Time‐Dependent Functions for Avoidance Behaviors Made in Response to the Circulation of HIV and AIDS

Interventions aimed at preventing HIV transmission include avoidance behaviors like condom use and reduced partner acquisition. In turn., engagement in such activities might also entail changed patterns of travel to evade contact with infected individuals. One method of estimating the effects of these actions on the observed distribution of HIV/AIDS involves the specification of space‐time models that imitate the epidemic process. This paper presents an application of this procedure where prevention is construed as a continuous population response to the evolving distribution of HIV and AIDS. This task entails the construction of models with time‐dependent parameters adapted to predicted prevalence or incidence measures to represent the effects of specific avoidance behaviors. In this respect, a multiregion model is described that serves as a baseline for analyzing the impact of preventative actions on the HIV/AIDS epidemic in Western Europe. A series of adaptation functions are derived within this system for imitating both changed rates of partner acquisition and altered travel behaviors. The results elicit modifications to the behavior of the baseline epidemic that are generated by each of these functions. Then, the conditions supporting space‐time variations in rates of survival between core countries (relatively low) and those in the periphery (relatively high) are investigated. The discussion considers the implications of these results for health policies that promote avoidance behaviors.     

Determining Market Areas Captured by Competitive Facilities: A Continuous Equilibrium Modeling Approach

Many existing models concerning locations and market areas of competitive facilities assume that customers patronize a facility based on distance to that facility, or perhaps on a function of distances between the customer and the different facilities available. Customers are generally assumed to be located at certain discrete demand points in a two-dimensional space, or continuously distributed over a one-dimensional line segment. In this paper these assumptions are relaxed by employment of a continuum optimization model to characterize the equilibrium choice behavior of customers for a given set of competitive facilities over a heterogeneous two-dimensional space. Customers are assumed to be scattered continuously over the space and each customer is assumed to choose a facility based on both congested travel time to the facility and on the attributes of the facility. The model is formulated as a calculus of variations problem and its optimality conditions are shown to be equivalent to the spatial customer-choice equilibrium conditions. An efficient numerical method using finite element technique is proposed and illustrated with a numerical example.     

Calibration and Test of a Discrete Choice Model with Endogenous Choice Sets

Conventional discrete choice models assume implicitly that the choice set is independent of the decisionmaker's preferences conditional on the explanatory variables of the models. This assumption is implausible in many choice situations where the decisionmaker selects his or her choice set. This paper estimates and tests a discrete choice model with endogenous choice sets based on Horowitz' theoretical work. To calibrate the model, a new probability simulator is introduced and a sequential estimation procedure is developed. The model and calibration methods are tested in an empirical application as well as Monte Carlo simulations. The empirical results are used to test the theory of endogenous choice sets and to examine the differences between the new model and a conventional choice model in parameter estimates and predicted choice probabilities. The empirical results strongly suggest that ignoring the endogeneity of choice sets in choice modeling can have serious consequences in applications.     

Using Decision Tree Induction Systems for Modeling Space‐Time Behavior

Discrete choice models are commonly used to predict individuals' activity and travel choices either separately or simultaneously in activity‐scheduling models. This paper investigates the possibilities of decision tree induction systems as an alternative approach. The ability of decision trees to represent heuristic decision rules is evaluated and a method of capturing interactions across decisions in a sequential decision model is outlined. Decision tree induction algorithms, such as C4.5, CART, and CHAID, are suited to derive the decision rules from empirical data. A case study to illustrate the approach considers decisions of individuals when they are faced with the choice to combine different out‐of‐home activities into a multipurpose, multistop trip or make a trip for each activity separately. Data from a large‐scale activity diary survey are used to induce the decision rules. Possible directions of future research are identified.     

Establishing Stage‐Discharge Relationships in Multiple‐Channelled,Ephemeral Rivers: a Case Study of the Diamantina River,Australia

The method for deriving a stage‐discharge relationship has a significant impact on the shape of the river's rating curve. We compare rating curves for a single gauging station on a mutiple‐channelled river in Australia compiled using three different methods – the Urban Runoff and Basin Systems (URBS) rainfall‐runoff model, an empirically‐based velocity‐area method, and the predictive Hydrologic Engineering Centre‐River Analysis System (HEC‐RAS) computer model. The rainfall‐runoff model was found to predict lower discharges for stage heights over 3.5 m than both the empirically‐based velocity‐area method and the HEC‐RAS model. The empirically‐based velocity‐area model predicts similar discharges to the rainfall‐runoff model for stage heights less than 3 m but much higher discharges for larger flood events. The HEC‐RAS model predicts higher discharges than both other rating curves at all stage heights probably due to under‐estimation of the impact of surface roughness on flow velocity. The three models are discussed with particular reference to their use on multiple‐channelled rivers.     

Nonparametric Significance Test for Discovery of Network‐Constrained Spatial Co‐Location Patterns

Spatial co‐location patterns are useful for understanding positive spatial interactions among different geographical phenomena. Existing methods for detecting spatial co‐location patterns are mostly developed based on planar space assumption; however, geographical phenomena related to human activities are strongly constrained by road networks. Although these methods can be simply modified to consider the constraints of networks by using the network distance or network partitioning scheme, user‐specified parameters or priori assumptions for determining prevalent co‐location patterns are still subjective. As a result, some co‐location patterns may be wrongly reported or omitted. Therefore, a nonparametric significance test without priori assumptions about the distributions of the spatial features is proposed in this article. Both point‐dependent and location‐dependent network‐constrained summary statistics are first utilized to model the distribution characteristics of the spatial features. Then, by using these summary statistics, a network‐constrained pattern reconstruction method is developed to construct the null model of the test, and the prevalence degree of co‐location patterns is modeled as the significance level. The significance test is evaluated using the facility points‐of‐interest data sets. Experiments and comparisons show that the significance test can effectively detect network‐constrained spatial co‐location patterns with less priori knowledge and outperforms two state‐of‐the‐art methods in excluding spurious patterns.     

Multidimensional Sequence Alignment Methods for Activity‐Travel Pattern Analysis: A Comparison of Dynamic Programming and Genetic Algorithms

Quantitative comparisons of space‐time activity‐travel patterns have been made at length in regional science. Traditionally, Euclidean Hamming distances have been widely used to measure the similarity between activity‐travel patterns that involve several attribute dimensions such as activity type, location, travel mode, accompanying person, etc. Other techniques, such as pattern recognition in signal processing theory, have also been introduced for this purpose. All these measures, however, lack the ability to capture the sequential information embedded in activity‐travel patterns. Recently, the sequence alignment methods (SAMs), developed in molecular biology that are concerned with the distances between DNA strings, have been introduced in time use research. These SAMs do capture the similarity of activity‐travel patterns, including sequential information, but based on a single attribute only. Unfortunately, the extension of the unidimensional SAMs to a multidimensional method induces the problem of combinatorial explosion. To solve this problem, this paper introduces effective heuristic methods for the comparison of multidimensional activity‐travel patterns. First, following a brief review of existing measures of activity‐travel pattern comparison, the problem of multidimensional sequential information comparison and the combinatorial nature of the method are discussed. The paper then develops alternative multidimensional SAMs employing heuristics based on dynamic programming and genetic algorithms, respectively. These heuristic SAMs are compared using empirical activity‐travel pattern data. The paper ends by discussing avenues of future research.     

Aggregation of Regional Economic Time Series with Different Spatial Correlation Structures. 具有不同空间相关结构的区域经济时间序列集成

In this article, we compare the relative efficiency of different forecasting methods of space‐time series when variables are spatially and temporally correlated. We consider two cases: (1) univariate forecasting (i.e., a space‐time series aggregated into a single time series) and (2) the more general instance of multivariate forecasting (i.e., a space‐time series aggregated into a coarser spatial partition). We extend the results in the literature by including the consideration of larger datasets and the treatment of edge effects and of negative spatial correlation. We first introduce a statistical framework based on the space‐time autoregressive class of random field models, which constitutes the basis of our simulation study, and we present the various alternative forecasting methods considered in the simulation. We then present the results of a Monte Carlo study related to univariate forecasting. In order to allow a comparison with the findings of Giacomini and Granger (2004), we consider the same forecasting strategies and the same combinations of the parameter values used there, but with a larger parametric set. Finally, we extend our analysis to the case of multivariate forecasting. The outcomes obtained provide operational suggestions about how to choose between alternative forecasting methods in empirical circumstances.     

Comparison of 3D Landmark and 3D Dense Cloud Approaches to Hominin Mandible Morphometrics Using Structure‐From‐Motion

The objective of this study is to compare landmark‐based assessment of hominin mandible morphological variation with that from a high‐density point cloud, using 3D models constructed from structure‐from‐motion (SfM) surface capture techniques. Surface models of nine hominin mandible casts were created using SfM photogrammetry. The morphology of these models was described using traditional geometric morphometrics based on identification of landmarks. This was compared to the morphological variation described by the differences between high‐density point clouds, which do not rely on anatomical landmarks, using an iterative closest point algorithm. The landmark‐based approach grouped the anatomically modern human and Neanderthal mandibles with reasonable success. The high‐density point cloud approach also grouped these successfully, but was able to incorporate information from a specimen that was insufficiently preserved to be included in the landmark data set. This improved the accuracy of the grouping. The use of high‐density point clouds from surface capture to analyse hominin mandible morphology allows for greater amounts of information to be included and offers a potential method to identify shape affinity that is as successful as landmark‐based geometric morphometrics.     

Investigating the Influence of Spatial and Temporal Granularities on Agent‐Based Modeling

Epidemic agent‐based models (ABMs) simulate individuals in artificial societies that are capable of movement, interaction, and transmitting disease among themselves. ABMs have been used to study the spread of disease at various spatial and temporal scales ranging from small communities to the world, over days, months, and years. The representations of space and time often vary between different epidemic ABMs and can be influenced by factors such as the size of a modeled population, computational requirements, population environments, and disease‐related data. The influence that the representations of space and time have on epidemic ABMs is difficult to assess. Here we show that the finest representations of space and time—termed spatial and temporal granularities (STGs)—in a parsimonious ABM affect speed, intensity, and spatial spread of a synthetic disease. Specifically, we found disease spread faster and more intensely as spatial granularity is coarsened, whereas disease spread slower and less intensely as temporal granularity is coarsened in a parsimonious ABM. Our study is the first to use the same epidemic ABM to examine the influence of STGs. Our results demonstrate that STGs influence ABM dynamics including early disease burnout and that an interrelationship exists between the coarsening of STGs and the speed and intensity at which disease spreads. Our parsimonious ABM is extended based on a structured community model and we found STGs also influence ABM dynamics in a more realistic context that includes hierarchical movement. Broadly, our study serves as a basis for further inquiry toward the influence of space–time representations on more realistic models that include multiscale mobility, routine movements (e.g., commuting), and heterogeneous population distributions.     

An Area‐Based Approach for Estimating Extreme Precipitation Probability

Accurate estimates of heavy rainfall probabilities reduce loss of life, property, and infrastructure failure resulting from flooding. NOAA's Atlas‐14 provides point‐based precipitation exceedance probability estimates for a range of durations and recurrence intervals. While it has been used as an engineering reference, Atlas‐14 does not provide direct estimates of areal rainfall totals which provide a better predictor of flooding that leads to infrastructure failure, and more relevant input for storm water or hydrologic modeling. This study produces heavy precipitation exceedance probability estimates based on basin‐level precipitation totals. We adapted a Generalized Extreme Value distribution to estimate Intensity‐Duration‐Frequency curves from annual maximum totals. The method exploits a high‐resolution precipitation data set and uses a bootstrapping approach to borrow spatially across homogeneous regions, substituting space in lieu of long‐time series. We compared area‐based estimates of 1‐, 2‐, and 4‐day annual maximum total probabilities against point‐based estimates at rain gauges within watersheds impacted by five recent extraordinary precipitation and flooding events. We found considerable differences between point‐based and area‐based estimates. It suggests that caveats are needed when using pointed‐based estimates to represent areal estimates as model inputs for the purpose of storm water management and flood risk assessment.     

Growth and Convergence in a Multiregional Model with Space–Time Dynamics. 多区域时空动态模型的增长与收敛

The goal of this article is to test four distinct hypotheses about whether the relative location of an economy affects economic growth and economic well‐being using an extended Solow–Swan neoclassical growth model that incorporates both space and time dynamics. We show that the econometric specification takes the form of an unconstrained spatial Durbin model, and we investigate whether the results depend on some methodological issues, such as the choice of the time span and the inclusion of fixed effects. To estimate the fixed effects spatial Solow–Swan model, we adjust the Arrelano and Bond (1991) generalized method‐of‐moments (GMM) estimator to deal with endogeneity not only arising from the initial income level, as in the basic model, but also from the initial income levels and economic growth rates observed in neighboring economies.     

A Hybrid Framework for Space–Time Modeling of Environmental Data. 环境数据时空建模的混合框架

The space–time autoregressive integrated moving average (STARIMA) model family provides useful tools for modeling space–time processes that exhibit stationarity (or near stationarity) in space and time. However, a more general method for routine use and efficient computation is needed to model the nonlinearities and nonstationarities of environmental space–time series. This article presents a hybrid framework combining machine learning and statistical methods to address this issue. It uses an artificial neural network (ANN) to extract global deterministic (nonlinear) space–time trends and a STARIMA model to extract local stochastic space–time variations in data. A four‐stage procedure is proposed for analyzing and modeling space–time series. The proposed framework and procedures are applied to forecast annual average temperature at 137 national meteorological stations in China. The results demonstrate that the hybrid framework achieves better forecasting accuracy than the STARIMA model alone. This finding suggests that the combination of machine learning and statistical methods provides a very powerful tool for analyzing and modeling space–time series of environmental data that have strong spatial nonlinear and nonstationary components.     

PLACE‐BASED ATTRIBUTES AND SPATIAL EXPENDITURE BEHAVIOR IN TOURISM

In recent discussions on local sustainable development, notions like “local for local” and “home bias” have often played a role. The purpose of this paper is to investigate whether preferences for specific place‐based attributes might constrain or support tourism participation and tourism destination choice of distinct socioeconomic groups of visitors. To test this proposition, a large data set from the Italian Households Budget Survey for the period 1997–2007 has been used and, by means of the double‐hurdle model, tourism participation and expenditure are modeled over the life‐cycle of tourists. These data are next merged with location‐specific attributes including natural amenities and infrastructural and regional‐economic context variables. Our results show that location‐specific or place‐based characteristics affect intra‐ and interregional tourism differently, as well as destination choices. Regional differences between residents in two different macroareas in Italy (North and Center‐South) are investigated. Location‐specific characteristics may be either push or constraint factors for tourism participation. For families living in the North, participation in the tourism market is supported by the tourist characteristics of their home region. For families living in the central and southern regions however, economic conditions of the area where they reside appear to be more significant.     

The Location Choice of Employment‐based Immigrants among U.S. Metro Areas*

Abstract. This paper examines the initial location choice of legal employment‐based immigrants to the United States using Immigration and Naturalization Service data on individual immigrants, as well as economic, demographic, and social data to characterize the 298 metropolitan areas we define as the universal choice set. Focusing on interactions between place characteristics and immigrant characteristics, we provide multinomial logit model estimates for the location choices of about 38,000 employment‐based immigrants to the United States in 1995, focusing on the top 10 source countries. We find that, as groups, immigrants from nearly all countries are attracted to large cities with superior climates, and to cities with relatively well‐educated adults and high wages. We also find evidence that employment‐based immigrants tend to choose cities where there are relatively few immigrants of nationalities other than their own. However, when we introduce interaction terms to account for the sociodemographic characteristics of the individual immigrants, we find that the estimated effects of location destination factors can reverse as one takes account of the age, gender, marital status, and previous occupation of the immigrants.     

Beyond ‘State Ibuism’: Empowerment Effects in State‐led Development in Indonesia

This article examines the ways women's processes of self‐formation are indicative (or not) of new possibilities for women's gendered selves in the post‐Reformasi period in Indonesia. It focuses on the development arena to reveal how shifts in state rhetoric, from top‐down guidance based on a patriarchal familial model to bottom‐up, inclusive development based on empowerment, have transformed what is referred to as the ‘topography for self’. The article draws upon theories of personhood a) to show how gendered selves emerge and are contested within particular historical conditions; and b) to develop an alternative framework of ‘empowerment’ that focuses not on capabilities and choice, but on an expansion in the possibilities for self. It argues that models of community‐driven development have provided new opportunities for women to hold and enact socially recognizable subject positions. This constitutes a form of empowerment for individual women but does not necessarily reflect challenges to patriarchy in Indonesia.     

Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity

Spatial nonstationarity is a condition in which a simple “global” model cannot explain the relationships between some sets of variables. The nature of the model must alter over space to reflect the structure within the data. In this paper, a technique is developed, termed geographically weighted regression, which attempts to capture this variation by calibrating a multiple regression model which allows different relationships to exist at different points in space. This technique is loosely based on kernel regression. The method itself is introduced and related issues such as the choice of a spatial weighting function are discussed. Following this, a series of related statistical tests are considered which can be described generally as tests for spatial nonstationarity. Using Monte Carlo methods, techniques are proposed for investigating the null hypothesis that the data may be described by a global model rather than a non-stationary one and also for testing whether individual regression coefficients are stable over geographic space. These techniques are demonstrated on a data set from the 1991 U.K. census relating car ownership rates to social class and male unemployment. The paper concludes by discussing ways in which the technique can be extended.     

Latent trajectory models for space‐time analysis: An application in deciphering spatial panel data

This article introduces latent trajectory models (LTMs), an approach often employed in social sciences to handle longitudinal data, to the arena of GIScience, particularly space‐time analysis. Using the space‐time data collected at county level for the whole United States through webpage search on the keyword “climate change,” we show that LTMs, when combined with eigenvector filtering of spatial dependence in data, are very useful in unveiling temporal trends hidden in such data: the webpage‐data derived popularity measure for climate change has been increasing from December 2011 to March 2013, but the increase rate has been slowing down. In addition, LTMs help reveal potential mechanisms behind observed space‐time trajectories through linking the webpage‐data derived popularity measure about climate change to a set of socio‐demographic covariates. Our analysis shows that controlling for population density, greater drought exposure, higher percent of people who are 16 years old or above, and higher household income are positively predictive of the trajectory slopes. Higher percentages of Republicans and number of hot days in summer are negatively related to the trajectory slopes. Implications of these results are examined, concluding with consideration of the potential utility of LTMs in space‐time analysis and more generally in GIScience.     

Contiguity Constraints for Single‐Region Site Search Problems

This paper proposes an explicit set of constraints as a general approach to the contiguity problem in site search modeling. Site search models address the challenging problem of identifying the best area in a study region for a particular land use, given that there are no candidate sites. Criteria that commonly arise in a search include a site's area, suitability, cost, shape, and proximity to surrounding geographic features. An unsolved problem in this modeling arena is the identification of a general set of mathematical programming constraints that can guarantee a contiguous solution (site) for any 0–1 integer‐programming site search formulation. The constraints proposed herein address this problem, and we evaluate their efficacy and efficiency in the context of a regular and irregular tessellation of geographic space. An especially efficient constraint form is derived from a more general form and similarly evaluated. The results demonstrate that the proposed constraints represent a viable, general approach to the contiguity problem.     

Worker Co‐operatives and Spaces of Possibility: An Investigation of Subject Space at Collective Copies

Abstract: If we are to understand the organization and growth of capitalist space, should we not also seek to understand the organization and expansion of noncapitalist economic spaces? In contrast to methods employed by theorists such as Harvey, Smith and other geographers focused on capitalist space, the diverse economies framework opens up to investigation such noncapitalist spaces. In this paper, using Gibson‐Graham's “politics of possibility”, I explore the production of work space and time in a growing worker owned co‐operative copy shop in order to gain insight into the organization and growth of co‐operative space. I argue that, in this instance, co‐operative growth emerges from the transformative experience of workers having a say in their daily work lives, having equal authority to govern work space and time and to appropriate and distribute surplus.