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Last modified by Hyunji Cho on 2022/07/04 14:39
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1 An important aim of the DivAirCity project is its acknowledgement and attempt to address data gaps where marginalised groups of people are concerned. It impacts the capacity of data to address equal participation and accessibility at which the DivAirCity project aims.
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3 Apart from the operation of the data framework in a general manner, the DivAirCity project needs to pay particular attention to building inclusive data. Inclusive data is to ensure that everyone is counted in evidence, and no one is forgotten. This is key to eliminating discrimination on the basis of personal characteristics such as disability, ethnicity, or gender and to accelerating global efforts towards inclusive programming.
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5 Greater concerns regarding biased data and the lack of visibility of diverse groups in data (or big data) started to be raised in recent years (Hand, 2020; Perez, 2019).  Still, not many data processing practices include this aspect. This section specifically addresses the key points to consider in order to provide the methods for inclusive data processing.
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7 == 1. Gathering and reviewing existing data ==
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9 Systematic analysis will be needed about available disaggregated data and other relevant quantitative and qualitative data. This includes official data from national statistical offices for example, data from census, standard national surveys, household consumption, administrative and registration systems.
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11 Published qualitative data from surveys or interviews including research by government, academic institutions, civil society, and community groups also should be analysed. The membership, activities, and participation data from local and national associations can be included. As data is increasingly geo-referenced, spatial aggregation becomes as useful tool. This will be directly used and visualised through DivAirCity geospatial maps (Deliverable 2.5).
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13 Data from diverse information sources are critical to complement information. For example, organisations working with groups and populations in vulnerable or marginalised situations can already have their own data. These data sources are identified further through DivAirCity data acquisition framework separately.
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15 == 2. Identifying and prioritising data gaps ==
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17 After reviewing the available data, it is vital to identify and prioritise the gaps in data. Following that, capacities and processes in order to monitor the data for 6+1 categories of diversity need to be identified. The DivAirCity project defines the 6+1 protected categories as the target groups of the project. However, the importance of the 6+1 groups can differ in each city. For example, a certain city can have more immigrant groups or refugees while another can have more elderly citizens. When cities define their targets and goals, they need to analyse which diverse groups inhabit their cities.  These identifying process of diverse groups in cities will provide practical guidance that stakeholders can use towards the goals of DivAirCity for including everyone in building sustainable cities.
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19 == 3. Filling data gaps ==
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21 More and better quantitative data is required, while it is also important to acquire new complementary qualitative data in which the evidence is generated directly by the 6+1 protected groups. Here, the two methods, collecting disaggregated data and engaging community in data collection, can be considered.
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23 ==== 3.1. Collecting disaggregated data ====
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25 Collecting disaggregated data is the first step toward the development inclusive data framework. Disaggregated data refers to the separation of compiled information into smaller units to elucidate underlying trends and patterns. For example, gender-disaggregated data is data that can be broken down by gender groups such as men, women, and gender minorities. It allows identify the status of each group. Disaggregated quantitative and qualitative data should be collected at the start of any planning process of data collection to expose related inequalities, inform data-driven solutions, and enable the measurement of project success over time.
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27 Disaggregating data by two dimensions, such as gender and income, is not challenging as much as more refined analysis of disadvantaged groups by using multilevel disaggregation.  For example, it is often possible to analyse sex and location or sex and income, but it is more challenging to analyse women from ethnic minorities living in poor households.
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29 These challenges in collecting disaggregated data are related to the quality of existing data and its timeliness as well. Indicators related to poverty can be a good example.  They mainly rely on household surveys, but those household surveys are typically not designed to measure living standards or to be representative at local levels. Also, households are used rather than individuals as a unit in surveys.  It may preclude assessments of intra-household discrimination for example gender gaps within the family and individual disadvantage. Existing tools are likely to remain insufficient to capture the range of people and communities being marginalised.
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31 Disaggregated data must be collected regardless of whether the plan or project is intended to address those groups' inequities.  It helps us to track whether there are inadvertent or unintentional adverse impacts on a certain group such as gender minorities.
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33 ==== 3.2. Involving communities in data collection and analysis ====
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35 Meaningful participation of people should be ensured in the data collection and analysis processes.  It needs to provide safe and trusted spaces by engaging with community groups and associations that include and represent the 6+1 groups. Partnerships and platforms should be strengthened by participation, decision making processes as well as feedback mechanisms.
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37 Some DivAirCity indicators use surveys or other qualitative methods through citizen participation to overcome data gaps. Data is disaggregated by distinguishing the types of populations with respect to the 6+1 categories. Along with this data collection through citizen participation, the DivAirCity indicators include KPIs to monitor this engagement processes.  This feedback mechanism can promote transparency, inclusiveness and consensus, especially including the most marginalized groups and those left behind.
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39 Digital devices including mobile smart phones can be helpful in this community engagement. When it comes to the digital device, inequities in digital affordability and availability should be considered. Special measures may be required to ensure that engagement processes are accessible for persons with disabilities, such as those using screen-reading software.  Digital devices might not be fully inclusive for all 6+1 groups but those can be helpful in a certain case.
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