Dissertations / Theses on the topic 'Spatial analysis (Statistics) Geography'

Basketball is a unique sport in which the use of space and time is greatly important for a team’s success. Furthermore, the National Basketball Association (NBA) is undergoing drastic change in terms of the way teams approach spatial issues as well as the spatio-temporal technologies and analytics. Given these facts, Geographic Information Systems (GIS) provide the opportunity to develop new analytic and visual methodologies to perform spatial analysis for team performances and meet the league’s changing needs. This project thus develops new approaches, methods, and toolsets using GIS to demonstrate its efficacy and potential for professional application in the NBA. The first application uses GIS to adapt Relative Motion analysis techniques to an existing play, seeking to represent the average motion characteristics entailed therein. The other application uses a tool developed to map, glean spatial statistics, and model the use and importance of floor spacing for teams in the NBA.

Traditional spatial analysis and data mining methods fall short of extracting temporal information from data. This inability makes their use difficult to study changes and the associated mechanisms of many geographic phenomena of interest, for example, land-use. On the other hand, the growing availability of land-change data over multiple time intervals and longer time frames, often based on satellite imagery, presents to land-change study a great opportunity, given that this information can be effectively utilized. This methodological gap highlights the need to better understand the analytical challenges brought by temporal complexities, and to investigate alternative analytical frameworks that could handle those challenges.

This dissertation attempted to achieve three goals: 1) finding metrics to capture temporal trends, 2) dealing with temporally imprecise data due to constraints of frequency, duration, and starting time of data collection, and 3) handling variables with time-changing values. A simulated land-change dataset based on an agent-based model of residential development and an empirical dataset from two case study sites in San Diego and Tijuana were used for this investigation.

Results from the simulation dataset indicated that the survival function and the hazard function are important metrics to reveal temporal trends. In general the results of land-change analysis are sensitive to time frequency, in particular when time-dependent variables are also present. Longer duration benefits land-change analysis since longer durations contains more information. However, time-dependent variables with measures over a long period are more difficult for detection, which may pose a challenge. Starting time also affects the analytical results because the level of process uncertainty varies at different starting times. Findings from real world data mostly agree with those from computational data. Time dependent variables present a major challenge in land-change analysis, and survival analysis can better handle time-independent variables and thus better forecast urban growth.

Archaeologists have been interested in relationship between environmental variability and cultural change for the last six decades. By understanding how, when, and why humans adapt to environmental change, archaeologists and anthropologists can better understand the development and complexity of human cultures. In northwest Alaska, archaeologists hypothesize that environmental variability was a major factor in both growing coastal population density, with large aggregated villages and large houses, between 1000 and 500 years ago (ya), and subsequent decreasing population density between 500 ya and the contact era. After 500 ya people are thought to have dispersed to smaller settlements with smaller house sizes in coastal areas, and perhaps, upriver. This settlement pattern was identified through research at four site locations over 30 years ago. The changing geographic distribution of sites, associated settlement size, and house size has not been examined in detail. A more careful examination of changing northwest Alaskan settlement patterns is needed before larger questions about socio-economic organization can be addressed. I use Geographic Information Systems (GIS) to evaluate the evidence for a geographic redistribution of Arctic peoples during the Late Holocene.

I constructed a database of settlement location and site attribute information, specifically the number of houses within each settlement and the size (m ²). Data were collected from a dataset of Western Arctic National Parklands (WEAR), the Alaska Heritage Resource Survey (AHRS) database of archaeological sites in Alaska, 409 unpublished site reports and field notes curated by the National Park Service (NPS) and Bureau of Indian Affairs (BIA), and the results of recent fieldwork in northwest Alaska. A total of 486 settlements were identified within the northwest Alaska with 128 settlements having temporal and site attribute data.

I incorporated settlement size data into a GIS database and then carried out global, Moran’s I, local Moran’s I, and local Getis-Ord spatial analyses to test whether settlement redistribution occurred and if key settlement locations shifted after 500 ya. The site attribute data (number of houses and average size of houses) are used to test the additional aspects of the proposed settlement pattern change after 500 ya. A total of 83 settlements with 465 houses are used to test if the average size of settlements and average house size changed after 500 ya.

The results of the spatial analyses indicate no statistically significant patterns in the spatial distribution of settlements. Site attribute analysis shows no statistical difference in the average number of houses per village or the average size of houses before or after 500 ya. The results of this work build our understanding of regional settlement patterns during the late Holocene. By testing settlement pattern change, i.e. settlement distribution, settlement size, and house size, future research into settlement pattern change can begin to evaluate likely causes for the observed changes. My method, specifically the use of GIS as a method for testing settlement pattern change, can be applied to other regions and temporal scales.

Groundwater in arid and semi-arid areas like the Karoo region of South Africa is an important source of domestic, agricultural and industrial source of fresh water. As a scarce resource, it requires extensive quality control and protection through innovative methods and efficient strategies. The town of Beaufort West and its vicinity use groundwater as a major source of municipal and private water supply. Forty nine groundwater samples were collected from spatially referenced boreholes located in and around the town of Beaufort West and were analyzed for EC, pH, TDS,TH, SAR, TA, Ca2+, Mg2+, Na+, K+, HCO3-, Cl-, NO3- and SO42- according to 
SANS 241 standards and tested for ionic balance. The groundwater of the study area was characterized using WHO and South African drinking water quality standards as well as TDS and Salinity hazard classifications. These comparisons and classifications characterized the groundwater of the study area as hard to very hard, with low to medium salinity hazard. These results are in accordance with the dominance of the ions Ca2+, Na+, HCO3 - and Cl- in the groundwater samples. Linear relationships between the hydrochemical variables were analysed through correlation and multiple regression analysis to relate the groundwater quality to the underlying hydrogeochemical processes. These linear relationships explained the contribution of the measured variables towards the salinity, hardness and anthropogenic contamination of the groundwater. The groundwater of the study area was also assessed using conventional trilinear diagrams and scatter plots to interpret the water quality and determine the major ion chemistry. The conventional methods highlighted the sources of the hydrochemical variables through analysis and interpretation of rock-water interaction and evaporations processes. To supplement these conventional methods and reveal hidden hydrogeochemical phenomenon, multivariate statistical analyses were employed. Factor analysis reduced the hydrochemical variables into three factors (Hardness, Alkalinity and Landuse) that characterize the groundwater quality in relation to the source of its hydrochemistry. Furthermore, combination of Cluster (CA) and Discriminant analyses (DA) were used to classify the groundwater in to different hydrochemical facies and determine the dominant hydrochemical variables that characterize these facies. The classification results were also compared with the trilinear diagrammatic interpretations to highlight the advantages of these multivariate statistical methods. The CA and DA classifications resulted in to six different hydrochemical facies that are characterized by NO3 -, Na+ and pH. These three hydrochemical variables explain 93.9% of the differences between the water types and highlight the influence of natural hydrogeochemical and anthropogenic processes on the groundwater quality. All the univariate, bivariate, multivariate statistical and conventional hydrogeochemical analyses results were analyzed spatially using ArcGIS 10.0. The spatial analysis employed the Inverse Distance Weighted (IDW) interpolation method to predict spatial distribution of unmeasured areas and reclassification of the interpolation results for classification purposes. The results of the different analyses methods employed in the thesis illustrate that the groundwater in the study area is generally hard but permissible in the absence of better alternative water source and useful for irrigation.

KIM, HYON-JUNG. Variance Estimation in Spatial Regression Using a NonparametricSemivariogram Based on Residuals. (Under the direction of Professor Dennis D. Boos.)The empirical semivariogram of residuals from a regression model withstationary errors may be used to estimate the covariance structure of the underlyingprocess.For prediction (Kriging) the bias of the semivariogram estimate induced byusing residuals instead of errors has only a minor effect because thebias is small for small lags. However, for estimating the variance of estimatedregression coefficients and of predictions,the bias due to using residuals can be quite substantial. Thus wepropose a method for reducing the bias in empirical semivariogram estimatesbased on residuals. The adjusted empirical semivariogram is then isotonizedand made positive definite and used to estimate the variance of estimatedregression coefficients in a general estimating equations setup.Simulation results for least squares and robust regression show that theproposed method works well in linear models withstationary correlated errors. Spectral Analysis with Spatial Periodogram and Data Tapers.(Under the direction of Professor Montserrat Fuentes.)The spatial periodogram is a nonparametric estimate of the spectral density, which is the Fourier Transform of the covariance function. The periodogram is a useful tool to explain the dependence structure of aspatial process.Tapering (data filtering) is an effective technique to remove the edge effects even inhigh dimensional problemsand can be applied to the spatial data in order to reduce the bias of the periodogram.However, the variance of the periodogram increases as the bias is reduced.We present a method to choose an appropriate smoothing parameter for datatapers and obtain better estimates of the spectral densityby improving the properties of the periodogram.The smoothing parameter is selected taking intoaccount the trade-off between bias and variance of the taperedperiodogram. We introduce a new asymptotic approach for spatial datacalled `shrinking asymptotics', which combines theincreasing-domain and the fixed-domain asymptotics.With this approach, the tapered spatial periodogram can be usedto determine uniquely the spectral density of the stationary process,avoiding the aliasing problem.