Spatial Optimization Methods And System For Redistricting Problems

Redistricting is the process of dividing space into districts or zones while optimizing a set of spatial criteria under certain constraints. Example applications of redistricting include political redistricting, school redistricting, business service planning, and city management, among many others. Redistricting is a mission-critical component in operating governments and businesses alike. In research fields, redistricting (or region building) are also widely used, such as climate zoning, traffic zone analysis, and complex network analysis. However, as a combinatorial optimization problem, redistricting optimization remains one of the most difficult research challenges. There are currently few automated redistricting methods that have the optimization capability to produce solutions that meet practical needs. The absence of effective and efficient computational approaches for redistricting makes it extremely time-consuming and difficult for an individual person to consider multiple criteria/constraints and manually create solutions using a trial-and-error approach. To address both the scientific and practical challenges in solving real-world redistricting problems, this research advances the methodology and application of redistricting by developing a new computational spatial optimization method and a system platform that can address a wide range of redistricting problems, in an automated and computation-assisted manner. The research has three main contributions. First, an efficient and effective spatial optimization method is developed for redistricting. The new method is based on a spatially constrained and Tabu-based heuristics, which can optimize multiple criteria under multiple constraints to construct high-quality optimization solutions. The new approach is evaluated with real-world redistricting applications and compared with existing methods. Evaluation results show that the new optimization algorithm is more efficient (being able to allow real-time user interaction), more flexible (considering multiple user-expressed criteria and constraints), and more powerful (in terms of optimization quality) than existing methods. As such, it has the potential to enable general users to perform complex redistricting tasks. Second, a redistricting system, iRedistrict, is developed based on the newly developed spatial optimization method to provide user-friendly visual interface for defining redistricting problems, incorporating domain knowledge, configuring optimization criteria and methodology parameters, and ultimately meeting the needs of real-world applications for tackling complex redistricting tasks. It is particularly useful for users of different skill levels, including researchers, practitioners, and the general public, and thus enables public participation in challenging redistricting tasks that are of immense public interest. Performance evaluations with real-world case studies are carried out. Further computational strategies are developed and implemented to handle large datasets. Third, the newly developed spatial optimization method is extended to solve a different spatial optimization problem, i.e., spatial community structure detection in complex networks, which is to partition networks to discover spatial communities by optimizing an objective function. Moreover, a series of new evaluations are carried out with synthetic datasets. This set of evaluations is different from the previous evaluations with case studies in that, the optimal solution is known with synthetic data and therefore it is possible to evaluate (1) whether the optimization method can discover the true pattern (global optima), and (2) how different data characteristics may affect the performance of the method. Evaluation results reveal that existing non-spatial methods are not robust in detecting spatial community structure, which may produce dramatically different outcomes for the same data with different characteristics, such as different spatial aggregations, sampling rates, or noise levels. The new optimization method with spatial constraints is significantly more stable and consistent. In addition to evaluations with synthetic datasets, a case study is also carried out to detect urban community structure with human movements, to demonstrate the application and effectiveness of the approach

A resectorization of fire brigades in the north of Portugal

Sectorization consists of grouping the basic units of a large territory to deal with a complex problem involving different criteria. Resectorization rearranges a current sectorization avoiding substantial changes, given a set of conditions. The paper considers the case of the distribution of geographic areas of fire brigades in the north of Portugal so that they can protect and rescue the population surrounding the fire stations. Starting from a current sectorization, assuming the geographic and population characteristics of the areas and the fire brigades’ response capacity, we provide an optimized resectorization considering two objectives: to reduce the rescue time by maximizing the compactness criterion, and to avoid overload situations by maximizing the equilibrium criterion. The solution method is based on the Non-dominated Sorting Genetic Algorithm (NSGA-II). Finally, computational results are presented and discussed.info:eu-repo/semantics/acceptedVersio

Districting Problems - New Geometrically Motivated Approaches

This thesis focuses on districting problems were the basic areas are represented by points or lines. In the context of points, it presents approaches that utilize the problem\u27s underlying geometrical information. For lines it introduces an algorithm combining features of geometric approaches, tabu search, and adaptive randomized neighborhood search that includes the routing distances explicitly. Moreover, this thesis summarizes, compares and enhances existing compactness measures

A spatial decision support system for autodistricting collection units for the taking of the Canadian census

This dissertation documents the districting requirements for collection units for taking the Canadian Census and provides a spatial decision support system for their automatic creation. In the context of the literature on autodistricting, this problem falls under the general category of creating districts for monitoring, surveillance and inventory applications since the Census is essentially an spatial inventory exercise. The basic requirement is to create an area-based categorical coverage such that the workload is equitably distributed amongst Census Representatives within the limits of a large number of constraints and conditions.A new omnibus automated districting process that combines a 3-stage cascading selection procedure for identifying sub-blockface, blockface and block level collection units with a 4- stage heuristic solution procedure for grouping blocks (termed 'assigns', 'annexes', 're-assigns' and 'adjusts') is contributed by this research to provide a systematic response to varying districting situations.The resulting spatial decision support system for autodistricting has been tested on test data sets and on one of the larger urban population centres of Canada. The set of test pattern sites mimicking typical settlement patterns was generated to ensure that the various alternative assignment or block grouping methods (i.e., unidirectional and bidirectional tessellations based on circular and rectangular grids and regular, random and 'extrema-based' seeds) performed as designed and specified. The Census Subdivision of Laval (in the Census Metropolitan Area of Montreal) was selected as the test site for comparing the performance of the autodistricting capacity to the actual, manually created, results from the 1986 Census.To permit the comparison of results from classical manual and automated processes, a set of satisficing evaluation functions that vary in accordance with data availability was implemented in the context of a competing set of districting objectives. The most sophisticated of these evaluation functions incorporates a composite index that combines the distribution and a measure of the 'density' of the dwellings with the length of the route that must be followed to complete the collection activity (including travel time to the start of the route and between route parts).To assess the continued acceptability of the districting from the previous Census, and/or to select between alternative results generated by computer-assisted approaches, a set of objective functions is provided that vary depending upon the available amount of geographic, cartographic or statistical data

Spatial organization of public services: models and applications

Location decisions are crucial in the spatial organization in both public and private sectors as they can have a long term impact on operational performances and on service levels. Social cost minimization, universality of services and equity, expressed in terms of users' accessibility, are the main objectives in public services contexts. Nevertheless, the enduring trend of public expenditures revision poses, also in the public sectors, the need to pursue objectives of economic efficiency. In the literature, two families of optimization problems are typically used to address these problems, namely Facility Location Problems (FLPs) and Districting Problems (DPs). The aim of this thesis is to show how FLPs and DPs can be used to underpin spatial organization processes of public services, providing analytical models able to assist the decision making. To this end, novel mathematical models are developed with application to the healthcare and postal service sectors. In particular, a hierarchical facility location model is formulated to reorganize an existing regional Blood Management System (BMS) while an integrated location-districting model is proposed for the organization of postal collection operations in urban areas. A constructive heuristic procedure is also devised to solve the latter problem. Extensive computational experiments are realized to validate the proposed models and to show their capability to provide insightful managerial implications. Finally, the thesis aims at filling another existing gap in the literature due to the absence of stochastic models for DPs. Hence, a two-stage stochastic program for districting is introduced and tested on real georgaphic data. Several extensions of the proposed modeling framework are also discussed

DISTRICT DESIGN AND ROUTE PLANNING FOR CUSTOMER-RELATED FIELD OPERATIONS OF NATURAL GAS DISTRIBUTION SYSTEMS: A CASE STUDY

Dünya enerji ihtiyacının yaklaşık % 24'ü doğalgazdan karşılanmaktadır. Bir şehirde, doğal gaz dağıtım sistemi, yapım, işletme, iç tesisat ve müşteri hizmetleri olmak üzere birbirine bağlı dört ana süreçten oluşur. Bu çalışmada, dağıtım sisteminin müşteri ile doğrudan ilişkili olan müşteri hizmetleri süreçleri ele alınmıştır. Bölgesel ayrım, rota planlaması ve mobil ekip iş yükü tahsisi gibi müşteri hizmetleri süreçleri için matematiksel modeller önerilmiş ve Konya'nın yetkili doğal gaz dağıtım şirketi olan Gaznet A.Ş.'de uygulanmıştır. Önerilen modellerin performansı, mobil ekip sayısı ve iş yükü miktarı açısından mevcut sistemle karşılaştırılmıştır. Deneysel çalışmalar, önerilen sistemin mobil ekip sayısının mevcut sistemden % 37 daha düşük olduğunu göstermektedir

2012 GREAT Day Program

SUNY Geneseo’s Sixth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1006/thumbnail.jp

Town of Hanover, New Hampshire 2020 report.

This is an annual report containing vital statistics for a town/city in the state of New Hampshire