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Automated interpretation of the spatial distribution of socio-economic conditions
Conventional Graphics Choropleth maps
Figure 1. is a map showing the spatial distribution of Italian born as a percentage of the total population for the City of Burnside in the southeast of the greater Adelaide metropolitan area of South Australia, Australia. Burnside is a statistical local area (SLA using the terminology of the Australian Bureau of Statistics (ABS)) comprising 80 census collection districts (CCDs again using the terminology of the ABS). Blue indicates 0.0% to less than 1.1% (21 CCDs), green 1.1% to less than 1.7% (21 CCDs), yellow 1.7% to less than 2.4% (17 CCDs), and red 2.4% to l8.3% (21 CCDs). Each of the CCDs contains several hundred households. The top of the page is north and a square surrounding the area is roughly 49 square kilometers so you have an idea of the scale. Isnt this a nice map to look at? That was a facetious, rhetorical question. You see this type of map every day! I have been looking at computer generated choropleth maps since 1966 when I saw one of the first outputs of SYMAP V, a FORTRAN IV implementation for line-printer output created at the Northwestern Technological Institute and popularized by the Harvard Laboratory for Computer Graphics and Spatial Analysis. In my opinion the map is not just boring. At best, it is an inappropriate representation of the spatial distribution and, at worst, it contributes manifestly to the already difficult task of interpretation. Five years ago I identified what I did not like about choropleth maps and the following section is a brief exposition of my criticisms. But I dont intend just to complain; if I did possibly I would be labeled a post-modernist and, in reality, I am a logical positivist. Instead, I have been trying to find a more effective way of representing the spatial distribution of a socio-economic condition.  Fig. 2: Two CCDs of Burnside Criticisms of choropleth maps First, persons who are faint-hearted are advised to avoid the interpretation of choropleth maps. The path from map to reality is circuitous with many traps for the uninitiated. Consider one coloured patch (in the case of the map in figure 1 one of eighty) and this patch represents one of two or more intervals of derived data. The derived data are usually expressed as percentages with the denominator as the total population, but this is by no means the only valid denominator it all depends on the purpose of the investigation. The raw data used as the numerator are frequency counts, eg in CCD 4121302 there are 52 persons who were born in Italy. Note well, the raw data are summary data not unit record data so of the 52 persons born in Italy we are unlikely to discover how many have a high school diploma, nor is it likely that we will discover how many are members of families residing in a semidetached house. Therefore, the choropleth map may stimulate enthusiasm for executing interesting and valuable secondary investigations, but the data used to produce these maps and confidentiality constraints on the use of unit record data often preclude the undertaking. Each frequency count is for a CCD containing several hundred households, so to see one of the 52 Italian born on the ground within CCD 4121302 may be considered a fortuitous experience. But on the ground is reality and the choropleth map the starting point for the interpretation of the spatial distribution of the socio-economic condition is a long and tortuous way from that reality. Second, look at the solid colors, the straight lines, and the crisp boundaries. Visually, a choropleth map is a strong statement. It implies intellectual rigor and scientific integrity. Rather than a hypothesis generating starting point, it appears to be a conclusion something arrived at after much experimentation, analysis and contemplation. But I know, and so do you, that a little change in one of the mapping specifications will dramatically alter the characterization of the spatial distribution of the socio-economic condition. I could specify equal interval instead of equal frequency classification; or, I could specify five classes instead of four. In other words the choropleth map in figure 1 is just one of an infinite set. It might be a reasonable characterization of the spatial distribution, but it might not! Third, the spatial distribution of the socio-economic condition, Italian born as a percentage of the total population, is not discontinuous. But look at two contiguous CCDs from figure 1 which have been enlarged and are presented in figure 2. Figure 2 implies that there is a sudden jump (a discontinuity) from relatively few Italian born living in the western (blue) CCD to relatively many Italian born living in the eastern (red) CCD. Quite frankly this is not true. We will never know the exact spatial distribution of Italian born as a percentage of the total population, but one thing we do know is that at all but the individual human level of generalization the distribution must be considered continuous. Therefore, it is characterized poorly by the colored patchwork quilt effect of the choropleth map. It is easy to write computer programs to generate choropleth maps. I suspect that this is the principal reason that choropleth mapping is the most common form of cartographic representation of census data. But if we accept the criticisms of choropleth mapping and we are inquisitive, then we are motivated to consider alternative types of graphics as the basis of interpretation. Two alternative types are contour maps and perspective views. |