Synthetic unit hydrographs, which assume uniform rainfall excess distribution and static watershed conditions, are frequently used to estimate hydrograph characteristics when observed data are unavailable. The objective of this research was to develop a spatially distributed unit hydrograph (SDUH) model that directly reflects spatial variation in the watershed in generating runoff hydrographs. The SDUH model is a time-area unit hydrograph technique that uses a geographic information system (GIS) to develop a cumulative travel time map of the watershed based on cell by cell estimates of overland and channel flow velocities. The model considers slope, land use, watershed position, channel characteristics, and rainfall excess intensity in determining flow velocities. The cumulative travel time map is divided into isochrones which are used to generate a time-area curve and the resulting unit hydrograph. Predictions of the SDUH model along with the Snyder, SCS, and Clark synthetic unit hydrographs were compared with forty observed storm events from an 1153-ha Virginia Piedmont watershed. The SDUH model predictions were comparable or slightly better than those from the other models, with the lowest relative error in the peak flow rate prediction for 12 of the 40 storms, and a model efficiency of at least 0.90 for 21 of the storms. Despite the good predictions of the hydrograph peak flow rate and shape, the time to peak was underpredicted for 34 of the 40 storms. Runoff from the 40 storms was also generated for two subwatersheds (C: 462 ha; D: 328 ha) in Owl Run to assess the effect of scale on the SDUH model. Peak flow rate predictions were more accurate for the entire watershed than for either subwatershed. The time to peak prediction and model efficiency statistics were comparable for the entire watershed and subwatershed D. Subwatershed C had poorer predictions, which were attributed to a large pond in the main channel, rather than to scale effects. The SDUH model provides a framework for predicting runoff hydrographs for ungauged watersheds that can reflect the spatially distributed nature of the rainfall-runoff process. Predictions were comparable to the other synthetic unit hydrograph techniques. Because the time to peak and model efficiency statistics were similar for the 1153-ha watershed and a 328-ha subwatershed, scale does not have a major impact on the accuracy of the SDUH model. / Master of Science

Physical characteristics of a site that plant species inhabit may be useful in deriving the range of the species. Current range maps for tree species of the United States were originally developed by Elbert Little. These range maps were based primarily on observations. The purpose of this study was to update Little’s (Little, 1971) range maps of select southern oak species in Mississippi by calculating the topological, soil, and climatic features of sites using a Geographic Information System (GIS) to analyze environmental variables associated with species distributions. Data collected from databases were input into ArcMap and site data extracted using Hawth’s Analyst Tools. Stepwise logistic regression performed with site variables yielded the parameters used in predictive models to generate probability maps for each species across Mississippi. These probability maps demonstrate the potential to efficiently manage forests by giving a more encompassing view of species occurrence.

This project developed a series of spatial models to classify the island karst landforms and predict the island karst feature distribution. Spatial models with unsupervised classified images, and fuzzy-based spatial models were used in this study. Forecasting verification and spatial regressions were used to validate the models. The case study was conducted on San Salvador Island, the Bahamas, a recognized carbonate island with island karst features. Fieldwork data on banana holes on the island were used for model validation. The results showed that most models had accuracy higher than 90%, and were statistically proved that they could be used as predictors of island karst features. Further study may be conducted to solve the Modified Areal Unit Problem (MAUP) in the future.

Although recent brownfields redevelopment research using theories of real estate valuation and neighborhood change have indicated negative effects on surrounding residential housing, little evidence exists to show price impacts and sociodemographic change after remediation. This study examines the extent and size of the economic impact of Superfund sites on surrounding single-family residential properties before and after remediation in Miami-Dade County and examines trends for contemporaneous sociodemographic changes. The study combines the economic impact from changes in environmental quality with contemporaneous sociodemographic changes within the purview of environmental and social justice. This study uses spatial hedonic price modeling on a comprehensive dataset of property-level data, with corresponding sales prices of housing transactions while controlling for other structural, neighborhood, and submarkets characteristics for assessing economic impact. Findings revealed that housing sales prices for single-family residential properties significantly increases as distance to the nearest contaminated Superfund increases. Following remediation, this negative impact declined and housing values increased significantly in neighborhoods with remedied Superfund sites albeit more so in low housing submarkets than premium submarkets. Spatial hedonic models outperformed traditional OLS models in presenting unbiased efficient parameter estimates, correcting for spatial dependence. Although no evidence for gentrification was observed, there existed significant differences between certain sociodemographic characteristics of neighborhoods around contaminated Superfund sites and those of properties located elsewhere leading to concerns of environmental and social justice. Findings suggest that low-income minority populations are more likely to be living in neighborhoods around contaminated Superfund sites and experience a greater negative effect on housing sales prices; these sites are also less likely to be remedied as compared to sites located elsewhere. The findings highlight not only the revealed preferences of homeowners with respect to environmental disamenities, but also help inform policymakers and researchers of the impact of brownfields redevelopment on economic and sociodemographic characteristics of a growing urban region with evolving cultural and social diversity. Incorporating influences of housing submarkets, neighborhood amenities, and spatial dependence help provide a holistic and comprehensive model for examining environmental disamenities and provide a better understanding for neighborhood change.

In Central Africa, wildlife populations are increasingly influenced by humans, even in protected areas. This raises the question how spatial patterns of wildlife abundance are affected by human activities and habitat and how these patterns change over time. I address these questions by developing spatial models combined with line transect survey data in two forest sites in Central Africa. In the Odzala National Park in the Republic of Congo, I examine elephant dung abundance data in relation to human threats and protection. In the Okapi Faunal Reserve in the Democratic Republic of Congo (DRC), I developed spatio-temporal models for elephants and other forest ungulates to examine temporal changes in their densities as a result of changes in human impact in the context of a civil war that took place in the region between the two surveys. Covariates related to human influences dominated the observed patterns at both sites. In Odzala, elephant dung densities were mainly influenced by protection. They were higher inside the protected area and closer to anti-poaching patrol routes. In the Okapi Faunal Reserve, populations of all ungulate species declined severely between the two survey periods. Declines in elephant abundance were relatively higher closer to the park boundary and areas of intense human activity. After the war, elephant densities were higher in a small area in the centre of the park that may have acted as a refuge. Forest duikers also declined, but the spatial pattern of their decline was different than that of elephants. Densities dropped more in the southern part of the reserve, probably due to pre-exisisting higher levels of hunting there. Besides explaining spatial patterns of abundance, spatial modeling was shown to be useful in improving the precision of density estimates and in predicting densities across a surface in the Odzala National Park. In summary, humans overwhelmingly determined the distribution and abundance of ungulates in both sites. The civil war in DRC led to a dramatic increase in elephant poaching for ivory which caused a major decline in elephant populations. It aggravated the bushmeat hunting of duikers whose populations also declined sharply.

In Central Africa, wildlife populations are increasingly influenced by humans, even in protected areas. This raises the question how spatial patterns of wildlife abundance are affected by human activities and habitat and how these patterns change over time. I address these questions by developing spatial models combined with line transect survey data in two forest sites in Central Africa. In the Odzala National Park in the Republic of Congo, I examine elephant dung abundance data in relation to human threats and protection. In the Okapi Faunal Reserve in the Democratic Republic of Congo (DRC), I developed spatio-temporal models for elephants and other forest ungulates to examine temporal changes in their densities as a result of changes in human impact in the context of a civil war that took place in the region between the two surveys. Covariates related to human influences dominated the observed patterns at both sites. In Odzala, elephant dung densities were mainly influenced by protection. They were higher inside the protected area and closer to anti-poaching patrol routes. In the Okapi Faunal Reserve, populations of all ungulate species declined severely between the two survey periods. Declines in elephant abundance were relatively higher closer to the park boundary and areas of intense human activity. After the war, elephant densities were higher in a small area in the centre of the park that may have acted as a refuge. Forest duikers also declined, but the spatial pattern of their decline was different than that of elephants. Densities dropped more in the southern part of the reserve, probably due to pre-exisisting higher levels of hunting there. Besides explaining spatial patterns of abundance, spatial modeling was shown to be useful in improving the precision of density estimates and in predicting densities across a surface in the Odzala National Park. In summary, humans overwhelmingly determined the distribution and abundance of ungulates in both sites. The civil war in DRC led to a dramatic increase in elephant poaching for ivory which caused a major decline in elephant populations. It aggravated the bushmeat hunting of duikers whose populations also declined sharply.

The degree of connectivity and proximity that results from the configuration of land uses and associated transport networks is an important concept in much of the transportation research agenda. A substantial body of work has developed around the idea that compact, mixed-use development with multimodal transport options will shape travel behavior, increasing the use of transit, walking, and cycling for routine travel. Yet empirical evidence is somewhat mixed. One of the reasons for this uncertainty is the difficulty of defining and measuring sprawl in a meaningful way for use in quantitative analyses, rather than using regionally idiosyncratic or mono-dimensional definitions of sprawl. A recently released national dataset measuring multiple dimensions of urban form offers an opportunity to explore the relationship between transportation and sprawl. This study uses a series of spatial regressions to model effects on the share of a county's workers who commute by driving alone. The results for income are found to be robust across various model specifications, confirming the well-established, positive relationship between income and d riving to work. The results for the Street Accessibility Factor suggest characteristics of the street network are related to the choice to commute by driving alone, with more compact street networks and greater connectivity associated with reduced driving alone. The Land Use Mixing Factor has little power in explaining travel behavior, despite its intuitive appeal as the land use component of the commute mode decision. / Series: SRE - Discussion Papers

In Central Africa, wildlife populations are increasingly influenced by humans, even in protected areas. This raises the question how spatial patterns of wildlife abundance are affected by human activities and habitat and how these patterns change over time. I address these questions by developing spatial models combined with line transect survey data in two forest sites in Central Africa. In the Odzala National Park in the Republic of Congo, I examine elephant dung abundance data in relation to human threats and protection. In the Okapi Faunal Reserve in the Democratic Republic of Congo (DRC), I developed spatio-temporal models for elephants and other forest ungulates to examine temporal changes in their densities as a result of changes in human impact in the context of a civil war that took place in the region between the two surveys. Covariates related to human influences dominated the observed patterns at both sites. In Odzala, elephant dung densities were mainly influenced by protection. They were higher inside the protected area and closer to anti-poaching patrol routes. In the Okapi Faunal Reserve, populations of all ungulate species declined severely between the two survey periods. Declines in elephant abundance were relatively higher closer to the park boundary and areas of intense human activity. After the war, elephant densities were higher in a small area in the centre of the park that may have acted as a refuge. Forest duikers also declined, but the spatial pattern of their decline was different than that of elephants. Densities dropped more in the southern part of the reserve, probably due to pre-exisisting higher levels of hunting there. Besides explaining spatial patterns of abundance, spatial modeling was shown to be useful in improving the precision of density estimates and in predicting densities across a surface in the Odzala National Park. In summary, humans overwhelmingly determined the distribution and abundance of ungulates in both sites. The civil war in DRC led to a dramatic increase in elephant poaching for ivory which caused a major decline in elephant populations. It aggravated the bushmeat hunting of duikers whose populations also declined sharply. / Science, Faculty of / Zoology, Department of / Graduate

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第21823号 / 農博第2336号 / 新制||農||1067(附属図書館) / 学位論文||H31||N5195(農学部図書室) / 京都大学大学院農学研究科生物資源経済学専攻 / (主査)教授 栗山 浩一, 教授 伊藤 順一, 教授 梅津 千恵子 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DGAM

Recent studies indicate a relationship between measures of urban form as applied to urban and suburban areas, and obesity, a risk factor for heart disease. Measures of urban form for exurban and rural areas are considerably scarce; such measures could prove useful in measuring relationships between urban form and both mortality and morbidity in such areas. In modeling area-level mortality, geographic relationships between counties warrant consideration because geographically adjacent areas tend to have more in common than areas farther from each other. We modify county-level indices of urban form found in the literature so that they can be applied to exurban and rural counties. We then use these indices in a Bayesian spatial model that accounts for spatial autocorrelation to determine if there is a relationship between such measures and cardiovascular disease mortality for white males age 35 and older for the time period 1999-2001. Issues related to the formation and usefulness of the indices, and issues related to the spatial model, are discussed. Maps of observed and expected relative risk of mortality are presented.