Dr Massimiliano Pittore

massimiliano
Researcher Centre for Early Warning Systems Helmholtz Zentrum Potsdam Deutsches GeoForschungsZentrum
Potsdam Brandenburg
DEU

Resources

Statistic exposure model for residential building stock in Kyrgyzstan

Resulting statistic exposure model for residential building stock in Kyrgyzstan, based on a mixed top-down / bottom-up methodology employing remote-sensing, ground-based mobile mapping systems and ancillary information. The included fields are as follows: gid unique id of the item osm_id unique OSM id is_city boolean, true if referring to a single settlement name Rayon name in Russian name_en Rayom name in English name_a2 short name id_oblast id of corresponding Oblast tot Total population tot_u Total urban population tot_r Total rural population mu_0_4 Male urban, 0 - 4 years mu_5_14 Male urban, 5 - 14 years mu_15_44 Male urban, 15 - 44 years mu_45_69 Male urban, 45 - 69 years mu_69_ Male urban, >69 years fu_0_4 Female urban, 0 - 4 years fu_5_14 Female urban, 5 - 14 years fu_15_44 Female urban, 15 - 44 years fu_45_69 Female urban, 45 - 69 years fu_69_ Female urban, >69 years mr_0_4 Male rural, 0 - 4 years mr_5_14 Male rural, 5 - 14 years mr_15_44 Male rural, 15 - 44 years mr_45_69 Male rural, 45 - 69 years mr_69_ Male rural, >69 years fr_0_4 Female rural, 0 - 4 years fr_5_14 Female rural, 5 - 14 years fr_15_44 Female rural, 15 - 44 years fr_45_69 Female rural, 45 - 69 years fr_69_ Female rural, >69 years rayon_en Rayon name in english nobs_u Number of inspected buildings in urban environment nobs_r Number of inspected buildings in rural environment a_u Dirichlet parameter for urban areas a_r Dirichlet parameter for rural areas c1.1_u Dirichlet posterior urban areas c1.2_u Dirichlet posterior urban areas c1.3_u Dirichlet posterior urban areas c2.1_u Dirichlet posterior urban areas c2.3_u Dirichlet posterior urban areas c2.4_u Dirichlet posterior urban areas c3.1_u Dirichlet posterior urban areas c3.3_u Dirichlet posterior urban areas c4_u Dirichlet posterior urban areas c5.1_u Dirichlet posterior urban areas c6_u Dirichlet posterior urban areas c1.1_r Dirichlet posterior rural areas c1.2_r Dirichlet posterior rural areas c1.3_r Dirichlet posterior rural areas c2.1_r Dirichlet posterior rural areas c2.3_r Dirichlet posterior rural areas c2.4_r Dirichlet posterior rural areas c3.1_r Dirichlet posterior rural areas c3.3_r Dirichlet posterior rural areas c4_r Dirichlet posterior rural areas c5.1_r Dirichlet posterior rural areas c6_r Dirichlet posterior rural areas c1.1_05p_u confidence interval urban areas - 5% percentile c1.2_05p_u confidence interval urban areas - 5% percentile c1.3_05p_u confidence interval urban areas - 5% percentile c2.1_05p_u confidence interval urban areas - 5% percentile c2.3_05p_u confidence interval urban areas - 5% percentile c2.4_05p_u confidence interval urban areas - 5% percentile c3.1_05p_u confidence interval urban areas - 5% percentile c3.3_05p_u confidence interval urban areas - 5% percentile c4_05p_u confidence interval urban areas - 5% percentile c5.1_05p_u confidence interval urban areas - 5% percentile c6_05p_u confidence interval urban areas - 5% percentile c1.1_50p_u confidence interval urban areas - 50% percentile c1.2_50p_u confidence interval urban areas - 50% percentile c1.3_50p_u confidence interval urban areas - 50% percentile c2.1_50p_u confidence interval urban areas - 50% percentile c2.3_50p_u confidence interval urban areas - 50% percentile c2.4_50p_u confidence interval urban areas - 50% percentile c3.1_50p_u confidence interval urban areas - 50% percentile c3.3_50p_u confidence interval urban areas - 50% percentile c4_50p_u confidence interval urban areas - 50% percentile c5.1_50p_u confidence interval urban areas - 50% percentile c6_50p_u confidence interval urban areas - 50% percentil c1.1_95p_u confidence interval urban areas - 95% percentile c1.2_95p_u confidence interval urban areas - 95% percentile c1.3_95p_u confidence interval urban areas - 95% percentile c2.1_95p_u confidence interval urban areas - 95% percentile c2.3_95p_u confidence interval urban areas - 95% percentile c2.4_95p_u confidence interval urban areas - 95% percentile c3.1_95p_u confidence interval urban areas - 95% percentile c3.3_95p_u confidence interval urban areas - 95% percentile c4_95p_u confidence interval urban areas - 95% percentile c5.1_95p_u confidence interval urban areas - 95% percentile c6_95p_u confidence interval urban areas - 95% percentile c1.1_05p_r confidence interval rural areas - 5% percentile c1.2_05p_r confidence interval rural areas - 5% percentile c1.3_05p_r confidence interval rural areas - 5% percentile c2.1_05p_r confidence interval rural areas - 5% percentile c2.3_05p_r confidence interval rural areas - 5% percentile c2.4_05p_r confidence interval rural areas - 5% percentile c3.1_05p_r confidence interval rural areas - 5% percentile c3.3_05p_r confidence interval rural areas - 5% percentile c4_05p_r confidence interval rural areas - 5% percentile c5.1_05p_r confidence interval rural areas - 5% percentile c6_05p_r confidence interval rural areas - 5% percentile c1.1_50p_r confidence interval rural areas - 50% percentile c1.2_50p_r confidence interval rural areas - 50% percentile c1.3_50p_r confidence interval rural areas - 50% percentile c2.1_50p_r confidence interval rural areas - 50% percentile c2.3_50p_r confidence interval rural areas - 50% percentile c2.4_50p_r confidence interval rural areas - 50% percentile c3.1_50p_r confidence interval rural areas - 50% percentile c3.3_50p_r confidence interval rural areas - 50% percentile c4_50p_r confidence interval rural areas - 50% percentile c5.1_50p_r confidence interval rural areas - 50% percentile c6_50p_r confidence interval rural areas - 50% percentil c1.1_95p_r confidence interval rural areas - 95% percentile c1.2_95p_r confidence interval rural areas - 95% percentile c1.3_95p_r confidence interval rural areas - 95% percentile c2.1_95p_r confidence interval rural areas - 95% percentile c2.3_95p_r confidence interval rural areas - 95% percentile c2.4_95p_r confidence interval rural areas - 95% percentile c3.1_95p_r confidence interval rural areas - 95% percentile c3.3_95p_r confidence interval rural areas - 95% percentile c4_95p_r confidence interval rural areas - 95% percentile c5.1_95p_r confidence interval rural areas - 95% percentile c6_95p_r confidence interval rural areas - 95% percentile nb_05p_u total number of buildings, confidence interval for urban areas - 5% percentile nb_50p_u total number of buildings, confidence interval for urban areas - 50% percentile nb_95p_u total number of buildings, confidence interval for urban areas - 95% percentile nb_05p_r total number of buildings, confidence interval rural areas - 5% percentile nb_50p_r total number of buildings, confidence interval rural areas - 50% percentile nb_95p_r total number of buildings, confidence interval for rural areas - 95% percentile

Shared on 10 dec 2015

Fire stations in the Kyrgyz Republic

Listing of fire stations in the Kyrgyz Republic. Compiled for the World Bank Project "Measuring Seismic Risk in Kyrgyz Republic" Only parameters available are: id tel

Shared on 09 dec 2015

Landsat images used for exposure and vulnerability assessment in Kyrgyzstan

Listing of landsat images used for exposure and vulnerability assessment in Kyrgyzstan Compiled for the World Bank Project "Measuring Seismic Risk in Kyrgyz Republic Entries are: gid unique id of the segments label_clas classification of the segments. currently defined labels are: "comm_industrial" (commercial, industrial) "res_highrise" (residential, high-rise buildings) "res_lowrise_highdens" (residential, low-rise buildings, high spatial density) "res_lowrise_lowdens" (residential, low-rise buildings, low spatial density) NOTE: low-rise refers to 1-5 storeys buildings. high-rise refers to 5-18 storeys buildings. High-density refers usually to urban environments, while low-density refers to rural areas. The dataset has been obtained by automatic processing of Landsat 8 (OLI) imagery. The labelling process has been carried out using machine learning techniques, and is based on a training / testing dataset manually labeled by an operator. As result of an automatic procedure, and since is based on medium resolution multi-spectral data, there is no warranty on the accuracy of the dataset over individual features (segments) and small areas.

Shared on 09 dec 2015