Kong Yunfeng. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (p. 280-312). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

Thesis (M.S.) in Spatial Information Science and Engineering--University of Maine, 2003. / Includes vita. Includes bibliographical references (leaves 93-99).

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Data-to-text systems are Natural Language Generation (NLG) systems that generate textural summaries of raw numerical data. To date such systems have concentrated exclusively on time series data. This is despite the increasing use and availability of low cost Geographical Information Systems (GIS), which has made analysis of georeferenced data commonplace in many scientific areas. This thesis describes original research in the field of NLG by addressing the problem of automatically generating textual summaries of georeferenced data; that is, any data that has a reference to its location on the Earth’s surface. The postulation that data-to-text technology can generate textural summaries of georeferenced data of comparable quality to human written ones for the same data set provides its focus. This research has carried out in the context of the RoadSafe project, whose primary outcome was development of a date-to-text application for generating road maintenance weather forecasts. This thesis is a thorough investigation of the practical and theoretical issues involved in generating good quality textural summaries of georeferenced data. It begins by surveying the current state of the art in data-to-text and the challenges that georeferenced data poses to such systems. Subsequently empirical observations are outlined that lead to the proposal of a model for georeferenced data-to-text. This model has been implemented and evaluated in a system fielded in the meteorology domain. Techniques for data analysis, content determination and generating spatial descriptions are outlined.

In this project, a range of characteristics describing farmers as decision-makers and details of their land holdings are integrated within a Geographic Information System (GIS) and used to predict overall farm response to defined policy scenarios, for a study area in the upland fringe of Grampian Region. Agriculture has undergone considerable adjustments over the past 40 years, characterised by the processes of intensification and specialisation of farming systems. These changes have been essentially policy-driven, although the precise interaction of policy, technological and macro-economic forces are not entirely understood. Further to this, rural areas are subject to an increasing range of consumption demands which has stimulated a new range of land-use policy schemes, and constraints, on farmers and landowners. From a position of assured government support which was enjoyed by the majority of farmers until the mid-1980s, the policy situation has developed into one of uncertainty and some confusion over the requirements and responsibilities placed on individual farmers. This research links spatial, behavioural and economic elements which combine to condition farm-level response to policy change. The complexity of individuals' decision environments, coupled with the diversity of individuals' values, objectives and resources is immense. The focus is on individual farms, and the implications of change at the individual level in developing a model of farm-level response to policy and market shifts.

archives@tulane.edu / The combination of remote sensing techniques and Geographic Information Systems (GIS) to measure water quality allows researchers to monitor changes in various water quality parameters over temporal and spatial scales that are not always readily apparent from in situ measurements. This study involves using Landsat images and in situ data within GIS to map urban expansion and its resulting influences on water quality in the Pontchartrain Basin over the last three decades. The Pontchartrain Basin is located in southeast Louisiana and covers an area of 25,000 km2 that encompasses sixteen parishes east of the Mississippi River and four Mississippi Counties (Penland et al., 2005). A Landsat Thematic Mapper (TM) image from 1985 and a Landsat Operational Land Imager (OLI) image from 2015 were processed using the Spectral Angle Mapper (SAM) algorithm to map urban expansion. In order to estimate how water quality has changed in the Pontchartrain Basin between 1985 and 2015, in situ water quality data from the Louisiana Department of Environmental Quality was interpolated using Empirical Bayesian Kriging (EBK). The results of this study demonstrated that high levels of fecal coliform were consistent with increased urbanization in water bodies in the Pontchartrain Basin. Phosphorous levels were higher in 2015 compared to 1985 and were at levels high enough to lead to eutrophic conditions. Dissolved oxygen levels were lower near the mouth of the Mississippi river in 2015 than in 1985. The results indicated that urbanization has negative impact on water quality. The GIS model is recommended to effectively manage and reduce the processing time of large water quality datasets. / 1 / Dana Carstens