京都大学 / 0048 / 新制・課程博士 / 博士(地域研究) / 甲第22562号 / 地博第265号 / 新制||地||100(附属図書館) / 京都大学大学院アジア・アフリカ地域研究研究科東南アジア地域研究専攻 / (主査)教授 岡本 正明, 准教授 甲山 治, 准教授 柳澤 雅之, 教授 水野 広祐 / 学位規則第4条第1項該当 / Doctor of Area Studies / Kyoto University / DGAM

The mode of carrying out literary spatial studies—or literary geography—has largely shifted to embrace digital methods and tools, culminating in the field of geospatial humanities. This shift has affected the scope of research questions that scholars can ask and answer using digital methods. Although there many continuities between non-digital and digital spatial studies, there are some fundamental points of departure in the critical processes that are involved in carrying out geospatial humanities research, including data modelling, prototyping, and multidisciplinary collaboration, that demand a revisit of the ways that knowledge production and analysis are carried out in the humanities. First there is thinking about how data models, prototypes, and digital projects embed within themselves spatial methodologies and spatial theory that form the foundation of humanities-oriented spatial inquiry. In addition, collaborating across multidisciplinary groups involves working toward shared project goals, while ideally ensuring that individual team members are drawing benefit from the collaborative research experience. Another factor has to do with creating rich and accurate data models that can capture the complexity of their subject of inquiry for meaningful humanities research. This dissertation addresses each of the aforementioned challenges through practical applications, by focusing not only on the literary contributions of geospatial humanities, but also engaging the critical processes involved in this form of digital research. By designing and co-creating three geospatial prototypes, TopoText, TopoText 2.0, and A Map of Paradise Lost, my goal is to demonstrate how digital objects can embody spatial theory and methodologies, and to portray how traditional literary studies approaches such as close reading and literary interpretation can be combined with digital methods that enable interactivity and mixed-media visualizations for an immersed literary geography analysis. The first two chapters translate a literary theory and method of analysis, geocriticism, into a digital prototype and iteratively improve on it to demonstrate the type of research made possible through a digital geocritical interpretation. In that part of the dissertation, I also address the challenges involved in translating a literary framework into a digital environment, such as designing under constraint, and discuss what is lost in translation alongside what is gained (McCarty 2008). Chapter three demonstrates how technological advances enable scholars to build community-university partnerships that can contribute to humanities scholarship while also making research findings publicly available. In particular, the chapter argues that scholars can draw on Volunteered Geographical Information to create rich cultural gazetteers that can inform spatial humanities research. The final two chapters demonstrate how a geospatial prototype that is fueled by rich data and embeds other types of media can inform literary interpretation and help make arguments. By focusing on the process of building A Map of Paradise Lost—a geospatial humanities text-to-map project that visualizes the locatable places in John Milton’s Paradise Lost—the closing chapter addresses the question “why map literature?” and demonstrates how the process of research prototyping is in itself a form of knowledge production. Since the methods and technologies that inform geospatial humanities research are rapidly evolving, this dissertation adopts a portfolio model and consists of five released and one forthcoming publications, as well as three published prototypes. Together, they form a digital dissertation, meaning that the digital component comprises a significant part of the intellectual work of the dissertation. Reflecting the collaborative nature of digital humanities research, some articles were co-authored and all three prototypes were co-developed. In all components of this dissertation, I took on the leading role in the publication and prototype development, which is detailed at the beginning of every chapter. / Graduate

Abstract Background Measles is a serious infectious disease that contributes significantly to the burden of disease in many developing countries. In most developed nations, such as Canada, endemic transmission of measles has been declared eliminated thanks to rigorous vaccination programs, but isolated outbreaks of the disease continue to happen. Therefore, a thorough understanding of the factors contributing to these outbreaks is necessary. Objectives There were two main objectives of this thesis. The first objective was to assess the geospatial distribution of reported measles cases in Ontario with a goal of identifying clusters of reported measles. For this objective, the main hypothesis was that measles cases would not be randomly distributed across Ontario and instead would cluster in certain regions. The second objective was to explore some of the factors that may be associated with measles clusters. For this objective, the main hypothesis was that the proportion of immigrants, population density, low-income prevalence and education level would be associated with measles clusters. Methods The first objective was achieved through a thorough geospatial analysis using SaTScan and R. Individual forward sortation areas were used as the spatial unit of analysis. The analysis leveraged data from multiple sources: 2016 Census data, Ontario measles cases data from iPHIS from 2008 to 2019, a shapefile of all forward sortation areas in Canada from Statistics Canada and centroid coordinates of forward sortation areas that were obtained using web scrapping techniques on the geolocation service of Natural Resources Canada. The maximal window size of the geospatial analysis was chosen using the maximum clustering heterogeneous set-proportion technique. The geospatial analysis was run with 99,999 Monte Carlo repetitions under a Poisson distribution using the purely spatial analysis. The Ontario population from the 2016 Census was used as the population at risk. Any cluster with a p ≤ 0.05 was deemed statistically significant. The second objective was achieved through a case-control study: Forward sortation areas that were within statistically significant measles clusters were considered as cases and the rest of the forward sortation areas were considered as controls. Demographic data necessary to assess the factors of interest were extracted from the 2016 Census. A univariable logistic regression model was run to compute the odds ratio and test the association between the factors of interest and measles clusters. 95% confidence intervals were computed for each odds ratio. Data-curation techniques and data analysis were performed in R 4.0.4. Results From 2008 through 2019, 178 measles cases were identified. 82% of cases lacked necessary vaccination or vaccination records against measles, 35% of cases were linked to traveling outside of Ontario, 20% of cases reported being in contact with a known case, and 72% of cases were less than 5 years old or older than 21. Ten measles clusters were identified of which six were deemed statistically significant. These six significant clusters represented 7% of the population at risk but contained nearly 40% of all reported measles cases between 2008 and 2019. Measles clusters had a strong association with the proportion of immigrants living within them, population density and prevalence of low-income. No association was found between education level and measles clusters. Conclusion The results indicate that most measles cases in Ontario are unvaccinated or lack proof of vaccination; arise through secondary transmission within the province; arise from undetected transmission; and are adults or infants. Additionally, it is possible to see that the risk of reported measles cases is not randomly distributed across the province, but instead measles cases tend to cluster in certain regions. Such clusters tend to be characterized by specific population-level factors that may be contributing to the risk of reported measles. Targeted and equitable interventions are needed as we continue on the path to eradication.

Abortion, the termination of pregnancy, is safe when provided as a surgical procedure by a trained provider or when the correct dosage of the drugs mifepristone and/or misoprostol are used. Despite this, many barriers to abortion care exist. In the United States (US), targeted state-level abortion restrictions create barriers to care, which make it so that people who wish to utilize abortion care face difficulty or are unable to do so. Such barriers to care have important public health implications, as studies have shown that individuals who cannot access wanted abortion care have poorer psychological, physical, social, and economic outcomes than those who obtained care. This dissertation aims to examine one component of abortion access, accessibility, operationalized as the drive time from a woman’s home to the nearest abortion-providing facility. We employ a novel measure of abortion accessibility constructed from three data sources: (1) the Advancing New Standards in Reproductive Health facility database; (2) US Census estimates and shapefiles; and (3) OpenStreetMap data. In the first study, we used geographic information systems (GIS) to explore the effect of programmatic and policy changes related to telemedicine for medication abortion services (TMAB) on population-level measures of abortion accessibility, or drive time to the nearest abortion-providing facility. We found that either expansions in TMAB services or removal of TMAB bans could improve abortion accessibility in the US. For these two exposure scenarios, compared to the current abortion provision scenario, increases in the proportion of women within a 30-, 60-, and 90-minute drive time of an abortion-providing facility ranged from 1.25 percentage points, or an additional 781,556 US women aged 15-44 years with accessibility, to 5.66 percentage points, or an additional 3,530,423 US women aged 15-44 years with accessibility. In the second study, we used GIS to assess the potential effect of the geographic unit of analysis (i.e., block group, ZIP code tabulation area [ZCTA], or county) on misclassification of the proportion of US women of reproductive age within a 30-minute drive time of an abortion-providing facility relative to a measure calculated using Census blocks. We found that block group- or ZCTA-based estimates of abortion accessibility were an underestimate, but resulted in little misclassification relative to measures constructed using Census blocks at the national level; however, county-based measures substantially underestimated abortion accessibility compared with Census block-based measures. Nationwide, the Census block-based abortion accessibility estimate was 0.35 percentage points greater than the block group-based estimate, 2.72 percentage points greater than the ZCTA-based estimate, and 24.21 percentage points greater than the county-based estimate. By state, the Census block-based abortion accessibility estimate ranged from 0 to 8.51 percentage points greater than the block group-based estimate, from 0 to 27.86 percentage points greater than the ZCTA-based estimate, and from 0 to 79.49 percentage points greater than the county-based estimate. Given that state-level ZCTA-based estimates could be substantially different from the Census block-based estimate, ZCTA-based estimates are likely not appropriate for state-level analyses or US analyses stratified by state. Finally, in the third study, we assessed the relationship between level of accessibility in an abortion client’s home ZCTA and the gestational age at which the client obtained abortion care, using fine stratification by propensity score to control confounding. We found that compared with living in a ZCTA with >0% accessibility, living in a ZCTA with 0% accessibility was associated with a decreased risk of being at or beyond 14 weeks’ gestation at abortion visit. These unexpected findings could be due to a selection bias induced by limiting the sample to those who obtained abortion care, uncontrolled or poorly controlled confounding, misclassification of exposure and/or outcome, and/or unidentified effect measure modification by state abortion provision landscape. Through these three dissertation studies, we highlighted the potential impact on abortion accessibility in the US with different changes in programming and policy, quantified misclassification of abortion accessibility, and examined how misclassification varied by geographic measure and location. The third study in this dissertation suggests a need for more research to identify how selection bias may affect studies of abortion access in the US that rely on data only from those who are able to access care.

>Magister Scientiae - MSc / Several attempts have been made to assess the impact of post-fire soil erosion; however, erosion occurs as a result of the complex interplay between many factors, such as climate, land cover, soil and topography, making precise estimation difficult. Additionally, these factors are far from constant in space and time, and often interact with one another. To assess the impact of wildfire on soil erosion and factors influencing its variability, the post-fire soil erosion response of two mountainous headwater sub-catchments namely Langrivier and Tierkloof, with different vegetation cover in the Jonkershoek Valley was examined using a systematic approach that combines efforts in field and laboratory work, spatial analysis and process-based numerical modelling. Geospatial modelling shows high spatial variability in erosion risk, with 56 % to 67 % of surfaces being highly susceptible excluding rock cover. The model highlights the importance of terrain and vegetation indices, with predicted erosion being more severe on steep slopes with lower vegetation cover. / 2021-08-30

Water shortages resulting from increased demand or reduced supply may be addressed, in part, by redirecting recycled water for irrigation, industrial reuse, groundwater recharge, and as effluent discharge returned to streams. Recycled water is an essential component of integrated water management and broader adoption of recycled water will increase water conservation in water-stressed coastal communities. This study examined spatial patterns of recycled water use in Florida in 2009 and 2015 to detect gaps in distribution, quantify temporal change, and identify potential areas for expansion. Databases of recycled water products and distribution centers for Florida in 2009 and 2015 were developed by combining the 2008 and 2012 Clean Water Needs Survey databases with Florida’s 2009 and 2015 Reuse Inventory databases, respectively. Florida increased recycled water production from 674.85 mgd in 2009 to 738.15 mgd in 2015, an increase of 63.30 mgd. The increase was primarily allocated to use in public access areas, groundwater recharge, and industrial reuse, all within the South Florida Water Management District (WMD). In particular, Miami was identified in 2009 as an area of opportunity for recycled water development, and by 2015 it had increased production and reduced the production gap. Overall, South Florida WMD had the largest increase in production of 44.38 mgd (69%), while Southwest Florida WMD decreased production of recycled water by 1.68 mgd, or 3%. Overall increase in use of recycled water may be related to higher demand due to increased population coupled with public programs and policy changes that promote recycled water use at both the municipal and individual level.

Karst springs are an essential source of private water supply for about 10% of households in Tennessee. However, these springs, which can be easily polluted, are unregulated. This study, therefore, assesses water quality spatial patterns and water quality rating of roadside springs in northeast Tennessee. Karst spring water samples collected from 50 springs were assessed using EPA Standard methods for pathogens, nutrients, radon, and physicochemical parameters. Springs generally met federal and state standards for physicochemical parameters, 90% of samples contained E. coli, and all samples contained fecal coliform. High E. coli was spatially clustered causing a fecal contamination hot spot on the border of Washington and Sullivan Counties, Tennessee. 60% of springs exceeded radon concentrations of 300 pCi/L. Water quality ratings were very poor or unfit for drinking, with 4% of springs ranked “good”. Therefore, microbial pollution purification procedures are advised before using these springs as a drinking water source.

This research assesses shifts in tornado occurrence pattens in space and time employing continental United States tornado records with an Enhanced Fujita (EF) rating equal or greater than 1. In similar research, most researchers discard tornado records prior to 1980 due to factors including: magnitude anomalies related to development of the Fujita Scale, unpredictability in tornado reporting (escalating populace, storm spotters, and technologic improvements), and better data records from the Census Bureau. We therefore constructed two datasets using tornados recorded in the National Weather Service Storm Prediction Center’s Severe Weather GIS (SVRGIS) database: 1950-2019 (dataset 1) and 1980-2019 (dataset 2). The goals for this study were to 1) determine whether spatiotemporal patterns of recorded tornado activity have shifted over time, and 2) determine whether inclusion of pre-1980 tornado data changes the findings from 1). This study employed Space-Time Pattern Mining (STPM) to construct four spacetime cubes (STC) in ArcGIS Pro. Emerging Hot Spot Analysis (EHS) was employed to identify the changes in tornado occurrence (number of incidents in a STC cell) and magnitude (sum of tornado EF ratings for all incidents in a STC cell). EHS displayed increased tornado activity in the Southeast and decreased activity for areas in the Great Plains for both occurrence and magnitude in both datasets. This is interpreted as significant intensifying hot spots in the Southeast region and diminishing hot spots in the Great Plains indicating an east-south-east shift for both datasets. Similar findings for both datasets indicate that inclusion of the less reliable pre-1980’s tornado data does not change the results and we recommend that the practice of discarding pre-1980’s tornado data in tornado occurrence research be reconsidered.

The Ebola outbreak in West Africa in 2014 to 2016 had more than 28,000 suspected, probable, and confirmed cases. It was the largest Ebola outbreak in history. Of the 28,000 cases in the three Ebola-affected countries, Liberia had 10,000 cases with almost 5,000 deaths. The Ebola Virus Disease (EVD) entered Liberia along the border of Guinea and moved to the capital city of Monrovia where the virus spread. Ebola Treatment Units (ETUs) were constructed throughout the response in locations where there were available facilities versus distance to care challenges. This study examined the association of distance from villages to ETUs and mortality. Using Geographic Information System (GIS) and statistics framed within the Social Ecological Model and the GIS Framework, this study geolocated the Ebola cases by village, mapped the travel routes and calculated the distance to the ETU. A logistic regression was then used to determine if there was an association between distance and mortality, with and without controlling for age and gender, and, to calculate the odds ratio. A logistic regression model showed there is an association between distance and mortality and that Ebola patients living within 12 kilometers of the ETU were 1.8 times less at risk of mortality (OR = 1.778, 95% CI [1.171 - 2.7]) than those living more than 12 kilometers. In addition, males had a 1.4 times lower risk of death due to EVD. This understanding can inform future outbreak responses and placement of treatment units. In addition, this information can lead to social change with respect to individual understanding of access to care, community expectations, and national health care planning.

Tularemia is a vector-borne disease of global concern with diverse regional foci. Arkansas is an endemic state with differences in case distribution and land suitability supporting host and vector sustainment. The aim of this study was to conduct a geospatial and spatiotemporal assessment of factors associated with case distribution and timeliness and completeness of public reporting. Guided with direction from spatial epidemiology and nidality, referring to the association of ecology, climate, and proximity of disease, analysis included secondary data collected from the Arkansas Department of Health between 1995 and 2018. Using Poisson-based software, 2 clusters were found: a high-risk cluster encompassing 23% of the total population within 24 counties spanning an 8-year period (RR = 4.98, p < 0.05), and a low risk cluster that included 25% of the population within 28 counties during a 12-year period (RR 0.14, p < 0.05). Analysis of ecological data revealed associations between annual precipitation within the high-risk cluster and total number of cases (AUC = 0.716 and AUC = 0.726, respectively) with trends toward higher incidence rates in suitable land cover and moderate to high elevation using maximum entropy software. Analysis of timeliness and completeness revealed gaps for clinical form and transmission mode determination (p < 0.05), while increases in probable cases followed decreases in confirmed cases revealing gaps in laboratory diagnostics. Positive social change necessitates multidisciplinary collaboration between climatologists, clinicians, and epidemiologists to reach high-risk populations and promote educational awareness. The potential for social change includes predictive modeling optimizing funding while representing underserved populations.