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系統識別號 U0026-1608201916092100
論文名稱(中文) 結合開放街圖(OSM)與夜間燈光影像於高解析度人口密度估計
論文名稱(英文) High-Resolution Population Density Estimation from Integration of Nighttime Light images and OpenStreetMap(OSM)
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系
系所名稱(英) Department of Geomatics
學年度 107
學期 2
出版年 108
研究生(中文) 楊承翰
研究生(英文) Chen-Han Yang
學號 P66061059
學位類別 碩士
語文別 中文
論文頁數 109頁
口試委員 指導教授-朱宏杰
口試委員-張升懋
口試委員-張巍勳
口試委員-周嘉辰
中文關鍵字 夜間燈光數據  OSM開放街圖  人口密度  迴歸模型 
英文關鍵字 night-time light  OSM  population density  regression model 
學科別分類
中文摘要 城市化與人類社會發展息息相關。如何根據遙感數據探索城市化議題是相關重要的。夜間燈光影像為城市化和社會經濟變量的研究提供了一個新的領域,這與傳統衛星遙感有很大的不同。美國國家海洋暨大氣總署的國家氣象衛星計劃/操作線掃描系統(DMSP/OLS)提供夜間燈光數據。許多研究利用DMSP/OLS傳感器監測近紅外光輻射(NIR),並量化人類活動與社會經濟變量和夜間亮度之間的關係。因此,本研究的宗旨在產製高解析度人口密度圖以了解人口連續性的分佈趨勢。對於夜間燈光影像,由於大氣條件的逐年變化和傳感器的周期性變化,不同年份獲得的夜間光照數據無法直接比較,因此,本研究使用多時期夜間燈光找出彼此的偽不辨特徵(PIFs),並依相對輻射校準參數校正影像使DN值更加穩定。在本研究中,考慮與人口密度相關的三個因素,如:夜間燈光、開放街圖(OSM)的道路密度和興趣點密度,此三個因素在迴歸模型裡設置為自變數,而依變數為實際人口密度,迴歸模型採用傳統的全域型迴歸(OLS),探討全域式自變數與人口之間的關係,局部型迴歸使用地理加權迴歸(GWR),該模型確定每個城市人口密度與自變數的空間變化關係,然而,該模型是一種局部迴歸方法,容易由變數之間的負相關得到無意義的負人口密度,因此,本文提出了一種利用調整最佳帶寬與非負最小二乘的約束條件改良GWR模型,並定義為非負地理加權迴歸(NGWR),該模型是探索人口密度分佈的有效方法,利用該方法可以解決人口密度負值和優化變數之間的擬合情況。與OLS和GWR相比,NGWR提供最佳的預測人口密度的方法,此外,本研究也考慮到時間變化的關係,考慮時間維度而建立非負時空地理加權迴歸(NGTWR),由2004~2013年的資料為基準,在此模型確立彼此之間的關係,進而獲得迴歸參數,確定好不同模型的迴歸參數後,藉由空間內插依已知迴歸參數估計未知迴歸參數,再使用網格運算的方式獲取人口密度圖。

本研究對不同的人口密度圖做精度驗證,不考慮時間因素下,最佳人口密度估計模型為NGWR,其樣點交叉驗證結果為相對誤差率17.95%,而NGTWR可降至7.87%,此外,校正後的夜間燈光影像可改善人口密度估計精度,其區域驗證下結果為均方根誤差230降至223,而多變數人口密度估計的誤差比單一變數低,其均方根誤差由230降至221,另一方面,本研究結合OSM水體、森林資料調整人口密度分佈,也針對都市化的議題,進行小區域的人口密度估計,研究區域選取北京市,此地方人口密度估計採用最新夜間燈光影像VIIRS/DNB數據,具有更高空間解析度以及更細部的空間分佈特徵,北京市所估計的人口密度圖為本研究對人口估計方面的延伸,此外,本文從不同研究所產製的人口密度圖進行比較,進而得知可改善的空間以及不同演算法在人口密度估計上的優勢。
英文摘要 Nighttime light imagery offers a unique perspective that greatly differs from that of conventional satellite remote sensing in studying urbanization and socio-economic variables. The National Oceanic and Atmospheric Administration provides night light data, including DMSP/OLS and VIIRS/DNB nighttime light images. However, given the periodic changes in satellite sensors and the yearly variations in atmospheric conditions, the nighttime light data obtained across different years cannot be directly compared. Therefore, the application of multi-temporal nighttime light calibration is proposed to enhance the stability of these images. This study extracts three factors related to population density, namely, nighttime light, road density, and POI density, from OpenStreetMap (OSM) and imports these factors into a regression model. After obtaining the coefficient of this model, population density maps of China and Beijing were generated and adjusted based on the water body and forest land distribution data from OSM. Result shows that the proposed model is more accurate than the existing models as reflected in its RMSE and MRE indexes. Therefore, the regional population of China can be reliably and effectively estimated based on OSM features and nighttime light images.
論文目次 目錄
摘要 I
英文延伸摘要 III
致謝 X
目錄 XI
圖目錄 XIV
表目錄 XIX
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 研究流程與方法 3
1.4 研究貢獻 6
1.5 研究架構 7
第二章 研究資料 8
2.1 夜間燈光影像 8
2.2 人口資料 10
2.3 開放街圖(OSM) 11
第三章 文獻回顧 12
3.1 人口密度估計 12
3.2 DMSP/OLS與VIIRS夜間燈光影像應用 13
3.3 DMSP/OLS與VIIRS夜間燈光影像率定 14
3.4 OSM開放街圖資料應用 15
3.5 迴歸模型預測法 15
第四章 研究方法 16
4.1 人口資料前處理 16
4.1.1 平均區域統計法 16
4.2 夜間燈光校正 17
4.2.1 偽不辨特徵相對輻射校正法 18
4.3 OSM密度分佈圖 22
4.3.1 道路核密度估計 22
4.3.2 興趣點核密度估計 24
4.4 迴歸模型 27
4.4.1 全域型迴歸(OLS) 27
4.4.2 地理加權迴歸(GWR) 29
4.4.3 非負地理加權迴歸(NGWR) 33
4.4.4 非負時空地理加權迴歸(NGTWR) 36
4.5 空間內插 39
4.5.1 反距離權重法(IDW) 39
4.6 驗證方法 42
4.6.1 殘差的空間自相關 42
4.6.2 區域型驗證 46
4.6.3 樣點交叉驗證 48
第五章 研究成果分析 49
5.1 地理加權迴歸帶寬測試 49
5.2 人口密度圖產製與分析 52
5.3 非負時空地理加權迴歸結果 60
5.4 人口密度調整 65
5.5 各迴歸模型殘差的local Moran’s I 值分佈 71
5.6 夜間燈光影像校正前後人口密度精度之差異 74
5.7 單一變數與多變數人口密度精度之差異 80
5.8 都市化下人口密度變遷 86
5.9 小區域案例探討 89
5.10 相關人口產品之比較 100
第六章 結論與研究限制 104
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