Datasets:

delen

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vistext

like 1

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Response Not too likely Being more active throughout the day 0.13 Spend more time exercising 0.18 Eating smaller portions of what you eat now 0.18 Limiting specific types of food or food types 0.19 Eating snacks less frequently 0.22 Eating smaller, more frequent meals or snacks 0.24 Tracking and increasing the amount of time you are physically active 0.19 Limit the number of calories in the food you eat 0.22 Eating more slowly and attentively 0.25 Eating out less often 0.25

It is most likely that people will use activity to manage weight. People were least likely to use eating more slowly and eating out less often to manage weight.

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Response Not too likely Being more active throughout the day 0.13 Spend more time exercising 0.18 Eating smaller portions of what you eat now 0.18 Limiting specific types of food or food types 0.19 Eating snacks less frequently 0.22 Eating smaller, more frequent meals or snacks 0.24 Tracking and increasing the amount of time you are physically active 0.19 Limit the number of calories in the food you eat 0.22 Eating more slowly and attentively 0.25 Eating out less often 0.25

The least likely responses are eating more slowly and eating out less often, both at 0.25. The most likely response is being more active through the day, at 0.13. There are 10 response categories.

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Month Facebook fans Aug 31, 2012 1.56 Jan 31, 2013 1.73 Aug 31, 2013 1.84 Feb 28, 2014 2.27 Aug 31, 2014 2.47 Feb 28, 2015 2.57 Aug 31, 2015 2.63 Feb 29, 2016 2.71 Aug 31, 2016 2.76 Feb 28, 2017 2.76 Aug 31, 2017 2.76 Feb 28, 2018 2.76 Aug 31, 2018 2.76 Feb 28, 2019 2.76 Aug 31, 2019 2.75 Feb 29, 2020 2.72 Aug 31, 2020 2.7

The amount of Facebook fans increased between 2014 and 2016 the fans have then been stable untill 2020 where a small stop can be seen.

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Month Facebook fans Aug 31, 2012 1.56 Jan 31, 2013 1.73 Aug 31, 2013 1.84 Feb 28, 2014 2.27 Aug 31, 2014 2.47 Feb 28, 2015 2.57 Aug 31, 2015 2.63 Feb 29, 2016 2.71 Aug 31, 2016 2.76 Feb 28, 2017 2.76 Aug 31, 2017 2.76 Feb 28, 2018 2.76 Aug 31, 2018 2.76 Feb 28, 2019 2.76 Aug 31, 2019 2.75 Feb 29, 2020 2.72 Aug 31, 2020 2.7

The number of Facebook fans increased steadily from 2012 to mid 2016, when it platoed. There has been a slight decline from mid 2019. The lowest number of fans was in 2012 with just over 1.5 million. It peaked at around 2.75 million mid 2016 and remained steady until mid 2019.

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Month Facebook fans Jul 31, 2012 0.66 Jan 31, 2013 0.83 Jul 31, 2013 0.88 Jan 31, 2014 1.24 Jul 31, 2014 1.69 Jan 31, 2015 2.01 Jul 31, 2015 1.99 Jan 31, 2016 2.08 Jul 31, 2016 2.14 Jan 31, 2017 2.17 Jul 31, 2017 2.17 Jan 31, 2018 2.17 Jul 31, 2018 2.17 Jan 31, 2019 2.19 Jul 31, 2019 2.18 Jan 31, 2020 2.17 Jul 31, 2020 2.16

In 20212 there are about 0.7 million Facebook fans. Then there is a slight increase until 2013 to about 0.8 and a massive increase to 2.0 in 2015. It stayed the same for a few months but then it started to increase slightly up to 2.25 in 2017 and stay at that level until 2020.

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Month Twitter followers Jul 31, 2012 0.13 Jan 31, 2013 0.21 Jul 31, 2013 0.27 Jan 31, 2014 0.35 Jul 31, 2014 0.39 Jan 31, 2015 0.53 Jul 31, 2015 0.65 Jan 31, 2016 0.81 Jul 31, 2016 1.01 Jan 31, 2017 1.34 Jul 31, 2017 1.54 Jan 31, 2018 1.8 Jul 31, 2018 1.82 Jan 31, 2019 1.85 Jul 31, 2019 1.9 Jan 31, 2020 1.93 Jul 31, 2020 1.9

In 2018, the rate of the increase of the number of twitter followers went down. The number of twitter followers start to go down in around 2020.

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Month Twitter followers Jul 31, 2012 0.13 Jan 31, 2013 0.21 Jul 31, 2013 0.27 Jan 31, 2014 0.35 Jul 31, 2014 0.39 Jan 31, 2015 0.53 Jul 31, 2015 0.65 Jan 31, 2016 0.81 Jul 31, 2016 1.01 Jan 31, 2017 1.34 Jul 31, 2017 1.54 Jan 31, 2018 1.8 Jul 31, 2018 1.82 Jan 31, 2019 1.85 Jul 31, 2019 1.9 Jan 31, 2020 1.93 Jul 31, 2020 1.9

The significant increase in users has now begun to flatten in terms of a data curve, indeed it now is decreasing.

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Family structure Father Only Dec 31, 1979 0.02 Dec 31, 1980 0.02 Dec 31, 1981 0.02 Dec 31, 1982 0.02 Dec 31, 1983 0.02 Dec 31, 1984 0.02 Dec 31, 1985 0.03 Dec 31, 1986 0.03 Dec 31, 1987 0.03 Dec 31, 1988 0.03 Dec 31, 1989 0.03 Dec 31, 1990 0.03 Dec 31, 1991 0.03 Dec 31, 1992 0.03 Dec 31, 1993 0.03 Dec 31, 1994 0.04 Dec 31, 1995 0.04 Dec 31, 1996 0.04 Dec 31, 1997 0.04 Dec 31, 1998 0.04 Dec 31, 1999 0.04 Dec 31, 2000 0.04 Dec 31, 2001 0.05 Dec 31, 2002 0.046 Dec 31, 2003 0.046 Dec 31, 2004 0.048 Dec 31, 2005 0.047 Dec 31, 2006 0.032 Dec 31, 2007 0.035 Dec 31, 2008 0.034 Dec 31, 2009 0.034 Dec 31, 2010 0.035 Dec 31, 2011 0.04 Dec 31, 2012 0.041 Dec 31, 2013 0.039 Dec 31, 2014 0.037 Dec 31, 2015 0.041 Dec 31, 2016 0.043 Dec 31, 2017 0.044 Dec 31, 2018 0.044

the time where most families had a father only was in around 2002-2005, and then it appears to decrease again slightly.

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Family structure No Parent Dec 31, 1979 0.04 Dec 31, 1980 0.04 Dec 31, 1981 0.03 Dec 31, 1982 0.03 Dec 31, 1983 0.03 Dec 31, 1984 0.03 Dec 31, 1985 0.03 Dec 31, 1986 0.03 Dec 31, 1987 0.03 Dec 31, 1988 0.03 Dec 31, 1989 0.03 Dec 31, 1990 0.03 Dec 31, 1991 0.03 Dec 31, 1992 0.03 Dec 31, 1993 0.04 Dec 31, 1994 0.04 Dec 31, 1995 0.04 Dec 31, 1996 0.04 Dec 31, 1997 0.04 Dec 31, 1998 0.04 Dec 31, 1999 0.04 Dec 31, 2000 0.04 Dec 31, 2001 0.04 Dec 31, 2002 0.041 Dec 31, 2003 0.043 Dec 31, 2004 0.045 Dec 31, 2005 0.046 Dec 31, 2006 0.035 Dec 31, 2007 0.038 Dec 31, 2008 0.04 Dec 31, 2009 0.041 Dec 31, 2010 0.039 Dec 31, 2011 0.036 Dec 31, 2012 0.037 Dec 31, 2013 0.038 Dec 31, 2014 0.039 Dec 31, 2015 0.038 Dec 31, 2016 0.04 Dec 31, 2017 0.043 Dec 31, 2018 0.04

The highest distribution of child population was around the 2015 mark whereas the lowest distribution in population was between 1981 and 1993.

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Family structure No Parent Dec 31, 1979 0.04 Dec 31, 1980 0.04 Dec 31, 1981 0.03 Dec 31, 1982 0.03 Dec 31, 1983 0.03 Dec 31, 1984 0.03 Dec 31, 1985 0.03 Dec 31, 1986 0.03 Dec 31, 1987 0.03 Dec 31, 1988 0.03 Dec 31, 1989 0.03 Dec 31, 1990 0.03 Dec 31, 1991 0.03 Dec 31, 1992 0.03 Dec 31, 1993 0.04 Dec 31, 1994 0.04 Dec 31, 1995 0.04 Dec 31, 1996 0.04 Dec 31, 1997 0.04 Dec 31, 1998 0.04 Dec 31, 1999 0.04 Dec 31, 2000 0.04 Dec 31, 2001 0.04 Dec 31, 2002 0.041 Dec 31, 2003 0.043 Dec 31, 2004 0.045 Dec 31, 2005 0.046 Dec 31, 2006 0.035 Dec 31, 2007 0.038 Dec 31, 2008 0.04 Dec 31, 2009 0.041 Dec 31, 2010 0.039 Dec 31, 2011 0.036 Dec 31, 2012 0.037 Dec 31, 2013 0.038 Dec 31, 2014 0.039 Dec 31, 2015 0.038 Dec 31, 2016 0.04 Dec 31, 2017 0.043 Dec 31, 2018 0.04

Between 1980 and 1990 the No Parent did not change. From 2000 onwards the No Parent fluctuated and does not seem to have settled down since then.

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Year Industry Dec 31, 2009 0.1888 Dec 31, 2010 0.1955 Dec 31, 2011 0.1792 Dec 31, 2012 0.1777 Dec 31, 2013 0.1756 Dec 31, 2014 0.1746 Dec 31, 2015 0.1747 Dec 31, 2016 0.1708 Dec 31, 2017 0.189 Dec 31, 2018 0.1911 Dec 31, 2019 0.192

The highest distribution was in 2010 and the lowest was in 2017.

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Year Industry Dec 31, 2007 0.109 Dec 31, 2008 0.1033 Dec 31, 2009 0.1186 Dec 31, 2010 0.1283 Dec 31, 2011 0.1364 Dec 31, 2012 0.1447 Dec 31, 2013 0.1439 Dec 31, 2014 0.1522 Dec 31, 2015 0.1469 Dec 31, 2016 0.1249 Dec 31, 2017 0.1247

the share of economic sectors in the GDP in Kiribati from 2008 to 2018 shows that the chart has a positive correlation.

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Year Industry Dec 31, 2007 0.109 Dec 31, 2008 0.1033 Dec 31, 2009 0.1186 Dec 31, 2010 0.1283 Dec 31, 2011 0.1364 Dec 31, 2012 0.1447 Dec 31, 2013 0.1439 Dec 31, 2014 0.1522 Dec 31, 2015 0.1469 Dec 31, 2016 0.1249 Dec 31, 2017 0.1247

The industry share steadily rose from 0.1 to 0.15 from 2009 to 2015. From 2015 to 2017 it dropped rapidly from 0.15 to around 0.12.

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Year Services Dec 31, 2007 0.6302 Dec 31, 2008 0.6172 Dec 31, 2009 0.6354 Dec 31, 2010 0.6162 Dec 31, 2011 0.6106 Dec 31, 2012 0.6079 Dec 31, 2013 0.6166 Dec 31, 2014 0.6071 Dec 31, 2015 0.6177 Dec 31, 2016 0.6331 Dec 31, 2017 0.6326

From 2008 to 2018 the services provided have stayed fairly linear.

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Year 20-39 years Dec 31, 2020 109714 Dec 31, 2021 110981 Dec 31, 2022 112007 Dec 31, 2023 108575 Dec 31, 2024 105558 Dec 31, 2025 102694 Dec 31, 2026 100063 Dec 31, 2027 97389 Dec 31, 2028 97648 Dec 31, 2029 98237

There is a peak in the population grown early in the chart, which then steadily declines into a dip in the latter years to the right of the chart. The population remains between around 90,000 to 110,000 across the 10 year period.

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Year 80 years and older Dec 31, 2020 12804 Dec 31, 2021 13020 Dec 31, 2022 13422 Dec 31, 2023 13885 Dec 31, 2024 14413 Dec 31, 2025 15074 Dec 31, 2026 15708 Dec 31, 2027 16448 Dec 31, 2028 17274 Dec 31, 2029 18084

The graph shows that more and more 80+ year old people were living longer over the years.

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Year 80 years and older Dec 31, 2020 12804 Dec 31, 2021 13020 Dec 31, 2022 13422 Dec 31, 2023 13885 Dec 31, 2024 14413 Dec 31, 2025 15074 Dec 31, 2026 15708 Dec 31, 2027 16448 Dec 31, 2028 17274 Dec 31, 2029 18084

According to the x-axis those aged 80 and older grow in population in almost 2-5,000 people every 2-4 years.

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Response Decrease Cybersecurity 0.03 Hybrid or multi-cloud 0.07 Automation 0.13 Smart analytics 0.11 AI 0.15 5G 0.1 Edge computing 0.12 IoT 0.16 AR/VR 0.22 Blockchain 0.24

In general the spending on all shown will decrease.

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Public insurance only Uninsured all year 95 359 116 394 140 363 177 351 154 383 149 389 229 544 221 482 326 634 385 613 441 583 496 551 352 561 384 449 392 453 359 348 308 346 207 304 162 274 98 155

I do not understand the correlation in this task at all.

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Year Labour Dec 31, 1996 0.42 Dec 31, 1997 0.45 Dec 31, 1998 0.43 Dec 31, 1999 0.4 Dec 31, 2000 0.45 Dec 31, 2001 0.41 Dec 31, 2002 0.37 Dec 31, 2003 0.32 Dec 31, 2004 0.4 Dec 31, 2005 0.33 Dec 31, 2006 0.34 Dec 31, 2007 0.27 Dec 31, 2008 0.26 Dec 31, 2009 0.2 Dec 31, 2010 0.32 Dec 31, 2011 0.36

There have been sharp peaks and troughs here with a record low in 2010 before they started to climb back up.

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Year Liberal Democrat Dec 31, 1996 0.1 Dec 31, 1997 0.1 Dec 31, 1998 0.1 Dec 31, 1999 0.1 Dec 31, 2000 0.13 Dec 31, 2001 0.11 Dec 31, 2002 0.11 Dec 31, 2003 0.13 Dec 31, 2004 0.13 Dec 31, 2005 0.12 Dec 31, 2006 0.09 Dec 31, 2007 0.09 Dec 31, 2008 0.1 Dec 31, 2009 0.13 Dec 31, 2010 0.07 Dec 31, 2011 0.06

The points are plotted not just on the lines representing every 2 years, or every .02, but also in between those lines, or every 1 year, and every .01.

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Year Any party Dec 31, 1996 0.83 Dec 31, 1997 0.84 Dec 31, 1998 0.82 Dec 31, 1999 0.81 Dec 31, 2000 0.85 Dec 31, 2001 0.81 Dec 31, 2002 0.77 Dec 31, 2003 0.77 Dec 31, 2004 0.82 Dec 31, 2005 0.77 Dec 31, 2006 0.75 Dec 31, 2007 0.75 Dec 31, 2008 0.73 Dec 31, 2009 0.77 Dec 31, 2010 0.73 Dec 31, 2011 0.76

In general, the percentage decreases. There are noticeable rises in percentage approximately every five years, in around 2000, 2005 and 2010. In 1998 the percentage is over 0.8. After 2006 the percentage is consistently below 0.8.

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Year Advertising revenue Dec 31, 2008 764 Dec 31, 2009 1868 Dec 31, 2010 3154 Dec 31, 2011 4279 Dec 31, 2012 6986 Dec 31, 2013 11492 Dec 31, 2014 17079 Dec 31, 2015 26885 Dec 31, 2016 39942 Dec 31, 2017 55013 Dec 31, 2018 69655

Facebooks advertising revenue seems to be growing exponentially over time. The revenue has increased each period, by a quantity greater than the last period.

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Public, state region of Italy 2168 Lombardy 1490 Campania 1400 Sicily 1373 Veneto 1268 Piedmont 1122 Lazio 939 Emilia-Romagna 929 Tuscany 833 Calabria 726 Apulia 477 Sardinia 438 Marche 420 Liguria 400 Abruzzo 364 Friuli-Venezia Giulia 285 Umbria 198 Basilicata 126 Molise 0 Aosta Valley 0 Trentino-South Tyrol

Lombardy has the greatest number of schools in Italian regions.

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region of Italy Public, not state Lombardy 0 Campania 1 Sicily 0 Veneto 0 Piedmont 0 Lazio 0 Emilia-Romagna 0 Tuscany 0 Calabria 0 Apulia 0 Sardinia 0 Marche 0 Liguria 0 Abruzzo 0 Friuli-Venezia Giulia 0 Umbria 0 Basilicata 0 Molise 0 Aosta Valley 81 Trentino-South Tyrol 530

most areas have state schools. only 3 areas have public schools, Trentino South has the most public schools and campania has the least.

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Response Online teens Preventing identity theft 0.55 Keeping my devices secure 0.41 How to identify fake emails, social posts, and texts 0.39 How to determine if a website is secure 0.35 How to report serious problems I encounter online 0.25 How young people are lured, groomed, or recruited online 0.27 How to manage my privacy online 0.32 Dealing with a bully 0.3 Blocking people 0.3 Risks of sexting 0.3 Adjusting settings on social networks 0.21 How to begin a conversation with (kids/my parents) about staying safe online 0.17

Although the chart says it gives data from online teens and parents, it only shows data from online teens. 12 areas of concern are plotted. The highest area of concern is preventing identity theft. The second highest is keeping my devices secure. The third highest is how to identify fake emails. The lowest area of concern is how to begin a conversation with.

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Response Online teens Preventing identity theft 0.55 Keeping my devices secure 0.41 How to identify fake emails, social posts, and texts 0.39 How to determine if a website is secure 0.35 How to report serious problems I encounter online 0.25 How young people are lured, groomed, or recruited online 0.27 How to manage my privacy online 0.32 Dealing with a bully 0.3 Blocking people 0.3 Risks of sexting 0.3 Adjusting settings on social networks 0.21 How to begin a conversation with (kids/my parents) about staying safe online 0.17

The highest response for which topic is the most important online safety topic for teens is preventing identity theft. The topic with the least responses is how to begin a conversation with. None of the responses received 0 votes and many were in the middle area of a score around 0.25.

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region of Canada 2017 Prince Edward Islan 328; region of Canada: Prince Edward Island Yukon Territorie 1886; region of Canada: Yukon Territories NW Territorie 1613; region of Canada: NW Territories Nova Scoti 2554; region of Canada: Nova Scotia Nunavu 2644; region of Canada: Nunavut Newfoundlan 5719; region of Canada: Newfoundland New Brunswic 6636; region of Canada: New Brunswick Manitob 13416; region of Canada: Manitoba Saskatchewa 10777; region of Canada: Saskatchewan British Columbi 26524; region of Canada: British Columbia Albert 34770; region of Canada: Alberta Quebe 47469; region of Canada: Quebec Ontari 51997; region of Canada: Ontario

Ontario has had the highest number of charter flights than any other providence or territory measured, coming in at 54,000 for 2017 and 2018.Quebec comes in as a close second at 48,000 for the same time frame. The lowest, Prince Edward Island, only had 1000 flights. All the other regions were in between the Ontario and Prince Edward Island.

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region of Canada 2018 Prince Edward Islan 291; region of Canada: Prince Edward Island Yukon Territorie 1842; region of Canada: Yukon Territories NW Territorie 1886; region of Canada: NW Territories Nova Scoti 2510; region of Canada: Nova Scotia Nunavu 3374; region of Canada: Nunavut Newfoundlan 5564; region of Canada: Newfoundland New Brunswic 6779; region of Canada: New Brunswick Manitob 10411; region of Canada: Manitoba Saskatchewa 12764; region of Canada: Saskatchewan British Columbi 27948; region of Canada: British Columbia Albert 36914; region of Canada: Alberta Quebe 48463; region of Canada: Quebec Ontari 55098; region of Canada: Ontario

The number of charter flights was the highest in Ontario and Quebec, and the lowest in Prince Edward Island. There were less than 10,000 charter flights in 8 out 14 regions in Canada.

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region of Canada 2018 Prince Edward Islan 291; region of Canada: Prince Edward Island Yukon Territorie 1842; region of Canada: Yukon Territories NW Territorie 1886; region of Canada: NW Territories Nova Scoti 2510; region of Canada: Nova Scotia Nunavu 3374; region of Canada: Nunavut Newfoundlan 5564; region of Canada: Newfoundland New Brunswic 6779; region of Canada: New Brunswick Manitob 10411; region of Canada: Manitoba Saskatchewa 12764; region of Canada: Saskatchewan British Columbi 27948; region of Canada: British Columbia Albert 36914; region of Canada: Alberta Quebe 48463; region of Canada: Quebec Ontari 55098; region of Canada: Ontario

We can see from the graph that Alberta, British Columbia, Quebec and Ontario are the most busy airports. It can also be noted that Prince Edward Island, Yukon Territories and NW territories are the least busy airports.

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region of Canada 2018 Prince Edward Islan 291; region of Canada: Prince Edward Island Yukon Territorie 1842; region of Canada: Yukon Territories NW Territorie 1886; region of Canada: NW Territories Nova Scoti 2510; region of Canada: Nova Scotia Nunavu 3374; region of Canada: Nunavut Newfoundlan 5564; region of Canada: Newfoundland New Brunswic 6779; region of Canada: New Brunswick Manitob 10411; region of Canada: Manitoba Saskatchewa 12764; region of Canada: Saskatchewan British Columbi 27948; region of Canada: British Columbia Albert 36914; region of Canada: Alberta Quebe 48463; region of Canada: Quebec Ontari 55098; region of Canada: Ontario

Ontario and Quebec have the most flights while Prince Edward Island has very little.

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Year Imports Dec 31, 1999 −53.71 Dec 31, 2000 −53.95 Dec 31, 2001 −56.15 Dec 31, 2002 −57.24 Dec 31, 2003 −60.47 Dec 31, 2004 −63.4 Dec 31, 2005 −67 Dec 31, 2006 −73.7 Dec 31, 2007 −74.56 Dec 31, 2008 −73.45 Dec 31, 2009 −74.12 Dec 31, 2010 −77.25 Dec 31, 2011 −76.27 Dec 31, 2012 −82.21 Dec 31, 2013 −84.16 Dec 31, 2014 −87.21 Dec 31, 2015 −88.49 Dec 31, 2016 −92.09 Dec 31, 2017 −93.45

Scotish imports have fallen steadily from 2000 to 2015. The decline in imports was slower between 2007 and 2012.

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Year Research* Dec 31, 1959 7 Dec 31, 1969 2 Dec 31, 1974 3.4 Dec 31, 1979 5.4 Dec 31, 1989 12.7 Dec 31, 1999 25.5 Dec 31, 2008 45.4 Dec 31, 2013 45.9 Dec 31, 2014 46.5 Dec 31, 2015 47.6 Dec 31, 2016 50.7

Dollars spent on U.S health investment has increased dramatically up to just over 50 billion since 1960. It was at the lowest spend in 1970.

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Year Structures and equipment Dec 31, 1959 1.8 Dec 31, 1969 5.6 Dec 31, 1974 8.8 Dec 31, 1979 14.4 Dec 31, 1989 34.6 Dec 31, 1999 57.8 Dec 31, 2008 93.6 Dec 31, 2013 105 Dec 31, 2014 107.9 Dec 31, 2015 110.6 Dec 31, 2016 116.9

Between 1960 - 2017 in the United States we seen strong investment growth in Structures & Equipment, from below $10 Billion in 1960 to approaching $120 Billion in 2017.

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Year Structures and equipment Dec 31, 1959 1.8 Dec 31, 1969 5.6 Dec 31, 1974 8.8 Dec 31, 1979 14.4 Dec 31, 1989 34.6 Dec 31, 1999 57.8 Dec 31, 2008 93.6 Dec 31, 2013 105 Dec 31, 2014 107.9 Dec 31, 2015 110.6 Dec 31, 2016 116.9

This chart covers many years and shows the sharp rises in recent years as opposed to the gradual increase in the 1960s.

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Year Structures and equipment Dec 31, 1959 1.8 Dec 31, 1969 5.6 Dec 31, 1974 8.8 Dec 31, 1979 14.4 Dec 31, 1989 34.6 Dec 31, 1999 57.8 Dec 31, 2008 93.6 Dec 31, 2013 105 Dec 31, 2014 107.9 Dec 31, 2015 110.6 Dec 31, 2016 116.9

Each year the US have invested more and more in structures and equipment. In 1960 there were barely any structures/ equipment invested in. In the year 2000, almost 60 billion dollars were invested in equipment and structures.

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Year Cargo 2020-2040* 0.029 2019-2039* 0.029 2018-2038* 0.016 2017-2037* 0.01 2016-2036* 0.012 2020* 0.036 2019* 0.036 2018 0.004 2017 0.056 2016 0.027 2015 0.027 2014 0.027 2013 −0.108

2013 was the worst year. 2017 was the best year. You can then see the performance of the individual years but not draw any conclusions from that.

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Year Cargo 2020-2040* 0.029 2019-2039* 0.029 2018-2038* 0.016 2017-2037* 0.01 2016-2036* 0.012 2020* 0.036 2019* 0.036 2018 0.004 2017 0.056 2016 0.027 2015 0.027 2014 0.027 2013 −0.108

the aircraft decreased significantly in 2013 but maintained consistency with an upsurge in 2017 and expecting to continue right to 2037.

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city of the United States Juvenile Dallas 6 San Francisco 6 Sacramento 6 Detroit 6 Milwaukee 6 Seattle 6 Los Angeles 5 Las Vegas 4 New Orleans 3 Oklahoma City 3 Chicago 3 Atlanta 3 Portland 3 Tampa 3 St. Louis 2 Miami 2 San Diego 2 Denver 2 Phoenix 2 Philadelphia 2 New York City 1 El Paso 1 Washington Field Office 1 Boston 1 Newark 0 Birmingham 0 Omaha 0 Richmond 0 Albuquerque 0 Baltimore 0 Indianapolis 0 San Antonio 0 Houston 0 Minneapolis 0 Cleveland 0 Knoxville 0

NUMBER OF RECOVERED JUVENILES AND ARRESTED PIPS ON JUNE 25 2012 BY FBI.

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The highest number of recovered juveniles on June 25 2012 was 6 and this was the number recovered in 6 different cities. In 12 different cities, 0 juveniles were recovered on that day,.

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7 states show no recorded results. New Orleans is the highest with 10. Following that there are 4 states coming in second highest with 7 recorded against them.

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Albuquerque, Birmingham, Dallas, Indianapolis, Knoxville, Milwaukee and Washington field office had no recovered juveniles or arrested pimps on June 25 2012. News Orleans has the highest number of recovered juveniles or arrested pimps on that day.

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Year Services Dec 31, 2007 0.4855 Dec 31, 2008 0.5492 Dec 31, 2009 0.5783 Dec 31, 2010 0.579 Dec 31, 2011 0.5509 Dec 31, 2012 0.5833 Dec 31, 2013 0.5759 Dec 31, 2014 0.5865 Dec 31, 2015 0.6041 Dec 31, 2016 0.6059 Dec 31, 2017 0.6128

The share of GDP has increased overall from 2008 until 2018 and appears to be continuning with that trend.

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Month Facebook fans Jul 31, 2012 2.54 Jan 31, 2013 2.67 Jul 31, 2013 2.73 Jan 31, 2014 3.15 Jul 31, 2014 3.85 Jan 31, 2015 4.2 Jul 31, 2015 4.04 Jan 31, 2016 4.08 Jul 31, 2016 4.13 Jan 31, 2017 4.13 Jul 31, 2017 4.13 Jan 31, 2018 4.13 Jul 31, 2018 4.13 Jan 31, 2019 4.03 Jul 31, 2019 4 Jan 31, 2020 3.97 Jul 31, 2020 3.94

After peaking in 2016 the amount of twitter and facebook followers has peaked and slightly decreased over time.

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Month Facebook fans Jul 31, 2012 2.54 Jan 31, 2013 2.67 Jul 31, 2013 2.73 Jan 31, 2014 3.15 Jul 31, 2014 3.85 Jan 31, 2015 4.2 Jul 31, 2015 4.04 Jan 31, 2016 4.08 Jul 31, 2016 4.13 Jan 31, 2017 4.13 Jul 31, 2017 4.13 Jan 31, 2018 4.13 Jul 31, 2018 4.13 Jan 31, 2019 4.03 Jul 31, 2019 4 Jan 31, 2020 3.97 Jul 31, 2020 3.94

The chart shows that from 2012 to 2015 there was a significant increase in the number of facebook fans/twitter followers from c. 2.3 million to over 4 million. From 2015 onwards the number has declined slowly and by 2020 has fallen below 4 million.

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Month Facebook fans Jul 31, 2012 2.54 Jan 31, 2013 2.67 Jul 31, 2013 2.73 Jan 31, 2014 3.15 Jul 31, 2014 3.85 Jan 31, 2015 4.2 Jul 31, 2015 4.04 Jan 31, 2016 4.08 Jul 31, 2016 4.13 Jan 31, 2017 4.13 Jul 31, 2017 4.13 Jan 31, 2018 4.13 Jul 31, 2018 4.13 Jan 31, 2019 4.03 Jul 31, 2019 4 Jan 31, 2020 3.97 Jul 31, 2020 3.94

the number of Chicago bears fans increased rapidly between the years of 2012 to 2015, and has since plateaued around the 4 million mark up until 2020.

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Year Retail and other Dec 31, 2008 0.84 Dec 31, 2009 0.95 Dec 31, 2010 1.12 Dec 31, 2011 1.2 Dec 31, 2012 1.3 Dec 31, 2013 1.45 Dec 31, 2014 1.62 Dec 31, 2015 1.71 Dec 31, 2016 1.58 Dec 31, 2017 1.59

The Walt Disney Company's product revenue peaked at 2016.

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One month or more in advance Response 0.82 United Kingdom 0.81 Germany 0.81 Belgium 0.8 Austria 0.79 Switzerland 0.78 France 0.69 Portugal 0.69 Spain 0.68 Italy 0.59 Poland

People in the United Kingdom book their holidays the longest in advance than any of the other countries, with people booking on average more than 0.8 months in advance. People in Poland book the closest to their holiday.

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Less than one month in advance Response 0.18 United Kingdom 0.19 Germany 0.19 Belgium 0.2 Austria 0.21 Switzerland 0.22 France 0.31 Portugal 0.31 Spain 0.32 Italy 0.41 Poland

Polish residents book holidays further in advance than the rest of European nations.

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Response Computer Balance 0.28 Send/receive money (domestic) 0.35 Transfer between accounts 0.37 Buy other insurance 0.63 Obtain insurance (home) 0.54 Make a payment 0.44 Statements 0.44 Make insurance claim (home) 0.63 Claim other insurance 0.61 Line of credit 0.33 Set up investments 0.34 Send/receive money (international) 0.6 Find advisor 0.23 Deal with advisor 0.27 Buy/sell stocks 0.28 Bank deposit 0.32 House/apartment loan 0.35 Car loan 0.32 Bank withdrawal 0.11

Bank withdrawals is the least common financial activity for millenials in Denmark is 2018.

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Phone call Response 0.04 Balance 0.04 Send/receive money (domestic) 0.04 Transfer between accounts 0.37 Buy other insurance 0.43 Obtain insurance (home) 0.04 Make a payment 0.05 Statements 0.3 Make insurance claim (home) 0.31 Claim other insurance 0.12 Line of credit 0.19 Set up investments 0.05 Send/receive money (international) 0.21 Find advisor 0.35 Deal with advisor 0 Buy/sell stocks 0.05 Bank deposit 0.22 House/apartment loan 0.14 Car loan 0.04 Bank withdrawal

Danish millennials tend to use their phones to make calls relating to insurance more than to complete other tasks.

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Response In real life Balance 0 Send/receive money (domestic) 0 Transfer between accounts 0 Buy other insurance 0 Obtain insurance (home) 0.02 Make a payment 0.05 Statements 0.05 Make insurance claim (home) 0.06 Claim other insurance 0.07 Line of credit 0.08 Set up investments 0.13 Send/receive money (international) 0.14 Find advisor 0.22 Deal with advisor 0.26 Buy/sell stocks 0.29 Bank deposit 0.38 House/apartment loan 0.39 Car loan 0.5 Bank withdrawal 0.6

Whilst people in Denmark use these devices to send and receive money, it is only being used for international transfers. Millennials tend to use these devices most to withdraw money. They are popular for car and property loans.

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Response In real life Balance 0 Send/receive money (domestic) 0 Transfer between accounts 0 Buy other insurance 0 Obtain insurance (home) 0.02 Make a payment 0.05 Statements 0.05 Make insurance claim (home) 0.06 Claim other insurance 0.07 Line of credit 0.08 Set up investments 0.13 Send/receive money (international) 0.14 Find advisor 0.22 Deal with advisor 0.26 Buy/sell stocks 0.29 Bank deposit 0.38 House/apartment loan 0.39 Car loan 0.5 Bank withdrawal 0.6

The highest use of digital devices was for bank withdrawals at 0.6, followed by car loans at 0.5 and house/apartment loans at 0.39. Fourth on the list was bank deposits at 0.38. Sending/receiving domestic funds and transferring between accounts had values of zero. Financial stock transactions, setting up investments and dealing with advisors generally had higher use of digital devices than insurance dealings and making payments.

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Year North America Dec 31, 2009 0.681 Dec 31, 2010 0.702 Dec 31, 2011 0.716 Dec 31, 2012 0.723 Dec 31, 2013 0.742 Dec 31, 2014 0.747 Dec 31, 2015 0.758 Dec 31, 2016 0.76 Dec 31, 2017 0.757 Dec 31, 2018 0.758

occupancy of marriott international hotels has increased in north america, from around 0.68 in 2010, to around 0.75 in2018.

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Year North America Dec 31, 2009 0.681 Dec 31, 2010 0.702 Dec 31, 2011 0.716 Dec 31, 2012 0.723 Dec 31, 2013 0.742 Dec 31, 2014 0.747 Dec 31, 2015 0.758 Dec 31, 2016 0.76 Dec 31, 2017 0.757 Dec 31, 2018 0.758

The Marriott International hotels North America occupancy rate increased steadily from 2010 to 2016. From 2016 onwards the Marriott International hotels North America occupancy rate remained steady. The area chart for the Marriott International hotels North America occupancy rate is shown as a bright pink infill colour against a white background with a pale grey line marking the maximum on the y-axis. The x-axis and both the x and y scales are shown in black. The text for the both axes is shown in bold black font as is the title at the top of the visualization.

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Year Middle East & Africa Dec 31, 2009 0.705 Dec 31, 2010 0.588 Dec 31, 2011 0.618 Dec 31, 2012 0.557 Dec 31, 2013 0.601 Dec 31, 2014 0.612 Dec 31, 2015 0.646 Dec 31, 2016 0.657 Dec 31, 2017 0.664 Dec 31, 2018 0.685

There is a general downward trend of occupancy in the hotels from 2010-2013. From 2013, there is a steady upward trend of occupancy to 2018. The occupancy rate of in 2019 is almost back to levels seen in 2010 of 0.7.

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Year Middle East & Africa Dec 31, 2009 0.705 Dec 31, 2010 0.588 Dec 31, 2011 0.618 Dec 31, 2012 0.557 Dec 31, 2013 0.601 Dec 31, 2014 0.612 Dec 31, 2015 0.646 Dec 31, 2016 0.657 Dec 31, 2017 0.664 Dec 31, 2018 0.685

Overall the occupancy rate of Marriott International hotels worldwide fell between 2010 and 2013 before rising steadily to hit 2010 levels by 2018.

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Exports from UK Country 0.39 North America 0.2 South America 0.27 Scandinavia 0.32 Western Europe −0.05 Mediterranean 0.33 Africa 0.42 Eastern Europe 0.46 Middle East 0.19 Indian subcontinent 0.36 Far East 0.45 Australia

Middle East and Australia were the countries to which there were the largest exports. The mederterainian may be an anonomly as it is a minus figure.

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Exports from UK Country 0.39 North America 0.2 South America 0.27 Scandinavia 0.32 Western Europe −0.05 Mediterranean 0.33 Africa 0.42 Eastern Europe 0.46 Middle East 0.19 Indian subcontinent 0.36 Far East 0.45 Australia

Exports have grown to all countries over the time period with the exception of the Mediterranean. The country with the largest increase in exports is the Middle East.

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Country Discretionary mandates Portugal 0.76 Turkey 0.68 Italy 0.67 United Kingdom 0.66 Belgium 0.53 Europe 0.48 France 0.46 Greece 0.38 Hungary 0.34 Netherlands 0.31 Germany 0.16 Romania 0.01 Bulgaria 0

Portugal had the most discretionary mandates and Bulgaria had the least. The range of mandates is between 0.0 and 0.8. The average amount was held by Hungary. Bulgaria had no mandates. There are 13 countries on the bar chart.

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Investment funds Country 0.24 Portugal 0.32 Turkey 0.33 Italy 0.34 United Kingdom 0.47 Belgium 0.52 Europe 0.54 France 0.62 Greece 0.66 Hungary 0.69 Netherlands 0.84 Germany 0.99 Romania 1 Bulgaria

Bulgaria has the highest investment fund and Portual has the lowest. Romania has the second hightest investment fund after Bulgaria.

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Year Men 2004/05 540 2005/06 526.4 2006/07 496.1 2007/08 499 2008/09 478.5 2009/10 461.5 2010/11 467.6 2011/12 454 2012/13 458.8 2013/14 438.2 2014/15 435.8 2015/16 426.4 2016/17 421.7 2017/18 432.4 2018/19* 435.7

A higher percentage of men have coronary heart disease in 2004/2005A steady decrease, with some years a little higher than othersMore healthy lifestyles are evident as the years moved on.

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Year 15-64 years Dec 31, 2008 0.6266 Dec 31, 2009 0.6297 Dec 31, 2010 0.6327 Dec 31, 2011 0.6358 Dec 31, 2012 0.6388 Dec 31, 2013 0.6414 Dec 31, 2014 0.6434 Dec 31, 2015 0.6453 Dec 31, 2016 0.6469 Dec 31, 2017 0.6481 Dec 31, 2018 0.6492

The average age of Ecuadorians has remained between 15 - 64 steadily over an 8 year period.

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Year 65 years and older Dec 31, 2008 0.0588 Dec 31, 2009 0.06 Dec 31, 2010 0.061 Dec 31, 2011 0.062 Dec 31, 2012 0.0631 Dec 31, 2013 0.0644 Dec 31, 2014 0.066 Dec 31, 2015 0.0676 Dec 31, 2016 0.0695 Dec 31, 2017 0.0716 Dec 31, 2018 0.0737

The average age has increased over the 10 years. The population is aging.

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Year Death by natural causes Dec 31, 2005 93793 Dec 31, 2006 94978 Dec 31, 2007 97135 Dec 31, 2008 97077 Dec 31, 2009 98465 Dec 31, 2010 97455 Dec 31, 2011 101995 Dec 31, 2012 102475 Dec 31, 2013 98095 Dec 31, 2014 103625 Dec 31, 2015 101364

Overall, deaths by natural causes have increased. Numbers of deaths showed a decrease from the previous year in 2009, 2011 and 2014.

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Year Death by natural causes Dec 31, 2005 93793 Dec 31, 2006 94978 Dec 31, 2007 97135 Dec 31, 2008 97077 Dec 31, 2009 98465 Dec 31, 2010 97455 Dec 31, 2011 101995 Dec 31, 2012 102475 Dec 31, 2013 98095 Dec 31, 2014 103625 Dec 31, 2015 101364

There are approximately 100,000 deaths by natural causes in a typical year in Belgium. There appears to be a slightly increasing trend of deaths by natural causes in Belgium from 2006 to 2016. The highest number of deaths by natural causes in the years given was in 2015. The lowest number of deaths by natural causes in the years given was in 2006.

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Year Death by natural causes Dec 31, 2005 93793 Dec 31, 2006 94978 Dec 31, 2007 97135 Dec 31, 2008 97077 Dec 31, 2009 98465 Dec 31, 2010 97455 Dec 31, 2011 101995 Dec 31, 2012 102475 Dec 31, 2013 98095 Dec 31, 2014 103625 Dec 31, 2015 101364

In general, the number of deaths have remained fairly steady between 2006 and 2016. The overall increase is less than 10,000 people. From 2011 to 2013, and between 2014 and 2015 there were the biggest increases in the number of deaths.

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Year Male Dec 31, 2007 2.91 Dec 31, 2008 2.92 Dec 31, 2009 2.93 Dec 31, 2010 2.93 Dec 31, 2011 2.94 Dec 31, 2012 2.95 Dec 31, 2013 2.96 Dec 31, 2014 2.97 Dec 31, 2015 2.99 Dec 31, 2016 3 Dec 31, 2017 3.01

From 2008 you can see the Male populates is just under 3million, over the course of 8-10 years there is a small increase of the male gender over time consistently.

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TV, radio or newspapers Response 0.45 Average 0.37 United States 0.66 India 0.54 Japan 0.54 South Africa 0.53 Brazil 0.49 South Korea 0.46 China 0.43 Italy 0.39 Canada 0.39 Germany 0.34 France 0.28 United Kingdom

The average amount is 0.45, thus India is far above average in their brand of communication. Whereas the U.K had a below average amount. All countries were above 0.2 though.

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TV, radio or newspapers Response 0.45 Average 0.37 United States 0.66 India 0.54 Japan 0.54 South Africa 0.53 Brazil 0.49 South Korea 0.46 China 0.43 Italy 0.39 Canada 0.39 Germany 0.34 France 0.28 United Kingdom

India population is the one who most prefers TV, radio or newspaper. This is followed by Japan and South Africa. UK is the country who least prefers TV, radio or newspaper. All other countries not mentioned above share more or less the same values.

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Response Facebook Average 0.31 United States 0.26 India 0.65 Japan 0.09 South Africa 0.48 Brazil 0.49 South Korea 0.19 China 0.18 Italy 0.38 Canada 0.28 Germany 0.18 France 0.22 United Kingdom 0.25

India is the country which most prefers Facebook as a channel of brand communication. Japan is the country which shows the least preference. Only one country is over 0.50. The majority of countries are under 0.30.

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Instagram Response 0.21 Average 0.15 United States 0.47 India 0.09 Japan 0.27 South Africa 0.46 Brazil 0.17 South Korea 0.17 China 0.19 Italy 0.16 Canada 0.12 Germany 0.09 France 0.13 United Kingdom

India and Brazil were the highest users of instagram and were the most engaged with it and had more users than the average. Japan and France had the least engagement.

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Instagram Response 0.21 Average 0.15 United States 0.47 India 0.09 Japan 0.27 South Africa 0.46 Brazil 0.17 South Korea 0.17 China 0.19 Italy 0.16 Canada 0.12 Germany 0.09 France 0.13 United Kingdom

Instagram was a successful channel for brand communication in India and Brazil (by a long way in comparison with other countries) during Covid.

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Twitter Response 0.19 Average 0.14 United States 0.47 India 0.2 Japan 0.28 South Africa 0.28 Brazil 0.09 South Korea 0.19 China 0.13 Italy 0.15 Canada 0.07 Germany 0.1 France 0.14 United Kingdom

India saw a significantly larger twitter response during the coronavirus pandemic than other countries included in the study. United Kingdom and United states have seen a near identical twitter response during the pandemic. No European or North American countries saw a twitter response which was larger than the average. The 'average' must be the mean and not the median. South Africa and Brazil are the only African or South American countries included in the study and both had a response which was higher than the average.

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Texts Response 0.18 Average 0.13 United States 0.37 India 0.08 Japan 0.28 South Africa 0.12 Brazil 0.31 South Korea 0.3 China 0.08 Italy 0.08 Canada 0.22 Germany 0.14 France 0.12 United Kingdom

Consumers in India preferred text as a channel of brand communication in relation to other countries. Consumers in Japan and Italy chose text as least favourite for brand communication from big companies.

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Secondary (new and old scrap) Year 130 2019* 117 2018 119 2017 220 2016 238 2015 135 2014 210 2013 215 2012 263 2011 198 2010 189 2009 181 2008

From 2008 to 2019 the refinery gold production in the United States was at it's highest during 2011, producing over 250 metric tons.

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Year Imports Dec 31, 2005 5312 Dec 31, 2006 4345 Dec 31, 2007 3187 Dec 31, 2008 2421 Dec 31, 2009 2498 Dec 31, 2010 2457 Dec 31, 2011 2539 Dec 31, 2012 2799 Dec 31, 2013 3292 Dec 31, 2014 3524 Dec 31, 2015 3615 Dec 31, 2016 3511

Imports dropped significantly from 2006 to 2009 and then levelled off slightly before beginning to rise again in 2012.

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Year Imports Dec 31, 2005 5312 Dec 31, 2006 4345 Dec 31, 2007 3187 Dec 31, 2008 2421 Dec 31, 2009 2498 Dec 31, 2010 2457 Dec 31, 2011 2539 Dec 31, 2012 2799 Dec 31, 2013 3292 Dec 31, 2014 3524 Dec 31, 2015 3615 Dec 31, 2016 3511

During the time between years 2006 and 2009 there was a significant decrease in the roundwood imports and exports from over 5000 million cubic feet to about 2500 million cubic feet. Between years 2010 and 2013 the number of cubic feet imported seems to be stable, although in the following years (2014 to 2016) an increase was observed, where the number roundwood import number reached about 3600 million cubic feet.

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Car brand Q2 2020 Ford 81541 Toyota 57286 Honda 41846 Hyundai 39199 Chevrolet 31636 Ram 28391 Nissan 27827 Kia 25090 GMC 22126 Mazda 19053

In 3rd quarter 2019 and 3rd quarter 2020 the most car sales by brand were Ford and Toyota. The average car sales between all of the brands is roughly 25k.

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Year Walloon Region Dec 31, 1999 30326 Dec 31, 2000 30949 Dec 31, 2001 31586 Dec 31, 2002 32256 Dec 31, 2003 33242 Dec 31, 2004 33912 Dec 31, 2005 34927 Dec 31, 2006 34530 Dec 31, 2007 36047 Dec 31, 2008 39205 Dec 31, 2009 38555

2000 WAS THE LOWEST YEAR FOR WALLOON REGION WITH 30,000 EUROS 2008 WAS THE HIGHEST YEAR FOR ANNUAL HOUSEHOLD DISPOSABLE INCOME WITH ALMOST 40,000 EUROSFROM 2000 TO 2010 ANNUAL HOUSEHOLD DISPOSABLE INCOME GREW STEADILY.

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Year 65 years and older Dec 31, 2008 0.0575 Dec 31, 2009 0.0585 Dec 31, 2010 0.0597 Dec 31, 2011 0.0609 Dec 31, 2012 0.0622 Dec 31, 2013 0.0636 Dec 31, 2014 0.0651 Dec 31, 2015 0.0669 Dec 31, 2016 0.0688 Dec 31, 2017 0.0708 Dec 31, 2018 0.073

Between 2009 and 2019 the percentage of over-65's shows a steady upward trend.

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Year Cellulose fibers Dec 31, 1999 2640 Dec 31, 2009 4400 Dec 31, 2010 4743 Dec 31, 2011 5040 Dec 31, 2012 5900 Dec 31, 2013 6200 Dec 31, 2014 6100 Dec 31, 2015 6400 Dec 31, 2016 6700 Dec 31, 2017 6800 Dec 31, 2018 7000

Cellulose finer production goes up yearly. The highest is 7000 in 2015.

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Year Total Dec 31, 1999 31040 Dec 31, 2009 49600 Dec 31, 2010 52700 Dec 31, 2011 56000 Dec 31, 2012 60300 Dec 31, 2013 63300 Dec 31, 2014 66800 Dec 31, 2015 71200 Dec 31, 2016 71600 Dec 31, 2017 73400 Dec 31, 2018 80500

The x axis goes up in intervals of 5 yearsThe graph has an upwards positive trendProduction is 2020 is double that of 2005.

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African field of study 35639 Business, Economics and Management Studies 27337 Education 9408 Engineering 7694 Social Sciences 7592 Health Professions and Related Clinical Sciences 5872 Public Management and Services 4673 Computer and Information Sciences 4358 Law 4216 Physical Sciences 3255 Agriculture, Agricultural Operations and Related Sciences 3210 Life Sciences 3186 Psychology 3111 Communication, Journalism and Related Studies 2035 Languages, Linguistics and Literature 1768 Architecture and the Built Environment 1573 Mathematics and Statistics 1360 Visual and Performing Arts 563 Philiosophy, Religion and Theology 385 Family Ecology and Consumer Sciences 1 Military Sciences

Twenty fields of study are listed, though full titles are sometimes cut off. The field of study with the most African graduates was "Business, Economics and Manage..." with 35,000. The second highest number of African graduates--approximately 27,500--received "Education" degrees. No other listed field of study had 10,000 or more African graduates.

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field of study Coloured Business, Economics and Management Studies 3209 Education 2111 Engineering 617 Social Sciences 642 Health Professions and Related Clinical Sciences 1178 Public Management and Services 369 Computer and Information Sciences 408 Law 580 Physical Sciences 292 Agriculture, Agricultural Operations and Related Sciences 92 Life Sciences 477 Psychology 560 Communication, Journalism and Related Studies 212 Languages, Linguistics and Literature 369 Architecture and the Built Environment 184 Mathematics and Statistics 102 Visual and Performing Arts 318 Philiosophy, Religion and Theology 135 Family Ecology and Consumer Sciences 28 Military Sciences 0

Business and economics was the most popular area of study. Education was second. There were no students studying the military. Only three subjects had more than 1000 students.

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Year Domestic 2020-2040** 0.019 2019-2039** 0.017 2018-2038** 0.019 2017-2037** 0.009 2016-2036** 0.013 2020 0.019 2019 0.028 2018 0.077 2017 0.095 2016 0.021 2015 0.033 2014 0.023 2013 0.007 2012 0.021 2011 −0.061

Highest projected growth is in 2017 and 2018. In 2011 growth projected is minus 0.05. 2013 shows lowest projected growth.

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Commodity group 2006 Miscellaneous mixed shipments, primarily intermoda 8536; Commodity group: Miscellaneous mixed shipments, primarily intermodal Coa 7574; Commodity group: Coal All other carload 2896; Commodity group: All other carloads Chemical 1969; Commodity group: Chemicals Farm product 1590; Commodity group: Farm products Food and similar product 1487; Commodity group: Food and similar products Non-metallic mineral 1470; Commodity group: Non-metallic minerals Motor vehicles and equipmen 1714; Commodity group: Motor vehicles and equipment Waste and scrap materia 701; Commodity group: Waste and scrap material Pulp, paper, and allied product 671; Commodity group: Pulp, paper, and allied products Metallic ore 674; Commodity group: Metallic ores Petroleum and cok 663; Commodity group: Petroleum and coke Metal and product 778; Commodity group: Metal and products Stone, clay, and glass product 570; Commodity group: Stone, clay, and glass products Lumber and wood product 548; Commodity group: Lumber and wood products Forwarder and shipper association traffi 276; Commodity group: Forwarder and shipper association traffic

remains low apart from coal and miscellaneous - the rest are steady.

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Commodity group 2006 Miscellaneous mixed shipments, primarily intermoda 8536; Commodity group: Miscellaneous mixed shipments, primarily intermodal Coa 7574; Commodity group: Coal All other carload 2896; Commodity group: All other carloads Chemical 1969; Commodity group: Chemicals Farm product 1590; Commodity group: Farm products Food and similar product 1487; Commodity group: Food and similar products Non-metallic mineral 1470; Commodity group: Non-metallic minerals Motor vehicles and equipmen 1714; Commodity group: Motor vehicles and equipment Waste and scrap materia 701; Commodity group: Waste and scrap material Pulp, paper, and allied product 671; Commodity group: Pulp, paper, and allied products Metallic ore 674; Commodity group: Metallic ores Petroleum and cok 663; Commodity group: Petroleum and coke Metal and product 778; Commodity group: Metal and products Stone, clay, and glass product 570; Commodity group: Stone, clay, and glass products Lumber and wood product 548; Commodity group: Lumber and wood products Forwarder and shipper association traffi 276; Commodity group: Forwarder and shipper association traffic

The highest number of carloads transported by US Class I railroads between 2004 and 2009 were ‘Miscellaneous mixed shipments, p…’. Coal was in second place. The least amount of carloads were ‘Forwarder and shipper association…’. 8 out of the 16 commodity groups had fewer than 1,000,000 carloads. 13 commodity groups had 2,000,000 or fewer carloads.

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2007 Commodity group 8465 Miscellaneous mixed shipments, primarily intermodal 7480 Coal 2588 All other carloads 2069 Chemicals 1681 Farm products 1493 Food and similar products 1398 Non-metallic minerals 1639 Motor vehicles and equipment 726 Waste and scrap material 652 Pulp, paper, and allied products 662 Metallic ores 671 Petroleum and coke 721 Metal and products 513 Stone, clay, and glass products 456 Lumber and wood products 246 Forwarder and shipper association traffic

MIscellaneous mixed shipments is the largest commodity transported by Class 1 Rail with over 8,000,000 carloads. The next highest is Coal with approximately 7,500,000 carloads. The commodity with the fewest carloads is Forwarder and Shipper Associates.

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2007 Commodity group 8465 Miscellaneous mixed shipments, primarily intermodal 7480 Coal 2588 All other carloads 2069 Chemicals 1681 Farm products 1493 Food and similar products 1398 Non-metallic minerals 1639 Motor vehicles and equipment 726 Waste and scrap material 652 Pulp, paper, and allied products 662 Metallic ores 671 Petroleum and coke 721 Metal and products 513 Stone, clay, and glass products 456 Lumber and wood products 246 Forwarder and shipper association traffic

Coal and mixed shipment is transported the most whilst forwarder is the least transported.

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Commodity group 2009 Miscellaneous mixed shipments, primarily intermoda 6897; Commodity group: Miscellaneous mixed shipments, primarily intermodal Coa 6842; Commodity group: Coal All other carload 2017; Commodity group: All other carloads Chemical 1909; Commodity group: Chemicals Farm product 1531; Commodity group: Farm products Food and similar product 1462; Commodity group: Food and similar products Non-metallic mineral 1054; Commodity group: Non-metallic minerals Motor vehicles and equipmen 912; Commodity group: Motor vehicles and equipment Waste and scrap materia 568; Commodity group: Waste and scrap material Pulp, paper, and allied product 546; Commodity group: Pulp, paper, and allied products Metallic ore 527; Commodity group: Metallic ores Petroleum and cok 479; Commodity group: Petroleum and coke Metal and product 416; Commodity group: Metal and products Stone, clay, and glass product 372; Commodity group: Stone, clay, and glass products Lumber and wood product 285; Commodity group: Lumber and wood products Forwarder and shipper association traffi 188; Commodity group: Forwarder and shipper association traffic

Coal and miscellaneous items were the most transported in 2009.

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Year Industry Dec 31, 2008 0.2474 Dec 31, 2009 0.2685 Dec 31, 2010 0.2707 Dec 31, 2011 0.2728 Dec 31, 2012 0.2678 Dec 31, 2013 0.2701 Dec 31, 2014 0.2706 Dec 31, 2015 0.275 Dec 31, 2016 0.2744 Dec 31, 2017 0.2746 Dec 31, 2018 0.2682

The chart shows that from 2010, industry remained above 0.25.

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11-15 years Response 0.564 2019/20 0.583 2018/19 0.604 2017/18 0.609 2016/17 0.703 2015/16 0.69 2014/15 0.736 2013/14 0.733 2012/13 0.774 2011/12 0.71 2010/11 0.709 2009/10 0.724 2008/09

Participation increased as the years went on and then decreased.

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Year 0-14 years Dec 31, 2008 0.2316 Dec 31, 2009 0.2246 Dec 31, 2010 0.2147 Dec 31, 2011 0.206 Dec 31, 2012 0.1986 Dec 31, 2013 0.1922 Dec 31, 2014 0.1867 Dec 31, 2015 0.1838 Dec 31, 2016 0.1803 Dec 31, 2017 0.1767 Dec 31, 2018 0.174

In Albania, between 2009 and 2019, there was a steady decline in the proportion of children aged 14 and under. The rate has decreased from around 0.28 in 2009 to around 0.175 in 2019.

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Year 15-64 years Dec 31, 2008 0.6665 Dec 31, 2009 0.669 Dec 31, 2010 0.6751 Dec 31, 2011 0.6801 Dec 31, 2012 0.6837 Dec 31, 2013 0.686 Dec 31, 2014 0.687 Dec 31, 2015 0.6866 Dec 31, 2016 0.6864 Dec 31, 2017 0.6858 Dec 31, 2018 0.684

There has been a slight increase in age structure between 2009 and 2019. Although there has been a slight increase in age structure, the figures are very close to each other and it could be argued there has been little if any real change.

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2018 Country 10057 Ukraine 7133 Germany 4858 United Kingdom 1446 Netherlands 1262 Vietnam 1247 USA 981 Belarus 797 Italy 708 Austria 709 France 862 Ireland 800 China 680 Russia 638 Norway 477 Sweden 435 Belgium 472 Spain

Poland received the most immigrants from Poland that year at 10,000. This was followed by Germany (around 7,000) and the UK (just under 5,000). All of the remaining countries received fewer than 2,000 people each.

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2018 Country 10057 Ukraine 7133 Germany 4858 United Kingdom 1446 Netherlands 1262 Vietnam 1247 USA 981 Belarus 797 Italy 708 Austria 709 France 862 Ireland 800 China 680 Russia 638 Norway 477 Sweden 435 Belgium 472 Spain

In 2018, emigration was at an average across most countries of less then 2000. A handful of countries had substantially higher , examples being Ukraine, UK and Germany. Ukraine was nearly 10k, UK 5k and Germany 7k. All of these countries are geographically located in Europe.

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2018 Country 10057 Ukraine 7133 Germany 4858 United Kingdom 1446 Netherlands 1262 Vietnam 1247 USA 981 Belarus 797 Italy 708 Austria 709 France 862 Ireland 800 China 680 Russia 638 Norway 477 Sweden 435 Belgium 472 Spain

In 2018 most people emigrating from Poland went to Ukraine, there were 10.000 people that emigrated from Poland to the Ukraine in this year. Spain Sweden and Belgium were the countries with the least amount of people emigrating from Poland with under 1000people durung theyear of 2018.

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Year Generation aged 18-29 Dec 31, 1971 0.46 Dec 31, 1975 0.51 Dec 31, 1979 0.44 Dec 31, 1983 0.4 Dec 31, 1987 0.47 Dec 31, 1991 0.43 Dec 31, 1995 0.53 Dec 31, 1999 0.48 Dec 31, 2003 0.54 Dec 31, 2007 0.66 Dec 31, 2011 0.6

This shows the increase in the 18 to 29 age group shows a big increase as the years moved forward.

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Country 2012/2013 Spain 39 USA 0 Thailand 4 France 5 Greece 12 United Arab Emirates 5 Cyprus 7 Australia 0 India 3 Germany 1 Turkey 16 China 5 Philippines 2 Pakistan 0 Portugal 3 Egypt 20 Canada 0 Switzerland 0 Italy 5 Jamaica 0

The chart, which has its Y axis sorted by alphabetical order, shows that Spain is the country with the most cases, with almost 40. There are 3 countries with between 10 and 20 cases (Egypt, Greece and Turkey). There are 10 countries with 1 to 10 cases. There are 6 countries (Australia, Canada, Jamaica, Pakistan, Switzwerland and the USA) with 0 cases of sexual assault of British residents.