Spatial Interactions in Business and Housing Location Models

The paper combines theoretical models of housing and business locations and shows that they have the same determinants. It evidences that classical, behavioural, new economic geography, evolutionary and co-evolutionary frameworks apply simultaneously, and one should consider them jointly when explaining urban structure. We use quantitative tools in a theory-guided factors induction approach to show the complexity of location models. The paper discusses and measures spatial phenomena as distance-decaying gradients, spatial discontinuities, densities, spillovers, spatial interactions, agglomerations, and as multimodal processes. We illustrate the theoretical discussion with an empirical case of interacting point-patterns for business, housing, and population. The analysis reveals strong links between housing valuation and business location and profitability, accompanied by the related spatial phenomena. It also shows that assumptions concerning unimodal spatial urban structure, the existence of rational maximisers, distance-decaying externalities, and a single pattern of behaviour, do not hold. Instead, the reality entails consideration of multimodality, a mixture of maximisers and satisfiers, incomplete information, appearance of spatial interactions, feed-back loops, as well as the existence of persistence of behaviour, with slow and costly adjustments of location.

Modelos de localização e análise econômica de unidade de serviço de atendimento médico de urgência

This study shows how location models such as p-median, p-center, and a proposed variation were employed to improve urgent and emergency care provided through the emergency mobile health units (SAMU). Besides incurring unnecessary additional operational costs, it is important to note that the failure or inefficiency of these mobile units can result in loss of human lives. The SAMU system in question serves a city with a population of approximately 213,576 inhabitants and it handles more than 1,400 calls per year. Operations research techniques like mixed integer linear programming and facility location principles were used to assertively and quantitatively define the best locations for SAMU units. The location problems were solved using the Julia 1.5.0 programming language, and other softwares were also used for organizing the data. The Lagrangian relaxation proved to be an efficient method to solve the problems which are considered NP-hard. Under the different scenarios tested, it was concluded that when compared with the p-median model, the p-center method found the best locations for the emergency mobile health units as it reduced the maximum distance between patient and the mobile units, in addition to other analyses.

Solving Classic Discrete Facility Location Problems Using Excel Spreadsheets

There are a variety of discrete facility location models that have practical relevance for operations management and management science courses. Integer linear programming (ILP) is the standard technique for solving such problems. An alternative approach that is often conceptually appealing to students is to pose the problem as one of finding the best possible subset of p facilities out of n possible candidates. I developed an Excel workbook that allows students to interactively evaluate the quality of different subsets, to run a VBA macro that finds the optimal subset, or to solve an ILP formulation that finds the optimal subset. Spreadsheets are available for five classic discrete location models: (1) the location set-covering problem, (2) the maximal covering location problem, (3) the p-median problem, (4) the p-centers problem, and (5) the simple plant location problem. The results from an assignment in a master’s-level business analytics course indicate that the workbook facilitates a better conceptual understanding of the precise nature of the discrete facility location problems by showing that they can be solved via enumeration of all possible combinations of p subsets that can be drawn from n candidate locations. More important, students directly observe the superiority of ILP as a solution approach as n increases and as p approaches n/2.

Commercial Classification and Location Modelling: Integrating Different Perspectives on Commercial Location and Structure

Commercial classification is essential to describe and compare the spatial patterns of commercial activity. Most classification systems consider a large set of dimensions that include detailed features such as store ownership or development type. Since new business models are continually being developed, the need to revise classification systems is constant. This makes generalisation hard, thus hindering the comparison of commercial structures in different places and periods. Recent studies have focused on cluster analysis and a smaller number of variables to gain insights into commercial structures, directly addressing this issue. Systematic bottom-up classification generates comparable structures, which is essential to contrast policy results in different situations. Furthermore, since form or accessibility are usually considered in classifications, cluster membership is precluded from most retail location models, often relying on the latter as an explanatory variable. Hence, a new classification system is proposed, based on cluster analysis (k-means) and a minimal set of variables: density, diversity, and clustering. This classification was implemented in 1995, 2002, and 2010 in Lisbon. Cross-sectional analysis of the commercial structures shows the system accurately describes commercial location and change, suggesting it can be generalised as a classification system. Since the minimal dataset also allows for cluster membership to be used on location models, the relationship between commercial classification and location modelling could be strengthened, reinforcing the role of commercial studies in urban planning and policymaking.

Open-source approaches for location cover models: capabilities and efficiency

Location cover models are aimed at siting facilities so as to provide service to demand efficiently. These models are crucial in the management, planning and decision-making of service systems in public and private sectors. As a result, location cover models have been incorporated in a range of GIS tools, either closed or open source. Among them, open-source tools are advantageous due to transparency and reproducibility. Nonetheless, the capabilities and limitations of location cover tools remain largely unknown, necessitating further investigation and assessment. To this end, this paper provides an overview of the open-source tools that are capable of structuring and solving location cover models. Case studies are provided to demonstrate access of location models through different open-source tools as well as exploring solution quality, scalability, computing performance and reproducibility. Directions for improving location cover models accessible through open-source tools are summarized based on this review.

Comparison between Highly Complex Location Models and GAMLSS

This paper presents a discussion regarding regression models, especially those belonging to the location class. Our main motivation is that, with simple distributions having simple interpretations, in some cases, one gets better results than the ones obtained with overly complex distributions. For instance, with the reverse Gumbel (RG) distribution, it is possible to explain response variables by making use of the generalized additive models for location, scale, and shape (GAMLSS) framework, which allows the fitting of several parameters (characteristics) of the probabilistic distributions, like mean, mode, variance, and others. Three real data applications are used to compare several location models against the RG under the GAMLSS framework. The intention is to show that the use of a simple distribution (e.g., RG) based on a more sophisticated regression structure may be preferable than using a more complex location model.