Health studies often rely on aggregated instead of individual-level data to protect patient privacy. However, aggregated data are subject to the modifiable areal unit problem (MAUP), meaning results of statistical analyses may differ depending on the data's scale and areal unit. Past studies have suggested MAUP is context-specific and analyzing multiple spatial scales may provide richer understandings of examined phenomena. More research is needed to understand the role of scale and areal unit in health-related analyses. This study examines associations between surface mining and birth outcomes from 1989 to 2015 in Central Appalachia at the individual; postal; county; and county-sized, non-administrative scales. Evidence from previous studies suggests associations exist between health outcomes and county-level measures of mining activity. This is the first study to examine associations between mining and birth outcomes at more spatially refined exposure estimates. We identified surface mines using Landsat imagery and geocoded birth records. Airsheds, used to quantify the influence area of potential airborne pollutants from surface mining activity, were built using HYSPLIT4. The frequency values of each airshed that intersected each geocoded birth record were summed. These cumulative frequency airshed values were then aggregated. Finally, we implemented multiple regression models, each at a different scale, to examine associations between airsheds and birth outcomes. Results suggest MAUP has minimal impacts on the statistical results of examining associations between surface mining and birth outcomes in Central Appalachia. Results also indicate surface mining is significantly associated with preterm birth and reduced birthweight at each scale. / Master of Science / Health studies often rely on data that has been grouped together within political boundaries (e.g. counties) instead of individual-level data to protect patient privacy. However, results from analyses using grouped data may differ depending on the data's scale and areal unit, which describes the modifiable areal unit problem (MAUP). Past studies have suggested MAUP is specific to the situation being analyzed and examining multiple scales may provide richer understandings of the situation. More research is needed to understand the role of scale and areal unit choice in health-related analyses. This study examines associations between surface mining and birth outcomes from 1989 to 2015 in Central Appalachia at the individual; postal; county; and county-sized, non-administrative scales. Evidence from previous studies suggests associations exist between health outcomes and county-level measures of mining activity. This is the first study to examine associations between mining and birth outcomes at finer scales. Surface mines were identified using satellite images, and we identified the locations of birth records using the mother's home address. Airsheds, used to determine the influence area of airborne pollutants from surface mining activity, were created. We then used statistical models, to examine associations between airsheds and birth outcomes at four spatial scales. Results suggest MAUP has minimal impacts on the statistical results of examining associations between surface mining and birth outcomes in Central Appalachia. Results also indicate surface mining is significantly associated with preterm birth and decreased birthweight in grams at each scale.

On December 26th, 2004, 280,000 people lost their lives. A massive earthquake struck Indonesia, triggering a tsunami that affected several countries, including Thailand. The disaster had important implications for health status of Thai citizens, as well as health system planning, and thus underscores the need to study its long-term effect. This dissertation examined the patterns, determinants, and spatial analysis of health service utilization following the tsunami in Thailand. The primary aim was to determine whether tsunami-affected status (personal injury or property loss) and distance to a health facility (public health center or hospital) influenced health service utilization. The study population included Thai citizens (aged 14+), living in the tsunami-affected Thai provinces: Phuket, Phang Nga, Krabi, and Ranong. Study participants were randomly selected from the ‘affected’ and ‘unaffected’ populations. One and two years after the tsunami, participants were interviewed in-person on demographic and socio-economic factors, disaster impact, health status, and health service utilization. Five types of health services were examined: outpatient services, inpatient services, home visits, medications, and informal (unpaid) care. Distance to a health facility was calculated using Geographic Information System’s Network Analyst. The Grossman model of the demand for health care and a distance decay concept provided the foundation for this study. A propensity score method and a two-part model were used to examine the study objectives. There were 1,889 participants. One year after the tsunami, individuals affected by property loss were more likely to use medications than unaffected participants. Two years after the tsunami, individuals with personal injury were more likely to use outpatient services, medications, and informal care than unaffected participants. Distance to a health facility was associated with the use of medications and informal care. The results confirmed the long-term effect of a tsunami. This dissertation may assist the decision- and policy-makers in the identification of those most likely to use health services and in the request of health resources to the affected areas. The patterns, determinants, and spatial analysis of health service utilization found in this study may not be specific to a tsunami and may provide insights on post-disaster contexts of other natural disasters.

On December 26th, 2004, 280,000 people lost their lives. A massive earthquake struck Indonesia, triggering a tsunami that affected several countries, including Thailand. The disaster had important implications for health status of Thai citizens, as well as health system planning, and thus underscores the need to study its long-term effect. This dissertation examined the patterns, determinants, and spatial analysis of health service utilization following the tsunami in Thailand. The primary aim was to determine whether tsunami-affected status (personal injury or property loss) and distance to a health facility (public health center or hospital) influenced health service utilization. The study population included Thai citizens (aged 14+), living in the tsunami-affected Thai provinces: Phuket, Phang Nga, Krabi, and Ranong. Study participants were randomly selected from the ‘affected’ and ‘unaffected’ populations. One and two years after the tsunami, participants were interviewed in-person on demographic and socio-economic factors, disaster impact, health status, and health service utilization. Five types of health services were examined: outpatient services, inpatient services, home visits, medications, and informal (unpaid) care. Distance to a health facility was calculated using Geographic Information System’s Network Analyst. The Grossman model of the demand for health care and a distance decay concept provided the foundation for this study. A propensity score method and a two-part model were used to examine the study objectives. There were 1,889 participants. One year after the tsunami, individuals affected by property loss were more likely to use medications than unaffected participants. Two years after the tsunami, individuals with personal injury were more likely to use outpatient services, medications, and informal care than unaffected participants. Distance to a health facility was associated with the use of medications and informal care. The results confirmed the long-term effect of a tsunami. This dissertation may assist the decision- and policy-makers in the identification of those most likely to use health services and in the request of health resources to the affected areas. The patterns, determinants, and spatial analysis of health service utilization found in this study may not be specific to a tsunami and may provide insights on post-disaster contexts of other natural disasters.

Detta examensarbete syftar till att svara på ett av Trafikverket fastställt problem: en gemensam regional cykelplanerings process skulle göra dem billigare och mer jämförbara. De erbjuder för närvarande planerarna en modell som utvecklades av Kågeson 2007. Denna modell har formen av en rapport som ger råd om när man ska bygga en cykelväg mellan städer eller platser i en region. Ändå används den bara i endast 6 av de 21 svenska länen. Trafikverket kräver ett nytt planeringsstödverktyg, mer interaktivt och komplett än Kågeson-modellen. Några nya önskade funktioner är separationen av efterfrågan per syfte, införandet av e-cyklar, olika resesyfte och en prioritering av investeringarna.  Examensarbetet är att designa och implementera det här verktyget, även kallat Planning Support System (PSS), som syftar till att jämföra utbud och efterfrågan på cykelväg till prioritering av infrastrukturförbättringar. En huvudbegränsning för modellen är att den måste vara billig datavis, men så komplett och exakt som möjligt. Det baseras på flera öppna dataleverantörer, till exempel OpenStreetMap, den svenska nationella vägdatabasen (NVDB) eller reseundersökningar från Sverige och Nederländerna. Resultatet är en modell, uppdelad efter turändamål och typ av cykel.  Del för efterfrågeuppskattning anpassar en klassisk fyrsteg transportmodell till cykelplanering och begränsad data. För olika resändamål genereras och distribueras resor tack vare en ursprungs begränsad gravitationsmodell. Valet av cykelläge är anpassat till det faktiska resebeteendet genom logistisk regression med en binär logit-modell. Resorna tilldelas sedan nätverket med tilldelnings metoden "allt-eller-ingenting" genom Dijkstras algoritm. För att utvärdera cykelförsörjningen använde vi ett mått som heter Level of Traffic Stress (LTS), som uppskattar den potentiella användningen av en nätverkslänk för olika delar av befolkningen som en funktion av vägnätvariablerna. Prioriteringsrankningen är då förhållandet mellan mått på efterfrågan och utbud.  Detta nya verktyg implementeras med opensource Geographic Information System (GIS) som heter QGIS och med Python 3 och testas i Södermanlands län / This Master Thesis main objective is to answer a problem set by the Swedish Transport Administration: a common regional bicycle planning process would them cheaper and more comparable. They currently offer the planners a model developed by Kågeson in 2007. This model takes the form of a report which advises on when to build a bicycle path between cities or places of a region. Still, it is only used in only 6 of the 21 Swedish counties. Trafikverket requires a new planning support tool, more interactive and complete than the Kågeson model. Some new desired features are the separation of demand per purpose, the inclusion of e-bikes, different trip purposes, and a prioritization of the investments.  The Degree Project work is to design and implement this tool, also called Planning Support System (PSS), which compares supply and demand for bicycle path to prioritizing infrastructure improvements. A main constraint for the model is that it needs to be cheap data-wise, but as complete and precise as possible. It bases on several open data providers, such as OpenStreetMap, the Swedish National Road Database (NVDB), or Travel Surveys from Sweden and the Netherlands. The result is a model, disaggregated by trip purpose and type of bicycle.  The demand estimation part adapts a classic four-step transportation model to bicycle planning and limited data. For different trip purposes, trips are generated and distributed thanks to an origin-constrained gravity model. Bicycle mode choice is fit to actual travel behaviour through logistic regression with a binary logit model. The trips are then assigned to the network using the "all-or-nothing" assignment method through the Dijkstra algorithm. To evaluate bicycle supply, we used a metric called Level of Traffic Stress (LTS), which estimates the potential use of a network link by different parts of the population as a function of the road network variables. The prioritization ranking is then the ratio between demand and supply metrics.  This new tool is implemented with the opensource Geographic Information System (GIS) called QGIS and with Python 3, and it is tested on Södermanland County.

人口流動具有各種型態。其中包含了遷移、移動、以及通勤人口。在宏觀模型框架下，空間互動模型(簡稱SIM)對於測量人口流動扮演了重要的角色。距離遞減效應為空間互動模型中重要的因子。該效應描述了人口流動的頻率會隨著移動距離而逐漸下降。然而，從實證上，本研究發現人口流動與移動距離的函數，並非在距離上保有恆定的關係。 在本文中，我們提出了對此非恆定的距離遞減效應之修正方法。本修正法運用了轉折點模型的特點，套入了空間互動模型的距離函數上。本文首先運用了電腦模擬驗證了此方法的穩定性與有效性。接下來，研究將此方法應用在兩個人口流動資料。第一個是從台灣健保資料庫觀察出的民眾就醫地變化。健保資料庫包含了總人口的5%抽樣資料。由於在抽樣上瑕疵不大，因此健保抽樣資料具有了一定的代表性。第二個資料則是英國統計局所提供的人口遷移普查資料。在這兩個資料上，我們發現本研究所提修正法，相較於傳統的空間互動模型具有更好的模型配適表現。此改善程度在非都市地區尤其更為明顯。 / Population movement encompasses various forms, such as migration, mobility, and commuting. Spatial Interaction Model (SIM) serves as an important tool to calibrate these movements in the sense of macro modelling. One of the important features of this model is that the number of migrants often decays with the distance. However, we found that this is not always the case in practice and the decay pattern may change with distance. In this study, we propose a distanced-based modification to the SIM, via applying the techniques of change-point problem to construct distance functional form. Computer simulation is illustrated to validate the method and the empirical analysis of flow data from Taiwan’s National Health Insurance Research Database (NHIRD), and also England & Wales internal migration data also provides sound evidences to support the proposed approach. Note that the flow data from the NHIRD consists of a sample of about one million people and can be treated as a fine sample representative of Taiwan’s whole population (about 23 million people). Our results show that the modified approach is more adequate than the traditional SIM, especially for describing the movements of suburban areas in Taiwan.

Contrary to the other forest ecosystems in the Democratic Republic of Congo (D.R. Congo), semi-deciduous forests have so far attracted little attention and studies regarding their ecological aspects remain sketchy. Yet semi-deciduous forests are among the most important non-flooded ecosystems in the Congo Basin and their importance is high, both ecologically and economically. They are home to a variety of species, some of them being exploited for timber by forest companies acting in the region. There is a constant need to focus on their composition and diversity, and to understand factors shaping their communities.<p>Using a sampling method broadly inspired from the synusial phytosociology approach, we examined plant communities within each of the forest layers composing the overstorey (canopy and emergent trees) and the understorey (shrub and herbaceous layers). <p>The role of edaphic heterogeneity on plant communities in the considered semi-deciduous forests was examined. We mainly focused on:<p>- Floristic parameters within these ecosystems;<p>- Spatial structure of edaphic variables;<p>- Species responses to edaphic heterogeneity;<p>- Distance decay in the considered ecosystems;<p>- Phylogenetic patterns within plant communities.<p>Some of the species found in the considered semi-deciduous forests are more related to a type of soil than another, defining some “edaphic specialists” species while many others can be considered “generalists”. Spatial distance effect in the considered plant communities is marked by a decrease of floristic similarity with the geographical distance and all the forest layers showed a pattern of spatial phylogenetic clustering meaning that species cohabiting within a same plot are more related than species from distant plots.<p> / Doctorat en Sciences / info:eu-repo/semantics/nonPublished