文章基本信息

标题：Social Media Geographic Information: Recent Findings and Opportunities for Smart Spatial Planning
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作者：Pierangelo Massa ; Michele Campagna
期刊名称：Tema. Journal of Land Use, Mobility and Environment
印刷版ISSN：1970-9870
出版年度：2014
DOI：10.6092/1970-9870/2500
语种：English
出版社：Tema. Journal of Land Use, Mobility and Environment
摘要：This paper deals with the opportunities of Social Media Geographic Information (SMGI) as valuable support for analysis, design and decision-making in urban and regional planning. In the light of the recent advances in digital geographic information, such as Spatial Data Infrastructures (SDI) and Volunteered Geographic Information (VGI) which are fostering innovation in urban and regional planning, the authors focus on the potentialities of Social Media as source of knowledge for the planning practices. The authors argue SMGI may play an important role to inform 'smart city' strategies extending authoritative and sensor data infrastructure with experiential and pluralist citizens knowledge. To support this thesis, an innovative methodology for the advance of Social Media Geographic Information (SMGI) Analytics is proposed. To this end, the authors present an original user-friendly tool able to extract information from popular Social Media such as Twitter.com and Youtube.com and to apply Spatial-Temporal Textual (STTx) analysis . Some examples are provided at the regional and at the local scale in order to demonstrate the potential of SMGI analytics in regional and urban planning. The results show how SMGI analytics can support design, analysis and decision making in planning, and add the value of pluralism to inform smart city initiatives. The paper ends with some brief conclusions on the opportunities of SMGI analytics for possibly affecting decision-making dynamics and urban and regional planning processes, through citizens’ dialogue and integration of experiential and professional knowledge.
关键词：Social Media Geographic Information SMGI;Urban Regional Planning;Volunteered Geographic Information VGI;Computational Social Science