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Geographical Data Sets
Hierarchical Matching
Some types of information, however, are collected in more detail and less frequently than other types of information. For example, financial and unemployment data covering a large area are collected quite frequently. On the other hand, population data are collected in small areas but at less frequent intervals. If the smaller areas nest (i.e., fit exactly) within the larger ones, then the way to make the data match of the same area is to use hierarchical matching -- add the data for the small areas together
until the grouped areas match the bigger ones and then match them exactly.
The hierarchical structure illustrated in the chart shows that this city is composed of several tracts. To obtain meaningful values for the city, the tract values must be added together.
| Tract |
Town |
Population |
| 101 |
P |
60,000 |
| 102 |
Q |
45,000 |
| 103 |
R |
35,000 |
| 104 |
S |
36,000 |
| 105 |
T |
57,000 |
| 106 |
Nakkhu |
25,000 |
| 107 |
Kupondole |
58,000 |
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| Tract 101 |
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Tract 102 |
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Tract 103
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Tract 104 |
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Tract 105
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Tract 107 |
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Tract 106 |
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Fuzzy Matching
On many occasions, the boundaries of the smaller areas do not match those of the larger ones. This occurs often while dealing with environmental data. For example, crop boundaries, usually defined by field edges, rarely match the boundaries between the soil types. If you want to determine the most productive soil for a particular crop, you need to overlay the two sets and compute crop productivity for each and every soil type. In principle, this is like laying one map over another and noting the combinations of soil and productivity.
A GIS can carry out all these operations because it uses geography, as a common key between the data sets. Information is linked only if it relates to the same geographical area.
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