系統識別號 U0026-2506201900444600
論文名稱(中文) 以人口群聚探究台灣的城鄉結構
論文名稱(英文) Study population agglomeration patterns for the urban-rural geomorphology of Taiwan
校院名稱 成功大學
系所名稱(中) 都市計劃學系
系所名稱(英) Department of Urban Planning
學年度 107
學期 2
出版年 108
研究生(中文) 蔡明華
研究生(英文) Ming-Hua Tsai
學號 P26064245
學位類別 碩士
語文別 英文
論文頁數 90頁
口試委員 口試委員-陳彥仲
中文關鍵字 城鄉結構  人口群聚  MAUP  點型態分析  空間自相關  空間內插法  台灣 
英文關鍵字 urban-rural geomorphology  population agglomeration  population cluster  Modifiable Areal Unit Problem  point pattern analysis  spatial autocorrelation  spatial interpolation  Taiwan. 
中文摘要 人口在空間中的分布被視為是探究城鄉結構的重要依據之一,都市計畫應遵從人口分布型態據以擬定地區發展計畫。然而,台灣的都市計畫長久以來依賴行政區來了解人口分布型態,卻輕忽行政界線僅是空間上虛構的線條─其劃設目的為公平分配國家資源,因此易隨政策改變界線,導致不同時空下不一致。因此,行政區界無法如實的呈現可靠的人口分布型態,可被視為是都市計畫的MAUP,說明連續的地理空間型態受人為劃分的可修改單元理解後,剝奪其原始的空間特徵。
英文摘要 The population distribution pattern is regarded as critical evidence to study the urban-rural geomorphology. While the scopes of urban planning should follow the patterns; however, Taiwan typically depends on the administrative boundaries and is regardless of their abstract and arbitrary spatial representation. To be precise, administrative areas were demarcated for the purpose of distributing national resources equitably, leading to the inclination to change with policies and making them become inconsistent through time. As a consequence, they fail to demonstrate reliable and applicable settlements distribution patterns—arises a potential risk of inconsistency to the reality—which is also regarded as one MAUP in the domain of urban planning, indicating continuous geomorphology being distorted by artificially modifiable spatial units.
The research attempt to reveal this disparity through observing population agglomeration patterns of two population demarcations, the administrative areas and the statistical units. The latter one was featured with the prominent characteristics of homogenization, fineness, and temporal stability, thus relatively fit well with the real population distribution. We regard cities as the most densely populous areas and should exist in clusters, therefore, the research methods apply statistical profiling, spatial interpolation, and spatial autocorrelation to obtain the patterns of population clusters. Next, we interpret the results into the urban-rural classification corresponding to empirical knowledge. Lastly, we propose an advised urban-rural classification and differentiate between two demarcations and approaches.
The main contributions include a recommended urban-rural classification based on the combination of statistical profiling and spatial autocorrelation, which is divided into five categories: downtown, city, town, village, and countryside, belonging to the urban, suburban, and rural areas. First, it is of interest that the advised patterns are substantially consistent with the statistical profiling; however, it remains a dissimilarity in the population size. Second, it is of certain that a more cogent pattern can we obtain from the statistical units, and we again confirm that a more average population delineation in the administrative areas. Third, the result claims that the Pareto exponent in Taiwan’s city-size distribution is not equal to 1, and demonstrates a phenomenon of urban primacy that the largest cities are overwhelming the secondary cities by around triple times. To conclude, the research provides new insight into the acknowledgment of Taiwan’s urban-rural patterns.
論文目次 中文摘要 i
Abstract ii
Acknowledgement iii
Contents iv
List of Tables vi
List of Figures vii
1 Introduction 1
1.1. Background 1
1.2. Research aim and objectives 2
1.3. Thesis structure 3
2 Perspectives for urban-rural geomorphology 4
2.1 Implications from settlement geography 4
2.2 Empirical studies for continuous settlement patterns 9
2.3 Delineation for urban-rural geomorphology 12
3 Research methodology 18
3.1. Study region 18
3.2. Population database 19
3.2.1. Spatial representation of population spatial units 19
3.2.2. Applied database 21
3.3. Methodology 23
3.3.1. Statistical profiling of population spatial units 24
3.3.2. Spatial interpolation and modeling 24
3.3.3. Spatial autocorrelation and association 26
4 Method implementation 29
4.1 Statistical profiling 30
4.2 Kernel Density Estimation, KDE 34
4.3 Spatial autocorrelation 36
4.3.1 Global Moran’s I index 36
4.3.2 Local Indicators of Spatial Association, LISA 38
4.4 Further classification of the original LISA patterns 48
5 Confirmation of Taiwan’s urban-rural geomorphology 54
5.1. Interpretation of the urban-rural geomorphology 54
5.1.1. The definition of the urban-rural patterns 54
5.1.2. Taiwan’s urban-rural patterns 55
5.2. A recommended urban-rural classification 59
5.3. Differentiate between urban-rural geomorphology 69
6 Conclusion 81
6.1. Summary of findings 81
6.2. Recommendations and future research 82
References 85
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