博碩士論文 109022601 詳細資訊




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姓名 萬詩文(Insan Wastuwidya Mahardiani)  查詢紙本館藏   畢業系所 遙測科技碩士學位學程
論文名稱 分析新型冠狀肺炎疫情對肺結核空間分布之影響 —以印尼日惹市為例
(Analyzing the Impact of COVID-19 on the Spatial Pattern of Tuberculosis in Yogyakarta City, Indonesia)
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摘要(中) 摘要
2018-2021年期間新型冠狀肺炎(COVID-19)的大流行對全球公共衛生系統構成了重大挑戰,同時也造成如肺結核等相關傳染防制工作的困難。特別是印尼作為肺結核高盛行的國家 。據此,COVID-19對於肺結核的傳播與分布的影響則為本研究關注的重點。本研究之目的為研究COVID-19於疫情前後在印尼日惹市結核病的空間分布型態的影響,並探討此影響與環境與社會經濟等因子之相關性。具體來說,本研究應用了地址對位(Geocoding)技術,將從印尼公共衛生局獲得之2018-2021各年之肺結核病例地址,轉換成具空間座標之資料,並使用集群分析方法(Clustering Analysis)以了解COVID-19疫情前與疫情期間之空間分布之變化。另外,本研究收集了與結核病傳播相關的七項包含了環境與社會經濟相關的重要因子進行關聯性分析。關於環境因子的收集工作,為仰賴應用遙測(Remote Sensing)技術來進行,包括土地覆蓋、建築密度、地表溫度以及植被密度;社會經濟因子的部分,主要為基於政府之統計資料,如健康設施可及性、人口密度和稅收收入等。本研究透過地理資訊系統(Geographic Information System, GIS)整合上述因子來進行空間集群分析。研究結果顯示,在群集分布的程度上,COVID-19之熱點地區集中在城市的中西部地區,在分布上於疫情前後大致相同,但上述地區於疫情期間有聚集顯著性增加的情形。另外,於疫情期間,環境和社會經濟因子與結核病聚集顯著性有不同的相關性:稅收收入和地表溫度與結核病聚集性的負相關性最大(-0.55與-0.36),而所有因子中僅有NDVI 呈較低的正相關(0.16)。研究結果顯示了利用集群分析可以有效地界定出結核病的主要聚集、具有高傳播風險的地區,且了解本研究區中COVID-19疫情是如何影響結核病之聚集分布情形,及其與環境、社經因子的關聯性。本研究相信上述成果對當地有關部門,於未來進行結核病的相關防治工作時能有相當的助益。


關鍵詞:結核病、地理資訊系統、新型冠狀肺炎、地址對位、群聚分析。
摘要(英) Abstract
The coronavirus disease 2019 (COVID-19) pandemic has presented significant challenges to global public health systems, potentially impeding the progress in Tuberculosis (TB) elimination efforts. Indonesia, a country burdened with high Tuberculosis prevalence, faces additional obstacles due to the current COVID-19 situation. Hence, the impact COVID-19 pandemic on the spread of Tuberculosis becomes a noticeable issue in Indonesia. This study aims to investigate the spatial patterns of Tuberculosis before and during the COVID-19 pandemic in Yogyakarta City and to explore the relationships between selected environmental and socioeconomic factors and Tuberculosis incidence. To analyze the TB spatial pattern in the study area, the geocoding method was used to convert the text-based Tuberculosis cases, from 2018 to 2021 into geo-coordinates. This spatial dataset was then used to understand the characteristic of TB spatial patterns using cluster analysis. Also, this study uses selected critical environmental and socioeconomic factors to correlate the spatial distribution of Tuberculosis hotspots. This study considers seven factors that are known as important factors relating to the spread of Tuberculosis. They are environmental factors, including land cover, building density, land surface temperature (LST), vegetation density, and socioeconomic factors, including health facility accessibility, population density, and tax income. For the collection of the environmental factors, remote sensing techniques were employed, such as land cover classification, LST estimation, and vegetation density mapping. The results show that (1) the clustering of Tuberculosis is a common phenomenon during the study period, whether before or during the pandemic; (2) the hotspots in the city generally persisted in the center and western areas, and these areas during the pandemic showed a greater clustering significance; and (3) during the pandemic, environmental and socioeconomic has a different correlation to the Tuberculosis incidence: tax income and land surface temperature have the greater negative correlations (-0.55 and -0.36), and among all factors, only NDVI has a positive correlation (0.16). The results of this study explore the areas where the possibility of Tuberculosis transmission is higher and have a better understanding of how the COVID-19 pandemic affects the Tuberculosis spatial patterns in Yogyakarta City, which can assist the local government to have better public health policies in the Tuberculosis transmission prevention practice in the future.
Keywords: Tuberculosis, GIS, COVID-19, Geocoding, Clustering Analysis
關鍵字(中) ★ 結核病
★ 地理資訊系統
★ 新型冠狀肺炎
★ 地址對位
★ 群聚分析
關鍵字(英) ★ Tuberculosis
★ GIS
★ COVID-19
★ Geocoding
★ Clustering Analysis
論文目次 Table of Contents

摘要 i
Abstract ii
Table of Contents iii
List of Figures v
List of Tables viii
CHAPTER I 1
1.1. Background 1
1.2. Objectives 3
CHAPTER II 5
2.1. Tuberculosis 5
2.2. Analyzing the Spatial Pattern of Tuberculosis using GIS 7
2.3. The Application of Clustering Analysis 9
2.4. Tuberculosis-Related Factors in Spatial Analysis 12
CHAPTER III 15
3.1. Study Area 15
3.2. Tuberculosis in Yogyakarta 20
3.3. COVID-19 Pandemic 24
3.4. Tuberculosis During the COVID-19 Pandemic 26
CHAPTER IV 29
4.1. Research Framework 29
4.2. Workflow 31
4.3. Dataset and Pre-processing 33
4.3.1. Remote Sensing Data 33
4.3.2. Statistical Data 36
4.4. Factors Used 37
4.4.1. Environmental Factors 39
4.4.2. Socioeconomic Factors 58
4.5. Geocoding 66
4.6. Clustering Analysis 67
4.7. Correlation between Environmental and Socioeconomic Factors on Tuberculosis Incidence 70
CHAPTER V 74
5.1. Tuberculosis Incidence Geocoding 74
5.2. The Clustering Analysis of Tuberculosis Incidence 77
5.3. The Impact of COVID-19 on Tuberculosis 81
5.4. The Influence of Environmental and Socioeconomic Factors 83
CHAPTER VI 103
6.1. Tuberculosis Incidence Geocoding 103
6.2. Spatial Pattern of Tuberculosis 105
6.3. The Impact of COVID-19 on Tuberculosis Spatial Pattern 106
6.4. The influence of environmental and socioeconomic factors 107
6.5. Limitations of this Study 110
6.6. Application of this Study 112
CHAPTER VII 115
References 119
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指導教授 姜壽浩 審核日期 2023-8-21
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