A few years ago, scientists across the internet were sending the breathtaking
image below (Figure 1), showing lights of the Earth at night, to colleagues,
friends, and family. When most scholars I know first looked at it,
they noted immediately that the image portrayed mirrored the locations
of the cities of the Earth. On second glance, they wondered
how the image was obtained: the entire surface of the Earth is never
dark, simultaneously. According to a NASA website (http://earthobservatory.nasa.gov/Study/Lights/lights_2.html),
"The images were taken by a Defense Meteorological Satellite Program’s (DMSP) Operational Linescan System (OLS). This network of satellites was originally designed to pick up on lunar illumination reflecting off of clouds at night in order to aid nighttime aircraft navigation. What the Air Force discovered is that on evenings when there was a new moon, the satellites were sensitive enough to record the illumination from city lights. Over a period of several new moons, the data the satellites retrieved could be pieced together to produce a global image of city lights." |
Marc Imhoff (see links below for references) apparently was the first
scholar to note the striking correspondence between the light pattern and
the spatial distribution of urban areas. His interests in developing
a spatial view of the global distribution of sprawl led him to work with
the city lights map. He noted that the light pattern overestimates
urbaninzation, when taking a more careful look at a larger-than-global
scale, and worked with a research team from Goddard Space Center to study,
with greater accuracy, the noted correspondence (http://earthobservatory.nasa.gov/Study/Lights/lights_3.html
):
"The researchers classified the lights left on the image, after [a] dimming process, as urban area. The previously lit areas on the image that shrank back were classified as peri-urban (low-density suburban areas or farmland). Any areas that had no lights to begin with were labeled as non-urban. They compared these classifications to the boundaries on the actual urban areas of the city and found there was a close match. Imhoff and his team now had a set of numbers (threshold values), which told them to what extent the lights from any portion of the United States should be dimmed to get an accurate and spatially explicit representation of urbanization."To look at the world's population, in a broad view only, consider using the data in the files of the Digital Chart of the World (DCW). The data is general, but is designed for viewing broad global pattern. Thus, Figure 2 shows a sequence of static images representing the world's urbanized areas and populated places. DCW categorizes both urbanized area and populated places in a hierarchy according to size, represented as 1, 2, and 3, from highest to lowest, in the maps below. There are also a few other categories noted in the legends below (Figure 2a-2j--scroll across to see the entire sequence). The urbanized areas are colored in tones of yellow from lightest at the high end to darkest at the low end; the populated places are colored in tones of khaki, from lightest at the high end to darkest at the low end. In addition, there is an overall continuum of shading from lightest yellow to darkest khaki across all the categories. |
Figure 2a. Layers of data: City (polygon), Kampong (polygon), City 1, City 2, City 3, Populated Place 1, Populated Place 2, Populated Place 3, Village. |
Figure 2b. Layers of data: Kampong (polygon), City 1, City 2, City 3, Populated Place 1, Populated Place 2, Populated Place 3, Village. |
Figure 2c. Layers of data: City 1, City 2, City 3, Populated Place 1, Populated Place 2, Populated Place 3, Village. |
Figure 2d. Layers of data: City 2, City 3, Populated Place 1, Populated Place 2, Populated Place 3, Village. |
Figure 2e. Layers of data: City 3, Populated Place 1, Populated Place 2, Populated Place 3, Village. |
Figure 2f. Layers of data: Populated Place 1, Populated Place 2, Populated Place 3, Village. |
Figure 2g. Layers of data: Populated Place 2, Populated Place 3, Village. |
Figure 2h. Layers of data: Populated Place 3, Village. |
Figure 2i. Layers of data: Village. |
Figure 2i. Layers of data: None. |
Data Layer information from:
Atlas Data Product Documentation, Digital Chart of the World (DCW), Strategic
Mapping, Inc., (File Release Jan 8, 1993). Extract from Defense
Mapping Agency's (DMA) Online Documentation for the Digital Chart of the
World (DCW) in Vector Product Format (VPF) [italics added below]:
"POPULATED PLACE (PP) AREA FEATURES |
The sequence of images in Figure 2 shows, successively, the entire hierarchy on the left with one hierarchical layer removed as the reader scrolls toward the right. One might also wish to see each layer individually rather than looking at the entire stack of layers and subsets of that stack. Figure 3 shows each of these layers, separately. |
When the sequence of images in Figure 3 is animated (Figure 4a), interesting visual comparisons emerge of the global distribution of urbanized areas, populated places, and lights. The animation begins with all layers showing and successively removes them while applying 5 stages of computer-generated (in Adobe ImageReady) between-smoothing-frames (tweening). It appears that it is urbanization, the yellow in Figure 4a, rather than mere population, the khaki in Figure 4a, that appears the greatest source of light in Figure 4b. |
Figure 4a. Animation of frames in Figure 3 with "tweening" applied in Adobe ImageReady (trademarked). Compare to Figure 4b, a copy of Figure 1. |
Indeed, this comparison offers support for the observation that (http://visibleearth.nasa.gov/cgi-bin/viewrecord?5826):
"The brightest areas of the Earth are the most urbanized, but not necessarily the most populated. (Compare western Europe with China and India.) Cities tend to grow along coastlines and transportation networks. Even without the underlying map, the outlines of many continents would still be visible. The United States interstate highway system appears as a lattice connecting the brighter dots of city centers. In Russia, the Trans-Siberian railroad is a thin line stretching from Moscow through the center of Asia to Vladivostok. The Nile River, from the Aswan Dam to the Mediterranean Sea, is another bright thread through an otherwise dark region."The information from the Digital Chart of the World offers a number of alternate ways to view information about population. No doubt a whole host of questions might be asked about the global pattern of lights, urbanization, and population. One question that is often discussed in the contemporary urban municipal setting is that of improving light dispersal in existing cities. One might imagine looking at the image in Figure 1, together with tables of population, and trying to find places with high population and low light use in relation to a set of population peers. Clearly, this sort of differencing would not be possible with maps that are as crude as those above. |
As a general strategy, though, one might consider an application of
Feigenbaum's graphical analysis to find critical values based on increasing
urbanization.
|
The graphical dynamics in Figure 5a are out of control; the initial seed (yellow) fired at the purple exponential bounces back and forth from curve to y=x in a fashion that goes on in ever-increasing steps toward infinity. When a lighting ordinance is applied as an upper bound, and that ordinance has sufficient strength to dampen the exponential so that it pushes back to the right of the line y=x (quickly), then the graphical dynamics can be made to settle down around the intersection point of the damped curve and y=x (as a fixed point that attracts graphical process). The latter situation suggests one form of intervention to control graphical dynamics representing real-world situations. The challenge then becomes implementation of such ideas as well as extension of them into more distant realms. |
References:
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