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Automating land parcel classification for neighborhood-scale urban analysis

International Journal of Digital Earth 2019年第12期12卷 1396-1405页

作者： Xinyue Ye V.Kelly Turner Bing She Urban Informatics&Spatial Computing Lab and Department of Informatics New Jersey Institute of TechnologyNewarkNJUSA Department of Geography Kent State UniversityKentOHUSA China Data Center University of MichiganAnn ArborMIUSA

Homeowners’Associations(HOAs)dictate landscape structure and management through legally enforceable land covenants at the neighborhood scale in the *** the location and spatial extent of HOAs is critical for examining its ***,such analysis is confounded by the lack of spatial data at the appropriate unit for such *** purpose of this paper is to develop and realize an open source implementation to automate land parcel classification,which is an initial step towards the goal of determining the impact of HOAs on urban land *** Maricopa County,Arizona as a testbed,we found that parcel merging processes reduce the number of subdivisions from 26,042 to 17,269,such that boundaries better align with neighborhood units to which rule sets like land covenants ***,after an initial training period,this process was completed in just over 7 *** research is an important first step in enabling a number of analysis including determining the location and spatial extent of HOAs regionally and,eventually,nationally and determining proposed links between HOAs and land management outcomes.

关键词： Land management land parcel classification open source neighborhood-scale urban analysis

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Detecting events from the social media through exemplar-enhanced supervised learning

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International Journal of Digital Earth 2019年第9期12卷 1083-1097页

作者： Xuan Shi Bowei Xue Ming-Hsiang Tsou Xinyue Ye Brian Spitzberg Jean Mark Gawron Heather Corliss Jay Lee Ruoming Jin Department of Geosciences University of ArkansasFayettevilleARUSA b Department of Geography San Diego State UniversitySan DiegoCAUSA Urban Informatics&Spatial Computing Lab Department of InformaticsNew Jersey Institute of TechnologyNewarkNJUSA School of Communication San Diego State UniversitySan DiegoCAUSA Department of Linguistics San Diego State UniversitySan DiegoCAUSA School of Public Health San Diego State UniversitySan DiegoCAUSA College of Environment and Planning Henan UniversityKaifengPeople’s Republic of China Department of Geography Kent State UniversityKentOHUSA Department of Computer Sciences Kent State UniversityKentOHUSA

Understanding and detecting the intended meaning in social media is challenging because social media messages contain varieties of noise and chaos that are irrelevant to the themes of *** example,conventional supervised classification approaches would produce inconsistent solutions to detecting and clarifying whether any given Twitter message is really about a wildfire ***,a renovated workflow was designed and *** workflow consists of four sequential procedures:(1)Apply the latent semantic analysis and cosine similarity calculation to examine the similarity between Twitter messages;(2)Apply Affinity Propagation to identify exemplars of Twitter messages;(3)Apply the cosine similarity calculation again to automatically match the exemplars to known training results,and(4)Apply accumulative exemplars to classify Twitter messages using a support vector machine *** overall correction ratio was over 90%when a series of ongoing and historical wildfire events were examined.

关键词： Social media Twitter wildfire supervised learning

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Evaluating the effect of city lock-down on controlling COVID-19 propagation through deep learning and network science models

arXiv

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arXiv 2020年

作者： Zhang, Xiaoqi Ji, Zheng Zheng, Yanqiao Ye, Xinyue Li, Dong National School of Development Southeast University Nanjing China School of Finance Zhejiang University of Finance and Economics Hangzhou China Urban Informatics-Spatial Computing Lab College of Computing New Jersey Institute of Technoloy New Jersey United States Innovation Center for Technology Beijing Tsinghua Tongheng Urban Planning & Design Institute China

The special epistemic characteristics of the COVID-19, such as the long incubation period and the infection through asymptomatic cases, put severe challenge to the containment of its outbreak. By the end of March, 2020, China has successfully controlled the within- spreading of COVID-19 at a high cost of locking down most of its major cities, including the epicenter, Wuhan. Since the low accuracy of outbreak data before the mid of Feb. 2020 forms a major technical concern on those studies based on statistic inference from the early outbreak. We apply the supervised learning techniques to identify and train NP-Net-SIR model which turns out robust under poor data quality condition. By the trained model parameters, we analyze the connection between population flow and the cross-regional infection connection strength, based on which a set of counterfactual analysis is carried out to study the necessity of lock-down and substitutability between lock-down and the other containment measures. Our findings support the existence of non-lock-down-typed measures that can reach the same containment consequence as the lock-down, and provide useful guideline for the design of a more flexible containment strategy. Copyright © 2020, The Authors. All rights reserved.

关键词： Supervised learning

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Extracting human emotions at different places based on facial expressions and spatial clustering analysis

arXiv

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arXiv 2019年

作者： Kang, Yuhao Jia, Qingyuan Gao, Song Zeng, Xiaohuan Wang, Yueyao Angsuesser, Stephan Liu, Yu Ye, Xinyue Fei, Teng Geospatial Data Science Lab Department of Geography University of Wisconsin Madison United States School of Resources and Environmental Sciences Wuhan University Wuhan China College of Urban and Environment Sciences Peking University Beijing China Institute of Remote Sensing and Geographical Information Systems Peking University Beijing China Urban Informatics and Spatial Computing Lab Department of Informatics New Jersey Institute of Technology Newark United States

The emergence of big data enables us to evaluate the various human emotions at places from a statistic perspective by applying affective computing. In this study, a novel framework for extracting human emotions from large-scale georeferenced photos at different places is proposed. After the construction of places based on spatial clustering of user generated footprints collected in social media websites, online cognitive services are utilized to extract human emotions from facial expressions using state-of-the-art computer vision techniques. And two happiness metrics are defined for measuring the human emotions at different places. To validate the feasibility of the framework, we take 80 tourist attractions around the world as an example and a happiness ranking list of places is generated based on human emotions calculated over 2 million faces detected out from over 6 million photos. Different kinds of geographical contexts are taken into consideration to find out the relationship between human emotions and environmental factors. Results show that much of the emotional variation at different places can be explained by a few factors such as openness. The research may offer insights on integrating human emotions to enrich the understanding of sense of place in geography and in place-based GIS. Copyright © 2019, The Authors. All rights reserved.

关键词： Big data

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The global spectrum of tree crown architecture

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Nature communications 2025年第1期16卷 4876页

作者： Tommaso Jucker Fabian Jörg Fischer Jérôme Chave David A Coomes John Caspersen Arshad Ali Grace Jopaul Loubota Panzou Ted R Feldpausch Daniel Falster Vladimir A Usoltsev Toby D Jackson Stephen Adu-Bredu Luciana F Alves Mohammad Aminpour Bhely Angoboy Ilondea Niels P R Anten Cécile Antin Yousef Askari Narayanan Ayyappan Lindsay F Banin Nicolas Barbier John J Battles Hans Beeckman Yannick E Bocko Ben Bond-Lamberty Frans Bongers Samuel Bowers Michiel van Breugel Arthur Chantrain Rajeev Chaudhary Jingyu Dai Michele Dalponte Kangbéni Dimobe Jean-Christophe Domec Jean-Louis Doucet Juan Manuel Dupuy Rada Remko A Duursma Moisés Enríquez Karin Y van Ewijk William Farfán-Rios Adeline Fayolle Marco Ferretti Eric Forni David I Forrester Hammad Gilani John L Godlee Matthias Haeni Jefferson S Hall Jie-Kun He Andreas Hemp José L Hernández-Stefanoni Steven I Higgins Robert J Holdaway Kiramat Hussain Lindsay B Hutley Tomoaki Ichie Yoshiko Iida Hai-Sheng Jiang Puspa Raj Joshi Hasan Kaboli Maryam Kazempour Larsary Tanaka Kenzo Brian D Kloeppel Takashi S Kohyama Suwash Kunwar Shem Kuyah Jakub Kvasnica Siliang Lin Emily R Lines Hongyan Liu Craig Lorimer Jean-Joël Loumeto Yadvinder Malhi Peter L Marshall Eskil Mattsson Radim Matula Jorge A Meave Sylvanus Mensah Xiangcheng Mi Stéphane T Momo Glenn R Moncrieff Francisco Mora Rodrigo Muñoz Sarath P Nissanka Zamah Shari Nur Hajar Kevin L O'Hara Steven Pearce Raphaël Pelissier Pablo L Peri Pierre Ploton Lourens Poorter Mohsen Javanmiri Pour Hassan Pourbabaei Sabina C Ribeiro Casey Ryan Anvar Sanaei Jennifer Sanger Michael Schlund Giacomo Sellan Alexander Shenkin Bonaventure Sonké Frank J Sterck Martin Svátek Kentaro Takagi Anna T Trugman Matthew A Vadeboncoeur Ahmad Valipour Mark C Vanderwel Alejandra G Vovides Peter Waldner Weiwei Wang Li-Qiu Wang Christian Wirth Murray Woods Wenhua Xiang Fabiano de Aquino Ximenes Yaozhan Xu Toshihiro Yamada Miguel A Zavala Niklaus E Zimmermann School of Biological Sciences University of Bristol Bristol BS8 1TQ UK. t.jucker@bristol.ac.uk. School of Biological Sciences University of Bristol Bristol BS8 1TQ UK. UMR5300 Centre de Recherche sur la Biodiversité et l'Environnement CNRS INPT IRD Université de Toulouse Bât 4R1 118 route de Narbonne 31062 Toulouse France. Conservation Research Institute University of Cambridge Cambridge CB2 3EA UK. Institute of Forestry and Conservation University of Toronto 33 Willcocks Street Toronto ON M5S 3B3 Canada. Forest Ecology Research Group College of Life Sciences Hebei University Baoding 071002 Hebei China. Institut Supérieur des Sciences Géographiques Environnementales et de l'Aménagement (ISSGEA) Université DENIS SASSOU-N'GUESSO Kintélé République du Congo. Laboratoire de Biodiversité de Gestion des Ecosystèmes et de l'Environnement (LBGE) Faculté des Sciences et Techniques Université Marien NGOUABI BP 69 Brazzaville Brazzaville République du Congo. Faculty of Environment Science and Economy University of Exeter Exeter EX4 4QE UK. Evolution & Ecology Research Centre University of New South Wales Sydney NSW Sydney Australia. Department of Forest Mensuration and Management Ural State Forest Engineering and Economic University Yekaterinburg Russia. Forestry Research Institute of Ghana Council for Scientific and Industrial Research University Kumasi Ghana. Center for Tropical Research Institute of the Environment and Sustainability University of California Los Angeles Los Angeles CA USA. Natural Recourses and Watershed Management Office West Azerbaijan Province Urmia Iran. Institut National pour l'Etude et la Recherche Agronomiques BP 2037 Kinshasa Democratic Republic of the Congo. Université Pédagogique Nationale BP 8815 Kinshasa-Ngaliema Democratic Republic of the Congo. Center for Crop Systems Analysis Wageningen University & Research Wageningen Netherlands. AMAP lab Montpellier University IRD CIRAD CNRS INRAE Montpellier France. Rese

Trees can differ enormously in their crown architectural traits, such as the scaling relationships between tree height, crown width and stem diameter. Yet despite the importance of crown architecture in shaping the structure and function of terrestrial ecosystems, we lack a complete picture of what drives this incredible diversity in crown shapes. Using data from 374,888 globally distributed trees, we explore how climate, disturbance, competition, functional traits, and evolutionary history constrain the height and crown width scaling relationships of 1914 tree species. We find that variation in height-diameter scaling relationships is primarily controlled by water availability and light competition. Conversely, crown width is predominantly shaped by exposure to wind and fire, while also covarying with functional traits related to mechanical stability and photosynthesis. Additionally, we identify several plant lineages with highly distinctive stem and crown forms, such as the exceedingly slender dipterocarps of Southeast Asia, or the extremely wide crowns of legume trees in African savannas. Our study charts the global spectrum of tree crown architecture and pinpoints the processes that shape the 3D structure of woody ecosystems.

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