Spatial optimization for land use allocation: accounting for sustainability concerns

Land-use allocation has long been an important area of research in regional science. Land-use patterns are fundamental to the functions of the biosphere, creating interactions that have substantial impacts on the environment. The spatial arrangement of land uses therefore has implications for activity and travel within a region. Balancing development, economic growth, social interaction, and the protection of the natural environment is at the heart of long-term sustainability. Since land-use patterns are spatially explicit in nature, planning and management necessarily must integrate geographical information system and spatial optimization in meaningful ways if efficiency goals and objectives are to be achieved. This article reviews spatial optimization approaches that have been relied upon to support land-use planning. Characteristics of sustainable land use, particularly compactness, contiguity, and compatibility, are discussed and how spatial optimization techniques have addressed these characteristics are detailed. In particular, objectives and constraints in spatial optimization approaches are examined

An integer programming approach for sensor location in a forest fire monitoring system

Forests worldwide have been devastated by fires. Forest fires cause incalculable damage to fauna and flora. In addition, a forest fire can lead to the death of people and financial damage in general, among other problems. To avoid wildfire catastrophes is fundamental to detect fire ignitions in the early stages, which can be achieved by monitoring ignitions through sensors. This work presents an integer programming approach to decide where to locate such sensors to maximize the coverage provided by them, taking into account different types of sensors, fire hazards, and technological and budget constraints. We tested the proposed approach in a real-world forest with around 7500 locations to be covered and about 1500 potential locations for sensors, showing that it allows obtaining optimal solutions in less than 20 min.This work has been supported by FCT Fundação para a Ciência e Tecnologia within the R&D Units Project Scope UIDB/00319/2020 and within project PCIF/GRF/0141/2019 “O3F - An Optimization Framework to reduce Forest Fire” and also the project UIDB/05757/2020 and Forest Alert Monitoring System (SAFe) Project through PROMOVE - Funda¸c˜ao La Caixa. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021, Thadeu Brito was supported by FCT PhD grant SFRH/BD/08598/2020

Using Contexts and Constraints for Improved Geotagging of Human Trafficking Webpages

Extracting geographical tags from webpages is a well-motivated application in many domains. In illicit domains with unusual language models, like human trafficking, extracting geotags with both high precision and recall is a challenging problem. In this paper, we describe a geotag extraction framework in which context, constraints and the openly available Geonames knowledge base work in tandem in an Integer Linear Programming (ILP) model to achieve good performance. In preliminary empirical investigations, the framework improves precision by 28.57% and F-measure by 36.9% on a difficult human trafficking geotagging task compared to a machine learning-based baseline. The method is already being integrated into an existing knowledge base construction system widely used by US law enforcement agencies to combat human trafficking.Comment: 6 pages, GeoRich 2017 workshop at ACM SIGMOD conferenc

A Constraint Programming Approach for Non-Preemptive Evacuation Scheduling

Large-scale controlled evacuations require emergency services to select evacuation routes, decide departure times, and mobilize resources to issue orders, all under strict time constraints. Existing algorithms almost always allow for preemptive evacuation schedules, which are less desirable in practice. This paper proposes, for the first time, a constraint-based scheduling model that optimizes the evacuation flow rate (number of vehicles sent at regular time intervals) and evacuation phasing of widely populated areas, while ensuring a nonpreemptive evacuation for each residential zone. Two optimization objectives are considered: (1) to maximize the number of evacuees reaching safety and (2) to minimize the overall duration of the evacuation. Preliminary results on a set of real-world instances show that the approach can produce, within a few seconds, a non-preemptive evacuation schedule which is either optimal or at most 6% away of the optimal preemptive solution.Comment: Submitted to the 21st International Conference on Principles and Practice of Constraint Programming (CP 2015). 15 pages + 1 reference pag

Spatial stochastic programming model for timber and core area management under risk of stand-replacing fire, A

2012 Fall.Includes bibliographical references.Forest harvest scheduling has been modeled using deterministic and stochastic programming models. Past models seldom address explicit spatial forest management concerns under the influence of natural disturbances. In this research study, we employ multistage full recourse stochastic programming models to explore the challenges and advantages of building spatial optimization models that account for the influences of random stand-replacing fires. Our exploratory test models simultaneously consider timber harvest and mature forest core area objectives. Each model run reports first-period harvesting decisions for each stand based on a sample set of random fire. We integrate multiple model runs to evaluate the persistence of period-one solutions under the influence of stochastic fires. Follow-up simulations were used to support multiple comparisons of different candidate forest management alternatives for the first time period. Test case results indicate that integrating the occurrence of stand-replacing fire into forest harvest scheduling models could improve the quality of long-term spatially explicit forest plans

Models and heuristics for forest management with environmental restrictions

Tese de doutoramento, Estatística e Investigação Operacional (Otimização), Universidade de Lisboa, Faculdade de Ciências, 2018The main focus of this thesis was to develop mathematical models and methods in integer programming for solving harvest scheduling problems with environmental restrictions. Constraints on maximum clearcut area, minimum total habitat area, minimum total core area and inter-habitat connectivity were addressed for this purpose. The research was structured in a collection of three papers, each one describing the study of a different forest harvest scheduling problem with respect to the environmental constraints. Problems of papers 1 and 2 aim at maximizing the net present value. A bi objective problem is considered in paper 3. The objectives are the maximization of the net present value and the maximization of the inter-habitat connectivity. The tree search methods branch-and-bound and multiobjective Monte Carlo tree search were designed specifically to solve the problems. The methods could be used as heuristics, as a time limit of 2 hours was imposed. All harvest scheduling problems were based on the socalled cluster formulation. The proposed models and methods were tested with sixteen real and hypothetical instances ranging from small to large. The results obtained for branch-and-bound and Monte Carlo tree search show that these methods were able to find solutions for all instances. The results suggest that it is possible to address the environmental restrictions with small reductions of the net present value. With respect to the forestry fragmentation caused by harvestings, the results suggest that, although clearcut size constraints tend to disperse clearcuts across the forest, compromising the development of large habitats, close to each other, the proposed models, with the other environmental constraints, attempt to mitigate this effect

Models and methods for the modelling of forest managing

A gestão florestal é uma actividade de grande valor económico e importância ecológica. As áreas florestais geridas podem abranger regiões muito grandes e a sua gestão adequada é muito importante para um desenvolvimento eficaz, tanto em termos de planeamento económico como de recursos naturais, e gerir uma floresta implica tipicamente a aplicação de escolhas políticas em diferentes parcelas de terra, aqui referidas como Stands ou Management Units (Unidades de Gestão). Este documento analisa vários métodos de gestão florestal, juntamente com as suas variações disponíveis na literatura ao longo de 4 capítulos, sendo esses métodos o Unit Restriction Model and Area Restriction Model por Alan T. Murray, the Area Restriction Model with Stand-Clear-Cut variables por Constantino et al, the Path Algorithm and the Generalized Management Unit formulation por McDill et al and the Full Adjacent Unit formulation por Gharbi et al. Os resultados apresentados nos artigos originais são discutidos nas conclusões de cada capítulo. Os 2 últimos capítulos apresentam uma formulação de Constraint Programming do problema e a sua implementação utilizando a biblioteca Choco da linguagem de programação Java e apresentam também os resultados, um capítulo relacionado com a primeira implementação que trata apenas da optimização do Madeira Total Obtida e o outro alargando o problema, juntamente com um novo conjunto de dados, para lidar com a optimização multicritério. Para o fazer, os princípios de Constraint Programming são primeiro enumerados juntamente com uma breve história da tecnologia de Constraint Programming. Finalmente, outros possíveis desenvolvimentos são discutidos numa secção de Trabalho Futuro; Abstract: Forest management is an activity of prime economic and ecological importance. Managed forest areas can span very large regions and their proper management is paramount to an effective development, in terms both of economic and natural resources planning and managing a forest typically implies applying policy choices to different patches of land, here referred to as Stands or Management Units. This paper reviews several methods of forest management alongside their variations available in the literature throughout 4 chapters, those methods being the Unit Restriction Model and Area Restriction Model by Alan T. Murray, the Area Restriction Model with Stand-Clear-Cut variables by Constantino et al, the Path Algorithm and the Generalized Management Unit formulation by McDill et al and the Full Adjacent Unit formulation by Gharbi et al. The results as presented in the original papers are discussed in the conclusions of each chapter. The final 2 chapters present a Constraint Programming formulation of the problem and its implementation using the Choco framework of the Java programming language and showcases the results, one chapter relating to the first implementation that deals only with the optimization of total Wood Yield and the other broadening the problem, alongside a new dataset, to deal with multi-criteria optimization. In order to do this the principles of Constraint Programming are first enumerated along with a short history of Constraint Programming technology