play_arrowdatasetViewer.description
This data set represents the levels of social vulnerability to various natural hazards in Chile using data from the 2002 Population Census. The model is based on the Social Vulnerability Index (SoVI) created by Cutter et al., (2003). This index was built based on demographic, socioeconomic and physical variables of the population at the district level, mainly through the Population and Housing Census of each of the years involved in the study (1992, 2002 and 2017).
On the map, 5 levels of vulnerability are represented, which range from “Very low” to “Very high”. Each of these levels were defined in standard deviation values with respect to the mean and are represented by a red scale, where the palest red indicates the lowest levels of vulnerability and the strongest the highest levels.
| SOVI range | Concept |
|:--|:--|
| $(-\infty , \mu - 1.5 \ \sigma ]$ | Very low |
| $(\mu - 1.5 \ \sigma , \mu - 0.5 \ \sigma]$ | Low |
| $(\mu - 0.5 \ \sigma , \mu + 0.5 \ \sigma]$ | Medium |
| $(\mu + 0.5 \ \sigma , \mu + 1.5 \ \sigma]$ | High |
| $(\mu + 1.5 \ \sigma , + \infty)$ | Very high |
The levels of vulnerability obtained for each year are explained by different factors. However, it is highlighted that in general socioeconomic status is the common factor to explain social vulnerability for 1992, 2002 and 2017. For more details refer to the article Temporal evolution in social vulnerability to natural hazards in Chile. Bronfman, N. C., Repetto, P. B., Guerrero, N., Castañeda, J. V., & Cisternas, P. C. (2021). Natural hazards, 107(2), 1757-1784. play_arrowdatasetViewer.preview
datasetViewer.previewCollectionInfo
| Nombre Región | Código Región | Número distritos | SOVI mínimo | SOVI máximo |
|---|
| 15 | Arica y Parinacota | 37 | -1.96 | 7.24 |
| 1 | Tarapacá | 38 | -5.43 | 7.8 |
| 2 | Antofagasta | 62 | -2.85 | 6.22 |
| 3 | Atacama | 74 | -1.06 | 13.59 |
| 4 | Coquimbo | 207 | -1.96 | 9.6 |
| ··· | ··· | ··· | ··· | ··· |
| 9 | La Araucanía | 299 | -2.5 | 18.72 |
| 14 | Los Ríos | 100 | -3.07 | 1.89 |
| 10 | Los Lagos | 238 | -3.21 | 2.4 |
| 11 | Aysén del General Carlos Ibáñez del Campo | 54 | -2.07 | 4.75 |
| 12 | Magallanes y de la Antártica Chilena | 36 | -3.61 | 2.79 |
play_arrowdatasetViewer.download
datasetViewer.download1 datasetViewer.here. datasetViewer.download3
play_arrowdatasetViewer.programaticAccess
datasetViewer.programaticAccess1 itrend-ds:fe85339b02cd8128
datasetViewer.programaticAccess2 Nombre Región
datasetViewer.programaticAccess3 datasetViewer.programaticAccess4 datasetViewer.programaticAccess5 datasetViewer.here
```python
import pytrend
import pandas as pd
session = pytrend.itrend_developer_tools()
session.set_credentials(
access_key_id = '', # Type your access_key_id
secret_access_key = '' # Type your secret_access_key
)
# Dataset
dataset_id = 'itrend-ds:fe85339b02cd8128'
collection_id = 'Nombre Región'
# Get available formats
dataset_formats = session.get_dataset_formats(dataset_id)
fmt = dataset_formats[0] # Choose your preferred format: dataset_formats = [csv]
# Download file
response = session.download_file(dataset_id, fmt)
# Download an element
filename = response.get('filename')
delimiter = response.get('delimiter')
df = pd.read_csv(filename, delimiter)
element_formats = session.get_element_formats(dataset_id)
efmt = element_formats[0] # Choose your preferred format: element_formats = [csv, xlsx, shp]
for r, row in df.iterrows():
element_id = row[collection_id]
element_response = session.download_file(dataset_id, efmt, element_id)
break
```
play_arrowdatasetViewer.attribution
datasetViewer.attributionMessage1
datasetViewer.attribution1 National Research Center for Integrated Natural Disaster Management, «Vulnerability index SoVI of the 2002 National Census by Bronfman et al. (2021)». datasetViewer.attribution2
file_copy
datasetViewer.attributionMessage2
datasetViewer.attribution3 National Research Center for Integrated Natural Disaster Management («Vulnerability index SoVI of the 2002 National Census by Bronfman et al. (2021)»), datasetViewer.attribution4
file_copy
play_arrowdatasetViewer.permanentLink
datasetViewer.permanentLinkDescription
https://www.plataformadedatos.cl/datasets/en/fe85339b02cd8128
file_copy
play_arrowdatasetViewer.metadata
datasetViewer.noMetadata
