On the search for spillovers from foreign direct investment (FDI) with spatial dependency

This paper examines various specifications that are used within the literature to test for spillovers from foreign direct investment (FDI). Analysis provides significant evidence of externalities from inward FDI, but it shows that these externalities are more localized than has previously been believed. Further, the results demonstrate that the econometric treatment of issues such as agglomeration, contiguity and spatial dependence significantly changes the conclusions regarding local and national spillovers from FDI, and productivity growth more generally.

Spatial Patterns and Irregularities of the electoral data: general elections in Canada

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Towards more precise sugar beet management based on geostatistical analysis of spatial variability within fields

Within-field variation in sugar beet yield and quality was investigated in three commercial sugar beet fields in the east of England to identify the main associated variables and to examine the possibility of predicting yield early in the season with a view to spatially variable management of sugar beet crops. Irregular grid sampling with some purposively-located nested samples was applied. It revealed the spatial variability in each sugar beet field efficiently. In geostatistical analyses, most variograms were isotropic with moderate to strong spatial dependency indicating a significant spatial variation in sugar beet yield and associated growth and environmental variables in all directions within each field. The Kriged maps showed spatial patterns of yield variability within each field and visual association with the maps of other variables. This was confirmed by redundancy analyses and Pearson correlation coefficients. The main variables associated with yield variability were soil type, organic matter, soil moisture, weed density and canopy temperature. Kriged maps of final yield variability were strongly related to that in crop canopy cover, LAI and intercepted solar radiation early in the growing season, and the yield maps of previous crops. Therefore, yield maps of previous crops together with early assessment of sugar beet growth may make an early prediction of within-field variability in sugar beet yield possible. The Broom’s Barn sugar beet model failed to account for the spatial variability in sugar yield, but the simulation was greatly improved when corrected for early canopy development cover and when the simulated yield was adjusted for weeds and plant population. Further research to optimize inputs to maximise sugar yield should target the irrigation and fertilizing of areas within fields with low canopy cover early in the season.

Spatial variability of infiltration and its relationship to some physical properties

Water infiltration into soil is one of the basic factors for estimating irrigation intensity according to the plants' requirements; this is aimed at avoiding problems of surface run-off and degradation. The purpose of the present investigation was to determine the spatial variation of infiltration and its relationship to some physical properties of soil by means of geostatistical techniques in Typic Plinthaquult soils having average texture and flat relief. A 113 point mesh was designned, having a regular distance of 10 m between points, samples being taken from 0 to 0.20 meters depth. Sand, silt and clay content, bulk density, macroporosity, microporosity and total porosity were determined. Infiltration tests were carried out in the field by means of a 15 cm diameter ring. Descriptive statistics and geostatistics were used for analysing the data. Infiltration, silt and microporosity data did not fit a normal distribution curve. Infiltration had high variability, having an average 36.03 mm h(-1). Total porosity was 56.73%, this being the only property that did not show spatial dependency. The smallest ranges were observed for bulk density, macroporosity and microporosity, having values of less than 40 m. The smallest degrees of spatial dependence were observed for infiltration, silt and clay, evidence also being shown of the influence of silt and clay on infiltration rate. Contour maps were constructed; fitting them to the semivariogram models, together with studying the correlations, led to establishing relationships between the properties.

Spatio-temporal variability behavior of land surface soil water content in shrub- and grass-land

The knowledge of the relationship between spatial variability of the surface soil water content (theta) and its mean across a spatial domain (theta(m)) is crucial for hydrological modeling and understanding soil water dynamics at different scales. With the aim to compare the soil moisture dynamics and variability between the two land uses and to explore the relationship between the spatial variability of theta and theta(m), this study analyzed sets of surface theta measurements performed with an impedance soil moisture probe, collected 136 times during a period of one year in two transects covering different land uses, i.e., korshinsk peashrub transect (KPT) and bunge needlegrass transect (BNT), in a watershed of the Loess Plateau, China. Results showed that the temporal pattern of theta behaved similarly for the two land uses, with both relative wetter soils during wet period and relative drier soils during dry period recognized in BNT. Soil moisture tended to be temporally stable among different dates, and more stable patterns could be observed for dates with more similar soil water conditions. The magnitude of the spatial variation of theta in KPT was greater than that in ENT. For both land uses, the standard deviation (SD) of theta in general increased as theta(m) increased, a behavior that could be well described with a natural logarithmic function. Convex relationship of CV and theta(m) and the maximum CV for both land uses (43.5% in KPT and 41.0% in BNT) can, therefore, be ascertained. Geostatistical analysis showed that the range in KPT (9.1 m) was shorter than that in BNT (15.1 m). The nugget effects, the structured variability, hence the total variability increased as theta(m) increased. For both land uses, the spatial dependency in general increased with increasing theta(m). 2011 Elsevier B.V. All rights reserved.

Distribuição espacial de atributos de solo e de plantas na cultura do mamoeiro no norte capixaba

No Brasil, as lavouras de mamoeiro das planícies dos tabuleiros costeiros são as que melhor desenvolvem e aplicam tecnologias para a produção de mamão no mundo. O objetivo foi aplicar a estatística clássica e a geoestatística no mapeamento e na correlação da variabilidade espacial de atributos químicos e físicos de solo e de plantas de mamoeiro (Carica papaya L.) de uma lavoura comercial do norte capixaba cultivada em um Argissolo típico dos tabuleiros costeiros. O solo de textura arenosa de caráter coeso foi preparado convencionalmente e cultivado com mamoeiro variedade Golden THB. Após a sexagem, procederam-se as amostragens de solo, amostrado na projeção da copa (0-0,20 e 0,20-0,40 m) para a determinação dos atributos químicos e físicos, e de atributos biométricos das plantas em uma área de 1,2 ha (114 x 110 m) totalizando 129 pontos amostrais georreferenciados. Ao nono mês após o transplantio, registrou-se a altura da colheita dos primeiros frutos, o número e a massa dos frutos colhidos para estimativa da produtividade, amostrando três plantas por ponto amostral durante três meses. Os dados foram submetidos à análise estatística descritiva e à correlação de Pearson. A dependência espacial das variáveis foi analisada através da ferramenta geoestatística, com obtenção de semivariograma e os mapas de distribuição das variáveis. A maior parte dos atributos de solo e de plantas de mamoeiro apresenta dependência espacial e é mapeada adequadamente. Há correlação de dependência vertical para densidade do solo, argila, silte, resistência do solo à penetração na linha de plantio e na rua e volume total de poros. Dos atributos químicos não ocorre este comportamento apenas para K, Al e Sat K. As frações areia e argila foram os principais atributos a constituírem correlação com os demais. Há poucas correlações dos atributos do solo com os atributos biométricos e a produtividade do mamoeiro. Ocorre correlação positiva entre a produtividade inicial do mamoeiro com características biométricas ideias para as plantas de mamoeiro. A fertilidade e o preparo do solo são expressivos para o desenvolvimento do mamoeiro e para a variabilidade espacial dos atributos avaliados.

Visualization of clusters in geo-referenced data using three-dimensional self-organizing maps

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

Regional consumption of antibiotics : a demand system approach

Through this paper. we have attempted to model the demand for different classes of antibiotics used for respiratory infections in outpatient care in Switzerland using a spatial version of the linear approximate Almost Ideal Demand System (AIDS) model. This model takes spatial dependency into account by means of spatial lags of antibiotic budget shares. We control for the health status of patients and the potential harmful effects of antibiotic use in terms of bacterial resistance. Elasticities to socioeconomic determinants of consumption and own- and cross-price elasticities between different groups of antibiotic have also been computed in this paper. Significant cross-price elasticities are found between newer or more expensive generations and older or less expensive generations of antibiotics. (C) 2009 Elsevier B.V. All rights reserved.

Sampling strategies for tropical forest nutrient cycling studies: a case study in São Paulo, Brazil

The precise sampling of soil, biological or micro climatic attributes in tropical forests, which are characterized by a high diversity of species and complex spatial variability, is a difficult task. We found few basic studies to guide sampling procedures. The objective of this study was to define a sampling strategy and data analysis for some parameters frequently used in nutrient cycling studies, i. e., litter amount, total nutrient amounts in litter and its composition (Ca, Mg, Κ, Ν and P), and soil attributes at three depths (organic matter, Ρ content, cation exchange capacity and base saturation). A natural remnant forest in the West of São Paulo State (Brazil) was selected as study area and samples were collected in July, 1989. The total amount of litter and its total nutrient amounts had a high spatial independent variance. Conversely, the variance of litter composition was lower and the spatial dependency was peculiar to each nutrient. The sampling strategy for the estimation of litter amounts and the amount of nutrient in litter should be different than the sampling strategy for nutrient composition. For the estimation of litter amounts and the amount of nutrients in litter (related to quantity) a large number of randomly distributed determinations are needed. Otherwise, for the estimation of litter nutrient composition (related to quality) a smaller amount of spatially located samples should be analyzed. The determination of sampling for soil attributes differed according to the depth. Overall, surface samples (0-5 cm) showed high short distance spatial dependent variance, whereas, subsurface samples exhibited spatial dependency in longer distances. Short transects with sampling interval of 5-10 m are recommended for surface sampling. Subsurface samples must also be spatially located, but with transects or grids with longer distances between sampling points over the entire area. Composite soil samples would not provide a complete understanding of the relation between soil properties and surface dynamic processes or landscape aspects. Precise distribution of Ρ was difficult to estimate.

Calibrating flood inundation models: identifying and addressing uncertainty in satellite observed flood extents

Improvements in the resolution of satellite imagery have enabled extraction of water surface elevations at the margins of the flood. Comparison between modelled and observed water surface elevations provides a new means for calibrating and validating flood inundation models, however the uncertainty in this observed data has yet to be addressed. Here a flood inundation model is calibrated using a probabilistic treatment of the observed data. A LiDAR guided snake algorithm is used to determine an outline of a flood event in 2006 on the River Dee, North Wales, UK, using a 12.5m ERS-1 image. Points at approximately 100m intervals along this outline are selected, and the water surface elevation recorded as the LiDAR DEM elevation at each point. With a planar water surface from the gauged upstream to downstream water elevations as an approximation, the water surface elevations at points along this flooded extent are compared to their ‘expected’ value. The pattern of errors between the two show a roughly normal distribution, however when plotted against coordinates there is obvious spatial autocorrelation. The source of this spatial dependency is investigated by comparing errors to the slope gradient and aspect of the LiDAR DEM. A LISFLOOD-FP model of the flood event is set-up to investigate the effect of observed data uncertainty on the calibration of flood inundation models. Multiple simulations are run using different combinations of friction parameters, from which the optimum parameter set will be selected. For each simulation a T-test is used to quantify the fit between modelled and observed water surface elevations. The points chosen for use in this T-test are selected based on their error. The criteria for selection enables evaluation of the sensitivity of the choice of optimum parameter set to uncertainty in the observed data. This work explores the observed data in detail and highlights possible causes of error. The identification of significant error (RMSE = 0.8m) between approximate expected and actual observed elevations from the remotely sensed data emphasises the limitations of using this data in a deterministic manner within the calibration process. These limitations are addressed by developing a new probabilistic approach to using the observed data.

The impact of uncertainty in satellite data on the assessment of flood inundation models

The performance of flood inundation models is often assessed using satellite observed data; however these data have inherent uncertainty. In this study we assess the impact of this uncertainty when calibrating a flood inundation model (LISFLOOD-FP) for a flood event in December 2006 on the River Dee, North Wales, UK. The flood extent is delineated from an ERS-2 SAR image of the event using an active contour model (snake), and water levels at the flood margin calculated through intersection of the shoreline vector with LiDAR topographic data. Gauged water levels are used to create a reference water surface slope for comparison with the satellite-derived water levels. Residuals between the satellite observed data points and those from the reference line are spatially clustered into groups of similar values. We show that model calibration achieved using pattern matching of observed and predicted flood extent is negatively influenced by this spatial dependency in the data. By contrast, model calibration using water elevations produces realistic calibrated optimum friction parameters even when spatial dependency is present. To test the impact of removing spatial dependency a new method of evaluating flood inundation model performance is developed by using multiple random subsamples of the water surface elevation data points. By testing for spatial dependency using Moran’s I, multiple subsamples of water elevations that have no significant spatial dependency are selected. The model is then calibrated against these data and the results averaged. This gives a near identical result to calibration using spatially dependent data, but has the advantage of being a statistically robust assessment of model performance in which we can have more confidence. Moreover, by using the variations found in the subsamples of the observed data it is possible to assess the effects of observational uncertainty on the assessment of flooding risk.

How do neighbouring populations affect local population change over time?

This study covers a period when society changed from a pre-industrial agricultural society to a post-industrial service-producing society. Parallel with this social transformation, major population changes took place. In this study, we analyse how local population changes are affected by neighbouring populations. To do so we use the last 200 years of local population change that redistributed population in Sweden. We use literature to identify several different processes and spatial dependencies in the redistribution between a parish and its surrounding parishes. The analysis is based on a unique unchanged historical parish division, and we use an index of local spatial correlation to describe different kinds of spatial dependencies that have influenced the redistribution of the population. To control inherent time dependencies, we introduce a non-separable spatial temporal correlation model into the analysis of population redistribution. Hereby, several different spatial dependencies can be observed simultaneously over time. The main conclusions are that while local population changes have been highly dependent on the neighbouring populations in the 19th century, this spatial dependence have become insignificant already when two parishes is separated by 5 kilometres in the late 20th century. Another conclusion is that the time dependency in the population change is higher when the population redistribution is weak, as it currently is and as it was during the 19th century until the start of industrial revolution.

Análise da distribuição espacial da hanseníase no Estado de São Paulo, 1991-2002

A hanseníase no Brasil ainda é um problema a ser equacionado e, no Estado de São Paulo, há varias regiões com altas taxas de detecção. O presente estudo objetivou analisar a distribuição e quantificar a dependência espacial das taxas médias de detecção da hanseníase no Estado de São Paulo, no período de 1991-2002, empregando técnicas geoestatísticas. Verificou-se tendência levemente decrescente das taxas médias de detecção para o Estado de São Paulo. Altos índices do indicador podem ser visualizados nas regiões oeste e noroeste de São Paulo. A dependência espacial encontrada foi de aproximadamente 30 km. Com os resultados encontrados, conclui-se que a análise de superfície das taxas médias de detecção pode auxiliar na escolha de áreas prioritárias visando aos exames de coletividade e ao incremento dos exames nos contatos dos casos detectados.

Variabilidade espacial de atributos químicos em latossolo e argissolos

O trabalho teve como objetivo caracterizar a variabilidade espacial de atributos químicos de Latossolos e Argissolos, sob cultivo de cana-de-açúcar em áreas com variações na forma do relevo. No presente estudo utilizou-se duas áreas, sendo uma em Latossolo em pedoforma convexa (158ha) e a outra em Argissolo na pedoforma linear (172ha). Foi coletada amostra de solo em malha na profundidade de 0,00-0,50m, realizando-se análise química de cada ponto amostrado. Os maiores coeficientes de variação e alcances foram observados na pedoforma convexa (Latossolo). Portanto, o Latossolo inserido na pedoforma convexa apresentou maior variabilidade espacial para os atributos químicos em relação ao Argissolo na pedoforma linear. O latossolo inserido pedoforma convexa necessita de maior número de pontos de coleta por apresentar maior variabilidade espacial. Recomenda-se que o intervalo de amostragem seja igual ao alcance da dependência espacial, para associar menor esforço de amostragem com maior representatividade.

Análise geoestatística de casos de hanseníase no Estado de São Paulo, 1991-2002

OBJETIVO: Analisar o padrão espacial da ocorrência dos casos de hanseníase para identificar áreas com probabilidade de riscos de transmissão da doença. MÉTODOS: Estudo ecológico, tendo como unidade de análise os municípios do Estado de São Paulo georreferenciados em seus centróides. A fonte de dados utilizada foi o banco informatizado dos casos de hanseníase notificados do Centro de Vigilância Epidemiológica do Estado de São Paulo, no período de 1991 a 2002. Utilizou-se de técnicas de geoestatística para a detecção das áreas de probabilidade de risco para hanseníase e quantificação da dependência espacial dos casos. RESULTADOS: Detectou-se o alcance de dependência espacial de 0,55 graus de coordenadas georreferenciadas, correspondendo aproximadamente a 60 km. As principais áreas de probabilidade de risco encontradas foram as regiões nordeste, norte e noroeste do Estado. CONCLUSÕES: A verificação de áreas com probabilidades de riscos de casos de hanseníase, utilizando-se a análise da dependência espacial, pode ser ferramenta útil para avaliar a situação de saúde e planejar alocação de recursos.