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Articles

Characterizing land-use change over space and time: applying principal components analysis in the Brazilian Legal Amazon

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Pages 19-37 | Received 21 Dec 2012, Accepted 29 Jul 2013, Published online: 10 Sep 2013

Figures & data

Table 1. Derivation of municipal-level variables included

Table 2. Descriptive statistics for municipal-level variables included

Figure 1. Variable loadings in the first four PCs for the combined 1996–2006 agricultural census dataset. Shade intensity represents the strength of the PC loadings, with bolder shades representing stronger loadings. Loadings to the left of the central axis in each PC are negative; loadings to the right of the central axis are positive.

Figure 1. Variable loadings in the first four PCs for the combined 1996–2006 agricultural census dataset. Shade intensity represents the strength of the PC loadings, with bolder shades representing stronger loadings. Loadings to the left of the central axis in each PC are negative; loadings to the right of the central axis are positive.

Figure 2. Changes in the first four PCs for conserved municipalities from 1996 to 2006.

Figure 2. Changes in the first four PCs for conserved municipalities from 1996 to 2006.

Figure 3. Changes in first four PCs in municipalities, organized by state. In each graph, a municipality’s PC score in 1996 is represented as a small circle, with the 2006 score represented by a large circle, connected to the 1996 score by a black vertical line. Municipalities are ordered from lowest to greatest 1996 PC score.

Figure 3. Changes in first four PCs in municipalities, organized by state. In each graph, a municipality’s PC score in 1996 is represented as a small circle, with the 2006 score represented by a large circle, connected to the 1996 score by a black vertical line. Municipalities are ordered from lowest to greatest 1996 PC score.

Figure 4. Fuzzy c-means clusters for n = 4 for the PC scores resulting from the combined 1996–2006 dataset, with clustering applied separátely to the 1996 and 2006 subsets. Each municipality has a fractional membership in each of the four clusters, with a fraction near 1 indicating near-complete membership in the cluster, a fraction near 0 indicating near-complete membership in other clusters, and fractions near 0.5 indicating weak membership in the cluster. PC loading fractions for each cluster are identified as BR (‘Big Ranching’), PF (‘Pre-frontier’), SF (‘Small Farming’), and MF (‘Mechanized Farming’).

Figure 4. Fuzzy c-means clusters for n = 4 for the PC scores resulting from the combined 1996–2006 dataset, with clustering applied separátely to the 1996 and 2006 subsets. Each municipality has a fractional membership in each of the four clusters, with a fraction near 1 indicating near-complete membership in the cluster, a fraction near 0 indicating near-complete membership in other clusters, and fractions near 0.5 indicating weak membership in the cluster. PC loading fractions for each cluster are identified as BR (‘Big Ranching’), PF (‘Pre-frontier’), SF (‘Small Farming’), and MF (‘Mechanized Farming’).

Map 1. Municipalities in the Brazilian Legal Amazon Region. Note that the Legal Amazon is bounded to the east by the 44th Meridian; our analysis includes municipalities in their entirety that have some share to the west of this line, and are included in the set of IBGE-defined Legal Amazon municipalities.

Map 1. Municipalities in the Brazilian Legal Amazon Region. Note that the Legal Amazon is bounded to the east by the 44th Meridian; our analysis includes municipalities in their entirety that have some share to the west of this line, and are included in the set of IBGE-defined Legal Amazon municipalities.
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