Geography

This article addresses social and spatial aspects of intraethnic identity transitions within an American city between 1880 and 1910. Set within a theoretical framework that views urban spaces as social and cultural creations that in turn affect the construction of identities, it focuses on Irish Catholic immigrants and their descendants in Buffalo, New York. Evidence is initially presented on their intergenerational residential movements within the city at a time of widening social distinctions and occupational mobility. This is then supplemented by material from more qualitative sources, chiefly a diocesan newspaper and an “urban-ethnic novel.” While Irish American popular culture drew broad lines between working-class “shanty” lifestyles and those of a more respectable “lace-curtain” middle class during this era, the Buffalo evidence demonstrates these categories to be overdrawn and of almost caricature quality. In bringing
the upwardly mobile portion of the group into focus, however, the article considers not simply the occupational characteristics of moving households and their destinations but also the sources and effects of place-based imaginations within the city and the relatively neglected roles of homes as sites of socialization and identity negotiation within Catholic parishes. Pursuing these latter lines of inquiry also enriches understandings of the place of women in the process of Irish American social mobility.

ABSTRACT Categorical recognition of a tree's genus is known to be valuable information for the effective management of forest inventories. In this paper, we present a method for learning a discriminative model using Random Forests to classify individual trees into three genera: pine, poplar, and maple. We believe that both internal and external geometric characteristics of the tree crown are related to tree form and therefore useful in classifying trees to the genus level. Our approach involves the extraction of both internal and external geometric features from a LiDAR point cloud as we believe that geometric features provide important information about the organization of the points inside the tree crown along with overall tree shape and form. We developed 24 geometric features and then reduced the number of features to increase efficiency. These geometric characteristics, computed for 160 sampled trees from eight field sites, were classified using Random Forests and achieved an 88.3% average accuracy rate by using 25% (40 trees) of the data for training.

The current provincial-extent digital elevation model (DEM) and corresponding hydrological maps for Ontario have been produced using traditional photogrammetry and aerial photograph interpretation. This process is labour-intensive and requires visual interpretation of stereo image pairs. The ground surface and small hydrological features may be inaccurately delineated in areas where vegetation is dense or the ground is otherwise shielded from aerial view. In an effort to improve and automate delineation of hydrological features, we examined the behaviour and final products of the D8 flowrouting algorithm in 2 software environments (TAS and TauDEM for ArcGIS) operating on a high spatial resolution DEM derived using canopy-penetrating light detection and ranging (LiDAR) technology in a pilot study in the Romeo Malette Forest (41.25°N, 81.50°W). Filtered LiDAR data points (5-m spacing) were interpolated using IDW, TIN, and splines, each resulting in a 2.5-m spatial resolution DEM. Resu...

The continual accumulation of categorical data sets, presented as nominal categories mapped onto regular grids, provides for the increased desire to compare the patterns observed between these maps. We present a measurement scheme for the comparison of categorical maps ...

ABSTRACT We present a simple simulation scheme to derive confidence intervals for measures computed based on a coincidence matrix. Our approach is based on the conditional distributions between two categorical maps, and is a direct interpretation of how much information one map (usually the classified image) carries about the other map (usually the reference image). The simulation algorithm creates a realization of the map created by visiting each pixel and drawing a random sample from the conditional distribution of reference categories (conditioned on the category of the pixel of the classified image). Confidence intervals can be derived by repeating the simulation many times and computing the coincidence measure(s). Handling the coincidence matrix as a set of conditional distributions also allows the comparison of maps with different numbers of categories. This approach is an extension of the traditional methodology widely used in accuracy assessment of data derived from remotely sensed images. We illustrate the usage and interpretation of the approach on artificial and Canadian land cover mapping data.