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Water resources in hard rock terrain are difficult to characterize due to heterogeneity and anisotropy in the fracture network, low porosities and limited recharge volumes available during the summer season. Three methods were developed and evaluated in order to assist in water supply planning. A groundwater resources potential index was estimated using multivariate statistics, where physical and geological variables were classified using Analysis of Variance and Fisher's Least Significant Difference tests according to their effect on hydraulic properties. Principal component analysis was used to assign weights to the different classed variables. Classes and weights were used to produce an index referred to as groundwater resources potential (GRP), which correlated significantly with well data. Nearly 80% of the wells with less than median specific capacity values also had GRP values at those locations of zero or lower. Non-stationary variance was observed in specific capacity sub-samples taken from the Geological Survey of Sweden's well archive, despite homogeneous geology and topography. Spatial statistical analyses showed that spatial correlations were weak in well archive samples, implying that regional approximations based on sparse point data are highly error prone. Kinematic porosity estimated using superficial fracture measurements correlated significantly with well archive data. However, low correlation coefficients indicated that well data is likely not a suitable method for predicting water supply characteristics. This approach is an efficient method which shows promise in preliminary estimations of groundwater storage in heterogenic terrains. A groundwater balance model which describes seasonal groundwater storage changes was created in order to better approximate the groundwater situation often found in Swedish urbanized and semi-urbanized hard rock terrains. The model was based on a water budget approach at the pixel scale, and allows for approximation of well extraction which is not uniformly distributed in space. The model showed that in specific regions groundwater extraction may lead to severe decreases in groundwater level, where these impacts may not otherwise be expected. Dry season modelling with 10% increased evapotranspiration showed that in several areas groundwater reservoir depletion may be influenced by more than 50%. / <p>QC 20140331</p>

In an era when the importance of Geographic Information Systems (GIS) is ever increasing, users still find them difficult to learn. The issue mainly derives from the fact that GIS interfaces’ learnability hurdles are not lowered for beginner users. Onboarding interaction is a way to tackle this issue, providing proper guidance to novice users the first time they use the software and consequently making it easier for them to learn the system. Throughout this paper, the study proposes a set of guidelines for designing onboarding interaction for assisting novice users utilizing GIS applications. Employing the method of Research through Design, a design prototype is developed through user studies, which in turn was used as a leverage to draw the guidelines as the final outcome of this research. The prototype addresses three different learnability problems of a GIS application identified through user studies and aims to provide design solutions for what users find difficult, important, and lacking in the GIS system. More specifically, the guidelines deal with helping users orient themselves in the application, learn easy functions by practicing them, and understand more complex features through watching instructive video materials.

Indiana University-Purdue University Indianapolis (IUPUI) / The fundamental principles colleges and universities use to recruit students have remained largely unchanged for decades. Traditionally, admissions professionals visit high schools and attend college fairs, while colleges and universities hold preview days and publish viewbooks all in the interest of attracting a high-quality and diverse student population. The recruiting process has been greatly improved through the application of modern technology. The analytic abilities of technologies such as geographic information systems (GISystems), which allow for the visualization and analysis of spatial data, presents previously underutilized strategies for higher education recruiting methods. In addition, the incorporation of a Hierarchical Bayesian model will effectively model influential enrollment factors, which successful students possess. Hierarchical Bayesian models use the prior distribution, and likelihood of an events occurrence to create the posterior distribution or Bayesian inference. The intelligence created by combining traditional recruiting techniques with GISystems and Hierarchical Bayesian modeling will allow admissions professionals to improve the success rate of enrollment efforts and expenditures. This paper will explore the application of Hierarchical Bayesian models and GISystems within higher education recruiting.

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The purpose of this thesis is to describe the relationship between surface water quality and land use. Water management recommendations will be divulged based upon the interaction of lake water quality and land use. The methodology developed for this research applied Data Envelopment Analysis (DEA), a performance measurement tool, to evaluate lake water quality in relation to surrounding land use. Lake performance ratings were generated by DEA software that assessed multiple variables describing surface water nutrient loads and surrounding land use. Results from this analysis revealed a significant trend between lake water quality and land use within the study area. Lakes located within a two mile radius of more naturally preserved land area typically attained higher performance ratings than lakes located within a two mile radius of less naturally preserved land area. The spatial quantity of naturally preserved land influenced lake nutrient concentrations. Also, lake performance ratings generally declined in two mile radius delineations that contained less naturally preserved land area indicating a direct relationship between natural land area and lake performance.

Geographic Information Systems (GIS) are increasingly used to examine fire activity. This study uses GIS to determine fire likelihood probabilities at an intermediate scale ( I-kilometer) on a daily basis given readily available data. Layers used for the analysis included slope, aspect, elevation, fuel type, proximity to existing fires, maximum temperature, minimum temperature, relative humidity, average vapor pressure deficit, precipitation, 1-hour fuel moisture, and 10-hour fuel moisture. There were three objectives of this study: 1. Establish a correlation between burn perimeters and readily available topographic and environmental data, and map the spatial distribution of these as fire likelihood areas; 2. Compare each day's fire likelihood areas to fire perimeters from the next day to determine to what extent areas deemed to be high fire likelihood on a given day could be used to identify likelihood areas for the subsequent day; 3. Create a generalized model using the fewest and most frequent significant variables and test this model as a general predictive tool for fire likelihood over a given season. Redundant variables and variables determined not to be significant at this scale were removed from the model. Variables that best explained the fire activity were identified and used to spatially map fire likelihood for any given day. By comparing subsequent days fire activity to the previous days fire likelihood areas, it was determined that the previous fire likelihood areas can be used as an indication for the subsequent day' s fire likelihood areas with a reasonable level of accuracy. Although factors changed from day to day, the most significant variables tended to be slope, elevation, fuel type, and 1-hour fuel moisture. These variables were incorporated into a generalized model which, when mapped spatially, provided a method to compare increasing or decreasing levels of fire likelihood on a temporal scale. The results were coarser, but still indicated that a generalized model could be used to identify the next day' s fire likelihood areas given the previous days spatial plots. When compared to new fire starts, fires occurred in areas of moderate fire likelihood probabilities and very few occurred in areas of low probability.

Many local planning departments have acquired and put into use advanced automated geocoding and Geographic Information Systems (GIS) to store, process, map and analyze geographic data. GIS technological advancements in hardware, software, and geographic databases - specifically, in geocoding methods to reference street address data to geographic locations - enable data to be integrated, mapped, and analyzed more efficiently and effectively. Also, technological advancements depend on organizational and institutional environments. The relationships between technological advancements and technical (data mapping and analysis), organizational, and institutional environments are not clear. The purpose of this study is to explain these relationships to help planning and development directors make better decisions in acquiring and using advanced geocoding and GIS technology. The findings are based on a mail survey of planning and development departments in cities with populations of 50,000 or more in the United States. The study found that planning departments with advanced geocoding and GIS technology are capable of conducting advanced geocoding applications. Data can be tabulated, aggregated, linked, and modeled for mapping and planning. Geocoding to aggregate data to small geographic areas helps by providing required and up-to-date information to solve urban problems. However, the study did not find that advanced geocoding systems enhance data quality as measured by spatial resolution and volume. Further studies are needed to explore this issue. The adoption and implementation of advanced geocoding and GIS technology are influenced by organizational and institutional environments. Large cities have more experience with hardware, software programs, computer professionals, and training programs, but they are dependent on centralized systems from an earlier computer era. Consequently, more recent entrants to using computers for geographic data processing are emerging rapidly. As technology is becoming more advanced, hardware and software costs are declining. Some of the organizational and institutional issues are eliminated while new ones are emerging. As a result, small area cities are adopting advanced geocoding and GIS technology more rapidly than they were previously, and sometimes they surpass large cities. This study improves understanding of automated street address geocoding methods and how these methods are related to advancements in GIS technology. The study also examines how technical, organizational, and institutional environments are interrelated in adopting and using geocoding and GIS technology. The challenge in the 1990s will not be how to fund and acquire a GIS, but how to integrate all of the pieces in order to make the technology work properly.

This thesis investigates the application of GIST features [13] to the problem of object detection in images. Object detection refers to locating instances of a given object category in an image. It is contrasted with object recognition, which simply decides whether an image contains an object, regardless of the object's location in the image. In much of computer vision literature, object detection uses a "sliding window" approach to finding objects in an image. This requires moving various sizes of windows across an image and running a trained classifier on the visual features of each window. This brute force method can be time consuming. I investigate whether global, easily computed GIST features can be used to classify the size and location of objects in the image to help reduce the number of windows searched before the object is found. Using K–means clustering and Support Vector Machines to classify GIST feature vectors, I find that object size and vertical location can be classified with 73–80% accuracy. These classifications can be used to constrain the search location and window sizes explored by object detection methods.

A technique is presented for estimating hourly and seasonal energy consumption profiles in the building sector at spatial scales down to the individual taxlot or parcel. The method combines annual building energy simulations for cityspecific prototypical buildings and commonly available geospatial data in a Geographical Information System (GIS) framework. Hourly results can be extracted for any day and exported as a raster output at spatial scales as fine as an individual parcel (