Applied Regional Growth and Innovation Models (Advances in Spatial Science)

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Ribarsky, T. Walsh and Thill, J. Gallicano, T. Wesslen and Thill, J. Tang and Thill, J. Cai and Thill, J.

Issel, M. Yonto, P. Jung and Thill, J. Kashiha and Thill, J. Lyu, C. Jung, and L. Villena J. Becerril and Thill, J. October Bourdeau-Lepage, H. Najaf, M. Findley, D. Strandow, R. Tao, J.


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Strandow, J. Yonto and M. Zhong, Y. Sun, and Z. Gong and W. Kashiha and C.

Applied Regional Growth and Innovation Models

Tsvetkova and D. Ogburn, K. Tiller, and Z. Tao and A. Davis and R. Delmelle, D. Kashiha, and K. Thomas, E. Delmelle, and D.

About this book

Chen, K. Baarson, J. Sanchez, S. Pulugurtha, H. Wancura, S. Thompson, D.

1 INTRODUCTION

Young, E. Hauser, M. Kane, P. Clinton, and P. Market analysis of water management technologies and conservation solutions. Pittsburgh, PA.

Son and M. Du and C. Long , Geoinformatics , Hong Kong, June Peeters, and I.

Geospatial Machine Learning for Urban Development

Thill J. Delmelle Thill, J-C. Land development trajectory of a southeastern urban region: sustainability in Charlotte, North Carolina. November Thill, J-C. The experiment shows that convergence to a perfect mix of coins can only be obtained if reciprocal exchanges are modeled, with a time horizon around — while non-reciprocal models indicate an imperfect mix converging in the year at the latest. In recent years, more than one million tons of sewage sludge has been discharged annually in Beijing. Untreated sewage sludge has critically polluted the waterways. The purpose of this paper is to analyze the impact of sewage sludge treatment on the development of the regional economy and on the environment.

In this report, we use Lingo software to simulate an economic model and an environmental model with an input-output table and perform a linear optimization of these models. The economic model describes the relationship between economic activities and the emission of water pollutants. The environmental model describes the change in the amount of water pollutants that are generated in the model. Beijing is divided into 11 sub-regions. A comprehensive environmental policy is coupled with the introduction of advanced technology to reduce water pollutants.

Based upon the results of the simulation, we can provide detailed information about economic growth, water pollutant reduction, policy subsidies and the number of new sewage and sewage sludge plants needed in each sub-region. This paper provides a selective review of the field of location modeling in spatial analysis.

The work synthesizes opinions and impressions from conference programs, informal conversations with colleagues at various stages of their careers, and general awareness of disciplinary norms. The paper points to several characteristics of a field in maturity. It also suggests that current ideas are, in many cases, quite well foreshadowed in the basic foundational literature; yet, at the same time, there are two kinds of emerging advances. One is a number of mathematical breakthroughs that allow an improved approach to previously difficult problems, and the other is a set of new problems that were unanticipated in the previous literature.

A key attribute of maturity is that techniques from the field have resulted in real world locational decisions. The paper is illustrated with examples from location theory and applied mathematical modeling. This is both a backward- and forward-looking appreciation for some ideas that have matured and are ready, in some cases, for new mathematical and computational tools.

Geographic information systems provide the capacity to digitally create, store, manipulate, analyze and display various types of spatial information. While these functionalities enable handling of spatial data in a much more rapid and precise way than traditional paper-based approaches, uncertainties nevertheless remain in digital information and are not likely to ever be completely eliminated. Location modeling, as an important spatial analytical approach, must therefore confront the various uncertainties and errors in spatial data. In this chapter we detail multi-objective models structured to account for data uncertainty in support of location siting when spatial dispersion is a prerequisite.

These models explicitly incorporate facets of data uncertainty and can be applied in a manner that enables evaluation of uncertainty impacts with statistical confidence. Solution approaches are discussed, along with the case study setting, to demonstrate how these models address spatial uncertainty in a context supporting facility location planning.

Bibliographic Information

Inspired from the behavior of real ants, the ant colony algorithm has provided a new approach for solving discrete optimization problems, such as the traveling salesman problem. However, traditional ant colony algorithms may consume an inordinate amount of computing time to converge to a solution. In this chapter, a new heuristic algorithm called the nearest neighbor ant colony system NNAC is proposed in order to reduce computing time, without sacrificing on the optimality properties of the solutions. The NNAC is an intelligent form of the original ant colony system that follows a spatial strategy.

Thanks to a search strategy that eliminates a large number of inefficient solutions up front on the basis of proximity-based neighborhoods, the NNAC is able to find the best solution in a fraction of the time that the conventional ant colony system consumes. The paper summarizes the principles of heuristics based on ant colony systems and highlighted some of their limitations. The proposed NNAC algorithm is presented in detail. The NNAC is tested on five different data sets and compared to a traditional ant colony system heuristic.

Applied regional growth and innovation models in SearchWorks catalog

Secure, sustainable, and cost-effective energy development will be one of the greatest global challenges in coming decades. This development will include an extensive range of energy resources including coal, conventional and unconventional oil and natural gas, wind, solar, biofuels, geothermal, and nuclear. Here, we highlight breakthroughs and future challenges for CCS infrastructure optimization and modeling. We start with the evolution of CCS infrastructure modeling from early attempts to represent the capture sources , transport network , and storage sinks of CO 2 , through to the integration of more advanced spatial optimization or location-allocation approaches including mixed integer-linear programming.

Tell us if something is incorrect. Only 5 left! Add to Cart. Free delivery. Arrives by Thursday, Oct Pickup not available. This volume contains a collection of unique and operational regional growth and innovation models.