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Scale is a fundamental geographic concept, and a substantial literature exists discussing the various roles that scale plays in different geographical contexts. Relatively little work exists, though, that provides a means of measuring the geographic scale over which different processes operate. Here we demonstrate how geographically weighted regression (GWR) can be adapted to provide such measures. GWR explores the potential spatial nonstationarity of relationships and provides a measure of the spatial scale at which processes operate through the determination of an optimal bandwidth. Classical GWR assumes that all of the processes being modeled operate at the same spatial scale, however. The work here relaxes this assumption by allowing different processes to operate at different spatial scales. This is achieved by deriving an optimal bandwidth vector in which each element indicates the spatial scale at which a particular process takes place. This new version of GWR is termed multiscale geographically weighted regression (MGWR), which is similar in intent to Bayesian nonseparable spatially varying coefficients (SVC) models, although potentially providing a more flexible and scalable framework in which to examine multiscale processes. Model calibration and bandwidth vector selection in MGWR are conducted using a back-fitting algorithm. We compare the performance of GWR and MGWR by applying both frameworks to two simulated data sets with known properties and to an empirical data set on Irish famine. Results indicate that MGWR not only is superior in replicating parameter surfaces with different levels of spatial heterogeneity but provides valuable information on the scale at which different processes operate.

The concept of disaster resilience has gained attention in political spheres and news outlets over the past few years, yet relatively few empirical measures of the concept exist. Furthermore, research into urban resilience has dwarfed our understanding of disaster resilience in rural places. This schism in what is known about the differences between urban and rural places becomes the topic of this article. Employing a suite of spatial and statistical techniques using an established measure of community resilience, the Baseline Resilience Indicators for Communities (BRIC), we focus on two key questions to better explain the resilience divide between urban and rural areas of the United States. Nonparametric rank analysis, analysis of variance, and logistic regression help describe the relationships between rurality and disaster resilience in contrast to resilience in urban areas. Pinpointing the driving factors, or characteristics, of resilience in rural America compared to metropolitan America, accomplished through binary logistic regression, revealed notable distinctions. Resilience in urban areas is primarily driven by economic capital, whereas community capital is the most important driver of disaster resilience in rural areas. Within rural areas there is considerable spatial variability in the components of disaster resilience. This suggests that attempts to enhance resilience cannot be approached using a one-size-fits-most strategy given the variability in the primary drivers of disaster resilience at county scales.

Global land-use and land-cover change (LUCC) data are crucial for modeling a wide range of environmental conditions. So far, access to high-resolution LUCC products at a global scale for public use is difficult because of data and technical issues. This article presents a Future Land-Use Simulation (FLUS) system to simulate global LUCC in relation to human-environment interactions, which is built and verified by using remote sensing data. IMAGE has been widely used in environmental studies despite its relatively coarse spatial resolution of 30 arc-min, which is about 55 km at the equator. Recently, an improved model has been developed to simulate global LUCC with a 5-min resolution (about 10 km at the equator). We found that even the 10-km resolution, however, still produced major distortions in land-use patterns, leading urban land areas to be underestimated by 19.77 percent at the global scale and global land change relating to urban growth to be underestimated by 60 to 97 percent, compared with the 1-km resolution model proposed through this article. These distortions occurred because a large percentage of small areas of urban land was merged into other land-use classes. During land-use change simulation, a majority of small urban clusters were also lost using the IMAGE product. Responding to these deficiencies, the 1-km FLUS product developed in this study is able to provide the spatial detail necessary to identify spatial heterogeneous land-use patterns at a global scale. We argue that this new global land-use product has strong potential in radically reducing uncertainty in global environmental modeling.

The emergence of big data brings new opportunities for us to understand our socioeconomic environments. We use the term social sensing for such individual-level big geospatial data and the associated analysis methods. The word sensing suggests two natures of the data. First, they can be viewed as the analogue and complement of remote sensing, as big data can capture well socioeconomic features while conventional remote sensing data do not have such privilege. Second, in social sensing data, each individual plays the role of a sensor. This article conceptually bridges social sensing with remote sensing and points out the major issues when applying social sensing data and associated analytics. We also suggest that social sensing data contain rich information about spatial interactions and place semantics, which go beyond the scope of traditional remote sensing data. In the coming big data era, GIScientists should investigate theories in using social sensing data, such as data representativeness and quality, and develop new tools to deal with social sensing data.

Accounts by geographers of the ways in which urban spaces are digitally mediated have proliferated in the last few years. This significant body of work pays particular attention to the production of urban space by software and digital hardware, and geographers have drawn on various kinds of posthumanist philosophies to theorize the agency of the technological nonhuman. The agency of the human, however, has been left undertheorized in this work, often appearing in the form of excessive resistance to the agency granted to the digital. This article contributes to understanding the digital mediation of cities by theorizing a specifically posthuman agency; that is, a human agency both mediated through technics and diverse. Drawing on the philosophy of Stiegler as well as a range of feminist digital scholarship, the article conceptualizes posthuman agency as always already coconstituted with technologies. Posthumans are simultaneously individuated and exteriorized in that coconstitution, and this permits agency understood as reinvention. The article also insists that such sociotechnical agency is differentiated, particularly in terms of the spatialities and temporalities through which it is organized. It concludes by arguing that geographers must reconfigure their understanding of digitally mediated cities and acknowledge the inventiveness and diversity of urban posthuman agency.

How communities respond to and recover from damaging hazard events could be contextualized in terms of their disaster resilience. Although numerous efforts have sought to explain the determinants of disaster resilience, the ability to measure the concept is increasingly being seen as a key step toward disaster risk reduction. The development of standards that are meaningful for measuring resilience remains a challenge, however. This is partially because there are few explicit sets of procedures within the literature that outline how to measure and compare communities in terms of their resilience. The primary purpose of this article is to advance the understanding of the multidimensional nature of disaster resilience and to provide an externally validated set of metrics for measuring resilience at subcounty levels of geography. A set of metrics covering social, economic, institutional, infrastructural, community-based, and environmental dimensions of resilience was identified, and the validity of the metrics is addressed via real-world application using Hurricane Katrina and the recovery of the Mississippi Gulf Coast in the United States as a case study.

This article considers critically how one of the oldest and most widely applied statistical methods, principal components analysis (PCA), is employed with spatial data. We first provide a brief guide to how PCA works: This includes robust and compositional PCA variants, links to factor analysis, latent variable modeling, and multilevel PCA. We then present two different approaches to using PCA with spatial data. First we look at the nonspatial approach, which avoids challenges posed by spatial data by using a standard PCA on attribute space only. Within this approach we identify four main methodologies, which we define as (1) PCA applied to spatial objects, (2) PCA applied to raster data, (3) atmospheric science PCA, and (4) PCA on flows. In the second approach, we look at PCA adapted for effects in geographical space by looking at PCA methods adapted for first-order nonstationary effects (spatial heterogeneity) and second-order stationary effects (spatial autocorrelation). We also describe how PCA can be used to investigate multiple scales of spatial autocorrelation. Furthermore, we attempt to disambiguate a terminology confusion by clarifying which methods are specifically termed \"spatial PCA\" in the literature and how this term has different meanings in different areas. Finally, we look at a further three variations of PCA that have not been used in a spatial context but show considerable potential in this respect: simple PCA, sparse PCA, and multilinear PCA.

Many fundamental notions in geographic and social science research still tend to be conceptualized largely in static spatial terms, ignoring how our understanding of the issues we study can be greatly enriched through the lenses of time and human mobility. This article revisits three such notions: racial segregation, environmental exposure, and accessibility. It argues for the need to expand our analytical focus from static residential spaces to other relevant places and times in people's everyday lives. Mobility is an essential element of people's spatiotemporal experiences, and these complex experiences cannot be fully understood by just looking at where people live. As many social scientists are interested in studying segregation, environmental exposure, and accessibility, geographers can contribute to advancing temporally integrated analysis of these issues through careful examination of people's everyday experiences as their lives unfold in space and time. Interdisciplinary research along this line could have a broad impact on many disciplines beyond geography. 诸多地理及社会学研究中的基础概念， 仍然被大幅概念化为静态的空间词彙， 忽略了我们如何得以透过时间与人类流动性的视角， 大加丰富我们所研究的议题。 本文重探三个此种类型的概念: 种族隔离、环境暴露， 以及可及性。 本文认为， 我们必须将分析焦点从静态的住宅空间扩张至人们日常生活中其他的相关地方与时间。 流动性是人们时空间验中的一个关键元素， 而这些经验皆无法仅透过观察人们的住处而充分理解之。 由于诸多社会学家对于研究隔离、 环境暴露与可及性感兴趣， 地理学家得以透过缜密地检视人们的生活在时空中展开的日常生活经验， 促进这些议题的时间性整合分析， 以此做出贡献。 沿着此一方向的跨领域研究， 能够对地理学之外的诸多领域产生广泛的影响。 Muchas de las nociones fundamentales de la investigación en ciencia geográfica y social en gran medida todavía tienden a conceptualizarse en términos espaciales estáticos, ignorando que nuestra comprensión de las cosas que estudiamos podrían enriquecerse mucho más escrutando el tiempo y la movilidad humana. Este artículo vuelve sobre tres de tales nociones: la segregación racial, la exposición ambiental y la accesibilidad. Se clama sobre la necesidad de expandir nuestro foco analítico desde los espacios residenciales estáticos hacia otros lugares y tiempos relevantes en la vida cotidiana de las personas. La movilidad es un elemento esencial de las experiencias espacio-temporales de la gente, y estas experiencias complejas no pueden aprehenderse plenamente con tan solo mirar donde la gente vive. En consideración al número de científicos sociales interesados en el estudio de la segregación, la exposición ambiental y la accesibilidad, los geógrafos pueden contribuir al desarrollo del análisis integrado temporalmente de estos tópicos examinando cuidadosamente las experiencias cotidianas de la gente, a medida que sus vidas se desenvuelven en el espacio y en el tiempo. Más allá de la geografía, la investigación interdisciplinaria a lo largo de esta línea podría tener un amplio impacto en muchas disciplinas.

The convergence of newly interactive Web-based technologies with growing practices of user-generated content disseminated on the Internet is generating a remarkable new form of geographic information. Citizens are using handheld devices to collect geographic information and contribute it to crowd-sourced data sets, using Web-based mapping interfaces to mark and annotate geographic features, or adding geographic location to photographs, text, and other media shared online. These phenomena, which generate what we refer to collectively as volunteered geographic information (VGI), represent a paradigmatic shift in how geographic information is created and shared and by whom, as well as its content and characteristics. This article, which draws on our recently completed inventory of VGI initiatives, is intended to frame the crucial dimensions of VGI for geography and geographers, with an eye toward identifying its potential in our field, as well as the most pressing research needed to realize this potential. Drawing on our ongoing research, we examine the content and characteristics of VGI, the technical and social processes through which it is produced, appropriate methods for synthesizing and using these data in research, and emerging social and political concerns related to this new form of information.

Although geographic features, such as mountains and coastlines, are fractal, some studies have claimed that the fractal property is not universal. This claim, which is dubious, is mainly attributed to the strict definition of fractal dimension as a measure or index for characterizing the complexity of fractals. In this article, we propose an alternative, ht-index, to quantify the fractal or scaling structure of geographic features. A geographic feature has ht-index (h) if the pattern of far more small things than large ones recurs (h - 1) times at different scales. The higher the ht-index, the more complex the geographic feature. We conduct three case studies to illustrate how the computed ht-indexes capture the complexity of different geographic features. We further discuss how ht-index is complementary to fractal dimension and elaborate on a dynamic view behind ht-index that enables better understanding of geographic forms and processes.