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CHIMERA WORKING PAPER NUMBER: 2007-08 CWP-2007-08-Time-To-Play.doc

Time to play: combining time-use surveys and census data to estimate small area distributions of potentially ICT mediated leisure Chimera Working Paper Number: 2007-08

Dr Ben Anderson, Chimera, University of Essex [email protected] There is an acknowledged need for a more locally oriented analysis of access to and usage of ‘information society’ services which can differentiate between patterns across relatively small geographical areas and which can support analysis of the local, individual and national factors that may be causing social, economic and digital exclusion. There is however a paucity of data and methods that can support this need. This paper reports preliminary results from the development of a method to estimate patterns of internet uptake and media time-use at small area levels (2001 Lower Layer Super Output Areas) using spatial microsimulation. The paper describes the method and provides a number of results for the Eastern region of England based on the 2000 Office for National Statistics Time-Use Survey. The paper discusses the reliability of the results, their potential value to policy makers and commercial strategists and indicates directions for future research. This paper may also be cited as: Anderson, B. (2007) Time to play: combining time-use surveys and census data to estimate small area distributions of potentially ICT mediated leisure. Paper presented at Internet Research 8.0, Association of Internet Researchers Annual Conference, 17-20 October 2007, Vancouver.

http://www.essex.ac.uk/chimera/

CHIMERA WORKING PAPER NUMBER: 2007-08 CWP-2007-08-Time-To-Play.doc

Chimera The work reported in this paper is part of the scientific programme of Chimera, the Institute for Sociotechnical Innovation and Research at the University of Essex. Chimera is a post-disciplinary institute employing social scientists, computer scientists, engineers, anthropologists, psychologists, HCI practitioners and interface designers specialising in ‘socio-technical’ research and consulting. It was set up in April 2002 at Adastral Park, Suffolk as a research institute of the University of Essex. Chimera carries out research which combines the social and technological sciences to: •

generate insights into personal and social use of information and communication technologies,

•

ground technological innovation in an understanding of people,

•

provide analysis to support evidence-based 'information society' strategies and policies in the public and commercial domain.

We achieve this through a balanced programme of basic and applied research projects, consultancy and publication. For more information see www.essex.ac.uk/chimera Contacting Chimera Chimera Institute of Socio-Technical Innovation and Research Ross Building (PP1, ROS-IP) Adastral Park, Martlesham Heath, Ipswich, Suffolk, IP5 3RE UK

Tel: +44 (0) 1473 631182 Fax: +44 (0) 1473 614936 E-mail: [email protected] Web: http://www.essex.ac.uk/chimera/

Citing This Paper Readers wishing to cite this paper are asked to use the following form of words: Anderson, B (2007) ‘Time to play: combining time-use surveys and census data to estimate small area distributions of potentially ICT mediated leisure’, Chimera Working Paper 2007-08. Ipswich: University of Essex. For an on-line version of this working paper and others in the series go to www.essex.ac.uk/chimera/publications.html © 2008, University of Essex All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted, in any form, or by any means, mechanical, photocopying, recording or otherwise, without the prior permission of the Director, Chimera.

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Table of Contents 1

Introduction............................................................................................................................................... 4

2

Previous work ........................................................................................................................................... 5

3

Spatially simulating ICT uptake and Media use..................................................................................... 5

4

The spatial microsimulation method ...................................................................................................... 6

5

Results..................................................................................................................................................... 10

6

Conclusions ............................................................................................................................................ 13

7

Acknowledgements................................................................................................................................ 14

8

REFERENCES ......................................................................................................................................... 14

Annex A

Constraint Tests......................................................................................................................... 16

A.1

Top level categories and internet access .......................................................................................... 16

A.2

Sub-categories .................................................................................................................................. 17

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1

Introduction

It is becoming increasingly apparent in most industrialised nations that access to new information and communication technologies (ICTs) is, in general, reproducing existing patterns of social and economic exclusion (Chen and Wellman 2005; Selwyn 2005; Selwyn 2005). In the context of policies aimed at using ICTs as a tool to reduce social divides this is clearly problematic. Recent publications by both the UK’s Office of Communications (Ofcom) and its Consumer Panel have, for example, highlighted the relative ‘telecommunications poverty’ of older people, those on low incomes, those with limiting long-term illnesses and, in particular the multiply disadvantaged who are members of a combination of these groups (Ofcom 2006; Ofcom Consumer Panel 2006). A large number of studies using representative sample surveys have shown that those with the social, economic and cultural capital are those who are able to gain most benefit from access to and usage of new technologies (Robinson, DiMaggio et al. 2003; Hargittai 2004; Anderson 2006; Kraut, Brynin et al. 2006). More recently these have been supported by in-depth qualitative studies that reveal the processes of cultural re-production at work (Selwyn 2005; Selwyn 2005; Ferlander and Timms 2006; Kvasny 2006). Throughout this work there has been a call for a more locally oriented analysis of access to and usage of ‘information society’ services which can differentiate between patterns across relatively small geographical 1 areas and which can support analysis of the local, individual and national factors that may be causing social, economic and digital exclusion (Fong, Wellman et al. 2001; Graham 2002; Crang, Crosbie et al. 2006; Ferlander and Timms 2006; Holloway 2006; Crang, Crosbie et al. To appear). Crang et al in particular use a combination of quantitative and qualitative methods to explore the ICT uptake and usage patterns in two contrasting wards of Newcastle, one relatively affluent (Jesmond) and one relatively deprived (Blakelaw). They show how the detailed ‘lived practices’ of ICT usage differ with individuals in the more affluent area using ICTs in a pervasive and continuous manner whilst those in the more deprived area using them in a more episodic manner. In the case of the former this usage supports the interweaving of multiple information and communication oriented activities whilst in the case of the latter usage is more focused, specific and confined. Following Crang and Graham the suggestion is that such disparities go beyond those one would expect to derive from individual and household level effects and include area or neighbourhood effects related in particular to local spatial configurations. To some extent this is re-emphasised in the recent Ofcom research which has illustrated regional disparities within the UK and noted that these cannot solely be explained by regional differences in socio-economic profiles such as the increasing wealth of the South and South East and differential infrastructure availability. However such analyses do not uncover the lower level intra2 regional variations that undoubtedly exist because they work with extremely high levels of aggregation . Thus findings that indicate relatively high uptake, usage and expenditure in London (Ofcom 2006) may well mask extremely low uptake in specific very disadvantaged areas. In contrast data such as that from the UK 2001 Census provides socio-demographic profiles and thus estimates of social exclusion at extremely small areas of geography such as the Output Area which comprises roughly 110 households. Considered at this level, intra-regional variation can be much greater than inter-regional variation (Dorling and Thomas 2004). Were we to recommend policy action to focus on lower uptake regions we would overlook the need to focus action on specific areas within regions of apparently high uptake and use as Holloway has demonstrated for Australia (Holloway 2006). Studies of local ICT uptake and use are relatively easy to carry out in case study form but are much less easy to undertake on a nationwide basis due to the paucity of appropriate data – unlike in Australia indicators of uptake and use at the small area level were not captured by the 2001 UK census. Such data 3 could only be gathered by an extremely large sample survey , a population census or a customer data synthesis exercise involving all known service providers. Clearly what we want to be able to do is to produce time-specific estimates of ICTs uptake and use at the small area level without having to conduct an ‘ICT census’. With such estimates to hand we can then provide analysis on the estimated patterns of ‘ICT poverty’ at the small area level which can then be ground checked to understand the effects of local social and spatial configurations. In combination with estimates of current media and entertainment time-use we can then move towards an assessment of the spatial distribution of the potential ‘content’ market by understanding who has ‘time to play’ and therefore where relevant infrastructure investments could prove most cost-effective. 1

For the purposes of this paper we refer to ‘small areas’ as equivalent to UK 2001 Lower Layer Super Output Areas (LSOAs) each of which comprises c.700 households. 2 For example the London Government Office region contained 3,016,058 households at the time of the 2001 UK Census. 3 For example sampling 100 households from all 32,482 GB LSOAs would produce a sample size of over 3 million households. © 2008, University of Essex http://www.essex.ac.uk/chimera/

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In this paper we focus on refining and testing an iterative proportional fitting method to produce time-specific estimates of household internet uptake and media time-use at the small area level for the UK. We compare the internet uptake estimates to known patterns of multiple deprivation to provide a more fine-grained analysis of the ‘digital access divide’ and use the media time-use results to provide a commentary on the potential spatial distribution of internet-mediated leisure. In addition we compare estimated employment time with data derived from the Census to validate the approach. We conclude with a discussion of future research directions.

2

Previous work

There have been a small number of attempts to generate small area estimates of some aspects of the distributions of ICT uptake and use in the UK. Early work reported by Foley carried out a number of analyses of the social outcomes of internet access at the small area level using address data (postcode) of customers provided by Internet Service Providers (ISPs) and analysed using Experian’s well-known postcode based Mosaic geodemographic system (Foley 2000). Whilst generally providing national level results Foley also reported a decreasing urban/rural divide and then went on to highlight the ‘most connected’ small areas (wards) and also to show, perhaps unsurprisingly that local authority districts (made up of multiple wards) with the highest deprivation also had lower internet uptake. However this analysis also showed that deprived districts might also contain relatively affluent small areas with high levels of internet uptake. More recently work by Longley and colleagues has developed a postcode level index of ‘digital engagement’ using a range of commercial lifestyle data drawn again from Experian, coupled to imputed household and individual level data in the form of a set of geodemographic codes (Li, Longley et al. 2004; Longley 2005). This data has then been used to produce clusters of user types which are geographically coded to produce 4 visually appealing spatial distributions of ‘digitally differentiated’ households . Whilst these approaches provide some characterisations of small areas in terms of uptake and use or ‘engagement’ in Longley et al’s terms, neither of these approaches allow us to model the possible social outcomes of increased uptake and usage at the small area level because they do not provide the appropriate levels of data (household or individual level) nor the contextual variables required. In order to do this we essentially need that which is impossible – detailed socio-economic and ICT update and use data at the individual and household level across a sample of (or ideally, all) households and individuals in all small areas in Great Britain – an ICT census.

3

Spatially simulating ICT uptake and Media use

There has been considerable recent interest in the estimation of non-census variables at the small area level using a range of approaches that go under the term ‘spatial microsimulation’. These have been driven by an agenda that assumes that ‘Governments need to predict the outcomes of their actions and produce forecasts at the local level.' (Openshaw 1995) Whilst a detailed literature survey is outside the scope of this paper the agenda requires the creation of datasets which measure the attributes of individuals, households or other micro-data units (such as firms) and which generate a synthetic census made up of these units to produce robust estimates of these attributes at the small area level (Birkin and Clarke 1989; Clarke 1996; Ballas, Clarke et al. 1999). Common to those efforts which have focused on individuals and households is the use of a number of variables from the UK decennial population census tables which describe the socio-demographics of small areas together with the same variables, known as constraints, from a representative sample survey (such as the UK ONS’ 2000 Time-Use Survey) to generate a synthetic population of households and/or individuals complete with all attributes measured by the sample survey. There are a wide range of possible methods to combine these data and they have recently been extensively tested and reviewed in the context of studies of population, income and health distributions (Ballas, Clarke et al. 1999; Ballas and Clarke 2001; Ballas, Clarke et al. 2005; Ballas, Rossiter et al. 2005). They key value of the approach is that, whichever simulation method is used, the product is a population dataset constructed by replicating the units (households or individuals) in the base sample survey so that they fit the socio-demographic distributions recorded by the census for each small area. This is done by giving each sample survey unit a series of weights, one for each small area so that cross tabulations of weighted units for each small area match those recorded by the census. Conceptually this can be thought of as a table where each row is a case from the sample survey and each column is one of the small areas. 4

See http://www.spatial-literacy.org/esocietyprofiler/ © 2008, University of Essex

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Each cell of the table contains the weight of that case in that small area. Thus the sum of weights for each column is equal to the number of units in that small area. By attaching weights to surveyed units it is possible to, in effect, create a synthetic census containing all the variables from the sample survey and to use this to estimate the small area distributions of each of them. However this provides us with just one snapshot in time, and a historical one at that. To generate more recent estimates we could use more recent time-use datasets (e.g. 2005) but we would have to assume no change in the Census socio-demographic distributions at the small area levels. More realistically and indeed to generate forecasts we need to be able to project both the Census and the ONS-TU forwards in time from 2001 and 2005 respectively. We are currently engaged in research on this aspect but do not report it here. Having constructed the synthetic census microdata (i.e. households or individuals) it is then possible to use microsimulation techniques (Mitton, Sutherland et al. 2000) to develop models linking a range of sociodemographic and other independent variables to the social outcomes under study. By varying the future distributions of the values of the independent variables under specific scenarios it then becomes possible to estimate the spatial distributions of such outcomes. As mentioned above it is this ability to conduct microlevel scenario analyses which marks this approach out from those which simply seek to produce spatial indicators or estimates of ‘current levels’.

4

The spatial microsimulation method

A comprehensive approach in the context of the analysis of poverty was developed by Ballas et al (Ballas, Clarke et al. 2005) who combined ward level data from the 1981, 1991 and 2001 censuses with British Household Panel Survey (BHPS) data to project income at the small area level. Whilst an identical approach could have been used here if we were only interested in internet uptake, we choose to use the Census 2001 and the ONS-TU-2000 (2003 release) as it provides a more detailed view of a range of media usage of interest to this paper as part of a wide-ranging time-use diary survey and also has a reasonably large household sample size. Whilst it would be possible to extend the method to all regions of the UK, for clarity we restrict our analysis to the Eastern region of England and provide baseline estimates for 2001. Following Ballas et al (Ballas, Clarke et al. 2005) we use an iterative proportional fitting procedure to repeatedly ‘fill’ each census area with households from the time use survey. This is done by calculating weights for each household for each of the n census areas, in this case the 2001 Lower Layer Super Output Areas. In order to do this we need to use a number of constraint variables which are available from both the census (as household counts) and the survey data (as microdata). The ONS-TU-2000 data contains 6,414 instances of households from across the UK together with extensive socio-demographic, ICT ownership and time-use data. The household and individual (all in household) interviews were carried out once and the twenty-four hour time use diaries were completed by each individual aged over 8 on two days, one a weekend and one a weekday. In addition a worksheet specifically focusing on paid work and employment covering a full week was completed by all those aged over sixteen. Altogether 11,667 individual interviews, 20,981 diaries and 6,134 worksheets were completed. Table 1: ONS Time Use Survey variables of interest. Values are mean minutes per day per person (aged 8+) over both week and weekend diary days. Variable Personal care/sleep Employment Study Household & family care Volunteer work & meetings Social life & entertainment Sports and outdoor activities Hobbies & Games Mass media Travel Unspecified time Arts Hobbies Games Reading (main) TV & Video/DVD (main) Radio & Music (main) Reading (secondary) TV & Video/DVD (secondary) Radio & Music (secondary) Reading (total) TV & Video/DVD (total)

Code dml1_0 dml1_1 dml1_2 dml1_3 dml1_4 dml1_5 dml1_6 dml1_7 dml1_8 dml1_9a dml1_9b dml2_71 dml2_72 dml2_73 dml2_81 dml2_82 dml2_83 dsl2_81 dsl2_82 dsl2_83 d2_81_tot d2_82_tot

Mean 651.44 154.98 40.78 175.05 15.62 82.35 16.13 29.64 180.68 83.61 9.72 2.42 9.22 18.00 25.95 147.08 7.65 10.49 21.28 34.72 36.44 168.35

Std. Dev. 126.72 235.10 120.38 161.11 52.12 103.41 50.19 69.34 139.31 79.01 22.91 17.91 37.00 56.21 50.61 123.47 30.33 27.08 47.61 69.71 59.17 129.94

Min

Max 50 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1440 1180 940 1090 840 980 850 790 1000 1270 760 570 700 790 920 910 630 600 820 770 920 910

% zeros 0.00% 65.16% 87.19% 11.71% 84.60% 28.22% 83.77% 71.26% 7.60% 11.75% 69.98% 96.64% 88.20% 82.80% 59.60% 12.53% 85.48% 73.93% 60.87% 57.84% 46.84% 7.96%

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d2_83_tot

42.37

78.90

0

850

52.58%

Note: Cases are weighted using the ONS’ supplied individual level diary non-response weight (weighted n = 19,898), ‘dml’ denotes main activities ‘dsl’ denotes secondary activities.

Whilst it would be possible to use the method to ‘import’ all variables from the ONS-TU-2000 into our spatial model we limit them to those of greatest interest to this paper. These are shown in Table 1 and include the top-level time-use aggregates (dml1_0-9b) together with a number of sub-categories such as elements of 5 media usage and hobbies and games. We also include the sum of main and secondary activities to reflect total media usage on the basis that as the table indicates TV and radio in particular are well known to be ‘background’ activities and it is not our purpose here to distinguish between ‘main’ and ‘secondary’ usage. The table shows the mean minutes per day spent on these activities averaged across all persons across the two days (weekday and weekend) of the diary, the standard deviation, the minimum and the maximum. As we can see for one person (a single retired person) the ‘mean’ minutes spent per day on personal care was 1440 (24 hours!) whilst one person reported only 50 minutes. The final column reports the proportion of individual diary days which recorded no time spent on these activities. Thus some 65 percent reported no paid work (employment) time, nearly 85 percent reported no ‘volunteer works or meetings’ time. Some 96 percent reported no internet usage time (not shown) even though 33% of people lived in a household with internet access. As with expenditure surveys (Bardazzi and Barnabani 1998) zero recorded time causes some analytic problems because it is not possible to tell if the activity is never done by the individual or it is done relatively infrequently in which case a two-day diary of this kind will be very unlikely to record it or it is done but not reported. In addition a number of studies have reported that attempting to collect relatively short-term transient activities such as telephone calls using timeuse diaries is prone to under-reporting even when it is done during the diary period (Lacohee and Anderson 2001; Crang and Graham 2005). Thus whilst the ONS Time-Use survey collected detailed data on internet usage in terms of a range of activities (e.g. ‘communications’ vs ‘information’ usages) the number of persons reporting this behaviour was to low to provide meaningful data. In an attempt to reduce the problem of under or non-reported activity for each category we use the household average time-use across all household members and in order to avoid conflating patterns of weekday and weekend time-use we analyse only weekday data in the remainder of this paper. Having identified the set of ‘ideal’ variables for the purposes of this paper we must then select those which are best predicted by the constraint variables that are common between the Census and the time use survey. The UK 2001 Census contains information at a range of levels of geography including the 32,482 Lower Super Output Areas (LSOAs) that constitute England. The data are mainly released as counts of households or people who fit within discrete criteria for each small area. In this paper we concentrate on household level analysis and so focus on those counts which are reported in terms of households, or more usually, household response persons. The 2001 Census provides a range of tables reporting household or HRP counts which are, in general cross-tabulations of the variables listed in Table 2. It can be seen that most of these either exist in the ONS 2000 Time Use Survey data or can be constructed using its base variables. Table 2: Potential constraint variables in 2001 UK Census and ONS 2000 Time Use Survey Census variable Accommodation type Cars/vans Tenure Number of rooms Number persons Composition Limiting long term illness HRP age, gender, marital status HRP NS-SEC Amenities (heating) Ethnicity Religion

Availability in ONS TU Y Y Y N Y Y Y Y Y N Y N

Whilst all of these variables could therefore be used as constraints to re-weight the time-use survey data it may be that some of them offer little in the way of predictive power and so, in the interests of efficiency can be excluded. In addition it is important to focus on those which provide the most predictive power in order to be confident that the resulting distributions are non-random (Chin and Harding 2006). Whilst Chin and 5

An activity that was recorded as being done ‘at the same time as’ a main activity – such as watching TV (secondary) whilst cooking (main). © 2008, University of Essex http://www.essex.ac.uk/chimera/

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Harding report the use of repeated bi-variate regressions to test each variable independently, we prefer to use a stepwise or nested multivariate method. The multivariate approach means that correlations between constraint variables are taken into account and thus the ‘pure’ effects of each constraint can be revealed whilst the use of the stepwise technique automatically includes only those variables which have a statistically significant effect on the model and orders the resulting indicators in decreasing order of their affects. The overall model R-squared score is then an indicator of how well the included constraints predict the outcome variables at the household level. The detailed results for the East of England can be found in Table 11 and Table 12 (Annex A) and a summary of the results is shown in Table 3. As we can see the available constraint variables are reasonable predictors of only some of the time-use categories of interest. The constraints are good predictors of Employment time, internet access, reading and radio/music as a main activity. They are also acceptable predictors of TV/video (main or total) but poor predictors of total radio/music time-use due in large part to the apparently random (or ubiquitous) nature of radio/music as a secondary activity. We will therefore estimate the spatial distributions of internet access, Reading (total), TV/Video/DVD (total) and Radio & Music (main). In addition we will also estimate the small area distribution of Employment time so that it can be validated against similar data from the Census itself. Table 3: Summaries of stepwise regression model results in decreasing order of statistical power for the selected time use categories and for household internet access (East of England only) Variable Employment Study Internet access Mass media Reading (main) Reading (total) Radio & Music (main) Household & family care Personal care/sleep TV & Video/DVD (main) TV & Video/DVD (total) Travel Social life & entertainment Games Radio & Music (total) TV & Video/DVD (secondary) Hobbies & Games Radio & Music (secondary) Volunteer work & meetings Reading (secondary) Unspecified time Hobbies Sports and outdoor activities Arts

R-square 52.5% 34.0% 31.7% 31.2% 24.3% 21.9% 21.6% 19.5% 18.4% 18.0% 17.9% 14.1% 8.6% 8.4% 4.4% 4.4% 3.4% 3.4% 3.3% 3.2% 2.8% 2.5% 0.0% 0.0%

The detailed regression results analysis (Annex A) suggest that the most appropriate constraint variables for the selected variables, in decreasing order of power are those shown in Table 4. Table 4: Final constraint variables in decreasing order of statistical power (East of England only) Order 1. 2. 3. 4. 5. R sq

Employment time Employment Number of children Age Number of earners

Have internet access Employment Number of persons Number of cars

Reading (total) Age Tenure

0.53

0.317

0.219

Radio & Music (main) Age Non-white HRP Employment status Number of persons Accommodation 0.216

TV & Video/DVD (total) Age Accommodation

0.179

Having identified the constraint variables it is then necessary to derive them from the Census data in terms of household counts at the LSOA level with as close a match to the definitions available in the time use survey as possible. Table 5 shows a simplified ONSTU 2000 dataset containing 5 hypothetical households with example constraint variables and time use indicators. Thus household one was recorded as having 2 persons, HRP = retired, composition = couple and no earners, spent no minutes in work and 133.6 minutes on home and family care. © 2008, University of Essex http://www.essex.ac.uk/chimera/

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Table 5: A hypothetical ONS TimeUse 2001 dataset of five households and four constraints. case 1 2 3 4 5

region 1 1 1 1 1

const_persons 2 2 1 1 1

const_nssec 4 3 0 4 3

const_comp 0 0 2 2 2

const_nearners 0 2 1 1 0

dml1_1 0 400 480 230 0

dml1_3 133.6 230 100 200 50

Table 6: A hypothetical Census 2001 dataset of four zones. zonecode E01000001 E01000002 E01000003 E01000004

region 7 7 7 7

N_hh 998 931 917 981

nearners_0 568 496 362 580

nearners _1 177 199 152 214

… … … … …

nearners _4 9 17 3 0

comp_0 264 227 170 231

comp_1 15 30 79 30

… … … … …

Table 6 shows a simplified partial Census 2001 dataset containing part of 2 constraint variables (number of persons, composition). With the census counts appropriately re-coded and with the ONSTU 2000 household survey data to hand we then turn to the spatial microsimulation process. The methodology used here is an adapted form of the deterministic reweighting approach described by Ballas et al (2005). The objective is to produce a set of weights linking all households to all zones, in the sense that the weights represent the ‘existence’ of the corresponding household in the corresponding area (i.e. how many of that household type exist in that area). This process begins with the first small area (LSOA) and the constraint variable with the least explanatory power and re-weights the cases so that the 6 survey distribution fits that of the small area census table for that LSOA . Having adjusted the weights for the first constraint the process then moves sequentially through each constraint variable multiplying each new weight by that produced by the previous step. Since the last constraint to be fitted will necessarily be fitted perfectly, it is necessary to order the variables in ‘r sq contribution’ order (cf. Table 3) so that the last to be fitted is the one which accounts for the most variation in the outcome variable of interest. Having passed over all constraints once the process then loops back to constraint one and repeats the reweighting starting from the weight produced in the last step (by the last constraint). Ballas et al found that iterating the procedure between 5 and 10 times produced weights that reduced the error in fitting households to areas to a point where it no longer declined. Our experimentation has suggested that 10 iterations were sufficient to achieve a stable indicator value. Thus after iterating over the re-weighting procedure 10 times the process then moves to the next zone. The spatial microsimulation process has been implemented as a platform independent java-based tool which produces an output file summarising the input variables of interest for each zone (in this case LSOAs) and also a weights file (Table 7). Zone E01015589 E01015589 E01015589 E01015589 E01015589

region

HH_id 6 6 6 6 6

2 3 4 5 6

WEIGHT 0.56 0.56 5.07 0.7 2.59

dml1_1 480 485 193.33 280 350

Table 7: Example simulation output file (partial) For each area (e.g. LSOA) this file records the weight attributed to the ONS TU households allocated to it. Notice that this weight could be zero. In addition there is the ONS TU household’s time use indicator as calculated in the source data. In addition any number of other variables can be included provided that we can be confident that they are predicted by the chosen constraint variables. In practice this means repeating the spatial microsimulation for each variable of interest since the constraint variables and their order will differ. Calculating the time-use indicator for each is thus a straightforward matter of summing the weighted time use variable (i.e. the sum of weight * dml1_1 in this case) for each area and dividing by the number of households in that area. Similarly any other statistic can be calculated – such as the median or the variance for each area.

6

For further details see Anderson, B. (2007). Estimating time spent on-line at small area levels: a spatial microsimulation approach. Chimera Working Paper 2007-01. Ipswich, Chimera, University of Essex. © 2008, University of Essex http://www.essex.ac.uk/chimera/

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5

Results

Table 8 shows the results of a spearman rank order correlation between all the results at the LSOA level and 7 two key independent indicators – the census derived mean work hours and the English Indices of Deprivation 2004 index score. As we can see there is a strong correlation between the simulated employment hours and the census derived weekly work hours (rho = 0.84) and this is further confirmed by Figure 1 suggesting that the spatial microsimulation method is relatively robust and this is supported by the strong negative correlation between estimated internet uptake and the index of deprivation (rho = -0.77, see also Figure 2) confirming Foley et al’s earlier results. Table 8: Spearman correlations between results at LSOA level (2001 estimates)

% households with internet access Mean work hours (census) Sim: Employment hours Sim: Reading (total) Sim: TV & video/DVD (total) Sim: Radio & Music (main)

ID 2004 index of deprivation -0.7662** -0.5221** -0.2668** -0.3467** 0.4426** 0.2165**

% Households with internet access 0.7260** 0.6023** -0.013 -0.7295** -0.4793**

Mean work hours (census)

0.8404** -0.2746** -0.5293** -0.3806**

Sim: Employment hours

-0.5787** -0.4876** -0.3621**

Sim: Reading (total)

0.1376** 0.2275**

Sim: TV & video/DVD (total)

0.6971**

Table notes: N = 3,350, ** = p < 0.01

Figure 1: Scatter with linear fit line of census derived mean work hours (2001) against simulated mean work hours 2001

Figure 2: Scatter with linear fit line of ID 2004 index against simulated % households with internet access 2001

7

Census work hours data is published in ranges. We have calculated the mean value for each range using the time-use survey and then taken this value as the ‘midpoint’ for each range in order to derive the census mean work time. This is particularly useful as it provides a specific value for the top-most census range which was “49 hours or more per week”. © 2008, University of Essex http://www.essex.ac.uk/chimera/

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Figure 3: Simulated % households with internet access in 2001 by rural/urban indicator (LSOA level)

Figure 4: Spatial distributions of simulated % households with internet access 2001 (East of England, LSOA level)

Figure 3 shows the distribution of simulated internet access across the LSOA level rural/urban indicator categories whilst Figure 4 shows the spatial distribution across all East of England LSOAs. Interestingly whilst the highest level of uptake was estimated to have been in an urban area of East Hertfordshire, mean take-up is highest in less sparse villages and lowest in sparse town/fringe. This confirms the suggestion made earlier that high uptake small areas may well be contained within lower uptake districts and thus that basing intervention decisions on high level areas may be inappropriate. Figure 4 confirms this general picture revealing a classic core-periphery distribution with the highest uptake estimated to be in areas closer to London and larger cities such as Cambridge and Norwich as well as along commuting transport corridors such as the London -Colchester - Ipswich - Norwich railway. By combining these LSOA level results with the Indices of Deprivation we are able to identify areas which have low deprivation scores and also low estimated internet uptake (Table 9) as well as those with high scores and high internet uptake (Table 10). In the case of the former these represent areas of apparently low demand that is less likely to be due to a lack of resources or skills and therefore may identify areas suitable for certain kinds of interventions. In the case of the latter these are areas which might be expected to have higher internet access uptake despite their relative deprivation and so could be a focus of research into the potential social benefits of internet-based services. Table 9: Zones with low deprivation scores and low estimated internet uptake in 2001 LSOA Name Waveney 014C North Norfolk 004B Southend-on-Sea 016E Uttlesford 002B Colchester 021E Ipswich 009E

Sim: % households with internet access

Index of Deprivation 2004 21 21 25 26 27 27

6.02 5.23 6.05 5.21 5.69 6.03

Table 10: Zones with high deprivation scores and high estimated internet uptake in 2001 LSOA Name Luton 009C Broadland 008A Luton 005C South Cambridgeshire 006E Tendring 003E Tendring 004F Bedford 007F

Sim: % households with internet access

Index of Deprivation 2004 40 40 39 39 39 38 38

27.11 27.38 22.3 21.87 28.07 23.81 29.56

Turning to the time-use data Figure 5 shows that the LSOAs with the highest mean household TV/Video/DVD usage are likely to be in less sparse urban areas as will the lowest even though the highest mean usage at the LSOA level is in sparse town and fringe LSOAs. Radio/Music usage is also highest in less sparse urban areas in contrast to mean household reading time which is highest in town and fringe. © 2008, University of Essex http://www.essex.ac.uk/chimera/

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Figure 5: Simulated media usage (LSOA level mean minutes per day) by rural/urban classification of LSOA (East of England, 2001)

Figure 6: Simulated media usage (LSOA level sum of minutes per day) by rural/urban classification of LSOA (East of England, 2001)

Figure 6 on the other hand shows the distribution of the total (sum) of minutes per day. This figure is more likely to be of use in infrastructure investment decisions since it is a potential proxy for small area revenue and bandwidth demand as it takes account of the number of households in each zone. In this case we can see that whilst the areas with highest total TV/video/DVD usage are still in less sparse urban areas, some town and fringe areas are equally high. Interestingly the sparse village zones have the highest mean total usage for reading and TV although there is considerable heterogeneity in the case of the latter. Figure 7 to Figure 10 show the estimated small area spatial distributions for the mean household minutes per day on the three time-use categories together with the distribution of total household minutes on TV. Mean household time spent reading appears concentrated in certain areas whilst mean household television time is relatively evenly distributed but with ‘hotspots’ in areas with larger proportions of older household response persons such as some coastal regions.

Figure 7: Simulated mean household minutes per day reading (Weekdays,all, LSOAs, East of England,

Figure 8: Simulated mean household minutes per day watching TV (Weekdays, all, LSOAs, East of England,

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2001)

2001)

Figure 9: Simulated mean household minutes per day listening to radio/music (Weekdays, Main activity only, LSOAs, East of England, 2001)

Figure 10: Simulated total household minutes per day watching TV (Weekdays, LSOAs, East of England, 2001)

This is emphasised by Figure 10 which emphasises the high overall volume of TV watching estimated for a number of rural/retirement districts. Whilst this is partially an artefact of the mapping method which overemphasises large rural areas it still suggests that infrastructure investments made into TV services in these areas might recoup significant revenue provided the demographic profile is able to pay for it.

6

Conclusions

This paper has argued for the development of methods to assess digital access/inclusion and ICT usage behaviour at small area levels in order to generate more nuanced understandings of the need for localised interventions or indeed the local effect of national policies and market actions. The paper has introduced a form of spatial microsimulation as one such method and has described its development, implementation and validation in the context of internet uptake and of a range of behaviour as measured by time use. The results have produced plausible small area estimates of time spent in work as validated against similar data collected by the census itself and given the power of the constraint variables in predicting other high level time-use variables we would expect the estimates for both internet uptake and different media usages to be similarly robust. Having created these kinds of estimates it is worth considering their potential value. Immediately apparent is their use as a baseline of what we might expect to find in a given small area. This could provide a means to compare the relative impact of, for example, localised ICT initiatives in transforming opportunities for economic and social participation and we have illustrated the use of the data in identifying specific areas for such interventions as well as ‘control’ areas with similar socio-demographic profiles. Given a spatially microsimulated baseline for 2001 in matched areas fieldwork such as that carried out by Crang et al could be used to analyse the change since the 2001 baseline that could be attributed to local interventions or spatial configurations. We have also illustrated potential commercial applications of this approach such as through the estimation of average and total small-area demand for media content and services which in turn can drive not only standard marketing activity but also decisions on infrastructure investment. Whilst the most obvious beneficiaries of these results would be telecommunications companies other infrastructure providers that are © 2008, University of Essex http://www.essex.ac.uk/chimera/

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increasingly linked to internet uptake and media consumption such as postal, parcel and courier as well as video/DVD rental services may also find the data of value. Other potential applications include the use of standard econometric microsimulation approaches to estimate and model the spatial distributions of potential time-use change under a range of policy conditions, market interventions or social trends. When combined with methods to forecast Census small-area data this would provide the ability to project change forwards in time at the small area level as a basis for both public policy and commercial strategy. Both of these are the subject of ongoing research (Anderson 2007; Anderson 2007). In the end of course the value of the method rests on the extent to which the behavioural variables of interest are adequately predicted by the available census constraint variables. As we have seen for the 2001 data this was the case for some categories of time use more than others. However this still leaves unresolved the issue of exactly what level of error is acceptable for the range of purposes to which the analysis could be put.

7

Acknowledgements

The research reported in this paper was supported by the ESRC (RES-341-25-0004). The ONS Time Use survey was collected by Ipsos-RSL and sponsored by the Office for National Statistics, the Department for Culture, Media and Sport; Department for Education and Skills; Department of Health; Department for Transport, Local Government and the Regions and the Economic and Social Research Council. It is distributed by the UK Data Archive, University of Essex, Colchester. The data is copyright and is reproduced with the permission of the Controller of HMSO and the Queen's Printer for Scotland. Census data were originally created and funded by the Office for National Statistics and are distributed by the Census Dissemination Unit, MIMAS (University of Manchester). Output is Crown copyright and is reproduced with the permission of the Controller of HMSO and the Queen's Printer for Scotland. This paper uses data provided through EDINA UKBORDERS with the support of the ESRC and JISC and uses boundary material which is copyright of the Crown. The Commission for Rural Communities’ Rural and Urban Area Classification is a product of a joint project to produce a single and consistent classification of urban and rural areas. The project was sponsored by the Countryside Agency (CA), the Department for Environment, Food and Rural Affairs (Defra), Office for National Statistics (ONS), the Office of the Deputy Prime Minister (ODPM) and the Welsh Assembly Government. A consortium consisting of South East Regional Research Laboratory (SERRL) at Birkbeck College and the Department of Town and Regional Planning at University of Sheffield carried out the work.

8

REFERENCES

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Birkin, M. and G. Clarke (1989). "The generation of individual and household incomes at the small area level." Regional Studies 23(6): 535-548. Chen, W. and B. Wellman (2005). Minding the Cyber-Gap, The Internet and Social Inequality. The Blackwell Companion to Social Inequalities. M. Romero and E. Margolis. Oxford, Blackwell. Chin, S.-F. and A. Harding (2006). Regional Dimensions: Creating Synthetic Small-area Microdata and Spatial Microsimulation Models. NATSEM Technical Paper no. 33. Canberra, National Centre for Social and Economic Modelling, University of Canberra. Clarke, G., Ed. (1996). Microsimulation for Urban and Regional Policy Analysis. London, Pion. Crang, M., T. Crosbie, et al. (2006). "Variable Geometries of Connection: Urban Digital Divides and the Uses of Information Technology." Urban Studies 43(13): 2551-2570. Crang, M., T. Crosbie, et al. (To appear). "Technology, Timespace and the Remediation of Neighbourhood Life." Environment and Planning A. Crang, M. and S. Graham (2005). Multispeed Cities and the Logistics of Living in the Information Age ESRC End of Award Research Report (RES-335-25-0015). Durham, University of Durham. Dorling, D. and B. Thomas (2004). People and places : a 2001 census atlas of the UK. Bristol, Policy Press. Ferlander, S. and D. Timms (2006). "Tackling the Dual Digital Divide: A Local Net and an Internet Café." Information Communication & Society 9(2): 137-159. Foley, P. (2000). Whose Net? Characteristics of Internet users in the UK. Report submitted to the Information Age and Policy Development Communications and Information Industries Directorate of the Department of Trade and Industry. Fong, E., B. Wellman, et al. (2001). Correlates of the Digital Divide: Individual, Household and Spatial Variation. Report to Office of LearningTechnologies, Human Resources Development Canada. Graham, S. (2002). "Bridging Urban Digital Divides? Urban Polarisation and Information and Communications Technologies (ICTs)." Urban Studies 39(1): 33-56. Hargittai, E. (2004). "Internet Access and Use in Context." New Media & Society 6(1): 137-143. Holloway, D. (2006). "The digital divide in Sydney: A sociospatial analysis." Information Communication & Society 8(2): 168 - 193. Kraut, R., M. Brynin, et al. (2006). Computers, Phones, and the Internet: Domesticating Information Technology Oxford, Oxford University Press. Kvasny, L. (2006). "Cultural (Re)production of digital inequality in a US community technology initiative." Information Communication & Society 9(2): 160-181. Lacohee, H. and B. Anderson (2001). "Interacting with the telephone." International Journal of Human Computer Studies 54(5): 665-700. Li, C., P. Longley, et al. (2004). GIS and Geodemographics: a national classification of ICT usages. Geographical Information Systems Research UK (GISRUK) 2004, Norwich, UK, GISRUK. Longley, P. (2005). Digital Differentiation: Consumption Profiles of Fracturing Digital Divides. ESRC End of Award Research Report (RES-335-25-0020). London, University College London. Mitton, L., H. Sutherland, et al. (2000). Microsimulation modelling for policy analysis : challenges and innovations. Cambridge, Cambridge University Press. Ofcom (2006). The Communications Market: Nations and Regions. London, Ofcom. Ofcom Consumer Panel (2006). Consumers and the communications market: 2006. London, Ofcom. Openshaw, S., Ed. (1995). Census Users’ Handbook. London, GeoInformation International. Robinson, J. P., P. DiMaggio, et al. (2003). "New Social Survey Persepctives on the Digital Divide." IT&Society 1(5): 1-22. Selwyn, N. (2005). "The social processes of learning to use computers." Social Science Computer Review 23(1): 122-135. Selwyn, N. (2005). "Whose internet is it anyway? Exploring adults' (non)use of the internet in everyday life." European Journal of Communication 20(1): 5-26.

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Annex A

Constraint Tests

A.1

Top level categories and internet access

Table 11: Results of stepwise regression models testing explanatory power of constraint variables on top level time use categories (East of England only) Have internet access Employment (NS-SEC 1) NS-SEC2 NS-SEC3 Inactive Retired Number of persons Number of cars Composition (Couple) Single parent Single person Other Number of children Age of HRP (16-24) age 25 to 34 age 35 to 44 age 45 to 54 age 55 to 64 age 65 to 74 age 75 to 84 age 85 or over Number of earners Sex of HRP Accommodation (Detached) Semi-Detached Terrace Flat/maisonette Other Presence of LLI Tenure (Own) Rent from council Social rent Private rent Ethnicity of HRP Constant R squared N P F

-0.184*** -0.286*** -0.351*** -0.288*** 0.192*** 0.168***

Personal care/sleep 0.096* 0.168*** 0.476*** 0.199***

Employment

Study

Household & family care

Volunteer work & meetings

Social life & entertainment

0.007 -0.041 -0.355*** -0.226***

Sports and outdoor activities

Hobbies & Games

Mass media

-0.026 0.028 0.264*** 0.184***

Unspecified time

Travel

-0.142** -0.140** -0.062 -0.111*

0.333*** 0.111* 0.118* -0.134** 0.018 -0.408***

0.119** 0.136* 0.071 0.313***

0.048 -0.048 -0.039 -0.200** -0.288*** -0.211** -0.094* 0.174**

-0.160** -0.103 -0.116** 0.118*

0.184*** 0.035 -0.044 0.057 0.151 0.366*** 0.356*** 0.223***

-0.468*** 0.106*

0.124 0.125 0.019 0.038 -0.058 -0.141 -0.118*

-0.182***

0.052 0.048 0.168*** 0.026 0.093*

*** 0.317 499 0.000 38.13614

*** 0.184 435 0.000 13.74783

*** 0.525 435 0.000 35.7261

** 0.34 435 0.000 44.1467

*** 0.195 435 0.000 17.32567

*** 0.033 435 0.000 14.88931

*** 0.086 435 0.000 10.0565

*** 0 435 . 0

*** 0.034 435 0.000 15.12965

*** 0.312 435 0.000 15.95204

0.064 0.065 -0.003 0.160***

*** 0.141 435 0.000 5.781433

*** 0.028 435 0.016 3.08369

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A.2

Sub-categories

Table 12: Results of stepwise regression models testing explanatory power of constraint variables on time use sub-categories (East of England only) Arts Employment (NS-SEC 1) NS-SEC2 NS-SEC3 Inactive Retired Number of persons Number of cars Composition (Couple) Single parent Single person Other Number of children Age of HRP (16-24) age 25 to 34 age 35 to 44 age 45 to 54 age 55 to 64 age 65 to 74 age 75 to 84 age 85 or over Number of earners Sex of HRP Accommodation (Detached) Semi-Detached Terrace Flat/maisonette Other Presence of LLI Tenure (Own) Rent from council Social rent Private rent Ethnicity of HRP Constant R squared N P F

Hobbies

Games

Reading (main)

TV & Video/DVD (main)

Radio & Music (main)

Reading (total)

TV & Video/DVD (total)

0.062 0.026 -0.021 0.174*** -0.132*

Radio & Music (total)

Reading (secondary)

TV & Video/DVD (secondary)

-0.209***

0.304***

Radio & Music (secondary)

-0.185***

-0.178*** 0.087 0.112 0.166 0.291*** 0.424*** 0.479*** 0.139**

-0.057 -0.204 -0.098 -0.018 0.179 0.156* 0.081

0.037 0.193 0.117 0.053 0.119 0.146 0.331***

0.04 0.022 0.11 0.228** 0.395*** 0.366*** 0.106*

0.018 -0.08 0.001 0.098 0.291** 0.222** 0.116*

-0.111*

-0.175***

-0.146** -0.140** -0.007 -0.056

-0.045 -0.088 0.065 -0.108* 0.173*** 0.038 0.029

0.078 0.056 0.185*** 0.05 -0.134** -0.007 0.015

-0.05 -0.141** -0.067

0.218*** *** 0.000 435 . 0.000

*** 0.025 435 0.028 2.746

*** 0.084 435 0.000 19.843

0.243 435 0.000 19.534

*** 0.180 435 0.000 9.287

0.216 435 0.000 6.749

0.219 435 0.000 11.866

*** 0.179 435 0.000 8.360

*** 0.044 435 0.000 19.835

*** 0.032 435 0.000 14.226

*** 0.044 435 0.001 4.906

*** 0.034 435 0.000 15.361

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