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List of potentially new Indicators

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melvinhlwong edited this page May 2, 2023 · 19 revisions

Introduction

The following list is a collection of indicators that could be potentially added to the package. The list is ordered according to their priority. Below you can

List

  1. Agricultural Aptitutde
  2. NDVI
  3. Land Use in Forest Loss Areas see here for a possible data source, here for a commodity-driven analysis approach and the Dynamic World Dataset
  4. Inland Surface Water
  5. Nightlights
  6. Conflict Data The political science literature differentiates different types of conflict. For example, terrorism has a very different definition than civil war. In practice, one has to be careful to be aware of these conceptual differences to avaoid pitfalls in the analysis. Here are three classical conflict data sources:
  7. Fire Burn Severity to assess vegetation damage from fire see here
  8. Atmospheric Emissions
  9. New Ecosystems Layer to complement or subsitute WWF (See Publication here)
  10. Nelson et al. Accessibility map from the year 2000 to avoid endogeneity in statistical estimations of land conversion between 2001 and 2020 (currently used is 2015)
  11. Keeping an eye on the development of a new global 30m DTM
  12. New Biomass and forest change estimations from ESA see here
  13. Intersected Area with Indigenous territories
  14. Anthromes: Anthromes are discusses as a superior classification system then biomes because most of the earth surface is altered by human beings see this paper. Data and R code for analysis is available here. There is also a paper here which discusses why anthromes could be better for conservation prioritization because it puts people and their land-use into the focus which is also increasingly discussed in global conservation forums.

Criteria that have to be fullfilled before developing a new indicator.

  • Definition of reasearch question
  • Definition of the indicator
  • Possible data-sources
  • spatial and temporal resolutions available
  • other available R packages or routines that could help for code development

Comments for discussion:

  • Deforestation and forest degradation 1990-now (JRC-TMF)
  • Nelson data from 2000 (to avoid endogeneity: matching variables must not be post-treatment and should not be possibly influenced by treatment, which is the case for road construction/improvement/maintenance).

Data inventories to review

Data source lists curated by Qiusheng Wu:

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