Friday, June 1, 2018

Regional ethnic diversity

Alesina and Zhuravskaya (2011) construct ethnic diversity measures at the sub-national region level for 97 countries. The data is available here (click "Download Data Set").

Gershman and Rivera (2018) construct alternative ethnic diversity measures at the sub-national region level for 36 African countries. The data is available as part of the replication files (see Appendix H on the journal webpage).

Thursday, May 24, 2018

Elevation

See also the Terrain Ruggedness Index.

The best elevation data as of 2016 seems to be WorldDEM, although I haven't seen any application in economics research. It's also not for free of charge. Below is the list of other elevation datasets (available for free of charge) that have been used by economists in the past.

GTOPO30

Developed by the U.S. Geological Survey's Center for Earth Resources Observation and Science (EROS) in 1996, GTOPO30 provides elevations at the 30 arc seconds (roughly 1km) grid level. See the USGS/EROS website for detail.

GTOPO30 was used by Deininger and Minten (2002), Nunn and Puga (2007) to measure the degree of ruggedness of the earth surface of each country, and Duflo and Pande (2007) to calculate river gradient in India.

GTOPO30 is now superseded by Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010).


SRTM3 / SRTM30

SRTM3 is an updated version of GTOPO30 (I suppose) at a higher spatial resolution of 3 arc-seconds (roughly 100m). SRTM30 is a version that aggregates SRTM3 to the 30 arc second resolution. SRTM30 is supposed to be better than GTOPO30. See Farr et al. (2007) for detail.

For SRTM3 (version 2.1), the data is available here and the documentation is available here. For SRTM30 (version 2.1), both the data and the documentation is available here. For a graphical interface to download the data, visit here.

SRTM30 has been widely used by economists: Taryn Dinkelman's working paper (now published in American Economic Review) "The Effects of Rural Electrification on Employment: New Evidence from South Africa" (to create an instrument for electricity grid placements); Melissa Dell's working paper (now published in Econometrica) "The Persistent Effects of Peru's Mining Mita" (to create control variables); Acemoglu and Dell's paper forthcoming in AEJ Macro "Productivity Differences Within and Between Countries" (to calculate the distance to paved roads that takes into account elevation); Olken (2009) (to obtain the strength of TV signals in each sub-district of Indonesia); and Yanagizawa (2009) "Propaganda and Conflict: Theory and Evidence from the Rwandan Genocide".

How to use GTOPO30 / SRTM30 in ArcGIS

Here is the tip for "How to import GTOPO30 or SRTM30 data into ArcMap (for ArcGIS 9.x)". Step 7 should be skipped because the SRTM30 version 2 uses the value 0 (instead of -9999) for the ocean (see section 1.2 of the documentation).

ASTER Global Digital Elevation Model Version 2

An alternative elevation data to SRTM. Rexer and Hirt (2014) validate SRTM and ASTER against elevation data in Australia, concluding that SRTM is superior in general, with ASTER better for mountainous areas.

Downloadable here.

Used by Mariaflavia Hariri's working paper entitled "Cities in Bad Shape: Urban Geometry in India".

TerrainBase
Elevation data is also available by TerrainBase, constructed by National Oceanic and Atmospheric Administration (NOAA) and U.S. National Geophysical Data Center (downloadable at the Atlas of Biosphere). This one is used by Michalopoulos (2008). It is not clear if this is the same as, better or worse than, GTOPO30 and SRTM30. However, if the study area is the whole globe, this data is easier to use because it comes in one file. (GTOPO30 and SRTM30 are provided in several files each of which covers a part of the whole globe.)

World Port Index

World Port Index provides "the location and physical characteristics of, and the facilities and services offered by major ports and terminals world-wide (approximately 3700 entries)"

Used by

River network

Natural Earth provides the global river network polyline shape file as "Rivers + lake centerline".

This data is supposed to supersede the DNNET (River Drainage Network Data), part of the Digital Chart of the World, which was used by Duflo and Pande (2007) to locate rivers in India and to calculate the average river gradient in each district of India to predict the number of dams constructed. Penn State University's Library, which used to host the Digital Chart of the World, now directs data users to Natural Earth.

CIA World DataBank II offers the spatial data on navigable rivers. See this post on CIA World DataBank II.

Wednesday, May 23, 2018

Water bodies (lakes etc.)

There are several data sources for lakes and other water bodies

The Global Lakes and Wetlands Database (GLWD)

Developed by the World Wildlife Fund (WWF) in partnership with the Center for Environmental Systems Research, University of Kassel, Germany. Downloadable at the WWF's website.



SRTM Water Body Data (SWBD)

Created by the National Geospatial-Intelligence Agency (NGA) as a by-product of SRTM elevation data (see this post). The data is available here as thousands of shapefiles, each of which contains 1 by 1 degree area.

Natural Earth

Natural and artificial lake polygons can be obtained from the Lakes + Reservoirs data of Natural Earth.

GSHHG (Global Self-consistent, Hierarchical, High-resolution Geography Database)

This database also contains "islands within lakes" and even "ponds within islands within lakes".

Coastline

There are several data sources for coastline across the globe. The coastline data is useful to calculate the distance to the nearest coastline (with the Near tool in ArcGIS), which can be used as a control variable in a cross-sectional regression.

Natural Earth (the resolution: 1 to 10 meters)

GSHHG (Global Self-consistent, Hierarchical, High-resolution Geography Database)
  • Very detailed. Based on the World Vector Shoreline project (Soluri and Woodson 1990). For Antarctica, it is based on Bohlander and Scambos (2007). See GSHHG's readme file for detail. 
  • Used by Henderson et al. (2018), to predict where nighttime lights are observed.

Terrestrial Ecoregions of the World

Compiled by Olson et al. (2001). Downloadable from a WWF webpage.

To browse the data, visit Data Basin.

Used by Henderson et al. (2016).