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  • 101.
    Ma, Ding
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    Sandberg, Mats
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Building, Energy and Environmental Engineering, Energy system.
    Jiang, Bin
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    Characterizing the Heterogeneity of the OpenStreetMap Data and Community2015In: ISPRS International Journal of Geo-Information, ISSN 2220-9964, Vol. 4, no 2, p. 535-550Article in journal (Refereed)
    Abstract [en]

    OpenStreetMap (OSM) constitutes an unprecedented, free, geographical information source contributed by millions of individuals, resulting in a database of great volume and heterogeneity. In this study, we characterize the heterogeneity of the entire OSM database and historical archive in the context of big data. We consider all users, geographic elements and user contributions from an eight-year data archive, at a size of 692 GB. We rely on some nonlinear methods such as power law statistics and head/tail breaks to uncover and illustrate the underlying scaling properties. All three aspects (users, elements, and contributions) demonstrate striking power laws or heavy-tailed distributions. The heavy-tailed distributions imply that there are far more small elements than large ones, far more inactive users than active ones, and far more lightly edited elements than heavy-edited ones. Furthermore, about 500 users in the core group of the OSM are highly networked in terms of collaboration.

  • 102.
    Omer, Itzhak
    et al.
    Department of Geography and Human Environment, Tel-Aviv University, Israel.
    Jiang, Bin
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    Can cognitive inferences be made from aggregate traffic flow data?2015In: Computers, Environment and Urban Systems, ISSN 0198-9715, E-ISSN 1873-7587, Vol. 54, p. 219-229Article in journal (Refereed)
    Abstract [en]

    Abstract Space syntax analysis or the topological analysis of street networks has illustrated that human traffic flow is highly correlated with some topological centrality measures, implying that human movement at an aggregate level is primarily shaped by the underlying topological structure of street networks. However, this high correlation does not imply that any individual's movement can be predicted by any street network centrality measure. In other words, traffic flow at the aggregate level cannot be used to make inferences about an individual's spatial cognition or conceptualization of space. Based on a set of agent-based simulations using three types of moving agents – topological, angular, and metric – we show that topological–angular centrality measures correlate better than does the metric centrality measure with the aggregate flows of agents who choose the shortest angular, topological or metric routes. We relate the superiority of the topological–angular network effects to the structural relations holding between street network to-movement and through-movement potentials. The study findings indicate that correlations between aggregate flow and street network centrality measures cannot be used to infer knowledge about individuals' spatial cognition during urban movement.

  • 103.
    Omer, Itzhak
    et al.
    Tel Aviv Univ, Tel Aviv, Israel.
    Jiang, Bin
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Urban and regional planning/GIS-institute.
    Imageability and topological eccentricity of urban streets2010In: Geospatial Analysis and Modelling of Urban Structure and Dynamics / [ed] Bin Jiang and Xiaobai Yao, Dordrecht: Springer , 2010, p. 163-175Chapter in book (Refereed)
    Abstract [en]

    Previous studies of the influence of structural qualities of urban street network on the image of the city are based mainly on centrality and connectivity measures taken from graph and space syntax theories. The paper suggests application of the structural property of eccentricity for considering the structural distinctiveness or differentiation of a given street in the overall street network. Eccentricity suggested by Q-analysis and based on the perspective of multidimensional chains of connectivity. This structural property is applied to the case of the city of Tel Aviv by using a geographic database of the street network and observed data acquired from Tel Aviv residents' production of sketch maps. The study's findings provide preliminary evidence for the relevance of the structural property of eccentricity for understanding the relationships between street network and the image of the city.

  • 104.
    Omer, Itzhak
    et al.
    Tel Aviv University, Israel.
    Kaplan, Nir
    Tel Aviv University, Israel.
    Jiang, Bin
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    Why angular centralities are more suitable for space syntax modeling?2017In: Proceedings - 11th International Space Syntax Symposium: 2. Cities and urban studies / [ed] Heitor T., Serra M., Bacharel M., Cannas da Silva L., Silva J.P., Instituto Superior Tecnico, Departamento de Engenharia Civil, Arquitetura e Georrecursos , 2017, Vol. 2, p. 100.1-100.12Conference paper (Refereed)
    Abstract [en]

    The street network's angular properties were found more suitable than metric properties for capturing the observed pedestrian and vehicle movement flows in space syntax modeling. Some studies relate this state to the underlying street network structure that create the potential for movement across the network. The aim of this paper is to clarify why the angular structure of the network has superiority over the metric structure. The investigation entailed analysis of street network' centralities and movement flows obtained through agent-based simulations conducted for two cities that differ in the pattern and size of street network. The findings indicate that the superiority of the angular structure can be explained by two structural properties: (i) a multi-scale correlation between to-movement and through-movement potentials (centrality measures) of the same distance type; and (ii) an overlap between movement potentials of different distance types across scales of the network. These structural properties create coherent and dominant angular foreground structures that fit movement flows in both study cities.

  • 105.
    Ren, Zheng
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Computer and Geospatial Sciences, Geospatial Sciences.
    Jiang, Bin
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Computer and Geospatial Sciences, Geospatial Sciences.
    Seipel, Stefan
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Computer and Geospatial Sciences, Computer Science.
    Capturing and characterizing human activities using building locations in America2019In: ISPRS International Journal of Geo-Information, ISSN 2220-9964, Vol. 8, no 5, article id 200Article in journal (Refereed)
    Abstract [en]

    Capturing and characterizing collective human activities in a geographic space have become much easier than ever before in the big era. In the past few decades it has been difficult to acquire the spatiotemporal information of human beings. Thanks to the boom in the use of mobile devices integrated with positioning systems and location-based social media data, we can easily acquire the spatial and temporal information of social media users. Previous studies have successfully used street nodes and geo-tagged social media such as Twitter to predict users’ activities. However, whether human activities can be well represented by social media data remains uncertain. On the other hand, buildings or architectures are permanent and reliable representations of human activities collectively through historical footprints. This study aims to use the big data of US building footprints to investigate the reliability of social media users for human activity prediction. We created spatial clusters from 125 million buildings and 1.48 million Twitter points in the US. We further examined and compared the spatial and statistical distribution of clusters at both country and city levels. The result of this study shows that both building and Twitter data spatial clusters show the scaling pattern measured by the scale of spatial clusters, respectively, characterized by the number points inside clusters and the area of clusters. More specifically, at the country level, the statistical distribution of the building spatial clusters fits power law distribution. Inside the four largest cities, the hotspots are power-law-distributed with the power law exponent around 2.0, meaning that they also follow the Zipf’s law. The correlations between the number of buildings and the number of tweets are very plausible, with the r square ranging from 0.53 to 0.74. The high correlation and the similarity of two datasets in terms of spatial and statistical distribution suggest that, although social media users are only a proportion of the entire population, the spatial clusters from geographical big data is a good and accurate representation of overall human activities. This study also indicates that using an improved method for spatial clustering is more suitable for big data analysis than the conventional clustering methods based on Euclidean geometry.

  • 106.
    Robinson, Anthony C.
    et al.
    GeoVISTA Center, Department of Geography, The Pennsylvania State University, University Park, PA, USA.
    Demšar, Urška
    School of Geography and Sustainable Development, University of St Andrews, St Andrews, Scotland.
    Moore, Antoni B.
    School of Surveying, University of Otago, Dunedin, New Zealand.
    Buckley, Aileen
    Esri, Inc, Redlands, USA.
    Jiang, Bin
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    Field, Kenneth
    Esri, Inc, Redlands, USA.
    Kraak, Menno-Jan
    Department of Geoinformation Processing, Faculty of Geoinformation Science and Earth Observation, University of Twente, Enschede, The Netherlands.
    Camboim, Silvana P.
    Department of Geomatics, Universidade Federal do Paraná, Curitiba, Brazil.
    Sluter, Claudia R.
    Department of Geomatics, Universidade Federal do Paraná, Curitiba, Brazil.
    Geospatial big data and cartography: research challenges and opportunities for making maps that matter2017In: International Journal of Cartography, ISSN 2372-9333, Vol. 3, no Suppl. 1, p. 32-60Article in journal (Refereed)
    Abstract [en]

    Geospatial big data present a new set of challenges and opportunities for cartographic researchers in technical, methodological and artistic realms. New computational and technical paradigms for cartography are accompanying the rise of geospatial big data. Additionally, the art and science of cartography needs to focus its contemporary efforts on work that connects to outside disciplines and is grounded in problems that are important to humankind and its sustainability. Following the development of position papers and a collaborative workshop to craft consensus around key topics, this article presents a new cartographic research agenda focused on making maps that matter using geospatial big data. This agenda provides both long-term challenges that require significant attention and short-term opportunities that we believe could be addressed in more concentrated studies.

  • 107.
    See, Linda
    et al.
    International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria.
    Mooney, Peter
    Department of Computer Science, Maynooth University, Maynooth, Ireland .
    Foody, Giles
    School of Geography, University of Nottingham, Nottingham, United Kingdom.
    Bastin, Lucy
    School of Engineering and Applied Science, Aston University, Birmingham, United Kingdom .
    Comber, Alexis
    School of Geography, University of Leeds, Leeds, United Kingdom.
    Estima, Jacinto
    NOVA IMS, Universidade Nova de Lisboa (UNL), Lisboa, Portugal .
    Fritz, Steffen
    International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria .
    Kerle, Norman
    Department of Earth Systems Analysis, ITC/University of Twente, Enschede, The Netherlands.
    Jiang, Bin
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    Laakso, Mari
    Finnish Geospatial Research Institute, Kirkkonummi, Finland .
    Liu, Hai-Ying
    Norwegian Institute for Air Research (NILU), Kjeller, Norway .
    Milčinski, Grega
    Sinergise Ltd., Cvetkova ulica 29, Ljubljana, Slovenia .
    Nikšič, Matej
    Urban Planning Institute of the Republic of Slovenia, Ljubljana, Slovenia .
    Painho, Marco
    NOVA IMS, Universidade Nova de Lisboa (UNL), Lisboa, Portugal .
    Pődör, Andrea
    Institute of Geoinformatics, Óbuda University Alba Regia Technical Faculty, Székesfehérvár, Hungary .
    Olteanu-Raimond, Ana-Maria
    Université Paris-Est, IGN-France, COGIT Laboratory, Saint-Mandé, Paris, France.
    Rutzinger, Martin
    Institute for Interdisciplinary Mountain Research, Austrian Academy of Sciences, Innsbruck, Austria .
    Crowdsourcing, citizen science or volunteered geographic information?: The current state of crowdsourced geographic information2016In: ISPRS International Journal of Geo-Information, ISSN 2220-9964, Vol. 5, no 5, article id 55Article in journal (Refereed)
    Abstract [en]

    Citizens are increasingly becoming an important source of geographic information, sometimes entering domains that had until recently been the exclusive realm of authoritative agencies. This activity has a very diverse character as it can, amongst other things, be active or passive, involve spatial or aspatial data and the data provided can be variable in terms of key attributes such as format, description and quality. Unsurprisingly, therefore, there are a variety of terms used to describe data arising from citizens. In this article, the expressions used to describe citizen sensing of geographic information are reviewed and their use over time explored, prior to categorizing them and highlighting key issues in the current state of the subject. The latter involved a review of 100 Internet sites with particular focus on their thematic topic, the nature of the data and issues such as incentives for contributors. This review suggests that most sites involve active rather than passive contribution, with citizens typically motivated by the desire to aid a worthy cause, often receiving little training. As such, this article provides a snapshot of the role of citizens in crowdsourcing geographic information and a guide to the current status of this rapidly emerging and evolving subject.

  • 108.
    Yao, Xiaobai Angela
    et al.
    Department of Geography, University of Georgia, Athens, USA.
    Huang, Haosheng
    Department of Geography, Universitat Zurich Institut fur Geographie, Zurich, Switzerland.
    Jiang, Bin
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Computer and Geospatial Sciences, Geospatial Sciences.
    Krisp, Jukka M.
    University of Augsburg, Augsburg, Germany.
    Representation and analytical models for location-based big data2019In: International Journal of Geographical Information Science, ISSN 1365-8816, E-ISSN 1365-8824, Vol. 33, no 4, p. 707-713Article in journal (Refereed)
  • 109.
    Yao, Xiaobai
    et al.
    University of Georgia, Athens, United States .
    Jiang, Bin
    University of Gävle, Department of Technology and Built Environment, Ämnesavdelningen för samhällsbyggnad.
    Geospatial modeling of urban environments2009In: Environment and Planning, B: Planning and Design, ISSN 0265-8135, E-ISSN 1472-3417, Vol. 36, no 5, p. 769-771Article in journal (Other academic)
  • 110. Yao, Xiaobai
    et al.
    Jiang, Bin
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    Liu, Yu
    Madden, Marguerite
    New insights gained from location-based social media data: VSI Preface for the special issue on New insights gained from location-based social media data2016In: Computers, Environment and Urban Systems, ISSN 0198-9715, E-ISSN 1873-7587, Vol. 58Article in journal (Refereed)
    Abstract [en]

    In the era of big data, increasingly sizeable datasets come from social media, particularly location-based social media, in the form that is widely known as user-generated contents. Many social media datasets are made available at the finest spatial and temporal scales. The availability of such data creates unprecedented opportunities for researchers to uncover what were previously hidden in the era of small data. What kinds of new research questions may be addressed with the available social media data? What are the social, ethical, and political implications of the wide use of social media platforms and the availability of such data? This special issue responds to the unique research opportunities and challenges from two broad perspectives. First, it looks at the need to develop new theories and data models for the management and analysis of social media data. Secondly, it advocates innovative acquisition and employment of social media data to enhance our understanding of human activities, social and spatial interactions, or the society as a whole. The inspiration for this special issue was the first ever International Conference on Location-based Social Media (ICLSM) held March 5-7, 2015 in Athens, Georgia, USA that brought together researchers from around the globe to discuss geosocial analysis and modeling of social media data. Geographers, GIScientists and social scientists gathered to report on the unique opportunities of collaboration and insights that can be gained from the analysis of location-based social media data collected from sources such as Facebook and Twitter. Participants shared innovative methods for social media data mining, big data analytics, social network analysis, social media data models and representations, human mobility and patterns of interaction.

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