Development of imputation methods for spatially dependent missing data
| Project/Area Number |
26820217
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Civil engineering project/Traffic engineering
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| Research Institution | Kobe University (2016)
Hiroshima University (2014-2015) |
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Principal Investigator |
SEYA HAJIME 神戸大学, 工学研究科, 准教授 (20584296)
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| Co-Investigator(Renkei-kenkyūsha) |
TSUTSUMI Morito 筑波大学, システム情報系社会工学域, 教授 (70292886)
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| Project Period (FY) |
2014-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2016: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2015: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2014: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | 地理情報 / 空間相関 / 欠損データ / 空間統計 / 土木計画 / 欠損値補完 / 空間情報 |
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| Outline of Final Research Achievements |
We developed new imputation method for spatially dependent missing data. The method attempts to apply spatial information to the prior distribution of missing values. We found that [1] By using spatial information for prior distribution, the imputation accuracy of some spatially auto-correlated explanatory variables can be improved (e.g., rent price, accessibility index). [2] Prior spatial information on missing data may not be effective for variables with a high {nugget/sill} ratio; in other words, the measurement error and/or micro-scale variation is dominant in total variation (e.g., FAR(Ratio) and age).
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Report
(4 results)
Research Products
(7 results)
