Youth from racially minoritized communities disproportionately receive exclusionary school discipline more severely and frequently. The racialization of school discipline has been linked to long-term deleterious impacts on students’ academic and life outcomes. In this article, we present a formative intervention, Learning Lab that addressed racial disparities in school discipline at a public high school. Learning Lab successfully united local stakeholders, specifically those who had been historically excluded from the school’s decision-making activities. Learning Lab members engaged in historical and empirical root cause analyses, mapped out their existing discipline system, and designed a culturally responsive schoolwide behavioral support model in response to diverse experiences, resources, practices, needs, and goals of local stakeholders. Analysis drew on the theory of expansive learning to examine how the Learning Lab process worked through expansive learning actions. Implications for research and practice are discussed.

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Commercial high spatial resolution satellite data now provide a synoptic and consistent source of digital imagery with detail comparable to that of aerial photography. In the work described here, per-pixel classification, image fusion, and GIS-based map refinement techniques were tailored to pan-sharpened 0.61 m QuickBird imagery to develop a six-category urban land-cover map with 89.3 percent overall accuracy (ϭ 0.87). The study area was a rapidly developing 71.5 km 2 part of suburban Raleigh, North Carolina, U.S.A., within the Neuse River basin. "Edge pixels" were a source of classification error as was spectral overlap between bare soil and impervious surfaces and among vegetated cover types. Shadows were not a significant source of classification error. These findings demonstrate that conventional spectral-based classification methods can be used to generate highly accurate maps of urban landscapes using high spatial resolution imagery.

A pixel level data fusion approach based on correspondence analysis (CA) is introduced for high spatial and spectral resolution satellite data. Principal component analysis (PCA) is a well-known multivariate data analysis and fusion technique in the remote sensing community. Related to PCA but a more recent multivariate technique, correspondence analysis, is applied to fuse panchromatic data with multispectral data in order to improve the quality of the final fused image. In the CA-based fusion approach, fusion takes place in the last component as opposed to the first component of the PCA-based approach. This new approach is then quantitatively compared to the PCA fusion approach using Landsat ETMϩ, QuickBird, and two Ikonos (with and without dynamic range adjustment) test imagery. The new approach provided an excellent spectral accuracy when synthesizing images from multispectral and high spatial resolution panchromatic imagery.

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