Presettlement land survey records provide baseline data on forest characteristics prior to major European settlement, but questions regarding surveyor bias and methodological consistency have limited confidence in quantitative analyses of this important data source. We propose new correction factors, calculated from bearings, distances, and species of bearing trees, to account for the effects of (i) inconsistency in quadrant configuration, (ii) bearing angle bias, and (iii) species bias on forest density and species composition estimated from presettlement land survey records. Computer simulations confirmed accuracy in random and nonuniform density forests, with moderate bias in very clustered and dispersed forests. A case study of township and quarter-section corners surveyed by the Holland Land Company in western New York demonstrates the potential magnitude of errors caused by surveyor inconsistency/bias in estimation of density and relative species frequency. The influence of nonuniform density, clustering, and dispersal on plotless density estimators remains an important obstacle to quantitative analysis of Presettlement land survey records. However, by accounting for uncertainties regarding surveyor methodology, the proposed correction factors add confidence to conclusions made regarding presettlement forest structure and composition.

Despite conceptual and technology advancements in cartography over the decades, choropleth map design and classification fail to address a fundamental issue: estimates that are statistically indifferent may be assigned to different classes on maps or vice versa. Recently, the class separability concept was introduced as a map classification criterion to evaluate the likelihood that estimates in two classes are statistical different. Unfortunately, choropleth maps created according to the separability criterion usually have highly unbalanced classes. To produce reasonably separable but more balanced classes, we propose a heuristic classification approach to consider not just the class separability criterion but also other classification criteria such as evenness and intra-class variability. A geovisual-analytic package was developed to support the heuristic mapping process to evaluate the trade-off between relevant criteria and to select the most preferable classification. Class break values can be adjusted to improve the performance of a classification.

This paper reports on a new Area Preserving Segment Collapse (APSC) algorithm for simplifying polygonal boundaries while preserving the polygonal area at simplified target scales and minimizing areal displacement. A general segment collapse algorithm is defined by iteratively collapsing segments to Steiner points in priority order, guided by placement and displacement functions. The algorithm is specified by defining functions that minimize areal displacement under the constraint that the areas of adjoining polygons are preserved exactly. Self-intersections can be avoided by testing for intersections with two new line segments associated with each segment collapse operation. The paper demonstrates simplification results for a sample of 10 lakes formed from alpine, Karst, glacial and arid desert processes as well as artificial dams. APSC results are compared with three other simplification routines and evaluated for area preservation, linear and areal displacement, complexity and introduction of boundary self-intersections. Results confirm that the APSC algorithm preserves area exactly and indicate that it outperforms the other tested algorithms for minimizing areal displacement while producing reasonably low measures of linear displacement. Self-intersections can occur more commonly with APSC than other algorithms but are avoided with the proposed topology check. The APSC algorithm additionally preserves polygon complexity better than other tested algorithms.

Changes in tree taxon composition and distribution in western New York over a 200 year time period ca. 1797–1993 were examined by comparing the presettlement land survey with the US Forest Inventory and Analysis (FIA) survey. To ensure data quality, biases in presettlement bearing tree selection and FIA plot location were assessed. A 6 mile × 6 mile grid of taxa abundance was then estimated using geostatistics. Overall, significant changes in taxon composition occurred, with the taxa most abundant in the presettlement land survey — beech (37.0%), sugar maple (21.0%), and eastern hemlock (8.3%) — being replaced by sugar maple (19.2%), ash (11.7%), and red maple (11.4%) in the FIA survey. Spatially resolved comparisons showed that the landscape changed from fairly spatially homogeneous to more heterogeneous; in the presettlement survey, the most abundant taxon in most of the 6 mile × 6 mile grid cells was beech, while in the present survey, the most abundant taxon in most of the cells was one of several early successional taxa that each displayed a distinctly clustered geographic pattern of dominance. The clusters of dominance of the different early successional taxa may correspond to environmental factors. This study demonstrates the insights available through spatially resolved analyses of changes in the forest landscape between presettlement and present.