Individual Level Analysis and the New Administrative DataDr. Karyn Morrissey,Department of Geography and Planning,University of Liverpool

DataMany forms;

Level 1: Micro and Macro Public policy – requires micro–level data Model impact of policy at the individual level,

Incorporate demographic, economic, social, environmental factors, plus…

Account for behavioural change (longitudinal models)Host of methods to analysis this data

Descriptive Statistics, Probability Equations, Deterministic Equations

Current MethodsHost of methods to analysis this data

Descriptive Statistics, Probability Equations, Deterministic Equations

Numerous methodologies to augment and refine this microdata;Add spatial referring: Spatial microsimulationAdd behavioural aspects: Dynamic simulation,

Agent Based Modelling We are quite good at this now (in theory)!

Big DataFuelled by continuing technological advances

Generation of enormous real-time datasets ‘Big data’Particularly in cities – ‘Smart Cities’

Potential – provide entirely new information on the functioning of societyAt various scales and across time

Understanding such data, remains a major challenge.

Data GenerationData is doubling every two years and last year (2011) 1.8

‘zettabytes’ was collectedCatone (2011) suggests that it is equivalent to the storage

in 57.5 billion 32 GB iPads but, Whatever the analogy - number is too big to comprehend.

The volume of data released each day exceeds anything that could be collected in the typical academic lifetime of a generation ago

Most of this information will be lost

Data Generators/HoldersPrivate companies collect for the first time more personal

information than central governmentAlso Volunteered data

Typically collected by individuals with no formal trainingThere is an increasing amount of such data that

could be, and in some cases is being, incorporated into formal scientific analyses.

Unlike company based dataMuch historical volunteered information is held by

public organisations and agencies who have an obligation to make their data holdings publicly available

Data IntegrityChange in who collects data is important;

Particularly with regard to its representativeness,And therefore reliability and robustness for analysis

More data - does not mean more quality dataStatistical integrity will or is often not be a priorityEspecially in comparison with national census

agencies.Most of these data collection processes differ from

that of a prescribed scientific experimentThis has to be taken into consideration when

analysing the data, calibrating models or testing hypotheses

Data IntegrityThis is a major issue given the ONS plans to phase

out the National CensusThe golden standard for population data

Data collected by infrastructure based agencies/companies – transport, telecommunication’s, energy Tend to be of a high standard – high-tech metering,

etc However, population data sets – Health records,

social welfare, etc,Prone to error and importantly - deemed highly

confidential

Big Data – Big Usage?Also, quantitative revolution: began to use ‘large’ datasets

to garner insights into the societal processes Most simulation models still find it difficult to provide

datasets for the population of the UKStill lack computational power

And many of our methods are yet to be harnessed for the latest datasets due to the rapidity and frequency of data releasesParticularly real time data – temporal modelling is difficult –

time as a continuous rather than a traditionally modelled discrete entity…

Admin Data The National Psychiatric In-patient

Reporting System (NPIRS) is maintained by the Health Research Board (HRB) in Ireland

Includes data including addresses on all admissions to and discharges from, psychiatric in-patient facilities in Ireland annually.

Patients addresses may be geo-coded for spatial analysis

Observe link between patient’s home ward and distance to hospital

Individuals that live beside hospitals – higher level of admissions?

Does proximity influence admissions?

Important policy question

New data – new stepsExamine the relationship further - need to use data

from two different micro-level data sourcesSMILE (Simulation Model of the Irish Local Economy):

spatial profile of individuals with depression & access scores to APH (Morrissey et al., 2010)

Admin data -NPIRS: spatial profile of individuals admitted to APH

No way to infer rates of psychiatric admission from SMILECannot extrapolate results from NPIRS to general

population – determinants are specific to the in-patient population

Need a linking methodology to join both datasets together

New data – new stepsUsing propensity score matching (PSM)

Found that closer proximity to an acute psychiatric hospital increases an individuals with self-reported depression probability of being admitted to an APH.

Brunsdon and Comber, 2012 – found climate change effects on flowering, by using random co-efficient modellingHowever their first model, a simple OLS, indicated that

flowering dates were becoming later!!!

New data – new stepsOver 32,000 respondents

!Great!However - potential survey biasI’m interested in extrapolating my

findings on depression from this survey to the general population

Non-random sample – people with a higher propensity/already have a CMD may specifically listen/respond to the show

Cannot use OLS/Binary ModelsNeed Sample Selection Models

University of Liverpool (Kinderman and Pontin) and BBC

Use BBC website to develop a wellbeing scale

Launched - All in the Mind a BBC Radio 4 programme

ConclusionIn ‘bite-size’ pieces, this new data, be it real-

time, volunteered, administrative, company-based, can be highly useful…

New methods, new ways of looking at dataNeed to be aware of it’s limitationsDefinite potential to link this new and big

data to older quantitative methods to help inform policy

Particularly as we as researchers become aware of it’s capacity and

Final Word

Need to question – Do we need all this data?

An answer to this may only be provided by turning to our ‘qualitative colleagues and the ‘data’ they collect