The home range encompasses an animal’s movements as it goes about its normal activity, and several home range estimators have been developed. I evaluated the performance of the Minimum Convex Polygon, Bivariate Normal, and several kernel home range estimators in a geographical information system environment using simulations and a large database of O. pumilio mark-recapture locations. A fixed 90% kernel estimator using Least-Square Cross-Validation (to select the bandwidth) outperformed other methods of estimating home range size and was effective with relatively few capture points. Home range size, core area size, intrasexual overlap, and movement rates among coordinates were higher in female frogs than in male frogs. These measures likely reflect behavioral differences related to territoriality (males only) and parental care (both sexes). The simple Biological Index of Vagility (BIV) generated movement values that scaled well with home range size while revealing more information than home range estimates alone.

Wage dispersion is one of the social and economic issues arousing public concern in Hong Kong. There are many studies exploring the possible causes and changes in wage dispersion. They often focus on the study of summary measures such as Gini and Theil indexes, or adopt OLS-based regression approach. In foreign studies on wage dispersion, Oaxaca-Blinder decomposition, originated from Oaxaca (1974) and Blinder (1973), is a common method of decomposing changes or differences in mean wages between two groups into wage structure effect and composition effect, and then further decomposing the two effects into contributions of each control variable. Nevertheless, focusing on summary measures or decomposing mean wages can just give people an insight into the causes and changes in general wage dispersion but not the entire wage distribution. As pointed out by Chi, Li and Yu (2007), the estimation of the entire wage distribution and decomposition of the distributional changes in wage dispersion has been attracting the attention of labour economists. This thesis adopts a distributional approach proposed by Firpo, Fortin and Lemieux (2007) to study the changes in wage dispersion of Hong Kong since 1980s. The FFL approach comprises a two-stage procedure. Firstly, changes in dispersion are divided into wage structure effect and composition effect without directly estimating a wage-setting model. This is done by doing a proper reweighting to obtain counterfactual wage vectors. Kernel density estimation is used for visualizing the wage distribution in different years and the counterfactuals; secondly, novel recentered influence function (RIF) regressions across quantiles are performed to further decompose the two effects into contributions of each control variable. The findings are outlined as follows: first, there was an increase in wage dispersion over the whole wage distribution from 1980s but a decrease from 2001 to 2006; second, the composition effect dominates the wage structure effect over years; third, changes in the distribution of characteristics and the returns to these characteristics are highly responsive to each other, suggesting that our labour market is highly responsive to structural changes; fourth, The common wage-determining factors may not be able to explain the earnings-profile of low wage earners well. In brief, the development of the economy since 1980s increased the wage dispersion over years. Nevertheless, the economic downturn due to external shocks and internal unfavourable events and general skill-upgrading in labour-intensive industries decreased the wage dispersion since 2000s.

Understanding spatial perspectives on the spread and incidence of a disease is invaluable for public health planning and intervention. Choropleth maps are commonly used to provide an abstraction of disease risk across geographic space. These maps are derived from aggregated population counts that are known to be affected by the small numbers problem. Kernel density estimation methods account for this problem by producing risk estimates that are based on aggregations of approximately equal population sizes. However, the process of aggregation often combines data from areas with non-uniform spatial and population characteristics. This thesis presents a new method to aggregate space in ways that are sensitive to their underlying risk factors. Such maps will enable better public health practice and intervention by enhancing our ability to understand the spatial processes that result in disparate health outcomes.

abstract: Infants born before 37 weeks of pregnancy are considered to be preterm. Typically, preterm infants have to be strictly monitored since they are highly susceptible to health problems like hypoxemia (low blood oxygen level), apnea, respiratory issues, cardiac problems, neurological problems as well as an increased chance of long-term health issues such as cerebral palsy, asthma and sudden infant death syndrome. One of the leading health complications in preterm infants is bradycardia - which is defined as the slower than expected heart rate, generally beating lower than 60 beats per minute. Bradycardia is often accompanied by low oxygen levels and can cause additional long term health problems in the premature infant.The implementation of a non-parametric method to predict the onset of brady- cardia is presented. This method assumes no prior knowledge of the data and uses kernel density estimation to predict the future onset of bradycardia events. The data is preprocessed, and then analyzed to detect the peaks in the ECG signals, following which different kernels are implemented to estimate the shared underlying distribu- tion of the data. The performance of the algorithm is evaluated using various metrics and the computational challenges and methods to overcome them are also discussed. It is observed that the performance of the algorithm with regards to the kernels used are consistent with the theoretical performance of the kernel as presented in a previous work. The theoretical approach has also been automated in this work and the various implementation challenges have been addressed. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2020

This thesis analyzes the end-user quality of service (QoS) in cellular mobile networks using device-side measurements. Quality of service in a wireless network is a significant factor in determining a user's satisfaction. Customers' perception of poor QoS is one of the core sources of customer churn for telecommunications companies. A core focus of this work is on assessing how user density impacts QoS within cellular networks. Kernel density estimation is used to produce user density estimates for high, medium, and low density areas. The QoS distributions are then compared across these areas. The k-sample Anderson-Darling test is used to determine the degree to which user densities vary over time. In general, it is shown that users in higher density areas tend to experience overall lower QoS levels than those in lower density areas, even though these higher density areas service more subscribers. The conducted analyses highlight the value of mobile device-side QoS measurements in augmenting traditional network-side QoS measurements. / Graduate

Lagrangian particle models are useful for modelling pollutants in the atmosphere. They simulate the spread of pollutants by modelling trajectories of individual particles. However, to be useful, these models require a density estimate. The standard method to use has been boxcounting, but kernel density estimator (KDE) is an alternative. How KDE is used varies as there is no standard implementation. Primarily, it is the choice of kernel and bandwidth estimator that determines the model. In this report I have implemented a KDE for FOI’s Lagrangian particle model LPELLO. The kernel I have used is a combination between a uniform and Gaussian kernel. Four different bandwidth estimators has been tested, where two are global and two are variable. The first variable bandwidth estimator is based on the age of released particles, and the second is based on the turbulence history of the particles. The methods have then been tested against boxcounting, which by using an exceedingly large number of particles can be seen as the true concentration. The tests indicate that KDE method generally performs better than boxcounting at low particle numbers. The variable bandwidth estimators also performed better than both global bandwidth estimators. To achive a firmer conclusion, more testing is needed. The results indicate that KDE in general, and variable bandwidth estimators in specific, are useful tools for concentration estimate.

Water shortages resulting from increased demand or reduced supply may be addressed, in part, by redirecting recycled water for irrigation, industrial reuse, groundwater recharge, and as effluent discharge returned to streams. Recycled water is an essential component of integrated water management and broader adoption of recycled water will increase water conservation in water-stressed coastal communities. This study examined spatial patterns of recycled water use in Florida in 2009 and 2015 to detect gaps in distribution, quantify temporal change, and identify potential areas for expansion. Databases of recycled water products and distribution centers for Florida in 2009 and 2015 were developed by combining the 2008 and 2012 Clean Water Needs Survey databases with Florida’s 2009 and 2015 Reuse Inventory databases, respectively. Florida increased recycled water production from 674.85 mgd in 2009 to 738.15 mgd in 2015, an increase of 63.30 mgd. The increase was primarily allocated to use in public access areas, groundwater recharge, and industrial reuse, all within the South Florida Water Management District (WMD). In particular, Miami was identified in 2009 as an area of opportunity for recycled water development, and by 2015 it had increased production and reduced the production gap. Overall, South Florida WMD had the largest increase in production of 44.38 mgd (69%), while Southwest Florida WMD decreased production of recycled water by 1.68 mgd, or 3%. Overall increase in use of recycled water may be related to higher demand due to increased population coupled with public programs and policy changes that promote recycled water use at both the municipal and individual level.

This research assesses shifts in tornado occurrence pattens in space and time employing continental United States tornado records with an Enhanced Fujita (EF) rating equal or greater than 1. In similar research, most researchers discard tornado records prior to 1980 due to factors including: magnitude anomalies related to development of the Fujita Scale, unpredictability in tornado reporting (escalating populace, storm spotters, and technologic improvements), and better data records from the Census Bureau. We therefore constructed two datasets using tornados recorded in the National Weather Service Storm Prediction Center’s Severe Weather GIS (SVRGIS) database: 1950-2019 (dataset 1) and 1980-2019 (dataset 2). The goals for this study were to 1) determine whether spatiotemporal patterns of recorded tornado activity have shifted over time, and 2) determine whether inclusion of pre-1980 tornado data changes the findings from 1). This study employed Space-Time Pattern Mining (STPM) to construct four spacetime cubes (STC) in ArcGIS Pro. Emerging Hot Spot Analysis (EHS) was employed to identify the changes in tornado occurrence (number of incidents in a STC cell) and magnitude (sum of tornado EF ratings for all incidents in a STC cell). EHS displayed increased tornado activity in the Southeast and decreased activity for areas in the Great Plains for both occurrence and magnitude in both datasets. This is interpreted as significant intensifying hot spots in the Southeast region and diminishing hot spots in the Great Plains indicating an east-south-east shift for both datasets. Similar findings for both datasets indicate that inclusion of the less reliable pre-1980’s tornado data does not change the results and we recommend that the practice of discarding pre-1980’s tornado data in tornado occurrence research be reconsidered.

京都大学 / 新制・課程博士 / 博士(経済学) / 甲第24375号 / 経博第662号 / 新制||経||302(附属図書館) / 京都大学大学院経済学研究科経済学専攻 / (主査)教授 西山 慶彦, 教授 江上 雅彦, 講師 柳 貴英 / 学位規則第4条第1項該当 / Doctor of Economics / Kyoto University / DFAM

This research estimated the spatial-temporal patterns of tornadoes in the continental United States from 1950-2019 using the National Weather Service Storm Prediction Center’s Severe Weather GIS (SVRGIS) database. This study employed Space-Time Cube Analysis and Linear Kernel Density (Kernel Density Linear Process, (KDLP)) rather than the standard Kernel Density Estimation (KDE) approach; to evaluate whether tornado hotspot locations and intensities shift over time. The first phase of the study utilized KDLP to map changes in tornado hotspots and qualitatively assess decadal shifts in hotspot locations and intensities by occurrence and magnitude between decades using ArcGIS Pro and CrimeStat. Next an Emerging Hot Spot Analysis (EHSA) was employed to identify the changes in tornado occurrence and magnitude. ESHA results identified, by both occurrence and magnitude, significant intensifying hot spots in the Southeast region and diminishing hot spots in the Great Plains indicating an east-south-east shift.