Developing and Applying a Disaggregated Retail Location Model with Extended Retail Demand Estimations

The spatial interaction model (SIM) is an important tool for retail location analysis and store revenue estimation, particularly within the grocery sector. However, there are few examples of SIM development within the literature that capture the complexities of consumer behavior or discuss model developments and extensions necessary to produce models which can predict store revenues to a high degree of accuracy. This article reports a new disaggregated model with more sophisticated demand terms which reflect different types of retail consumer (by income or social class), with different shopping behaviors in terms of brand choice. We also incorporate seasonal fluctuations in demand driven by tourism, a major source of non‐residential demand, allowing us to calibrate revenue predictions against seasonal sales fluctuations experienced at individual stores. We demonstrate that such disaggregated models need empirical data for calibration purposes, without which model extensions are likely to remain theoretical only. Using data provided by a major grocery retailer, we demonstrate that statistically, spatially, and in terms of revenue estimation, models can be shown to produce extremely good forecasts and predictions concerning store patronage and store revenues, including much more realistic behavior regarding store selection. We also show that it is possible to add a tourist demand layer, which can make considerable forecasting improvements relative to models built only with residential demand.     

A Unified Model for Dispersing Facilities

One of the important classes of facility dispersion problems involves the location of a number of facilities where the intent is to place them as far apart from each other as possible. Four basic forms of the p‐facility dispersion problem appear in the literature. Erkut and Neuman present a classification system for these four classic constructs. More recently, Curtin and Church expanded upon this framework by the introduction of “multiple types” of facilities, where the dispersion distances between specific types are weighted differently. This article explores another basic assumption found in all four classic models (including the multitype facility constructs of Curtin and Church): that dispersion is accounted for in terms of either distance to the closest facility or distances to all facilities (from a given facility), whether applied to a single type of facility or across a set of facility types. In reality, however, measuring dispersion in terms of whether neighboring facilities to a given facility are dispersed rather than whether all facilities are dispersed away from the given facility often makes more sense. To account for this intermediate measure of dispersion, we propose a construct called partial‐sum dispersion. We propose four “partial‐sum” dispersion problem forms and show that these are generalized forms of the classic set of four models codified by Erkut and Neuman. Further, we present a unifying model that is a generalized form of all four partial‐sum models as well as a generalized form of the original four classic model constructs. Finally, we present computational experience with the general model and conclude with a few examples and suggestions for future research. Una de las clases importantes dentro de los problemas de dispersión de instalaciones de servicios/infraestructura es el caso en el que la localización de un número de instalaciones debe cumplir la condición de maximizar la distancia entre cada par. La literatura especializada cita cuatro formas básicas del problema de dispersión llamados tipo p‐instalación (p‐facility) (Shier 1977; Luna y Chaudhry 1984; Kuby 1987; Erkut y Neuman, 1991). Erkut y Neuman (1991) presentan un sistema de clasificación para estas cuatro formas clásicas. Recientemente, Curtin e Iglesia (2006) ampliaron este marco metodológico al incorporar múltiples tipos de instalaciones, permitiendo que las distancias de dispersión entre diferentes tipos específicos de instalaciones sean ponderadas de manera diferente. El artículo presente explora otro supuesto básico que se encuentra en los cuatro modelos clásicos (y las modifcaciones para acomodar instalaciones multi‐tipo de Curtin e Iglesia): la dispersión es cuantificada en términos de la distancia entre una instalación dada y la instalación más cercana, o entre una instalación dada y la totalidad de las instalaciones. Este supuesto se mantiene si las distancias son aplicadas a un solo tipo de instalación o a múltiples tipos de instalaciones. Sin embargo, en realidad, tiene más sentido medir la dispersión en relación a las instalaciones vecinas, en vez de en relación a la totalidad las instalaciones. Para incorporar esta realidad a un nuevo tipo de medida intermedia de dispersión, se propone una medida llamada dispersión de suma parcial (partial‐sum dispersion). Proponemos cuatro tipos de problemas de dispersión de tipo parcial‐sum y demostramos que éstas son formas generalizadas de los cuatro modelos clásicos presentados por Erkut y Neuman (1991). Además, se presenta un modelo unificado que es una forma generalizada de los cuatro modelos tipo partial‐sum, así como una forma generalizada de las cuatro tipos en el modelo clásico. Por último, se presenta los resultados de pruebas computacionales usando el modelo general y se concluye con algunos ejemplos y sugerencias para investigaciones futuras. 设施分散问题中重要的一类是大量设施的布局,其意图是将它们在空间上尽可能离得更远。目前文献中主要讨论了4种基本形式(Shier 1977; Moon and Chaudhry 1984; Kuby 1987; Erkut and Neuman 1991)。Erkut and Neuman (1991)提出了这4种经典结构的一种分类系统。Curtin and Church (2006)引入设施“多种类型”对上述分类框架进行拓展,在特定类别之间的分散距离的权重存在不同。本文探索了在4种经典模型中所发现的另一种基本假设（包含Curtin and Church的多种类型设施结构）：无论是在单一类型设施或包括多种类型设施中,分散度在解释某一给定设施到最近设施的距离或到所有设施的距离方面都是合理的。然而,在现实中设施分散度度量方面,测量某一给定设施的邻近设施的分散度特征相比于测量给定设施的所有其他设施的散布特征通常更有意义。为解释这种分散度的中间度量,本文提出了一种称为“局部和整体”的结构,包括4种分散问题形式,它们是Erkut and Neuman 4种传统类型的广义形式。本文进而提出了一个统一模型,即所有 “局部和整体”模型和经典类型结构一种广义形式。最后,对统一模型进行了计算检验,并基于几个实证进行了总结,还提出了未来的研究建议。     

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.     

A Spatial Search-Location Model of Retail Centers

Central place theory can be modeled using two types of optimization problems. Location-allocation models have been widely applied to operationalize central place theory as an aggregate optimization problem. This paper constructs a spatial search-location model to formulate central place theory as an individual optimization problem.     

A Partitioning Model of Urban Retail Structure

A model based on renewal theory generates the number of retail establishments in a place as the outcome of a competitive partitioning process. The available market, measured for example by population or by existing retail sales, is shared among businesses until no market potential market remains. Competing businesses obtain different shares of the market, and the number of establishments is predicted as a discrete random variable. Several alternative formulations are presented of varying generality. One version is successfully tested, using GLIM, on ten business types (SIC two-digit classes) in 232 cities of New York State for 1977 and 1987. The model correctly predicts the form and the variance structure of the relationship between number of establishments and place size. It is shown how the model may be combined with models of city-size distributions to predict aggregate frequency distributions of retail establishments across urban systems.     

A Covering Tour Model for Planning Mobile Health Care Facilities in SuhumDistrict, Ghama

The provision of adequate primary health care in developing countries is often troublesome. The problem is to provide a sufficient number of facilities to be geographically accessible, yet few enough to be properly stocked and staffed. In many less developed countries accessibility problems are exacerbated by extensive rainy seasons in which travel is only possible on paved roads. Using the covering tour model we investigate the use of mobile facilities to resolve this dilemma in Suhum District, Ghana. The model minimizes a mobile facility's travel while serving all population centers within range of a feasible stop. Computational results show that in the rainy season the model cannot provide full coverage; over six percent of the population is beyond a covering distance of eight kilometers. In the dry season, 99 percent of the population can be served by a tour at a covering distance of seven kilometers.Beyond a distance of four kilometers, the dry season problem becomes a trade-off between the distance traveled by healthcare patrons and mobile facilities. These results illustrate the importance of flexibility of mobile systems: if accessibility cannot be provided in all seasons it may still be provided at favorable times of the year.     

A Cost-Benefit Location-Allocation Model for Public Facilities: An Econometric Approach

This research develops and operationalizes a facility location-allocation model based on cost-benefit principles derived from welfare economics. Despite the theoretical advantages of cost-benefit location-allocation models, the difficulties associated with estimating household preferences for public facilities have heretofore prevented their application. This research demonstrates that the hedonic-pricing methodology can be effectively used to estimate preferences for public facilities. Specifically, household preferences for Baltimore public middle schools were estimated from the spatial variation in housing prices using the random bidding model. To provide an example of the methodology, the cost-benefit location-allocation objective function was maximized to simultaneously determine the optimal number, quality, and locations of Baltimore middle schools. The cost-benefit approach to facility location constitutes a major improvement over existing methods because it directly incorporates user preferences into the objective function and because the number and quality of facilities can be determined endogenously rather than being specified as a constraint a priori.     

An Equity Model for Locating Environmentally Hazardous Facilities

This paper develops a multiobjective mathematical location model to identify possible locations for environmentally hazardous facilities. Risk and equity are recognized as the most important criteria in determining site selection. In contrast to earlier models, the equity objective explicitly considers the existing distribution of environmental burdens when siting new hazardous facilities. Proposed environmentally hazardous facilities are located so that the burdens associated with new and existing hazards are shared as equally as possible among all areas. The application of the model, in a case study of the Greenpoint/Williamsburg neighborhood in Brooklyn, New York, illustrates the trade‐offs associated with various risk and equity scenarios. Sensitivity analyses demonstrate how the existing distribution of environmental burdens may act as a constraint and limit the degree of equity that may be obtained when locating new facilities.     

The Optimal Nodal Location of Public Facilities With Price-Sensitive Demand

The purpose of this paper is to present some models for the location of public facilities in nodal networks that explicitly maximize social welfare by accounting for price-elastic demand functions. The models presented here are general; yet they are mathematically equivalent to the plant location problem and are therefore amenable to solution procedures developed for the plant location problem. The models presented here distinguish between two institutional environments that reflect the degree of power of the consumer to choose which facility to patronize. If consumers can be assigned arbitrarily to facilities and can be denied service, then the environment is one of public fiat. If consumers must be served at the facility of their choice, then a “serve-allcomers” environment exists. Separate models for each environment are specified, and the relationship between optimal assignments and pricing policies is developed.     

Multiple Facilities Location in the Plane Using the Gravity Model

Two problems are considered in this article. Both problems seek the location of p facilities. The first problem is the p median where the total distance traveled by customers is minimized. The second problem focuses on equalizing demand across facilities by minimizing the variance of total demand attracted to each facility. These models are unique in that the gravity rule is used for the allocation of demand among facilities rather than assuming that each customer selects the closest facility. In addition, we also consider a multiobjective approach, which combines the two objectives. We propose heuristic solution procedures for the problem in the plane. Extensive computational results are presented.     

Bi‐objective Model for Optimal Number and Size of Circular Facilities

This article presents a bi‐objective model for determining the number and size of finite size facilities. The objectives are to minimize both the average closest distance to facilities and the probability that a random line intersects facilities. The former represents the accessibility of customers, whereas the latter represents the interference to travelers. The average closest distance and the probability of intersecting facilities are derived for circular facilities randomly distributed in a circular city. The analytical expressions for the average closest distance and the probability of intersecting facilities demonstrate how the number and size of facilities affect the accessibility of customers and the interference to travelers. The model focuses on the tradeoff between the accessibility and interference, and the tradeoff curve provides planners with alternatives for the number and size of facilities.     

A Note on the Geographic Interdependencies of Retail Market Areas

Central place theory describes an orderly hierarchy of places, with particular retail services developing for lower-ordered places as they reach a threshold. Yet it is likely that nearby areas could serve simultaneously as a source of demand and a source of competing supply for retail stores in a place. This paper contributes to the understanding of local economic development by modeling and estimating the geographic interdependence between a place and its neighboring areas. The simultaneous equation Tobit results suggest that such geographical interdependence exists for most retail industries, with spatial competition on the supply side being particularly important.     

Gender,Retail Employment and the Clothing Commodity Chain

This article analyzes the conditions of employment in fashion retailing, arguing that fashion retailing constitutes a distinct sector of retailing. The fashion commodity chain is characterized by a unique spatiality and temporality and as a result, female retail workers share much in common with women situated at production, advertising and consumption sites in the chain. Given that many of the issues women confront at different sites are similar, the sector is amenable to organizing across the chain.     

Tang Stele at the Snowy Foot of the Mountain - A Series of Archeological Trips in Gyirong County

At the northern flank of the downtown of Gyirong County, a mountain valley heads from south to north directly to Mt. Mala. Overlooking the valley to the north, the snowy mountain range pops into view. In the past the valley was the traffic artery for the Gyirong to communicate with the outside world. After the 1960s, Gyirong built its modern road. This valley was naturally erased from people＇s memory. On the way out of the valley two mountains, opposite to each other, are facing the extensive Gyirong basin. One of the mountain precipices features a small platform, known as ＂Ngawang Gyaye＂ （meaning ＂the name of the father＂）. Twenty years ago, it was at this point that I, with my fellow members of an archeological study, found the well-     

A Micro-Econometric Store Choice Model Incorporating Multi- and Omni-Channel Shopping: The Case of Furniture Retailing in Germany

Online retailing and multi-/omni-channel shopping are gaining in importance. However, there is a significant lack of research focused on incorporating online shopping into models of spatial shopping behavior. The present study aims (1) to construct a store choice model which includes both physical and online stores as well as the opportunity for omni-channel shopping, and (2) to identify the main drivers of spatial shopping behavior given the availability of both channels. Based on a representative survey, this study employs a revealed-preference approach toward store choice and expenditures in furniture retailing. The statistical analysis is performed using a hurdle model approach, with the expenditures of individual consumers at (online or physical) furniture stores serving as the dependent variable. Results show that channel choice (online vs. offline) is mainly influenced by psychographic characteristics, place of residence, and age of the consumers. Store choice and expenditures are primarily explained by store features such as assortment size, omni-channel integration, and accessibility. This study demonstrates that e-shopping can be integrated into a store choice model and that both the modeling approach and the subsequent findings are of significance for retail companies and spatial planning.