Global Data Hound

Last month we looked at human impacts on oceans; this month we turn to the human impact on land. The NASA-funded Socioeconomic Data and Applications Center (SEDAC) operated by CIESIN has long been a pioneer in supporting the development of data pertaining to the “human dimensions” of global environmental change.  This spring ISciences has partnered with CIESIN to release the 2008 version of the TerraViva! SEDAC data viewer.  A free version of GDV on a CD preloaded with 51 SEDAC data sets is available from CIESIN, and several of the SEDAC data sets are available here at TerraViva.net from ISciences.

Now, however, we focus on one particular set of products from CIESIN, version 2 of “The Last of the Wild” produced by CIESIN in conjunction with the Wildlife Conservation Society (WCS). LTW2 (as it is known) includes three constituent data sets: the Human Influence Index, the Human Footprint, and the Last of the Wild.  It is a substantial improvement over LOTW1  because the new version uses updated data on human population density, urban boundaries (GRUMP), lower-level administrative units, roads (especially in Africa and Latin America), and navigable rivers.  (CIESIN notes, however, that LTW2 cannot be compared retrospectively to LTW1 because too many of the data layers are different.)

Before digging into the details of the data available via GDV, I should mention that the WCS site devoted to the Human Footprint adds a valuable perspective with photographs and case studies that humanize the data far more than I am about to do.

The Human Influence Index is calculated using a point scoring system that is described in detail at http://sedac.ciesin.columbia.edu/wildareas/methods.jsp.

To illustrate how the HII is computed, and to provide some insight into how indices like this are constructed, I’ll rate a favorite 1 km x 1 km pixel, the former Ann Arbor headquarters  of ISciences LLC, which is located at 300 North Fifth Avenue in the picturesque Kerrytown neighborhood, right near a Farmer’s Market and our arguably world famous deli Zingerman’s.  As  you can see, our pixel  is right on the border between two pixels, but it looks like it should get an HII rating of 56, out of a maximum of 64.

According to SEDAC’s Gridded Population of the World (available on TerraViva.net) the population density in this mixed use residential and retail area is 2,067 per square km,  or well in excess of the 10 people/sq km that produces an HII subscore of 10.

The pixel is  less than 2 km from the nearest railroad.  The HII uses VMAP 0 Roads and Railways, but, not having time to fire up Arc or import the dataset into GDA, I eyeballed things using Google Earth.  Score: 8.

We are within 2 km of a major road–the M-14 runs north of Ann Arbor.  Ditto on the source data.  Score: 8.

Stretches of the Huron River, which runs through Ann Arbor, are  popular locations for canoeing, but a religious group that recently asked the city of Ann Arbor for permission to conduct a baptism in its waters was recently advised “not a good idea.” However, the HII doesn’t care about this particular form of water quality; it only asks whether there is a navigable river nearby.  The Huron is not, thank goodness, suitable for barges, so it scores 0.

Although Michigan is surrounded by the longest freshwater coastline in the world, Ann Arbor is about fifty miles from the lakeshore, and here in the Midwest we are a long way from any oceans. Score: 0.

The HII uses night lights as a proxy for human activity.  In TerraViva, we use the Radiance-Calibrated Lights of the World (RCLW) database produced by the NOAA National Geophysical Data Center (NGDC) using the Defense Meteorological Satellite Project.  There is a wrinkle: our presentation of the data displays radiant energy received by the pixel, but the HII index is calculated by assigning the score based on the percentage of days night lights are visible in the pixel.  In other words, they rely on a different view of the same underlying data.  In this case, I know from walking around the pixel, which includes portions of Ann Arbor’s restaurant district and some student ghetto housing, that there are lights on all the time.   Score: 10.

It’s interesting that both this night lights image and the Gridded Population of the World give a more pronounced image of Ann Arbor as a “peninsula” jutting out from metropolitan Detroit than the Human Influence Index does.  The HII may be an accurate reflection of a human influence that is more subtle than mere habitation. I have driven through the areas north and west of Ann Arbor on many occasions, and although they are lightly populated relative to the city, they are crisscrossed by a rectangular grid of parallel county roads that effectively subdivide the habitats into ~ 1 km square.

Our pixel is within an urban polygon, according to SEDAC’s Gridded Urban/Rural Mask Polygons (GRUMP).  Score: 10.

Our land cover type is urban. Score: 10.

The total HII score for our pixel is, thus, 56, as predicted.

Now let’s look at how the Human Footprint is computed.  We know that some areas of the world are more easily influenced by humans than others. Thus, an HII score of 25 that occurs in a rain forest pixel is more “impressive” evidence of human influence than one that occurs in a temperate forest biome like ours.  Thus, the Human Footprint is calculated by ranking all the pixels that share the same biome. Here is what our pixel looks like in the Human Footprint.

As we would expect from the discussion above, our pixel is among the most heavily influenced in its biome.

The Last of the Wild is simply the bottom 10% of the Human Footprint. What’s really striking (and rather sad) is that the nearest relatively wild areas are found in the northern portion of the Lower Peninsula, several hours away by automobile.