ESQUIVEL: Avance del inventario estructural de edicaciones del cantón de San José
118
Hu, Z., Huyck, C., Eguchi, M., & Bevington, J. (2014). User guide: Tool for spatial inventory data development.
GEM Technical Report, 60. https://doi.org/10.13117/GEM.DATA-CAPTURE.TR2014.05
Instituto Geográco Nacional. (2014). Sistema Nacional de Información Territorial. http://www.snitcr.go.cr/
Miyamoto Internacional Inc. (2016a). Apéndice A: Datos suplementarios. The USAID / OFDA PREPARE
Program. Phase I., 1–143.
Miyamoto Internacional Inc. (2016b). Appendix B: Building Exposure Model. The USAID / OFDA PREPARE
Program. Phase I., 28.
Miyamoto Internacional Inc. (2016c). Assessment of Earthquakes Risks. The USAID / OFDA PREPARE
Program. Phase I., December, 1–59.
Mora, M. G., Valcárcel, J. A., Cardona, O. D., Pujades, L. G., Barbat, A. H., & Bernal, G. A. (2015).
Prioritizing interventions to reduce seismic vulnerability in school facilities in Colombia. Earthquake
Spectra, 31(4), 2535–2552. https://doi.org/10.1193/040412EQS151T
Mouroux, P., Bertrand, E., Bour, M., Le Brun, B., Depinois, S., & Masure, P. (2004). The European RISK-UE
project: an advanced approach to earthquake risk scenarios. 13th World Conference on Earthquake
Engineering, paper No. 3329, 14.
Municipalidad de San José. (2016). Diagnóstico cantonal 2016. Dirección de Planicación y Evaluación, 236.
Recuperado de https://www.msj.go.cr/MSJ/Municipalidad/Lists/Diagnstico Cantonal/DispForm.aspx?I-
D=2&Source=https%3A%2F%2Fwww.msj.go.cr%2FMSJ%2FMunicipalidad%2FSitePages%2FSJC_
diagnostico_cantonal.ASPX&ContentTypeId=0x0100455673D594F2D14DADEBA26DA2A907DA
Parolai, S. (2016a). DB2 Software platform and processing tools. SIBYL Project Derivable, 1–33.
Parolai, S. (2016b). DB3 Guidelines of the mobile mapping system and remote rapid visual screening. SIBYL
Project Derivable, 1–36.
Pittore, M. (2014). D3.5 Sampling framework. SENSUM Project Derivable, 1–72.
Pittore, M., Grant, D., Parolai, S., Free, M., Mambetalyev, E., & Sheraliev, T. (2017). Exposure and vul-
nerability assessment via the integration of remote and in situ information: case study of Kyrgyzstan
[Artículo de conferencia].
Pittore, M., Haas, M., & Megalooikonomou, K. G. (2018). Risk-Oriented, Bottom-Up Modeling of Building
Portfolios With Faceted Taxonomies. Frontiers in Built Environment, 4(October), 1–14. https://doi.
org/10.3389/fbuil.2018.00041
Pittore, M., Wieland, M., Errize, M., Kariptas, C., & Güngör, I. (2015). Improving post-earthquake insurance
claim management: A novel approach to prioritize geospatial data collection. ISPRS International
Journal of Geo-Information, 4(4), 2401–2427. https://doi.org/10.3390/ijgi4042401
PostgreSQL Global Development Group. (2018). PostgreSQL. The PostgreSQL Global Development Group.
2626. Recuperado de https://www.postgresql.org/les/documentation/pdf/9.6/postgresql-9.6-US.pdf
Santa-María, H., Hube, M. A., Rivera, F., Yepes-Estrada, C., & Valcárcel, J. A. (2017). Development of
national and local exposure models of residential structures in Chile. Natural Hazards, 86, 55–79.
https://doi.org/10.1007/s11069-016-2518-3
Simpson, A., Murnane, R., Saito, K., Phillips, E., Reid, R., & Himmelfarb, A. (2014). Understanding risk.
In GFDRR-World Bank. World Bank. https://doi.org/10.1136/bmj.329.7474.1086
Stone, H. (2017). Exposure and vulnerability for seismic risk evaluations [University College London].
Recuperado de https://discovery.ucl.ac.uk/id/eprint/10051591
Taubenböck, H., Geib, C., & Klotz, M. (2013). D.2.1 Present day and future remote sensing data. SENSUM
Project Derivable, 1–59.