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 .”     

Generating Alternative Land‐use Allocation for Mixed Use Areas: Multi‐Objective Optimization Approach

Current research is carried out with an intention to present an optimization approach for the urban land‐use allocation problem by generating Pareto optimum solutions considering two objectives—maximizing compatibility among adjacent space uses of a study area without compromising the area’s total land price and maximizing the price of plot of each individual owner. Considering the non‐linear characteristics of the objective functions, a multi‐objective evolutionary algorithm approach called Non‐Dominated Sorting Genetic Algorithm‐II (NSGA‐II) is applied to obtain Pareto optimal land‐use allocation subject to different set of constraints. The objective functions are tested over a case study area of Dhaka, Bangladesh. The resulting NSGA‐II model produces 24 Pareto optimal solutions of land‐use allocation, allowing tradeoff between maximizing compatibility and land price from one solution to other. This research also expresses the potential of the model to aid the policymakers and city planners of development authorities by providing alternative land‐use plans, and thereby predicting the consequences of any plan before practical application.     

Dynamics of Land Surface Temperature in Response to Land‐Use/Cover Change

In this study, we employed Geographical Information Systems and remote sensing techniques to investigate the impact of land‐use/cover change on land surface temperature (LST) in a rapidly urbanisation city, Kunming in south‐west China. Spatial patterns of LST and land use for 1992 and 2006 were derived from Landsat images to examine how LST responded to urban growth. Remote sensing indices were used to quantify land‐use types and employed as explanatory variables in LST modelling. The geographically weighted regression (GWR), a location dependent model, was performed to explore the influences of the spatially varied land‐use conditions on the LST patterns. Results revealed that rapid urbanisation in Kunming altered the local thermal environment, particularly in increasing the LST in the zone surrounding the urban core. Remote sensing indices demonstrated that water and vegetation played an important role in mitigating the urban heat island effect, while built‐up and barren land accounted for the increase in LST. The GWR improved the goodness‐of‐fit for LST modelling and provided insights into the spatially varied relationship between LST and land‐use conditions.     

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.     

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 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.     

The Redevelopmental State: Governing Surplus Life and Land in the ‘Urban Age’

This article theorizes changing configurations of development governance emerging as states attempt to reconcile two contradictory pressures of global urbanization: dispossessing capitalist accumulation and demands for inclusive welfare. It introduces the ‘redevelopmental state’ as a dynamic spatio‐political framework for understanding how hegemonic rule is tenuously forged amid potentially volatile urban land struggles. Whereas Northern urban redevelopment theories are less attentive to post‐colonial urbanization processes and most developmental state scholarship has not focused on cities, the redevelopmental state offers an alternative conceptualization. It centres on how emerging regimes of territorial rule, development and political participation contour access to land and social benefits in Southern cities. Forged at key conjunctures of social pressure, these redevelopmental state spaces work through and beyond formal policies and institutions, and articulate with nationalist cultural politics of belonging and aspiration that foster consent for redevelopment while also legitimating exclusions, violence and dispossession. A case study of Mumbai illustrates redevelopmental state spaces that suture ethno‐religious nationalism, urbanized accumulation and populist welfare to unevenly distribute life capacities, garnering both cooperation and contestation. The article concludes by suggesting ways this spatially attuned framing can provide insights into the recent rise of ethno‐nationalism and authoritarian populism around the world.     

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.     

Monitoring Land‐Cover and Land‐Use Change in a Rapidly Urbanising Coastal Environment: the Maroochy and Mooloolah Rivers Catchments,Southeast Queensland, 1988–1997

Effective catchment monitoring requires an integrative approach for selecting remotely sensed data to monitor land‐cover change. Catchment analyses and monitoring requirements in the Maroochy and Mooloolah River Catchments (south‐east Queensland) were addressed by linking government and community information needs to appropriate scales of remotely sensed data and processing routines. A hybrid image classification approach applied to Landsat Thematic Mapper image data acquired in 1988 and 1997 provided catchment scale land‐cover maps (with accuracies of 73% and 84%). Land‐cover change maps derived from post‐classification comparison provided information on the spatial distribution and type of land‐cover changes between 1988–1997. Land‐cover change was dominated by activities related to urban residential expansion (suburban and rural residential) and agricultural expansion (sugar cane farms). Significant spatial variations in the geometric registration accuracy of the image data sets and classification accuracies produced moderate to low change detection accuracies. However, the framework and community consultation used for selecting and processing data ensured the resource managers and community groups were fully aware of the limitations of the land‐cover change mapping process and that the output images were suitable for incorporating in resource monitoring activities.     

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.     

Ethno‐religious ‘unmixing’ of ‘Turkey’: 6–7 September riots as a case in Turkish nationalism*

Abstract. This article examines the structural and ideological factors that paved the way for the eruption of violence against non‐Muslims in Turkey on 6 September 1955. I argue that the conventional explanations that treat this instance of collective violence either as spontaneous rioting caused by over‐excited masses or as a government conspiracy that eventually got out of control are insufficient in that they fail to answer how and why so many people participated in these riots when we know that nothing on this scale ever took place in the history of the republic. In order to adequately understand the dynamics behind these riots one first needs to situate them in the broader historical context of the emergence, development and crystallisation of Turkish nationalism and national identity that marked the non‐Muslim citizens of the republic as the ‘others’ and potential enemies of the real Turkish nation. This historical analysis constitutes the first part of the article. Since ethno‐national riots do not always occur whenever there are conflicting identities, one also needs to explain the processes through which ethno‐national identities become radicalized and polarized. Thus, in the second part of the article, I focus on the economic, political and social conditions of the post‐single‐party era (post‐1950) that helped to radicalise the sentiments of the growing urban populace against the non‐Muslim ‘others’. I argue that it was the socio‐economic, ideological and political transformations of the Democrat Party era that made it possible for ethnic entrepreneurs and state provocateurs to mobilise the masses against a fictitious enemy.     

Measurement of Urban Fringe Sprawl in Nanjing between 1984 and 2010 Using Multidimensional Indicators

Due to the global increases in urbanisation rates, urban sprawl has become a worldwide issue. In China, the development of rural areas has been affected by urban expansion and adjustments of metropolitan areas by administrative divisions that have led to losses in agricultural lands and ecosystem services. There is the ‘paradoxical’ desire for governments to promote rural development and protect land resources. Urban fringe sprawl is a prominent issue in the process of rural development. Identifying and measuring sprawl at the urban fringe could assist policymakers to either encourage or discourage this phenomenon. This paper demonstrates use of multidimensional indicators to effectively measure urban fringe sprawl by remote sensing, in addition to demographic and socio‐economic data. Using integrated indicators observed in the recent Chinese development context, urban fringe sprawl was measured comprehensively for Jiangning, a district in the city of Nanjing, China. This study indicates that Jiangning is sprawling rapidly. The results also show that the multidimensional indicator approach could provide decision support to create effective long‐term land‐use planning policies for controlling urban sprawl patterns.     

A NEW SPATIAL MULTIPLE DISCRETE‐CONTINUOUS MODELING APPROACH TO LAND USE CHANGE ANALYSIS

This paper formulates a multiple discrete‐continuous probit (MDCP) land use model within a spatially explicit economic structural framework for land use change decisions. The spatial MDCP model is capable of predicting both the type and intensity of urban development patterns over large geographic areas, while also explicitly acknowledging geographic proximity‐based spatial dependencies in these patterns. At a methodological level, the paper focuses on specifying and estimating a spatial MDCP model that allows the dependent variable to exist in multiple discrete states with an intensity associated with each discrete state. The formulation also accommodates spatial dependencies, as well as spatial heterogeneity and heteroskedasticity, in the dependent variable, and should be applicable in a wide variety of fields where social and spatial dependencies between decision agents (or observation units) lead to spillover effects in multiple discrete‐continuous choices (or states). A simulation exercise is undertaken to evaluate the ability of the proposed maximum approximate composite marginal likelihood (MACML) approach to recover parameters from a cross‐sectional spatial MDCP model. The results show that the MACML approach does well in recovering parameters. An empirical demonstration of the approach is undertaken using the city of Austin parcel level land use data.     

The Impact of Neutral and Nonneutral Productivity Growth on Creative Capital in a Trading Regional Economy. 区域经济贸易中中性和非中性生产力增长对创新资本的影响

We analyze a two‐sector model of a trading creative regional economy (TCRE) of the sort studied by Florida. Our analysis focuses on two cases. The first case concerns the impact of faster neutral, whereas the second case concerns the impact of faster nonneutral, productivity growth in the tradable sector on the employment of creative people in each of a TCRE's two sectors. In both cases, the sign of the percentage change in the steady‐state use of creative capital in the nontradable sector is ambiguous. In other words, the potential departure of creative people from the tradable to the nontradable sector does not depend upon whether the faster productivity growth being studied is neutral or nonneutral. In addition, because the effects of faster productivity growth in the tradable sector are not necessarily innocuous, a potential role exists for activist policy designed to countermand the possibly negative effects of faster productivity growth on either the tradable or the nontradable sector. This finding of possible “uneven development” is consistent with Florida's view that TCREs can give rise to externalities such as uneven regional development.     

An Empirically Based Implementation and Evaluation of a Hierarchical Model for Commuting Flows. 一种基于经验的层状通勤流模型实现与评估

This article provides an empirical evaluation of a hierarchical approach to modeling commuting flows. As the gravity family of spatial interaction models represents a benchmark for empirical evaluation, we begin by reviewing basic aspects of these models. The hierarchical modeling framework is the same that Thorsen, Ubøe, and Nævdal (1999) used. However, because some modifications are required to construct a more workable model, we undertake a relatively detailed presentation of the model, rather than merely referring to the presentation in Thorsen, Ubøe, and Nævdal (1999) . The model uses a hierarchical specification of a transportation network and the individual search procedure. Journeys to work are determined by the effects of distance deterrence and of intervening opportunities, and by the location of potential destinations relative to alternatives at subsequent levels in a transportation network. The model calibration uses commuting data from a region in western Norway. The estimated parameter values are reasonable, and the explanatory power is very satisfactory when compared with the results of a competing destinations approach.     

Determinants of Variation in Population–Employment Interaction Findings: A Quasi‐Experimental Meta‐Analysis. 人口‐就业相互作用的决定因素研究：一种准实验元分析法

This article reports about a metaregression analysis of empirical results generated using data for the northern Netherlands (1988–2002) in order to investigate the ambiguity in results in the population–employment interaction literature. Specifically, the analysis deals with the issue whether “jobs follow people” or “people follow jobs.” The article starts with introducing the basics of quasi‐experimental meta‐analysis and with identifying some advantages of using quasi‐experimental meta‐analysis as compared with the standard meta‐analysis approach. Two subsequent sections document the selection of the population–employment interaction model and salient characteristics of the data set as well as the setup of the primary analyses. A total of 4,050 quasi‐experimental empirical results for the jobs–people direction of causality are generated using different specifications and estimators for a spatial econometric interaction model. The subsequent metaregression analysis reveals that the empirical results are largely shaped by the spatial, temporal, and employment characteristics of the data sampling. The results also appear much more sensitive to different measurements of the model's key variables when compared with alternative specifications of the spatial weights matrix. The main determinant driving empirical results about jobs–people causality are differences in model specification and estimation, as revealed by an inherent bias in parameter estimates and misguided inferences for some of the commonly used specifications. Finally, suggestions for future research are identified.     

Private agricultural colonization on a Brazilian frontier, 1970–1980

Private colonization is the use of companies and cooperatives to survey, demarcate and occupy land, build infrastructure, open roads, plan urban areas, and provide health services and education. Although state-directed colonization projects are strongly implicated in recent environmental and social changes in the Brazilian Amazon, areas settled by private colonization were larger than state-led settlement. The paper considers this poorly examined aspect of the region's recent settlement history by focusing upon a colonization cooperative and private company that settled smallholders from southern Brazil to eastern Mato Grosso State between 1970 and 1980. The analysis emphasizes how private colonization cooperatives successfully secured land title, setting the stage for subsequent commercial agricultural development. This study rejects prevailing interpretations of private colonization as a tool of authoritarian government in Brazil. Rather, private colonization secured land tenure and organized an economically viable production system in a frontier environment of unpredictable state bureaucracies, high transaction costs, risk, and precarious markets.     

Comparison of GIS‐Based Logic Scoring of Preference and Multicriteria Evaluation Methods: Urban Land Use Suitability

Multi‐criteria evaluation (MCE) methods are useful tools to evaluate the land suitability for various uses and assist in the effective management of available land. Many common GIS‐based MCE methods, such as analytical hierarchy process (AHP), ordered weighted averaging (OWA), and a combination of AHP and OWA methods (AHP–OWA) are not able to fully represent all the logic that constitute a wide range of human decision‐making reasoning. Consequently, improved GIS‐based MCE methods such as Logic Scoring of Preference (LSP) method are needed. The main objectives of this study are to: (1) implement the GIS‐based LSP method for land suitability evaluation and (2) compare qualitatively and quantitatively the suitability maps generated by LSP and three GIS‐based MCE methods. This study was implemented with data sets from Boulder County, Colorado, USA for the case study of the urban land suitability evaluation. The qualitative properties of MCE methods and the Receiver Operating Characteristic (ROC) statistics were used as comparison metrics. The results indicate that soft computing methods and particularly LSP performed the best among GIS‐based MCE methods for the urban land use application.     

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.     

Integrating Multi‐Sensor Remote Sensing Data for Land Use/Cover Mapping in a Tropical Mountainous Area in Northern Thailand

Accurate mapping of land use/cover conditions provides essential information for managing natural resources and is critical for further examination of land use/cover change and its subsequent impacts on the environment. Remote sensing offers a means of acquiring land use/cover data in a timely manner, with optical remote sensing images commonly being used in land use/cover related studies. The persistent cloud cover during the rainy season in Southeast Asia, however, presents a challenge for using optical images in land use/cover mapping. Integrating multi‐sensor images of different spectral domains is thus desirable because more information can be extracted to improve the mapping accuracy. The purpose of this study is to assess the potential of using multi‐sensor data sets for land use/cover mapping in a tropical mountainous area in northern Thailand. Optical data from Landsat Thematic Mapper, radar images from Advanced Land Observing Satellite/Phased Array type L‐band Synthetic Aperture Radar (PALSAR), and topographical data were used, providing complementary information on land use/cover. Classification and accuracy assessment were conducted for 12 different combinations of the data sets. The results suggested that short crop mapping using multi‐temporal Phased Array type L‐band Synthetic Aperture Radar images offered insights into the distributions of crop and paddy fields. Because of the mountainous environment of the study area, combining topographic data of elevation and slope into the classification greatly reduced the confusion between different land use/cover types. Improvement of classification accuracy was evident especially in separating evergreen and deciduous forests from other vegetation types and discriminating urban village and the fallow field classes.