Content uploaded by Marc Armstrong
Author content
All content in this area was uploaded by Marc Armstrong on Aug 08, 2014
Content may be subject to copyright.
A spatial decision support system for school redistricting
A sustainable process satisfies the current needs without compromising the ability of future generations to satisfy their own needs; that is, it must have a triple impact (sustainability): social, economic, and environmental. In México, there are several services that the government must provide to society for its proper development, for example, the collection of solid waste. Urban logistics include all the processes and operations that provide a service to the community, such as water, safety, health, waste collection, etc., providing the service with the lowest possible cost (economic, social, and environmental) that contributes to the sustainability of the city. Due to the accelerated growth of the world population, several environmental problems have arisen, among them, the generation of solid waste in important quantities; their proper management is relevant for adequate development of the population. The collection of solid waste in municipal areas aims to grant green spaces and recreation areas for the citizens. Although an outstanding effort has been made by the government to provide an adequate service, there are still gaps in the application of correct tools that guarantee efficiency in operations and continuity in services. This article presents a proposal to improve the planning of the design of territories for the cleaning, weeding, and collection of solid waste in municipal areas, using two MILP (Mixed Integer Linear Programming) models. The main contribution of the adaptation of this model is the application to the weeding and waste collection service municipality of the Monterrey Metropolitan Area, which considers important factors among which are the amount of waste, frequency, and service coverage.
The p-median problem (PMP) is one of the most applied location problems in urban and regional planning. As an NP-hard problem, the PMP remains challenging to solve optimally, especially for large-sized problems. A number of heuristics have been developed to obtain PMP solutions in a fast manner. Among the heuristics, the Teitz and Bart (TB) algorithm has been found effective for finding high-quality solutions. In this article, we present a spatial-knowledge-enhanced Teitz and Bart (STB) heuristic method for solving PMPs. The STB heuristic prioritizes candidate facility sites to be examined in the solution set based on the spatial distribution of demand and service provision. Tests based on a range of PMPs demonstrate the effectiveness of the STB heuristic. This new algorithm can be incorporated into current commercial GIS packages to solve a wide range of location-allocation problems.
Spatial aggregation methods have generated interest in recent years due to their ease of adaptation to different objectives. Moreover, there are statistical reasons to support their use: they reduce the bias caused by the modifiable area unit problem (MAUP). This article presents a bibliographical review of this issue and the different aggregation methods, together with the application of an algorithm to data from the Metropolitan Area of the Valley of Mexico.
Los métodos de agregación espacial han generado interés en los últimos años debido a su fácil adecuación a diferentes objetivos. Más allá de eso existen razones estadísticas para aconsejar su aplicación: reducen el sesgo generado por el problema de unidad de área modificable (MAUP). En este artículo se presenta una revisión bibliográfica de este problema y de los diferentes métodos de agregación, así como la aplicación de un algoritmo a datos de la Zona Metropolitana del Valle de México.AbstractSpatial aggregation methods have generated interest in recent years due to their ease of adaptation to different objectives. Moreover, there are statistical reasons to support their use: they reduce the bias caused by the modifiable area unit problem (MAUP). This article presents a bibliographical review of this issue and the different aggregation methods, together with the application of an algorithm to data from the Metropolitan Area of the Valley of Mexico.
The current method for delineating U . S . metropolitan and micropolitan statistical areas (collectively referred to as core‐based statistical areas— CBSA s) is to cluster counties based on the strength of commuting interactions between outlying areas and urban cores. The social and economic integration of a metropolitan or a micropolitan statistical area is operationalized using what we believe is an outdated, monocentric perspective on functional spatial structure: one that fixates solely on inward and reverse commuting. We propose a new spatial optimization model for delineating CBSA s that can better account for polycentric urban structure by considering all intercounty commuting linkages. Our model seeks to find the boundaries that maximize the containment of entire webs of intra‐ CBSA intercounty commuting. We apply the proposed method to delineate alternative sets of metropolitan and micropolitan statistical areas for comparison with the federal government's currently defined (2010 O ffice of M anagement and B udget [ OMB ] standards) official CBSA s.
El método actual para delimitar áreas estadísticas micropolitanas y metropolitana en los Estados Unidos (denomidado genéricamente áreas estadísticos nucleares o core‐statistical areas‐ CBSA) consiste en agrupar condados en base a la fuerza de las interacciones de los desplazamientos residencia‐trabajo entre las zonas periféricas y los núcleos urbanos. La integración social y económica de un área estadística metro y micro politana se pone en operación usando un enfoque funcional que los autores consideran obsoleto que se basa en una estructura espacial urbana monocéntrica. Dicho enfoque se limita únicamente a los desplazamientos hacia el interior y de vuelta. Los autores proponen un nuevo modelo de optimización espacial para delinear CBSAs que respresenta mejor una estructura urbana policéntrica que considera todos los desplazamientos entre condados. El modelo busca encontrar los límites que maximizan la inclusión de redes enteras de desplazamientos intra CBSA y entre condados. El método propuesto es aplicado para delinear conjuntos alternativos de áreas estadísticas metropolitanas y micropolitanas y es comparado con los límites oficiales actuales de CBSA proporcionados por gobierno federal (de acuerdo a los lineamientos de la Oficina de Administración y Presupuesto u Office of Management Budget ‐OMB) en 2010).
目前用于描述美国大都市区与居住区统计区域（共同地被称为基于核心的统计区域—CBSAs）的方法，主要是基于边远地区和城市核心区间的相互作用强度对郡县进行聚类。大都市区或居住区统计区域社会与经济一体化的运作方式，采用的是一种功能空间结构上落伍的单中心视角，即仅关注内部交换或反向交换。本文提出了一种新的空间优化模型用于描述CBSAs，将所有内部通勤联系考虑在内，能更好地解释多中心城市结构。该模型力图发现能最大化包含内部CBSA通勤联系网的边界。最后，将该模型应用于大都市区和居住区的统计区域描述，并与联邦政府目前定义的官方CBSAs（管理与预算 [OMB]标准2010办公室）进行了对比研究。
Map algebra is a methodology for organizing and processing digital cartographic data in a geographic information system (GIS). While its capabilities to describe patterns existing (or hidden) in data have been well studied, its capabilities to prescribe new patterns in response to specific requirements have not been much explored. The latter, prescriptive capabilities help planners address a class of spatial problems called “cartographic allocation” (Tomlin, 1990), which concerns allocation of subsets (or zones) of a cartographic space to certain uses according to one or more criteria. Taking a school districting problem as an example, this paper introduces a systematic approach to designing a map algebraic procedure for a cartographic allocation problem with capacity constraints. It is found that a classical trial-and-error heuristic can be refined to a more formal approximation method and serve as a good alternative to other solution methods when the problem involves a large number of spatial units as is often the case with a raster-based GIS.
SUMMARY Today, GIS is an efficient tool to tackle with many issues. The matters based on geographical information like determination of school catchments areas, the routes for school service buses, and Automatic Student Registration (ASR) are not a problem anymore with GIS in education sector. But deficiencies in residential address infrastructure, graphic and non-graphic data problems, and school capacity issues are the most important difficulties for ASR especially in developing countries. Geographical Information Systems (GIS) mapping procedures are used widely in ASR process. Furthermore, this is very important tool for determination of school catchments areas and route of school service buses. The researches have shown that deficiency of GIS mapping procedures in developing countries is an important issue in school site management. In these countries, school catchments areas are mostly not determined, because address information have not enough accuracy with up-to-date, and routes for school buses are not so efficient. In this study, firstly, student registration processes are examined and system requirements are defined. In accordance, a prototype model was developed for Turkey where there are 36.712 public primary and secondary schools. Using this model, applications were carried out with actual data. This model include several graphic data layers as roads, buildings, school locations, administrative boundaries, and non-graphic layers as street numbers, residential addresses and school capacities. As a result, students had an opportunity to enroll the nearest school via internet using address information while school buses have been managed efficiently by minimizing costs with GIS functions.
Collaborative spatial decision‐making environments in which group members individually and collectively pursue solutions to semi‐structured problems have a unique set of geographic visualization requirements. Group members often pursue diverse strategies as they attempt to solve such problems. As a consequence, numerous mapped representations of alternative solutions are generated. It is difficult to compare and synthesize these results, especially when decision‐makers have little or no previous cartographic training. In this paper, we derive several map types that synthesize representations of alternative solutions to location‐selection problems. These synthetic maps, designed to be accessible to group members, are created by decomposing solutions into a collection of atomic elements that are then placed into an accounting framework. Network map algebra operations are performed within this framework, and the results are accumulated and displayed as maps. Group members can use these maps to identify similar and dissimilar elements of alternative solutions to a problem. Such maps are intended to promote discussion and support group consensus‐building activities.
AbstractA model that emphasizes possible alternative sequences of events that occur over time is presented in this article. Representing alternative or branching events captures additional semantics unrealized by linear or non‐branching approaches. Two basic elements of branching, divergence and convergence are discussed. From these elements, many complex branching models can be built capturing a perspective of events that will take place in the future or have occurred in the past. This produces likely sequences of events that a user may compare and analyze using spatial or temporal criteria. The branching events model is especially useful for spatiotemporal decision support systems, as decision‐makers are able to identify alternative locations and times of events and, depending on the context, also identify regions of multiple possible events. A branching events viewer application is presented illustrating a case study based on a tornado response.
Unlike developed countries, no enforced attendance areas exist in Iran to guide school selection or allow students to make the most convenient commutes to the nearest schools. Without defined school attendance areas, parents seeking better and more reputable schools often have no choice but to send children to schools outside the local area. In the case of the Northern Iranian city of Rasht, this parental-based system of schools selection has resulted in commutes that are longer than necessary for the female students of junior high school who journey on foot or on the city's public and private transport. Long commutes by vehicle and on foot are a heavy burden on students and parents in terms of travel expenses, stress and the possibility of physical harm. This paper presents the realities of the parental-based system of school selection through the case study of Rasht City using questionnaires. The criteria in the selection of school by parents were clarified through the use of questionnaire surveys. In order to reduce the time and distance of commutes to school, the research proposed to eliminate parental choice and to create school attendance areas that allocate students to the closest possible public female junior high (PFJH) schools of Rasht City. It concluded that through the introduction of school attendance areas based on the multiplicatively weighted Voronoi diagram method, it would be possible to not only eliminate most hazardous vehicle travel but also make walking commute shorter and more convenient.
ResearchGate has not been able to resolve any references for this publication.