| 研究課題/領域番号 |
20K10447
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| 研究機関 | 東京大学 |
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研究代表者 |
新井 亜弓 東京大学, 地球観測データ統融合連携研究機構, 特任研究員 (10788574)
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| 研究分担者 |
金杉 洋 東京大学, 空間情報科学研究センター, 客員研究員 (00526907)
ウィタヤンクーン アピチョン 東京大学, 空間情報科学研究センター, 特任助教 (90726407)
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| 研究期間 (年度) |
2020-04-01 – 2024-03-31
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| キーワード | mobile phone data / human mobility / developing country / malaria / public health |
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| 研究実績の概要 |
Work Package I: Linking seasonal demography and mobile phone patterns to map seasonal demography
Data access is already secured. We already set up data processing environment in the premise of the ICT regulator who facilitated mobile phone data collection from three mobile network operators (MNOs). The mobile phone data of the three MNOs cover more than 90% of the market so we consider that the data well represent the general population of study site. We examined the correspondence of residential population observed from mobile phone data with known population data (census). The result shows that the population distribution estimated from mobile phone data in terms of residential population is relevant for this study (we used the data of July 2019). We conducted the analysis on mobility patterns by different seasons. We estimated the population distribution at different times of days to examine how population distributions change seasonally; we also examined changes in residential locations. It enables us to indicate the temporal changes in residential location. It can be used as a proxy for seasonal migration. To compare the seasonal characteristics of mobility patters, we employed the following mobility metrics: OD matrices, average distance traveled, and radius of gyration.
Estimated demographic attributes using our existing model where we found that the model is not robust under COVID-19 setting as people's mobility patterns are different from usual patterns.
Work Package 2: Focusing on high transmission seasons and extending to other countries
Data collection is ongoing.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
3: やや遅れている
理由
We found that our existing model for estimating demographic attributes is too sensitive to the changes in time spent at home and work places, which are key parameters in the model. The estimation result is not encouraging.
We are considering to taking a different approach for estimating the attributes of mobile phone users.
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| 今後の研究の推進方策 |
1) Attempting to change data period for this study due to the impact on mobility patterns under COVID-19
We already have access to mobile phone data of July 2019 (dry season) and February 2020 (rainy season) where the mobility patterns of February 2020 is affected by social-distancing policy. We are trying to obtain February 2019 as the alternative to February 2020 where we expect to be able to extract usual seasonal patterns without any effects of COVID-19.
2) Possibility of developing a different approach to infer the sociodemographic attributes of mobile phone users
We are considering that we may use a different way of classifying population groups by sociodemographic attributes. In our original plan, our model estimate demographic attributes based on routines in mobility patterns. However, parameters used for the estimation are closely related to mobility patterns and may not work effectively under COVID-19. Instead, we plan to use satellite images to estimate socioeconomic groups by the pattern of buildings and human settlements.
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| 次年度使用額が生じた理由 |
We could not hold any face-to-face workshop and field surveys so could not use any travel expenses.
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