Using Local Features to Measure Land Development in Urban Regions

Monitoring urban development in a given region provides valuable information to researchers. Currently available, very high resolution satellite images can be used for this purpose. However, manually monitoring land development using these large and complex images is time consuming and prone to errors. To handle this problem, an automated system is needed to measure development in urban regions. Therefore, in this study we propose such an automated method to measure land development in a given urban region imaged in different times. We benefit from novel land development measures for this purpose. They are based on local features obtained from sequential images. As a novel contribution, we represent these local features in a spatial voting matrix. Then, we propose five different land development measures on the formed voting matrix. We test our method on 19 sets of sequential panchromatic Ikonos images. Our test results indicate the possible use of our method in measuring land development automatically

Agglomeration within and between regions: Two econometric based indicators

We propose two indexes to measure the agglomeration forces acting within and between different regions. Unlike the existing measures of agglomeration, our model-based indexes allow for simultaneous treatment of both aspects. Local plant diffusion in a given industry is modelled as a spatial error components process (SEC). Maximum likelihood inference on model parameters is dealt with, including the problem of data censoring. The statistical properties of standard agglomeration indexes in the data environment provided by our SEC model are then treated. Finally, our methodology is applied to Italian census data for both manufacturing and service industries.agglomeration, spatial autocorrelation, spatial error components model

Income Mobility in Latin America

[Excerpt] In the last decades Latin American countries have experienced substantial macroeconomic instability. While the region as a whole experienced economic growth during most of the 1990’s and 2000’s, there were also years of stagnation as well as economic decline

Predicting water quality and ecological responses

Abstract Changes to climate are predicted to have effects on freshwater streams. Stream flows are likely to change, with implications for freshwater ecosystems and water quality. Other stressors such as population growth, community preferences and management policies can be expected to interact in various ways with climate change and stream flows, and outcomes for freshwater ecosystems and water quality are uncertain. Managers of freshwater ecosystems and water supplies could benefit from being able to predict the scales of likely changes. This project has developed and applied a linked modelling framework to assess climate change impacts on water quality regimes and ecological responses. The framework is designed to inform water planning and climate adaptation activities. It integrates quantitative tools, and predicts relationships between future climate, human activities, water quality and ecology, thereby filling a gap left by the considerable research effort so far invested in predicting stream flows. The modelling framework allows managers to explore potential changes in the water quality and ecology of freshwater systems in response to plausible scenarios for climate change and management adaptations. Although set up for the Upper Murrumbidgee River catchment in southern NSW and ACT, the framework was planned to be transferable to other regions where suitable data are available. The approach and learning from the project appear to have the potential to be broadly applicable. We selected six climate scenarios representing minor, moderate and major changes in flow characteristics for 1oC and 2oC temperature increases. These were combined with four plausible alternative management adaptations that might be used to modify water supply, urban water demand and stream flow regimes in the Upper Murrumbidgee catchment. The Bayesian Network (BN) model structure we used was developed using both a ‘top down’ and ‘bottom up’ approach. From analyses combined with expert advice, we identified the causal structure linking climate variables to stream flow, water quality attributes, land management and ecological responses (top down). The ‘bottom up’ approach focused on key ecological outcomes and key drivers, and helped produce efficient models. The result was six models for macroinvertebrates, and one for fish. In the macroinvertebrate BN models, nodes were discretised using statistical/empirical derived thresholds using new techniques. The framework made it possible to explore how ecological communities respond to changes in climate and management activities. Particularly, we focused on the effects of water quality and quantity on ecological responses. The models showed a strong regional response reflecting differences across 18 regions in the catchment. In two regions the management alternatives were predicted to have stronger effects than climate change. In three other regions the predicted response to climate change was stronger. Analyses of water quality suggested minor changes in the probability of water quality exceeding thresholds designed to protect aquatic ecosystems. The ‘bottom up’ approach limited the framework’s transferability by being specific to the Upper Murrumbidgee catchment data. Indeed, to meet stakeholder questions models need to be specifically tailored. Therefore the report proposes a general model-building framework for transferring the approach, rather than the models, to other regions.  Please cite this report as: Dyer, F, El Sawah, S, Lucena-Moya, P, Harrison, E, Croke, B, Tschierschke, A, Griffiths, R, Brawata, R, Kath, J, Reynoldson, T, Jakeman, T 2013 Predicting water quality and ecological responses, National Climate Change Adaptation Research Facility, Gold Coast, pp. 110 Changes to climate are predicted to have effects on freshwater streams. Stream flows are likely to change, with implications for freshwater ecosystems and water quality. Other stressors such as population growth, community preferences and management policies can be expected to interact in various ways with climate change and stream flows, and outcomes for freshwater ecosystems and water quality are uncertain. Managers of freshwater ecosystems and water supplies could benefit from being able to predict the scales of likely changes. This project has developed and applied a linked modelling framework to assess climate change impacts on water quality regimes and ecological responses. The framework is designed to inform water planning and climate adaptation activities. It integrates quantitative tools, and predicts relationships between future climate, human activities, water quality and ecology, thereby filling a gap left by the considerable research effort so far invested in predicting stream flows. The modelling framework allows managers to explore potential changes in the water quality and ecology of freshwater systems in response to plausible scenarios for climate change and management adaptations. Although set up for the Upper Murrumbidgee River catchment in southern NSW and ACT, the framework was planned to be transferable to other regions where suitable data are available. The approach and learning from the project appear to have the potential to be broadly applicable. We selected six climate scenarios representing minor, moderate and major changes in flow characteristics for 1oC and 2oC temperature increases. These were combined with four plausible alternative management adaptations that might be used to modify water supply, urban water demand and stream flow regimes in the Upper Murrumbidgee catchment. The Bayesian Network (BN) model structure we used was developed using both a ‘top down’ and ‘bottom up’ approach. From analyses combined with expert advice, we identified the causal structure linking climate variables to stream flow, water quality attributes, land management and ecological responses (top down). The ‘bottom up’ approach focused on key ecological outcomes and key drivers, and helped produce efficient models. The result was six models for macroinvertebrates, and one for fish. In the macroinvertebrate BN models, nodes were discretised using statistical/empirical derived thresholds using new techniques. The framework made it possible to explore how ecological communities respond to changes in climate and management activities. Particularly, we focused on the effects of water quality and quantity on ecological responses. The models showed a strong regional response reflecting differences across 18 regions in the catchment. In two regions the management alternatives were predicted to have stronger effects than climate change. In three other regions the predicted response to climate change was stronger. Analyses of water quality suggested minor changes in the probability of water quality exceeding thresholds designed to protect aquatic ecosystems. The ‘bottom up’ approach limited the framework’s transferability by being specific to the Upper Murrumbidgee catchment data. Indeed, to meet stakeholder questions models need to be specifically tailored. Therefore the report proposes a general model-building framework for transferring the approach, rather than the models, to other regions.&nbsp

Do Agglomeration Economies Reduce the Sensitivity of Firm Location to Tax Differentials?

Low corporate taxes can help attract new firms. This is the main mechanism underpinning the standard race-to-the-bottom view of tax competition. A recent theoretical literature has qualified this view by formalizing the argument that agglomeration forces can reduce firms' sensitivity to tax differentials across locations. We test this proposition using data on firm startups across Swiss municipalities. We found that, on average, high corporate income taxes do deter new firms, but that this relationship is significantly weaker in the most spatially concentrated sectors. Location choices of firms in sectors with an agglomeration intensity at the twentieth percentile of the sample distribution are estimated to be twice as responsive to a given difference in local corporate tax burdens as firms in sectors with an agglomeration intensity at the eightieth percentile. Hence, our analysis confirms the theoretical prediction: agglomeration economies can neutralize the impact of tax differentials on firms' location choices.Firm location, agglomeration economies, local taxation, count models, Switzerland

A class of spatial econometric methods in the empirical analysis of clusters of firms in the space

In this paper we aim at identifying stylized facts in order to suggest adequate models of spatial co–agglomeration of industries. We describe a class of spatial statistical methods to be used in the empirical analysis of spatial clusters. Compared to previous contributions using point pattern methods, the main innovation of the present paper is to consider clustering for bivariate (rather than univariate) distributions, which allows uncovering co–agglomeration and repulsion phenomena between the different industrial sectors. Furthermore we present the results of an empirical application of such methods to a set of European Patent Office (EPO) data and we produce a series of empirical evidences referred to the the pair–wise intra–sectoral spatial distribution of patents in Italy in the nineties. In this analysis we are able to identify some distinctive joint patterns of location between patents of different sectors and to propose some possible economic interpretations

Estimating impervious surfaces from a small urban watershed in Baton Rouge, Louisiana, using LANDSAT thematic mapper imagery

Many urban areas are using estimations of impervious surfaces as a means for better environmental management. This is because research over the last two decades indicate a consistent, inverse relationship between the percentage of impervious surfaces in a watershed and the environmental problems urban areas are experiencing. Although various methods for estimating impervious surfaces can be identified, few result in accurate and defensible estimations by which environmental problems can be assessed. This is especially important to rapidly expanding urban areas such as Baton Rouge, Louisiana where detailed records and planimetric data are lacking. Numerous studies have shown a potential for estimating impervious surfaces using remotely sensed satellite imagery however, none were performed in a sub-tropical geographical area such as southern Louisiana. Three different dates of Landsat TM multi-spectral imagery, corresponding to seasonal differences, were acquired for land cover type classification purposes. Seasonal dates of imagery were used to determine tree canopy effects and the optimum season for estimating impervious surfaces from satellite imagery. Unique to this study, the derived classified estimates were compared to an impervious surfaces reference estimate developed from high resolution, true color aerial photography. The impervious surfaces reference estimate was developed by digitizing over 15,000 polygons of impervious features throughout the watershed such as roads, buildings, and parking lots. Statistical evaluation of the seasonal classified images included the error matrix analysis, Kappa analysis (both overall and conditional), and the Pair-Wise Z test statistic. Results obtained in this research indicate overall accuracies of the derived classified estimates ranged between 75.33 percent and 81.33 percent while differing from the reference estimate by 10 percent or less

A class of spatial econometric methods in the empirical analysis of clusters of firms in the space

In this paper we aim at identifying stylized facts in order to suggest adequate models of spatial co–agglomeration of industries. We describe a class of spatial statistical methods to be used in the empirical analysis of spatial clusters. Compared to previous contributions using point pattern methods, the main innovation of the present paper is to consider clustering for bivariate (rather than univariate) distributions, which allows uncovering co–agglomeration and repulsion phenomena between the different industrial sectors. Furthermore we present the results of an empirical application of such methods to a set of European Patent Office (EPO) data and we produce a series of empirical evidences referred to the the pair–wise intra–sectoral spatial distribution of patents in Italy in the nineties. In this analysis we are able to identify some distinctive joint patterns of location between patents of different sectors and to propose some possible economic interpretations.Agglomeration, Bivariate K–functions, co–agglomeration, Non parametric concentration measures, Spatial clusters, Spatial econometrics

Cellular automata and urban studies: a literature survey

Este artigo apresenta uma pesquisa bibliográfica sobre a técnica matemática de autómatos celulares (CA) e a sua aplicação a estudos urbanos. Os modelos baseados em CA são actualmente alvo de intensa investigação não só em termos teóricos como também na sua aplicação operacional. Diversos modelos são já aplicados a diversas áreas urbanas e regiões metropolitanas em todo o mundo. É feita a apresentação da formulação clássica de CA bem como das suas primeiras aplicações a problemas geográficos e urbanos, sendo ainda discutidas as principais evoluções da técnica. É apresentada uma série de aplicações de modelos baseados em CA e é dedicada uma atenção particular às suas medidas de desempenho e a diversas abordagens de calibração dos modelos.Peer Reviewe