Análise espacial dos resultados eleitorais em Portugal – Aplicação do Geographically Weighted Regression

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

Análise espacial dos resultados eleitorais em Portugal – Aplicação do Geographically Weighted Regression

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

Geographic weighted regression: applicability to epidemiological studies of leprosy

Abstract: INTRODUCTION: Geographic information systems (GIS) enable public health data to be analyzed in terms of geographical variability and the relationship between risk factors and diseases. This study discusses the application of the geographic weighted regression (GWR) model to health data to improve the understanding of spatially varying social and clinical factors that potentially impact leprosy prevalence. METHODS: This ecological study used data from leprosy case records from 1998-2006, aggregated by neighborhood in the Duque de Caxias municipality in the State of Rio de Janeiro, Brazil. In the GWR model, the associations between the log of the leprosy detection rate and social and clinical factors were analyzed. RESULTS: Maps of the estimated coefficients by neighborhood confirmed the heterogeneous spatial relationships between the leprosy detection rates and the predictors. The proportion of households with piped water was associated with higher detection rates, mainly in the northeast of the municipality. Indeterminate forms were strongly associated with higher detections rates in the south, where access to health services was more established. CONCLUSIONS: GWR proved a useful tool for epidemiological analysis of leprosy in a local area, such as Duque de Caxias. Epidemiological analysis using the maps of the GWR model offered the advantage of visualizing the problem in sub-regions and identifying any spatial dependence in the local study area.

Estimation of average annual daily traffic (AADT) using geographically weighted regression (GWR) method and geographic information system (GIS)

Annual average daily traffic (AADT) is important information for many transportation planning, design, operation, and maintenance activities, as well as for the allocation of highway funds. Many studies have attempted AADT estimation using factor approach, regression analysis, time series, and artificial neural networks. However, these methods are unable to account for spatially variable influence of independent variables on the dependent variable even though it is well known that to many transportation problems, including AADT estimation, spatial context is important. ^ In this study, applications of geographically weighted regression (GWR) methods to estimating AADT were investigated. The GWR based methods considered the influence of correlations among the variables over space and the spatially non-stationarity of the variables. A GWR model allows different relationships between the dependent and independent variables to exist at different points in space. In other words, model parameters vary from location to location and the locally linear regression parameters at a point are affected more by observations near that point than observations further away. ^ The study area was Broward County, Florida. Broward County lies on the Atlantic coast between Palm Beach and Miami-Dade counties. In this study, a total of 67 variables were considered as potential AADT predictors, and six variables (lanes, speed, regional accessibility, direct access, density of roadway length, and density of seasonal household) were selected to develop the models. ^ To investigate the predictive powers of various AADT predictors over the space, the statistics including local r-square, local parameter estimates, and local errors were examined and mapped. The local variations in relationships among parameters were investigated, measured, and mapped to assess the usefulness of GWR methods. ^ The results indicated that the GWR models were able to better explain the variation in the data and to predict AADT with smaller errors than the ordinary linear regression models for the same dataset. Additionally, GWR was able to model the spatial non-stationarity in the data, i.e., the spatially varying relationship between AADT and predictors, which cannot be modeled in ordinary linear regression. ^

GIS-integrated mathematical modeling of social phenomena at macro- and micro- levels—a multivariate geographically-weighted regression model for identifying locations vulnerable to hosting terrorist safe-houses: France as case study

Adaptability and invisibility are hallmarks of modern terrorism, and keeping pace with its dynamic nature presents a serious challenge for societies throughout the world. Innovations in computer science have incorporated applied mathematics to develop a wide array of predictive models to support the variety of approaches to counterterrorism. Predictive models are usually designed to forecast the location of attacks. Although this may protect individual structures or locations, it does not reduce the threat—it merely changes the target. While predictive models dedicated to events or social relationships receive much attention where the mathematical and social science communities intersect, models dedicated to terrorist locations such as safe-houses (rather than their targets or training sites) are rare and possibly nonexistent. At the time of this research, there were no publically available models designed to predict locations where violent extremists are likely to reside. This research uses France as a case study to present a complex systems model that incorporates multiple quantitative, qualitative and geospatial variables that differ in terms of scale, weight, and type. Though many of these variables are recognized by specialists in security studies, there remains controversy with respect to their relative importance, degree of interaction, and interdependence. Additionally, some of the variables proposed in this research are not generally recognized as drivers, yet they warrant examination based on their potential role within a complex system. This research tested multiple regression models and determined that geographically-weighted regression analysis produced the most accurate result to accommodate non-stationary coefficient behavior, demonstrating that geographic variables are critical to understanding and predicting the phenomenon of terrorism. This dissertation presents a flexible prototypical model that can be refined and applied to other regions to inform stakeholders such as policy-makers and law enforcement in their efforts to improve national security and enhance quality-of-life.

Method validation using weighted linear regression models for quantification of UV filters in water samples

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Lazy Lasso for local regression

Locally weighted regression is a technique that predicts the response for new data items from their neighbors in the training data set, where closer data items are assigned higher weights in the prediction. However, the original method may suffer from overfitting and fail to select the relevant variables. In this paper we propose combining a regularization approach with locally weighted regression to achieve sparse models. Specifically, the lasso is a shrinkage and selection method for linear regression. We present an algorithm that embeds lasso in an iterative procedure that alternatively computes weights and performs lasso-wise regression. The algorithm is tested on three synthetic scenarios and two real data sets. Results show that the proposed method outperforms linear and local models for several kinds of scenarios

Metodologia para a suavização de dados biomecanicos por função não parametrica ponderada local robusta

Universidade Estadual de Campinas. Faculdade de Educação Física

Determinants of salivary cotinine level: a population-based study in Brazil

OBJECTIVE: A cross-sectional population-based study was conducted to assess, in active smokers, the relationship of number of cigarettes smoked and other characteristics to salivary cotinine concentrations. METHODS: A random sample of active smokers aged 15 years or older was selected using a stepwise cluster sample strategy, in the year 2000 in Rio de Janeiro, Brazil. The study included 401 subjects. Salivary cotinine concentration was determined using gas chromatography with nitrogen-phosphorus detection. A standard questionnaire was used to collect demographic and smoking behavioral data. The relation between the number of cigarettes smoked in the last 24h and cotinine level was examined by means of a nonparametric fitting technique of robust locally weighted regression. RESULTS: Significantly (p<0.05) higher adjusted mean cotinine levels were found in subjects smoking their first cigarette within five minutes after waking up, and in those smoking 1-20 cigarettes in the last 24h who reported inhaling more than ½ the time. In those smoking 1-20 cigarettes, the slope was significantly higher for those subjects waiting for more than five minutes before smoking their first cigarette after waking up, and those smoking "light" cigarettes when compared with their counterparts. These heterogeneities became negligible and non-significant when subjects with cotinine >40 ng/mL per cigarette were excluded. CONCLUSIONS: There was found a positive association between self-reporting smoking five minutes after waking up, and inhaling more than ½ the time are consistent and higher cotinine levels. These can be markers of dependence and higher nicotine intake. Salivary cotinine proved to be a useful biomarker of recent smoking and can be used in epidemiological studies and smoking cessation programs.

Investigating the use of dasymetric techniques for assessing employment containment in Melbourne, Australia

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

Analysing and visualising areal crime data. A case study of residential burglary in San Francisco, USA

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

The effects of cultural heritage on residential property values: Evidence from Lisbon, Portugal

This paper examines the impact of historic amenities on residential housing prices in the city of Lisbon, Portugal. Our study is directed towards identifying the spatial variation of amenity values for churches, palaces, lithic (stone) architecture and other historic amenities via the housing market, making use of both global and local spatial hedonic models. Our empirical evidence reveals that different types of historic and landmark amenities provide different housing premiums. While having a local non-landmark church within 100 meters increases housing prices by approximately 4.2%, higher concentrations of non-landmark churches within 1000 meters yield negative effects in the order of 0.1% of prices with landmark churches having a greater negative impact around 3.4%. In contrast, higher concentration of both landmark and non-landmark lithic structures positively influence housing prices in the order of 2.9% and 0.7% respectively. Global estimates indicate a negative effect of protected zones, however this significance is lost when accounting for heterogeneity within these areas. We see that the designation of historic zones may counteract negative effects on property values of nearby neglected buildings in historic neighborhoods by setting additional regulations ensuring that dilapidated buildings do not damage the city’s beauty or erode its historic heritage. Further, our results from a geographically weighted regression specification indicate the presence of spatial non-stationarity in the effects of different historic amenities across the city of Lisbon with variation between historic and more modern areas.

Evaluation of geostatistical techniques to estimate the spatial distribution of aboveground biomass in the Amazon rainforest using high-resolution remote sensing data

ABSTRACT The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression - kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos - Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand.

Regional abundance of on-premise outlets and drinking patterns among Swiss young men: District level analyses and geographic adjustments.

INTRODUCTION AND AIMS: This study investigated the associations of alcohol outlet density with specific alcohol outcomes (consumption and consequences) among young men in Switzerland and assessed the possible geographically related variations. DESIGN AND METHODS: Alcohol consumption and drinking consequences were measured in a 2010-2011 study assessing substance use risk factors (Cohort Study on Substance Use Risk Factors) among 5519 young Swiss men. Outlet density was based on the number of on- and off-premise outlets in the district of residence. Linear regression models were run separately for drinking level, heavy episodic drinking (HED) and drinking consequences. Geographically weighted regression models were estimated when variations were recorded at the district level. RESULTS: No consistent association was found between outlet density and drinking consequences. A positive association between drinking level and HED with on-premise outlet density was found. Geographically weighted regressions were run for drinking level and HED. The predicted values for HED were higher in the southwest part of Switzerland (French-speaking part). DISCUSSION AND CONCLUSIONS: Among Swiss young men, the density of outlets and, in particular, the abundance of bars, clubs and other on-premise outlets was associated with drinking level and HED, even when drinking consequences were not significantly affected. These findings support the idea that outlet density needs to be considered when developing and implementing regional-based prevention initiatives. [Astudillo M, Kuendig H, Centeno-Gil A, Wicki M, Gmel G. Regional abundance of on-premise outlets and drinking patterns among Swiss young men: District level analyses and geographic adjustments. Drug Alcohol Rev 2014;33:526-33].

Cardiovascular risk factor profiles and their social gradient from adolescence to age 74 in a Swiss region.

BACKGROUND: Few European studies have investigated how cardiovascular risk factors (CRF) in adults relate to those observed in younger generations. OBJECTIVE: To explore this issue in a Swiss region using two population health surveys of 3636 adolescents ages 9-19 years and 3299 adults ages 25-74 years. METHODS: Age patterns of continuous CRF were estimated by robust locally weighted regression and those of high-risk groups were calculated using adult criteria with appropriate adjustment for children. RESULTS: Gender differences in height, weight, blood pressure, and HDL cholesterol observed in adults were found to emerge in adolescents. Overweight, affecting 10-12% of adolescents, was increasing steeply in young adults (three times among males and twice among females) in parallel with inactivity. Median age at smoking initiation was decreasing rapidly from 18 to 20 years in young adults to 15 in adolescents. A statistically significant social gradient in disfavor of the lower education level was observed for overweight in all age groups of women above 16 (odds ratios (ORs) 2.4 to 3.3, P < 0.01), for inactivity in adult males (ORs 1.6 to 2.0, P < 0.05), and for regular smoking in older adolescents (OR 1.9 for males, 2.7 for females, P < 0.005), but not for elevated blood pressure. CONCLUSION: Discontinuities in the cross-sectional age patterns of CRF indicated the emergence of a social gradient and the need for preventive actions against the early adoption of persistent unhealthy behaviors, to which low-educated girls and women are particularly exposed.