Geography Theses and Dissertations

Permanent URI for this collection


Recent Submissions

Now showing 1 - 5 of 182
  • Item
    Land Tenure, Property Ownership, and Home Mortgages in the Late Nineteenth Century: A Case Study of Baltimore's Germans
    (1976) Vill, Martha J.; Groves, Paul A.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, MD)
    During the late nineteenth century the rapidly expanding urban population of the United States created an increased demand for housing. At the same time, mortgage money for the finance of home purchases was in short supply because of the availability of more lucrative investment opportunities elsewhere and because there were legal restrictions on the power of banks to lend money on real estate . Recent literature has emphasized the importance of property ownership among different components of the population, including immigrant groups. Little attention has been paid to the process of property acquisition or to the patterns of land tenure which resulted. An immigrant population, handicapped in numerous ways, was likely to have limited access to available mortgage financing, thereby limiting its ability to purchase property. Yet, the literature suggests that immigrants actively acquired property. This study examines some preliminary ideas about tenure patterns and home mortgages within immigrant residential areas, using a sample of Baltimore's Germans as a case study. The argument presented is that housing acquisition was facilitated by the activities of the immigrants themselves. In view of the restrictions on the supply of mortgage money, financing for property purchases had to come from sources independent of the city's major financial institutions, and the immigrants had to generate their own sources of capital. It was expected that tenants and landlords would have common national origins, another reflection of the immigrants' reliance on members of their own group for housing. Another expectation of the study was that Germans of different origins in Germany would exhibit different tenure patterns. Arguing that the term "German" was an imprecise indicator of national origins, and that the residential patterns of immigrants from different parts of Germany were distinct, it was expected that this diversity would also find expression in tenure patterns. The selection of the sample areas in the study was, therefore, conditioned by the need to isolate areas inhabited by Germans of diverse origins. Land tenure, property ownership, and relationships between landlords and tenants were analyzed. The hoped for differences in rates of property ownership did not materialize, and home ownership was not systematically related to age, income, or family employment. The findings do indicate, however, that home ownership was within the grasp of people with relatively low income. The mechanism which enabled home purchasers to obtain mortgages was the building and loan associations which were organized and directed by men whose origins, occupations, and residences reflected those of the associations' clientele. Thus, the hypothesis that immigrants generated their own mortgage funds was confirmed. The findings of the study concerning landlords and tenants further substantiate the argument that the provision of housing was accomplished by the immigrants themselves. Landlords' residences were close to the properties they rented, and there was a marked tendency for tenants to rent from landlords who shared their German origins.
  • Item
    Quantifying the impact of remotely sensed photosynthetically active radiation retrievals on empirical crop models in the United States
    (2023) Brown, Meredith Guenevere Longshore; Skakun, Sergii; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Photosynthetically active radiation (PAR), is an essential component for life onEarth and one of the essential climate variables. Due to the differences in biochemistry, cell structure, and photosynthetic pathways, different plant species absorb PAR with varying efficiency and have evolved to thrive in different conditions, such as direct, intense sunlight or indirect, diffuse light conditions. Ground-based measurements allow for direct estimation of PAR; however, those are available in select locations, e.g. through the Surface Radiation Budget (SURFRAD) Network. Remote sensing-based methods, on the other hand, enable spatially explicit estimates of PAR on a regular basis. Current methods and models for satellite-based PAR retrievals require many ancillary atmospheric datasets as well as a large computing infrastructure. PAR, as one of the parameters influencing plant productivity, has not been previously used in the empirical crop yields and as such can lead to better satellite-based yield estimates. Having the advantages of spatially explicit PAR estimates, spatial and temporal patterns of the PAR can reveal differences in the land uses and the level of crop productivity. Therefore, the overarching goal of my dissertation is to advance the science of satellite-based PAR estimation and agricultural applications. This is done through the use of machine-learning models to reduce data input requirements for PAR estimation from daily Moderate Resolution Imaging Spectroradiometer (MODIS) acquisitions and by incorporating PAR into the empirical crop yield models over the US. In order to obtain satellite-based PAR estimates without the need for ancillary atmospheric data, I developed an empirical approach making use of machine learning methods as an efficient way to capture the non-linear relationship between top of atmosphere radiance and PAR at the surface. I found that the bootstrap aggregated decision tree (Bagged Tree), Gaussian Process Regression (GPR), and Multilayer Perceptron (MLP) yielded the best results with minimal input and training data requirements with an R2 of 0.77, 0.78, and 0.78 respectively, and a relative RMSE of 22-23%. While these results underperform compared with the look up table (LUT) approach, it does not require the same atmospheric parameters as input, such as atmospheric water vapor, aerosol optical depth, and others that might not be available in near real time or are only available at coarser spatial resolution. I incorporated MODIS-based PAR estimates into empirical corn and soybean yield models over the US. By explicitly adding PAR into the crop yield models, I found a maximum R2 of 0.81 and 0.80 for corn and soybean, respectively, whereas models that do not include PAR showed a maximum R2 of 0.60 for corn and soybean. By adding PAR directly into the empirical yield model and demonstrating additional explained variability, I show that my model is in closer agreement with process-based models than previous empirical models. I found that MODIS- derived coefficient of absorption of PAR (αPAR), which corresponds to the plant canopy chlorophyll content (CCC) and consequently productivity, corresponds to the ground-based αPAR measurements. Specifically, I found that for the US-Ne sites of corn and soybean fields in Eastern Nebraska R2 was 0.97 and RMSE was 1.34 (11%) when comparing MODIS-derived αPAR with the in situ measurements. I also found that the relationships between MODIS-based αPAR and CCC for corn and soybean corresponded to the ones obtained from in situ data. The relationships between αPAR and CCC for corn and soybean are distinct due to the different photosynthetic pathways of corn (C4) and soybean (C3), differences in cell structure, and chloroplast distribution between the two crops. Crop yield and productivity are also related to CCC, meaning αPAR can be used as a crop specific indicator of yield. Through this research, I have demonstrated the added value of incorporating PAR directly into crop yield models, by improving crop yield estimates over empirical models based on vegetation indices or surface reflectance alone. The research also provides the basis for further work using crop specific measures of the absorption of PAR into the same empirical models at large spatial scales that were previously impractical due to the spatial discrepancies between in situ- and MODIS- derived measurements.
  • Item
    (2023) Hu, Guangxiao; Sun, Laixiang; Feng, Kuishuang; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Toxic chemicals pose significant threats to ecosystems, climate change, and human health. Unfortunately, pollution inequality is pervasive in the United States, with a disproportionate exposure of racial/ethnic minorities and low-socioeconomic groups to toxic releases. This inequality is especially pronounced in Houston, Texas. Moreover, income inequality has widened over decades, and the distribution of toxic releases has changed over time in the USA. To better understand the problem of pollution inequality, it is necessary to investigate the embodied toxic release in final demand by states and income groups. Notably, pollution inequality is non-uniform across regions and over time in the USA. The relationship between socioeconomic development and toxic risk necessitates analysis to comprehend the resultant health outcomes at different spatial scales and locations. Further, identifying the spatial heterogeneity of the association between environmental hazards and socioeconomic indicators is critical for addressing environmental inequality. Investigating the spatiotemporal heterogeneity of the impact of racial disparities and socioeconomic development on toxic risk can reveal disparities between regions and trends in pollution inequality. This study employs an extended U.S. multi-regional input-output (MRIO) model with toxic chemical release data to analyze the inter-regional transfer of embodied toxic release between states and their unequal distribution between income groups from a consumption-based perspective. Additionally, this study analyzes the spatial non-stationarity in the associations between toxic chemical hazard risk and community characteristics of census block groups in Texas, USA, for 2017 using a multiscale geographically weighted regression (MGWR). Further, this study uses Houston, a city with a history of segregation and discrimination and a diverse racial/ethnic makeup, as an example to analyze the spatiotemporal heterogeneity of the impact of racial disparities and socioeconomic development on toxic risk using geographical and temporal weighted regression (GTWR) models. The study's outcomes are instrumental in determining whether pollution inequality has improved or worsened. Results indicate that non-metallic and metallic products manufacturing sectors are crucial for interregional flows of embodied toxic release from the Great Lake Region to Southeast, Mid-Atlantic, and Northeast regions, and are the most important sectors for most states from the consumption-based perspective. The findings also highlight the significance of identifying the spatial patterns of the association between toxic chemical hazard risks and community characteristics at the census block group level to address environmental inequality.
  • Item
    (2023) Liu, Zheng; Oshan, Taylor; Geography/Library & Information Systems; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Spatial interactions provide insights into urban mobility that reflects urban livability. A range of traditional and modern urban mobility models have been developed to analyze and model spatial interaction. The study of bike-sharing systems has emerged as a new area of research, offering expanded opportunities to understand the dynamics of spatial interaction processes. This dissertation proposes new methods and frameworks to model and understand the high-frequency changes in the spatial interaction of a bike share system. Three challenges related to the spatial and temporal dynamics of spatial interaction within a bike share system are discussed via three studies: 1) Predicting spatial interaction demand at new stations as part of system infrastructure expansion; 2) Understanding the dynamics of determinants in the context of the COVID-19 pandemic; and 3) Detecting events that lead to changes in the spatial interaction process of bike share trips from a model-based proxy. The first study proposes a hybrid strategy to predict 'cold start' trips by comparing flow interpolation and spatial interaction methods. The study reveals 'cold start' stations with different classifications based on their locations have different best model choices as a hybrid strategy for the research question. The second study demonstrates a disaggregated comparative framework to capture the dynamics of determinants in bike share trip generation before, during, and after the COVID-19 lockdown and to identify long-term bike share usage behavioral changes. The third study investigates an event detection approach combining martingale test and spatial interaction model with specification evaluation from simulated data and explorative examination from bike share datasets in New York City, Washington, DC, and San Francisco. Results from the study recognize events from exogenous factors that induced changes in spatial interactions which are critical for model evaluation and improvement toward more flexible models to high-frequency changes. The dissertation elaborated and expanded the spatial interaction model to more effectively meet the research demands for the novel transportation mode of bike-share cycling in the context of a high-frequency urban environment. Taken as a whole, this dissertation contributes to the field of transportation geography and geographic information science and contributes methods toward the creation of improved transport systems for more livable cities.
  • Item
    Estimation and Spatiotemporal Analysis of All-sky Land Surface Temperature from Multiple Satellite Data
    (2023) Jia, Aolin; Wang, Dongdong; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The daily surface temperature variability, characterizing the dispersion of day-to-day temperature anomalies, is a fundamental aspect of the climate. It can be represented by the temperature standard deviation in a week. Studies reveal that daily temperature variability is a critical determinant of societal and natural outcomes, such as public health, crop yield, economic growth, etc. Although the overall warming trend is now well established in the scientific community, previous studies have shown little consensus about changes in daily temperature variability over the globe in recent decades; this is due to limited simulation accuracy and in-situ measurement distribution. Therefore, it is urgently needed to generate a reliable, global, long-term, observation-derived, daily temperature dataset in order to analyze variability changes and potential driving factors. The Advanced Very High-Resolution Radiometer (AVHRR) data provide an exceptional chance to record long-term land surface temperature (LST) over the entire globe. However, the AVHRR LST suffers from two restrictions: cloud contamination and orbital drift. Accordingly, we develop a surface energy balance (SEB)-based algorithm to recover the LST under clouds, and a two-step method to correct the artificial spurious temperature variation due to orbital drift. In the SEB method, 1) the hypothetical LST of missing pixels is first reconstructed by assimilating dispersed clear-sky retrievals into a continuous LST time-evolving model built by reanalysis data, and 2) the reconstructed LST is then corrected by superposing the cloud effect, estimated by satellite radiation products based on SEB theory. The two-step correction includes 1) calibrating the systematic bias of diurnal temperature cycles (DTCs) simulated from reanalysis data using satellite product climatology, 2) correcting the calibrated DTCs in detail by historical AVHRR LSTs during the years 1981-2021, and averaging the corrected DTCs to get daily mean LSTs. Global, 5-km, all-sky, daily mean LSTs from 1982 to 2021 are produced for the daily variability analysis. In order to mitigate the impact of orbital drift, the SEB method is examined by MODIS and VIIRS LST products. Ground validation suggests that the cloudy-sky VIIRS LST exhibits a root mean square error (RMSE) of 3.54 K, a bias of −0.36 K, and R2 of 0.94, comparable to the accuracy of clear-sky LST and the MODIS results. Thus, the algorithm is sensor independent and also works for AVHRR data. To obtain satellite-derived DTC climatology for calibrating simulated DTCs, an optimization module is created to extend the feasibility of the SEB method at night. By collecting clear-sky LSTs from geostationary satellite sensors and two MODIS sensors, global, hourly, 5 km, all-sky LSTs from 2011 to 2021 are produced. The overall RMSE of the hourly LSTs is 3.38 K, with a bias of −0.53 K based on 197 global sites. Finally, after integrating the SEB method and two-step correction method, the target AVHRR LST is recovered with an RMSE of 1.97 K over the globe and few biases. Spatiotemporal analysis of the AVHRR LST suggests that the globally averaged daily LST variability does not have a significant trend from 1982 to 2021 under the global warming background, whereas it showed diverse variation both regionally and seasonally. A significant decrease/increase is detected at high/low latitudes, which matches previous simulation conclusions. However, contrary to the simulation, it reveals significant variability increases in the mid-latitudes, such as the western US, the Mediterranean Basin, and northern China. Historical auxiliary observations indicate that the variability decrease at high-latitudes is driven by downward longwave (DLW) radiation. Arctic amplification mitigates cold temperature anomalies at high latitudes in winter. The enhanced atmospheric convection in the tropics causes the increasing variability of cloud cover and downward shortwave radiation (DSR), and the LST variability has also increased. Climate internal variability, DLW, and DSR all show considerable impact at mid-latitudes. This study proposed innovative cloud-sky LST estimation and orbital drift correction methods. The first global, all-sky, 5-km, daily mean LST product (1982 - 2021) was generated, which shows great potential for long-term energy budget and hydrological cycling analysis. Furthermore, the study fills the knowledge gap about the unknown daily temperature variability trend over the globe and provides an attribution based on historical observations, which will assist the community in understanding the mechanism of high-frequency temperature change, improving model prediction, and coordinating resources for extreme weather adaptation.