155","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}

{"title":"Multi-View Spatial-Temporal Model for Travel Time Estimation","authors":"Zichuan Liu, Zhaoyang Wu, Meng Wang, Rui Zhang","doi":"10.1145/3474717.3488239","DOIUrl":"https://doi.org/10.1145/3474717.3488239","url":null,"abstract":"Taxi arrival time prediction is essential for building intelligent transportation systems. Traditional prediction methods mainly rely on extracting features from traffic maps, which cannot model complex situations and nonlinear spatial and temporal relationships. Therefore, we propose Multi-View Spatial-Temporal Model (MVSTM) to capture the mutual dependence of spatial-temporal relations and trajectory features. Specifically, we use graph2vec to model the spatial view, dual-channel temporal module to model the trajectory view, and structural embedding to model traffic semantics. Experiments on large-scale taxi trajectory data have shown that our approach is more effective than the existing novel methods. The source code can be found at https://github.com/775269512/SIGSPATIAL-2021-GISCUP-4th-Solution.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129726553","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}

{"title":"Worbel: Aggregating Point Labels into Word Clouds","authors":"S. Bhore, R. Ganian, Guangping Li, M. Nöllenburg, J. Wulms","doi":"10.1145/3474717.3483959","DOIUrl":"https://doi.org/10.1145/3474717.3483959","url":null,"abstract":"Point feature labeling is a classical problem in cartography and GIS that has been extensively studied for geospatial point data. At the same time, word clouds are a popular visualization tool to show the most important words in text data which has also been extended to visualize geospatial data (Buchin et al. PacificVis 2016). In this paper, we study a hybrid visualization, which combines aspects of word clouds and point labeling. In the considered setting, the input data consists of a set of points grouped into categories and our aim is to place multiple disjoint and axis-aligned rectangles, each representing a category, such that they cover points of (mostly) the same category under some natural quality constraints. In our visualization, we then place category names inside the computed rectangles to produce a labeling of the covered points which summarizes the predominant categories globally (in a word-cloud-like fashion) while locally avoiding excessive misrepresentation of points (i.e., retaining the precision of point labeling). We show that computing a minimum set of such rectangles is NP-hard. Hence, we turn our attention to developing heuristics and exact SAT models to compute our visualizations. We evaluate our algorithms quantitatively, measuring running time and quality of the produced solutions, on several artificial and real-world data sets. Our experiments show that the heuristics produce solutions of comparable quality to the SAT models while running much faster.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123555462","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}

{"title":"Quantifying Intrinsic Value of Information of Trajectories","authors":"Kien Nguyen, John Krumm, C. Shahabi","doi":"10.1145/3474717.3483912","DOIUrl":"https://doi.org/10.1145/3474717.3483912","url":null,"abstract":"A trajectory, defined as a sequence of location measurements, contains valuable information about movements of an individual. Its value of information (VOI) may change depending on the specific application. However, in a variety of applications, knowing the intrinsic VOI of a trajectory is important to guide other subsequent tasks or decisions. This work aims to find a principled framework to quantify the intrinsic VOI of trajectories from the owner's perspective. This is a challenging problem because an appropriate framework needs to take into account various characteristics of the trajectory, prior knowledge, and different types of trajectory degradation. We propose a framework based on information gain (IG) as a principled approach to solve this problem. Our IG framework transforms a trajectory with discrete-time measurements to a canonical representation, i.e., continuous in time with continuous mean and variance estimates, and then quantifies the reduction of uncertainty about the locations of the owner over a period of time as the VOI of the trajectory. Qualitative and extensive quantitative evaluation show that the IG framework is capable of effectively capturing important characteristics contributing to the VOI of trajectories.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123598899","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}

{"title":"HTF: Homogeneous Tree Framework for Differentially-Private Release of Location Data","authors":"Sina Shaham, Gabriel Ghinita, Ritesh Ahuja, John Krumm, C. Shahabi","doi":"10.1145/3474717.3483943","DOIUrl":"https://doi.org/10.1145/3474717.3483943","url":null,"abstract":"Mobile apps that use location data are pervasive, spanning domains such as transportation, urban planning and healthcare. Important use cases for location data rely on statistical queries, e.g., identifying hotspots where users work and travel. Such queries can be answered efficiently by building histograms. However, precise histograms can expose sensitive details about individual users. Differential privacy (DP) is a mature and widely-adopted protection model, but most approaches for DP-compliant histograms work in a data-independent fashion, leading to poor accuracy. The few proposed data-dependent techniques attempt to adjust histogram partitions based on dataset characteristics, but they do not perform well due to the addition of noise required to achieve DP. We identify density homogeneity as a main factor driving the accuracy of DP-compliant histograms, and we build a data structure that splits the space such that data density is homogeneous within each resulting partition. We show through extensive experiments on large-scale real-world data that the proposed approach achieves superior accuracy compared to existing approaches.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"200 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124492559","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}

{"title":"Route Reconstruction from Traffic Flow via Representative Trajectories","authors":"B. Custers, Wouter Meulemans, B. Speckmann, Kevin Verbeek","doi":"10.1145/3474717.3483650","DOIUrl":"https://doi.org/10.1145/3474717.3483650","url":null,"abstract":"Understanding human mobility patterns is an important aspect of traffic analysis and urban planning. Trajectory data provide detailed views on specific routes, but typically do not capture all traffic. On the other hand, loop detectors built into the road network capture all traffic flow at specific locations, but provide no information on the individual routes. Given a set of loop-detector measurements as well as a (small) set of representative trajectories, our goal is to investigate how one can effectively combine these two partial data sources to create a more complete picture of the underlying mobility patterns. Specifically, we want to reconstruct a realistic set of routes from the loop-detector data, using the given trajectories as representatives of typical behavior. We model the loop-detector data as a network flow field that needs to be covered by the reconstructed routes and we capture the realism of the routes via the strong Fréchet distance to the representative trajectories. We prove that several forms of the resulting algorithmic problem are NP-hard. Hence we explore heuristic approaches which decompose the flow well while following the representative trajectories to varying degrees. We propose an iterative Fréchet Routes (FR) heuristic which generates candidates routes with bounded Fréchet distance to the representative trajectories. We also describe a variant of multi-commodity min-cost flow (MCMCF) which is only loosely coupled to the trajectories. We perform an extensive experimental evaluation of our two proposed approaches in comparison to a global min-cost flow (GMCF), which is essentially agnostic to the representative trajectories. To make meaningful claims in terms of quality, we derive a ground truth by map-matching real-world trajectories. We find that GMCF explains the flow best, but produces a large number of often nonsensical routes (significantly more than the ground truth). MCMCF produces a large number of mostly realistic routes which explain the flow reasonably well. In contrast, FR produces much smaller sets of realistic routes which still explain the flow well, at the cost of a higher running time. Finally, we report on the results of a case study which combines real-world loop detector data and representative trajectories for the region around The Hague, the Netherlands.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131334851","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}

{"title":"Auxiliary-task learning for geographic data with autoregressive embeddings","authors":"Konstantin Klemmer, Daniel B. Neill","doi":"10.1145/3474717.3483922","DOIUrl":"https://doi.org/10.1145/3474717.3483922","url":null,"abstract":"Machine learning is gaining popularity in a broad range of areas working with geographic data. Here, data often exhibit spatial effects, which can be difficult to learn for neural networks. We propose SXL, a method for embedding information on the autoregressive nature of spatial data directly into the learning process using auxiliary tasks. We utilize the local Moran's I, a measure of local spatial autocorrelation, to \"nudge\" the model to learn the direction and magnitude of local spatial effects, complementing learning of the primary task. We further introduce a novel expansion of Moran's I to multiple resolutions, capturing spatial interactions over longer and shorter distances simultaneously. The novel multi-resolution Moran's I can be constructed easily and offers seamless integration into existing machine learning frameworks. Over a range of experiments using real-world data, we highlight how our method consistently improves the training of neural networks in unsupervised and supervised learning tasks. In generative spatial modeling experiments, we propose a novel loss for auxiliary task GANs utilizing task uncertainty weights. SXL outperforms domain-specific spatial interpolation benchmarks, highlighting its potential for downstream applications.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114514820","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}