The Spatial Structuring of Central Places

The spatial structuring of “classical” central place systems is the primary concern of this research. Emphasizing the development of such systems from basic spatial relations between centers and hinterland locations (rather than as a result of an underlying geometry of regions), this paper takes an optimization approach to the siting of central places in a single-good system. The objective function is shown to be one that maximizes both market coverage of demand and market overlap. Access to this objective is given through the natural slack covering model.     

Analysis of Errors Due to Demand Data Aggregation in the Set Covering and Maximal Covering Location Problems

In this paper, we extend the concepts of demand data aggregation error to location problems involving coverage. These errors, which arise from losses in locational information, may lead to suboptimal location patterns. They are potentially more significant in covering problems than in p-median problems because the distance metric is binary in covering problems. We examine the Hillsman and Rhoda (1978) Source A, B, and C errors, identify their coverage counterparts, and relate them to the cost and optimality errors that may result. Three rules are then presented which, when applied during data aggregation, will reduce these errors. The third rule will, in fact, eliminate all loss of locational information, but may also limit the amount of aggregation possible. Results of computational tests on a large-scale problem are presented to demonstrate the performance of rule 3.     

Classical Central Places as Protected Thresholds

Previous research efforts demonstrate the use of location covering in the spatial structuring of central places within a single-good context. In a multilevel context, this paper similarly develops a mathematical programming approach to the siting of central places based on the “protection” of inner (or threshold) markets. The objective function maximizes both market coverage of demand and market overlap, subject to an upper bound on market overlap; this allows the formation of different market structures consistent with the various K-valued central place systems of Christaller and Lösch. Siting examples serve to illustrate the working of the protected threshold model in isotropic settings. Furthermore, ways in which the present model might be extended for other situations are examined.     

Estimating Spatial Models Within A Disequilibrium Framework

Modeling demand in a spatial context requires careful handling of regional interactions. In situations where there are constraints in some markets that lead to spillovers to others it is useful to build this explicitly into the model. In this paper I present a theoretical model that is related to the disequilibrium and the spatial econometric literature. Under certain conditions the model may be estimable and an appropriate estimation technique that uses the EM algorithm, is put forward. A data set from the U.K. market for secondary school places provides a test for the procedure.     

HEDONIC PRICES,IDENTIFICATION, AND THE DEMAND FOR PUBLIC SAFETY*

In this paper, we use a two-stage intercity hedonic model to estimate household demand for public safety. This approach is shown to readily address the identification problem inherent in the hedonic model. Data from the 1980 Public Use Microdata Sample are used to estimate a willingness-to-pay function for the public-safety good. Income is found to be the primary determinant of willingness to pay. Indeed, the influence of income outweighs the combined impact of family life-cycle considerations.     

A Simple Trade-off Model for Maximal and Multiple Coverage

A natural slack model, which allows for the trade-off of single coverage of demand for multiple coverage, is developed in this paper. Locational options structured within this multiobjective perspective extend from the most dispersed pattern possible (i.e., the maximal covering solution) to the most clustered (i.e., the multiple covering solution). It is believed that this extension of the location covering approach can better inform decision makers who must address questions of spatial and temporal access to services. A numerical example, demonstrating the use of the model and the identification of the noninferior set of siting choices, is suggested and solved.     

A Route Map for Successful Applications of Geographically Weighted Regression

Geographically Weighted Regression (GWR) is increasingly used in spatial analyses of social and environmental data. It allows spatial heterogeneities in processes and relationships to be investigated through a series of local regression models rather than a single global one. Standard GWR assumes that relationships between the response and predictor variables operate at the same spatial scale, which is frequently not the case. To address this, several GWR variants have been proposed. This paper describes a route map to decide whether to use a GWR model or not, and if so which of three core variants to apply: a standard GWR, a mixed GWR or a multiscale GWR (MS-GWR). The route map comprises 3 primary steps that should always be undertaken: (1) a basic linear regression, (2) a MS-GWR, and (3) investigations of the results of these in order to decide whether to use a GWR approach, and if so for determining the appropriate GWR variant. The paper also highlights the importance of investigating a number of secondary issues at global and local scales including collinearity, the influence of outliers, and dependent error terms. Code and data for the case study used to illustrate the route map are provided.     

LONG-TERM FORECASTING OF NONCOINTEGRATED AND COINTEGRATED REGIONAL AND NATIONAL MODELS*

ABSTRACT. Johansen's (1988) multivariate test for cointegration is first applied to four models involving quarterly state data and five variables, along with a national model based on Friedman and Kuttner's (1992) model of money demand, which uses three variables. Each regional model consists of frequently used national and state series, for which theory suggests the possible cointegration of several series pairs. Beginning with all five series, however, one state model is found to be cointegrated over each of 20 successive estimation intervals. The money demand model and one state model are not cointegrated over the same intervals. In the cointegrated case, five-year experimental forecasts show that error correction mechanism (ECM) and Bayesian ECM models outperform all other approaches. More importantly, forecasting performance improves further by respecifying the ECM model based on three cointegrated series pairs rather than the five-component cointegrating vector. For the two noncointegrated systems, the first-difference model suggested by the cointegration/ error correction literature is far superior to VAR in levels over both shortand long-term horizons.     

Undocumented Immigration and Host‐Country Welfare: Competition Across Segmented Labor Markets*

Abstract. In this paper's model, undocumented workers are endogenously sorted into secondary labor markets. When further illegal immigration occurs, some new migrants follow their fellows into already migrant‐dominated jobs, lowering migrant wages and raising real incomes of host‐country labor and capital. Some submarkets switch from employing legal workers to employing migrants, lowering demand for and wages of legal workers. Undocumented immigration is Pareto‐improving when enforcement reserves primary‐sector jobs for legal workers. Pareto‐dominant policies target the number of migrant‐dominated submarkets, not the number of migrants. This appears consistent with U.S. enforcement practices. The effects of deportations, employer sanctions, and amnesties are explored.     

A QUARTERLY ECONOMETRIC MODEL OF FACTOR DEMANDS IN REGIONAL MANUFACTURING

This paper presents a model of demand for factor inputs in regional manufacturing industry. Demands for labor and capital are distinguished from demands for factor services using a generalization of the disequilibrium adjustment framework of Nadiri and Rosen. The generalization allows for a richer variety of responses to external shocks and involves the distinction between systematic and error dynamics.
The model is estimated for the period 1963:I-1980:I using data from the Cincinnati SMSA and appropriate econometric techniques. Substantial attention is paid to the dynamic response of the estimated equations.     

Definition of Seismic Input for Out-of-Plane Response of Masonry Walls: I. Parametric Study

Response of masonry walls to out-of-plane excitation is a complex, yet inadequately addressed theme in seismic analysis. The seismic input expected on an out-of-plane wall (or a generic “secondary system”) in a masonry building is the ground excitation filtered by the in-plane response of the walls and the floor diaphragm response. More generally, the dynamic response of the primary structure, which can be nonlinear, contributes to the filtering phenomenon. The current article delves into the details and results of several nonlinear dynamic time-history analyses executed within a parametric framework. The study addresses masonry structures with rigid diaphragm response to lateral loads. The scope of the parametric study is to demonstrate the influence of inelastic structural response on the seismic response of secondary systems and eventually develop an expression to estimate the seismic input on secondary systems that explicitly accounts for the level of inelasticity in the primary structure in terms of the displacement ductility demand. The proposed formulation is discussed in the companion article.     

Spatial optimisation of fire service coverage: a case study of Brisbane,Australia

In the context of changing demand for fire services, spatial optimisation of fire coverage has attracted little scholarly attention despite its potential to improve emergency response and to inform future service planning for fire stations. Drawing on small area population forecasts, this paper extends the application of the Maximum Coverage Location Model to compute and delineate the spatial coverage of current and proposed new fire stations to align with population growth estimates for Brisbane, Australia. Our results reveal important gaps in fire cover that are likely to emerge as a result of predicted population growth, the spatial patterns of which varies across the Brisbane metropolitan area. We draw on these results to delineate a series of new potential sites for fire stations to ameliorate the reduction in spatial coverage as a consequence of predicted population growth demonstrating the utility of our analytic approach for decision‐making and operational planning in the fire services.     

Housing Market Dynamics Under Stochastic Growth: An Application to the Housing Market in Boulder, Colorado

Recent housing-market studies have modeled slow stock and price adjustment with some success. However, the empirical procedures used in these models break down if housing stocks or prices are driven by stochastic growth. In this paper I suggest an error-correction model for analyzing housing supply and demand under conditions of stochastic growth for a regional housing market. The model is applied to the housing market in Boulder, Colorado from 1981 through 1995—a period of rapid growth in housing values in the area. Long-run housing supply and demand are shown to be inelastic with respect to changes in the price of housing. The results indicate that developers respond more accurately to housing-market disequilibrium attributable to supply-side disturbances than to disturbances generated by changes in the demand for housing. On the other hand, price appreciation is driven primarily by demand disturbances.     

Monitoring the Near‐surface Urban Heat Island in Beijing,China by Satellite Remote Sensing

Urban heat island (UHI) effect is an important impact factor of the regional climate and ecological environment. How to observe and analyse the spatial distribution of UHI has become an important issue of urban environmental research. In this paper, the near‐surface air temperature of Beijing was derived based on the Landsat/TM satellite imagery on 26 July 2011 to study the near‐surface UHI. A statistical model at 195‐m window size was established to estimate the air temperature, using land surface temperature, normalized difference vegetation index, altitude, and surface albedo as independent variables. The mean absolute error (MAE) of the model was 0.87°C, and the R² was 0.66, indicating that the method can be used to effectively estimate the air temperature. The air temperature distribution obtained from remote sensing revealed that the UHI effect in Beijing was very significant and showed a concentrated pattern. The heat island intensity was stronger in the southern part than in the northern part of the city. In addition, the relationship between the air temperature and impervious surfaces was analysed. The air temperature increased with increasing impervious surface coverage, and the rate of change depended on the impervious surface coverage. When the impervious surface coverage was below 40 per cent, the temperature increased rapidly with increasing impervious surface coverage, and when the impervious surface coverage was above 40 per cent, the temperature increased slowly. This study provides a new approach to monitor near‐surface UHI and reveals its relationship with impervious surface, providing a scientific reference for urban planning and environmental assessment.     

The Multi‐level Location Set Covering Model

The classical Location Set Covering Problem involves finding the smallest number of facilities and their locations so that each demand is covered by at least one facility. It was first introduced by Toregas in 1970. This problem can represent several different application settings including the location of emergency services and the selection of conservation sites. The Location Set Covering Problem can be formulated as a 0–1 integer‐programming model. Roth (1969) and Toregas and ReVelle (1973) developed reduction approaches that can systematically eliminate redundant columns and rows as well as identify essential sites. Such approaches can often reduce a problem to a size that is considerably smaller and easily solved by linear programming using branch and bound. Extensions to the Location Set Covering Model have been proposed so that additional levels of coverage are either encouraged or required. This paper focuses on one of the extended model forms called the Multi‐level Location Set Covering Model. The reduction rules of Roth and of Toregas and ReVelle violate properties found in the multi‐level model. This paper proposes a new set of reduction rules that can be used for the multi‐level model as well as the classic single‐level model. A demonstration of these new reduction rules is presented which indicates that such problems may be subject to significant reductions in both the numbers of demands as well as sites.     

Educational Innovation and the Market for Geographers in Hungary

This paper examines the status of geography in higher education in Hungary. Stress is placed on reforms begun in the 1990s to launch new curricula for training professional geographers. The authors played an important role in developing this new curriculum by introducing new subjects into geography programmes, working out the scope and sequence of courses, obtaining accreditation and carrying out market research for graduates. The project was motivated by a decline in demand for geography and geography teachers in secondary schools accompanied by an increase in demand for geographers trained to work in public administration, government and business. The graduates of the new professional geographer curriculum receive a practice-oriented education designed to cultivate their spatial problem solving and applied geographical skills. In this paper the authors present the steps in the curriculum reform and suggest that it may serve as a model for reform in a number of nearby countries planning to join the EU.     

Location Modeling Utilizing Maximum Service Distance Criteria

The location set-covering problem (LSCP) and the maximal covering location problem (MCLP) have been the subject of considerable interest. As originally defined, both problems allowed facility placement only at nodes. This paper deals with both problems for the case when facility placement is allowed anywhere on the network. Two theorems are presented that show that when facility placement is unrestricted, for either the LSCP or MCLP at least one optimal solution exists that is composed entirely of points belonging to a finite set of points called the network intersect point set (NIPS). Optimal solution approaches to the unrestricted site LSCP and MCLP problems that utilize the NIPS and previously developed solution methodologies are presented. Example solutions show that considerable improvement in the amount of coverage or the number of facilities needed to insure total coverage can be achieved by allowing facility placement along arcs of the network. In addition, extensions to the arc-covering model and the ambulance-hospital model of ReVelle, Toregas, and Falkson are developed and solved.     

COMPETITIVE LOCATION UNDER UNCERTAINTY OF COSTS

ABSTRACT. In this paper, we study the centroid problem from competitive location theory for a linear market with uniform demand, assuming that the leader has imperfect information about the follower's fixed and marginal costs. It is shown that the general version of this problem can be formulated as a nonlinear programming problem and the exact solution can be obtained analytically in a special case. A simple strategy is also given for the general problem, and it is proven that this strategy has a guaranteed error bound. It is demonstrated that uncertainty of costs might lead to market failure in the centroid problem, but this disappears if the game is repeated and the firms learn from observing each other's moves. It is also shown that it is possible for the leader to obtain optimal expected profit at a low perceived risk, with only sufficient, and not necessarily perfect, information. These two observations lead to our primary conclusion from the study that although cost uncertainty is a realistic feature of most competitive location models, there are very effective ways of dealing with it.     

Accounting for Temporal Demand Variations in Retail Location Models

This article develops and calibrates a spatial interaction model (SIM) incorporating additional temporal characteristics of consumer demand for the U.K. grocery market. SIMs have been routinely used by the retail sector for location modeling and revenue prediction and have a good record of success, especially in the supermarket/hypermarket sector. However, greater planning controls and a more competitive trading environment in recent years has forced retailers to look to new markets. This has meant a greater focus on the convenience market which creates new challenges for retail location models. In this article, we present a custom built SIM for the grocery market in West Yorkshire incorporating trading and consumer data provided by a major U.K. retailer. We show that this model works well for supermarkets and hypermarkets but poorly for convenience stores. We then build a series of new demand layers taking into account the spatial distributions of demand at the time of day that consumers are likely to use grocery stores. These new demand layers include workplace populations, university student populations and secondary school children. When these demand layers are added to the models, we see a very promising increase in the accuracy of the revenue forecasts.     

The Aggregation Problem in Location-Allocation

Location-allocation solutions based on aggregate estimates of demand are subject to error because of a loss of locational information during aggregation. It is shown that any method to remove or reduce uncertainty must be solution-specific and therefore impractical, for both median and center classes of problems. The significance of the error is illustrated by simulation of solutions to a number of artificial and real problems. It is suggested that aggregation problems be specifically addressed in applications of location-allocation models, and possible methods are proposed.