Project MUSE®: Geographical Analysis - Latest Articles
http://muse.jhu.edu/journal/66
Project MUSE®: Latest articles in Africa Today.daily12016-09-30T00:00:00-05:00text/htmlen-USThe Ohio State University PressVol. 34 (2002) - vol. 36 (2004)Latest Articles: Geographical AnalysisTWOProject MUSE®Geographical Analysis1538-46320016-7363Latest articles in Geographical Analysis. Feed provided by Project MUSE®Aggregation Decomposition and Aggregation Guidelines for a Class of Minimax and Covering Location Models
http://muse.jhu.edu/article/173522
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For various sorts of analytical models in geography, there is often a question of how much detail to build into the models. The question is particularly acute for location models, since the underlying problem may involve determining the location of one or more new facilities to serve a large population. For example, if demand is generated by all private residences in a major metropolitan area, there can be hundreds of thousands of demand points. Instead of modeling such a problem with all of its detail, an alternative is to first do demand point aggregation, a process that reduces the level of detail in the model by replacing demand points by aggregate demand points. However, it is well known that this aggregation
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Project MUSE®http://muse.jhu.edu/2016-09-30T00:00:00-05:00http://muse.jhu.edu/journal/66/image/coversmallAggregation Decomposition and Aggregation Guidelines for a Class of Minimax and Covering Location Models2004-09-29text/htmlen-USThe Ohio State University PressAggregation Decomposition and Aggregation Guidelines for a Class of Minimax and Covering Location ModelsIndustrial locationPublic utilitiesStore locationGeographical analysisScaling (Social sciences)NeighborhoodFactor analysisPolitical participationGeographyDivergence (Biology)Spatial ecologySensitivity theory (Mathematics)Neighborhood planning2004-09-292004TWOProject MUSE®1372662016-09-30T00:00:00-05:002004-09-29Annual Index: Volume 36, (2004)
http://muse.jhu.edu/article/173523
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January ............................................................ 1-86April.............................................................. 87-196July.............................................................. 197-298October ...................................................... 299-385Aggregation Decomposition and Aggregation Guidelines for a Class of Minimax and Covering Location Models (36:4, pp. 332-349)Akella, Mohan (36:2, pp. 177-194)Aldstadt, Jared (36:2, pp. 90-104)Analysis of Qualitative Similarity between Surfaces (36:3, pp. 217-233)Anisotropic Variance Functions in Geographically Weighted Regression Models (36:4, pp. 299-314)Arentze, Theo (36:1, pp. 85-86)A Bayesian Approach to Modeling Binary Data:
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Project MUSE®http://muse.jhu.edu/2016-09-30T00:00:00-05:00http://muse.jhu.edu/journal/66/image/coversmallAnnual Index: Volume 36, (2004)2004-09-29text/htmlen-USThe Ohio State University PressAnnual Index: Volume 36, (2004)Industrial locationPublic utilitiesStore locationGeographical analysisScaling (Social sciences)NeighborhoodFactor analysisPolitical participationGeographyDivergence (Biology)Spatial ecologySensitivity theory (Mathematics)Neighborhood planning2004-09-292004TWOProject MUSE®66132016-09-30T00:00:00-05:002004-09-29Scale, Factor Analyses, and Neighborhood Effects
http://muse.jhu.edu/article/173524
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Although they have long since ceased to attract attention as research topics in and of themselves, factorial ecologies continue to provide indicators of neighborhood characteristics that can be employed in studies of relationships between individual behavior and local milieux. In particular, the utility of factor scores as indicators of neighborhood composition for ecological analyses has been realized on many occasions in recent decades. Morenoff and Sampson (1997), for example, used a factorial ecology research design to identify four separate dimensions of neighborhood differences in Chicago in their study of the changing geography of crime there—one example of the output of their large program of research in
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Project MUSE®http://muse.jhu.edu/2016-09-30T00:00:00-05:00http://muse.jhu.edu/journal/66/image/coversmallScale, Factor Analyses, and Neighborhood Effects2004-09-29text/htmlen-USThe Ohio State University PressScale, Factor Analyses, and Neighborhood EffectsIndustrial locationPublic utilitiesStore locationGeographical analysisScaling (Social sciences)NeighborhoodFactor analysisPolitical participationGeographyDivergence (Biology)Spatial ecologySensitivity theory (Mathematics)Neighborhood planning2004-09-292004TWOProject MUSE®1307272016-09-30T00:00:00-05:002004-09-29Anisotropic Variance Functions in Geographically Weighted Regression Models
http://muse.jhu.edu/article/173525
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Most standard methods of statistical analysis used in the social and environmental sciences are built upon the basic assumptions of serial independence, homogeneity, and isotropy. A majority of these methods were originally developed within fields for which said assumptions were reasonable, or at a time when they were needed to make the problems tractable (Hepple 1998). A consequence of this historic development is that over time these basic assumptions were transmitted to, and in some cases unconsciously adopted by, different fields for which statistical methods became important tools of analysis. The assumption of independence, however, has long been recognized to be at odds with certain fundamental premises in a
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Project MUSE®http://muse.jhu.edu/2016-09-30T00:00:00-05:00http://muse.jhu.edu/journal/66/image/coversmallAnisotropic Variance Functions in Geographically Weighted Regression Models2004-09-29text/htmlen-USThe Ohio State University PressAnisotropic Variance Functions in Geographically Weighted Regression ModelsIndustrial locationPublic utilitiesStore locationGeographical analysisScaling (Social sciences)NeighborhoodFactor analysisPolitical participationGeographyDivergence (Biology)Spatial ecologySensitivity theory (Mathematics)Neighborhood planning2004-09-292004TWOProject MUSE®949932016-09-30T00:00:00-05:002004-09-29Divergence, Sensitivity, and Nonequilibrium in Ecosystems
http://muse.jhu.edu/article/173526
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A fundamental debate in biogeography and ecology is whether, or the extent to which, communities and ecosystems follow a developmental pathway leading toward a stable, steady-state equilibrium condition. The absence of, deviation from, or variation in such monotonic developmental pathways is likewise a focus of debate, particularly on the roles and relative importance of external disturbances, intrinsic complex dynamics, and historical or path dependencies. The purpose of this paper is not to provide a comprehensive review or critique of these debates. Rather, the goal is to attempt to redirect the focus to observable manifestations of (non)equilbrium, (in)stability, and other phenomena in ecosystems. Rather than
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Project MUSE®http://muse.jhu.edu/2016-09-30T00:00:00-05:00http://muse.jhu.edu/journal/66/image/coversmallDivergence, Sensitivity, and Nonequilibrium in Ecosystems2004-09-29text/htmlen-USThe Ohio State University PressDivergence, Sensitivity, and Nonequilibrium in EcosystemsIndustrial locationPublic utilitiesStore locationGeographical analysisScaling (Social sciences)NeighborhoodFactor analysisPolitical participationGeographyDivergence (Biology)Spatial ecologySensitivity theory (Mathematics)Neighborhood planning2004-09-292004TWOProject MUSE®1359852016-09-30T00:00:00-05:002004-09-29A Scale-Sensitive Test of Attraction and Repulsion Between Spatial Point Patterns
http://muse.jhu.edu/article/173527
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There currently exist a variety of tests for the presence of attraction and repulsion effects between spatial point populations, most notably those involving either nearest-neighbor or cell-count statistics (as reviewed for example in Cressie 1993, section 8.6). The advantage of nearest-neighbor approaches is that it is often possible to obtain exact (or at least asymptotic) distributions for certain test statistics under the null hypothesis of statistically independent populations. Most notable here is the approach of Diggle and Cox (1981), who showed that a powerful nearest-neighbor test of independence between two spatial point patterns could be constructed using Kendall's rank correlation coefficient. But by
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Project MUSE®http://muse.jhu.edu/2016-09-30T00:00:00-05:00http://muse.jhu.edu/journal/66/image/coversmallA Scale-Sensitive Test of Attraction and Repulsion Between Spatial Point Patterns2004-09-29text/htmlen-USThe Ohio State University PressA Scale-Sensitive Test of Attraction and Repulsion Between Spatial Point PatternsIndustrial locationPublic utilitiesStore locationGeographical analysisScaling (Social sciences)NeighborhoodFactor analysisPolitical participationGeographyDivergence (Biology)Spatial ecologySensitivity theory (Mathematics)Neighborhood planning2004-09-292004TWOProject MUSE®901082016-09-30T00:00:00-05:002004-09-29