diff --git a/Theme-1/changeOverTime/allYearsLinePlot.png b/Theme-1/changeOverTime/allYearsLinePlot.png new file mode 100644 index 0000000000000000000000000000000000000000..e4067c9e9efecff7cd8d8ec955c1ede2ef58fac3 Binary files /dev/null and b/Theme-1/changeOverTime/allYearsLinePlot.png differ diff --git a/Theme-1/changeOverTime/changingPeakDemandMtus1974_2014_v2.0.html b/Theme-1/changeOverTime/changingPeakDemandMtus1974_2014_v2.0.html index 839e4c773f34aba785deb46f8e6b12171644cda6..01524b4423dd1cae5c3d83c813b513583214403c 100644 --- a/Theme-1/changeOverTime/changingPeakDemandMtus1974_2014_v2.0.html +++ b/Theme-1/changeOverTime/changingPeakDemandMtus1974_2014_v2.0.html @@ -94,6 +94,9 @@ img { .tabbed-pane { padding-top: 12px; } +.html-widget { + margin-bottom: 20px; +} button.code-folding-btn:focus { outline: none; } @@ -218,12 +221,12 @@ div.tocify { <h1 class="title toc-ignore">The Changing Nature of Peak Demand in the UK: 1974 - 2014</h1> <h4 class="author"><em>Ben Anderson (<a href="mailto:b.anderson@soton.ac.uk">b.anderson@soton.ac.uk</a>, <code>@dataknut</code>), Jacopo Torriti (<a href="mailto:j.torriti@reading.ac.uk">j.torriti@reading.ac.uk</a>, <code>@JTorriti</code>)</em></h4> -<h4 class="date"><em>Last run: 2018-03-08 04:01:31</em></h4> +<h4 class="date"><em>Last run: 2018-07-03 14:14:04</em></h4> </div> -<pre><code>## [1] "Loading functions from /Users/ben/gitlabSoton/SERG/DEMAND/demandFunctions.R"</code></pre> +<pre><code>## [1] "Loading functions from /Users/ben/git.soton/SERG/DEMAND/demandFunctions.R"</code></pre> <pre><code>## [1] "Loading the following libraries using lb_myRequiredPackages: data.table" ## [2] "Loading the following libraries using lb_myRequiredPackages: ggplot2" ## [3] "Loading the following libraries using lb_myRequiredPackages: readr" @@ -233,16 +236,25 @@ div.tocify { ## [7] "Loading the following libraries using lb_myRequiredPackages: stargazer" ## [8] "Loading the following libraries using lb_myRequiredPackages: survey" ## [9] "Loading the following libraries using lb_myRequiredPackages: doBy" -## [10] "Loading the following libraries using lb_myRequiredPackages: robustbase" -## [11] "Loading the following libraries using lb_myRequiredPackages: knitr"</code></pre> +## [10] "Loading the following libraries using lb_myRequiredPackages: hms" +## [11] "Loading the following libraries using lb_myRequiredPackages: robustbase" +## [12] "Loading the following libraries using lb_myRequiredPackages: knitr"</code></pre> +<div id="about" class="section level1"> +<h1><span class="header-section-number">1</span> About</h1> +<p>Version of code to match version 2.0 of paper submitted to Energy Policy:</p> +<ul> +<li>removed all code & results not used in paper</li> +<li>removed code will be foind in v1.9 of Rmd</li> +</ul> +</div> <div id="to-do" class="section level1"> -<h1><span class="header-section-number">1</span> To do</h1> +<h1><span class="header-section-number">2</span> To Do</h1> <ul> -<li>re-introduce regression models for early vs late evening food</li> +<li></li> </ul> </div> <div id="introduction" class="section level1"> -<h1><span class="header-section-number">2</span> Introduction</h1> +<h1><span class="header-section-number">3</span> Introduction</h1> <ul> <li>Longitudinal data on yearly average consumption is largely available, but longitudinal data on time of the day is not</li> <li>This work investigates changing patterns in household energy demand over thirty years through UK time use data (1985, 2000-2001 and 2014-2015)</li> @@ -253,7 +265,7 @@ div.tocify { </ul> </div> <div id="trends-over-time-in-household-energy-demand" class="section level1"> -<h1><span class="header-section-number">3</span> Trends over time in household energy demand</h1> +<h1><span class="header-section-number">4</span> Trends over time in household energy demand</h1> <ul> <li>Trends are generally intended to synthetically capture how yearly energy demand changes on average</li> <li>Brief review of what we know about longitudinal energy demand: UK DIGEST</li> @@ -261,16 +273,21 @@ div.tocify { <li>Areas in which time use data has been used longitudinally (not in energy)</li> </ul> <div id="national-grid-system-peaks" class="section level2"> -<h2><span class="header-section-number">3.1</span> National Grid system peaks</h2> +<h2><span class="header-section-number">4.1</span> National Grid system peaks</h2> <p>We use this data to try to examine changing peaks over time. Note that these demand values include <em>all</em> demand, not just households.</p> <p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/load%20NG%20demand%20data-1.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/load%20NG%20demand%20data-2.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/load%20NG%20demand%20data-3.png" /><!-- --></p> <p>The last chart shows a quite substantial drop in demand over the last 10 years… at all times of year.</p> -<p>The next two charts show demand levels in January 2006 & 2016 as mean MW per hour.</p> -<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/compareNGJanuary2006toJanuary2016-1.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/compareNGJanuary2006toJanuary2016-2.png" /><!-- --></p> +<p>The next two charts show demand levels in January 2006 & 2016 as mean MW per hour. Figure (fig:compareTotal) shows mean MW across weekdays compared with each weekend day. Fig 1 in paper.</p> +<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/compareTotal-1.png" alt="NG demand totals over time" /><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/compareTotal-2.png" alt="NG demand totals over time" /></p> +<p>Figure (fig:compareNormalised) shows the same data but normalised to the overall mean. This shows the relative distribution of demand rather than absolute and illustrates the increased ‘peakiness’. Fig 2 in paper.</p> +<div class="figure"> +<img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/compareNormalised-1.png" alt="NG Demand normalised" /> +<p class="caption">NG Demand normalised</p> +</div> </div> </div> <div id="methods" class="section level1"> -<h1><span class="header-section-number">4</span> Methods</h1> +<h1><span class="header-section-number">5</span> Methods</h1> <ul> <li>Details about longitudinal analysis <ul> @@ -281,7 +298,7 @@ div.tocify { </ul> </div> <div id="data" class="section level1"> -<h1><span class="header-section-number">5</span> Data</h1> +<h1><span class="header-section-number">6</span> Data</h1> <p>Data used:</p> <ul> <li><a href="(http://www.timeuse.org/mtus)">MTUS World 6</a> - Multinational Timeuse Survey sample for the UK 1974-2005. Note that the most recent MTUS release is W9 but as far as we can tell it has no additional data of use in this paper;</li> @@ -290,7 +307,7 @@ div.tocify { </ul> <p>The following section loads the <a href="https://dataknut.github.io/UK-TU-2014/convertToMTUS/createMTUSFromUKTU2014.html">harmonised</a> MTUS & UK TU 2014-2015 data which has then been processed to form a <a href="">synthetic half-hour dataset</a> that can enable comparisons over time. Much of this output will not be necessary for the paper but is useful detail here to understand what we are doing.</p> <div id="survey-data" class="section level2"> -<h2><span class="header-section-number">5.1</span> Survey data</h2> +<h2><span class="header-section-number">6.1</span> Survey data</h2> <p>Load the MTUS survey data and check the age distributions.</p> <pre><code>## ba_survey ## ba_age_r 1974 1985 1995 2000 2005 2014 <NA> @@ -442,7 +459,7 @@ div.tocify { </tbody> </table> <table> -<caption>MTUS 1974-2015 Survay data: age ranges (weighted)</caption> +<caption>MTUS 1974-2015 Survey data: age ranges (weighted)</caption> <thead> <tr class="header"> <th></th> @@ -534,7 +551,7 @@ div.tocify { </table> </div> <div id="synthetic-halfhour-mtus-1974-2014" class="section level2"> -<h2><span class="header-section-number">5.2</span> Synthetic halfhour MTUS 1974-2014</h2> +<h2><span class="header-section-number">6.2</span> Synthetic halfhour MTUS 1974-2014</h2> <p>Now load the synthetic ‘half hour’ MTUS 1974 - 2014 data we previously created.</p> <table> <caption>Number of episodes, diaries & respondents by year</caption> @@ -920,8 +937,8 @@ div.tocify { </table> </div> </div> -<div id="demand-acts" class="section level1"> -<h1><span class="header-section-number">6</span> DEMAND Acts</h1> +<div id="code-demand-acts" class="section level1"> +<h1><span class="header-section-number">7</span> Code DEMAND Acts</h1> <p>We use this data to code the main and secondary activities into a non-arbitrary but highly aggregated set of 10 DEMAND ‘Acts’. This enables us to more easily depict change over time at a coarse level. The aggregated codes are as follows:</p> <ul> <li>Sleep @@ -2421,9 +2438,194 @@ div.tocify { </tr> </tbody> </table> +<div id="check-secondary-acts" class="section level2"> +<h2><span class="header-section-number">7.1</span> Check secondary acts</h2> +<table> +<caption>MTUS 1974-2015 Survey data: Main ‘DEMAND Act’ (weighted, col %)</caption> +<thead> +<tr class="header"> +<th></th> +<th align="right">1974</th> +<th align="right">1985</th> +<th align="right">2000</th> +<th align="right">2014</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td>Education</td> +<td align="right">0.27</td> +<td align="right">0.61</td> +<td align="right">0.54</td> +<td align="right">0.84</td> +</tr> +<tr class="even"> +<td>Food</td> +<td align="right">9.94</td> +<td align="right">10.78</td> +<td align="right">11.69</td> +<td align="right">10.32</td> +</tr> +<tr class="odd"> +<td>Media</td> +<td align="right">10.94</td> +<td align="right">11.76</td> +<td align="right">12.94</td> +<td align="right">13.85</td> +</tr> +<tr class="even"> +<td>Personal/home</td> +<td align="right">11.68</td> +<td align="right">16.70</td> +<td align="right">15.55</td> +<td align="right">16.03</td> +</tr> +<tr class="odd"> +<td>Shopping</td> +<td align="right">1.69</td> +<td align="right">2.34</td> +<td align="right">2.26</td> +<td align="right">2.42</td> +</tr> +<tr class="even"> +<td>Sleep</td> +<td align="right">36.84</td> +<td align="right">31.15</td> +<td align="right">31.75</td> +<td align="right">30.73</td> +</tr> +<tr class="odd"> +<td>Social/leisure</td> +<td align="right">7.75</td> +<td align="right">8.31</td> +<td align="right">7.41</td> +<td align="right">6.81</td> +</tr> +<tr class="even"> +<td>Sport</td> +<td align="right">1.10</td> +<td align="right">1.14</td> +<td align="right">2.06</td> +<td align="right">1.75</td> +</tr> +<tr class="odd"> +<td>Travel</td> +<td align="right">5.34</td> +<td align="right">5.21</td> +<td align="right">6.74</td> +<td align="right">6.70</td> +</tr> +<tr class="even"> +<td>Work</td> +<td align="right">14.41</td> +<td align="right">11.37</td> +<td align="right">8.58</td> +<td align="right">8.32</td> +</tr> +<tr class="odd"> +<td>X: Not recorded</td> +<td align="right">0.03</td> +<td align="right">0.63</td> +<td align="right">0.49</td> +<td align="right">2.24</td> +</tr> +</tbody> +</table> +<table> +<caption>MTUS 1974-2015 Survey data: Secondary ‘DEMAND Act’ (weighted, col %)</caption> +<thead> +<tr class="header"> +<th></th> +<th align="right">1974</th> +<th align="right">1985</th> +<th align="right">2000</th> +<th align="right">2014</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td>Education</td> +<td align="right">0.00</td> +<td align="right">0.03</td> +<td align="right">0.02</td> +<td align="right">0.05</td> +</tr> +<tr class="even"> +<td>Food</td> +<td align="right">2.11</td> +<td align="right">3.15</td> +<td align="right">2.29</td> +<td align="right">2.26</td> +</tr> +<tr class="odd"> +<td>Media</td> +<td align="right">7.10</td> +<td align="right">1.96</td> +<td align="right">3.83</td> +<td align="right">4.23</td> +</tr> +<tr class="even"> +<td>Personal/home</td> +<td align="right">1.84</td> +<td align="right">4.25</td> +<td align="right">2.90</td> +<td align="right">3.53</td> +</tr> +<tr class="odd"> +<td>Shopping</td> +<td align="right">0.11</td> +<td align="right">0.40</td> +<td align="right">0.05</td> +<td align="right">0.21</td> +</tr> +<tr class="even"> +<td>Sleep</td> +<td align="right">0.24</td> +<td align="right">0.77</td> +<td align="right">0.50</td> +<td align="right">0.81</td> +</tr> +<tr class="odd"> +<td>Social/leisure</td> +<td align="right">2.51</td> +<td align="right">4.74</td> +<td align="right">6.36</td> +<td align="right">5.92</td> +</tr> +<tr class="even"> +<td>Sport</td> +<td align="right">0.04</td> +<td align="right">0.10</td> +<td align="right">0.04</td> +<td align="right">0.13</td> +</tr> +<tr class="odd"> +<td>Travel</td> +<td align="right">0.14</td> +<td align="right">0.25</td> +<td align="right">0.01</td> +<td align="right">0.09</td> +</tr> +<tr class="even"> +<td>Work</td> +<td align="right">0.04</td> +<td align="right">0.12</td> +<td align="right">0.05</td> +<td align="right">0.36</td> +</tr> +<tr class="odd"> +<td>X: Not_recorded</td> +<td align="right">85.84</td> +<td align="right">84.23</td> +<td align="right">83.95</td> +<td align="right">82.41</td> +</tr> +</tbody> +</table> +</div> </div> <div id="analysis" class="section level1"> -<h1><span class="header-section-number">7</span> Analysis</h1> +<h1><span class="header-section-number">8</span> Analysis</h1> <ul> <li>general descriptive picture of changing components of peak – highlight major changes/no-changes?</li> <li>Laundry – by age & gender?</li> @@ -2431,11 +2633,9 @@ div.tocify { <li>Cooking</li> <li>Occupancy – number of persons or just ‘any person’?</li> </ul> -<div id="demand-activities-over-time" class="section level2"> -<h2><span class="header-section-number">7.1</span> DEMAND activities over time</h2> <p>Analysis below uses unweightd data except where explicitly stated.</p> -<div id="test-weights" class="section level3"> -<h3><span class="header-section-number">7.1.1</span> Test weights</h3> +<div id="test-weights" class="section level2"> +<h2><span class="header-section-number">8.1</span> Test weights</h2> <p>Some quick tests on weighted vs un-weighted data.</p> <p>The following tables show the unweighted and weighted % of half hours in which the given act was reported across the different years of the MTUS data. As we can see the weighted estimates do not vary substantially from the unweighted values.</p> <table> @@ -2625,33 +2825,24 @@ div.tocify { <p>Again, these results show minor variations with the maximum difference being +/- 2%. Sleep (weekends 2014) and work (weekdays 2000 & 2014) show the largest weighting effects.</p> <p>All subsequent analysis uses weighted data unless specifically stated (e.g. for testing purposes)</p> </div> -<div id="demand-all-age-groups-combined" class="section level3"> -<h3><span class="header-section-number">7.1.2</span> DEMAND: All age groups combined</h3> -<div id="whole-day" class="section level4"> -<h4><span class="header-section-number">7.1.2.1</span> Whole day</h4> +<div id="demand-all-age-groups-combined" class="section level2"> +<h2><span class="header-section-number">8.2</span> DEMAND: All age groups combined</h2> <p>The following charts use the ‘any in a half hour’ data to show the percentage of half hours reported as a given act at a given time of day.</p> <p>The percentage charts are useful for assessing the changing composition of in-home vs out-of-home activities but can over-emphasise the apparent prevalence of certain acts when it is less common to be out of the home - such as out of home sleep during 01:00 - 05:00.</p> -<p>As there is the potential for more than one activity to be reported in a given half hour the activities may sum to greater than 100% and this is especially visible for diaries with smaller duration slots and more additional variables which lead to more frequent episode boundaries (e.g. 2000 & 2014/15).</p> -<p>First we present results just for 2014 to give a snapshot of the most recent data.</p> -<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/2014%20as%20line%20chart-1.png" /><!-- --></p> -<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/plot%20DEMAND%20acts%20(stacked)-1.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/plot%20DEMAND%20acts%20(stacked)-2.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/plot%20DEMAND%20acts%20(stacked)-3.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/plot%20DEMAND%20acts%20(stacked)-4.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/plot%20DEMAND%20acts%20(stacked)-5.png" /><!-- --></p> -<p>Finally we plot all years combined.</p> -<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/all%20years%20stacked-1.png" /><!-- --></p> -<p>Compare in home & out of home but without looking at day of the week (paper Fig 1). Adjust height of chart to fit single A4 page?</p> -<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/repeat%20all%20acts%20but%20split%20by%20at%20home%20vs%20not%20at%20home-1.png" /><!-- --></p> -<p>Now plot % point change ignoring day of the week (paper Fig 2, adjust height & width to suit single A4)</p> -<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/pc%20point%20change%20all%20days-1.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/pc%20point%20change%20all%20days-2.png" /><!-- --></p> -<p>Next we separate out ‘in home’ vs ‘not in home’ activities (as reported) - Fig 3.</p> -<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/repeat%20all%20acts%20but%20split%20by%20day%20of%20week%20and%20at%20home%20vs%20not%20at%20home-1.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/repeat%20all%20acts%20but%20split%20by%20day%20of%20week%20and%20at%20home%20vs%20not%20at%20home-2.png" /><!-- --></p> -<blockquote> -<p>2nd acts not useful</p> -</blockquote> -<p>As the above representation is difficult to interpret, we next present a derived graph which shows the change in % of halfhours in which these activities were recorded from 1974 to 2014. This chart therefore shows aboslute change of percentage points and highlights those relatively frequent activities which have changed a lot.</p> -<p>Fig 3</p> +<p>Fig (fig:allYearsStackedByWeekday) shows all DEMAND activities stacked by day of the week. (no longer used in paper)</p> +<div class="figure"> +<img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/allYearsStackedByWeekday-1.png" alt="Synthetic MTUS 1974 - 2014, UK sample (% of halfhours on given day reporting X as main activity, weighted)" /> +<p class="caption">Synthetic MTUS 1974 - 2014, UK sample (% of halfhours on given day reporting X as main activity, weighted)</p> +</div> +<p>Fig (fig:allYearsStackedByWeekday) shows all DEMAND activities stacked by day of the week uses line chart to show all acts in vs out of home. Fig 3 in paper.</p> +<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/allYearsLineByLocation-1.png" /><!-- --></p> +<p>Separate out weekdays from weekend days and select specific variables to plot…</p> +<p>Fig (fig:selectedActsDeltaPlot) drawa % point change plot 1974 - 2014 for selected variables. (paper v2.0 Fig 4)</p> <blockquote> <p>NB: ignores hours < 06:00 to avoid noisy data</p> </blockquote> -<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/all%20-%20plot%20pc%20change%201974_2014-1.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/all%20-%20plot%20pc%20change%201974_2014-2.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/all%20-%20plot%20pc%20change%201974_2014-3.png" /><!-- --></p> +<p>All activities at except Travel or where labelled.</p> +<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/selectedActsDeltaPlot-1.png" /><!-- --></p> <p>This charts shows:</p> <ul> <li>decrease in incidence of ‘work’ - most probably due to a decreasing % of the population in work (ageing population)</li> @@ -2660,24 +2851,91 @@ div.tocify { <li>decrease in ‘social’ later in evenings</li> <li>increase in media use especially on weekday evenings and at weekends (NB this data excludes children…)</li> </ul> -<div id="in-detail" class="section level5"> -<h5><span class="header-section-number">7.1.2.1.1</span> In detail</h5> -<p>We now produce a few more detailed plots (for edf presentation).</p> -<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/food%20related-1.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/food%20related-2.png" /><!-- --></p> -<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/personal%20or%20home%20care%20related-1.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/personal%20or%20home%20care%20related-2.png" /><!-- --></p> -<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/media%20related-1.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/media%20related-2.png" /><!-- --></p> -</div> </div> -<div id="evening-peak" class="section level4"> -<h4><span class="header-section-number">7.1.2.2</span> Evening Peak</h4> -<p>The first chart shows the (weighted) proportion of half-hours in which the DEMAND acts were reported in 2014 during this period as context.</p> -<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/create%202014%20peak%20time%20DEMAND%20acts%20plot%20weighted-1.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/create%202014%20peak%20time%20DEMAND%20acts%20plot%20weighted-2.png" /><!-- --></p> -<p>Next we use the 1974 - 2014 changes to present the changes in the peak demand period for weekdays vs Saturdays and Sundays. The first version keeps the at own home/out of home panels as the vertical columns, the second switches them to highlight change. Need to be consistent inthe paper…</p> -<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/All%20-%20Evening%20peak%20change%20by%20day%20of%20the%20week-1.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/All%20-%20Evening%20peak%20change%20by%20day%20of%20the%20week-2.png" /><!-- --></p> +<div id="compare-tu-ng-demand-data" class="section level2"> +<h2><span class="header-section-number">8.3</span> Compare TU & NG demand data</h2> +<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/check%20TU%20act%20change%20against%20NG-1.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/check%20TU%20act%20change%20against%20NG-2.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/check%20TU%20act%20change%20against%20NG-3.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/check%20TU%20act%20change%20against%20NG-4.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/check%20TU%20act%20change%20against%20NG-5.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/check%20TU%20act%20change%20against%20NG-6.png" /><!-- --></p> </div> -</div> -<div id="demand-working-age-group-16-64-vs-retired-65" class="section level3"> -<h3><span class="header-section-number">7.1.3</span> DEMAND: Working age group (16-64) vs retired (65+)</h3> +<div id="demand-working-age-group-16-64-vs-retired-65" class="section level2"> +<h2><span class="header-section-number">8.4</span> DEMAND: Working age group (16-64) vs retired (65+)</h2> +<p>Weighted table of respondents by age (repeats table above?)</p> +<table> +<caption>MTUS 1974-2015 Survey data: age of respondents (weighted, row %)</caption> +<thead> +<tr class="header"> +<th></th> +<th align="right">16-25</th> +<th align="right">26-35</th> +<th align="right">36-45</th> +<th align="right">46-55</th> +<th align="right">56-65</th> +<th align="right">66-75</th> +<th align="right">75+</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td>1974</td> +<td align="right">18.71</td> +<td align="right">21.40</td> +<td align="right">18.16</td> +<td align="right">20.17</td> +<td align="right">12.47</td> +<td align="right">7.10</td> +<td align="right">1.99</td> +</tr> +<tr class="even"> +<td>1985</td> +<td align="right">16.73</td> +<td align="right">25.49</td> +<td align="right">21.41</td> +<td align="right">17.47</td> +<td align="right">11.64</td> +<td align="right">5.49</td> +<td align="right">1.77</td> +</tr> +<tr class="odd"> +<td>1995</td> +<td align="right">11.22</td> +<td align="right">20.54</td> +<td align="right">17.76</td> +<td align="right">17.29</td> +<td align="right">13.23</td> +<td align="right">13.38</td> +<td align="right">6.59</td> +</tr> +<tr class="even"> +<td>2000</td> +<td align="right">12.06</td> +<td align="right">19.41</td> +<td align="right">19.17</td> +<td align="right">17.82</td> +<td align="right">13.11</td> +<td align="right">11.54</td> +<td align="right">6.90</td> +</tr> +<tr class="odd"> +<td>2005</td> +<td align="right">12.68</td> +<td align="right">17.10</td> +<td align="right">19.38</td> +<td align="right">16.32</td> +<td align="right">15.82</td> +<td align="right">10.30</td> +<td align="right">8.39</td> +</tr> +<tr class="even"> +<td>2014</td> +<td align="right">13.72</td> +<td align="right">17.38</td> +<td align="right">16.44</td> +<td align="right">17.32</td> +<td align="right">14.23</td> +<td align="right">11.98</td> +<td align="right">8.92</td> +</tr> +</tbody> +</table> <p>The following table shows the % of respondents aged 16+ in different work status over the period. The decline in the % of those in full time work is partly due to increased numbers of people in the older (retired) age cohorts.</p> <table> <caption>MTUS 1974-2015 Survey data: employment status (weighted, row %)</caption> @@ -2751,7 +3009,7 @@ div.tocify { </table> <p>This is corrected by the following table which shows the work status of all aged 16-64 in each survey for men and for women. As we can see there has been a general reduction in the % of male respondents in full time work over the period alongside an increase in the % in part time work. In contrast the opposite trends are seen for women with a steady increase in the % in full time work and a similar (but less steep) upward trend in the % in part-time work. Given the ongoing gendered nature of energy-using domestic acitivties (refs) these trends provide an important context to the patterns of time-use we discuss below.</p> <table> -<caption>MTUS 1974-2014 Survey data: Male employment status (weighted, row %, all aged 16-65)</caption> +<caption>MTUS 1974-2014 Survey data: Male employment status (weighted, row %, all aged 16-64)</caption> <thead> <tr class="header"> <th></th> @@ -2821,7 +3079,7 @@ div.tocify { </tbody> </table> <table> -<caption>MTUS 1974-2014 Survey data: Female employment status (weighted, row %, all aged 16-65)</caption> +<caption>MTUS 1974-2014 Survey data: Female employment status (weighted, row %, all aged 16-64)</caption> <thead> <tr class="header"> <th></th> @@ -2993,30 +3251,39 @@ div.tocify { <blockquote> <p>NB: ignores hours < 06:00 to avoid noisy data</p> </blockquote> -<p>Out of home: Fig 4</p> -<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/16-64%20-%20plot%20day%20of%20week%20change%20for%201974%20-%202014%20out%20of%20home-1.png" /><!-- --></p> -<p>Essentially repeat the above analysis but for evening peak.</p> -<p>Out of home evening peak:</p> -<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/16-64%20-%20plot%20day%20of%20week%20change%20for%201974%20-%202014%20out%20of%20home%20evening%20peak-1.png" /><!-- --></p> -<p>At home: Fig 5</p> -<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/16-64%20-%20plot%20day%20of%20week%20change%20for%201974%20-%202014%20at%20own%20home-1.png" /><!-- --></p> -<p>At home evening peak</p> -<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/16-64%20-%20plot%20day%20of%20week%20change%20for%201974%20-%202014%20at%20own%20home%20evening%20peak-1.png" /><!-- --></p> -<p>This charts shows:</p> -<blockquote> -<p>to do</p> -</blockquote> +<p>16-64 - All activities at home except Travel or where labelled (Figure (fig:deltaPlotDow16_64) - Fig 5 in paper).</p> +<div class="figure"> +<img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/deltaPlotDow16_64-1.png" alt="deltaPlotDow16_64" /> +<p class="caption">deltaPlotDow16_64</p> +</div> +<p>65+ - All activities at home except Travel or where labelled (Figure (fig:deltaPlotDow64m) - Fig 6 in paper).</p> +<div class="figure"> +<img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/deltaPlotDow64m-1.png" alt="deltaPlotDow64m" /> +<p class="caption">deltaPlotDow64m</p> </div> -<div id="food-media-regression-analysis" class="section level3"> -<h3><span class="header-section-number">7.1.4</span> Food & Media: Regression analysis</h3> +</div> +<div id="detailed-activities-descriptive-analysis" class="section level2"> +<h2><span class="header-section-number">8.5</span> Detailed Activities: Descriptive analysis</h2> <p>However, such descriptive analysis does not enable robust statistical claims regarding change during the evening peak period. To provide such evidence we developed two Poisson models - one for the early evening period from 16:00 to 18:00 on weekdays () and one for 18:00 – 21:00 () on weekdays, reflecting the descriptive analysis/charts above. For each model we first assess the statistical effect of survey year (sub-model one) and then year plus age group (sub-model two) on the number of half hours that the given activity was reported in those periods.</p> <p>It looks like we have a shift of eating to later in the evening on weekdays…</p> <p>Construct two models - one for the early evening period before 18:00 on weekdays and one for after (reflecting descriptive analysis/charts above).</p> <p>Test this using two poisson models - one for ‘early’ eating and one for ‘late’ eating on weekdays at home. We use the sum of number of half hours where any food activity is reported in the two periods for each person as the dependent variable. We need robust SE as we (may) have multiple observations where diaries included more than 1 weekday as the table below shows. Poisson models test predictors of number of reported half-hours in which act reported - so a decrease could be a reduction in doing the act (at all) <em>and/or</em> or a reduction in time devoted to it.</p> <p>Also use two logit to test p(any reported) - this tests predictors of reporting any activity in the time period and may be easier to interpret.</p> +<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/plotChange-1.png" /><!-- --></p> <p>NB: we use interaction terms for survey year & working age to understand how older people’s reported activities have changed over time.</p> -<p>Before we start regression analysis, construct line charts showing % reporting this activity per half hour for each survey year - to give a better sense of the trajectory of change than the 1974 -> 2014 total diff.</p> -<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/yearly%20change%20-%20food-1.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/yearly%20change%20-%20food-2.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/yearly%20change%20-%20food-3.png" /><!-- --></p> +<p>Before we start regression analysis, construct line charts showing % reporting this activity per half hour for each survey year - to give a better sense of the trajectory of change than the 1974 -> 2014 total diff. (not used in final version of paper)</p> +<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/allActsYearlyChangeByAgeGender-1.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/allActsYearlyChangeByAgeGender-2.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/allActsYearlyChangeByAgeGender-3.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/allActsYearlyChangeByAgeGender-4.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/allActsYearlyChangeByAgeGender-5.png" /><!-- --></p> +</div> +<div id="regression-analysis" class="section level2"> +<h2><span class="header-section-number">8.6</span> Regression analysis</h2> +<p>Change of purpose of models - to estimate statistical effects of being in empstat on p(late food) etc. This uses respondents aged 16-64 only as we are interested in the effects of changing work patterns (primarily)</p> +<div id="run-food-models" class="section level3"> +<h3><span class="header-section-number">8.6.1</span> Run Food models</h3> +<ul> +<li>Poisson (counts of half-hours)</li> +<li>Logit (any half-hours)</li> +</ul> +<p>Model 1: survey year * sex Model 2: sex * empl for all in 2014 only</p> <table> <caption>Obs counts for ‘early food’ model</caption> <thead> @@ -3030,27 +3297,27 @@ div.tocify { <tbody> <tr class="odd"> <td align="right">1974</td> -<td align="right">7994</td> -<td align="right">2274</td> -<td align="right">7994</td> +<td align="right">9161</td> +<td align="right">2451</td> +<td align="right">9161</td> </tr> <tr class="even"> <td align="right">1985</td> -<td align="right">10799</td> -<td align="right">2727</td> -<td align="right">10799</td> +<td align="right">11606</td> +<td align="right">2787</td> +<td align="right">11606</td> </tr> <tr class="odd"> <td align="right">2000</td> -<td align="right">6007</td> -<td align="right">5990</td> -<td align="right">6007</td> +<td align="right">6648</td> +<td align="right">6628</td> +<td align="right">6648</td> </tr> <tr class="even"> <td align="right">2014</td> -<td align="right">5087</td> -<td align="right">5082</td> -<td align="right">5087</td> +<td align="right">5626</td> +<td align="right">5618</td> +<td align="right">5626</td> </tr> </tbody> </table> @@ -3067,146 +3334,93 @@ div.tocify { <tbody> <tr class="odd"> <td align="right">1974</td> -<td align="right">9931</td> -<td align="right">2529</td> -<td align="right">9931</td> +<td align="right">9503</td> +<td align="right">2516</td> +<td align="right">9503</td> </tr> <tr class="even"> <td align="right">1985</td> -<td align="right">12321</td> -<td align="right">2838</td> -<td align="right">12321</td> +<td align="right">11819</td> +<td align="right">2824</td> +<td align="right">11819</td> </tr> <tr class="odd"> <td align="right">2000</td> -<td align="right">7371</td> -<td align="right">7348</td> -<td align="right">7371</td> +<td align="right">7186</td> +<td align="right">7163</td> +<td align="right">7186</td> </tr> <tr class="even"> <td align="right">2014</td> -<td align="right">6360</td> -<td align="right">6352</td> -<td align="right">6360</td> +<td align="right">6244</td> +<td align="right">6238</td> +<td align="right">6244</td> </tr> </tbody> </table> -<table> -<caption>Obs counts for ‘early media’ model</caption> -<thead> -<tr class="header"> -<th align="right">ba_survey</th> -<th align="right">nObs</th> -<th align="right">nPeople</th> -<th align="right">nDiaries</th> -</tr> -</thead> -<tbody> -<tr class="odd"> -<td align="right">1974</td> -<td align="right">7994</td> -<td align="right">2274</td> -<td align="right">7994</td> +<p>The following tables report the regression results more neatly.</p> +<p>Early food:</p> +<pre><code>## ba_pid ba_diarypid ba_survey ba_sex +## Length:33041 Length:33041 Min. :1974 Male :14020 +## Class :character Class :character 1st Qu.:1974 Female:19021 +## Mode :character Mode :character Median :1985 +## Mean :1990 +## 3rd Qu.:2000 +## Max. :2014 +## ba_workingAge empstat sumDA_Food_m minTime +## 16-64:28490 not in paid work :13049 Min. :0.00 Length:33041 +## 65+ : 4551 full-time :12793 1st Qu.:1.00 Class1:hms +## missing : 274 Median :1.00 Class2:difftime +## part-time : 6362 Mean :1.47 Mode :numeric +## unknown job hours: 543 3rd Qu.:2.00 +## NA's : 20 Max. :5.00 +## maxTime anyDA_Food_m +## Length:33041 Min. :0.0000 +## Class1:hms 1st Qu.:1.0000 +## Class2:difftime Median :1.0000 +## Mode :numeric Mean :0.7993 +## 3rd Qu.:1.0000 +## Max. :1.0000</code></pre> +<pre><code>## [1] "From:"</code></pre> +<pre><code>## 16:00:00</code></pre> +<pre><code>## [1] "To:"</code></pre> +<pre><code>## 18:00:00</code></pre> +<table style="text-align:center"> +<tr> +<td colspan="3" style="border-bottom: 1px solid black"> +</td> </tr> -<tr class="even"> -<td align="right">1985</td> -<td align="right">10799</td> -<td align="right">2727</td> -<td align="right">10799</td> +<tr> +<td style="text-align:left"> +</td> +<td colspan="2"> +<em>Dependent variable:</em> +</td> </tr> -<tr class="odd"> -<td align="right">2000</td> -<td align="right">6007</td> -<td align="right">5990</td> -<td align="right">6007</td> +<tr> +<td> +</td> +<td colspan="2" style="border-bottom: 1px solid black"> +</td> </tr> -<tr class="even"> -<td align="right">2014</td> -<td align="right">5087</td> -<td align="right">5082</td> -<td align="right">5087</td> -</tr> -</tbody> -</table> -<table> -<caption>Obs counts for ‘late media’ model</caption> -<thead> -<tr class="header"> -<th align="right">ba_survey</th> -<th align="right">nObs</th> -<th align="right">nPeople</th> -<th align="right">nDiaries</th> -</tr> -</thead> -<tbody> -<tr class="odd"> -<td align="right">1974</td> -<td align="right">9931</td> -<td align="right">2529</td> -<td align="right">9931</td> -</tr> -<tr class="even"> -<td align="right">1985</td> -<td align="right">12321</td> -<td align="right">2838</td> -<td align="right">12321</td> -</tr> -<tr class="odd"> -<td align="right">2000</td> -<td align="right">7371</td> -<td align="right">7348</td> -<td align="right">7371</td> -</tr> -<tr class="even"> -<td align="right">2014</td> -<td align="right">6360</td> -<td align="right">6352</td> -<td align="right">6360</td> -</tr> -</tbody> -</table> -<div id="run-food-models" class="section level4"> -<h4><span class="header-section-number">7.1.4.1</span> Run food models:</h4> -<ul> -<li>Poisson (counts of half-hours)</li> -<li>Logit (any half-hours)</li> -</ul> -<p>The following tables report the regression results more neatly.</p> -<p>Early food:</p> -<p>First, the poisson model which predicts counts of half-hours.</p> -<table style="text-align:center"> -<tr> -<td colspan="3" style="border-bottom: 1px solid black"> -</td> -</tr> -<tr> -<td style="text-align:left"> -</td> -<td colspan="2"> -<em>Dependent variable:</em> -</td> -</tr> -<tr> -<td> -</td> -<td colspan="2" style="border-bottom: 1px solid black"> -</td> -</tr> -<tr> -<td style="text-align:left"> -</td> -<td colspan="2"> -sumDA_Food_m -</td> +<tr> +<td style="text-align:left"> +</td> +<td> +simpleModel +</td> +<td> +fullModel +</td> </tr> <tr> <td style="text-align:left"> </td> <td> -Early: Model 1 (poisson) +Food Early: Model 1 (poisson) </td> <td> -Early: Model 2 (poisson) +Food Early: Model 2 (poisson) </td> </tr> <tr> @@ -3228,10 +3442,10 @@ Early: Model 2 (poisson) as.factor(ba_survey)1985 </td> <td> -0.076<sup>***</sup> (0.050, 0.101) +0.263<sup>***</sup> (0.221, 0.305) </td> <td> -0.067<sup>***</sup> (0.040, 0.095) +0.001 (-0.022, 0.024) </td> </tr> <tr> @@ -3239,10 +3453,10 @@ as.factor(ba_survey)1985 as.factor(ba_survey)2000 </td> <td> --0.055<sup>***</sup> (-0.085, -0.024) +0.196<sup>***</sup> (0.147, 0.244) </td> <td> --0.103<sup>***</sup> (-0.137, -0.068) +-0.062<sup>***</sup> (-0.089, -0.034) </td> </tr> <tr> @@ -3250,212 +3464,131 @@ as.factor(ba_survey)2000 as.factor(ba_survey)2014 </td> <td> --0.225<sup>***</sup> (-0.260, -0.191) +0.082<sup>**</sup> (0.029, 0.134) </td> <td> --0.278<sup>***</sup> (-0.317, -0.238) +-0.203<sup>***</sup> (-0.233, -0.173) </td> </tr> <tr> <td style="text-align:left"> -ba_workingAge65+ +ba_sexFemale </td> <td> +0.717<sup>***</sup> (0.679, 0.756) </td> <td> --0.063 (-0.127, 0.002) +0.366<sup>***</sup> (0.334, 0.398) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)1985:ba_workingAge65+ +as.factor(ba_survey)1985:ba_sexFemale </td> <td> +-0.325<sup>***</sup> (-0.375, -0.275) </td> <td> -0.072 (-0.015, 0.159) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)2000:ba_workingAge65+ +as.factor(ba_survey)2000:ba_sexFemale </td> <td> +-0.342<sup>***</sup> (-0.400, -0.283) </td> <td> -0.246<sup>***</sup> (0.160, 0.331) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)2014:ba_workingAge65+ +as.factor(ba_survey)2014:ba_sexFemale </td> <td> +-0.352<sup>***</sup> (-0.416, -0.289) </td> <td> -0.223<sup>***</sup> (0.134, 0.312) </td> </tr> <tr> <td style="text-align:left"> -Constant +empstatfull-time </td> <td> -0.269<sup>***</sup> (0.249, 0.289) </td> <td> -0.277<sup>***</sup> (0.255, 0.298) -</td> -</tr> -<tr> -<td colspan="3" style="border-bottom: 1px solid black"> +-0.295<sup>***</sup> (-0.331, -0.259) </td> </tr> <tr> <td style="text-align:left"> -Observations +empstatmissing </td> <td> -29,887 </td> <td> -29,887 -</td> -</tr> -<tr> -<td colspan="3" style="border-bottom: 1px solid black"> +-0.339<sup>***</sup> (-0.521, -0.157) </td> </tr> <tr> <td style="text-align:left"> -<em>Note:</em> -</td> -<td colspan="2" style="text-align:right"> -<sup><em></sup>p<0.05; <sup><strong></sup>p<0.01; <sup></strong></em></sup>p<0.001 +empstatpart-time </td> -</tr> -</table> -<p>Second, the logit model which is predicting any food related activities in that period.</p> -<table style="text-align:center"> -<tr> -<td colspan="3" style="border-bottom: 1px solid black"> -</td> -</tr> -<tr> -<td style="text-align:left"> -</td> -<td colspan="2"> -<em>Dependent variable:</em> -</td> -</tr> -<tr> <td> </td> -<td colspan="2" style="border-bottom: 1px solid black"> -</td> -</tr> -<tr> -<td style="text-align:left"> -</td> -<td colspan="2"> -anyDA_Food_m -</td> -</tr> -<tr> -<td style="text-align:left"> -</td> <td> -Early: Model 1 (logit) -</td> -<td> -Early: Model 2 (logit) +-0.172<sup>***</sup> (-0.234, -0.109) </td> </tr> <tr> <td style="text-align:left"> +empstatunknown job hours </td> <td> -(1) </td> <td> -(2) -</td> -</tr> -<tr> -<td colspan="3" style="border-bottom: 1px solid black"> +-0.273<sup>***</sup> (-0.400, -0.147) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)1985 +ba_sexFemale:empstatfull-time </td> <td> -0.260<sup>***</sup> (0.190, 0.330) </td> <td> -0.240<sup>***</sup> (0.166, 0.314) +-0.047<sup>*</sup> (-0.094, -0.0002) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)2000 +ba_sexFemale:empstatmissing </td> <td> --0.193<sup>***</sup> (-0.269, -0.117) </td> <td> --0.288<sup>***</sup> (-0.370, -0.205) +0.001 (-0.235, 0.237) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)2014 +ba_sexFemale:empstatpart-time </td> <td> --0.659<sup>***</sup> (-0.735, -0.583) </td> <td> --0.735<sup>***</sup> (-0.820, -0.650) +0.137<sup>***</sup> (0.069, 0.204) </td> </tr> <tr> <td style="text-align:left"> -ba_workingAge65+ +ba_sexFemale:empstatunknown job hours </td> <td> </td> <td> --0.067 (-0.226, 0.092) -</td> -</tr> -<tr> -<td style="text-align:left"> -as.factor(ba_survey)1985:ba_workingAge65+ -</td> -<td> -</td> -<td> -0.215 (-0.023, 0.453) -</td> -</tr> -<tr> -<td style="text-align:left"> -as.factor(ba_survey)2000:ba_workingAge65+ -</td> -<td> -</td> -<td> -0.529<sup>***</sup> (0.310, 0.749) -</td> -</tr> -<tr> -<td style="text-align:left"> -as.factor(ba_survey)2014:ba_workingAge65+ -</td> -<td> -</td> -<td> -0.327<sup>**</sup> (0.122, 0.533) +0.232<sup>**</sup> (0.079, 0.386) </td> </tr> <tr> @@ -3463,10 +3596,10 @@ as.factor(ba_survey)2014:ba_workingAge65+ Constant </td> <td> -1.117<sup>***</sup> (1.066, 1.168) +-0.041<sup>*</sup> (-0.074, -0.007) </td> <td> -1.125<sup>***</sup> (1.070, 1.179) +0.346<sup>***</sup> (0.313, 0.379) </td> </tr> <tr> @@ -3478,10 +3611,10 @@ Constant Observations </td> <td> -29,887 +33,041 </td> <td> -29,887 +33,021 </td> </tr> <tr> @@ -3498,7 +3631,31 @@ Observations </tr> </table> <p>Late food:</p> -<p>First, the poisson model which predicts counts of half-hours.</p> +<pre><code>## ba_pid ba_diarypid ba_survey ba_workingAge +## Length:34752 Length:34752 Min. :1974 16-64:30279 +## Class :character Class :character 1st Qu.:1974 65+ : 4473 +## Mode :character Mode :character Median :1985 +## Mean :1990 +## 3rd Qu.:2000 +## Max. :2014 +## ba_sex empstat sumDA_Food_m +## Male :15364 not in paid work :12873 Min. :0.0000 +## Female:19388 full-time :14613 1st Qu.:0.0000 +## missing : 285 Median :0.0000 +## part-time : 6427 Mean :0.6929 +## unknown job hours: 538 3rd Qu.:1.0000 +## NA's : 16 Max. :5.0000 +## minTime maxTime anyDA_Food_m +## Length:34752 Length:34752 Min. :0.0000 +## Class1:hms Class1:hms 1st Qu.:0.0000 +## Class2:difftime Class2:difftime Median :0.0000 +## Mode :numeric Mode :numeric Mean :0.4646 +## 3rd Qu.:1.0000 +## Max. :1.0000</code></pre> +<pre><code>## [1] "From:"</code></pre> +<pre><code>## 18:30:00</code></pre> +<pre><code>## [1] "To:"</code></pre> +<pre><code>## 20:30:00</code></pre> <table style="text-align:center"> <tr> <td colspan="3" style="border-bottom: 1px solid black"> @@ -3520,18 +3677,21 @@ Observations <tr> <td style="text-align:left"> </td> -<td colspan="2"> -sumDA_Food_m +<td> +simpleModel +</td> +<td> +fullModel </td> </tr> <tr> <td style="text-align:left"> </td> <td> -Late: Model 1 (poisson) +Food Late: Model 1 (poisson) </td> <td> -Late: Model 2 (poisson) +Food Late: Model 2 (poisson) </td> </tr> <tr> @@ -3553,10 +3713,10 @@ Late: Model 2 (poisson) as.factor(ba_survey)1985 </td> <td> --0.089<sup>***</sup> (-0.119, -0.058) +0.225<sup>***</sup> (0.160, 0.290) </td> <td> --0.106<sup>***</sup> (-0.137, -0.074) +0.006 (-0.033, 0.046) </td> </tr> <tr> @@ -3564,10 +3724,10 @@ as.factor(ba_survey)1985 as.factor(ba_survey)2000 </td> <td> -0.293<sup>***</sup> (0.261, 0.324) +0.683<sup>***</sup> (0.618, 0.748) </td> <td> -0.290<sup>***</sup> (0.257, 0.324) +0.477<sup>***</sup> (0.437, 0.517) </td> </tr> <tr> @@ -3575,223 +3735,325 @@ as.factor(ba_survey)2000 as.factor(ba_survey)2014 </td> <td> -0.346<sup>***</sup> (0.313, 0.378) +0.795<sup>***</sup> (0.730, 0.860) </td> <td> -0.329<sup>***</sup> (0.294, 0.364) +0.554<sup>***</sup> (0.514, 0.595) </td> </tr> <tr> <td style="text-align:left"> -ba_workingAge65+ +ba_sexFemale </td> <td> +0.507<sup>***</sup> (0.446, 0.568) </td> <td> --0.445<sup>***</sup> (-0.538, -0.352) +0.288<sup>***</sup> (0.235, 0.340) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)1985:ba_workingAge65+ +as.factor(ba_survey)1985:ba_sexFemale </td> <td> +-0.406<sup>***</sup> (-0.487, -0.325) </td> <td> -0.226<sup>***</sup> (0.099, 0.353) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)2000:ba_workingAge65+ +as.factor(ba_survey)2000:ba_sexFemale </td> <td> +-0.383<sup>***</sup> (-0.465, -0.302) </td> <td> -0.256<sup>***</sup> (0.144, 0.369) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)2014:ba_workingAge65+ +as.factor(ba_survey)2014:ba_sexFemale </td> <td> +-0.462<sup>***</sup> (-0.544, -0.380) </td> <td> -0.376<sup>***</sup> (0.267, 0.485) </td> </tr> <tr> <td style="text-align:left"> -Constant +empstatfull-time </td> <td> --0.149<sup>***</sup> (-0.171, -0.127) </td> <td> --0.117<sup>***</sup> (-0.139, -0.094) -</td> -</tr> -<tr> -<td colspan="3" style="border-bottom: 1px solid black"> +0.277<sup>***</sup> (0.224, 0.330) </td> </tr> <tr> <td style="text-align:left"> -Observations +empstatmissing </td> <td> -35,983 </td> <td> -35,983 -</td> -</tr> -<tr> -<td colspan="3" style="border-bottom: 1px solid black"> +0.213<sup>*</sup> (0.015, 0.412) </td> </tr> <tr> <td style="text-align:left"> -<em>Note:</em> +empstatpart-time </td> -<td colspan="2" style="text-align:right"> -<sup><em></sup>p<0.05; <sup><strong></sup>p<0.01; <sup></strong></em></sup>p<0.001 +<td> </td> -</tr> -</table> -<p>Second, the logit model which is predicting any food related activities in that period.</p> -<table style="text-align:center"> -<tr> -<td colspan="3" style="border-bottom: 1px solid black"> +<td> +0.270<sup>***</sup> (0.188, 0.352) </td> </tr> <tr> <td style="text-align:left"> +empstatunknown job hours </td> -<td colspan="2"> -<em>Dependent variable:</em> -</td> -</tr> -<tr> <td> </td> -<td colspan="2" style="border-bottom: 1px solid black"> +<td> +0.081 (-0.130, 0.293) </td> </tr> <tr> <td style="text-align:left"> +ba_sexFemale:empstatfull-time </td> -<td colspan="2"> -anyDA_Food_m +<td> +</td> +<td> +-0.061 (-0.129, 0.006) </td> </tr> <tr> <td style="text-align:left"> +ba_sexFemale:empstatmissing </td> <td> -Late: Model 1 (logit) </td> <td> -Late: Model 2 (logit) +-0.035 (-0.300, 0.231) </td> </tr> <tr> <td style="text-align:left"> +ba_sexFemale:empstatpart-time </td> <td> -(1) </td> <td> -(2) -</td> -</tr> -<tr> -<td colspan="3" style="border-bottom: 1px solid black"> +-0.162<sup>***</sup> (-0.255, -0.070) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)1985 +ba_sexFemale:empstatunknown job hours </td> <td> --0.105<sup>***</sup> (-0.158, -0.052) </td> <td> --0.149<sup>***</sup> (-0.204, -0.093) +0.056 (-0.207, 0.320) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)2000 +Constant </td> <td> -0.512<sup>***</sup> (0.448, 0.575) +-0.913<sup>***</sup> (-0.963, -0.863) </td> <td> -0.524<sup>***</sup> (0.454, 0.593) +-0.940<sup>***</sup> (-0.994, -0.886) +</td> +</tr> +<tr> +<td colspan="3" style="border-bottom: 1px solid black"> </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)2014 +Observations </td> <td> -0.533<sup>***</sup> (0.467, 0.600) +34,752 </td> <td> -0.496<sup>***</sup> (0.422, 0.570) +34,736 +</td> +</tr> +<tr> +<td colspan="3" style="border-bottom: 1px solid black"> </td> </tr> <tr> <td style="text-align:left"> -ba_workingAge65+ +<em>Note:</em> </td> -<td> +<td colspan="2" style="text-align:right"> +<sup><em></sup>p<0.05; <sup><strong></sup>p<0.01; <sup></strong></em></sup>p<0.001 </td> -<td> --0.749<sup>***</sup> (-0.888, -0.610) +</tr> +</table> +</div> +<div id="run-personalhome-care-models" class="section level3"> +<h3><span class="header-section-number">8.6.2</span> Run Personal/home care models:</h3> +<ul> +<li>Poisson (counts of half-hours)</li> +</ul> +<table> +<caption>Obs counts for ‘early personal’ model</caption> +<thead> +<tr class="header"> +<th align="right">ba_survey</th> +<th align="right">nObs</th> +<th align="right">nPeople</th> +<th align="right">nDiaries</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="right">1974</td> +<td align="right">9161</td> +<td align="right">2451</td> +<td align="right">9161</td> +</tr> +<tr class="even"> +<td align="right">1985</td> +<td align="right">11606</td> +<td align="right">2787</td> +<td align="right">11606</td> +</tr> +<tr class="odd"> +<td align="right">2000</td> +<td align="right">6648</td> +<td align="right">6628</td> +<td align="right">6648</td> +</tr> +<tr class="even"> +<td align="right">2014</td> +<td align="right">5626</td> +<td align="right">5618</td> +<td align="right">5626</td> +</tr> +</tbody> +</table> +<table> +<caption>Obs counts for ‘late personal’ model</caption> +<thead> +<tr class="header"> +<th align="right">ba_survey</th> +<th align="right">nObs</th> +<th align="right">nPeople</th> +<th align="right">nDiaries</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="right">1974</td> +<td align="right">9503</td> +<td align="right">2516</td> +<td align="right">9503</td> +</tr> +<tr class="even"> +<td align="right">1985</td> +<td align="right">11819</td> +<td align="right">2824</td> +<td align="right">11819</td> +</tr> +<tr class="odd"> +<td align="right">2000</td> +<td align="right">7186</td> +<td align="right">7163</td> +<td align="right">7186</td> +</tr> +<tr class="even"> +<td align="right">2014</td> +<td align="right">6244</td> +<td align="right">6238</td> +<td align="right">6244</td> +</tr> +</tbody> +</table> +<p>The following tables report the regression results more neatly.</p> +<p>Early personal:</p> +<pre><code>## ba_pid ba_diarypid ba_survey ba_workingAge +## Length:33041 Length:33041 Min. :1974 16-64:28490 +## Class :character Class :character 1st Qu.:1974 65+ : 4551 +## Mode :character Mode :character Median :1985 +## Mean :1990 +## 3rd Qu.:2000 +## Max. :2014 +## ba_sex empstat sumDA_Personal_m +## Male :14020 not in paid work :13049 Min. :0.0000 +## Female:19021 full-time :12793 1st Qu.:0.0000 +## missing : 274 Median :1.0000 +## part-time : 6362 Mean :0.9951 +## unknown job hours: 543 3rd Qu.:2.0000 +## NA's : 20 Max. :5.0000 +## minTime maxTime anyDA_Personal_m +## Length:33041 Length:33041 Min. :0.0000 +## Class1:hms Class1:hms 1st Qu.:0.0000 +## Class2:difftime Class2:difftime Median :1.0000 +## Mode :numeric Mode :numeric Mean :0.5663 +## 3rd Qu.:1.0000 +## Max. :1.0000</code></pre> +<pre><code>## [1] "From:"</code></pre> +<pre><code>## 16:00:00</code></pre> +<pre><code>## [1] "To:"</code></pre> +<pre><code>## 18:00:00</code></pre> +<table style="text-align:center"> +<tr> +<td colspan="3" style="border-bottom: 1px solid black"> </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)1985:ba_workingAge65+ </td> -<td> +<td colspan="2"> +<em>Dependent variable:</em> </td> +</tr> +<tr> <td> -0.420<sup>***</sup> (0.228, 0.612) +</td> +<td colspan="2" style="border-bottom: 1px solid black"> </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)2000:ba_workingAge65+ </td> <td> +simpleModel </td> <td> -0.306<sup>**</sup> (0.119, 0.493) +fullModel </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)2014:ba_workingAge65+ </td> <td> +Personal Early: Model 1 (poisson) </td> <td> -0.609<sup>***</sup> (0.422, 0.796) +Personal Early: Model 2 (poisson) </td> </tr> <tr> <td style="text-align:left"> -Constant </td> <td> -0.261<sup>***</sup> (0.222, 0.301) +(1) </td> <td> -0.331<sup>***</sup> (0.289, 0.373) +(2) </td> </tr> <tr> @@ -3800,159 +4062,156 @@ Constant </tr> <tr> <td style="text-align:left"> -Observations +as.factor(ba_survey)1985 </td> <td> -35,983 +0.391<sup>***</sup> (0.334, 0.447) </td> <td> -35,983 -</td> -</tr> -<tr> -<td colspan="3" style="border-bottom: 1px solid black"> +0.508<sup>***</sup> (0.474, 0.541) </td> </tr> <tr> <td style="text-align:left"> -<em>Note:</em> +as.factor(ba_survey)2000 </td> -<td colspan="2" style="text-align:right"> -<sup><em></sup>p<0.05; <sup><strong></sup>p<0.01; <sup></strong></em></sup>p<0.001 +<td> +0.503<sup>***</sup> (0.442, 0.565) </td> -</tr> -</table> -<p>Now repeat for media use.</p> -</div> -<div id="run-media-models" class="section level4"> -<h4><span class="header-section-number">7.1.4.2</span> Run media models:</h4> -<ul> -<li>Poisson (counts of half-hours)</li> -<li>Logit (any half-hours)</li> -</ul> -<p>The following tables report the regression results more neatly.</p> -<p>Early media:</p> -<p>First, the poisson model which predicts counts of half-hours.</p> -<table style="text-align:center"> -<tr> -<td colspan="3" style="border-bottom: 1px solid black"> +<td> +0.546<sup>***</sup> (0.509, 0.583) </td> </tr> <tr> <td style="text-align:left"> +as.factor(ba_survey)2014 </td> -<td colspan="2"> -<em>Dependent variable:</em> +<td> +0.471<sup>***</sup> (0.407, 0.535) +</td> +<td> +0.506<sup>***</sup> (0.467, 0.545) </td> </tr> <tr> +<td style="text-align:left"> +ba_sexFemale +</td> <td> +0.296<sup>***</sup> (0.239, 0.353) </td> -<td colspan="2" style="border-bottom: 1px solid black"> +<td> +0.521<sup>***</sup> (0.478, 0.564) </td> </tr> <tr> <td style="text-align:left"> +as.factor(ba_survey)1985:ba_sexFemale </td> -<td colspan="2"> -sumDA_Media_m +<td> +0.180<sup>***</sup> (0.110, 0.249) +</td> +<td> </td> </tr> <tr> <td style="text-align:left"> +as.factor(ba_survey)2000:ba_sexFemale </td> <td> -Early: Model 1 (poisson) +0.062 (-0.015, 0.138) </td> <td> -Early: Model 2 (poisson) </td> </tr> <tr> <td style="text-align:left"> +as.factor(ba_survey)2014:ba_sexFemale </td> <td> -(1) +0.046 (-0.033, 0.126) </td> <td> -(2) </td> </tr> <tr> -<td colspan="3" style="border-bottom: 1px solid black"> +<td style="text-align:left"> +empstatfull-time +</td> +<td> +</td> +<td> +0.110<sup>***</sup> (0.063, 0.157) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)1985 +empstatmissing </td> <td> -0.315<sup>***</sup> (0.270, 0.360) </td> <td> -0.378<sup>***</sup> (0.327, 0.429) +0.296<sup>***</sup> (0.120, 0.472) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)2000 +empstatpart-time </td> <td> -0.522<sup>***</sup> (0.474, 0.571) </td> <td> -0.508<sup>***</sup> (0.451, 0.565) +0.039 (-0.039, 0.116) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)2014 +empstatunknown job hours </td> <td> -0.558<sup>***</sup> (0.508, 0.607) </td> <td> -0.482<sup>***</sup> (0.421, 0.543) +-0.026 (-0.185, 0.134) </td> </tr> <tr> <td style="text-align:left"> -ba_workingAge65+ +ba_sexFemale:empstatfull-time </td> <td> </td> <td> -1.044<sup>***</sup> (0.964, 1.123) +-0.360<sup>***</sup> (-0.421, -0.300) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)1985:ba_workingAge65+ +ba_sexFemale:empstatmissing </td> <td> </td> <td> --0.244<sup>***</sup> (-0.348, -0.139) +-0.401<sup>***</sup> (-0.639, -0.163) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)2000:ba_workingAge65+ +ba_sexFemale:empstatpart-time </td> <td> </td> <td> --0.335<sup>***</sup> (-0.438, -0.232) +-0.050 (-0.135, 0.034) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)2014:ba_workingAge65+ +ba_sexFemale:empstatunknown job hours </td> <td> </td> <td> --0.324<sup>***</sup> (-0.428, -0.220) +0.038 (-0.164, 0.240) </td> </tr> <tr> @@ -3960,10 +4219,10 @@ as.factor(ba_survey)2014:ba_workingAge65+ Constant </td> <td> --0.875<sup>***</sup> (-0.911, -0.839) +-0.643<sup>***</sup> (-0.689, -0.598) </td> <td> --1.032<sup>***</sup> (-1.074, -0.990) +-0.777<sup>***</sup> (-0.824, -0.730) </td> </tr> <tr> @@ -3975,10 +4234,10 @@ Constant Observations </td> <td> -29,887 +33,041 </td> <td> -29,887 +33,021 </td> </tr> <tr> @@ -3994,7 +4253,32 @@ Observations </td> </tr> </table> -<p>Second, the logit model which is predicting any media related activities in that period.</p> +<p>Late personal:</p> +<pre><code>## ba_pid ba_diarypid ba_survey ba_workingAge +## Length:34752 Length:34752 Min. :1974 16-64:30279 +## Class :character Class :character 1st Qu.:1974 65+ : 4473 +## Mode :character Mode :character Median :1985 +## Mean :1990 +## 3rd Qu.:2000 +## Max. :2014 +## ba_sex empstat sumDA_Personal_m +## Male :15364 not in paid work :12873 Min. :0.000 +## Female:19388 full-time :14613 1st Qu.:0.000 +## missing : 285 Median :1.000 +## part-time : 6427 Mean :1.052 +## unknown job hours: 538 3rd Qu.:2.000 +## NA's : 16 Max. :5.000 +## minTime maxTime anyDA_Personal_m +## Length:34752 Length:34752 Min. :0.0000 +## Class1:hms Class1:hms 1st Qu.:0.0000 +## Class2:difftime Class2:difftime Median :1.0000 +## Mode :numeric Mode :numeric Mean :0.5619 +## 3rd Qu.:1.0000 +## Max. :1.0000</code></pre> +<pre><code>## [1] "From:"</code></pre> +<pre><code>## 18:30:00</code></pre> +<pre><code>## [1] "To:"</code></pre> +<pre><code>## 20:30:00</code></pre> <table style="text-align:center"> <tr> <td colspan="3" style="border-bottom: 1px solid black"> @@ -4016,18 +4300,21 @@ Observations <tr> <td style="text-align:left"> </td> -<td colspan="2"> -anyDA_Media_m +<td> +simpleModel +</td> +<td> +fullModel </td> </tr> <tr> <td style="text-align:left"> </td> <td> -Early: Model 1 (logit) +Personal Late: Model 1 (poisson) </td> <td> -Early: Model 2 (logit) +Personal Late: Model 2 (poisson) </td> </tr> <tr> @@ -4049,10 +4336,10 @@ Early: Model 2 (logit) as.factor(ba_survey)1985 </td> <td> -0.335<sup>***</sup> (0.275, 0.396) +0.387<sup>***</sup> (0.332, 0.442) </td> <td> -0.381<sup>***</sup> (0.316, 0.447) +0.447<sup>***</sup> (0.415, 0.479) </td> </tr> <tr> @@ -4060,10 +4347,10 @@ as.factor(ba_survey)1985 as.factor(ba_survey)2000 </td> <td> -0.492<sup>***</sup> (0.422, 0.561) +0.520<sup>***</sup> (0.461, 0.578) </td> <td> -0.471<sup>***</sup> (0.393, 0.548) +0.526<sup>***</sup> (0.491, 0.561) </td> </tr> <tr> @@ -4071,224 +4358,289 @@ as.factor(ba_survey)2000 as.factor(ba_survey)2014 </td> <td> -0.448<sup>***</sup> (0.375, 0.521) +0.426<sup>***</sup> (0.364, 0.488) </td> <td> -0.375<sup>***</sup> (0.291, 0.459) +0.478<sup>***</sup> (0.442, 0.515) </td> </tr> <tr> <td style="text-align:left"> -ba_workingAge65+ +ba_sexFemale </td> <td> +0.467<sup>***</sup> (0.413, 0.521) </td> <td> -1.152<sup>***</sup> (1.011, 1.294) +0.790<sup>***</sup> (0.740, 0.840) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)1985:ba_workingAge65+ +as.factor(ba_survey)1985:ba_sexFemale </td> <td> +0.042 (-0.025, 0.109) </td> <td> --0.027 (-0.226, 0.172) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)2000:ba_workingAge65+ +as.factor(ba_survey)2000:ba_sexFemale </td> <td> +-0.042 (-0.114, 0.030) </td> <td> --0.350<sup>***</sup> (-0.540, -0.160) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)2014:ba_workingAge65+ +as.factor(ba_survey)2014:ba_sexFemale </td> <td> +-0.015 (-0.091, 0.062) </td> <td> --0.355<sup>***</sup> (-0.544, -0.166) </td> </tr> <tr> <td style="text-align:left"> -Constant +empstatfull-time </td> <td> --0.753<sup>***</sup> (-0.800, -0.706) </td> <td> --0.899<sup>***</sup> (-0.950, -0.847) -</td> -</tr> -<tr> -<td colspan="3" style="border-bottom: 1px solid black"> +0.544<sup>***</sup> (0.493, 0.595) </td> </tr> <tr> <td style="text-align:left"> -Observations +empstatmissing </td> <td> -29,887 </td> <td> -29,887 -</td> -</tr> -<tr> -<td colspan="3" style="border-bottom: 1px solid black"> +0.445<sup>***</sup> (0.260, 0.630) </td> </tr> <tr> <td style="text-align:left"> -<em>Note:</em> +empstatpart-time </td> -<td colspan="2" style="text-align:right"> -<sup><em></sup>p<0.05; <sup><strong></sup>p<0.01; <sup></strong></em></sup>p<0.001 +<td> </td> -</tr> -</table> -<p>Late media:</p> -<p>First, the poisson model which predicts counts of half-hours.</p> -<table style="text-align:center"> -<tr> -<td colspan="3" style="border-bottom: 1px solid black"> +<td> +0.273<sup>***</sup> (0.191, 0.355) </td> </tr> <tr> <td style="text-align:left"> +empstatunknown job hours </td> -<td colspan="2"> -<em>Dependent variable:</em> -</td> -</tr> -<tr> <td> </td> -<td colspan="2" style="border-bottom: 1px solid black"> +<td> +-0.043 (-0.241, 0.155) </td> </tr> <tr> <td style="text-align:left"> +ba_sexFemale:empstatfull-time </td> -<td colspan="2"> -sumDA_Media_m +<td> +</td> +<td> +-0.501<sup>***</sup> (-0.563, -0.440) </td> </tr> <tr> <td style="text-align:left"> +ba_sexFemale:empstatmissing </td> <td> -Late: Model 1 (poisson) </td> <td> -Late: Model 2 (poisson) +-0.478<sup>***</sup> (-0.721, -0.235) </td> </tr> <tr> <td style="text-align:left"> +ba_sexFemale:empstatpart-time </td> <td> -(1) </td> <td> -(2) -</td> -</tr> -<tr> -<td colspan="3" style="border-bottom: 1px solid black"> +-0.117<sup>**</sup> (-0.205, -0.028) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)1985 +ba_sexFemale:empstatunknown job hours </td> <td> -0.100<sup>***</sup> (0.080, 0.119) </td> <td> -0.139<sup>***</sup> (0.118, 0.159) +0.048 (-0.186, 0.283) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)2000 +Constant </td> <td> -0.147<sup>***</sup> (0.125, 0.168) +-0.663<sup>***</sup> (-0.706, -0.619) </td> <td> -0.131<sup>***</sup> (0.107, 0.155) +-1.072<sup>***</sup> (-1.124, -1.020) +</td> +</tr> +<tr> +<td colspan="3" style="border-bottom: 1px solid black"> </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)2014 +Observations </td> <td> -0.132<sup>***</sup> (0.110, 0.155) +34,752 </td> <td> -0.082<sup>***</sup> (0.056, 0.109) +34,736 +</td> +</tr> +<tr> +<td colspan="3" style="border-bottom: 1px solid black"> </td> </tr> <tr> <td style="text-align:left"> -ba_workingAge65+ +<em>Note:</em> </td> -<td> +<td colspan="2" style="text-align:right"> +<sup><em></sup>p<0.05; <sup><strong></sup>p<0.01; <sup></strong></em></sup>p<0.001 </td> -<td> -0.739<sup>***</sup> (0.701, 0.777) +</tr> +</table> +</div> +<div id="run-media-models" class="section level3"> +<h3><span class="header-section-number">8.6.3</span> Run Media models</h3> +<ul> +<li>Poisson (counts of half-hours)</li> +</ul> +<p>test decrease in media at peak time</p> +<table> +<caption>Obs counts for ‘peak media’ model</caption> +<thead> +<tr class="header"> +<th align="right">ba_survey</th> +<th align="right">nObs</th> +<th align="right">nPeople</th> +<th align="right">nDiaries</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="right">1974</td> +<td align="right">9161</td> +<td align="right">2451</td> +<td align="right">9161</td> +</tr> +<tr class="even"> +<td align="right">1985</td> +<td align="right">11606</td> +<td align="right">2787</td> +<td align="right">11606</td> +</tr> +<tr class="odd"> +<td align="right">2000</td> +<td align="right">6648</td> +<td align="right">6628</td> +<td align="right">6648</td> +</tr> +<tr class="even"> +<td align="right">2014</td> +<td align="right">5626</td> +<td align="right">5618</td> +<td align="right">5626</td> +</tr> +</tbody> +</table> +<p>The following tables report the regression results more neatly.</p> +<p>Late media:</p> +<pre><code>## ba_pid ba_diarypid ba_survey ba_workingAge +## Length:33041 Length:33041 Min. :1974 16-64:28490 +## Class :character Class :character 1st Qu.:1974 65+ : 4551 +## Mode :character Mode :character Median :1985 +## Mean :1990 +## 3rd Qu.:2000 +## Max. :2014 +## ba_sex empstat sumDA_Media_m +## Male :14020 not in paid work :13049 Min. :0.0000 +## Female:19021 full-time :12793 1st Qu.:0.0000 +## missing : 274 Median :0.0000 +## part-time : 6362 Mean :0.9212 +## unknown job hours: 543 3rd Qu.:2.0000 +## NA's : 20 Max. :5.0000 +## minTime maxTime anyDA_Media_m +## Length:33041 Length:33041 Min. :0.0000 +## Class1:hms Class1:hms 1st Qu.:0.0000 +## Class2:difftime Class2:difftime Median :0.0000 +## Mode :numeric Mode :numeric Mean :0.4592 +## 3rd Qu.:1.0000 +## Max. :1.0000</code></pre> +<pre><code>## [1] "From:"</code></pre> +<pre><code>## 16:00:00</code></pre> +<pre><code>## [1] "To:"</code></pre> +<pre><code>## 18:00:00</code></pre> +<table style="text-align:center"> +<tr> +<td colspan="3" style="border-bottom: 1px solid black"> </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)1985:ba_workingAge65+ </td> -<td> +<td colspan="2"> +<em>Dependent variable:</em> </td> +</tr> +<tr> <td> --0.189<sup>***</sup> (-0.243, -0.136) +</td> +<td colspan="2" style="border-bottom: 1px solid black"> </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)2000:ba_workingAge65+ </td> <td> +simpleModel </td> <td> --0.216<sup>***</sup> (-0.268, -0.164) +fullModel </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)2014:ba_workingAge65+ </td> <td> +Media Peak: Model 1 (poisson) </td> <td> --0.226<sup>***</sup> (-0.278, -0.173) +Media Peak: Model 2 (poisson) </td> </tr> <tr> <td style="text-align:left"> -Constant </td> <td> -0.673<sup>***</sup> (0.658, 0.687) +(1) </td> <td> -0.583<sup>***</sup> (0.567, 0.599) +(2) </td> </tr> <tr> @@ -4297,149 +4649,156 @@ Constant </tr> <tr> <td style="text-align:left"> -Observations +as.factor(ba_survey)1985 </td> <td> -35,983 +0.296<sup>***</sup> (0.249, 0.344) </td> <td> -35,983 -</td> -</tr> -<tr> -<td colspan="3" style="border-bottom: 1px solid black"> +0.133<sup>***</sup> (0.099, 0.168) </td> </tr> <tr> <td style="text-align:left"> -<em>Note:</em> +as.factor(ba_survey)2000 </td> -<td colspan="2" style="text-align:right"> -<sup><em></sup>p<0.05; <sup><strong></sup>p<0.01; <sup></strong></em></sup>p<0.001 +<td> +0.302<sup>***</sup> (0.248, 0.356) </td> -</tr> -</table> -<p>Second, the logit model which is predicting any media related activities in that period.</p> -<table style="text-align:center"> -<tr> -<td colspan="3" style="border-bottom: 1px solid black"> +<td> +0.217<sup>***</sup> (0.178, 0.255) </td> </tr> <tr> <td style="text-align:left"> +as.factor(ba_survey)2014 </td> -<td colspan="2"> -<em>Dependent variable:</em> +<td> +0.339<sup>***</sup> (0.283, 0.394) +</td> +<td> +0.146<sup>***</sup> (0.106, 0.186) </td> </tr> <tr> +<td style="text-align:left"> +ba_sexFemale +</td> <td> +-0.377<sup>***</sup> (-0.432, -0.322) </td> -<td colspan="2" style="border-bottom: 1px solid black"> +<td> +-0.645<sup>***</sup> (-0.679, -0.612) </td> </tr> <tr> <td style="text-align:left"> +as.factor(ba_survey)1985:ba_sexFemale </td> -<td colspan="2"> -anyDA_Media_m +<td> +-0.050 (-0.120, 0.019) +</td> +<td> </td> </tr> <tr> <td style="text-align:left"> +as.factor(ba_survey)2000:ba_sexFemale </td> <td> -Late: Model 1 (logit) +0.116<sup>**</sup> (0.039, 0.193) </td> <td> -Late: Model 2 (logit) </td> </tr> <tr> <td style="text-align:left"> +as.factor(ba_survey)2014:ba_sexFemale </td> <td> -(1) +0.047 (-0.033, 0.127) </td> <td> -(2) </td> </tr> <tr> -<td colspan="3" style="border-bottom: 1px solid black"> +<td style="text-align:left"> +empstatfull-time +</td> +<td> +</td> +<td> +-0.962<sup>***</sup> (-1.000, -0.925) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)1985 +empstatmissing </td> <td> -0.261<sup>***</sup> (0.204, 0.319) </td> <td> -0.286<sup>***</sup> (0.226, 0.345) +-1.151<sup>***</sup> (-1.381, -0.920) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)2000 +empstatpart-time </td> <td> -0.465<sup>***</sup> (0.397, 0.534) </td> <td> -0.417<sup>***</sup> (0.345, 0.489) +-0.661<sup>***</sup> (-0.728, -0.595) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)2014 +empstatunknown job hours </td> <td> -0.321<sup>***</sup> (0.252, 0.391) </td> <td> -0.235<sup>***</sup> (0.160, 0.310) +-0.414<sup>***</sup> (-0.537, -0.291) </td> </tr> <tr> <td style="text-align:left"> -ba_workingAge65+ +ba_sexFemale:empstatfull-time </td> <td> </td> <td> -1.083<sup>***</sup> (0.893, 1.273) +0.190<sup>***</sup> (0.124, 0.256) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)1985:ba_workingAge65+ +ba_sexFemale:empstatmissing </td> <td> </td> <td> --0.196 (-0.466, 0.074) +0.904<sup>***</sup> (0.598, 1.210) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)2000:ba_workingAge65+ +ba_sexFemale:empstatpart-time </td> <td> </td> <td> --0.160 (-0.424, 0.104) +0.052 (-0.030, 0.134) </td> </tr> <tr> <td style="text-align:left"> -as.factor(ba_survey)2014:ba_workingAge65+ +ba_sexFemale:empstatunknown job hours </td> <td> </td> <td> --0.202 (-0.452, 0.047) +-0.063 (-0.274, 0.147) </td> </tr> <tr> @@ -4447,10 +4806,10 @@ as.factor(ba_survey)2014:ba_workingAge65+ Constant </td> <td> -0.705<sup>***</sup> (0.663, 0.746) +-0.288<sup>***</sup> (-0.326, -0.250) </td> <td> -0.625<sup>***</sup> (0.581, 0.668) +0.423<sup>***</sup> (0.386, 0.459) </td> </tr> <tr> @@ -4462,10 +4821,10 @@ Constant Observations </td> <td> -35,983 +33,041 </td> <td> -35,983 +33,021 </td> </tr> <tr> @@ -4482,179 +4841,13 @@ Observations </tr> </table> </div> -<div id="detailed-plots-misc---for-various-presentations" class="section level4"> -<h4><span class="header-section-number">7.1.4.3</span> Detailed plots (misc - for various presentations)</h4> -<p>We now produce a few more detailed plots (for edf presentation).</p> -<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/food%20related%20by%20working%20age-1.png" /><!-- --><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/food%20related%20by%20working%20age-2.png" /><!-- --></p> -</div> -</div> -</div> -<div id="specific-activity-analysis" class="section level2"> -<h2><span class="header-section-number">7.2</span> Specific activity analysis</h2> -<div id="car-travel-ending-at-home" class="section level3"> -<h3><span class="header-section-number">7.2.1</span> Car travel ending at home</h3> -<p>This section analyses the patterns of car trips that end at home on the assumption that this might predict the timing of ‘charging load’ in the context of a direct substitution from petrol/diesel vehicles to EVs. Of course it is likely that car-use practices will evolve to reflect the new affordances of EVs but we do not attempt to model such change here.</p> -<table> -<caption>% halfhours which followed a car trip ending at home</caption> -<thead> -<tr class="header"> -<th align="right">ba_survey</th> -<th align="left">r_wday</th> -<th align="right">% halfhours</th> -</tr> -</thead> -<tbody> -<tr class="odd"> -<td align="right">1974</td> -<td align="left">Monday-Friday</td> -<td align="right">0.1961266</td> -</tr> -<tr class="even"> -<td align="right">1974</td> -<td align="left">Saturday</td> -<td align="right">0.0486145</td> -</tr> -<tr class="odd"> -<td align="right">1974</td> -<td align="left">Sunday</td> -<td align="right">0.0234394</td> -</tr> -<tr class="even"> -<td align="right">1985</td> -<td align="left">Monday-Friday</td> -<td align="right">0.8157822</td> -</tr> -<tr class="odd"> -<td align="right">1985</td> -<td align="left">Saturday</td> -<td align="right">0.8015492</td> -</tr> -<tr class="even"> -<td align="right">1985</td> -<td align="left">Sunday</td> -<td align="right">0.7063325</td> -</tr> -<tr class="odd"> -<td align="right">2000</td> -<td align="left">Monday-Friday</td> -<td align="right">1.4024300</td> -</tr> -<tr class="even"> -<td align="right">2000</td> -<td align="left">Saturday</td> -<td align="right">1.4470898</td> -</tr> -<tr class="odd"> -<td align="right">2000</td> -<td align="left">Sunday</td> -<td align="right">1.2340825</td> -</tr> -<tr class="even"> -<td align="right">2014</td> -<td align="left">Monday-Friday</td> -<td align="right">1.1278345</td> -</tr> -<tr class="odd"> -<td align="right">2014</td> -<td align="left">Saturday</td> -<td align="right">1.1151661</td> -</tr> -<tr class="even"> -<td align="right">2014</td> -<td align="left">Sunday</td> -<td align="right">0.9647050</td> -</tr> -</tbody> -</table> -<p>First we test the unweighted patterns to check the patterns look sensible.</p> -<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/test%20unweighted%20patterns%20for%20confidence-1.png" /><!-- --></p> -<p>The above unweighted chart appears to show virtually no car trips ending at home outside weekdays in 1974. However the following table shows that such acts are extremely rare so the result is correct. We note also the unspecified transport rates higher in 1974 suggesting that at least some car use may not be classified as such.</p> -<table> -<caption>Proportion of weekend half hours reporting car travel (unweighted)</caption> -<thead> -<tr class="header"> -<th></th> -<th align="right">1974</th> -<th align="right">1985</th> -<th align="right">2000</th> -<th align="right">2014</th> -</tr> -</thead> -<tbody> -<tr class="odd"> -<td>missing</td> -<td align="right">0.02</td> -<td align="right">0.02</td> -<td align="right">0.11</td> -<td align="right">0.00</td> -</tr> -<tr class="even"> -<td>not travelling</td> -<td align="right">94.62</td> -<td align="right">93.63</td> -<td align="right">90.51</td> -<td align="right">91.48</td> -</tr> -<tr class="odd"> -<td>other physical transport</td> -<td align="right">0.00</td> -<td align="right">0.03</td> -<td align="right">0.13</td> -<td align="right">0.11</td> -</tr> -<tr class="even"> -<td>other/unspecified transport</td> -<td align="right">4.64</td> -<td align="right">1.86</td> -<td align="right">0.54</td> -<td align="right">0.98</td> -</tr> -<tr class="odd"> -<td>public transport</td> -<td align="right">0.00</td> -<td align="right">0.47</td> -<td align="right">0.57</td> -<td align="right">0.78</td> -</tr> -<tr class="even"> -<td>travel by car etc</td> -<td align="right">0.10</td> -<td align="right">2.61</td> -<td align="right">4.99</td> -<td align="right">4.60</td> -</tr> -<tr class="odd"> -<td>walk / on foot</td> -<td align="right">0.62</td> -<td align="right">1.37</td> -<td align="right">3.15</td> -<td align="right">2.05</td> -</tr> -</tbody> -</table> -<p>Now the weighted version. First we show the overall results for the whole population and we exclude 1974 for the purposes of clarity.</p> -<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/car%20trips%20ending%20at%20home%20-%20all%20weighted-1.png" /><!-- --></p> -<p>Interesting that the weight makes a bit of difference here.</p> -<p>Just 2014.</p> -<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/2014%20patterns%20only%20-%20all-1.png" /><!-- --></p> -<p>Now repeat but show different patterns for those aged 16-64 vs 65+, again excluding 1974.</p> -<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/car%20trips%20ending%20at%20home%20-%2016-64%20vs%2065+%20weighted-1.png" /><!-- --></p> -<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/2014%20patterns%20only%20-%20by%20age%20group-1.png" /><!-- --></p> -<p>Now repeat but show different patterns for different seasons, excluding 1974 & 1985 for clarity (as some months not defined or have so few cases they skew the charts).</p> -<p><img src="changingPeakDemandMtus1974_2014_v2.0_files/figure-html/car%20trips%20ending%20at%20home%20-%20by%20season%20weighted-1.png" /><!-- --></p> -<p>to be developed further.</p> -</div> -<div id="media-ict-use-for-janinemike" class="section level3"> -<h3><span class="header-section-number">7.2.2</span> Media & ICT use (for Janine/Mike)</h3> -<blockquote> -<p>update a comparative graph over time, and the breakdown of patterns by season and day of week. Both for TV and computer/devices</p> -</blockquote> -<p>Some patterns can be discerned from the ‘DEAMND acts’ charts above.</p> </div> </div> +<div id="run-to-here" class="section level1"> +<h1><span class="header-section-number">9</span> Run To Here</h1> </div> <div id="discussion-and-conclusions" class="section level1"> -<h1><span class="header-section-number">8</span> Discussion and conclusions</h1> +<h1><span class="header-section-number">10</span> Discussion and conclusions</h1> <ul> <li>What can be learned from longitudinal time use analysis</li> <li>Forthcoming BEIS initiative to collect “behind the meter” information on household energy demand through longitudinal phone surveys – interesting, what are they planning to ask?</li> @@ -4664,7 +4857,7 @@ Observations </ul> </div> <div id="acknowledgements" class="section level1"> -<h1><span class="header-section-number">9</span> Acknowledgements</h1> +<h1><span class="header-section-number">11</span> Acknowledgements</h1> <p>This work was funded by RCUK through the End User Energy Demand Centres Programme via the “DEMAND: Dynamics of Energy, Mobility and Demand” Centre:</p> <ul> <li><a href="http://www.demand.ac.uk" class="uri">http://www.demand.ac.uk</a></li> @@ -4672,7 +4865,7 @@ Observations </ul> </div> <div id="copyright-licensing" class="section level1"> -<h1><span class="header-section-number">10</span> Copyright & licensing</h1> +<h1><span class="header-section-number">12</span> Copyright & licensing</h1> <p>This work is (c) 2018, University of Southampton.</p> <p>The work is published under the Creative Commons Attribution-ShareAlike 4.0 International (<a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a>) License.</p> <ul> @@ -4686,15 +4879,15 @@ Observations <p>Re-use of the software code contained within this work is governed by an additional <a href="https://github.com/dataknut/UK-TU-2014/blob/master/LICENSE">license</a>.</p> </div> <div id="citation" class="section level1"> -<h1><span class="header-section-number">11</span> Citation</h1> +<h1><span class="header-section-number">13</span> Citation</h1> <p>If you wish to cite this work please use:</p> <ul> <li>Anderson, B. & Torriti, J. (2018) The Changing Nature of Peak Demand in the UK: 1974 - 2014, DEMAND Centre Working Paper, Southampton: University of Southampton.</li> </ul> </div> -<div id="about" class="section level1"> -<h1><span class="header-section-number">12</span> About</h1> -<p>Analysis completed in: 7.095 seconds using <a href="https://cran.r-project.org/package=knitr">knitr</a> with R version 3.4.2 (2017-09-28) running on x86_64-apple-darwin15.6.0.</p> +<div id="about-1" class="section level1"> +<h1><span class="header-section-number">14</span> About</h1> +<p>Analysis completed in: 3.144 seconds using <a href="https://cran.r-project.org/package=knitr">knitr</a> with R version 3.5.0 (2018-04-23) running on x86_64-apple-darwin15.6.0.</p> <p>R packages used:</p> <ul> <li>base R - for the basics <span class="citation">[@baseR]</span></li> @@ -4711,7 +4904,7 @@ Observations </ul> </div> <div id="references" class="section level1"> -<h1><span class="header-section-number">13</span> References</h1> +<h1><span class="header-section-number">15</span> References</h1> </div> diff --git a/Theme-1/changeOverTime/changingPeakDemandMtus1974_2014_v2.0.md b/Theme-1/changeOverTime/changingPeakDemandMtus1974_2014_v2.0.md index 29e6a97e977b109f91d170da3c46435fd28fcbdf..4efa3239f7fd196ab0e76295cbb992c7cbcef539 100644 --- a/Theme-1/changeOverTime/changingPeakDemandMtus1974_2014_v2.0.md +++ b/Theme-1/changeOverTime/changingPeakDemandMtus1974_2014_v2.0.md @@ -1,7 +1,7 @@ --- title: "The Changing Nature of Peak Demand in the UK: 1974 - 2014" author: "Ben Anderson (b.anderson@soton.ac.uk, `@dataknut`), Jacopo Torriti (j.torriti@reading.ac.uk, `@JTorriti`)" -date: 'Last run: 2018-03-08 04:01:31' +date: 'Last run: 2018-07-03 14:14:04' output: html_document: fig_caption: yes @@ -24,7 +24,7 @@ output: ``` -## [1] "Loading functions from /Users/ben/gitlabSoton/SERG/DEMAND/demandFunctions.R" +## [1] "Loading functions from /Users/ben/git.soton/SERG/DEMAND/demandFunctions.R" ``` @@ -38,15 +38,23 @@ output: ## [7] "Loading the following libraries using lb_myRequiredPackages: stargazer" ## [8] "Loading the following libraries using lb_myRequiredPackages: survey" ## [9] "Loading the following libraries using lb_myRequiredPackages: doBy" -## [10] "Loading the following libraries using lb_myRequiredPackages: robustbase" -## [11] "Loading the following libraries using lb_myRequiredPackages: knitr" +## [10] "Loading the following libraries using lb_myRequiredPackages: hms" +## [11] "Loading the following libraries using lb_myRequiredPackages: robustbase" +## [12] "Loading the following libraries using lb_myRequiredPackages: knitr" ``` -# To do +# About + +Version of code to match version 2.0 of paper submitted to Energy Policy: + + * removed all code & results not used in paper + * removed code will be foind in v1.9 of Rmd - * re-introduce regression models for early vs late evening food +# To Do + + * # Introduction @@ -71,9 +79,15 @@ We use this data to try to examine changing peaks over time. Note that these dem The last chart shows a quite substantial drop in demand over the last 10 years... at all times of year. -The next two charts show demand levels in January 2006 & 2016 as mean MW per hour. -<!-- --><!-- --> + +The next two charts show demand levels in January 2006 & 2016 as mean MW per hour. Figure \ref(fig:compareTotal) shows mean MW across weekdays compared with each weekend day. Fig 1 in paper. + + + +Figure \ref(fig:compareNormalised) shows the same data but normalised to the overall mean. This shows the relative distribution of demand rather than absolute and illustrates the increased 'peakiness'. Fig 2 in paper. + + # Methods @@ -140,7 +154,7 @@ NA 0 0 0 0 0 0 0 0 0 -Table: MTUS 1974-2015 Survay data: age ranges (weighted) +Table: MTUS 1974-2015 Survey data: age ranges (weighted) (0,16] 16-25 26-35 36-45 46-55 56-65 66-75 75+ NA ----- ------- ------ ------ ------ ------ ------ ------ ----- --- @@ -267,8 +281,7 @@ ba_age_r nEpisodes nDiaries nRespondents 66-75 329189 5822 2245 75+ 155258 2730 1180 - -# DEMAND Acts +# Code DEMAND Acts We use this data to code the main and secondary activities into a non-arbitrary but highly aggregated set of 10 DEMAND 'Acts'. This enables us to more easily depict change over time at a coarse level. The aggregated codes are as follows: @@ -516,6 +529,42 @@ Table: Lost cases by survey (due to diaryWeight == 0 or NA 1985 42 2014 1 +## Check secondary acts + + +Table: MTUS 1974-2015 Survey data: Main 'DEMAND Act' (weighted, col %) + + 1974 1985 2000 2014 +---------------- ------ ------ ------ ------ +Education 0.27 0.61 0.54 0.84 +Food 9.94 10.78 11.69 10.32 +Media 10.94 11.76 12.94 13.85 +Personal/home 11.68 16.70 15.55 16.03 +Shopping 1.69 2.34 2.26 2.42 +Sleep 36.84 31.15 31.75 30.73 +Social/leisure 7.75 8.31 7.41 6.81 +Sport 1.10 1.14 2.06 1.75 +Travel 5.34 5.21 6.74 6.70 +Work 14.41 11.37 8.58 8.32 +X: Not recorded 0.03 0.63 0.49 2.24 + + +Table: MTUS 1974-2015 Survey data: Secondary 'DEMAND Act' (weighted, col %) + + 1974 1985 2000 2014 +---------------- ------ ------ ------ ------ +Education 0.00 0.03 0.02 0.05 +Food 2.11 3.15 2.29 2.26 +Media 7.10 1.96 3.83 4.23 +Personal/home 1.84 4.25 2.90 3.53 +Shopping 0.11 0.40 0.05 0.21 +Sleep 0.24 0.77 0.50 0.81 +Social/leisure 2.51 4.74 6.36 5.92 +Sport 0.04 0.10 0.04 0.13 +Travel 0.14 0.25 0.01 0.09 +Work 0.04 0.12 0.05 0.36 +X: Not_recorded 85.84 84.23 83.95 82.41 + # Analysis @@ -525,11 +574,9 @@ Table: Lost cases by survey (due to diaryWeight == 0 or NA * Cooking * Occupancy – number of persons or just ‘any person’? -## DEMAND activities over time - Analysis below uses unweightd data except where explicitly stated. -### Test weights +## Test weights Some quick tests on weighted vs un-weighted data. @@ -578,49 +625,32 @@ Again, these results show minor variations with the maximum difference being +/- All subsequent analysis uses weighted data unless specifically stated (e.g. for testing purposes) -### DEMAND: All age groups combined - -#### Whole day +## DEMAND: All age groups combined The following charts use the 'any in a half hour' data to show the percentage of half hours reported as a given act at a given time of day. The percentage charts are useful for assessing the changing composition of in-home vs out-of-home activities but can over-emphasise the apparent prevalence of certain acts when it is less common to be out of the home - such as out of home sleep during 01:00 - 05:00. -As there is the potential for more than one activity to be reported in a given half hour the activities may sum to greater than 100% and this is especially visible for diaries with smaller duration slots and more additional variables which lead to more frequent episode boundaries (e.g. 2000 & 2014/15). - -First we present results just for 2014 to give a snapshot of the most recent data. - -<!-- --> - --1.png)<!-- -->-2.png)<!-- -->-3.png)<!-- -->-4.png)<!-- -->-5.png)<!-- --> - -Finally we plot all years combined. +Fig \ref(fig:allYearsStackedByWeekday) shows all DEMAND activities stacked by day of the week. (no longer used in paper) -<!-- --> + -Compare in home & out of home but without looking at day of the week (paper Fig 1). Adjust height of chart to fit single A4 page? +Fig \ref(fig:allYearsStackedByWeekday) shows all DEMAND activities stacked by day of the week uses line chart to show all acts in vs out of home. Fig 3 in paper. -<!-- --> +<!-- --> -Now plot % point change ignoring day of the week (paper Fig 2, adjust height & width to suit single A4) +Separate out weekdays from weekend days and select specific variables to plot... -<!-- --><!-- --> -Next we separate out 'in home' vs 'not in home' activities (as reported) - Fig 3. -<!-- --><!-- --> -> 2nd acts not useful - -As the above representation is difficult to interpret, we next present a derived graph which shows the change in % of halfhours in which these activities were recorded from 1974 to 2014. This chart therefore shows aboslute change of percentage points and highlights those relatively frequent activities which have changed a lot. - -Fig 3 +Fig \ref(fig:selectedActsDeltaPlot) drawa % point change plot 1974 - 2014 for selected variables. (paper v2.0 Fig 4) > NB: ignores hours < 06:00 to avoid noisy data +All activities at except Travel or where labelled. -<!-- --><!-- --><!-- --> - +<!-- --> This charts shows: @@ -630,28 +660,27 @@ This charts shows: * decrease in 'social' later in evenings * increase in media use especially on weekday evenings and at weekends (NB this data excludes children...) -##### In detail - -We now produce a few more detailed plots (for edf presentation). - - -<!-- --><!-- --> -<!-- --><!-- --> +## Compare TU & NG demand data -<!-- --><!-- --> -#### Evening Peak +<!-- --><!-- --><!-- --><!-- --><!-- --><!-- --> -The first chart shows the (weighted) proportion of half-hours in which the DEMAND acts were reported in 2014 during this period as context. +## DEMAND: Working age group (16-64) vs retired (65+) -<!-- --><!-- --> +Weighted table of respondents by age (repeats table above?) -Next we use the 1974 - 2014 changes to present the changes in the peak demand period for weekdays vs Saturdays and Sundays. The first version keeps the at own home/out of home panels as the vertical columns, the second switches them to highlight change. Need to be consistent inthe paper... -<!-- --><!-- --> +Table: MTUS 1974-2015 Survey data: age of respondents (weighted, row %) -### DEMAND: Working age group (16-64) vs retired (65+) + 16-25 26-35 36-45 46-55 56-65 66-75 75+ +----- ------ ------ ------ ------ ------ ------ ----- +1974 18.71 21.40 18.16 20.17 12.47 7.10 1.99 +1985 16.73 25.49 21.41 17.47 11.64 5.49 1.77 +1995 11.22 20.54 17.76 17.29 13.23 13.38 6.59 +2000 12.06 19.41 19.17 17.82 13.11 11.54 6.90 +2005 12.68 17.10 19.38 16.32 15.82 10.30 8.39 +2014 13.72 17.38 16.44 17.32 14.23 11.98 8.92 The following table shows the % of respondents aged 16+ in different work status over the period. The decline in the % of those in full time work is partly due to increased numbers of people in the older (retired) age cohorts. @@ -670,7 +699,7 @@ Table: MTUS 1974-2015 Survey data: employment status (weighted, row %) This is corrected by the following table which shows the work status of all aged 16-64 in each survey for men and for women. As we can see there has been a general reduction in the % of male respondents in full time work over the period alongside an increase in the % in part time work. In contrast the opposite trends are seen for women with a steady increase in the % in full time work and a similar (but less steep) upward trend in the % in part-time work. Given the ongoing gendered nature of energy-using domestic acitivties (refs) these trends provide an important context to the patterns of time-use we discuss below. -Table: MTUS 1974-2014 Survey data: Male employment status (weighted, row %, all aged 16-65) +Table: MTUS 1974-2014 Survey data: Male employment status (weighted, row %, all aged 16-64) full-time missing not in paid work part-time unknown job hours NA ----- ---------- -------- ----------------- ---------- ------------------ ----- @@ -683,7 +712,7 @@ Table: MTUS 1974-2014 Survey data: Male employment status (weighted, row %, all -Table: MTUS 1974-2014 Survey data: Female employment status (weighted, row %, all aged 16-65) +Table: MTUS 1974-2014 Survey data: Female employment status (weighted, row %, all aged 16-64) full-time missing not in paid work part-time unknown job hours NA ----- ---------- -------- ----------------- ---------- ------------------ ----- @@ -729,30 +758,15 @@ We run the 'whole day' and 'eveing peak' analysis in direct sequence as one is j -Out of home: Fig 4 - -<!-- --> - - -Essentially repeat the above analysis but for evening peak. - -Out of home evening peak: - -<!-- --> +16-64 - All activities at home except Travel or where labelled (Figure \ref(fig:deltaPlotDow16_64) - Fig 5 in paper). -At home: Fig 5 + -<!-- --> +65+ - All activities at home except Travel or where labelled (Figure \ref(fig:deltaPlotDow64m) - Fig 6 in paper). -At home evening peak + -<!-- --> - -This charts shows: - -> to do - -### Food & Media: Regression analysis +## Detailed Activities: Descriptive analysis However, such descriptive analysis does not enable robust statistical claims regarding change during the evening peak period. To provide such evidence we developed two Poisson models - one for the early evening period from 16:00 to 18:00 on weekdays () and one for 18:00 – 21:00 () on weekdays, reflecting the descriptive analysis/charts above. For each model we first assess the statistical effect of survey year (sub-model one) and then year plus age group (sub-model two) on the number of half hours that the given activity was reported in those periods. @@ -764,332 +778,443 @@ Test this using two poisson models - one for 'early' eating and one for 'late' e Also use two logit to test p(any reported) - this tests predictors of reporting any activity in the time period and may be easier to interpret. -NB: we use interaction terms for survey year & working age to understand how older people's reported activities have changed over time. - -Before we start regression analysis, construct line charts showing % reporting this activity per half hour for each survey year - to give a better sense of the trajectory of change than the 1974 -> 2014 total diff. +<!-- --> -<!-- --><!-- --><!-- --> +NB: we use interaction terms for survey year & working age to understand how older people's reported activities have changed over time. +Before we start regression analysis, construct line charts showing % reporting this activity per half hour for each survey year - to give a better sense of the trajectory of change than the 1974 -> 2014 total diff. (not used in final version of paper) -Table: Obs counts for 'early food' model +<!-- --><!-- --><!-- --><!-- --><!-- --> - ba_survey nObs nPeople nDiaries ----------- ------ -------- --------- - 1974 7994 2274 7994 - 1985 10799 2727 10799 - 2000 6007 5990 6007 - 2014 5087 5082 5087 +## Regression analysis +Change of purpose of models - to estimate statistical effects of being in empstat on p(late food) etc. This uses respondents aged 16-64 only as we are interested in the effects of changing work patterns (primarily) -Table: Obs counts for 'late food' model - ba_survey nObs nPeople nDiaries ----------- ------ -------- --------- - 1974 9931 2529 9931 - 1985 12321 2838 12321 - 2000 7371 7348 7371 - 2014 6360 6352 6360 +### Run Food models + * Poisson (counts of half-hours) + * Logit (any half-hours) + +Model 1: survey year * sex +Model 2: sex * empl for all in 2014 only -Table: Obs counts for 'early media' model +Table: Obs counts for 'early food' model ba_survey nObs nPeople nDiaries ---------- ------ -------- --------- - 1974 7994 2274 7994 - 1985 10799 2727 10799 - 2000 6007 5990 6007 - 2014 5087 5082 5087 + 1974 9161 2451 9161 + 1985 11606 2787 11606 + 2000 6648 6628 6648 + 2014 5626 5618 5626 -Table: Obs counts for 'late media' model +Table: Obs counts for 'late food' model ba_survey nObs nPeople nDiaries ---------- ------ -------- --------- - 1974 9931 2529 9931 - 1985 12321 2838 12321 - 2000 7371 7348 7371 - 2014 6360 6352 6360 - -#### Run food models: - - * Poisson (counts of half-hours) - * Logit (any half-hours) - - - - + 1974 9503 2516 9503 + 1985 11819 2824 11819 + 2000 7186 7163 7186 + 2014 6244 6238 6244 The following tables report the regression results more neatly. Early food: -First, the poisson model which predicts counts of half-hours. +``` +## ba_pid ba_diarypid ba_survey ba_sex +## Length:33041 Length:33041 Min. :1974 Male :14020 +## Class :character Class :character 1st Qu.:1974 Female:19021 +## Mode :character Mode :character Median :1985 +## Mean :1990 +## 3rd Qu.:2000 +## Max. :2014 +## ba_workingAge empstat sumDA_Food_m minTime +## 16-64:28490 not in paid work :13049 Min. :0.00 Length:33041 +## 65+ : 4551 full-time :12793 1st Qu.:1.00 Class1:hms +## missing : 274 Median :1.00 Class2:difftime +## part-time : 6362 Mean :1.47 Mode :numeric +## unknown job hours: 543 3rd Qu.:2.00 +## NA's : 20 Max. :5.00 +## maxTime anyDA_Food_m +## Length:33041 Min. :0.0000 +## Class1:hms 1st Qu.:1.0000 +## Class2:difftime Median :1.0000 +## Mode :numeric Mean :0.7993 +## 3rd Qu.:1.0000 +## Max. :1.0000 +``` +``` +## [1] "From:" +``` -<table style="text-align:center"><tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="2"><em>Dependent variable:</em></td></tr> -<tr><td></td><td colspan="2" style="border-bottom: 1px solid black"></td></tr> -<tr><td style="text-align:left"></td><td colspan="2">sumDA_Food_m</td></tr> -<tr><td style="text-align:left"></td><td>Early: Model 1 (poisson)</td><td>Early: Model 2 (poisson)</td></tr> -<tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td></tr> -<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">as.factor(ba_survey)1985</td><td>0.076<sup>***</sup> (0.050, 0.101)</td><td>0.067<sup>***</sup> (0.040, 0.095)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2000</td><td>-0.055<sup>***</sup> (-0.085, -0.024)</td><td>-0.103<sup>***</sup> (-0.137, -0.068)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2014</td><td>-0.225<sup>***</sup> (-0.260, -0.191)</td><td>-0.278<sup>***</sup> (-0.317, -0.238)</td></tr> -<tr><td style="text-align:left">ba_workingAge65+</td><td></td><td>-0.063 (-0.127, 0.002)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)1985:ba_workingAge65+</td><td></td><td>0.072 (-0.015, 0.159)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2000:ba_workingAge65+</td><td></td><td>0.246<sup>***</sup> (0.160, 0.331)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2014:ba_workingAge65+</td><td></td><td>0.223<sup>***</sup> (0.134, 0.312)</td></tr> -<tr><td style="text-align:left">Constant</td><td>0.269<sup>***</sup> (0.249, 0.289)</td><td>0.277<sup>***</sup> (0.255, 0.298)</td></tr> -<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>29,887</td><td>29,887</td></tr> -<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="2" style="text-align:right"><sup>*</sup>p<0.05; <sup>**</sup>p<0.01; <sup>***</sup>p<0.001</td></tr> -</table> +``` +## 16:00:00 +``` -Second, the logit model which is predicting any food related activities in that period. +``` +## [1] "To:" +``` + +``` +## 18:00:00 +``` <table style="text-align:center"><tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="2"><em>Dependent variable:</em></td></tr> <tr><td></td><td colspan="2" style="border-bottom: 1px solid black"></td></tr> -<tr><td style="text-align:left"></td><td colspan="2">anyDA_Food_m</td></tr> -<tr><td style="text-align:left"></td><td>Early: Model 1 (logit)</td><td>Early: Model 2 (logit)</td></tr> +<tr><td style="text-align:left"></td><td>simpleModel</td><td>fullModel</td></tr> +<tr><td style="text-align:left"></td><td>Food Early: Model 1 (poisson)</td><td>Food Early: Model 2 (poisson)</td></tr> <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td></tr> -<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">as.factor(ba_survey)1985</td><td>0.260<sup>***</sup> (0.190, 0.330)</td><td>0.240<sup>***</sup> (0.166, 0.314)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2000</td><td>-0.193<sup>***</sup> (-0.269, -0.117)</td><td>-0.288<sup>***</sup> (-0.370, -0.205)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2014</td><td>-0.659<sup>***</sup> (-0.735, -0.583)</td><td>-0.735<sup>***</sup> (-0.820, -0.650)</td></tr> -<tr><td style="text-align:left">ba_workingAge65+</td><td></td><td>-0.067 (-0.226, 0.092)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)1985:ba_workingAge65+</td><td></td><td>0.215 (-0.023, 0.453)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2000:ba_workingAge65+</td><td></td><td>0.529<sup>***</sup> (0.310, 0.749)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2014:ba_workingAge65+</td><td></td><td>0.327<sup>**</sup> (0.122, 0.533)</td></tr> -<tr><td style="text-align:left">Constant</td><td>1.117<sup>***</sup> (1.066, 1.168)</td><td>1.125<sup>***</sup> (1.070, 1.179)</td></tr> -<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>29,887</td><td>29,887</td></tr> +<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">as.factor(ba_survey)1985</td><td>0.263<sup>***</sup> (0.221, 0.305)</td><td>0.001 (-0.022, 0.024)</td></tr> +<tr><td style="text-align:left">as.factor(ba_survey)2000</td><td>0.196<sup>***</sup> (0.147, 0.244)</td><td>-0.062<sup>***</sup> (-0.089, -0.034)</td></tr> +<tr><td style="text-align:left">as.factor(ba_survey)2014</td><td>0.082<sup>**</sup> (0.029, 0.134)</td><td>-0.203<sup>***</sup> (-0.233, -0.173)</td></tr> +<tr><td style="text-align:left">ba_sexFemale</td><td>0.717<sup>***</sup> (0.679, 0.756)</td><td>0.366<sup>***</sup> (0.334, 0.398)</td></tr> +<tr><td style="text-align:left">as.factor(ba_survey)1985:ba_sexFemale</td><td>-0.325<sup>***</sup> (-0.375, -0.275)</td><td></td></tr> +<tr><td style="text-align:left">as.factor(ba_survey)2000:ba_sexFemale</td><td>-0.342<sup>***</sup> (-0.400, -0.283)</td><td></td></tr> +<tr><td style="text-align:left">as.factor(ba_survey)2014:ba_sexFemale</td><td>-0.352<sup>***</sup> (-0.416, -0.289)</td><td></td></tr> +<tr><td style="text-align:left">empstatfull-time</td><td></td><td>-0.295<sup>***</sup> (-0.331, -0.259)</td></tr> +<tr><td style="text-align:left">empstatmissing</td><td></td><td>-0.339<sup>***</sup> (-0.521, -0.157)</td></tr> +<tr><td style="text-align:left">empstatpart-time</td><td></td><td>-0.172<sup>***</sup> (-0.234, -0.109)</td></tr> +<tr><td style="text-align:left">empstatunknown job hours</td><td></td><td>-0.273<sup>***</sup> (-0.400, -0.147)</td></tr> +<tr><td style="text-align:left">ba_sexFemale:empstatfull-time</td><td></td><td>-0.047<sup>*</sup> (-0.094, -0.0002)</td></tr> +<tr><td style="text-align:left">ba_sexFemale:empstatmissing</td><td></td><td>0.001 (-0.235, 0.237)</td></tr> +<tr><td style="text-align:left">ba_sexFemale:empstatpart-time</td><td></td><td>0.137<sup>***</sup> (0.069, 0.204)</td></tr> +<tr><td style="text-align:left">ba_sexFemale:empstatunknown job hours</td><td></td><td>0.232<sup>**</sup> (0.079, 0.386)</td></tr> +<tr><td style="text-align:left">Constant</td><td>-0.041<sup>*</sup> (-0.074, -0.007)</td><td>0.346<sup>***</sup> (0.313, 0.379)</td></tr> +<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>33,041</td><td>33,021</td></tr> <tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="2" style="text-align:right"><sup>*</sup>p<0.05; <sup>**</sup>p<0.01; <sup>***</sup>p<0.001</td></tr> </table> Late food: -First, the poisson model which predicts counts of half-hours. +``` +## ba_pid ba_diarypid ba_survey ba_workingAge +## Length:34752 Length:34752 Min. :1974 16-64:30279 +## Class :character Class :character 1st Qu.:1974 65+ : 4473 +## Mode :character Mode :character Median :1985 +## Mean :1990 +## 3rd Qu.:2000 +## Max. :2014 +## ba_sex empstat sumDA_Food_m +## Male :15364 not in paid work :12873 Min. :0.0000 +## Female:19388 full-time :14613 1st Qu.:0.0000 +## missing : 285 Median :0.0000 +## part-time : 6427 Mean :0.6929 +## unknown job hours: 538 3rd Qu.:1.0000 +## NA's : 16 Max. :5.0000 +## minTime maxTime anyDA_Food_m +## Length:34752 Length:34752 Min. :0.0000 +## Class1:hms Class1:hms 1st Qu.:0.0000 +## Class2:difftime Class2:difftime Median :0.0000 +## Mode :numeric Mode :numeric Mean :0.4646 +## 3rd Qu.:1.0000 +## Max. :1.0000 +``` -<table style="text-align:center"><tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="2"><em>Dependent variable:</em></td></tr> -<tr><td></td><td colspan="2" style="border-bottom: 1px solid black"></td></tr> -<tr><td style="text-align:left"></td><td colspan="2">sumDA_Food_m</td></tr> -<tr><td style="text-align:left"></td><td>Late: Model 1 (poisson)</td><td>Late: Model 2 (poisson)</td></tr> -<tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td></tr> -<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">as.factor(ba_survey)1985</td><td>-0.089<sup>***</sup> (-0.119, -0.058)</td><td>-0.106<sup>***</sup> (-0.137, -0.074)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2000</td><td>0.293<sup>***</sup> (0.261, 0.324)</td><td>0.290<sup>***</sup> (0.257, 0.324)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2014</td><td>0.346<sup>***</sup> (0.313, 0.378)</td><td>0.329<sup>***</sup> (0.294, 0.364)</td></tr> -<tr><td style="text-align:left">ba_workingAge65+</td><td></td><td>-0.445<sup>***</sup> (-0.538, -0.352)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)1985:ba_workingAge65+</td><td></td><td>0.226<sup>***</sup> (0.099, 0.353)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2000:ba_workingAge65+</td><td></td><td>0.256<sup>***</sup> (0.144, 0.369)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2014:ba_workingAge65+</td><td></td><td>0.376<sup>***</sup> (0.267, 0.485)</td></tr> -<tr><td style="text-align:left">Constant</td><td>-0.149<sup>***</sup> (-0.171, -0.127)</td><td>-0.117<sup>***</sup> (-0.139, -0.094)</td></tr> -<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>35,983</td><td>35,983</td></tr> -<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="2" style="text-align:right"><sup>*</sup>p<0.05; <sup>**</sup>p<0.01; <sup>***</sup>p<0.001</td></tr> -</table> +``` +## [1] "From:" +``` -Second, the logit model which is predicting any food related activities in that period. +``` +## 18:30:00 +``` + +``` +## [1] "To:" +``` + +``` +## 20:30:00 +``` <table style="text-align:center"><tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="2"><em>Dependent variable:</em></td></tr> <tr><td></td><td colspan="2" style="border-bottom: 1px solid black"></td></tr> -<tr><td style="text-align:left"></td><td colspan="2">anyDA_Food_m</td></tr> -<tr><td style="text-align:left"></td><td>Late: Model 1 (logit)</td><td>Late: Model 2 (logit)</td></tr> +<tr><td style="text-align:left"></td><td>simpleModel</td><td>fullModel</td></tr> +<tr><td style="text-align:left"></td><td>Food Late: Model 1 (poisson)</td><td>Food Late: Model 2 (poisson)</td></tr> <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td></tr> -<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">as.factor(ba_survey)1985</td><td>-0.105<sup>***</sup> (-0.158, -0.052)</td><td>-0.149<sup>***</sup> (-0.204, -0.093)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2000</td><td>0.512<sup>***</sup> (0.448, 0.575)</td><td>0.524<sup>***</sup> (0.454, 0.593)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2014</td><td>0.533<sup>***</sup> (0.467, 0.600)</td><td>0.496<sup>***</sup> (0.422, 0.570)</td></tr> -<tr><td style="text-align:left">ba_workingAge65+</td><td></td><td>-0.749<sup>***</sup> (-0.888, -0.610)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)1985:ba_workingAge65+</td><td></td><td>0.420<sup>***</sup> (0.228, 0.612)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2000:ba_workingAge65+</td><td></td><td>0.306<sup>**</sup> (0.119, 0.493)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2014:ba_workingAge65+</td><td></td><td>0.609<sup>***</sup> (0.422, 0.796)</td></tr> -<tr><td style="text-align:left">Constant</td><td>0.261<sup>***</sup> (0.222, 0.301)</td><td>0.331<sup>***</sup> (0.289, 0.373)</td></tr> -<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>35,983</td><td>35,983</td></tr> +<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">as.factor(ba_survey)1985</td><td>0.225<sup>***</sup> (0.160, 0.290)</td><td>0.006 (-0.033, 0.046)</td></tr> +<tr><td style="text-align:left">as.factor(ba_survey)2000</td><td>0.683<sup>***</sup> (0.618, 0.748)</td><td>0.477<sup>***</sup> (0.437, 0.517)</td></tr> +<tr><td style="text-align:left">as.factor(ba_survey)2014</td><td>0.795<sup>***</sup> (0.730, 0.860)</td><td>0.554<sup>***</sup> (0.514, 0.595)</td></tr> +<tr><td style="text-align:left">ba_sexFemale</td><td>0.507<sup>***</sup> (0.446, 0.568)</td><td>0.288<sup>***</sup> (0.235, 0.340)</td></tr> +<tr><td style="text-align:left">as.factor(ba_survey)1985:ba_sexFemale</td><td>-0.406<sup>***</sup> (-0.487, -0.325)</td><td></td></tr> +<tr><td style="text-align:left">as.factor(ba_survey)2000:ba_sexFemale</td><td>-0.383<sup>***</sup> (-0.465, -0.302)</td><td></td></tr> +<tr><td style="text-align:left">as.factor(ba_survey)2014:ba_sexFemale</td><td>-0.462<sup>***</sup> (-0.544, -0.380)</td><td></td></tr> +<tr><td style="text-align:left">empstatfull-time</td><td></td><td>0.277<sup>***</sup> (0.224, 0.330)</td></tr> +<tr><td style="text-align:left">empstatmissing</td><td></td><td>0.213<sup>*</sup> (0.015, 0.412)</td></tr> +<tr><td style="text-align:left">empstatpart-time</td><td></td><td>0.270<sup>***</sup> (0.188, 0.352)</td></tr> +<tr><td style="text-align:left">empstatunknown job hours</td><td></td><td>0.081 (-0.130, 0.293)</td></tr> +<tr><td style="text-align:left">ba_sexFemale:empstatfull-time</td><td></td><td>-0.061 (-0.129, 0.006)</td></tr> +<tr><td style="text-align:left">ba_sexFemale:empstatmissing</td><td></td><td>-0.035 (-0.300, 0.231)</td></tr> +<tr><td style="text-align:left">ba_sexFemale:empstatpart-time</td><td></td><td>-0.162<sup>***</sup> (-0.255, -0.070)</td></tr> +<tr><td style="text-align:left">ba_sexFemale:empstatunknown job hours</td><td></td><td>0.056 (-0.207, 0.320)</td></tr> +<tr><td style="text-align:left">Constant</td><td>-0.913<sup>***</sup> (-0.963, -0.863)</td><td>-0.940<sup>***</sup> (-0.994, -0.886)</td></tr> +<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>34,752</td><td>34,736</td></tr> <tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="2" style="text-align:right"><sup>*</sup>p<0.05; <sup>**</sup>p<0.01; <sup>***</sup>p<0.001</td></tr> </table> -Now repeat for media use. - - -#### Run media models: +### Run Personal/home care models: * Poisson (counts of half-hours) - * Logit (any half-hours) +Table: Obs counts for 'early personal' model + ba_survey nObs nPeople nDiaries +---------- ------ -------- --------- + 1974 9161 2451 9161 + 1985 11606 2787 11606 + 2000 6648 6628 6648 + 2014 5626 5618 5626 -The following tables report the regression results more neatly. - -Early media: -First, the poisson model which predicts counts of half-hours. +Table: Obs counts for 'late personal' model -<table style="text-align:center"><tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="2"><em>Dependent variable:</em></td></tr> -<tr><td></td><td colspan="2" style="border-bottom: 1px solid black"></td></tr> -<tr><td style="text-align:left"></td><td colspan="2">sumDA_Media_m</td></tr> -<tr><td style="text-align:left"></td><td>Early: Model 1 (poisson)</td><td>Early: Model 2 (poisson)</td></tr> -<tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td></tr> -<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">as.factor(ba_survey)1985</td><td>0.315<sup>***</sup> (0.270, 0.360)</td><td>0.378<sup>***</sup> (0.327, 0.429)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2000</td><td>0.522<sup>***</sup> (0.474, 0.571)</td><td>0.508<sup>***</sup> (0.451, 0.565)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2014</td><td>0.558<sup>***</sup> (0.508, 0.607)</td><td>0.482<sup>***</sup> (0.421, 0.543)</td></tr> -<tr><td style="text-align:left">ba_workingAge65+</td><td></td><td>1.044<sup>***</sup> (0.964, 1.123)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)1985:ba_workingAge65+</td><td></td><td>-0.244<sup>***</sup> (-0.348, -0.139)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2000:ba_workingAge65+</td><td></td><td>-0.335<sup>***</sup> (-0.438, -0.232)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2014:ba_workingAge65+</td><td></td><td>-0.324<sup>***</sup> (-0.428, -0.220)</td></tr> -<tr><td style="text-align:left">Constant</td><td>-0.875<sup>***</sup> (-0.911, -0.839)</td><td>-1.032<sup>***</sup> (-1.074, -0.990)</td></tr> -<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>29,887</td><td>29,887</td></tr> -<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="2" style="text-align:right"><sup>*</sup>p<0.05; <sup>**</sup>p<0.01; <sup>***</sup>p<0.001</td></tr> -</table> + ba_survey nObs nPeople nDiaries +---------- ------ -------- --------- + 1974 9503 2516 9503 + 1985 11819 2824 11819 + 2000 7186 7163 7186 + 2014 6244 6238 6244 -Second, the logit model which is predicting any media related activities in that period. +The following tables report the regression results more neatly. +Early personal: -<table style="text-align:center"><tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="2"><em>Dependent variable:</em></td></tr> -<tr><td></td><td colspan="2" style="border-bottom: 1px solid black"></td></tr> -<tr><td style="text-align:left"></td><td colspan="2">anyDA_Media_m</td></tr> -<tr><td style="text-align:left"></td><td>Early: Model 1 (logit)</td><td>Early: Model 2 (logit)</td></tr> -<tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td></tr> -<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">as.factor(ba_survey)1985</td><td>0.335<sup>***</sup> (0.275, 0.396)</td><td>0.381<sup>***</sup> (0.316, 0.447)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2000</td><td>0.492<sup>***</sup> (0.422, 0.561)</td><td>0.471<sup>***</sup> (0.393, 0.548)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2014</td><td>0.448<sup>***</sup> (0.375, 0.521)</td><td>0.375<sup>***</sup> (0.291, 0.459)</td></tr> -<tr><td style="text-align:left">ba_workingAge65+</td><td></td><td>1.152<sup>***</sup> (1.011, 1.294)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)1985:ba_workingAge65+</td><td></td><td>-0.027 (-0.226, 0.172)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2000:ba_workingAge65+</td><td></td><td>-0.350<sup>***</sup> (-0.540, -0.160)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2014:ba_workingAge65+</td><td></td><td>-0.355<sup>***</sup> (-0.544, -0.166)</td></tr> -<tr><td style="text-align:left">Constant</td><td>-0.753<sup>***</sup> (-0.800, -0.706)</td><td>-0.899<sup>***</sup> (-0.950, -0.847)</td></tr> -<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>29,887</td><td>29,887</td></tr> -<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="2" style="text-align:right"><sup>*</sup>p<0.05; <sup>**</sup>p<0.01; <sup>***</sup>p<0.001</td></tr> -</table> -Late media: +``` +## ba_pid ba_diarypid ba_survey ba_workingAge +## Length:33041 Length:33041 Min. :1974 16-64:28490 +## Class :character Class :character 1st Qu.:1974 65+ : 4551 +## Mode :character Mode :character Median :1985 +## Mean :1990 +## 3rd Qu.:2000 +## Max. :2014 +## ba_sex empstat sumDA_Personal_m +## Male :14020 not in paid work :13049 Min. :0.0000 +## Female:19021 full-time :12793 1st Qu.:0.0000 +## missing : 274 Median :1.0000 +## part-time : 6362 Mean :0.9951 +## unknown job hours: 543 3rd Qu.:2.0000 +## NA's : 20 Max. :5.0000 +## minTime maxTime anyDA_Personal_m +## Length:33041 Length:33041 Min. :0.0000 +## Class1:hms Class1:hms 1st Qu.:0.0000 +## Class2:difftime Class2:difftime Median :1.0000 +## Mode :numeric Mode :numeric Mean :0.5663 +## 3rd Qu.:1.0000 +## Max. :1.0000 +``` -First, the poisson model which predicts counts of half-hours. +``` +## [1] "From:" +``` +``` +## 16:00:00 +``` -<table style="text-align:center"><tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="2"><em>Dependent variable:</em></td></tr> -<tr><td></td><td colspan="2" style="border-bottom: 1px solid black"></td></tr> -<tr><td style="text-align:left"></td><td colspan="2">sumDA_Media_m</td></tr> -<tr><td style="text-align:left"></td><td>Late: Model 1 (poisson)</td><td>Late: Model 2 (poisson)</td></tr> -<tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td></tr> -<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">as.factor(ba_survey)1985</td><td>0.100<sup>***</sup> (0.080, 0.119)</td><td>0.139<sup>***</sup> (0.118, 0.159)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2000</td><td>0.147<sup>***</sup> (0.125, 0.168)</td><td>0.131<sup>***</sup> (0.107, 0.155)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2014</td><td>0.132<sup>***</sup> (0.110, 0.155)</td><td>0.082<sup>***</sup> (0.056, 0.109)</td></tr> -<tr><td style="text-align:left">ba_workingAge65+</td><td></td><td>0.739<sup>***</sup> (0.701, 0.777)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)1985:ba_workingAge65+</td><td></td><td>-0.189<sup>***</sup> (-0.243, -0.136)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2000:ba_workingAge65+</td><td></td><td>-0.216<sup>***</sup> (-0.268, -0.164)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2014:ba_workingAge65+</td><td></td><td>-0.226<sup>***</sup> (-0.278, -0.173)</td></tr> -<tr><td style="text-align:left">Constant</td><td>0.673<sup>***</sup> (0.658, 0.687)</td><td>0.583<sup>***</sup> (0.567, 0.599)</td></tr> -<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>35,983</td><td>35,983</td></tr> -<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="2" style="text-align:right"><sup>*</sup>p<0.05; <sup>**</sup>p<0.01; <sup>***</sup>p<0.001</td></tr> -</table> +``` +## [1] "To:" +``` -Second, the logit model which is predicting any media related activities in that period. +``` +## 18:00:00 +``` <table style="text-align:center"><tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="2"><em>Dependent variable:</em></td></tr> <tr><td></td><td colspan="2" style="border-bottom: 1px solid black"></td></tr> -<tr><td style="text-align:left"></td><td colspan="2">anyDA_Media_m</td></tr> -<tr><td style="text-align:left"></td><td>Late: Model 1 (logit)</td><td>Late: Model 2 (logit)</td></tr> +<tr><td style="text-align:left"></td><td>simpleModel</td><td>fullModel</td></tr> +<tr><td style="text-align:left"></td><td>Personal Early: Model 1 (poisson)</td><td>Personal Early: Model 2 (poisson)</td></tr> <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td></tr> -<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">as.factor(ba_survey)1985</td><td>0.261<sup>***</sup> (0.204, 0.319)</td><td>0.286<sup>***</sup> (0.226, 0.345)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2000</td><td>0.465<sup>***</sup> (0.397, 0.534)</td><td>0.417<sup>***</sup> (0.345, 0.489)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2014</td><td>0.321<sup>***</sup> (0.252, 0.391)</td><td>0.235<sup>***</sup> (0.160, 0.310)</td></tr> -<tr><td style="text-align:left">ba_workingAge65+</td><td></td><td>1.083<sup>***</sup> (0.893, 1.273)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)1985:ba_workingAge65+</td><td></td><td>-0.196 (-0.466, 0.074)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2000:ba_workingAge65+</td><td></td><td>-0.160 (-0.424, 0.104)</td></tr> -<tr><td style="text-align:left">as.factor(ba_survey)2014:ba_workingAge65+</td><td></td><td>-0.202 (-0.452, 0.047)</td></tr> -<tr><td style="text-align:left">Constant</td><td>0.705<sup>***</sup> (0.663, 0.746)</td><td>0.625<sup>***</sup> (0.581, 0.668)</td></tr> -<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>35,983</td><td>35,983</td></tr> +<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">as.factor(ba_survey)1985</td><td>0.391<sup>***</sup> (0.334, 0.447)</td><td>0.508<sup>***</sup> (0.474, 0.541)</td></tr> +<tr><td style="text-align:left">as.factor(ba_survey)2000</td><td>0.503<sup>***</sup> (0.442, 0.565)</td><td>0.546<sup>***</sup> (0.509, 0.583)</td></tr> +<tr><td style="text-align:left">as.factor(ba_survey)2014</td><td>0.471<sup>***</sup> (0.407, 0.535)</td><td>0.506<sup>***</sup> (0.467, 0.545)</td></tr> +<tr><td style="text-align:left">ba_sexFemale</td><td>0.296<sup>***</sup> (0.239, 0.353)</td><td>0.521<sup>***</sup> (0.478, 0.564)</td></tr> +<tr><td style="text-align:left">as.factor(ba_survey)1985:ba_sexFemale</td><td>0.180<sup>***</sup> (0.110, 0.249)</td><td></td></tr> +<tr><td style="text-align:left">as.factor(ba_survey)2000:ba_sexFemale</td><td>0.062 (-0.015, 0.138)</td><td></td></tr> +<tr><td style="text-align:left">as.factor(ba_survey)2014:ba_sexFemale</td><td>0.046 (-0.033, 0.126)</td><td></td></tr> +<tr><td style="text-align:left">empstatfull-time</td><td></td><td>0.110<sup>***</sup> (0.063, 0.157)</td></tr> +<tr><td style="text-align:left">empstatmissing</td><td></td><td>0.296<sup>***</sup> (0.120, 0.472)</td></tr> +<tr><td style="text-align:left">empstatpart-time</td><td></td><td>0.039 (-0.039, 0.116)</td></tr> +<tr><td style="text-align:left">empstatunknown job hours</td><td></td><td>-0.026 (-0.185, 0.134)</td></tr> +<tr><td style="text-align:left">ba_sexFemale:empstatfull-time</td><td></td><td>-0.360<sup>***</sup> (-0.421, -0.300)</td></tr> +<tr><td style="text-align:left">ba_sexFemale:empstatmissing</td><td></td><td>-0.401<sup>***</sup> (-0.639, -0.163)</td></tr> +<tr><td style="text-align:left">ba_sexFemale:empstatpart-time</td><td></td><td>-0.050 (-0.135, 0.034)</td></tr> +<tr><td style="text-align:left">ba_sexFemale:empstatunknown job hours</td><td></td><td>0.038 (-0.164, 0.240)</td></tr> +<tr><td style="text-align:left">Constant</td><td>-0.643<sup>***</sup> (-0.689, -0.598)</td><td>-0.777<sup>***</sup> (-0.824, -0.730)</td></tr> +<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>33,041</td><td>33,021</td></tr> <tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="2" style="text-align:right"><sup>*</sup>p<0.05; <sup>**</sup>p<0.01; <sup>***</sup>p<0.001</td></tr> </table> -#### Detailed plots (misc - for various presentations) - -We now produce a few more detailed plots (for edf presentation). - - -<!-- --><!-- --> - -## Specific activity analysis +Late personal: -### Car travel ending at home - -This section analyses the patterns of car trips that end at home on the assumption that this might predict the timing of 'charging load' in the context of a direct substitution from petrol/diesel vehicles to EVs. Of course it is likely that car-use practices will evolve to reflect the new affordances of EVs but we do not attempt to model such change here. - - -Table: % halfhours which followed a car trip ending at home - - ba_survey r_wday % halfhours ----------- -------------- ------------ - 1974 Monday-Friday 0.1961266 - 1974 Saturday 0.0486145 - 1974 Sunday 0.0234394 - 1985 Monday-Friday 0.8157822 - 1985 Saturday 0.8015492 - 1985 Sunday 0.7063325 - 2000 Monday-Friday 1.4024300 - 2000 Saturday 1.4470898 - 2000 Sunday 1.2340825 - 2014 Monday-Friday 1.1278345 - 2014 Saturday 1.1151661 - 2014 Sunday 0.9647050 - -First we test the unweighted patterns to check the patterns look sensible. - -<!-- --> - -The above unweighted chart appears to show virtually no car trips ending at home outside weekdays in 1974. However the following table shows that such acts are extremely rare so the result is correct. We note also the unspecified transport rates higher in 1974 suggesting that at least some car use may not be classified as such. +``` +## ba_pid ba_diarypid ba_survey ba_workingAge +## Length:34752 Length:34752 Min. :1974 16-64:30279 +## Class :character Class :character 1st Qu.:1974 65+ : 4473 +## Mode :character Mode :character Median :1985 +## Mean :1990 +## 3rd Qu.:2000 +## Max. :2014 +## ba_sex empstat sumDA_Personal_m +## Male :15364 not in paid work :12873 Min. :0.000 +## Female:19388 full-time :14613 1st Qu.:0.000 +## missing : 285 Median :1.000 +## part-time : 6427 Mean :1.052 +## unknown job hours: 538 3rd Qu.:2.000 +## NA's : 16 Max. :5.000 +## minTime maxTime anyDA_Personal_m +## Length:34752 Length:34752 Min. :0.0000 +## Class1:hms Class1:hms 1st Qu.:0.0000 +## Class2:difftime Class2:difftime Median :1.0000 +## Mode :numeric Mode :numeric Mean :0.5619 +## 3rd Qu.:1.0000 +## Max. :1.0000 +``` +``` +## [1] "From:" +``` -Table: Proportion of weekend half hours reporting car travel (unweighted) +``` +## 18:30:00 +``` - 1974 1985 2000 2014 ----------------------------- ------ ------ ------ ------ -missing 0.02 0.02 0.11 0.00 -not travelling 94.62 93.63 90.51 91.48 -other physical transport 0.00 0.03 0.13 0.11 -other/unspecified transport 4.64 1.86 0.54 0.98 -public transport 0.00 0.47 0.57 0.78 -travel by car etc 0.10 2.61 4.99 4.60 -walk / on foot 0.62 1.37 3.15 2.05 +``` +## [1] "To:" +``` -Now the weighted version. First we show the overall results for the whole population and we exclude 1974 for the purposes of clarity. +``` +## 20:30:00 +``` +<table style="text-align:center"><tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="2"><em>Dependent variable:</em></td></tr> +<tr><td></td><td colspan="2" style="border-bottom: 1px solid black"></td></tr> +<tr><td style="text-align:left"></td><td>simpleModel</td><td>fullModel</td></tr> +<tr><td style="text-align:left"></td><td>Personal Late: Model 1 (poisson)</td><td>Personal Late: Model 2 (poisson)</td></tr> +<tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td></tr> +<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">as.factor(ba_survey)1985</td><td>0.387<sup>***</sup> (0.332, 0.442)</td><td>0.447<sup>***</sup> (0.415, 0.479)</td></tr> +<tr><td style="text-align:left">as.factor(ba_survey)2000</td><td>0.520<sup>***</sup> (0.461, 0.578)</td><td>0.526<sup>***</sup> (0.491, 0.561)</td></tr> +<tr><td style="text-align:left">as.factor(ba_survey)2014</td><td>0.426<sup>***</sup> (0.364, 0.488)</td><td>0.478<sup>***</sup> (0.442, 0.515)</td></tr> +<tr><td style="text-align:left">ba_sexFemale</td><td>0.467<sup>***</sup> (0.413, 0.521)</td><td>0.790<sup>***</sup> (0.740, 0.840)</td></tr> +<tr><td style="text-align:left">as.factor(ba_survey)1985:ba_sexFemale</td><td>0.042 (-0.025, 0.109)</td><td></td></tr> +<tr><td style="text-align:left">as.factor(ba_survey)2000:ba_sexFemale</td><td>-0.042 (-0.114, 0.030)</td><td></td></tr> +<tr><td style="text-align:left">as.factor(ba_survey)2014:ba_sexFemale</td><td>-0.015 (-0.091, 0.062)</td><td></td></tr> +<tr><td style="text-align:left">empstatfull-time</td><td></td><td>0.544<sup>***</sup> (0.493, 0.595)</td></tr> +<tr><td style="text-align:left">empstatmissing</td><td></td><td>0.445<sup>***</sup> (0.260, 0.630)</td></tr> +<tr><td style="text-align:left">empstatpart-time</td><td></td><td>0.273<sup>***</sup> (0.191, 0.355)</td></tr> +<tr><td style="text-align:left">empstatunknown job hours</td><td></td><td>-0.043 (-0.241, 0.155)</td></tr> +<tr><td style="text-align:left">ba_sexFemale:empstatfull-time</td><td></td><td>-0.501<sup>***</sup> (-0.563, -0.440)</td></tr> +<tr><td style="text-align:left">ba_sexFemale:empstatmissing</td><td></td><td>-0.478<sup>***</sup> (-0.721, -0.235)</td></tr> +<tr><td style="text-align:left">ba_sexFemale:empstatpart-time</td><td></td><td>-0.117<sup>**</sup> (-0.205, -0.028)</td></tr> +<tr><td style="text-align:left">ba_sexFemale:empstatunknown job hours</td><td></td><td>0.048 (-0.186, 0.283)</td></tr> +<tr><td style="text-align:left">Constant</td><td>-0.663<sup>***</sup> (-0.706, -0.619)</td><td>-1.072<sup>***</sup> (-1.124, -1.020)</td></tr> +<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>34,752</td><td>34,736</td></tr> +<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="2" style="text-align:right"><sup>*</sup>p<0.05; <sup>**</sup>p<0.01; <sup>***</sup>p<0.001</td></tr> +</table> +### Run Media models -<!-- --> + * Poisson (counts of half-hours) + +test decrease in media at peak time -Interesting that the weight makes a bit of difference here. -Just 2014. +Table: Obs counts for 'peak media' model -<!-- --> + ba_survey nObs nPeople nDiaries +---------- ------ -------- --------- + 1974 9161 2451 9161 + 1985 11606 2787 11606 + 2000 6648 6628 6648 + 2014 5626 5618 5626 -Now repeat but show different patterns for those aged 16-64 vs 65+, again excluding 1974. +The following tables report the regression results more neatly. -<!-- --> +Late media: -<!-- --> -Now repeat but show different patterns for different seasons, excluding 1974 & 1985 for clarity (as some months not defined or have so few cases they skew the charts). +``` +## ba_pid ba_diarypid ba_survey ba_workingAge +## Length:33041 Length:33041 Min. :1974 16-64:28490 +## Class :character Class :character 1st Qu.:1974 65+ : 4551 +## Mode :character Mode :character Median :1985 +## Mean :1990 +## 3rd Qu.:2000 +## Max. :2014 +## ba_sex empstat sumDA_Media_m +## Male :14020 not in paid work :13049 Min. :0.0000 +## Female:19021 full-time :12793 1st Qu.:0.0000 +## missing : 274 Median :0.0000 +## part-time : 6362 Mean :0.9212 +## unknown job hours: 543 3rd Qu.:2.0000 +## NA's : 20 Max. :5.0000 +## minTime maxTime anyDA_Media_m +## Length:33041 Length:33041 Min. :0.0000 +## Class1:hms Class1:hms 1st Qu.:0.0000 +## Class2:difftime Class2:difftime Median :0.0000 +## Mode :numeric Mode :numeric Mean :0.4592 +## 3rd Qu.:1.0000 +## Max. :1.0000 +``` -<!-- --> +``` +## [1] "From:" +``` -to be developed further. +``` +## 16:00:00 +``` -### Media & ICT use (for Janine/Mike) +``` +## [1] "To:" +``` -> update a comparative graph over time, and the breakdown of patterns by season and day of week. Both for TV and computer/devices +``` +## 18:00:00 +``` -Some patterns can be discerned from the 'DEAMND acts' charts above. +<table style="text-align:center"><tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="2"><em>Dependent variable:</em></td></tr> +<tr><td></td><td colspan="2" style="border-bottom: 1px solid black"></td></tr> +<tr><td style="text-align:left"></td><td>simpleModel</td><td>fullModel</td></tr> +<tr><td style="text-align:left"></td><td>Media Peak: Model 1 (poisson)</td><td>Media Peak: Model 2 (poisson)</td></tr> +<tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td></tr> +<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">as.factor(ba_survey)1985</td><td>0.296<sup>***</sup> (0.249, 0.344)</td><td>0.133<sup>***</sup> (0.099, 0.168)</td></tr> +<tr><td style="text-align:left">as.factor(ba_survey)2000</td><td>0.302<sup>***</sup> (0.248, 0.356)</td><td>0.217<sup>***</sup> (0.178, 0.255)</td></tr> +<tr><td style="text-align:left">as.factor(ba_survey)2014</td><td>0.339<sup>***</sup> (0.283, 0.394)</td><td>0.146<sup>***</sup> (0.106, 0.186)</td></tr> +<tr><td style="text-align:left">ba_sexFemale</td><td>-0.377<sup>***</sup> (-0.432, -0.322)</td><td>-0.645<sup>***</sup> (-0.679, -0.612)</td></tr> +<tr><td style="text-align:left">as.factor(ba_survey)1985:ba_sexFemale</td><td>-0.050 (-0.120, 0.019)</td><td></td></tr> +<tr><td style="text-align:left">as.factor(ba_survey)2000:ba_sexFemale</td><td>0.116<sup>**</sup> (0.039, 0.193)</td><td></td></tr> +<tr><td style="text-align:left">as.factor(ba_survey)2014:ba_sexFemale</td><td>0.047 (-0.033, 0.127)</td><td></td></tr> +<tr><td style="text-align:left">empstatfull-time</td><td></td><td>-0.962<sup>***</sup> (-1.000, -0.925)</td></tr> +<tr><td style="text-align:left">empstatmissing</td><td></td><td>-1.151<sup>***</sup> (-1.381, -0.920)</td></tr> +<tr><td style="text-align:left">empstatpart-time</td><td></td><td>-0.661<sup>***</sup> (-0.728, -0.595)</td></tr> +<tr><td style="text-align:left">empstatunknown job hours</td><td></td><td>-0.414<sup>***</sup> (-0.537, -0.291)</td></tr> +<tr><td style="text-align:left">ba_sexFemale:empstatfull-time</td><td></td><td>0.190<sup>***</sup> (0.124, 0.256)</td></tr> +<tr><td style="text-align:left">ba_sexFemale:empstatmissing</td><td></td><td>0.904<sup>***</sup> (0.598, 1.210)</td></tr> +<tr><td style="text-align:left">ba_sexFemale:empstatpart-time</td><td></td><td>0.052 (-0.030, 0.134)</td></tr> +<tr><td style="text-align:left">ba_sexFemale:empstatunknown job hours</td><td></td><td>-0.063 (-0.274, 0.147)</td></tr> +<tr><td style="text-align:left">Constant</td><td>-0.288<sup>***</sup> (-0.326, -0.250)</td><td>0.423<sup>***</sup> (0.386, 0.459)</td></tr> +<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>33,041</td><td>33,021</td></tr> +<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="2" style="text-align:right"><sup>*</sup>p<0.05; <sup>**</sup>p<0.01; <sup>***</sup>p<0.001</td></tr> +</table> +# Run To Here # Discussion and conclusions @@ -1130,7 +1255,7 @@ If you wish to cite this work please use: # About -Analysis completed in: 7.095 seconds using [knitr](https://cran.r-project.org/package=knitr) with R version 3.4.2 (2017-09-28) running on x86_64-apple-darwin15.6.0. +Analysis completed in: 3.144 seconds using [knitr](https://cran.r-project.org/package=knitr) with R version 3.5.0 (2018-04-23) running on x86_64-apple-darwin15.6.0. 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