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Emociones, espacio público e imágenes urbanas en el contexto de COVID-19
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Publicado:
Feb 24, 2022
Palabras clave:
imágenes, emociones, COVID-19, espacio público, machine learning, social network analysis
Contenido principal del artículo
Resumen
El confinamiento y las restricciones de movilidad durante la pandemia de COVID-19 han dado lugar auna serie de dilemas sobre el uso y percepción del espacio público, donde sus propiedades relacionalesy contextuales pueden dar lugar a una diversidad de emociones. Con la aplicación de herramientas machinelearning y social network analysis, exploramos emociones sobre el espacio público basadas enatributos de imágenes fotográficas en la ciudad de Quito, Ecuador, tomadas entre abril y junio de 2020.Los resultados muestran emociones positivas y negativas asociadas a un mismo atributo del paisajeurbano, mientras que atributos que pueden considerarse opuestos (como “brillante” y “sucio”) podríantener mayor influencia en los sentimientos positivos sobre dicho espacio. Esta investigación abre unnuevo campo de estudio en la región sobre las emociones urbanas, y ofrece un mejor entendimiento delas percepciones de los ciudadanos sobre el espacio público durante la crisis de la pandemia.
Detalles del artículo
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Miscelánea
Licencia
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Referencias
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Aslam, F., Awan, T. M., Syed, J. H., Kashif, A., & Parveen, M. (2020). Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak. Humanities and Social Sciences Communications, 7(1), 23. https://doi.org/10.1057/s41599-020-0523-3
Bondi, L. (2005). Making connections and thinking through emotions: between geography and psychotherapy. Transactions of the Institute of British Geographers, 30(4), 433–448. https://doi.org/10.1111/j.1475-5661.2005.00183.x
Brown, G., Raymond, C. M., & Corcoran, J. (2015). Mapping and measuring place attachment. Applied Geography, 57, 42–53. https://doi.org/https://doi.org/10.1016/j.apgeog.2014.12.011
Cabrera-Barona, P., & Carrión, A. (2020). Voiding Public Spaces, Enclosing Domestic Places: Place Attachment at the Onset of the Pandemic in Quito, Ecuador. Journal of Latin American Geography. https://doi.org/10.1353/lag.0.0145
Choudrie, J., Patil, S., Kotecha, K., Matta, N., & Pappas, I. (2021). Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study. Information Systems Frontiers, 23(6), 1431–1465. https://doi.org/10.1007/s10796-021-10152-6
Datta, R, Li, J., & Wang, J. Z. (2008). Algorithmic inferencing of aesthetics and emotion in natural images: An exposition. 2008 15th IEEE International Conference on Image Processing, 105–108. https://doi.org/10.1109/ICIP.2008.4711702
Datta, Ritendra, Joshi, D., Li, J., & Wang, J. Z. (2006). Studying Aesthetics in Photographic Images Using a Computational Approach. In A. Leonardis, H. Bischof, & A. Pinz (Eds.), Computer Vision – ECCV 2006 (pp. 288–301). Berlin, Heidelberg: Springer Berlin Heidelberg.
Devine-Wright, P., Pinto De Carvalho, L., Masso, A. Di, Lewicka, M., Manzo, L., & Williams, D. R. (2020). “Re-placed”-Reconsidering relationships with place and lessons from a pandemic. Journal of Environmental Psychology, 72, 101514. https://doi.org/10.1016/j.jenvp.2020.101514
Dhar, S., Ordonez, V., & Berg, T. L. (2011). High level describable attributes for predicting aesthetics and interestingness. CVPR 2011, 1657–1664. https://doi.org/10.1109/CVPR.2011.5995467
Dong, H., Ma, S., Jia, N., & Tian, J. (2021). Understanding public transport satisfaction in post COVID-19 pandemic. Transport Policy, 101, 81–88. https://doi.org/https://doi.org/10.1016/j.tranpol.2020.12.004
Ekman, P. (1999). Basic emotions. In T. Dalgleish & M. Power (Eds.), Handbook of Cognition and Emotion (pp. 45–60). Wiley & Sons Ltd.
Gholami, R., & Fakhari, N. (2017). Chapter 27 - Support Vector Machine: Principles, Parameters, and Applications. In P. Samui, S. Sekhar, & V. E. Balas (Eds.), Handbook of Neural Computation (pp. 515–535). https://doi.org/https://doi.org/10.1016/B978-0-12-811318-9.00027-2
Goodchild, M. F. (2007). Citizens as sensors: The world of volunteered geography. GeoJournal, 69, 211–221. https://doi.org/10.1007/s10708-007-9111-y
Hanzl, M. (2020). Urban forms and green infrastructure – the implications for public health during the COVID-19 pandemic. Cities & Health, 1–5. https://doi.org/10.1080/23748834.2020.1791441
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. https://doi.org/10.1109/CVPR.2016.90
Hernández Magaña, A., & Güiza Valverde, F. (2016). Información Geográfica Voluntaria (IGV), estado del arte en Latinoamérica. Revista Cartográfica, (93), 35–55.
Hidalgo, M. C. (2013). Operationalization of place attachment: A consensus proposal. Studies in Psychology, 34(3), 251–259. https://doi.org/10.1174/021093913808295190
Hidalgo, M. C., & Hernandez, B. (2001). Place Attachment: Conceptual and Empirical Questions. Journal of Environmental Psychology, 21(3), 273–281. https://doi.org/10.1006/JEVP.2001.0221
Honey-Rosés, J., Anguelovski, I., Chireh, V. K., Daher, C., Konijnendijk van den Bosch, C., Litt, J. S., … Nieuwenhuijsen, M. J. (2020). The impact of COVID-19 on public space: an early review of the emerging questions – design, perceptions and inequities. Cities & Health, 1–17. https://doi.org/10.1080/23748834.2020.1780074
Huang, Y., Fei, T., Kwan, M.-P., Kang, Y., Li, J., Li, Y., … Bian, M. (2020). GIS-Based Emotional Computing: A Review of Quantitative Approaches to Measure the Emotion Layer of Human–Environment Relationships. ISPRS International Journal of Geo-Information, 9(9). https://doi.org/10.3390/ijgi9090551
Izard, C. E. (2007). Basic Emotions, Natural Kinds, Emotion Schemas, and a New Paradigm. Perspectives on Psychological Science, 2(3), 260–280. Retrieved from https://about.jstor.org/terms
Jasi?ski, A. (2020). Public space or safe space – remarks during the COVID-19 pandemic. Technical Transactions, e2020020, 20200020. https://doi.org/https://doi.org/10.37705/TechTrans/e2020020
Kamalipour, H., Yeganeh, A. J., & Alalhesabi, M. (2012). Predictors of Place Attachment in Urban Residential Environments: A Residential Complex Case Study. Procedia - Social and Behavioral Sciences, 35, 459–467. https://doi.org/10.1016/J.SBSPRO.2012.02.111
Kaur, S., Kaul, P., & Zadeh, P. M. (2020). Monitoring the Dynamics of Emotions during COVID-19 Using Twitter Data. Procedia Computer Science, 177, 423–430. https://doi.org/https://doi.org/10.1016/j.procs.2020.10.056
Kleinberg, J. M. (1999). Authoritative Sources in a Hyperlinked Environment. J. ACM, 46(5), 604–632. https://doi.org/10.1145/324133.324140
Lewicka, M. (2008). Place attachment, place identity, and place memory: Restoring the forgotten city past. Journal of Environmental Psychology, 28(3), 209–231. https://doi.org/10.1016/J.JENVP.2008.02.001
Li, Y., Fei, T., Huang, Y., Li, J., Li, X., Zhang, F., … Wu, G. (2020). Emotional habitat: mapping the global geographic distribution of human emotion with physical environmental factors using a species distribution model. International Journal of Geographical Information Science, 1–23. https://doi.org/10.1080/13658816.2020.1755040
Low, S., & Smart, A. (2020). Thoughts about Public Space During Covid-19 Pandemic. City & Society (Washington, D.C.), 32(1), 10.1111/ciso.12260. https://doi.org/10.1111/ciso.12260
Marin, A., & Wellman, B. (2014). Social Network Analysis: An Introduction. In J. Scott & P. Carrington (Eds.), The SAGE Handbook of Social Network Analysis (pp. 11–25). https://doi.org/10.4135/9781446294413.n2
Meng, M., & Zhao, C. (2015). Application of Support Vector Machines to a Small-Sample Prediction. Advances in Petroleum Exploration and Development, 10(2), 72–75.
Orellana, D., Bustos, M. E., Marín-Palacios, M., Cabrera-Jara, N., & Hermida, M. A. (2020). Walk’n’roll: Mapping street-level accessibility for different mobility conditions in Cuenca, Ecuador. Journal of Transport and Health, 16, 100821. https://doi.org/10.1016/j.jth.2020.100821
Resch, B., & Szell, M. (2019). Human-Centric Data Science for Urban Studies. ISPRS International Journal of Geo-Information, 8(12). https://doi.org/10.3390/ijgi8120584
Ricart, N., & Remesar, A. (2013). Reflexiones sobre el espacio público . On the W@terfront, (25), 5–35.
Sagl, G., Resch, B., & Blaschke, T. (2015). Contextual Sensing: Integrating Contextual Information with Human and Technical Geo-Sensor Information for Smart Cities. Sensors, 15(7), 17013–17035. https://doi.org/10.3390/s150717013
Tuan, Y.-F. (1976). Humanistic Geography. Annals of the Association of American Geographers, 66(2), 266–276.
Tuan, Y.-F. (1990). Topophilia: A Study of Environmental Perceptions, Attitudes, and Values. Columbia University Press.
Ujang, N. (2012). Place Attachment and Continuity of Urban Place Identity. Procedia - Social and Behavioral Sciences, 49, 156–167. https://doi.org/10.1016/J.SBSPRO.2012.07.014
van Eck, E., van Melik, R., & Schapendonk, J. (2020). Marketplaces as Public Spaces in Times of The Covid-19 Coronavirus Outbreak: First Reflections. Tijdschrift Voor Economische En Sociale Geografie, 111(3), 373–386. https://doi.org/https://doi.org/10.1111/tesg.12431
Woodford, L., & Bussey, L. (2021). Exploring the Perceived Impact of the COVID-19 Pandemic Social Distancing Measures on Athlete Wellbeing: A Qualitative Study Utilising Photo-Elicitation. Frontiers in Psychology, 12, 2727. https://doi.org/10.3389/fpsyg.2021.624023
Xue, J., Chen, J., Hu, R., Chen, C., Zheng, C., Su, Y., & Zhu, T. (2020). Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach. J Med Internet Res, 22(11), e20550. https://doi.org/10.2196/20550
Zhang, F., Zhou, B., Liu, L., Liu, Y., Fung, H. H., Lin, H., & Ratti, C. (2018). Measuring human perceptions of a large-scale urban region using machine learning. Landscape and Urban Planning, 180, 148–160. https://doi.org/10.1016/j.landurbplan.2018.08.020
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A. (2017). Places: A 10 million Image Database for Scene Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence.
Aslam, F., Awan, T. M., Syed, J. H., Kashif, A., & Parveen, M. (2020). Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak. Humanities and Social Sciences Communications, 7(1), 23. https://doi.org/10.1057/s41599-020-0523-3
Bondi, L. (2005). Making connections and thinking through emotions: between geography and psychotherapy. Transactions of the Institute of British Geographers, 30(4), 433–448. https://doi.org/10.1111/j.1475-5661.2005.00183.x
Brown, G., Raymond, C. M., & Corcoran, J. (2015). Mapping and measuring place attachment. Applied Geography, 57, 42–53. https://doi.org/https://doi.org/10.1016/j.apgeog.2014.12.011
Cabrera-Barona, P., & Carrión, A. (2020). Voiding Public Spaces, Enclosing Domestic Places: Place Attachment at the Onset of the Pandemic in Quito, Ecuador. Journal of Latin American Geography. https://doi.org/10.1353/lag.0.0145
Choudrie, J., Patil, S., Kotecha, K., Matta, N., & Pappas, I. (2021). Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study. Information Systems Frontiers, 23(6), 1431–1465. https://doi.org/10.1007/s10796-021-10152-6
Datta, R, Li, J., & Wang, J. Z. (2008). Algorithmic inferencing of aesthetics and emotion in natural images: An exposition. 2008 15th IEEE International Conference on Image Processing, 105–108. https://doi.org/10.1109/ICIP.2008.4711702
Datta, Ritendra, Joshi, D., Li, J., & Wang, J. Z. (2006). Studying Aesthetics in Photographic Images Using a Computational Approach. In A. Leonardis, H. Bischof, & A. Pinz (Eds.), Computer Vision – ECCV 2006 (pp. 288–301). Berlin, Heidelberg: Springer Berlin Heidelberg.
Devine-Wright, P., Pinto De Carvalho, L., Masso, A. Di, Lewicka, M., Manzo, L., & Williams, D. R. (2020). “Re-placed”-Reconsidering relationships with place and lessons from a pandemic. Journal of Environmental Psychology, 72, 101514. https://doi.org/10.1016/j.jenvp.2020.101514
Dhar, S., Ordonez, V., & Berg, T. L. (2011). High level describable attributes for predicting aesthetics and interestingness. CVPR 2011, 1657–1664. https://doi.org/10.1109/CVPR.2011.5995467
Dong, H., Ma, S., Jia, N., & Tian, J. (2021). Understanding public transport satisfaction in post COVID-19 pandemic. Transport Policy, 101, 81–88. https://doi.org/https://doi.org/10.1016/j.tranpol.2020.12.004
Ekman, P. (1999). Basic emotions. In T. Dalgleish & M. Power (Eds.), Handbook of Cognition and Emotion (pp. 45–60). Wiley & Sons Ltd.
Gholami, R., & Fakhari, N. (2017). Chapter 27 - Support Vector Machine: Principles, Parameters, and Applications. In P. Samui, S. Sekhar, & V. E. Balas (Eds.), Handbook of Neural Computation (pp. 515–535). https://doi.org/https://doi.org/10.1016/B978-0-12-811318-9.00027-2
Goodchild, M. F. (2007). Citizens as sensors: The world of volunteered geography. GeoJournal, 69, 211–221. https://doi.org/10.1007/s10708-007-9111-y
Hanzl, M. (2020). Urban forms and green infrastructure – the implications for public health during the COVID-19 pandemic. Cities & Health, 1–5. https://doi.org/10.1080/23748834.2020.1791441
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. https://doi.org/10.1109/CVPR.2016.90
Hernández Magaña, A., & Güiza Valverde, F. (2016). Información Geográfica Voluntaria (IGV), estado del arte en Latinoamérica. Revista Cartográfica, (93), 35–55.
Hidalgo, M. C. (2013). Operationalization of place attachment: A consensus proposal. Studies in Psychology, 34(3), 251–259. https://doi.org/10.1174/021093913808295190
Hidalgo, M. C., & Hernandez, B. (2001). Place Attachment: Conceptual and Empirical Questions. Journal of Environmental Psychology, 21(3), 273–281. https://doi.org/10.1006/JEVP.2001.0221
Honey-Rosés, J., Anguelovski, I., Chireh, V. K., Daher, C., Konijnendijk van den Bosch, C., Litt, J. S., … Nieuwenhuijsen, M. J. (2020). The impact of COVID-19 on public space: an early review of the emerging questions – design, perceptions and inequities. Cities & Health, 1–17. https://doi.org/10.1080/23748834.2020.1780074
Huang, Y., Fei, T., Kwan, M.-P., Kang, Y., Li, J., Li, Y., … Bian, M. (2020). GIS-Based Emotional Computing: A Review of Quantitative Approaches to Measure the Emotion Layer of Human–Environment Relationships. ISPRS International Journal of Geo-Information, 9(9). https://doi.org/10.3390/ijgi9090551
Izard, C. E. (2007). Basic Emotions, Natural Kinds, Emotion Schemas, and a New Paradigm. Perspectives on Psychological Science, 2(3), 260–280. Retrieved from https://about.jstor.org/terms
Jasi?ski, A. (2020). Public space or safe space – remarks during the COVID-19 pandemic. Technical Transactions, e2020020, 20200020. https://doi.org/https://doi.org/10.37705/TechTrans/e2020020
Kamalipour, H., Yeganeh, A. J., & Alalhesabi, M. (2012). Predictors of Place Attachment in Urban Residential Environments: A Residential Complex Case Study. Procedia - Social and Behavioral Sciences, 35, 459–467. https://doi.org/10.1016/J.SBSPRO.2012.02.111
Kaur, S., Kaul, P., & Zadeh, P. M. (2020). Monitoring the Dynamics of Emotions during COVID-19 Using Twitter Data. Procedia Computer Science, 177, 423–430. https://doi.org/https://doi.org/10.1016/j.procs.2020.10.056
Kleinberg, J. M. (1999). Authoritative Sources in a Hyperlinked Environment. J. ACM, 46(5), 604–632. https://doi.org/10.1145/324133.324140
Lewicka, M. (2008). Place attachment, place identity, and place memory: Restoring the forgotten city past. Journal of Environmental Psychology, 28(3), 209–231. https://doi.org/10.1016/J.JENVP.2008.02.001
Li, Y., Fei, T., Huang, Y., Li, J., Li, X., Zhang, F., … Wu, G. (2020). Emotional habitat: mapping the global geographic distribution of human emotion with physical environmental factors using a species distribution model. International Journal of Geographical Information Science, 1–23. https://doi.org/10.1080/13658816.2020.1755040
Low, S., & Smart, A. (2020). Thoughts about Public Space During Covid-19 Pandemic. City & Society (Washington, D.C.), 32(1), 10.1111/ciso.12260. https://doi.org/10.1111/ciso.12260
Marin, A., & Wellman, B. (2014). Social Network Analysis: An Introduction. In J. Scott & P. Carrington (Eds.), The SAGE Handbook of Social Network Analysis (pp. 11–25). https://doi.org/10.4135/9781446294413.n2
Meng, M., & Zhao, C. (2015). Application of Support Vector Machines to a Small-Sample Prediction. Advances in Petroleum Exploration and Development, 10(2), 72–75.
Orellana, D., Bustos, M. E., Marín-Palacios, M., Cabrera-Jara, N., & Hermida, M. A. (2020). Walk’n’roll: Mapping street-level accessibility for different mobility conditions in Cuenca, Ecuador. Journal of Transport and Health, 16, 100821. https://doi.org/10.1016/j.jth.2020.100821
Resch, B., & Szell, M. (2019). Human-Centric Data Science for Urban Studies. ISPRS International Journal of Geo-Information, 8(12). https://doi.org/10.3390/ijgi8120584
Ricart, N., & Remesar, A. (2013). Reflexiones sobre el espacio público . On the W@terfront, (25), 5–35.
Sagl, G., Resch, B., & Blaschke, T. (2015). Contextual Sensing: Integrating Contextual Information with Human and Technical Geo-Sensor Information for Smart Cities. Sensors, 15(7), 17013–17035. https://doi.org/10.3390/s150717013
Tuan, Y.-F. (1976). Humanistic Geography. Annals of the Association of American Geographers, 66(2), 266–276.
Tuan, Y.-F. (1990). Topophilia: A Study of Environmental Perceptions, Attitudes, and Values. Columbia University Press.
Ujang, N. (2012). Place Attachment and Continuity of Urban Place Identity. Procedia - Social and Behavioral Sciences, 49, 156–167. https://doi.org/10.1016/J.SBSPRO.2012.07.014
van Eck, E., van Melik, R., & Schapendonk, J. (2020). Marketplaces as Public Spaces in Times of The Covid-19 Coronavirus Outbreak: First Reflections. Tijdschrift Voor Economische En Sociale Geografie, 111(3), 373–386. https://doi.org/https://doi.org/10.1111/tesg.12431
Woodford, L., & Bussey, L. (2021). Exploring the Perceived Impact of the COVID-19 Pandemic Social Distancing Measures on Athlete Wellbeing: A Qualitative Study Utilising Photo-Elicitation. Frontiers in Psychology, 12, 2727. https://doi.org/10.3389/fpsyg.2021.624023
Xue, J., Chen, J., Hu, R., Chen, C., Zheng, C., Su, Y., & Zhu, T. (2020). Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach. J Med Internet Res, 22(11), e20550. https://doi.org/10.2196/20550
Zhang, F., Zhou, B., Liu, L., Liu, Y., Fung, H. H., Lin, H., & Ratti, C. (2018). Measuring human perceptions of a large-scale urban region using machine learning. Landscape and Urban Planning, 180, 148–160. https://doi.org/10.1016/j.landurbplan.2018.08.020
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A. (2017). Places: A 10 million Image Database for Scene Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence.