Monitoring growth in rapidly urbanizing areas using remotely sensed data

Urbanization and the ability to manage for a sustainable future present numerous challenges for geographers and planners in metropolitan regions. Remotely sensed data are inherently suited to provide information on urban land cover characteristics, and their change over time, at various spatial and temporal scales. Data models for establishing the range of urban land cover types and their biophysical composition (vegetation, soil, and impervious surfaces) are integrated to provide a hierarchical approach to classifying land cover within urban environments. These data also provide an essential component for current simulation models of urban growth patterns, as both calibration and validation data. The first stages of the approach have been applied to examine urban growth between 1988 and 1995 for a rapidly developing area in southeast Queensland, Australia. Landsat Thematic Mapper image data provided accurate (83% adjusted overall accuracy) classification of broad land cover types and their change over time. The combination of commonly available remotely sensed data, image processing methods, and emerging urban growth models highlights an important application for current and next generation moderate spatial resolution image data in studies of urban environments.

Integrating attribute and space characteristics in choropleth display and spatial data mining

This paper develops an interactive approach for exploratory spatial data analysis. Measures of attribute similarity and spatial proximity are combined in a clustering model to support the identification of patterns in spatial information. Relationships between the developed clustering approach, spatial data mining and choropleth display are discussed. Analysis of property crime rates in Brisbane, Australia is presented. A surprising finding in this research is that there are substantial inconsistencies in standard choropleth display options found in two widely used commercial geographical information systems, both in terms of definition and performance. The comparative results demonstrate the usefulness and appeal of the developed approach in a geographical information system environment for exploratory spatial data analysis.

A landscape-scale test of the predictive ability of a spatially explicit model for population viability analysis

1. Although population viability analysis (PVA) is widely employed, forecasts from PVA models are rarely tested. This study in a fragmented forest in southern Australia contrasted field data on patch occupancy and abundance for the arboreal marsupial greater glider Petauroides volans with predictions from a generic spatially explicit PVA model. This work represents one of the first landscape-scale tests of its type. 2. Initially we contrasted field data from a set of eucalypt forest patches totalling 437 ha with a naive null model in which forecasts of patch occupancy were made, assuming no fragmentation effects and based simply on remnant area and measured densities derived from nearby unfragmented forest. The naive null model predicted an average total of approximately 170 greater gliders, considerably greater than the true count (n = 81). 3. Congruence was examined between field data and predictions from PVA under several metapopulation modelling scenarios. The metapopulation models performed better than the naive null model. Logistic regression showed highly significant positive relationships between predicted and actual patch occupancy for the four scenarios (P = 0.001-0.006). When the model-derived probability of patch occupancy was high (0.50-0.75, 0.75-1.00), there was greater congruence between actual patch occupancy and the predicted probability of occupancy. 4. For many patches, probability distribution functions indicated that model predictions for animal abundance in a given patch were not outside those expected by chance. However, for some patches the model either substantially over-predicted or under-predicted actual abundance. Some important processes, such as inter-patch dispersal, that influence the distribution and abundance of the greater glider may not have been adequately modelled. 5. Additional landscape-scale tests of PVA models, on a wider range of species, are required to assess further predictions made using these tools. This will help determine those taxa for which predictions are and are not accurate and give insights for improving models for applied conservation management.

Disaggregation of polygons of surficial geology and soil maps using spatial modelling and legacy data

Examples from the Murray-Darling basin in Australia are used to illustrate different methods of disaggregation of reconnaissance-scale maps. One approach for disaggregation revolves around the de-convolution of the soil-landscape paradigm elaborated during a soil survey. The descriptions of soil ma units and block diagrams in a soil survey report detail soil-landscape relationships or soil toposequences that can be used to disaggregate map units into component landscape elements. Toposequences can be visualised on a computer by combining soil maps with digital elevation data. Expert knowledge or statistics can be used to implement the disaggregation. Use of a restructuring element and k-means clustering are illustrated. Another approach to disaggregation uses training areas to develop rules to extrapolate detailed mapping into other, larger areas where detailed mapping is unavailable. A two-level decision tree example is presented. At one level, the decision tree method is used to capture mapping rules from the training area; at another level, it is used to define the domain over which those rules can be extrapolated. (C) 2001 Elsevier Science B.V. All rights reserved.

Spatial data mining for enhanced soil map modelling

The principle of using induction rules based on spatial environmental data to model a soil map has previously been demonstrated Whilst the general pattern of classes of large spatial extent and those with close association with geology were delineated small classes and the detailed spatial pattern of the map were less well rendered Here we examine several strategies to improve the quality of the soil map models generated by rule induction Terrain attributes that are better suited to landscape description at a resolution of 250 m are introduced as predictors of soil type A map sampling strategy is developed Classification error is reduced by using boosting rather than cross validation to improve the model Further the benefit of incorporating the local spatial context for each environmental variable into the rule induction is examined The best model was achieved by sampling in proportion to the spatial extent of the mapped classes boosting the decision trees and using spatial contextual information extracted from the environmental variables.

A strategy to fill gaps in soil survey over large spatial extents: an example from the Murray-Darling basin of Australia

We re-mapped the soils of the Murray-Darling Basin (MDB) in 1995-1998 with a minimum of new fieldwork, making the most out of existing data. We collated existing digital soil maps and used inductive spatial modelling to predict soil types from those maps combined with environmental predictor variables. Lithology, Landsat Multi Spectral Scanner (Landsat MSS), the 9-s digital elevation model (DEM) of Australia and derived terrain attributes, all gridded to 250-m pixels, were the predictor variables. Because the basin-wide datasets were very large data mining software was used for modelling. Rule induction by data mining was also used to define the spatial domain of extrapolation for the extension of soil-landscape models from existing soil maps. Procedures to estimate the uncertainty associated with the predictions and quality of information for the new soil-landforms map of the MDB are described. (C) 2002 Elsevier Science B.V. All rights reserved.

Visualization of learning in multilayer perceptron networks using principal component analysis

This paper is concerned with the use of scientific visualization methods for the analysis of feedforward neural networks (NNs). Inevitably, the kinds of data associated with the design and implementation of neural networks are of very high dimensionality, presenting a major challenge for visualization. A method is described using the well-known statistical technique of principal component analysis (PCA). This is found to be an effective and useful method of visualizing the learning trajectories of many learning algorithms such as back-propagation and can also be used to provide insight into the learning process and the nature of the error surface.

Spatial and temporal variation in distribution of mangroves in Moreton Bay, subtropical Australia: a comparison of pattern metrics and change detection analyses based on aerial photographs

An assessment of the changes in the distribution and extent of mangroves within Moreton Bay, southeast Queensland, Australia, was carried out. Two assessment methods were evaluated: spatial and temporal pattern metrics analysis, and change detection analysis. Currently, about 15,000 ha of mangroves are present in Moreton Bay. These mangroves are important ecosystems, but are subject to disturbance from a number of sources. Over the past 25 years, there has been a loss of more than 3800 ha, as a result of natural losses and mangrove clearing (e.g. for urban and industrial development, agriculture and aquaculture). However, areas of new mangroves have become established over the same time period, offsetting these losses to create a net loss of about 200 ha. These new mangroves have mainly appeared in the southern bay region and the bay islands, particularly on the landward edge of existing mangroves. In addition, spatial patterns and species composition of mangrove patches have changed. The pattern metrics analysis provided an overview of mangrove distribution and change in the form of single metric values, while the change detection analysis gave a more detailed and spatially explicit description of change. An analysis of the effects of spatial scales on the pattern metrics indicated that they were relatively insensitive to scale at spatial resolutions less than 50 m, but that most metrics became sensitive at coarser resolutions, a finding which has implications for mapping of mangroves based on remotely sensed data. (C) 2003 Elsevier Science B.V. All rights reserved.

Multiresolution spatial databases: Making web-based spatial applications faster

Spatial data has now been used extensively in the Web environment, providing online customized maps and supporting map-based applications. The full potential of Web-based spatial applications, however, has yet to be achieved due to performance issues related to the large sizes and high complexity of spatial data. In this paper, we introduce a multiresolution approach to spatial data management and query processing such that the database server can choose spatial data at the right resolution level for different Web applications. One highly desirable property of the proposed approach is that the server-side processing cost and network traffic can be reduced when the level of resolution required by applications are low. Another advantage is that our approach pushes complex multiresolution structures and algorithms into the spatial database engine. That is, the developer of spatial Web applications needs not to be concerned with such complexity. This paper explains the basic idea, technical feasibility and applications of multiresolution spatial databases.

Neighborhood and Efficiency in Manufacturing in Brazilian Regions: A Spatial Markov Chain Analysis

This paper analyzes the geography of regional competitiveness in manufacturing in Brazil. The authors estimate stochastic frontiers to calculate regional efficiency of representative firms in 137 regions in the period 2000-2006, in four sectors defined by technological intensity. The efficiency results are analyzed using Markov Spatial Transition Matrices to provide insights into the transition of regions between efficiency levels, considering their local spatial context. The results indicate that geography plays an important role in manufacturing competitiveness. In particular, regions with more competitive neighbors are more likely to improve their relative efficiency (pull effect) over time, and regions with less competitive neighbors are more likely to lose relative efficiency (drag effect). The authors find that the pull effect is stronger than the drag effect.

Spatial aspects of trade liberalization in Colombia: A general equilibrium approach

This paper offers some preliminary steps in the marriage of some of the theoretical foundations of new economic geography with spatial computable general equilibrium models. Modelling the spatial economy of Colombia using the traditional assumptions of computable general equilibrium (CGE) models makes little sense when one territorial unit, Bogota, accounts for over one quarter of GDP and where transportation costs are high and accessibility low compared to European or North American standards. Hence, handling market imperfections becomes imperative as does the need to address internal spatial issues from the perspective of Colombia`s increasing involvement with external markets. The paper builds on the Centro de Estudios de Economia Regional (CEER) model, a spatial CGE model of the Colombian economy; non-constant returns and non-iceberg transportation costs are introduced and some simulation exercises carried out. The results confirm the asymmetric impacts that trade liberalization has on a spatial economy in which one region, Bogota, is able to more fully exploit scale economies vis--vis the rest of Colombia. The analysis also reveals the importance of different hypotheses on factor mobility and the role of price effects to better understand the consequences of trade opening in a developing economy.

Non-Spatial Government Policies and Regional Income Inequality in Brazil

Silveira Neto R. Da M. and Azzoni C. R. Non-spatial government policies and regional income inequality in Brazil, Regional Studies. This paper uses both macro- and micro-data to analyse the role of social programmes in the recent reduction in Brazilian regional income inequality. Convergence indicators are presented for different sources of regional income in the period 1995-2006. A decomposition of the Gini indicator allows the identification of the role of each of these income sources with respect to the reduction of regional inequality during the period. The results point out that both labour productivity and government non-spatial policies - mainly minimum wage changes and income transference programmes - do have a role in explaining regional inequality reduction during the period. [image omitted] Silveira Neto R. Da M. et Azzoni C. R. Les politiques gouvernementales non-spatiales et l`ecart des revenus regionaux au Bresil, Regional Studies. Cet article emploie des donnees a la fois macroeconomiques et microeconomiques afin d`analyser le role des programmes d`actions sociales quant a la baisse recente de l`ecart des revenus regionaux au Bresil. On presente des indicateurs de convergence pour diverses sources des revenus regionaux pour la periode allant de 1995 a 2006. Une decomposition du coefficient de Gini permet d`identifier le role de chacune de ces sources des revenus par rapport a la baisse de l`ecart des revenus pendant cette periode. Les resultats indiquent que la productivite du travail et les politiques gouvernementales non-spatiales - notamment la modification du salaire minimum et les programmes visant le transfert des revenus - ont un role a jouer pour expliquer la baisse de l`ecart des revenus regionaux pendant la periode en question. Convergence Productivite du travail Transfert des revenus Salaire minimum Effets spatiaux des politiques non-spatiales Silveira Neto R. Da M. und Azzoni C. R. Nicht raumliche Regierungspolitiken und das regionale Einkommensungleichgewicht in Brasilien, Regional Studies. In diesem Beitrag analysieren wir mit Hilfe von Makro- und Mikrodaten die Rolle von sozialen Programmen bei der unlangst erzielten Verringerung des regionalen Einkommensungleichgewichts in Brasilien. Wir stellen Konvergenz-Indikatoren fur verschiedene regionale Einkommensquellen im Zeitraum von 1995 bis 2006 vor. Eine Dekomposition des Gini-Indikators ermoglicht die Identifizierung der jeweiligen Rolle dieser Einkommensquellen fur die Verringerung des regionalen Ungleichgewichts im betreffenden Zeitraum. Die Ergebnisse weisen darauf hin, dass sowohl die Produktivitat der Arbeitskrafte als auch die nicht raumlichen Regierungspolitiken - in erster Linie Veranderungen beim Mindestlohn und Programme fur Einkommenstransfers - als Grunde fur die Verringerung des regionalen Ungleichgewichts in dieser Periode durchaus eine Rolle spielen. Konvergenz Arbeitsproduktivitat Einkommenstransfer Mindestlohn Raumliche Auswirkungen nicht raumlicher Politiken Silveira Neto R. Da M. y Azzoni C. R. Politicas gubernamentales no espaciales y desigualdades de ingresos regionales en Brasil, Regional Studies. En este articulo utilizamos datos macro y micro para analizar el papel de los programas sociales en la reciente reduccion en las desigualdades de ingresos regionales de Brasil. Presentamos los indicadores de convergencia para diferentes fuentes de ingresos regionales en el periodo de 1995 a 2006. Una descomposicion del indice Gini permite identificar el papel de cada una de estas fuentes de ingresos con respecto a la reduccion de las desiguadades regionales durante este periodo. Los resultados destacan que tanto la productividad laboral como las politicas no espaciales del gobierno - principalmente los cambios de salario minimo y los programas de transferencias de ingresos - desempenan una funcion a la hora de explicar la reduccion de las desigualdades regionales durante este periodo. Convergencia Productividad laboral Transferencias de ingresos Salario minimo Efectos espaciales de politicas no espaciales.

Regional Effects of Port Infrastructure: A Spatial CGE Application to Brazil

This article attempts to elucidate one of the mechanisms that link trade barriers, in the form of port costs, and subsequent growth and regional inequality. Prior attention has focused on inland or link costs, but port costs can be considered as a further barrier to enhancing trade liberalization and growth. In contrast to a highway link, congestion at a port may have severe impacts that are spread over space and time whereas highway link congestion may be resolved within several hours. Since a port is part of the transportation network, any congestion/disruption is likely to ripple throughout the hinterland. In this sense, it is important to model properly the role nodal components play in the context of spatial models and international trade. In this article, a spatial computable general equilibrium (CGE) model that is integrated to a transport network system is presented to simulate the impacts of increases in port efficiency in Brazil. The role of ports of entry and ports of exit are explicitly considered to grasp the holistic picture in an integrated interregional system. Measures of efficiency for different port locations are incorporated in the calibration of the model and used as the benchmark in our simulations. Three scenarios are evaluated: (1) an overall increase in port efficiency in Brazil to achieve international standards; (2) efficiency gains associated with decentralization in port management in Brazil; and (3) regionally differentiated increases in port efficiency to reach the boundary of the national efficiency frontier.