De-mystifying data

Vivienne Avery

Demography and Policy Analysis Manager

Outline

• Types of data and ways of classifying it

• How statistics are derived from data

• Key data sources for London

• Examples of data use in City Intelligence Unit

Types of data

• Numbers

• Text

• Multi-media• Images

• Video

• Audio

Other ways of classifying data

• Quantitative v Qualitative

• Primary v Secondary data

• Purpose of measurement e.g. research, monitor, evaluate impact

• Types of statistics produced from these data – estimates, forecasts, composite statistics

Quantitative v Qualitative

Quantitative research: uses measurable data to formulate facts and uncover patterns in research

Qualitative research: primarily exploratory research. It is used to gain an understanding of underlying reasons, opinions, and motivations

When to use quantitative research

• Measure a population and/or feature of a population

• Test a hypothesis

• Model relationships between different variables in a population

• Make population projections and forecasts

• Estimate policy impact

• Estimate costs and benefits

When to use qualitative research

• Deep understanding of individual needs, values, motives

• Understand info obtained in a quantitative study e.g. satisfaction levels

• Develop new theories/hypotheses for testing

• Generate policy ideas, improvements, policy direction and development

• Evaluate policy effectiveness / communication / marketing messages

Primary data

• Population census

• Surveys

• Qualitative research

• Citizen science

• Consultation

Secondary data – public sector data

Secondary data – commercial data

Secondary data – commercial – social media

Purpose of data collection

• Research question

• Hypothesis / Logic model

• Monitoring progress

• Evaluating impact

Composite statistics – bringing together data

• Population classifications

• Index of Multiple Deprivation

Population classifications – London Output Area Classification (LOAC)

Consumer Population Classifications: Acorn (CACI): N1 9PW – Urban adversity, struggling estatesMOSAIC (Experian)

Index of Multiple Deprivation

• Overall IMD

• Education & Skills

• Crime

• Living environment

• Older people income

Statistical Modelling

• Mathematical model using statistical assumptions to understand relationships between different variables (such as the characteristics of the population)

• Examples of well known models• Relationship between variables - Birth cohort studies – longitudinal

research - relationship between smoking and physical health

• Projections or forecasts

• Tax-benefit models – micro-simulation models e.g. welfare reform

• Cost-benefit analysis – evaluating the impact of policy e.g. new transport infrastructure, health benefits of a new drug.

Population projections

• Use trends in birth, deaths and migration to estimate future population. In London, we also use planned housing development data to allocate population growth across the city

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2011 2013 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041

Barnsbury Population

Where can I find data?

• London Datastore

• Boroughs

• Office for National Statistics• https://www.ons.gov.uk/

• Release calendar

• https://www.ons.gov.uk/releasecalendar

• gov.uk• Fingertips – https://fingertips.phe.org.uk/

• NHS Digital

• Academic, independent research organisations (e.g. JRF, Trust for London)

What is the London Datastore?

Examples of Social Evidence Base outputs

Examples of how we use data

• Decision Support tools – Sports Unite, Violence Reduction Unit

• Schools Atlas / School roll modelling

• Public Health approach to Serious Youth Violence

Decision support tools to assist resource allocation

• Decision-support tools have been designed for resource allocation and funding for the following areas:

• Violence Reduction Unit• Sport Unites• Good Growth Fund

• These tools bring together data from a range of sources – to produce scoring systems that highlight areas with highest need

• Social integration• Crime statistics and perceptions about crime• Public health measures• “People, place and prosperity” for economic regeneration

Decision support tools to assist resource allocation

Schools Atlas / School roll projections

Violence in Islington: ambulance data

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10_14 15_ 19 20_24 25_29 30_34 35_39 Over 40 n/a

Victims Age Range

• Led to changes in monitoring adults (Integrated Offender Monitoring team) and in street population (knife offences were often drug-related

Mapping vulnerable areas to food insecurity

• Estimates of food insecurity from Survey of Londoners

• IMD, LOAC

• data from 360 Giving on projects related to food

• NPI report on Borough support and assistance

Conclusions - how can you use data?

• There is a vast amount of data to improve your use of evidence

• There are different ways of classifying data types and these help understand the different sources

• You have to invest some time – understanding what the data is, tools to use it – all the training you need is on YouTube

• 4 things data can do for you• Help you understand your area as a whole and in relation to the rest of

London• Help you understand your target population• Understand what works with the type of outcomes you want to achieve• Help you understand what services and projects are already funded