Adaptive Cell Tower Location Using Geostatistics. 基于地统计学的适应性基站发射塔选址研究

In this article, we address the problem of allocating an additional cell tower (or a set of towers) to an existing cellular network, maximizing the call completion probability. Our approach is derived from the adaptive spatial sampling problem using kriging, capitalizing on spatial correlation between cell phone signal strength data points and accounting for terrain morphology. Cell phone demand is reflected by population counts in the form of weights. The objective function, which is the weighted call completion probability, is highly nonlinear and complex (nondifferentiable and discontinuous). Sequential and simultaneous discrete optimization techniques are presented, and heuristics such as simulated annealing and Nelder–Mead are suggested to solve our problem. The adaptive spatial sampling problem is defined and related to the additional facility location problem. The approach is illustrated using data on cell phone call completion probability in a rural region of Erie County in western New York, and accounts for terrain variation using a line‐of‐sight approach. Finally, the computational results of sequential and simultaneous approaches are compared. Our model is also applicable to other facility location problems that aim to minimize the uncertainty associated with a customer visiting a new facility that has been added to an existing set of facilities.     

The p‐Regions Problem. p‐区域问题

The p‐regions problem involves the aggregation or clustering of n small areas into p spatially contiguous regions while optimizing some criteria. The main objective of this article is to explore possible avenues for formulating this problem as a mixed integer‐programming (MIP) problem. The critical issue in formulating this problem is to ensure that each region is a spatially contiguous cluster of small areas. We introduce three MIP models for solving the p regions problem. Each model minimizes the sum of dissimilarities between all pairs of areas within each region while guaranteeing contiguity. Three strategies designed to ensure contiguity are presented: (1) an adaptation of the Miller, Tucker, and Zemlin tour‐breaking constraints developed for the traveling salesman problem; (2) the use of ordered‐area assignment variables based upon an extension of an approach by Cova and Church for the geographical site design problem; and (3) the use of flow constraints based upon an extension of work by Shirabe. We test the efficacy of each formulation as well as specify a strategy to reduce overall problem size.     

Inferences for Selected Location Quotients with Applications to Health Outcomes. 选择性区位商推断及其在医疗成效中的应用

The location quotient (LQ) is an index frequently used in geography and economics to measure the relative concentration of activities. This quotient is calculated in a variety of ways depending on which group is used as a reference. Here, we focus on a simultaneous inference for the ratios of the individual proportions to the overall proportion based on binomial data. This is a multiple comparison problem and inferences for LQs with adjustments for multiplicity have not been addressed before. The comparisons are negatively correlated. The quotients can be simultaneously tested against unity, and simultaneous confidence intervals can be constructed for the LQs based on existing probability inequalities and by directly using the asymptotic joint distribution of the associated test statistics. The proposed inferences are appropriate for analysis based on sample surveys. Two real data sets are used to demonstrate the application of multiplicity‐adjusted LQs. A simulation study is also carried out to assess the performance of the proposed methods to achieve a nominal coverage probability. For the LQs considered, the coverage of the simple Bonferroni‐adjusted Fieller intervals for LQs is observed to be almost as good as the coverage of the method that directly takes the correlations into account.     

A Geostatistical Linear Regression Model for Small Area Data. 一种适用于小区域数据的地统计线性回归模型

We present a new linear regression model for use with aggregated, small area data that are spatially autocorrelated. Because these data are aggregates of individual‐level data, we choose to model the spatial autocorrelation using a geostatistical model specified at the scale of the individual. The autocovariance of observed small area data is determined via the natural aggregation over the population. Unlike lattice‐based autoregressive approaches, the geostatistical approach is invariant to the scale of data aggregation. We establish that this geostatistical approach also is a valid autoregressive model; thus, we call this approach the geostatistical autoregressive (GAR) model. An asymptotically consistent and efficient maximum likelihood estimator is derived for the GAR model. Finite sample evidence from simulation experiments demonstrates the relative efficiency properties of the GAR model. Furthermore, while aggregation results in less efficient estimates than disaggregated data, the GAR model provides the most efficient estimates from the data that are available. These results suggest that the GAR model should be considered as part of a spatial analyst's toolbox when aggregated, small area data are analyzed. More important, we believe that the GAR model's attention to the individual‐level scale allows for a more flexible and theory‐informed specification than the existing autoregressive approaches based on an area‐level spatial weights matrix. Because many spatial process models, both in geography and in other disciplines, are specified at the individual level, we hope that the GAR covariance specification will provide a vehicle for a better informed and more interdisciplinary use of spatial regression models with area‐aggregated data.     

Weighted Bidimensional Regression. 加权二维回归

Shape analysis is useful for a wide variety of disciplines and has many applications. One of the many approaches to shape analysis focuses on shapes that are represented by predefined landmarks on an object. Some landmarks may be measured with greater precision, exhibit more natural variation, or be more important than others to an analysis. This article introduces a method for including this information when estimating mapping relations or assessing the degree of similarity between two objects that are represented by a set of two‐dimensional landmarks. Weighted bidimensional regression combines aspects of weighted least squares regression and bidimensional regression as a way to weight variables that are represented by a set of two‐dimensional spatial coordinates. One possible weighting scheme is suggested, and the effect of weighting is demonstrated through a face‐matching application. Results indicate that appropriate weighting increases the ability to correctly match two faces and that weighting has the largest effect when used with a projective transformation.     

A Framework for Exploring Urban Retail Discontinuities. 探索城市零售业不连续性的框架

A key area in the analysis of urban structural evolution is identifying discontinuities. Effective analysis could improve long‐term forecasting and provide a better understanding of how to steer an urban system toward a desirable future state. We use a simple aggregate retail model to demonstrate an algorithm for identifying discontinuities in model parameter space. Explorations of retailing in both Greater London and South Yorkshire in the United Kingdom illustrate how understanding a system's potential for discontinuity can provide insights for both policy makers and retail businesses. The Harris and Wilson model, described in the section so‐named, is used as a simple archetype to illustrate the new framework. This model can be developed in a straightforward way to incorporate further refinement. In “ Executing the model and visualizing the results ,” we describe a single model run and in “ Investigating discontinuities ,” we explain our framework for detecting and analyzing discontinuities. “ Identifying discontinuities in the London retail system ” shows the results of applying this methodology to the Greater London retail system, and in “ Practical applications ,” we explore the policy applications for this technique as related to the decline of town centers in the South Yorkshire retail system. Some concluding comments are offered in “ Conclusions .”     

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.     

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.     

A Theory‐Based Cellular Automata for the Simulation of Land‐Use Change. 基于理论细胞自动机的土地利用变化模拟

This study proposes a theory‐based cellular automata (CA) in which CA rules are constructed on the basis of a theory of urban land‐use change and other local conditions. Rent gap theory that describes urban redevelopment and exogenous conditions that reflect urban development are embedded into a CA to simulate changes of land use. The theoretical framework provides a strong conceptual background for the CA simulation. A case study demonstrates the flexibility of the integrated framework for simulating land‐use changes in complex settings. This research aims to help decision‐makers formulate appropriate development plans.     

A Fractal Approach to Identifying Urban Boundaries. 城市边界识别的分形方法

Fractal geometry can be used for determining the morphological boundaries of metropolitan areas. A two‐step method is proposed here: (1) Minkowski's dilation is applied to detect any multiscale spatial discontinuity and (2) a distance threshold is located on the dilation curve corresponding to a major change in its behavior. We therefore measure the maximum curvature of the dilation curve. The method is tested on theoretical urban patterns and on several European cities to identify their morphological boundaries and to track boundary changes over space and time. Results obtained show that cities characterized by comparable global densities may exhibit different distance thresholds. The less the distances separating buildings differ between an urban agglomeration and its surrounding built landscape, the greater the distance threshold. The fewer the buildings that are connected across scales, the greater the distance threshold.     

Gibrat Revisited: An Urban Growth Model Incorporating Spatial Interaction and Innovation Cycles. 再评吉尔布瑞特：顾及空间相互作用和创新周期的城市增长模型

We designed a geographical model for simulating the distribution of urban growth in systems of cities. The model incorporates the hierarchical and spatial diffusion of innovation cycles through gravitational interactions within a set of cities. Using theoretical simulations, we demonstrate that this model is able to reproduce the observed properties of urban systems for the log‐normal distribution of city sizes as well as the observed distribution of growth rates. Our experimentation was performed on a large harmonized historical database that includes a few hundred French urban agglomerations between 1831 and 1999 (Pumain‐INED database). Both spatial interaction and innovation cycles are necessary ingredients to explain the evolution of urban hierarchies. We suggest that Gibrat's generic stochastic growth model based on independent entities should be replaced by a more relevant model of spatially and temporally interdependent geographical entities.     

Dealing with Timing and Synchronization in Opportunities for Joint Activity Participation. 公共活动参与机会中实时性与同步性处理

The ability of people to access opportunities offered by the built environment is circumscribed by various sets of space–time constraints, including the requirements to meet other persons at particular times and places to undertake activities together. While models of space–time accessibility recognize that joint activities may constrain the performance of activities in space and time, their specifications do not explicitly acknowledge the opportunities that individuals of a group have for joint activity participation. Therefore, this article focuses on joint activity participation and argues that collective activity decisions are the outcome of a complex process involving various aspects of timing, synchronization, and social hierarchy. The utility‐theoretic model proposed here quantifies the extent to which opportunities can be jointly accessed by a particular group of people within a specific time period. Central to the approach are three key variables: the attractiveness of an opportunity, the time available for activity participation, and the travel time to an activity location. Because of the multiperson character of joint activities, the determination of these variables is subject to individual preferences, privileges, and power differentials within a group. Specific attention is given to how time‐of‐day and synchronization effects influence the opportunities accessible to a group of individuals. The impact of these factors on joint accessibility is illustrated by a real‐world example of an everyday rendezvous scenario. The outcomes of a simulation exercise suggest that time‐of‐day and synchronization effects significantly affect the benefits that can be gained from opportunities for joint activities.     

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.     

Minority–Majority Relations in the Schelling Model of Residential Dynamics. 住宅动态迁移Schelling模型中的少数‐多数关系

The Schelling model describing segregation between two groups of residential agents, reflects the most abstract, basic view of noneconomic forces motivating residential migrations: be close to people of “your own” kind. The model assumes that residential agents, located in neighborhoods where the fraction of “friends” is less than a predefined threshold value F, try to relocate to neighborhoods where this fraction is F or higher. For groups of equal size, Schelling's residential pattern converges either to complete integration (random pattern) or segregation, depending on F. We investigate Schelling model pattern dynamics as a function of F in addition to two other parameters—the ratio of groups' numbers, and neighborhood size. We demonstrate that the traditional integration–segregation pattern dichotomy should be extended. In the case of groups of different sizes, a wide interval of F‐values exists that entails a third persistent residential pattern, one in which a portion of the majority population segregates while the rest remains integrated with the minority. We also demonstrate that Schelling model dynamics essentially depend on the formalization of urban agents' residential behavior. To obtain realistic results, the agents should be satisficers, and the fraction of the agents relocating irrespective of the neighborhood's state should be nonzero. We discuss the relationship between our results and real‐world residential dynamics.     

A New Perspective about Moran's Coefficient: Spatial Autocorrelation as a Linear Regression Problem. Moran系数的新视角：空间自相关视为线性回归问题

The computation of Moran's index of spatial autocorrelation requires the definition of a spatial weighting matrix. The eigendecomposition of this doubly centered matrix (i.e., one that forces the sums of all rows and columns to equal zero) has interesting properties that have been exploited in various contexts: distribution properties of the Moran coefficient (MC), spatial filtering in linear models, generalized linear models, and multivariate analysis. In this article, this eigendecomposition is used to propose a new view of MC based on its interpretation in the simple context of linear regression. I use this interpretation to demonstrate the different properties of MC and also the inefficiency of this index in some situations involving simultaneous positive and negative spatial autocorrelation. I propose some new statistics and procedures for testing spatial autocorrelation, and conduct a simulation study to evaluate these new approaches.