Todays urban environment is produced, both physically and perceptually, by a myriad of factors. Through a comparative study of two Chicago neighborhoods along the Chicago River, the Loop and Bridgeport, I construct the network of political, social, and economic factors that create the urban environment of the Chicago River in and along these places. I explore residents perception of who controls the production of the urban environment as it relates to the Chicago River, and how the socio-environment is created. In this study, I utilize an urban political ecology approach that recognizes that environments produced simultaneously by social and physical processes are historically situated and that there is nothing inherently unnatural about urban, produced environments, but that these environments are a result of historically and spatially contextualized socio-environmental processes. Using this approach in conjunction with interviews, surveys, and participant observation, I analyze these factors to determine who the residents of these neighborhoods perceive as exerting control over the production of the environment of the Chicago River and how these various entities, along with the numerous processes that influence the environment of the river, are mobilized to serve particular purposes. This research allows me to view the interrelated entities that contribute to the production of the environment of the Chicago River as well as to understand that social factors and pressures that have both historically and recently played a larger role in this production than are often ascribed to them by residents.

Population growth and hydrological drought threaten the water supply of Atlanta, Georgia. In Georgia, water policy changed rapidly due to stress on the resource, regulation increased, and the state promoted a culture of conservation to combat water scarcity. This study examines how regulatory measures, including legislative acts and Governors executive orders, have impacted the populations attitudes and behaviors towards water consumption. Furthermore, public spaces are used to measure public perception of water policy, and serve as a physical representation of the culture shift.

In this dissertation, I investigate the determinants on Americans perceptions of global warming and individuals environmentally significant behaviors to reduce global warming. Specially, I examine how contextual variables, primarily represented by local weather and climate, attitudinal variables, and socio-demographic characteristics affect public opinion towards global warming, personal voluntary actions and willingness to address global warming. The research of this dissertation reveals some important findings. First, local weather and climaterepresented by long-term temperature trendsis found to have significant effects on public perceptions of global warming and private-sector environmentally significant behaviors. In particular, the summer temperature trend over the past 10 years has consistently shown to have positive effects on public acceptance of anthropogenic global warming and concern for global warming. In other words, individuals are most sensitive to summer temperature and more likely to translate increasingly hot summers into perceptions of anthropogenic global warming and their concern for this issue. Second, consistent with the results of previous studies, global warming has become a politically polarized issue. Specifically, Democrats and political liberals are more likely than Republicans and political conservatives to accept the notion of anthropogenic global warming, show higher level of concern for global warming, and participate in private-sector environmentally significant behaviors to reduce global warming. Third, attitudinal variables play an important role in affecting public perceptions of global warming and individuals environmentally significant behaviors. For instance, personal attitudes toward scientists are found to be a strong group of predictors on public opinion toward global warming. In addition, attitudinal variablesincluding individuals environmental views and perceptions of global warmingoutperform socio-demographic characteristics and contextual forces in explaining the variance of personal actions and public willingness to pay more to reduce global warming. Finally, objective macro-economic conditions, represented by county-level unemployment rate in this dissertation are not found to have any consistently significant effect on either public perceptions of global warming or individuals environmentally significant behaviors.

This dissertation revolves around three issues on the urban area of Port-au-Prince, Haiti: the population distribution pattern, its estimation from remote sensing images, and its relationship with environmental quality. It follows a three-paper format. Paper 1 examines the population density pattern by the monocentric and polycentric models, based on the 2003 census data. The regression results show a poor fitting power of monocentric functions, and improved but less than satisfactory R2 by polycentric functions. A five-sector conceptual model is proposed to capture the urban structure shaped by the absence or lack of institutional enforcement of land use regulations and urban planning. Paper 2 proposes a population estimation model based on Landsat ETM+ images that are widely available. The subpixel vegetation-impervious surface-soil (VIS) fractions derived from the Landsat multispectral bands (the mean value of houses fraction image, the mean value of vegetation and the standard deviation of vegetation fraction image) are used as predictors for urban population density. The research indicates that the geographically weighted regression (GWR) model, which accounts for spatial non-stationarity, performs much better than its Ordinary Least Square counterpart. Paper 3 uses multiple factors to assess and map the urban environmental quality (UEQ). In addition to parameters typically considered in previous studies, this study includes natural hazards and other parameters unique to Port-au-Prince. Crowdedness, waste, lack of vegetation, presence of slums and water body pollutions are considered as the most critical factors (negatively) affecting the quality of the environment in Port-au-Prince. All are exacerbated by population pressure on the resources, i.e., population density. The scores for corresponding factors are integrated together by weights extracted from a panel of local experts. The overall UEQ results are validated by field surveys. Each paper discusses important implications of major findings for public policy and planning

Tropical cyclone generate storm surge is responsible for damage to lives and livelihoods on a global scale. In spite of this inherent danger, the scientific community currently lacks a global database of peak storm surge events. Thus, SURGEDAT was created to fill this void. Research began on the Gulf of Mexico, and the research presented here creates a database of peak surges for the Atlantic Coast of the United States (ACUS) between the years of 1898-2011. A total of 25 sources were utilized for creation of this database, with many more being consulted but not included in the final product. A database of 72 surge events ≥ 1.22 m was created, with the largest event being a 6.49 m surge in Fairhaven, MA in 1938. Spatial analysis reveals high levels of surge activity in south Florida and North Carolina, with diminished activity from Virginia to Maine. Statistical analysis tested surge frequency and magnitude versus two teleconnections: the Atlantic Multidecadal Oscillation (AMO), and the Southern Oscillation Index (SOI). Statistically significant links were found between AMO phase and surge frequencies; however, no links were found between AMO phase and surge height. No statistically significant links between SOI and magnitude or frequency were detected. Return periods associated with surge events were calculated through three quantile estimation methods the Pareto distribution and the Huff-Angel and Southern Regional Climate Center (SRCC) linear regression methods. The SRCC method produced the best quantile estimates of surge heights along the ACUS, with a 100-yr event of 6.49 m and a 2-yr event of 1.55 m. A K-means cluster analysis was performed to split the ACUS region into 10 zones, and the SRCC method was employed to produce quantile estimates on a regional level. Zone 7, centered on Charleston, SC, produced the highest 100-yr return period (5.64 m), followed by Zone 1, centered on New Bedford, MA (5.61 m).

This dissertation provides deeper understanding on the application of Vegetation-Impervious Surface-Soil (V-I-S) model in the urban land use characterization and population modeling, focusing on New Orleans area. Previous research on the V-I-S model used in urban land use classification emphasized on the accuracy improvement while ignoring the discussion of the stability of classifiers. I developed an evaluation framework by using randomization techniques and decision tree method to assess and compare the performance of classifiers and input features. The proposed evaluation framework is applied to demonstrate the superiority of V-I-S fractions and LST for urban land use classification. It could also be applied to the assessment of input features and classifiers for other remote sensing image classification context. An innovative urban land use classification based on the V-I-S model is implemented and tested in this dissertation. Due to the shape of the V-I-S bivariate histogram that resembles topological surfaces, a pattern that honors the Lu-Wengs urban model, the V-I-S feature space is rasterized into grey-scale image and subsequently partitioned by marker-controlled watershed segmentation, leading to an urban land use classification. This new approach is proven to be insensitive to the selection of initial markers as long as they are positioned around the underlying watershed centers. This dissertation links the population distribution of New Orleans with its physiogeographic conditions indicated by the V-I-S sub-pixel composition and the land use information. It shows that the V-I-S fractions cannot be directly used to model the population distribution. Both the OLS and GWR models produced poor model fit. In contrast, the land use information extracted from the V-I-S information and LST significantly improved regression models. A three-class land use model is fitted adequately. The GWR model reveals the spatial nonstationarity as the relationship between the population distribution and the land use is relatively poor in the city center and becomes stronger towards the city fringe, depicting a classic urban concentric pattern. It highlighted that New Orleans is a complex metropolitan area, and its population distribution cannot be fully modeled with the physiogeographic measurements.

Yunnan, with the largest number of minority languages in China, is labeled as The Museum of Dialects. The rich diversity of ethnic groups is reflected in its toponyms (place names). The objectives of this study are to (1) construct a GIS database of toponyms in Yunnan at the prefecture, county and township levels from a comprehensive toponymical dictionary series of China; (2) analyze the spatial distribution of Han, Tibeto-Burman (Zang-Mian) and Kam-Tai (Zhuang-Dong) toponyms and its association with environmental factors; and (3) examine the historical evolution of toponyms. Results show that the study shows that the highest concentrations of Zang-Mian toponyms are in north and the eastern mountainous areas, and Zhuang-Dong concentrate in the southwest and southeast areas with lower elevations. Statistical analysis reveals that Zhuang-Dong toponyms tend to have lower elevations than Han toponyms, and Zang-Mian toponyms tend to in places higher than Han toponyms. Both minority groups of toponyms are slightly farther from rivers but closer to railways or the major cities than Han toponyms. The standard distance and standard deviation methods help reveal the historical trend of gradual expansion of Han settlement in Yunnan as recorded in the time stamps of toponyms. In Ming Dynasty,Han people spreaded out into south and north, especially the north area. In the Qing Dynasty, Han people expanded to the frontier areas. This suggests that the most significant spread of Han settlement in Yunnan happened in the Qing dynasty.

Impervious surfaces are manmade surfaces which are highly resistant to infiltration of water. Previous attempts to classify impervious surfaces from high spatial resolution imagery with pixel-based techniques have proven to be unsuitable for automated classification because of its high spectral variability and complex land covers in urban areas. Accurate and rapid classification of impervious surfaces would help in emergency management after extreme events like flooding, earthquakes, fires, tsunami, and hurricanes, by providing quick estimates and updated maps for emergency response. The objectives of this study were to: (1) compare classification accuracy between pixel-based and OBIA methods, (2) examine whether the object-based image analysis (OBIA) could better detect urban impervious surfaces, and (3) develop an automated, generalized OBIA classification method for impervious surfaces. <br><br> This study analyzed urban impervious surfaces using a 1-meter spatial resolution, four band Digital Orthophoto Quarter Quad (DOQQ) aerial imagery of downtown New Orleans, Louisiana taken as part of post Hurricane Katrina and Rita dataset. The study compared the traditional pixel-based classification with four variations of the rule-based OBIA approach for classification accuracy. A four-class classification scheme was used for the analysis, including impervious surfaces, vegetation, shadow, and water. The results show that OBIA accuracy ranges from 85.33% through 91.41% compared with 80.67% classification accuracy from using the pixel-based approach. OBIA rule-based method 4 utilizing a multi-resolution segmentation approach and derived spectral indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and the Spectral Shape Index (SSI) was the best method, yielding a 91.41% classification accuracy. OBIA rule-based method 4 can be automated and generalized for multiple study areas. A test of the segmentation parameters show that parameter values of scale ≤ 20, color/shape ranging from 0.1 - 0.3, and compactness/smoothness ranging from 0.4 - 0.6 yielded the highest classification accuracies. These results show that the developed OBIA method was accurate, generalizable, and capable of automation for the classification of urban impervious surfaces.

Based on the 2000 and 2010 census tract data from the U.S. Census Bureau, this thesis examines the change of population distribution patterns in New Orleans in the pre- and post-Katrina eras by the monocentric and polycentric density functions. The study area is the mostly urbanized part of the New Orleans Metropolitan Statistical Area (MSA), including Orleans, Jefferson and St. Bernard parishes. The post-Katrina New Orleans has experienced an uneven recovery reflected in the geographic disparities in population change. The density function approach investigates what function best captures the population density distribution, how the density patterns have changed over time, how many significant centers can be identified in the study area, and how influential each center has been on the population distribution throughout the area. The regression results of the monocentric model show that New Orleans has experienced suburbanization captured by the negative exponential density function with a lower CBD intercept and a smoother gradient in 2010 than 2000. Two subcenters are identified besides the CBD in New Orleans based on the GIS surface modeling of employment density pattern in combination of field observation. The regression results from the polycentric model indicate that the CBD has significant influence over the citywide population density pattern in both 2000 and 2010, and only one subcenter is significant in 2000 but none in 2010. This indicates that the urban structure in New Orleans has regressed from more of polycentricity in 2010 to monocentricity in 2000.

The Toncrey site (16PL07), located in the coastal marsh of Plaquemines Parish, Louisiana, is a prehistoric mound site containing three earthen mounds, on the north, west, and south edges of a plaza. The layout of the mounds in cardinal directions around the plaza suggests that this component of the site dates to the Coles Creek period. On the eastern side of the site an extensive, wave-washed and re-deposited oyster (Crassostrea virginica) shell midden contains diagnostic Coles Creek, Plaquemine, and Mississippian pottery. Archaeological research at the site was conducted to confirm the cultural affiliation of the mound group and midden, and to gather information on the relationship between what appears to be a Coles Creek occupation and possible Mississippian culture influence or intrusion in the area. Approximately 2,876 pottery sherds were collected and analyzed. The results of this investigation produced more data that will allow archaeologists to more accurately compare prehistoric coastal Louisiana and inland regions, and to clarify the role of Mississippian culture along the coast.