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
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
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