查看更多>>摘要：Since the rapid growth of urban populations,the study of urban systems has gained considerable attention from researchers,decision makers,governments,and organizations.Urban systems are complex and dynamic such that they produce emergent patterns such as self-organization and nonlinearity.Agent-based modelling presents an approach to simulating and abstracting urban systems to reveal and study emergent patterns from urban-related entities.However,agent-based models are difficult to effectively optimize and validate without high quality real-world data.Geosocial media data provides agent-based models with location-enabled data at high volumes and frequencies.Integrating agent-based models with geosocial media data presents opportunities in advancing and developing studies in urban systems.This paper provides a general overview of concepts,review of recent applications,and discussion of challenges and opportunities in the context of using geosocial media data in agent-based models for urban systems.We argue that ABMs focused on studying urban systems can benefit greatly from geosocial media data,given that research moves towards standard guidelines that enable the comparison and effective use of ABMs,and geosocial media data under appropriate circumstances and applications.
查看更多>>摘要：The Internet of Moving Things is rapidly becoming a reality where intelligent devices and infrastructures are fostering real-time data sus-tainability in smart cities and advancing crowdsourced tasks to improve energy consumption,waste management,and traffic operations.These intelligent devices create a complex network scenario in which they often move together or in conjunction with one another to complete crowdsourced tasks.Our research premise is that mobility relationships matter when performing these tasks,and therefore,a graph model based on representing the changes in mobility relation-ships is needed to help identify the neighbour devices that are moving close to one another in our physical world but also seamlessly con-nected in their virtual world.We propose a bi-partite community mobility graph model for linking intelligent devices in both virtual and physical worlds,as well as reaching a trade-off between crowd-sourced tasks designed with explicit and implicit citizen participation.This paper aims to explore a bi-partite graph as a promising spatio-temporal representation of IoMT networks since changes in mobility relationships over time can indicate volunteer organisation at the device and community levels.The Louvain community detection method is proposed to find communities of intelligent devices to reveal a value conscious participation of citizens.The proposed bi-partite graph model is evaluated using a real-world scenario in transportation,confirming the main role of evolving communities in developing crowdsourcing IoMT networks.
查看更多>>摘要：The applications of 3D building models are limited as producing them requires massive labor and time costs as well as expensive devices.In this paper,we aim to propose a novel and web-based interactive platform,VGI3D,to overcome these challenges.The platform is designed to reconstruct 3D building models by using free images from internet users or volunteered geographic informa-tion(VGI)platform,even though not all these images are of high quality.Our interactive platform can effectively obtain each 3D building model from images in 30 seconds,with the help of user interaction module and convolutional neural network(CNN).The user interaction module provides the boundary of building facades for 3D building modeling.And this CNN can detect facade elements even though multiple architectural styles and complex scenes are within the images.Moreover,user interaction module is designed as simple as possible to make it easier to use for both of expert and non-expert users.Meanwhile,we conducted a usability testing and collected feedback from participants to better optimize platform and user experience.In general,the usage of VGI data reduces labor and device costs,and CNN simplifies the process of elements extraction in 3D building modeling.Hence,our proposed platform offers a promising solution to the 3D modeling community.
查看更多>>摘要：Without explicit description of map application themes,it is difficult for users to discover desired map resources from massive online Web Map Services(WMS).However,metadata-based map application theme extraction is a challenging multi-label text classification task due to limited training samples,mixed vocabularies,variable length and content arbitrariness of text fields.In this paper,we propose a novel multi-label text classification method,Text GCN-SW-KNN,based on geographic semantics and collaborative training to improve classifica-tion accuracy.The semi-supervised collaborative training adopts two base models,i.e.a modified Text Graph Convolutional Network(Text GCN)by utilizing Semantic Web,named Text GCN-SW,and widely-used Multi-Label K-Nearest Neighbor(ML-KNN).Text GCN-SW is improved from Text GCN by adjusting the adjacency matrix of the heterogeneous word document graph with the shortest semantic distances between themes and words in metadata text.The distances are calculated with the Semantic Web of Earth and Environmental Terminology(SWEET)and WordNet dictionaries.Experiments on both the WMS and layer metadata show that the proposed methods can achieve higher F1-score and accuracy than state-of-the-art baselines,and demonstrate better stability in repeating experiments and robustness to less training data.Text GCN-SW-KNN can be extended to other multi-label text classification scenario for better supporting metadata enhancement and geospatial resource discovery in Earth Science domain.
查看更多>>摘要：Under the global health crisis of COVID-19,timely,and accurate epi-demic data are important for observation,monitoring,analyzing,modeling,predicting,and mitigating impacts.Viral case data can be jointly analyzed with relevant factors for various applications in the context of the pandemic Current COVID-19 case data are scattered across a variety of data sources which may consist of low data quality accompanied by inconsistent data structures.To address this short-coming,a multi-scale spatiotemporal data product is proposed as a public repository platform,based on a spatiotemporal cube,and allows the integration of different data sources by adopting various data standards.Within the spatiotemporal cube,a comprehensive data processing workflow gathers disparate COVID-19 epidemic data-sets at the global,national,provincial/state,county,and city levels.This proposed framework is supported by an automatic update with a 2-h frequency and the crowdsourcing validation team to produce and update data on a daily time step.This rapid-response dataset allows the integration of other relevant socio-economic and environ-mental factors for spatiotemporal analysis.The data is available in Harvard Dataverse platform(https://dataverse.harvard.edu/dataset.xhtml?persistentld=doi:10.7910/DVN/8HGECN)and GitHub open source repository(https://github.com/stccenter/COVID-19-Data).
查看更多>>摘要：In the Big Data era,Earth observation is becoming a complex process integrating physical and social sectors.This study presents an approach to generating a 100 m population grid in the Contiguous United States(CONUS)by disaggregating the US cen-sus records using 125 million of building footprints released by Microsoft in 2018.Land-use data from the OpenStreetMap(OSM),a crowdsourcing platform,was applied to trim original footprints by removing the non-residential buildings.After trimming,several metrics of building measurements such as building size and build-ing count in a census tract were used as weighting scenarios,with which a dasymetric model was applied to disaggregate the American Community Survey(ACS)5-year estimates(2013-2017)into a 100 m population grid product.The results confirm that the OSM trimming process removes non-residential buildings and thus provides a better representation of population distribution within complicated urban fabrics.The building size in the census tract is found in the optimal weighting scenario.The product is 2.5Gb in size containing 800 million populated grids and is currently hosted by ESRI(http://arcg.is/19S4qK)for visualization.The data can be accessed via https://doi.org/10.7910/DVN/DLGP7Y.With the accel-erated acquisition of high-resolution spatial data,the product could be easily updated for spatial and temporal continuity.
查看更多>>摘要：Many cities,countries and transport operators around the world are striving to design intelligent transport systems.These systems cap-ture the value of multisource and multiform data related to the functionality and use of transportation infrastructure to better sup-port human mobility,interests,economic activity and lifestyles.They aim to provide services that can enable transportation custo-mers and managers to be better informed and make safer and more efficient use of infrastructure.In developing principles,guidelines,methods and tools to enable synergistic work between humans and computer-generated informa-tion,the science of visual analytics continues to expand our under-standing of data through effective and interactive visual interfaces.In this paper,we describe an application of visual analytics related to the study of movement and transportation systems.This application documents the use of rapid,2D and 3D web visualisation and data analytics libraries and explores their potential added value to the analysis of big public transport performance data.A novel approach to displaying such data through a generalisable framework visualisa-tion system is demonstrated.This framework recalls over a year's worth of public transport performance data at a highly granular level in a fast,interactive browser-based environment.Greater Sydney,Australia forms a case study to highlight poten-tial uses of the visualisation of such large,passively-collected data sets as an applied research scenario.In this paper,we argue that such highly visual systems can add data-driven rigour to service planning and longer-term transport decision-making.Furthermore,they enable the sharing of quality of service statistics with various stakeholders and citizens and can showcase improvements in ser-vices before and after policy decisions.The paper concludes by making recommendations on the value of this approach in embed-ding these or similar web-based systems in transport planning practice,performance management,optimisation and understand-ing of customer experience.
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