Assessing reduction of cluster size to estimate wood volume in an Amazonian forest

ABSTRACT While the Brazilian National Forest Inventory (NFI) is in progress, there is a growing demand to understand the effect of cluster size on the accuracy and precision of forest-attribute estimation. We aimed to find the minimum cluster size (in area) to estimate merchantable volume (MV) with the same accuracy and precision as the estimates derived from the original cluster of 8,000 m2. We used data from an inventory carried out in a forest unit (Bom Futuro National Forest) in the southwestern Brazilian Amazon, where 22 clusters were distributed as a two-stage sampling design. Three products were evaluated: (i) MV of trees with a diameter at breast height (DBH) ≥ 20 cm (P1); (ii) MV of trees with DBH ≥ 50 cm (P2); and (iii) MV of commercial species with DBH ≥ 50 cm and stem quality ‘level 1’ or ‘level 2’ (P3). We assessed ten scenarios in which the cluster size was reduced from 8,000 m2 to 800 m2. The accuracy of P1, P2 and P3 was highly significantly lower for reductions < 2,400 m². The precision was more sensitive to variations in cluster size, especially for P2 and P3. Minimum cluster sizes were ≥ 2,400 m² to estimate P1, ≥ 4,800 m² to estimate P2, and ≥ 7,200 m² to estimate P3. We concluded that it is possible to reduce the cluster size without losing the accuracy and precision given by the original NFI cluster. A cluster of 2,400 m² provides estimates as accurate as the original cluster, regardless of the evaluated product.

MIGRATION PROCESSES IN THE TYUMEN REGION

The article discusses some aspects of population migration in the territory of the Tyumen region (except autonomous districts). Special attention is paid to studying three levels of migration processes in the territory: - Comparison of population migration in the regional center with migration in the region as a whole. - Finding the typical features of intraregional, interregional, and international migration. - Identification of differences in male and female migration. As an information basis for the study, we used official statistics of municipalities for 6 years (from 2014 to 2019), namely the number of arriving and leaving migrants by gender and by type of migration. Spatial analysis and mapping of the results were performed with the help of ArcGIS Pro tools. Using Getis-Ord Gi * metric (Hot Spot Analysis tool from the Spatial Statistics set), statistically significant spatial clusters of high and low values (hot and cold spots) were identified for the selected indicators. The minimum cluster size was defined as a set of municipalities sharing a common border with the given municipal formation. The paper provides conclusions concerning the identified features of migration processes and outlines directions for further research.

EXACT AND APPROXIMATE METHODS FOR DATA DIRECTED MICROAGGREGATION IN ONE OR MORE DIMENSIONS

Microaggregation is a technique for the protection of the confidentiality of respondents in microdata releases. It is used for economic data where respondent identifiability is high. Microaggregation releases the averages of small groups in which no single respondent is dominant. It was developed for univariate data. The data was sorted and the averages of adjacent fixed size groups were reported. The groups can be allowed to have varying sizes so that no group will include a large gap in the sorted data. The groups become more homogeneous when their boundaries are sensitive to the distribution of the data. This is like clustering but with the number of clusters chosen to be as large as possible subject to homogeneous clusters and a minimum cluster size. Approximate methods based on comparisons are developed. Exact methods based on linear optimization are also developed. For bivariate, or higher dimensional, data the notion of adjacency is defined even though sorting is no longer well defined. The constraints for minimum cluster size are also more elaborate and not so easily solved. We may also use only a triangulation to limit the number of adjacencies to be considered in the algorithms. Hybrids of the approximate and exact methods combine the strengths of each strategy.