The integration of GIS and multicriteria decision analysis has attracted significant interest over the last 15 years or so This paper surveys the GIS‐based multicriteria decision analysis (GIS‐MCDA) approaches using a literature review and classification of articles from 1990 to 2004 An electronic search indicated that over 300 articles appeared in refereed journals The paper provides taxonomy of those articles and identifies trends and developments in GIS‐MCDA

https://www.tandfonline.com/doi/pdf/10.1080/13658810600661508

GIS‐based multicriteria decision analysis: a survey of the literature

Cellular automata models for the simulation of real-world urban processes: A review and analysis

Being newly immersed in the upstream part of the oil business, I just recently had my first work session with data in ARC–GIS®. The project involves subsurface geographical modeling. Obviously I had considerable interest in discovering if the methodology in this book would enhance my modeling capabilities. The book is for social scientists, but I had no difficulty imagining my own important oil exploration application within the framework of geographically weighted regression (GWR). The first chapter nicely explains what is unique in this book. A standard regression model using geographically oriented data (the example is housing prices across all of England) is a global representation of a spatial relationship, an average that does not account for any local differences. In y = f (x), imagine a whole family of f ’s that are indexed by spatial location. That is the focus of this book. It is about one form of local spatial modeling, which is GWR. A more general resource for this topic is the earlier book by Fotheringham and Wegener (2000), which escaped the notice of Technometrics. Imagine a display of model parameters in a geographical information system (GIS) and you will understand the focus for this book. The authors note, “only where there is no significant spatial variation in a measured relationship can global models be accepted” (p. 10). The second chapter develops the basis of GWR. It analyzes the housing sales prices versus the 33 boroughs in London and begins by fitting a conventional multiple regression model versus housing characteristics. The GWR is motivated by differences in the regression models fitted separately by borough. The GWR is a spatial moving-window approach with all data distances weighted versus a specific data point using a weighting function and a bandwidth. A GIS can then be used to evaluate the spatial dependency of the parameters. As in kriging, local standard errors also are calculated. The chapter also provides all the math. Chapter 3 comprises several further considerations: parameters that are globally constant, outliers, and spatial heteroscedasticity. The first issue leads to hypothesis tests for model comparison using an Akaike information criterion (AIC). Local outliers are hard to detect. Studentized (deletion) residuals are recommended. The outliers can be plotted geographically. Robust regression is suggested as a less computationally intensive alternative. Hetereoscedasticity is harder to handle. Chapter 4 adds statistical inference to the capabilities of GWR: both a confidence interval approach using local likelihood and an AIC method. Four additional methodology chapters present various extensions of GWR. Chapter 5 considers the relationship between GWR and spatial autocorrelation, and includes a combined version of GWR and spatial regression using some complex hybrid models. Chapter 6 examines the relationship of scale and zoning problems in spatial analysis to GWR. Chapter 7 introduces the use of initial exploratory data analysis using geographically weighted statistics, which are based on the idea of using a kernel around each data point to create weights. Univariate statistics and correlation coefficients are defined for exploring local patterns in data. A final set of extensions in Chapter 8 discusses regression models with non-Gaussian errors, logistic regression, local principal components analysis, and local probability density estimation. The methods all use some kind of distributional model. The million-dollar question for me is always, “What about software?” The authors have a stand-alone program, GWR 3, available in CD–ROM by contacting the authors. Basically the drill with GWR 3 is to gather your data, use Excel to transform and reformat the data for GWR 3, use GWR 3 to produce a set of coefficients, and feed those coefficients to your favorite GIS to produce your maps. Forty pages of discussion about using the software are provided. A final epilogue chapter also discusses embedding GWR in R or Matlab and includes some references to people who have done that type of work. I probably would not have read this book if I had not happened to have had it in my briefcase on a visit with the exploration technologists. Though inclusive of appropriate mathematical development, this material is readily approachable because of the many illustrations and the pages and pages of GIS displays. The authors unabashedly present much of the material as their developmental work, so GWR offers a lot of opportunity for research and further development through novel applications and extensions.

Geographically Weighted Regression

GIS-based approach for optimized siting of municipal solid waste landfill.

http://www.lib.udec.cl/archivos_descargas_pdf/pdf_publicaciones_2007_2006/pauchard-urbanization.pdf

Multiple effects of urbanization on the biodiversity of developing countries: the case of a fast-growing metropolitan area (Concepción, Chile).

Spatial-temporal model for demand and allocation of waste landfills in growing urban regions

The twentieth century saw a dramatic increase in the production of urban solid waste, reflecting unprecedented global levels of economic activity. Despite some efforts to reduce and recover the waste, disposal in landfills is still the most usual destination. However, landfill has become more difficult to implement because of its increasing cost, community opposition to landfill siting, and more restrictive environmental regulations regarding the siting and operation of landfills. Moreover, disposal in landfill is the waste destination method with the largest demand for land, while land is a resource whose availability has been decreasing in urban systems. Shortage of land for landfills is a problem frequently cited in the literature as a physical constraint. Nonetheless, the shortage of land for waste disposal has not been fully studied and, in particular, quantified. This paper presents a method to quantify the relationship between the demand and supply of suitable land for waste disposal over time using a geographic information system and modelling techniques. Based on projections of population growth, urban sprawl and waste generation the method can allow policy and decision-makers to measure the dimension of the problem of shortage of land into the future. The procedure can provide information to guide the design and schedule of programs to reduce and recover waste, and can potentially lead to a better use of the land resource. Porto Alegre City, Brazil was used as the case study to illustrate and analyse the approach. By testing different waste management scenarios, the results indicated that the demand for land for waste disposal overcomes the supply of suitable land for this use in the study area before the year 2050.

/pdf/assessing-the-demand-of-solid-waste-disposal-in-urban-region-10cby9gg9s.pdf

Assessing the demand of solid waste disposal in urban region by urban dynamics modelling in a GIS environment

Urbanization is one of the most evident global changes. Research in the field of urban growth modelling has generated models that explore for drivers and components of the urban growth dynamics. Cellular automata (CA) modeling is one of the recent advances, and a number of CA-based models of urban growth have produced satisfactory simulations of spatial urban expansion over time. Most application and test of CA-based models of urban growth which provide likely and reliable simulations has been developed in urban regions of developed nations; urban regions in the United States, in particular. This is because most of the models were developed in universities and research centers of developed nations, and these regions have the required data, which is extensive. Most of the population growth in the world, however, occurs in the developing world. While some European countries show signs of stabilization of their population, in less developed countries, such as India, population still grows exponentially. And this growth is normally uncoordinated, which results in serious environmental and social problems in urban areas. Therefore, the use of existing dynamic-spatial models of urban growth in regions of developing nations could be a means to assist planners and decision makers of these regions to understand and simulate the process of urban growth and test the results of different development strategies. The pattern of growth of urban regions of developing nations, however, seems to be different of the pattern of developed countries. The former use to be more dense and centralized, normally expanding outwards from consolidated urban areas; while the second is normally more fragmented and sparse. The present paper aims to investigate to how extent existing CA-based urban growth models tested in developed nations can also be applied to a developing country urban area. The urban growth model was applied to Porto Alegre City, Brazil. An expected contiguous expansion from existing urban areas has been obtained as following the historical trends of growth of the region. Moreover, the model was sensitive and able to portray different pattern of growth in the study area by changing the value of its parameters.

Simulating urban growth in a developing nation's region using a cellular automata-based model

Despite ample medical evidence of the adverse impacts of traffic noise on health, most policies for traffic noise management are arbitrary or incomplete, resulting in serious social and economic impacts. Surprisingly, there is limited information about citizen’s exposure to traffic noise worldwide. This paper presents the 2Loud? mobile phone application, developed and tested as a methodology to monitor, assess and map the level of exposure to traffic noise of citizens with focus on the night period and indoor locations, since sleep disturbance is one of the major triggers for ill health related to traffic noise. Based on a community participation experiment using the 2Loud? mobile phone application in a region close to freeways in Australia, the results of this research indicates a good level of accuracy for the noise monitoring by mobile phones and also demonstrates significant levels of indoor night exposure to traffic noise in the study area. The proposed methodology, through the data produced and the participatory process involved, can potentially assist in planning and management towards healthier urban environments.

http://brownfields-toolbox.org/download/other_resources/2Loud%20noise%20art_10.1007_s10661-014-3848-9.pdf

2Loud?: Community mapping of exposure to traffic noise with mobile phones

One of the challenges in creating plans for sustainable urban futures is the ability to work across government agencies and to break down traditional barriers to truly collaborative planning. Typic...

Simone Leao

Papers

Spatial-temporal model for demand and allocation of waste landfills in growing urban regions

Assessing the demand of solid waste disposal in urban region by urban dynamics modelling in a GIS environment

Simulating urban growth in a developing nation's region using a cellular automata-based model

2Loud?: Community mapping of exposure to traffic noise with mobile phones

Breaking down the silos through geodesign – Envisioning Sydney’s urban future: