{"id":1311,"date":"2018-09-12T12:44:24","date_gmt":"2018-09-12T11:44:24","guid":{"rendered":"https:\/\/marriott-stats.com\/nigels-blog\/?p=1311"},"modified":"2020-02-07T22:09:07","modified_gmt":"2020-02-07T22:09:07","slug":"uk-weather-trends-6-summer-2018","status":"publish","type":"post","link":"https:\/\/marriott-stats.com\/nigels-blog\/uk-weather-trends-6-summer-2018\/","title":{"rendered":"UK Weather Trends #6 &#8211; Summer 2018"},"content":{"rendered":"<p>Meteorologists define summer in the UK to be the period from June to August so summer is now over and we are officially in autumn.\u00a0 The 2018 summer was the joint hottest on record and in doing so blew apart <a href=\"https:\/\/marriott-stats.com\/nigels-blog\/uk-weather-trends-5-spring-2018\/\">my prediction<\/a> that we would have to wait till next year for our next good summer!<\/p>\n<p><!--more--><\/p>\n<p>I analyse the long term trends in the UK weather using a statistical tool known as <a href=\"https:\/\/en.wikipedia.org\/wiki\/Standard_score\" target=\"_blank\" rel=\"noopener noreferrer\"><strong>Standardisation<\/strong><\/a>.\u00a0 This means that the 3 key variables of Temperature, Sunshine and Rainfall are recalculated so that they all have the same units, which is number of standard deviations above or below the mean.\u00a0 Such variables are known as <strong>Z-Scores<\/strong>\u00a0which by definition will have a mean value of 0 and a standard deviation of 1.\u00a0 For more information on how I have done this, <a href=\"https:\/\/marriott-stats.com\/nigels-blog\/uk-weather-trends-2-summer-2017\/\" target=\"_blank\" rel=\"noopener noreferrer\">please read my post on trends in the UK summer of 2017.<\/a><\/p>\n<h4><span style=\"color: #008000\"><strong>Latest Z-Scores<\/strong><\/span><\/h4>\n<p>The Z-Scores for Temperature, Sunshine and Rainfall are shown in the 3 charts below.\u00a0 Each chart also contains an 11-year centred moving average which gives an idea of the underlying trend.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-1305 alignnone\" src=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM1-300x127.png\" alt=\"\" width=\"619\" height=\"262\" srcset=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM1-300x127.png 300w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM1-768x326.png 768w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM1-1024x435.png 1024w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM1-450x191.png 450w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM1.png 1253w\" sizes=\"auto, (max-width: 619px) 100vw, 619px\" \/><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-1306 alignnone\" src=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM2-300x127.png\" alt=\"\" width=\"619\" height=\"262\" srcset=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM2-300x127.png 300w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM2-768x326.png 768w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM2-1024x435.png 1024w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM2-450x191.png 450w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM2.png 1253w\" sizes=\"auto, (max-width: 619px) 100vw, 619px\" \/><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-1307 alignnone\" src=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM3-300x127.png\" alt=\"\" width=\"619\" height=\"262\" srcset=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM3-300x127.png 300w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM3-768x326.png 768w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM3-1024x435.png 1024w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM3-450x191.png 450w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM3.png 1253w\" sizes=\"auto, (max-width: 619px) 100vw, 619px\" \/><\/p>\n<p>Standardised variables aid interpretation of data in many ways.\u00a0 If the standardised value is positive, it means that the value is above your average or expected value.\u00a0 If it is negative, then the value is below your expected value. \u00a0If the original variable is approximately normal in its distribution then the vertical scale gives us an idea of how typical or atypical each year is.\u00a0 Z-Scores in the range -1 to +1 are considered typical values and completely unremarkable.\u00a0 Z-scores in the ranges -2 to -1 and +1 to +2 are considered to be uncommon values but still entirely plausible and such values should not cause us concern.\u00a0 When Z-Scores get into the ranges -3 to -2 and +2 to +3, we should start paying closer attention and asking ourselves if something has changed especially if we get a sequence of successive points in these ranges. Finally, if the Z-scores are less than -3 or greater than +3, that is normally regarded as a clear call to action.\u00a0\u00a0There are in fact many ways of interpreting Z-Scores and\u00a0what I have said so far\u00a0merely a gives an overview of the most basic interpretations.\u00a0 A whole field of study known as <a href=\"https:\/\/en.wikipedia.org\/wiki\/Statistical_process_control\">Statistical Process Control (SPC) <\/a>is dedicated to building and interpreting such charts (known as Control Charts).<\/p>\n<p><span style=\"float: none;background-color: transparent;color: #333333;cursor: text;font-family: Georgia,'Times New Roman','Bitstream Charter',Times,serif;font-size: 16px;font-style: normal;font-variant: normal;font-weight: 400;letter-spacing: normal;text-align: left;text-decoration: none;text-indent: 0px\">For the summer of 2018, the z-scores for temperature, sunshine and rainfall were respectively +2.0, +1.9 and -1.0.\u00a0 So across all three dimensions, the summer was 1 to 2 standard deviations away from the mean indicating a very untypical summer.\u00a0 If you have been following <a href=\"https:\/\/marriott-stats.com\/nigels-blog\/uk-weather-tracker-13-february-2018\/\" target=\"_blank\" rel=\"noopener noreferrer\">my monthly weather tracker<\/a> then you will know that August was unremarkable whilst June and July were hot, dry and sunny.\u00a0\u00a0<\/span><\/p>\n<h4><span style=\"color: #008000\"><strong>Long Term Climate Trends<\/strong><\/span><\/h4>\n<p>Since the 3 moving averages in the above 3 charts all use the same units, they can be plotted onto the same chart as below.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-1308 alignnone\" src=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM4-300x125.png\" alt=\"\" width=\"624\" height=\"260\" srcset=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM4-300x125.png 300w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM4-768x319.png 768w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM4-1024x425.png 1024w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM4-450x187.png 450w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM4.png 1281w\" sizes=\"auto, (max-width: 624px) 100vw, 624px\" \/><\/p>\n<p>This clearly shows a shift in our summer climate over the last 100 years of roughly 1 standard deviation in some cases.\u00a0 Recall that the baseline for the z-score calculation is based on the idea of &#8220;living memory&#8221; which I have defined to be the last 50 years of 1968 to 2017. \u00a0 We can characterise our summers broadly as follows:<\/p>\n<ul>\n<li>1915-1970 &#8211; we had cold and damp summers.<\/li>\n<li>1970-1995 &#8211; we had dryer and almost normal temperature summers.<\/li>\n<li>1995-today &#8211; a clear shift in our climate occurred to warm and wet summers.<\/li>\n<\/ul>\n<h4><span style=\"color: #008000\"><strong>How many dimensions does Summer have?<\/strong><\/span><\/h4>\n<p>The long term trends chart above suggests that the z-scores for temperature, sunshine and rainfall all appear to be correlated.\u00a0 In fact this can be illusory as the above chart uses moving averages.\u00a0 If we look at the actual z-scores, we can see what the correlation is in the 3 scatter plots below.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-1302 alignnone\" src=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM5-300x72.png\" alt=\"\" width=\"800\" height=\"192\" srcset=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM5-300x72.png 300w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM5-768x185.png 768w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM5-1024x246.png 1024w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM5-450x108.png 450w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/09\/UKweatherTracker2018SUM5.png 1851w\" sizes=\"auto, (max-width: 800px) 100vw, 800px\" \/><\/p>\n<p>The brown square in each chart is 2018.\u00a0 Scatter plots can be useful to identify unusual years that do not follow the normal relationships.\u00a0 Here we see that 2018 was completely consistent with historical scatters.<\/p>\n<p>Looking at the 3 scatter plots in turn, we see that all 3 variables are correlated with each other.\u00a0 A statistician would look at these charts and say that what at first appears to be 3-dimensional data (temperature, sunshine and rainfall being the 3 dimensions) is in fact closer to be being 1-dimensional.\u00a0 This new dimension would be a weighted average of these 3 z-scores known as a <strong>Component<\/strong>.\u00a0 There are in fact many possible weighted averages of the z-scores (components) that could be used and in my Spring 2018 Trends post, <a href=\"https:\/\/marriott-stats.com\/nigels-blog\/uk-weather-trends-5-spring-2018\/\" target=\"_blank\" rel=\"noopener noreferrer\">I introduced the method of PCA (Principal Components Analysis)<\/a> which can take our 3-dimensional data set, calculate 3 new components that are statistically uncorrelated with each other, and with the property that the first component would account for the greatest possible share of the total 3-dimensional variance and thereby reducing the effective dimensionality of the data to 2 or even 1 dimension.\u00a0 Please do read that post if you need to familiarise yourself with PCA.<\/p>\n<p>If I carry out a PCA with summer datya, the first output of PCA is the scree plot shown here.\u00a0 We are analysing a 3 dimensional data set here and the scree plot shows what proportion of the 3 <img loading=\"lazy\" decoding=\"async\" class=\"alignright wp-image-1380\" src=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/10\/UKweatherTracker2018SUM7-300x234.png\" alt=\"\" width=\"396\" height=\"309\" srcset=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/10\/UKweatherTracker2018SUM7-300x234.png 300w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/10\/UKweatherTracker2018SUM7.png 384w\" sizes=\"auto, (max-width: 396px) 100vw, 396px\" \/>dimensions is accounted for by each component.\u00a0 Here, the first component PC1 accounts for 2.25 dimensions or more strictly 76% of the total variance across the 3 dimensions.\u00a0 PC2 &amp; PC3 each account for less than 1 dimension\u00a0 Before, had we chosen any one of the three z-scores, we would only account for 1\/3 of the total variance so PCA has resulted in a single new components that almost account for a clear majority of the variation seen normally in 3 dimensions.\u00a0 So it is quite correct to say that our summer weather is effectively 1-dimensional<\/p>\n<p>The Correlation Bi-Plot\u00a0shows the correlation of the original variables (3 z-scores in this instance) with the two new principal components PC1 &amp; PC2.\u00a0 Each axis label shows how much the components account for of the total variance and since the total of these values is less than 100%, this warns us that the bi-plot is still an approximation of the entire dataset and therefore some information is missing.\u00a0 Despite that, it is still very informative.\u00a0 Note, for ease of interpretion, I have replaced Rainfall with Dryness where Dryness is just the negative of Rainfall i.e. Rainfall multiplied by -1.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-1378 alignleft\" src=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/10\/UKweatherTracker2018SUM8-300x292.png\" alt=\"\" width=\"379\" height=\"369\" srcset=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/10\/UKweatherTracker2018SUM8-300x292.png 300w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/10\/UKweatherTracker2018SUM8-768x748.png 768w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/10\/UKweatherTracker2018SUM8-359x350.png 359w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/10\/UKweatherTracker2018SUM8.png 898w\" sizes=\"auto, (max-width: 379px) 100vw, 379px\" \/>You can see that PC1 is highly positively correlated with Sunshine since the Sunshine label is almost at +1 on the PC1 axis.\u00a0 Temperature is also positively correlated with PC1 but not quite as strongly as Sunshine.\u00a0 Finally, Dryness is well correlated with PC1 and is also correlated with PC2.\u00a0 So this tells us that the UK Summer 1st Principal Component is basically an average of the 3 z-scores with slightly more weight given to Sunshine.\u00a0 The more positive PC1 is, the better our summer and the more negative PC1 is, the worse our summer.<\/p>\n<p>Let&#8217;s not forget that Principal Components are by definition uncorrelated with each other, which means you can analyse each component independently. The bi-plot tells us that the 2nd Principal Component is basically the difference between the Dryness Z-Score and the Temperature Z-Score.\u00a0 So when PC2 is positive this indicates the summer is dry and cool and when it is negative, it is warm and wet.\u00a0 However, PC2 only accounts for 16% of the total variance so from now on, I will ignore this and concentrate on PC1.<\/p>\n<h4><span style=\"color: #008000\"><strong>Using PC1 to Predict UK Summers!<\/strong><\/span><\/h4>\n<p>The great value of PCA is that we can look at long term trends in our climate using a single index.\u00a0 I have done this for PC1 in the chart below.\u00a0 The scale used is similar to the z-score concept.\u00a0 0 represents the long term average, positive numbers above average and negative numbers below average.\u00a0 The numbers themselves broadly correspond to the number of standard deviations above or below average.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-1379 alignnone\" src=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/10\/UKweatherTracker2018SUM9-300x123.png\" alt=\"\" width=\"724\" height=\"297\" srcset=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/10\/UKweatherTracker2018SUM9-300x123.png 300w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/10\/UKweatherTracker2018SUM9-768x316.png 768w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/10\/UKweatherTracker2018SUM9-1024x421.png 1024w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/10\/UKweatherTracker2018SUM9-450x185.png 450w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/10\/UKweatherTracker2018SUM9.png 1105w\" sizes=\"auto, (max-width: 724px) 100vw, 724px\" \/><\/p>\n<p>When I first performed PCA on UK summer data after 2003, I was startled to see an apparent pattern emerging.\u00a0 My understanding of weather is that long term predictions are not possible and <img loading=\"lazy\" decoding=\"async\" class=\"alignright wp-image-1381\" src=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/10\/UKweatherTracker2018SUM10-111x300.png\" alt=\"\" width=\"161\" height=\"435\" srcset=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/10\/UKweatherTracker2018SUM10-111x300.png 111w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/10\/UKweatherTracker2018SUM10-130x350.png 130w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2018\/10\/UKweatherTracker2018SUM10.png 313w\" sizes=\"auto, (max-width: 161px) 100vw, 161px\" \/>yet our good summers seemed to coming every 6 to 8 years.\u00a0 If we define a good summer to be one where PC1 is greater than 2 (as highlighted by the green checked bars in the chart), the first thing that is clear is that the good summers seem to be discontinuous from our other summers.\u00a0 By this I mean, there are very few summers with PC1 values between 1 &amp; 2.\u00a0 More notably, the gaps between the good summers did not appear to be random as shown in the chart to the left.\u00a0 Either the next good summer came straightaway or we had to wait 6 or 7 for the next one.<\/p>\n<p>It was this chart that prompted to say all and sundry that 2018 would not be a good summer!\u00a0 Our previous good summer was in 2013 so adding 6\/7 years implied that our next summer would be 2019\/20.\u00a0 However I had forgotten that 1989 came 5 years after 1984 whereas I had been counting that year as 6 years after 1983.\u00a0 Of course we can now add an extra sun to the 5th row of the chart.<\/p>\n<p>A final point from the PC1 chart.\u00a0 The long term trend as shown by the black line shows a clear shift in the 1970s to better summers.\u00a0 However, there has been no discernible trend since then.\u00a0 I think this is an extremely important point to bear in mind when discussing the effect of Global Warming on the UK.\u00a0 Our summers have not been getting warmer and warmer.\u00a0 Instead, they underwent a step change in the 1970s but have not had any noticeable trend since.\u00a0 In fact, it appears that the main effect of Global Warming on the UK has been warmer winters most of all.\u00a0 Therefore, if we are trying to anticipate future climate change in the UK and how to adapt, we should not be expecting warmer and warmer summers.\u00a0 The summer of 2018 set some new records but it does not mean that all of our summers in the future are going to be like that and the UK will continue to have poor summers as well.<\/p>\n<hr \/>\n<p>If you want to read <a href=\"https:\/\/marriott-stats.com\/nigels-blog\/tag\/weather-trends\/\" target=\"_blank\" rel=\"noopener noreferrer\">my other Weather Trends posts<\/a>, please click on the link or the Weather Trends hashtag below this post.\u00a0 Otherwise, please click the relevant season from the list below.<\/p>\n<ul>\n<li>2018 &#8211; <a href=\"https:\/\/marriott-stats.com\/nigels-blog\/uk-weather-trends-4-winter-2018\/\" target=\"_blank\" rel=\"noopener noreferrer\">Winter<\/a>, <a href=\"https:\/\/marriott-stats.com\/nigels-blog\/uk-weather-trends-5-spring-2018\/\" target=\"_blank\" rel=\"noopener noreferrer\">Spring<\/a><em>, Summer, Autumn<\/em><\/li>\n<li>2017 &#8211; <em>Winter<\/em>, <a href=\"https:\/\/marriott-stats.com\/nigels-blog\/uk-weather-trends-1-spring-2017\/\" target=\"_blank\" rel=\"noopener noreferrer\">Spring<\/a>, <a href=\"https:\/\/marriott-stats.com\/nigels-blog\/uk-weather-trends-2-summer-2017\/\" target=\"_blank\" rel=\"noopener noreferrer\">Summer<\/a>, <a href=\"https:\/\/marriott-stats.com\/nigels-blog\/uk-weather-trends-3-autumn-2017\/\" target=\"_blank\" rel=\"noopener noreferrer\">Autumn<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Meteorologists define summer in the UK to be the period from June to August so summer is now over and we are officially in autumn.\u00a0 The 2018 summer was the joint hottest on record and in doing so blew apart my prediction that we would have to wait till next year for our next good [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":1381,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[90],"tags":[48,40,72,51,46,34,47,52],"class_list":{"0":"post-1311","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-archive","8":"tag-multivariate-data","9":"tag-presenting-data","10":"tag-principal-components-analysis","11":"tag-standardisation","12":"tag-trend-analysis","13":"tag-weather","14":"tag-weather-trends","15":"tag-z-scores","16":"entry","17":"override"},"_links":{"self":[{"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/posts\/1311","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/comments?post=1311"}],"version-history":[{"count":4,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/posts\/1311\/revisions"}],"predecessor-version":[{"id":2303,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/posts\/1311\/revisions\/2303"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/media\/1381"}],"wp:attachment":[{"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/media?parent=1311"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/categories?post=1311"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/tags?post=1311"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}