Project MUSE®: Geographical Analysis - Latest Articles
https://muse.jhu.edu/journals/geographical_analysis
Project MUSE®: Latest articles in Geographical Analysis.daily12015-04-18T04:00:39-05:00text/htmlen-USThe Johns Hopkins University PressVol. 34 (2002) - vol. 36 (2004)Latest Articles: Geographical AnalysisGeographyTWOProject 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
https://muse.jhu.edu/journals/geographical_analysis/v036/36.4francis.pdf
<p>By R. L. Francis, Timothy J. Lowe, Arie Tamir, Hulya Emir-Farinas</p>
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 ... <a href="https://muse.jhu.edu/journals/geographical_analysis/v036/36.4francis.pdf">Read More</a>
Project MUSE®https://muse.jhu.edu/2015-04-18T04:00:39-05:00https://muse.jhu.edu/images/journals/coverImages/geocoversmall.gifAggregation Decomposition and Aggregation Guidelines for a Class of Minimax and Covering Location Models2004-09-29text/htmlen-USThe Johns Hopkins University PressAggregation Decomposition and Aggregation Guidelines for a Class of Minimax and Covering Location ModelsStore location2004-09-292004TWOProject MUSE®02004-09-29T00:00:00-05:002004-09-29Scale, Factor Analyses, and Neighborhood Effects
https://muse.jhu.edu/journals/geographical_analysis/v036/36.4johnston.pdf
<p>By Ron Johnston, Kelvyn Jones, Simon M. Burgess, Carol Propper</p>
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 ... <a href="https://muse.jhu.edu/journals/geographical_analysis/v036/36.4johnston.pdf">Read More</a>
Project MUSE®https://muse.jhu.edu/2015-04-18T04:00:39-05:00https://muse.jhu.edu/images/journals/coverImages/geocoversmall.gifScale, Factor Analyses, and Neighborhood Effects2004-09-29text/htmlen-USThe Johns Hopkins University PressScale, Factor Analyses, and Neighborhood EffectsPolitical participation2004-09-292004TWOProject MUSE®02004-09-29T00:00:00-05:002004-09-29Anisotropic Variance Functions in Geographically Weighted Regression Models
https://muse.jhu.edu/journals/geographical_analysis/v036/36.4paez.pdf
<p>By Antonio Paez</p>
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 href="https://muse.jhu.edu/journals/geographical_analysis/v036/36.4paez.pdf">Read More</a>
Project MUSE®https://muse.jhu.edu/2015-04-18T04:00:39-05:00https://muse.jhu.edu/images/journals/coverImages/geocoversmall.gifAnisotropic Variance Functions in Geographically Weighted Regression Models2004-09-29text/htmlen-USThe Johns Hopkins University PressAnisotropic Variance Functions in Geographically Weighted Regression ModelsGeography2004-09-292004TWOProject MUSE®02004-09-29T00:00:00-05:002004-09-29Divergence, Sensitivity, and Nonequilibrium in Ecosystems
https://muse.jhu.edu/journals/geographical_analysis/v036/36.4phillips.pdf
<p>By Jonathan D. Phillips</p>
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 ... <a href="https://muse.jhu.edu/journals/geographical_analysis/v036/36.4phillips.pdf">Read More</a>
Project MUSE®https://muse.jhu.edu/2015-04-18T04:00:39-05:00https://muse.jhu.edu/images/journals/coverImages/geocoversmall.gifDivergence, Sensitivity, and Nonequilibrium in Ecosystems2004-09-29text/htmlen-USThe Johns Hopkins University PressDivergence, Sensitivity, and Nonequilibrium in EcosystemsSensitivity theory (Mathematics)2004-09-292004TWOProject MUSE®02004-09-29T00:00:00-05:002004-09-29A Scale-Sensitive Test of Attraction and Repulsion Between Spatial Point Patterns
https://muse.jhu.edu/journals/geographical_analysis/v036/36.4smith.pdf
<p>By Tony E. Smith</p>
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 ... <a href="https://muse.jhu.edu/journals/geographical_analysis/v036/36.4smith.pdf">Read More</a>
Project MUSE®https://muse.jhu.edu/2015-04-18T04:00:39-05:00https://muse.jhu.edu/images/journals/coverImages/geocoversmall.gifA Scale-Sensitive Test of Attraction and Repulsion Between Spatial Point Patterns2004-09-29text/htmlen-USThe Johns Hopkins University PressA Scale-Sensitive Test of Attraction and Repulsion Between Spatial Point PatternsStore location2004-09-292004TWOProject MUSE®02004-09-29T00:00:00-05:002004-09-29A Multivariate Model for Spatio-temporal Health Outcomes with an Application to Suicide Mortality
https://muse.jhu.edu/journals/geographical_analysis/v036/36.3congdon.pdf
<p>By P Congdon</p>
The evolution over time of interdependent and spatially defined health outcomes has relevance in several modeling contexts, including Bayesian smoothing and regression modeling of social and environmental risk factors. Authors such as Waller et al. (1997), Knorr-Held and Besag (1998), Gelfand et al. (1998), and Sun et al. (2000) have considered the benefits of a fully Bayesian estimation approach to spatio-temporal disease mapping, using general linear model techniques applied to a count variable with Poisson sampling. However, such applications have been confined to a single (i.e., univariate) outcome. Only recently has the possibility of spatio-temporal analyses that extend to multivariate outcomes been ... <a href="https://muse.jhu.edu/journals/geographical_analysis/v036/36.3congdon.pdf">Read More</a>
Project MUSE®https://muse.jhu.edu/2015-04-18T04:00:39-05:00https://muse.jhu.edu/images/journals/coverImages/geocoversmall.gifA Multivariate Model for Spatio-temporal Health Outcomes with an Application to Suicide Mortality2004-07-09text/htmlen-USThe Johns Hopkins University PressA Multivariate Model for Spatio-temporal Health Outcomes with an Application to Suicide MortalitySuicide2004-07-092004TWOProject MUSE®02004-07-09T00:00:00-05:002004-07-09A Geostatistical Framework for Area-to-Point Spatial Interpolation
https://muse.jhu.edu/journals/geographical_analysis/v036/36.3kyriakidis.pdf
<p>By Phaedon C. Kyriakidis</p>
Going from one spatial support (domain informed by each measurement or unknown value) to another is of critical importance to numerous scientific disciplines. Coarse spatial resolution predictions of general circulation models, for example, need to be downscaled to the watershed level (or even finer in the case of spatially distributed models) for hydrologic impact assessment studies. Similarly, socioeconomic variables reported on census tracts need to be downscaled to smaller regions for detailed modeling. Scaling issues continue to be a critical and vibrant research topic in Geography; a recent review of such issues and some of their geostatistical solutions can be found in Atkinson and Tate (2000).Area-to-point ... <a href="https://muse.jhu.edu/journals/geographical_analysis/v036/36.3kyriakidis.pdf">Read More</a>
Project MUSE®https://muse.jhu.edu/2015-04-18T04:00:39-05:00https://muse.jhu.edu/images/journals/coverImages/geocoversmall.gifA Geostatistical Framework for Area-to-Point Spatial Interpolation2004-07-09text/htmlen-USThe Johns Hopkins University PressA Geostatistical Framework for Area-to-Point Spatial InterpolationGeology2004-07-092004TWOProject MUSE®02004-07-09T00:00:00-05:002004-07-09A Bayesian Approach to Modeling Binary Data: The Case of High-Intensity Crime Areas
https://muse.jhu.edu/journals/geographical_analysis/v036/36.3law.pdf
<p>By Jane Law, Robert P. Haining</p>
High-intensity crime areas, or HIAs, are areas identified by urban police forces in England that experience high levels of violent, often drug-related, crime. Violence involves the use of knives and/or firearms. There may be further problems when bringing charges because of high levels of witness intimidation. The reason for this is that individuals or families resident in the neighborhood often perpetrate the crimes. HIAs therefore are more than simply areas with high levels of particular types of offenses ("hot spots"); they are areas with a particularly dangerous cocktail of violent crime perpetrated by offenders who are also resident in the area. They present particularly difficult policing problems.Craglia ... <a href="https://muse.jhu.edu/journals/geographical_analysis/v036/36.3law.pdf">Read More</a>
Project MUSE®https://muse.jhu.edu/2015-04-18T04:00:39-05:00https://muse.jhu.edu/images/journals/coverImages/geocoversmall.gifA Bayesian Approach to Modeling Binary Data: The Case of High-Intensity Crime Areas2004-07-09text/htmlen-USThe Johns Hopkins University PressA Bayesian Approach to Modeling Binary Data: The Case of High-Intensity Crime AreasCriminal statistics2004-07-092004TWOProject MUSE®02004-07-09T00:00:00-05:002004-07-09A Critical Comment on the Taylor Approach for Measuring World City Interlock Linkages
https://muse.jhu.edu/journals/geographical_analysis/v036/36.3nordlund.pdf
<p>By Carl Nordlund</p>
At the Globalization and World Cities Study Group and Network (GaWC), Peter Taylor and his colleagues have developed a method for analyzing the world city network and its structural features through an analysis, and subsequent data processing, of office establishments in different cities of a set of transnational service-producing firms (Taylor 2001; Taylor, Catalano, and Walker 2002). Due to the lack of available data sets on interurban flows and structures (Short et al. 1996), Taylor and his colleagues explicitly prefer to generate structural data instead of relying on scarce existing sources (Beaverstock et al. 2000, p. 44).There is no doubt that the data acquisition of such service-producing firms is relevant ... <a href="https://muse.jhu.edu/journals/geographical_analysis/v036/36.3nordlund.pdf">Read More</a>
Project MUSE®https://muse.jhu.edu/2015-04-18T04:00:39-05:00https://muse.jhu.edu/images/journals/coverImages/geocoversmall.gifA Critical Comment on the Taylor Approach for Measuring World City Interlock Linkages2004-07-09text/htmlen-USThe Johns Hopkins University PressA Critical Comment on the Taylor Approach for Measuring World City Interlock LinkagesGeography2004-07-092004TWOProject MUSE®02004-07-09T00:00:00-05:002004-07-09Analysis of Qualitative Similarity between Surfaces
https://muse.jhu.edu/journals/geographical_analysis/v036/36.3sadahiro.pdf
<p>By Yukio Sadahiro, Masae Masui</p>
The surface is a computational model of a scalar field defined in a region. It can be used for modeling elevation of the earth's surface (Pike 1988; Hutchinson 1989; Etzelmuller 2000), distribution of geological measures (Isaaks and Srivastava 1989; Cressie 1993; Bailey and Gatrell 1995), population distribution (Bracken 1993; Bracken and Martin 1995), and so forth, in order to perform spatial analysis in a Geographical Information System (GIS) environment.Given a set of derived surfaces in a region, we are often interested in the spatial relationship between them. In geodemography, for instance, we often discuss similarities and differences in population distributions between different races. Spatial econometric ... <a href="https://muse.jhu.edu/journals/geographical_analysis/v036/36.3sadahiro.pdf">Read More</a>
Project MUSE®https://muse.jhu.edu/2015-04-18T04:00:39-05:00https://muse.jhu.edu/images/journals/coverImages/geocoversmall.gifAnalysis of Qualitative Similarity between Surfaces2004-07-09text/htmlen-USThe Johns Hopkins University PressAnalysis of Qualitative Similarity between Surfaces2004-07-092004TWOProject MUSE®02004-07-09T00:00:00-05:002004-07-09