• Asian Journal of Innovation and Policy
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• v.9 no.2
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• pp.223-239
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• 2020

It is important for urban planners and policy makers to understand complex, diverse urban demands and social structure, but this is not easy due to lack of data that represents the dynamics of residents at micro-geographical level. This paper explores how to create population data at at a micro-level by allocating population data to building. It attempted to allocate population data stored in a grid layer (100 meters by 100 meters) into a building footprint layer that represents the appearance of physical buildings. For the allocation, this paper describes a systemic approach that classifies grid cells into five prototypical patterns based on the composition of residential building types in a grid cell. This approach enhances allocation accuracy by accommodating heterogeneity of urban space rather than relying on the assumption of uniform spatial homogeneity of populations within an aerial unit. Unlike the methods that disaggregate population data to the parcel, this approach is more applicable to Asian cities where large multifamily residential parcels are common. However, it should be noted that this paper does not demonstrate the validity of the allocated population since there is a lack of the actual data available to be compared with the current estimated population. In the case of water and electricity, the data is already attached to an individual address, and hence, it can be considered to the purpose of the validation for the allocation. By doing so, it will be possible to identify innovative methods that create a population distribution dataset representing the comprehensive and dynamic nature of the population at the micro geographical level.

• Journal of Korea Water Resources Association
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• v.49 no.4
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• pp.283-291
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• 2016

As the infrastructures and populations are the condensed in the mega city, urban flood management becomes very important due to the severe loss of lives and properties. For the more accurate calculation of runoff from the urban catchment, hourly or even minute rainfall data have been utilized. However, the time steps of the measured or forecasted data under climate change scenarios are longer than hourly, which causes the difficulty on the application. In this study, daily rainfall data was disaggregated into hourly using the stochastic method. Based on the historical hourly precipitation data, Gram Schmidt orthonormalization process and K-Nearest Neighbor Resampling (KNNR) method were applied to disaggregate daily precipitation into hourly. This method was originally developed to disaggregate yearly runoff data into monthly. Precipitation data has smaller probability density than runoff data, therefore, rainfall patterns considering the previous and next days were proposed as 7 different types. Disaggregated rainfall was resampled from the only same rainfall patterns to improve applicability. The proposed method was applied rainfall data observed at Seoul weather station where has 52 years hourly rainfall data and the disaggregated hourly data were compared to the measured data. The proposed method might be applied to disaggregate the climate change scenarios.