Data Repository - Supplementary Materials to Droughts and deforestation: Does seasonality matter? Vaglietti et al. 2022
Principal Investigator(s): View help for Principal Investigator(s) Giulia Vaglietti; Philippe Delacote; Antoine Leblois
Version: View help for Version V1
| Name | File Type | Size | Last Modified |
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| Geographic-grid | 08/25/2022 07:59:AM | ||
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text/x-rsrc | 13.6 KB | 09/01/2022 10:15:AM |
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text/csv | 165 MB | 09/01/2022 09:57:AM |
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text/html | 7.9 MB | 04/08/2022 07:22:AM |
Project Citation:
Vaglietti, Giulia, Delacote, Philippe, and Leblois, Antoine. Data Repository - Supplementary Materials to Droughts and deforestation: Does seasonality matter? Vaglietti et al. 2022. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2022-09-01. https://doi.org/10.3886/E178361V1
Project Description
Summary:
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Lastly is provided a geographic information system data (shapefile) to match the data contained in the .csv with a specific location.
To reproduce the econometric analysis is also provided a R code recalling the same variables displayed in Vaglietti et al. 2022 (Programming language - R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria).
This data repository collects the supporting materials concerning the working paper on deforestation and weather shocks proposed by Vaglietti G., Delacote P., and Leblois A.,(2022), Droughts and deforestation: does seasonality matter?.
It is constituted of 4 main files: a data paper, a code file, a data file and a shapefile.
The data paper aims to present the databases used during the analysis and how those have been processed in order to obtain the final results. Moreover, it presents additional databases, which have not been applied in the analysis but may be useful to answer future questions connected to the same topics. In particular, for each database will be provided the main characteristics in terms of data availability, space/temporal coverage as well as the procedure followed to make the data comparable.
The results have been then aggregated in a panel dataset used to perform the econometric analysis presented in the paper: the data are here available in .csv format.
The data paper aims to present the databases used during the analysis and how those have been processed in order to obtain the final results. Moreover, it presents additional databases, which have not been applied in the analysis but may be useful to answer future questions connected to the same topics. In particular, for each database will be provided the main characteristics in terms of data availability, space/temporal coverage as well as the procedure followed to make the data comparable.
The results have been then aggregated in a panel dataset used to perform the econometric analysis presented in the paper: the data are here available in .csv format.
Lastly is provided a geographic information system data (shapefile) to match the data contained in the .csv with a specific location.
To reproduce the econometric analysis is also provided a R code recalling the same variables displayed in Vaglietti et al. 2022 (Programming language - R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria).
Scope of Project
Subject Terms:
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deforestation;
drought;
adaptation
Geographic Coverage:
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DRC
Time Period(s):
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1/2001 – 12/2020
Data Type(s):
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aggregate data;
geographic information system (GIS) data
Methodology
Data Source:
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Data are a reanalysis of the ones provided by Hansen et al. (2013) for the Forest Cover and the Forest Loss, by Funk et al. (2014) for precipitations, by the UNEP-WCMC - UNEP (2019) for protected areas and by Nelson (2008) for proximity to cities.
Precipitations have been aggregated in function of the cropping calendars provided by Sacks et al. (2010).
Additional databases, how they should be treated to be added to the actual analysis have been reported in the Data Paper.
References:
Precipitations have been aggregated in function of the cropping calendars provided by Sacks et al. (2010).
Additional databases, how they should be treated to be added to the actual analysis have been reported in the Data Paper.
References:
- Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros, D.H., Verdin, J.P., Rowland, J.D., Romero, B.E., Husak, G.J., Michaelsen, J.C., and Verdin, A.P., (2014), A quasi-global precipitation time series for drought monitoring: U.S. Geological Survey Data Series 832, 4
- Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., … & Townshend, J. (2013). High-resolution global maps of 21st-century forest cover change. science, 342(6160), 850-853
- Nelson, A., 2008. Travel time to major cities: A global map of accessibility. Office for Official Publications of the European Communities, Luxembourg. doi:10.2788/95835
- Sacks, W.J., D. Deryng, J.A. Foley, and N. Ramankutty (2010). Crop planting dates: an analysis of global patterns. Global Ecology and Biogeography 19, 607-620. DOI: 10.1111/j.1466-8238.2010.00551.x
- UNEP-WCMC - UN Environment Programme World Conservation Monitoring Centre (2019). User Manual for the World Database on Protected Areas and world database on other effective area-based conservation measures: 1.6. UNEP-WCMC: Cambridge, UK.
Unit(s) of Observation:
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Cell based
Geographic Unit:
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30 sqkm at the Equator ca.
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