Optimizing the location of weather monitoring stations using estimation uncertainty

In this article, we address the problem of planning a network of weather monitoring stations observing average air temperature (AAT). Assuming the network planning scenario as a location problem, an optimization model and an operative methodology are proposed. The model uses the geostatistical uncertainty of estimation and the indicator formalism to consider in the location process a variable demand surface, depending on the spatial arrangement of the stations. This surface is also used to express a spatial representativeness value for each element in the network. It is then possible to locate such a network using optimization techniques, such as the used methods of simulated annealing (SA) and construction heuristics. This new approach was applied in the optimization of the Portuguese network of weather stations monitoring the AAT variable. In this case study, scenarios of reduction in the number of stations were generated and analysed: the uncertainty of estimation was computed, interpreted and applied to model the varying demand surface that is used in the optimization process. Along with the determination of spatial representativeness value of individual stations, SA was used to detect redundancies on the existing network and establish the base for its expansion. Using a greedy algorithm, a new network for monitoring average temperature in the selected study area is proposed and its effectiveness is compared with the current distribution of stations. For this proposed network distribution maps of the uncertainty of estimation and the temperature distribution were created. Copyright (c) 2011 Royal Meteorological Societyinfo:eu-repo/semantics/publishedVersio

Bayesian Optimization with Hidden Constraints via Latent Decision Models

Bayesian optimization (BO) has emerged as a potent tool for addressing intricate decision-making challenges, especially in public policy domains such as police districting. However, its broader application in public policymaking is hindered by the complexity of defining feasible regions and the high-dimensionality of decisions. This paper introduces the Hidden-Constrained Latent Space Bayesian Optimization (HC-LSBO), a novel BO method integrated with a latent decision model. This approach leverages a variational autoencoder to learn the distribution of feasible decisions, enabling a two-way mapping between the original decision space and a lower-dimensional latent space. By doing so, HC-LSBO captures the nuances of hidden constraints inherent in public policymaking, allowing for optimization in the latent space while evaluating objectives in the original space. We validate our method through numerical experiments on both synthetic and real data sets, with a specific focus on large-scale police districting problems in Atlanta, Georgia. Our results reveal that HC-LSBO offers notable improvements in performance and efficiency compared to the baselines.Comment: 8 pages, 8 figures (exclude appendix

Redistricting using Heuristic-Based Polygonal Clustering

Redistricting is the process of dividing a geographic area into districts or zones. This process has been considered in the past as a problem that is computationally too complex for an automated system to be developed that can produce unbiased plans. In this paper we present a novel method for redistricting a geographic area using a heuristic-based approach for polygonal spatial clustering. While clustering geospatial polygons several complex issues need to be addressed – such as: removing order dependency, clustering all polygons assuming no outliers, and strategically utilizing domain knowledge to guide the clustering process. In order to address these special needs, we have developed the Constrained Polygonal Spatial Clustering (CPSC) algorithm that holistically integrates domain knowledge in the form of cluster-level and instance-level constraints and uses heuristic functions to grow clusters. In order to illustrate the usefulness of our algorithm we have applied it to the problem of formation of unbiased congressional districts. Furthermore, we compare and contrast our algorithm with two other approaches proposed in the literature for redistricting, namely – graph partitioning and simulated annealing

Zone design of specific sizes using adaptive additively weighted voronoi diagrams

Territory or zone design processes entail partitioning a geographic space, organized as a set of areal units, into different regions or zones according to a specific set of criteria that are dependent on the application context. In most cases, the aim is to create zones of approximately equal sizes (zones with equal numbers of inhabitants, same average sales, etc.). However, some of the new applications that have emerged, particularly in the context of sustainable development policies, are aimed at defining zones of a predetermined, though not necessarily similar, size. In addition, the zones should be built around a given set of seeds. This type of partitioning has not been sufficiently researched; therefore, there are no known approaches for automated zone delimitation. This study proposes a new method based on a discrete version of the adaptive additively weighted Voronoi diagram that makes it possible to partition a two-dimensional space into zones of specific sizes, taking both the position and the weight of each seed into account. The method consists of repeatedly solving a traditional additively weighted Voronoi diagram, so that each seed?s weight is updated at every iteration. The zones are geographically connected using a metric based on the shortest path. Tests conducted on the extensive farming system of three municipalities in Castile-La Mancha (Spain) have established that the proposed heuristic procedure is valid for solving this type of partitioning problem. Nevertheless, these tests confirmed that the given seed position determines the spatial configuration the method must solve and this may have a great impact on the resulting partition

A multiple server location–allocation model for service system design

Service systems are endemic in a service economy, and effective system design is fundamental to the competitiveness of service organizations such as retailers, distributors, and healthcare providers. This is because system design may significantly facilitate (or hinder) the attainment of important organizational objectives such as minimizing system cost and maximizing service level. This paper develops and solves a comprehensive nonlinear location–allocation model for service system design that incorporates several relevant costs and considerations. These include, for instance, transportation, facility, and waiting costs, queuing considerations, multiple servers, multiple order priority levels, multiple service sites, and service distance limits. The model is first converted to an equivalent linear form and then solved using Lagrangian relaxation. A computational study shows problems with 250 service districts, 60 service sites, and 250 candidate locations are solved in about two and a half minutes. An extensive managerial experiment is conducted that evaluates alternative system designs from a number of important perspectives including centralization versus decentralization, system configuration, and service distance limit. Each scenario is evaluated with respect to two fundamental criteria, namely, total cost and service level. The analysis provides insights into important tradeoffs that must be taken into consideration in designing an effective service system

Police districting problem: literature review and annotated bibliography

The police districting problem concerns the efficient and effective design of patrol sectors in terms of performance attributes. Effectiveness is particularly important as it directly influences the ability of police agencies to stop and prevent crime. However, in this problem, a homogeneous distribution of workload is also desirable to guarantee fairness to the police agents and an increase in their satisfaction. This chapter provides a systematic review of the literature related to the police districting problem, whose history dates back to almost 50 years ago. Contributions are categorized in terms of attributes and solution methodology adopted. Also, an annotated bibliography that presents the most relevant elements of each research is given

Dense urban typologies and the game of life: evolving cellular automata.

The ongoing rate of urbanization in China is the motivator behind this paper. As a response to the observed monotonous housing developments in Suzhou Industrial Park (SIP) and elsewhere, our method exploits Cellular Automata (CA) combined with fitness evaluation algorithms to explore speculatively the potential of existing developments and respective building regulations for increased density and diversity through an automated design algorithm. The well-known Game of Life CA is extended from its original two-dimensional functionality into the realm of three dimensions and enriched with the opportunity of resizing the involved cells according to their function. Moreover, our method integrates an earlier technique of constrcuctivists - namely the social condenser as a means of diversifying functional distribution within the Cellular Automata - as well as solar radiation as requested by the existing building regulation. The method achieves a densification of the development from 31% to 39% ratio of footprint to occupied volume whilst obeying the solar radiation rule and offering a more diverse functional occupation. This proof of concept demonstrates a solid approach to the automated design of housing developments at an urban scale with a limited evaluation procedure including solar radiation, which can be extended to other performance criteria in future work