Estimación de índice de pobreza multidimensional (IPM) en Bogotá D.C. y algunas ciudades cercanas usando imágenes satelitales
ilustraciones (principalmente a color), diagramas, fotografías
- Autores:
-
Oviedo Mozo, Juan Sebastián
- Tipo de recurso:
- Fecha de publicación:
- 2024
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/86359
- Palabra clave:
- 330 - Economía::339 - Macroeconomía y temas relacionados
Pobreza -- Índices
Pobreza - Investigaciones
Poverty - research
IPM
imagen satelital
Regresión tensorial
Red convolucional
Métricas de desempeño
Pixel
MPI
Satellite image
Tensor regression
Convolutional network
Performance metrics
Pixel
Modelos de redes neurales
Imágenes satelitales
Neural network simulation
Satellite imagery
- Rights
- openAccess
- License
- Reconocimiento 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Estimación de índice de pobreza multidimensional (IPM) en Bogotá D.C. y algunas ciudades cercanas usando imágenes satelitales |
| dc.title.translated.eng.fl_str_mv |
Multidimensional poverty index (MPI) estimation in Bogotá D.C. and some nearby cities using satellite imagery |
| title |
Estimación de índice de pobreza multidimensional (IPM) en Bogotá D.C. y algunas ciudades cercanas usando imágenes satelitales |
| spellingShingle |
Estimación de índice de pobreza multidimensional (IPM) en Bogotá D.C. y algunas ciudades cercanas usando imágenes satelitales 330 - Economía::339 - Macroeconomía y temas relacionados Pobreza -- Índices Pobreza - Investigaciones Poverty - research IPM imagen satelital Regresión tensorial Red convolucional Métricas de desempeño Pixel MPI Satellite image Tensor regression Convolutional network Performance metrics Pixel Modelos de redes neurales Imágenes satelitales Neural network simulation Satellite imagery |
| title_short |
Estimación de índice de pobreza multidimensional (IPM) en Bogotá D.C. y algunas ciudades cercanas usando imágenes satelitales |
| title_full |
Estimación de índice de pobreza multidimensional (IPM) en Bogotá D.C. y algunas ciudades cercanas usando imágenes satelitales |
| title_fullStr |
Estimación de índice de pobreza multidimensional (IPM) en Bogotá D.C. y algunas ciudades cercanas usando imágenes satelitales |
| title_full_unstemmed |
Estimación de índice de pobreza multidimensional (IPM) en Bogotá D.C. y algunas ciudades cercanas usando imágenes satelitales |
| title_sort |
Estimación de índice de pobreza multidimensional (IPM) en Bogotá D.C. y algunas ciudades cercanas usando imágenes satelitales |
| dc.creator.fl_str_mv |
Oviedo Mozo, Juan Sebastián |
| dc.contributor.advisor.none.fl_str_mv |
Arrieta-Prieto, Mario |
| dc.contributor.author.none.fl_str_mv |
Oviedo Mozo, Juan Sebastián |
| dc.contributor.orcid.spa.fl_str_mv |
Oviedo Mozo, Juan Sebastián [0000-0002-1125-954X] |
| dc.subject.ddc.spa.fl_str_mv |
330 - Economía::339 - Macroeconomía y temas relacionados |
| topic |
330 - Economía::339 - Macroeconomía y temas relacionados Pobreza -- Índices Pobreza - Investigaciones Poverty - research IPM imagen satelital Regresión tensorial Red convolucional Métricas de desempeño Pixel MPI Satellite image Tensor regression Convolutional network Performance metrics Pixel Modelos de redes neurales Imágenes satelitales Neural network simulation Satellite imagery |
| dc.subject.lemb.spa.fl_str_mv |
Pobreza -- Índices Pobreza - Investigaciones |
| dc.subject.lemb.eng.fl_str_mv |
Poverty - research |
| dc.subject.proposal.spa.fl_str_mv |
IPM imagen satelital Regresión tensorial Red convolucional Métricas de desempeño Pixel |
| dc.subject.proposal.eng.fl_str_mv |
MPI Satellite image Tensor regression Convolutional network Performance metrics Pixel |
| dc.subject.umls.spa.fl_str_mv |
Modelos de redes neurales Imágenes satelitales |
| dc.subject.umls.eng.fl_str_mv |
Neural network simulation Satellite imagery |
| description |
ilustraciones (principalmente a color), diagramas, fotografías |
| publishDate |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2024-07-02T22:45:42Z |
| dc.date.available.none.fl_str_mv |
2024-07-02T22:45:42Z |
| dc.date.issued.none.fl_str_mv |
2024 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
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info:eu-repo/semantics/publishedVersion |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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Text |
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http://purl.org/redcol/resource_type/TM |
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publishedVersion |
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https://repositorio.unal.edu.co/handle/unal/86359 |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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Abraham, N. and Khan, N. M. (2019). A novel focal tversky loss function with improved attention u-net for lesion segmentation. In 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019), pages 683–687. IEEE. Alkire, S., Calderon, M., Evans, M., Fortacz, A., Jahic, A., Ghorai, M., Sjolander, C., Mirza, T., Nogales, R., Scharlin-Pettee, S., Soomro, M., Shrestha, S., Suppa, N., and Tapia, H. (2022). Unpacking deprivation bundles to reduce multidimensional poverty global multidimensional poverty index 2022. Alkire, S. and Foster, J. (2011). Counting and multidimensional poverty measurement. Journal of Public Economics, 95(7):476–487. Angulo, R., Cuervo, Y. D., and Pardo, R. (2011). Índice de pobreza multidimensional para Colombia. Technical report, Departamento Nacional de Planeación. Benedetti, P., Femminella, M., and Reali, G. (2022). Mixed-sized biomedical image segmentation based on u-net architectures. Applied Sciences, 13(1):329. Berisha, B., M¨eziu, E., and Shabani, I. (2022). Big data analytics in cloud computing: an overview. Journal of Cloud Computing, 11(1):24. Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587. Chen, T., Zhu, Z., Hu, S.-M., Cohen-Or, D., and Shamir, A. (2016). Extracting 3d objects from photographs using 3-sweep. Communications of the ACM, 59(12):121–129. Cichocki, A., Mandic, D., De Lathauwer, L., Zhou, G., Zhao, Q., Caiafa, C., and Phan, H. A. (2015). Tensor decompositions for signal processing applications: From two-way to multiway component analysis. IEEE signal processing magazine, 32(2):145–163. DANE (2019). Ficha metodológica censo nacional de población y vivienda 2018. https:// www.dane.gov.co/files/censo2018/informacion-tecnica/DSO-CNPV-FME-01-v2.pdf. Accessed: 2022-08-06. DANE (2020). Índice de Pobreza Multidimensional: Predicción del IPM Censal Usando Aprendizaje de Máquinas e Imágenes Satelitales. PDF document. Author: Departamento Administrativo Nacional de Estadísticas, Year: 2020. Available at: https://www.dane.gov.co/files/investigaciones/experimentales/ipm/IPM-documento-metodologico.pdf. DANE-DPS-DNP (2012). Conpes 150 - metodologías oficiales y arreglos institucionales para la medición de la pobreza en Colombia. https://www.dane.gov.co/files/acerca/ Normatividad/CONPES-150.pdf. Accessed: 2022-12-08. DigitalSreeni (2021). 225 - attention u-net. what is attention and why is it needed for u-net? https://www.youtube.com/watch?v=KOF38xAvo8I. Fu, H., Meng, D., Li, W., and Wang, Y. (2021). Bridge crack semantic segmentation based on improved deeplabv3+. Journal of Marine Science and Engineering, 9(6):671. Fukushima, K., Miyake, S., and Ito, T. (1983). Neocognitron: A neural network model for a mechanism of visual pattern recognition. IEEE transactions on systems, man, and cybernetics, (5):826–834. Gábor, H. (2022). The tversky loss function and its modifications for medical image segmentation. Ghuffar, S. (2018). Dem generation from multi satellite planetscope imagery. Remote Sensing, 10(9). Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning. MIT press. Guo, W., Kotsia, I., and Patras, I. (2011). Tensor learning for regression. IEEE Transactions on Image Processing, 21(2):816–827. Hand, D. J. and Till, R. J. (2001). A simple generalisation of the area under the roc curve for multiple class classification problems. Machine learning, 45:171–186. He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep residual learning for image recognition. CoRR, abs/1512.03385. Head, A., Manguin, M., Tran, N., and Blumenstock, J. E. (2017). Can human development be measured with satellite imagery? In Proceedings of the Ninth International Conference on Information and Communication Technologies and Development, ICTD ’17, New York, NY, USA. Association for Computing Machinery. Heitmann, S. and Buri, S. (2019). Poverty estimation with satellite imagery at neighborhood levels : Results and lessons for financial inclusion from ghana and uganda. In Washington, D.C., World Bank Group. Hou, C., Nie, F., Yi, D., and Wu, Y. (2012). Efficient image classification via multiple rank regression. IEEE Transactions on Image Processing, 22(1):340–352. Huang, J., Zhou, W., Li, H., and Li, W. (2015). Sign language recognition using 3d convolutional neural networks. In 2015 IEEE International Conference on Multimedia and Expo (ICME), pages 1–6. Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., and Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301):790–794. Kolda, T. G. and Bader, B. W. (2009). Tensor decompositions and applications. SIAM review, 51(3):455–500. Liu, J., Wu, Z., Xiao, L., Sun, J., and Yan, H. (2019). Generalized tensor regression for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 58(2):1244–1258. Metz, C. E. (1978). Basic principles of roc analysis. In Seminars in nuclear medicine, volume 8, pages 283–298. Elsevier. Moncada, G. and Lee, H. (2005). Mecovi : Improving the survey and measurement of living conditions in latin america and the caribbean. en breve; No. 63. License: CC BY 3.0 IGO. Niu, Z., Zhou, M., Wang, L., Gao, X., and Hua, G. (2016). Ordinal regression with multiple output cnn for age estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4920–4928. Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., Mori, K., Mc- Donagh, S., Hammerla, N. Y., Kainz, B., et al. (2018). Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999. O’Shea, K. and Nash, R. (2015). An introduction to convolutional neural networks. CoRR, abs/1511.08458. Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. CoRR, abs/1505.04597. Salazar, R. C. A., Cuervo, Y. D., and Pardo, R. (2011). Índice de Pobreza Multidimensional para Colombia. Archivos de Economía 009228, Departamento Nacional de Planeación. Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., and Rueckert, D. (2019). Attention gated networks: Learning to leverage salient regions in medical images. Medical Image Analysis, 53. SEN, A. (1979). Issues in the measurement of poverty, en scandinavian journal of economic. Steele, J., Sundsøy, P., Pezzulo, C., Alegana, V., Bird, T., Blumenstock, J., Bjelland, J., Engø-Monsen, K., Montjoye, Y.-A., Iqbal, A., Hadiuzzaman, K., Lu, X.,Wetter, E., Tatem, A., and Bengtsson, L. (2017). Mapping poverty using mobile phone and satellite data. Journal of The Royal Society Interface, 14:20160690. Sudre, C. H., Li, W., Vercauteren, T., Ourselin, S., and Cardoso, M. J. (2017). Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pages 240–248. Springer International Publishing. Wikle, C. K., Zammit-Mangion, A., and Cressie, N. (2019). Spatio-temporal statistics with R. CRC Press. Wu, J. (2017). Introduction to convolutional neural networks. National Key Lab for Novel Software Technology. Nanjing University. China, 5(23):495. |
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Universidad Nacional de Colombia |
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Bogotá - Ciencias - Maestría en Ciencias - Estadística |
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Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Arrieta-Prieto, Marioc0991efb9f75e8850dbc1abbaa64900d600Oviedo Mozo, Juan Sebastián9a2ebcbb970db3e7f31da9ea6f062f43Oviedo Mozo, Juan Sebastián [0000-0002-1125-954X]2024-07-02T22:45:42Z2024-07-02T22:45:42Z2024https://repositorio.unal.edu.co/handle/unal/86359Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones (principalmente a color), diagramas, fotografíasEn este documento se estudia una alternativa para obtener estimaciones de pobreza utilizando imágenes satelitales de Bogotá, D.C. y algunos municipios cercanos. Para lograr este fin, se comparan las dos metodologías presentadas en este trabajo: un modelo de redes neuronales convolucionales (CNN) y un modelo de regresión tensorial (GTR), los dos modelos aplicados para la clasificación de pixeles. A partir de estos modelos se definen unos criterios o métricas que nos permiten seleccionar el modelo que mejor captura la distribución del Índice de pobreza multidimensional (IPM) a nivel de píxeles. Finalmente, se lleva a cabo una aplicación de estimación de pobreza utilizando las imágenes de Planet Scope (PS) y la información a nivel de manzanas del ´ultimo Censo Nacional de Población y Vivienda (CNPV2018) donde se encuentra que el modelo de GTR presenta mejores métricas de desempeño en comparación del modelo de CNN (Texto tomado de la fuente).This document explores an alternative method to obtain poverty estimates using satellite images of Bogot´a, D.C., and some nearby municipalities. To achieve this goal, the two methodologies proposed in this study are compared: Convolutional Neural Networks (CNN) and a Tensor Regression Model (GTR). Based on these models, criteria or metrics are defined to select the model that best captures the distribution of the Multidimensional Poverty Index (MPI) at the pixel level. Finally, a poverty estimation application is conducted using Planet Scope (PS) images and block-level information from the latest National Population and Housing Census (CNPV2018). The results reveal that the GTR model demonstrates superior performance metrics compared to the CNN model (Texto tomado de la fuente).MaestríaMagíster en Ciencias - EstadísticaAprendizaje Estadísticoxviii, 82 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - EstadísticaFacultad de CienciasUniversidad Nacional de Colombia - Sede Bogotá330 - Economía::339 - Macroeconomía y temas relacionadosPobreza -- ÍndicesPobreza - InvestigacionesPoverty - researchIPMimagen satelitalRegresión tensorialRed convolucionalMétricas de desempeñoPixelMPISatellite imageTensor regressionConvolutional networkPerformance metricsPixelModelos de redes neuralesImágenes satelitalesNeural network simulationSatellite imageryEstimación de índice de pobreza multidimensional (IPM) en Bogotá D.C. y algunas ciudades cercanas usando imágenes satelitalesMultidimensional poverty index (MPI) estimation in Bogotá D.C. and some nearby cities using satellite imageryTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/TMBogotáColombiaAbraham, N. and Khan, N. M. (2019). A novel focal tversky loss function with improved attention u-net for lesion segmentation. In 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019), pages 683–687. IEEE.Alkire, S., Calderon, M., Evans, M., Fortacz, A., Jahic, A., Ghorai, M., Sjolander, C., Mirza, T., Nogales, R., Scharlin-Pettee, S., Soomro, M., Shrestha, S., Suppa, N., and Tapia, H. (2022). Unpacking deprivation bundles to reduce multidimensional poverty global multidimensional poverty index 2022.Alkire, S. and Foster, J. (2011). Counting and multidimensional poverty measurement. Journal of Public Economics, 95(7):476–487.Angulo, R., Cuervo, Y. D., and Pardo, R. (2011). Índice de pobreza multidimensional para Colombia. Technical report, Departamento Nacional de Planeación.Benedetti, P., Femminella, M., and Reali, G. (2022). Mixed-sized biomedical image segmentation based on u-net architectures. Applied Sciences, 13(1):329.Berisha, B., M¨eziu, E., and Shabani, I. (2022). Big data analytics in cloud computing: an overview. Journal of Cloud Computing, 11(1):24.Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587.Chen, T., Zhu, Z., Hu, S.-M., Cohen-Or, D., and Shamir, A. (2016). Extracting 3d objects from photographs using 3-sweep. Communications of the ACM, 59(12):121–129.Cichocki, A., Mandic, D., De Lathauwer, L., Zhou, G., Zhao, Q., Caiafa, C., and Phan, H. A. (2015). Tensor decompositions for signal processing applications: From two-way to multiway component analysis. IEEE signal processing magazine, 32(2):145–163.DANE (2019). Ficha metodológica censo nacional de población y vivienda 2018. https:// www.dane.gov.co/files/censo2018/informacion-tecnica/DSO-CNPV-FME-01-v2.pdf. Accessed: 2022-08-06.DANE (2020). Índice de Pobreza Multidimensional: Predicción del IPM Censal Usando Aprendizaje de Máquinas e Imágenes Satelitales. PDF document. Author: Departamento Administrativo Nacional de Estadísticas, Year: 2020. Available at: https://www.dane.gov.co/files/investigaciones/experimentales/ipm/IPM-documento-metodologico.pdf.DANE-DPS-DNP (2012). Conpes 150 - metodologías oficiales y arreglos institucionales para la medición de la pobreza en Colombia. https://www.dane.gov.co/files/acerca/ Normatividad/CONPES-150.pdf. Accessed: 2022-12-08.DigitalSreeni (2021). 225 - attention u-net. what is attention and why is it needed for u-net? https://www.youtube.com/watch?v=KOF38xAvo8I.Fu, H., Meng, D., Li, W., and Wang, Y. (2021). Bridge crack semantic segmentation based on improved deeplabv3+. Journal of Marine Science and Engineering, 9(6):671.Fukushima, K., Miyake, S., and Ito, T. (1983). Neocognitron: A neural network model for a mechanism of visual pattern recognition. IEEE transactions on systems, man, and cybernetics, (5):826–834.Gábor, H. (2022). The tversky loss function and its modifications for medical image segmentation.Ghuffar, S. (2018). Dem generation from multi satellite planetscope imagery. Remote Sensing, 10(9).Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning. MIT press.Guo, W., Kotsia, I., and Patras, I. (2011). Tensor learning for regression. IEEE Transactions on Image Processing, 21(2):816–827.Hand, D. J. and Till, R. J. (2001). A simple generalisation of the area under the roc curve for multiple class classification problems. Machine learning, 45:171–186.He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep residual learning for image recognition. CoRR, abs/1512.03385.Head, A., Manguin, M., Tran, N., and Blumenstock, J. E. (2017). Can human development be measured with satellite imagery? In Proceedings of the Ninth International Conference on Information and Communication Technologies and Development, ICTD ’17, New York, NY, USA. Association for Computing Machinery.Heitmann, S. and Buri, S. (2019). Poverty estimation with satellite imagery at neighborhood levels : Results and lessons for financial inclusion from ghana and uganda. In Washington, D.C., World Bank Group.Hou, C., Nie, F., Yi, D., and Wu, Y. (2012). Efficient image classification via multiple rank regression. IEEE Transactions on Image Processing, 22(1):340–352.Huang, J., Zhou, W., Li, H., and Li, W. (2015). Sign language recognition using 3d convolutional neural networks. In 2015 IEEE International Conference on Multimedia and Expo (ICME), pages 1–6.Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., and Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301):790–794.Kolda, T. G. and Bader, B. W. (2009). Tensor decompositions and applications. SIAM review, 51(3):455–500.Liu, J., Wu, Z., Xiao, L., Sun, J., and Yan, H. (2019). Generalized tensor regression for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 58(2):1244–1258.Metz, C. E. (1978). Basic principles of roc analysis. In Seminars in nuclear medicine, volume 8, pages 283–298. Elsevier.Moncada, G. and Lee, H. (2005). Mecovi : Improving the survey and measurement of living conditions in latin america and the caribbean. en breve; No. 63. License: CC BY 3.0 IGO.Niu, Z., Zhou, M., Wang, L., Gao, X., and Hua, G. (2016). Ordinal regression with multiple output cnn for age estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4920–4928.Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., Mori, K., Mc- Donagh, S., Hammerla, N. Y., Kainz, B., et al. (2018). Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999.O’Shea, K. and Nash, R. (2015). An introduction to convolutional neural networks. CoRR, abs/1511.08458.Ronneberger, O., Fischer, P., and Brox, T. (2015). 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