R is Recession

For data, I traveled to the US Department of Labor. Bureau of Labor Statistics and was met with an overwhelming amount of files, charts, tables and numbers.

week 3 -- improved hockey stick?

I don’t believe that this graph is an improvement to the traditional hockey stick image. Even with access to the raw data or even processed data, I was not able to understand the information well enough to provide a critical reinterpretation. However this graph changes the aspect of the graph by showing simply the estimated surface temperatures by using data collected from lake cores from Lake Tanganika in East Africa. I chose this data set because in Mann et al. 1998 they acknowledged few if any data from the African continent. While I understand that Mann et al. were working with the data sets they had available, I thought it would be interesting to work with data that are from a drastically under-represented area.


Where these charts differ the greatest from the hockey stick projection, is that they do not show the departures of temperature from the 1961 to 1990 average but rather just display the estimated temperatures based on lake core analyses. In fact they do not show contemporary temperatures at all. I tried this same projection with other data sets and I came up with similar looking charts that seem not to have any consistent pattern. In this way we can see that perhaps one single study cannot add to current climate change debates, however, only aggregate compilations of these studies can give weight and force to the international, scholarly and secular, and very public debate on climate change.

The elementary graphs above were produced with excel based on data from

Tierney, J.E., J.M. Russell, Y. Huang, J.S. Sinninghe Damsté, E.C. Hopmans, and A.S. Cohen. 2008.  "Northern Hemisphere Controls on Tropical Southeast African 
Climate During the Past 60,000 Years" Science, Vol. 322, No. 5899, pp. 252-255, 10 October 2008.  
available here.

climate science essay -- the power of the visual

It seems that the debate on climate science in the media has similar cycles--peaks and valleys--just as line graph of climate variation on earth has. In the months before and also following a high profile international conference, newspaper articles, blog posts and TV media stories increase in frequency and intensity. Debates become heated as each side attempts to poke holes in their opponents’ arguments and destabilize their opponents’ positions. New information comes to light, it is challenged and rebutted. Then the public interest in the discussion dies down and we concern ourselves with other debates such as health care, the failing economy, and many, many more. The interest in climate change and climate science debates seems to lie dormant until the next international meeting or vote on this issue. Yet the total trajectory of how climate science has fared in this arena is most interesting. In the March 18 issue of The Economist it states that “…the scientists’ shameful mistakes have certainly changed perceptions. They have not, however, changed science itself.” If indeed the science hasn’t changed, then what exactly was it that led and still feeds the on-going rollercoaster public debate? Did scientists simply learn to present their findings to a larger audience? Can we blame the media for yet again making a sensation out of a molehill? And what is most influential—the methodology, the findings, how they are visually presented, or how the discussion plays out for the general public?

            Possibly the beginning of this debate was with the publishing of Mann et al in 1998 in Nature (often referred to as MBH98) and the first appearance of the famous hockey stick graph. Using various paleoclimate data sets and recent instrument-recorded surface temperatures, they were able to construct a northern hemisphere projection of past climates calibrated with current temperature data. Their conclusion was that, in the northern hemisphere, the last 50 years were unnaturally warm compared to the previous 2000 and then suggests this is due to anthropogenic forces. Their line graph was then challenged by outside observers for its correctness and completeness. They were accused of misrepresenting the data, leaving data out and operating in a cohort of scientists that “rubber stamp” each other’s work. Critics claimed that climate scientists were withholding their data which suggested they had something to hide. Blogs and articles produced by climate change skeptic watchdogs appeared and gathered support while picking apart details of the various reports. 

            Perhaps most influential on the opposing side were Stephen McIntyre and Ross McKitrick who published papers questioning the statistical analyses of MBH98. The blog of McIntyre (Climate Audit) provided fuel for the climate skeptics and a controversy for the media to cover.  

            The role of imagery and data projections is certainly central to the debate. While the climate change debate might have started among peer-reviewed journals and academic circles of the physical science world, this discussion morphed onto the secular scene. If we think about the phrase an image is worth a thousand words, then we must understand the images that the general public is viewing when ideas such as global warming, increasing CO2 levels, and melting ice caps in addition to data centric statistically rendered graphs and charts. 

            While the famed “hockey stick” projection, with its estimates of error and red line climbing high in the late 20^th century may be the visualization that most scientists point out to give weight to the vast and diverse studies of surface temperatures, proxy measurements of lake, ice or soil cores and tree ring, and atmospheric gases, it is certainly a target for skeptics. Blog post such as “the broken hockey stick” or “the hockey stick debunked” produced completely flat line or U-shaped graphs. 

I would argue that these types of images aren’t the ones that have the most impact. Instead think of all the times images of polar bears trapped on melting ice caps or barren, dry and cracked soil are shown in conjunction with climate change debates. Or other images like maps of projected temperature increases, sea level rise and historic/contemporary glaciers.  

            Still even more powerful, especially early in the climate science debates, might be Al Gore’s Inconvenient Truth. This documentary film is based off the scholarship of the science behind the publications and recommendations of the IPCC. Here a hockey stick-like chart needs an elevator to raise Gore high enough to show the rising levels of CO2 in the atmosphere. Inconvenient Truth brought together statistical charts, historic photographs, and heart wrenching cinematography of ecosystems in harsh transitions. I believe that it is these bundles of images that provide the greatest impact to the greatest population. Debate on whether or not scientists maliciously left out data or conveniently tweaked data into conformity will continue. Hockey stick charts and accompanying images of an earth in potentially traitorous transition were the first to bring climate change and especially human driven climate change to the general public. They are now what all science and rhetoric has to build off of and react to. 

Images of "debunked hockey sticks" are found on Jo Nova's blog
Other images come from a wiki called Global Warming Art
And Al Gore on an lift can be watched here

Gallery-Remote Sensing

Fig. 1. Example of remote-sensing images used to assess flooding regimes on the Connecticut River. a) Additive image of ETM+ bands 4 and 7 for September; b) same for April; c) slope layer; d) composite image (R:G:B = September:April:slope. Areas of flooding are in pink. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 2. Extraction of spring flooded areas, Northampton, MA. a) original imagery of September 2001; b) original imagery of April 2001; c) composite image; d) binary grid, with darker areas representing areas of overbank flooding.

 Fig. 3. Example output of the ecological condition model for longleaf pine sandhills at Eglin AFB, Florida, scaled to 1-ha monitoring units for a) 2001, and b) 2007. Tier 1 represents high-quality sandhills while Tier 4 reflects degraded sandhills.

 Fig. 4. a) Change in longleaf pine condition tier by area (ha) from 2001 to 2007 at Eglin AFB as assessed by the ecological condition model. b) A matrix showing how the tier values of Eglin's 1-ha management units transitioned from 2001 to 2007. For example, 7703 ha moved from Tier 2 to Tier 1 over the time period. Shaded cells indicate 1-ha management units that did not experience a change in condition from 2001 to 2007 (total of 80,141 ha).

 Fig. 5. Flow chart summarizing the spatial modeling approach used to assess the ecological condition of longleaf pine sandhills across Eglin AFB.

 Fig. 5. Flow chart summarizing the spatial modeling approach used to assess the ecological condition of longleaf pine sandhills across Eglin AFB.

All images are from
Wiens J., Sutter R., Anderson M., Blanchard J., Barnett A., Aguilar-Amuchastegui N., Avery C., Laine S.(2009) "Selecting and conserving lands for biodiversity: The role of remote sensing" Remote Sensing of Environment, 113 (7), pp. 1370-1381. 

week 1 assignment

To find images for this assignment, I used a Google Scholar search and browsed through journal articles looking for maps or map-like projections. I wanted to look specifically at images that show spatial relationships in a traditional way. While I know that much of the data in environmental geography journals are not in map-like form, I was shocked to see just how little there really was. I was especially shocked to not find images that were easily understandable or were more confusing than helpful.




These first two images are from the same article (Petit et al. 2001) only the top one appears in grey scale. Working with a lot of satellite images, I see this kind of projection often. At first I thought I would post the grey scale to show just how absurd it is to publish this type of map in a print journal without color plates or even how un-useful it becomes when printed on a grey scale printer. Yet the color version doesn't seem to be helpful either. Without any other features on the image except a grid and simple key, this doesn't give an additional information to the reader. It is as if the authors put the image in the article simply prove that they did the data analysis with Landsat scene. (Or perhaps in the late 90s when the authors did this study, they paid a lot of money for the single image and were sure to use it as much as they could!)

The second image is of the relationship between baobab trees and villages in Mali. Here I imagine the collection of these data was through field measurements with most of the coded information not having a direct spatial relationship, yet the purpose of the study was to examine how attributes like age of both the village and the trees relates spatially. While I think visualization adds to the analysis, I still don't see the spatial relationships clearly.

Therefore, as a final image I wanted to show a geovisualization that would really say more with a map-like image (or here a time series of images) than with the text or another sort of chart or graph. I turned to the recent New York Times article about where to get a cab. (h/t Timur) Here the a heat map of where to find a cab in NYC displays information that could not be understood in other ways...at least not to the extent or ease that it is here.

Therefore I think these images call into question how we visualize certain kinds of data (obviously) and whether or not a geovisulization is the best way to display the data. Particularly with the satellite images, these data are, at their creation, spatially linked--each pixel of information is directly bordered by 4 (to 8) other pixels of information. So when does breaking this relationship benefit the viewer? What kinds of data are better represented in tabular or discriptive terms? I know that when I work with these satellite derived data, I feel I cannot betray them by representing them in any other form, yet I can see how it's not necessarily helpful to view in this way.


Articles referenced
Duvall C.S. (2007) Human settlement and baobab distribution in southwestern Mali. Journal of Biogeography 34(11):1947–1961

Grynbaum, M. (2010) "Need a Cab? New Analysis Shows Where to Find One" The New York Times. April 2.

Petit, C. , Scudder, T. andLambin, E.(2001) 'Quantifying processes of land-cover change by remote sensing: resettlement and rapid land-cover changes in south-eastern Zambia', International Journal of Remote Sensing, 22: 17, 3435 — 3456