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研究生:簡欣民
研究生(外文):CHIEN, HSIN-MING
論文名稱:原住民人口熱點偵測及聚落分析—以桃園市為例
論文名稱(外文):Hotspot Detection and Settlement Analysis on Indigenous Population, taking Taoyuan City as an Example
指導教授:陳建州陳建州引用關係
指導教授(外文):Chen, Chien-Chou
口試委員:陳建州許凱翔盧宏益
口試委員(外文):Chen, Chien-ChouHsu, Kai-ShyangLu, Hung-Yi
口試日期:2022-07-01
學位類別:碩士
校院名稱:輔仁大學
系所名稱:統計資訊學系應用統計碩士班
學門:數學及統計學門
學類:統計學類
論文種類:學術論文
論文出版年:2022
畢業學年度:110
語文別:英文
論文頁數:43
中文關鍵詞:階層式分群法自然斷點法原住民聚落熱區偵測
外文關鍵詞:hierarchical clusteringhot spot analysisindigenous settlement patternclustering analysisJenks natural breaks
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原住民人口約占台灣總人口數2.4%,且是臺灣歷史與文化的重要根源,各族群皆擁有各自不同的風俗習慣及部落結構。隨著現代都市化的發展,原住民族與主流社會間雖然變得更緊密不可分,然而在原住民人口統計上的數據仍存在顯著的落差。如何將原住民人口實際現狀精準呈現對於政府政策制定或文化保存等方面是十分重要,然而現存的研究鮮少提供完整且詳盡的資訊。本研究旨在發展依據各地區不同的特性為核心,聚落為單位的統計模式,並採互動式WebGIS視覺化研究結果。
本研究以台灣原住民族基礎開放研究資料庫(TIPD)中的桃園原住民人口分佈資料為研究實例,採用Moran’s I選取三個地區並初步標示出該地區的熱區所在,並進一步探討。然而在龜山地區使用Moran’s I時無法準確標示出所有原住民人口熱區所在,因此改採自然分級法 (Natural Breaks method) 劃分出各熱區的邊界。由於傳統行政界域在統計上的侷限,為了能更好描述各地區相異的人口因素,本研究採用階層式分群法將各人口統計數據相近的行政區域分群方便研究進行。
我們在研究實例當中發現原住民傾向於以小型部落等方式聚居,而在偏鄉地區此種情形尤為明顯。在偏鄉地區原住民人口聚落密度(15.36人/公頃)較都會地區(12.61人/公頃)高。除此之外在就業機會較多的地區,如工業區(如觀音工業區)亦可發現到較高的原住民人口比例,可能出於求職等原因聚集。本研究應用展現階層式分群法與自然斷點法,對統計區域進行分級,藉由保證一定的隱私程度來描述原住民人口分佈。

The indigenous population account for about 2.4% of Taiwan’s total population and plays a vital role in the nation’s history and culture. With the progress and development of modern society, the indigenous people and main social stream seem inseparable as ever before; still there remains a significant difference in both social and economic status. Therefore, to precisely depict the current conditions of the aboriginal people is of essential
Importance for either government policy formulation, cultural preservation etc.… However prior studies fail to provide complete and detailed information, thus this paper aims to develop a statistical method based on different characteristics of each region.
We obtained data from the Taiwan indigenous Peoples open research Data (TIPD) database and uses Moran’s I and Jenks Natural Breaks to marks the hot spots. Due to statistical limitations of the current geographical statistical hierarchy, we defined a custom hierarchy implementing hierarchical clustering methods. In this research case, we found that the aborigines tended to live together in tribes and other ways, especially in remote rural areas. The density of aboriginal population in rural areas (15.36 persons/ha) is higher than that in urban areas (12.61 persons/ha). In addition, in areas with more employment opportunities, such as industrial zones (Guanyin Industrial Zone), a higher proportion of aboriginal population can also be found, which may gather for reasons such as job hunting. In this study, our method is based on a 100m*100m grid system, we first divided Taoyuan City into two clusters with results from hierarchical clustering, then a settlement is based on a grade3(Jenks natural breaks) level grids as center with grade2 level grids as outliers, nearby settlement within distance of 150m are agglomerated together as a single settlement.

Contents
Pages
Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Research Objectives 2
Chapter 2 Literature Review 3
2.1 Aboriginal Settlement 3
2.2 TIPD Usage and Limits 3
2.3 Clustering Algorithms 4
2.3.1 Moran’s I 4
2.3.2 Jenks Natural Breaks Classification Method 5
2.3.3 Hierarchical Clustering 6
Chapter 3 Research Design and Methodology 7
3.1 Research Design 7
3.2 Data Collection 8
3.2.1 Taoyuan City Population 8
3.3 Measurement Units 9
3.3.1 Coordinate System 9
3.3.2 Basic Statistical Area 9
3.4 Hot Spot Analysis 11
3.4.1 Sampling Area 12
3.4.2 Hotspot Detection 14
3.5 Self-defined Regions 21
3.6 Website Development 24
3.6.1 Website Structure 26
3.6.2 Website Feature 26

Chapter 4 Results 28
4.1 Settlement Statistics 28
4.1.1 Taoyuan City Overview 28
4.1.2 Statistics of Every District 30
Chapter 5 Conclusion 41
5.1 Findings 41
5.2 Limitation and Future Work 41
Reference 43



















LIST OF TABLES
Pages
TABLE 3-1 GVF Value Chart 7
TABLE 3-2 Jenks Classes Population Count Range 24
TABLE 4-1 Indigenous Settlement of Taoyuan City 31



































LIST OF FIGURES
Pages
FIGURE 3-1 Research design 7
FIGURE 3-2 Geographical statistical classification hierarchy 10
FIGURE 3-3 Guanyin District population heat map 11
FIGURE 3-4 Guanyin District indigenous population distribution map 12
FIGURE 3-5 Guishan District indigenous population distribution map 13
FIGURE 3-6 Daxi District indigenous population distribution map 14
FIGURE 3-7 Guanyin District local Moran’s I (significance = 0.1) 15
FIGURE 3-8 Guanyin District local Moran’s I (significance = 0.05) 16
FIGURE 3-9 Guishan District local Moran’s I (significance = 0.1) 16
FIGURE 3-10 Guishan District local Moran’s I (significance = 0.05) 17
FIGURE 3-11 Daxi District local Moran’s I (significance = 0.1) 17
FIGURE 3-12 Daxi District local Moran’s I (significance = 0.05) 18
FIGURE 3-13 Hierarchical clustering result 22
FIGURE 3-14 Website structure 24
FIGURE 3-15 Backend structure 25
FIGURE 3-16 Marker popup example 25
FIGURE 3-17 indigenous population heatmap 26
FIGURE 3-18 Column definition of database 27
FIGURE 4-1 Taoyuan District indigenous population heat map 30
FIGURE 4-2 Chongli District indigenous population heat map 32
FIGURE 4-3 Pingzhen District indigenous population heat map 32
FIGURE 4-4 Bade District indigenous population heat map 33
FIGURE 4-5 Yangmei District indigenous population heat map 34
FIGURE 4-6 Luzhu District indigenous population heat map 35
FIGURE 4-7 Guishan District indigenous population heat map 36
FIGURE 4-8 Longtan District indigenous population heat map 37
FIGURE 4-9 Daxi District indigenous population heat map 37
FIGURE 4-10 Dayuan District indigenous population heat map 38
FIGURE 4-11 Guanyin District indigenous population heat map 39
FIGURE 4-12 Xinwu District indigenous population heat map 40


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