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Determining Urban Emotion using a Supervised Learning Approach: A Case Study around Majitar, East
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 144 - Number 5 |
| Year of Publication: 2016 |
| Authors: Supriya Choudhury, Mohan P. Pradhan, Pratikshya Sharma, S. K. Kar |
10.5120/ijca2016910291
|
Supriya Choudhury, Mohan P. Pradhan, Pratikshya Sharma, S. K. Kar . Determining Urban Emotion using a Supervised Learning Approach: A Case Study around Majitar, East. International Journal of Computer Applications. 144, 5 ( Jun 2016), 37-44. DOI=10.5120/ijca2016910291
Abstract
Perception and expectation of citizens is an important factor in urban settlement, planning and management. Hence, there is a need of a participatory citizen centric planning of urban settlement based on spatial data. These perception and expectation may be represented in terms of emotions. Determining Urban Emotions is an approach which can be used to map different types of emotions associated with urbanization. In the recent years, some new methods have been presented for the area of urban and spatial planning, which resulted in a fundamental change of the issues and understanding of urban planning. Geographical information system acts as a key factor for analyzing urban emotions from various types of data. This paper presents the supervised learning approach for determining urban emotions using K-Nearest Neighbor algorithm.
References
- Supriya Choudhury, Mohan P. Pradhan, S. K. Kar, “A Survey on Determining Urban Emotions using Geo-Data Classification: A Case Study around Majitar, East District, Sikkim ”, International Journal of Computer Applications, (0975 – 8887), Volume 135 – No.2, February, 2016, ISBN : 973-93-80891-05-5.
- Peter Zeile, Bernd Resch, Linda Dorrzapf, Jan-Philipp Exner, Gunter Sagl, Anja Summa, Martin Sudmanns, Urban Emotions–Tools of Integrating People’s Perception into Urban Planning, Conference Proceedings REAL CORP 2015 Tagungsband, 5-7 May 2015, Ghent, Belgium. ISBN: 978-3-9503110-8-2 (CD-ROM); ISBN: 978-3-9503110-9-9 (Print).
- Peter Zeile, Bernd Resch, Jan-Philipp Exner and Gunther Sagl, Urban Emotions Benefits and Risks in Using Human Sensory Assessment for the Extraction of Contextual Emotion Information in Urban Planning, Springer International Publishing, 2015.
- Bernd Resch, Martin Sudmanns, Gunther Sagl, Anja Summa, Peter Zeile, and Jan-Philipp Exner, Crowd-sourcing Physiological Conditions and Subjective Emotions by Coupling Technical and Human Mobile Sensors, GI_Forum ‒ Journal for Geographic Information Science, 1-2015, Berlin, ISBN 978-3-87907-558-4, ISSN 2308-1708, doi:10.1553/giscience2015s514.
- Gunther Sagl, Bernd Resch, and Thomas Blaschke, Contextual Sensing: Integrating Contextual Information with Human and Technical Geo-Sensor Information for Smart Cities, Open Access Sensors, 2015, 15, 17013-17035; doi: 10.3390/s150717013, ISSN 1424-8220.
- Bernd Resch, Anja Summa, Gunther Sagl, Peter Zeile, Jan-Philipp Exner, Urban Emotions–Geo-Semantic Emotion Extraction from Technical Sensors, Human Sensors, Springer International Publishing, 2014.
- Chrysaida-Aliki Papadopoulou and Maria Giaoutzi, Crowd-sourcing as a Tool for Knowledge Acquisition in Spatial Planning”, Future Internet 2014, 6, 109-125; ISSN 1999-5903, doi:10.3390/fi6010109.
- Benjamin S. Bergner, Jan-Philipp Exner, Martin Memmel, Rania Raslan, Dina Taha, Manar Talal, Peter Zeile,”Human Sensory Assessment Methods in Urban Planning – a Case Study in Alexandria”, Conference Proceedings REAL CORP 2013, Tagungsband, 20-23 May 2013, Rome, Italy, ISBN: 978-3-9503110-4-4 (CD-ROM); ISBN: 978-3-9503110-5-1 (Print).
- Bernd Resch, “People as Sensors and Collective Sensing-Contextual Observations Complementing Geo-Sensor Network Measurements”, Springer International Publishing, 2013.
- Peter Zeile, Martin Memmel, Jan-Philipp Exner, “A New Urban Sensing and Monitoring Approach: Tagging the City with the RADAR SENSING App”, Reviewed Paper of Conference Proceedings REAL CORP 2012, Tagungsband, 14-16 May 2012, Schwechat, ISBN: 978-3-9503110-2-0 (CD-ROM); ISBN: 978-3-9503110-3-7 (Print).
- About K-nearest neighbors algorithm, https://en.wikipedia.org/wiki/Knearest_neighbors_algorithm.
- About Majitar, https://en.wikipedia.org/wiki/Majitar
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