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Version 5.1 by Hyunji Cho on 2022/07/04 14:40
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1 This chapter aims to define best practices for handling data for the mentioned datasets. This is an important stage of the project because for each indicator required, many types of data are needed which can be collected in different ways. Having data collected and handled uniformly throughout the project will ensure comparability across cities and over time.
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3 The outcomes created by datasets can vary hugely depending on the temporal resolution at which the data was collected. In particular, air pollution measurements can vary depending on the time of day, the weather and the season. Therefore, measurements must be taken at multiple times throughout the day and span multiple months or even years, in order to create an unbiased picture of air quality.
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5 On the other hand, data including health and census records will vary on considerably longer timescales than that of air pollution. Census data may only be collected every few years and thus its values will remain constant for a long period of time compared to constantly changing air pollution values. A meaningful timeseries of social data may only be created by comparing values across years or decades, compared to air pollution which can be observed to change day by day.
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7 This chapter aims to specify data handling process including those temporal and spatial resolution. Along with that, Chapter 9 of this document provides some sample visualisation options with various temporal resolution examples.