The thesis is concerned with development of efficient regional frequency analysis (RFA) approaches to estimate quantiles of hydrometeorological events. The estimates are necessary for various applications in water resources engineering. The classical approach to estimate quantiles involves fitting frequency distribution to at-site data. However, this approach cannot be used when data at target site are inadequate or unavailable to compute parameters of the frequency distribution. This impediment can be overcome through RFA, in which sites having similar attributes are identified to form a region, and information is pooled from all the sites in the region to estimate the quantiles at target site. The thesis proposes new approaches to RFA of precipitation, meteorological droughts and floods, and demonstrates their effectiveness. The approach proposed for RFA of precipitation overcomes shortcomings of conventional approaches with regard to delineation and validation of homogeneous precipitation regions, and estimation of precipitation quantiles in ungauged and data sparse areas. For the first time in literature, distinction is made between attributes/variables useful to form homogeneous rainfall regions and to validate the regions. Another important issue is that some of the attributes considered for regionalization vary dynamically with time. In conventional approaches, there is no provision to consider dynamic aspects of time varying attributes. This may lead to delineation of ineffective regions. To address this issue, a dynamic fuzzy clustering model (DFCM) is developed. The results obtained from application to Indian summer monsoon and annual rainfall indicated that RFA based on DFCM is more effective than that based on hard and fuzzy clustering models in arriving at rainfall quantile estimates. Errors in quantile estimates for the hard, fuzzy and dynamic fuzzy models based on the proposed approach are shown to be significantly less than those computed for Indian summer monsoon rainfall regions delineated in three previous studies. Overall, RFA based on DFCM and large scale atmospheric variables appeared promising. The performance of DFCM is followed by that of fuzzy and hard clustering models. Next, a new approach is proposed for RFA of meteorological droughts. It is suggested that homogeneous precipitation regions have to be delineated before proceeding to develop drought severity - areal extent - frequency (SAF) curves. Drought SAF curves are constructed at annual and summer monsoon time scales for each of the homogeneous rainfall regions that are newly delineated in India based on the proposed approach. They find use in assessing spatial characteristics and frequency of meteorological droughts. It overcomes shortcomings associated with classical approaches that construct SAF curves for political (e.g., state, country) and physiographic regions (e.g., river basin), based on spatial patterns of at-site values of drought indices in the study area, without testing homogeneity in rainfall. Advantage of the new approach can be noted especially in areas that have significant variations in temporal and spatial distribution of precipitation (possibly due to variations in topography, landscape and climate). The DFCM is extended to RFA of floods, and its effectiveness in prediction of flood quantiles is demonstrated by application to Godavari basin in India, considering precipitation as time varying attribute. Six new homogeneous regions are formed in Godavari basin and errors in quantile estimates based on those regions are shown to be significantly less than those computed based on sub-zones delineated in Godavari basin by Central Water Commission in a previous study.

Regionalization of hydrometeorological variables such as rainfall and temperature is necessary for various applications related to water resources planning and management. Sampling variability and randomness associated with the variables, as well as non-availability and paucity of data pose a challenge in modelling the variables. This challenge can be addressed by using stochastic models that utilize information from hydrometeorologically similar locations for modelling the variables. A set of locations that are hydrometeorologically similar are referred to as homogeneous region or pooling group and the process of identifying a homogeneous region is referred to as regionalization. The thesis concerns development of new approaches to regionalization of (i) extreme rainfall,(ii) maximum and minimum temperatures, and (iii) rainfall together with maximum and minimum temperatures. Regionalization of extreme rainfall and frequency analysis based on resulting regions yields quantile estimates that find use in design of water control (e.g., barrages, dams, levees) and conveyance structures (e.g., culverts, storm sewers, spillways) to mitigate damages that are likely due to floods triggered by extreme rainfall, and land-use planning and management. Regionalization based on both rainfall and temperature yield regions that could be used to address a wide spectrum of problems such as meteorological drought analysis, agricultural planning to cope with water shortages during droughts, downscaling of precipitation and temperature. Conventional approaches to regionalization of extreme rainfall are based extensively on statistics derived from extreme rainfall. Therefore delineated regions are susceptible to sampling variability and randomness associated with extreme rainfall records, which is undesirable. To address this, the idea of forming regions by considering attributes for regionalization as seasonality measure and site location indicators (which could be determined even for ungauged locations) is explored. For regionalization, Global Fuzzy c-means (GFCM) cluster analysis based methodology is developed in L-moment framework. The methodology is used to arrive at a set of 25 homogeneous extreme rainfall regions over India considering gridded rainfall records at daily scale, as there is dearth of regionalization studies on extreme rainfall in India Results are compared with those based on commonly used region of influence (ROI) approach that forms site-specific regions for quantile estimation, but lacks ability to delineate a geographical area into a reasonable number of homogeneous regions. Gridded data constitute spatially averaged rainfall that might originate from a different process (more synoptic) than point rainfall (more convective). Therefore to investigate utility of the developed GFCM methodology in arriving at meaningful regions when applied to point rainfall data, the methodology is applied to daily rainfall records available for 1032 gauges in Karnataka state of India. The application yielded 22 homogeneous extreme rainfall regions. Experiments carried out to examine utility of GFCM and ROI based regions in arriving at quantile estimates for ungauged sites in the study area reveal that performance of GFCM methodology is fairly close to that of ROI approach. Errors were marginally lower in the case of GFCM approach in analysis with observed point rainfall data over Karnataka, while its converse was noted in the case of analysis with gridded rainfall data over India. Neither of the approaches (CA, ROI) was found to be consistent in yielding least error in quantile estimates over all the sites. The existing approaches to regionalization of temperature are based on temperature time series or their related statistics, rather than attributes effecting temperature in the study area. Therefore independent validation of the delineated regions for homogeneity in temperature is not possible. Another drawback of the existing approaches is that they require adequate number of sites with contemporaneous temperature records for regionalization, because the delineated regions are susceptible to sampling variability and randomness associated with the temperature records that are often (i) short in length, (ii) limited over contemporaneous time period and (iii) spatially sparse. To address these issues, a two-stage clustering approach is developed to arrive at regions that are homogeneous in terms of both monthly maximum and minimum temperatures ( and ). First-stage of the approach involves (i) identifying a common set of possible predictors (LSAVs) influencing and over the entire study area, and (ii) using correlations of those predictors with and along with location indicators (latitude, longitude and altitude) as the basis to delineate sites in the study area into hard clusters through global k-means clustering algorithm. The second stage involves (i) identifying appropriate LSAVs corresponding to each of the first-stage clusters, which could be considered as potential predictors, and (ii) using the potential predictors along with location indicators (latitude, longitude and altitude) as the basis to partition each of the first-stage clusters into homogeneous temperature regions through global fuzzy c-means clustering algorithm. A set of 28 homogeneous temperature regions was delineated over India using the proposed approach. Those regions are shown to be effective when compared to an existing set of 6 temperature regions over India for which inter-site cross-correlations were found to be weak and negative for several months, which is undesirable. Effectiveness of the newly formed regions is demonstrated. Utility of the proposed maxTminT homogeneous temperature regions in arriving at PET estimates for ungauged locations within the study area was demonstrated. The estimates were found to be better when compared to those based on the existing regions. The existing approaches to regionalization of hydrometeorological variables are based on principal components (PCs)/ statistics/indices determined from time-series of those variables at monthly and seasonal scale. An issue with use of PCs for regionalization is that they have to be extracted from contemporaneous records of hydrometeorological variables. Therefore delineated regions may not be effective when the available records are limited over contemporaneous time period. A drawback associated with the use of statistics/indices is that they (i) may not be meaningful when data exhibit nonstationarity and (ii) do not encompass complete information in the original time series. Consequently the resulting regions may not be effective for the desired purpose. To address these issues, a new approach is proposed. It considers information extracted from wavelet transformations of the observed multivariate hydrometeorological time series as the basis for regionalization by global fuzzy c-means clustering procedure. The approach can account for dynamic variability in the time series and its nonstationarity (if any). Effectiveness of the proposed approach in forming homogeneous hydrometeorological regions is demonstrated by application to India, as there are no prior attempts to form such regions over the country. The investigations resulted in identification of 29 regions over India, which are found to be effective and meaningful. Drought Severity-Area-Frequency (SAF) curves are developed for each of the newly formed regions considering the drought index to be Standardized Precipitation Evapotranspiration Index (SPEI).

The aim of this master's thesis was to get acquainted with cluster analysis, clustering methods and their theoretical properties. It was necessary select clustering algorithms whose properties will be analyzed, find and select data sets on which these algorithms will be triggered. Also, the goal was to design and implement an application that will evaluate and display clustering results in an appropriate manner. The last step was to analyze the results and compare them with theoretical assumptions.

Indiana University-Purdue University Indianapolis (IUPUI) / This research performs a comparative study of techniques for combining spatial data and multivariate statistical methods for characterizing water quality conditions in a river basin. The study has been performed on the White River basin in central Indiana, and uses sixteen physical and chemical water quality parameters collected from 44 different monitoring sites, along with various spatial data related to land use – land cover, soil characteristics, terrain characteristics, eco-regions, etc. Various parameters related to the spatial data were analyzed using ArcHydro tools and were included in the multivariate analysis methods for the purpose of creating classification equations that relate spatial and spatio-temporal attributes of the watershed to water quality data at monitoring stations. The study compares the use of various statistical estimates (mean, geometric mean, trimmed mean, and median) of monitored water quality variables to represent annual and seasonal water quality conditions. The relationship between these estimates and the spatial data is then modeled via linear and non-linear multivariate methods. The linear statistical multivariate method uses a combination of principal component analysis, cluster analysis, and discriminant analysis, whereas the non-linear multivariate method uses a combination of Kohonen Self-Organizing Maps, Cluster Analysis, and Support Vector Machines. The final models were tested with recent and independent data collected from stations in the Eagle Creek watershed, within the White River basin. In 6 out of 20 models the Support Vector Machine more accurately classified the Eagle Creek stations, and in 2 out of 20 models the Linear Discriminant Analysis model achieved better results. Neither the linear or non-linear models had an apparent advantage for the remaining 12 models. This research provides an insight into the variability and uncertainty in the interpretation of the various statistical estimates and statistical models, when water quality monitoring data is combined with spatial data for characterizing general spatial and spatio-temporal trends.

Master of Science / Department of Statistics / John E. Boyer Jr / The purpose of this study is to evaluate the effectiveness of the methods used for allocating disaster funds to assist commercial fishery participants as a result of Hurricanes Katrina and Rita of 2005 and to examine alternative methods to aid in determining an efficient criterion for allocating public funds for fisheries assistance. The trip ticket data managed by the Louisiana Department of Wildlife and Fisheries were used and analyzed using a cluster analysis. Results from the clustering procedures show that commercial fishermen consist of seven clusters, while wholesale/retail seafood dealers consist of six clusters. The three tiers into which commercial fishermen were originally classified can be extended to at least eleven (11) clusters, made up of three (3) clusters in tier 1 and an equal number of clusters (4) clusters in tier 2 and tier 3. Similarly, the original three tiers of wholesale/retail seafood dealers can be reclassified into at least nine (9) clusters with two clusters in tier 1, four (4) clusters in tier 2 and three (3) clusters in tier 3. As a result of the clustering reclassifications, alternative compensation plans were developed for the commercial fishermen and wholesale/retail seafood dealers. These alternative compensation plans suggest a reallocation of disaster assistance funds among individual groups of fishermen and among individual groups of dealers. We finally recommend that alternative classification methods should always be considered in order to select the most efficient criterion for allocating public funds in the future.

我國「都市計畫容積移轉辦法」中規定容積接受區須與容積送出區為同一都市計畫地區，以臺北市為例，全區屬同一主要計畫，申請者於房價較低之行政區取得公共設施用地後，將其容積移入房價較高之行政區，產生某些行政區開發量暴增、公共設施服務品質不佳、都市景觀衝擊、都市交通擁擠及缺乏最適容積總量管制等課題。 本研究以都市容受力理論觀點，提出最適容積總量的評估機制，利用ArcGIS系統了解臺北市各行政區及街廓發展現況，掌握其各種屬性，作為建立分類指標參考，再藉由因子分析、集群分析等研究方法，進行都市容受力評估以及街廓條件分類。 最後，根據實證研究結果，建議臺北市容積移轉制度須檢討各行政區容積發展總量、評估都市發展容受力、考量其他容積管制政策、調整容積移轉範圍、規定各行政區接受基地街廓分類與移入容積上限及擬定容積移轉接受基地相關規範，以作為容積移轉政策之執行參考。 / As stipulated in the Regulations of Urban Building Capacity Transfer, the recipient and donor of transferred building capacity need to be located in the same urban planned district. Take Taipei City for example, it is covered in the same urban planned area. As developers acquire public facilities lands in administrative areas of low land prices, and then transfer the building capacity got from the former to the administrative areas of high land prices. The behavior of developers mentioned above, will trigger a rapid increase of development capacity in certain administrative areas, impact the service quality of public facilities, urban landscape, traffic condition, and optimal control of building capacity. Based on the theory of urban carrying capacity, the study proposes an optimal building capacity assessment mechanism. ArcGIS is used to trace the current development and understand the various features of the administrative areas and street blocks in Taipei City so as to help establish relevant classification indicators. Factor and cluster analyses are then conducted to facilitate urban carrying capacity assessment and classification of street blocks. Finally, on the basis of the empirical results , this study offers the following suggestions for achieving more effective implementation of the urban building capacity transfer system in Taipei City: reviewing the total building capacity in every administrative areas, assessing urban carrying capacity, exploring other policies for building capacity control, adjusting the scope of building capacity transfer, classifying the street blocks of the recipient sites in every administrative areas, limiting the volume of transferred building capacity, and drafting regulations for recipient sites of transferred building capacity.

Inequalities in levels of health between regions within a country are frequently regarded as a problem. Zimbabwe is characterised by poor and unequal conditions of health (both the state of people‘s health and health services). The health system of the country shows severe spatial inequalities that are manifested at provincial, district and even local levels. The current research therefore examines and analyses the spatial inequalities and temporal variation of health conditions in Zimbabwe. Composite indices were used to determine the people‘s state of health in Zimbabwe. Administrative districts were ranked according to the level of people‘s state of health. Cluster analysis was also performed to demarcate administrative districts according the level of health service provision. Districts with minimum difference were demarcated in a single cluster. Clusters were delineated using data on patterns of diseases and health and such clusters were used to demarcate the country‘s spatial health system according to the Adapted Epidemiological Transition Model. This was meant to evaluate the applicability of the model to Zimbabwe. It emerged from the research that generally the country‘s health conditions are poor and the health system is characterised by severe spatial inequalities. Some districts are experiencing poor health service provision and serious health challenges and are still in the age of pestilence and famine but others have good health service provision as well as highly developed health conditions and are in the age of degenerative and man-made diseases of the epidemiological transition model. It further emerged that the country‘s health has been evolving with signs of improvement since the 1990s. Some proposals are made in research for spatial development of health in the country. Recommendations were made regarding possible adjustment to previous strategies and policies used in Zimbabwe, for the development of the health system of the country. New strategies were also recommended for the improvement of the health system of the country. / Geography / Ph.D. (Geography)

Developing and retaining competitive advantage was a major concern for all companies; it fundamentally relied on being aware of the external environment and customer satisfaction. Modifications of the environment conditions and unexpected economic events could cause of a loss of the level of organisational adjustment and subsequent loss in competitiveness, only those organisations able to rapidly adjust to these dynamics would be able to remain. In some instances, companies decided to geographically co-locate seeking economies of scale and benefiting from complementarities. Literature review revealed the strong support that clusters had from Government and Local Authorities, but it also highlighted the limited practical research in the field. The aim of this research was to measure the dynamism of the cluster formed by the geographical concentration of diverse manufacturers within the Mondragon Cooperativa Group in the Basque region of Spain, and compared it to the individual dynamism of these organisations in order to have a better understanding the actual complementarities and synergies of this industrial colocation. Literature review identified dynamic capabilities as the core enablers of organisation when competing in dynamic environments; based on these capabilities, a model was formulated. This model combined with the primary data collected via questionnaire and interviews helped measure the dynamism of the individual cluster members and the cluster as whole as well as provided an insight on the complementarities and synergies of this type of alliance. The findings of the research concluded that the cluster as a whole was more dynamic than the individual members; nevertheless, the model suggested that there were considerable differences in speed among the cluster members. These differences on speed were determined by the size of the company and their performance in dimensions such as marketing, culture and management. The research also suggested that despite of the clear differences in the level of dynamism among cluster members, all companies benefited in some way from being part of the cluster; these benefits were different in nature depending on each specific members.

published_or_final_version / Education / Master / Master of Education

This study aims to explore policy implications, production and consumption processes between the airline and tourism industries. In particular, policy initiatives, product developments, innovation and consumption patterns are taken into consideration in order to identify the relationship between the two industries within the context of Cyprus. The airline industry, after the implementation of liberalization, has changed considerably in terms of market size, type of airlines and operations. In the case of destination management, innovation and policy planning are key parameters of success. Additionally, new business production methods are imperative, given the emergence of a ‘new-tourist’ who is educated, seeking shorter breaks and more frequent and cheaper trips in unique and unexplored destinations. Both quantitative and qualitative methods of analysis are employed. In particular, 26 interviews of ‘power-elite’ policymakers and stakeholders in Cyprus are used to explore policy implications for the identification of implementation outcomes and their impact on product developments and innovation. Furthermore, 300 self-administered questionnaires were distributed to British travellers to Cyprus, so as to identify the role of the airlines and the extent of the importance attributed to destination. The results of the study suggest a gap in the relation of the tourism and airline industries’ interaction at policy level, outcome, and implementation. More specifically, the airline policy enables the industry to become more adaptive and creative, and innovation is depicted via low-cost carriers (LCCs). The tourism industry has developed a policy that reflects the post/neo-Fordism trends of consumption and production, which refers to niche products. However, there is a dearth of policy theory and implementation, with consistent failures and delays. Thus, tourism is at the stage of renovation without essential innovation in contrast to the airline industry, which is a leader, and a proponent of innovation. In terms of consumption, Factor Analysis suggests that British tourists tend to book their holiday trips based on three categories of airline attributes: ‘Customer service’, ‘Price-sensitive & Internet’ and ‘Selection in travel behaviour’. Cluster analysis suggests three main categories of tourists, namely, ‘Traditional’, ‘Demanding/Opportunists’ and ‘Ambivalent’. The results confirm that consumers have changed and tourism destinations must be able to adapt to their demands and to offer a variety of services and products in order to survive in a competitive global market. In the case of the airline industry and holiday trips, convenience and the airport that the airline is flying from is more important than the cost of the ticket.