166,533 research outputs found

    Tourism as community development: a comparative analysis of the Vieux Carre and the Lower Garden District from 1950 to 1990

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    Employing housing and population data available from the U.S. Census of Housing for 1970 and 1980, we compare block level delineations of gentrification with tract level delineations within several historic neighborhoods of New Orleans, Louisiana. Contrary to Spain\u27s (1992: 132) assertion that block level census data are not adequate to detect the presence of gentrification, a geographical delineation of gentrifying activity during the 1970\u27s is achieved. Methodologically, there are two main findings. First, we display the inadequacies of census tract level definitions of where gentrification is occurring. We conclude that gentrification is a small-scale process that is best examined on a larger scale than census tracts allow. A related implication is that previous analysis of locational relationships based upon census tract definitions of gentrification may be severely flawed (cf., Laska et. al., 1982). Finally, we show that heightened real estate activity is an unsatisfactory indirect measure of gentrification. More importantly, the block level delineation developed allows a fine-grain analysis of several theoretical issues regarding gentrification. First, support for residential location theory is shown by the occurrence of gentrifying blocks along the edges of solidly European-American, middle-class neighborhoods. Concomitantly, the avoidance of large African American residential areas is also demonstrated. Secondly, our results are consistent with rent gap theory as evidenced by the close correlation of gentrification with accelerated average rents. Gentrifying blocks also illustrate contagious behavior by their concentration and clustering in discrete locales within the study area. Our findings also support theories that argue gentrification occurs in concert with decline. Finally, we examine the timing of historic district designation relative to gentrifying activity. In the eastern portion of the study area, the data supports the view that gentrification occurs contemporaneously with district designation. On the other hand, gentrification occurred in the central and western portions of the study area without such designation

    Tourism as community development: a comparative analysis of the Vieux Carre and the Lower Garden District from 1950 to 1990

    Get PDF
    Employing housing and population data available from the U.S. Census of Housing for 1970 and 1980, we compare block level delineations of gentrification with tract level delineations within several historic neighborhoods of New Orleans, Louisiana. Contrary to Spain\u27s (1992: 132) assertion that block level census data are not adequate to detect the presence of gentrification, a geographical delineation of gentrifying activity during the 1970\u27s is achieved. Methodologically, there are two main findings. First, we display the inadequacies of census tract level definitions of where gentrification is occurring. We conclude that gentrification is a small-scale process that is best examined on a larger scale than census tracts allow. A related implication is that previous analysis of locational relationships based upon census tract definitions of gentrification may be severely flawed (cf., Laska et. al., 1982). Finally, we show that heightened real estate activity is an unsatisfactory indirect measure of gentrification. More importantly, the block level delineation developed allows a fine-grain analysis of several theoretical issues regarding gentrification. First, support for residential location theory is shown by the occurrence of gentrifying blocks along the edges of solidly European-American, middle-class neighborhoods. Concomitantly, the avoidance of large African American residential areas is also demonstrated. Secondly, our results are consistent with rent gap theory as evidenced by the close correlation of gentrification with accelerated average rents. Gentrifying blocks also illustrate contagious behavior by their concentration and clustering in discrete locales within the study area. Our findings also support theories that argue gentrification occurs in concert with decline. Finally, we examine the timing of historic district designation relative to gentrifying activity. In the eastern portion of the study area, the data supports the view that gentrification occurs contemporaneously with district designation. On the other hand, gentrification occurred in the central and western portions of the study area without such designation

    Biotope mapping to compare and contrast Columbia, Missouri neighborhoods

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    Abstract only availableHow people manage their property can ultimately have an affect on biodiversity, and ecosystem services. Our project seeks to understand the relationship of socioeconomic status to property management and how it affects biodiversity and ecosystem services. Our project compares neighborhoods with different socioeconomic characteristics by studying vegetation pattern and structure at three different scales: neighborhood, block, and lot. In this poster we compare vegetation pattern at a neighborhood scale. Our objectives are to classify vegetation in eight Columbia, Missouri neighborhoods and to determine if there are associated with socioeconomic differences. We defined neighborhoods as eight census block groups, and used data from the 2000 census to group the eight block groups into four socioeconomic categories. We used biotope mapping, a method of vegetation classification, to describe the different vegetation types in each block group. We used a similarity analysis to group the block groups based on biotope (vegetation) type. We used percent cover of the dominant biotope types to test for differences among the four socioeconomic groups. We identified 46 biotope types among the eight neighborhoods. Residential biotope types with lawn, trees and shrubs were dominant. These were divided into two subtypes, yard trees and fence rows, based on location of trees. Fence row were most common in the inner city area where the income is much lower than the areas with yard trees. The classification of the eight block groups based on the percentage of each biotope type in the block group did not match the classification based on socioeconomic data. The biotope classification did join the two block groups dominated by renters, Black residents, and low median income, a relationship supported by census data.NSF Undergraduate Mentoring in Environmental Biolog

    Spatial Analysis of Landscape and Sociodemographic Factors Associated With Green Stormwater Infrastructure Distribution in Baltimore, Maryland and Portland, Oregon

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    This study explores the spatial distribution of green stormwater infrastructure (GSI) relative to sociodemographic and landscape characteristics in Portland, OR, and Baltimore, MD, USA at census block group (CBG) and census tract scales. GSI density is clustered in Portland, while it is randomly distributed over space in Baltimore. Variables that exhibit relationships with GSI density are varied over space, as well as between cities. In Baltimore, GSI density is significantly associated with presence of green space (+), impervious surface coverage (+), and population density (−) at the CBG scale; though these relationships vary over space. At the census tract scale in Baltimore, a different combination of indicators explains GSI density, including elevation (+), population characteristics, and building characteristics. Spatial regression analysis in Portland indicates that GSI density at the CBG scale is associated with residents identifying as White (−) and well-draining hydrologic soil groups A and B (−). At both census tract and CBG scales, GSI density is associated with median income (−) and sewer pipe density (−). Hierarchical modelling of GSI density presents significant spatial dependence as well as group dependence implicit to Portland at the census tract scale. Significant results of this model retain income and sewer pipe density as explanatory variables, while introducing the relationship between GSI density and impervious surface coverage. Overall, this research offers decision-relevant information for urban resilience in multiple environments and could serve as a reminder for cities to consider who is inherently exposed to GSI benefits

    A case study of social vulnerability mapping: issues of scale and aggregation

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    This study uses geographic information systems to determine if the aggregation of census block data are better than census block group data for analyzing social vulnerability. This was done by applying a social vulnerability method that used census block group data for a countywide analysis and converting it to use census blocks for a countywide analysis and a municipal-wide analysis to determine which level of aggregation provided a more precise representation of social vulnerability. In addition to calculating the social vulnerability, the results were overlaid with an evacuation zone for the threat of a train derailment, determining which aggregation better depicted at-risk populations. The results of the study showed that the census blocks enable a more exact measurement of social vulnerability because they are better at capturing small pockets of high-risk areas. This study concludes that census block are more advantageous than census block groups because they are more sensitive and geographically exact in measuring social vulnerability, allow for a better interpretation of social vulnerability for smaller areas, and show spatial patterns of vulnerability at a finer spatial scale

    A factorial ecology of Omaha: Using 1980 census data at block group scale

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    This thesis is an urban factorial ecology of the Omaha/Douglas County area. The ecological unit used in the analysis is the Census Block Group. As a result, the data used are based on a more homogeneous areal unit, and the regionalization constructed yields social areas which more accurately display residential differentiation. The input data consists of a matrix of 84 variables by 399 block groups. Through factor analysis, the matrix is reduced to a factor score profile matrix of 10 factors by 399 block groups. The first eight factors are interpreted into social dimensions. From this factor structure, a regionalization is constructed for the Omaha/Douglas County area, consisting of twenty-eight social areas. These social areas are further grouped into an ecological model consisting of five concentric zones and four radial sectors. The regionalization and model demonstrate a comparability in social dimensions and ecological structure between Omaha and other American cities. And, the social areas constructed are also comparable to the real residential districts of the Omaha/Douglas County area. In addition, the automation of this study demonstrates a promising application potential of factorial ecology in urban planning and marketing analysis

    Scaling Nonparametric Bayesian Inference via Subsample-Annealing

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    We describe an adaptation of the simulated annealing algorithm to nonparametric clustering and related probabilistic models. This new algorithm learns nonparametric latent structure over a growing and constantly churning subsample of training data, where the portion of data subsampled can be interpreted as the inverse temperature beta(t) in an annealing schedule. Gibbs sampling at high temperature (i.e., with a very small subsample) can more quickly explore sketches of the final latent state by (a) making longer jumps around latent space (as in block Gibbs) and (b) lowering energy barriers (as in simulated annealing). We prove subsample annealing speeds up mixing time N^2 -> N in a simple clustering model and exp(N) -> N in another class of models, where N is data size. Empirically subsample-annealing outperforms naive Gibbs sampling in accuracy-per-wallclock time, and can scale to larger datasets and deeper hierarchical models. We demonstrate improved inference on million-row subsamples of US Census data and network log data and a 307-row hospital rating dataset, using a Pitman-Yor generalization of the Cross Categorization model.Comment: To appear in AISTATS 201
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