3,386 Matching Annotations
  1. Aug 2018
    1. 0.855

      0.855

    2. 0.847

      0.847

    3. 0.847

      0.847

    4. 0.847

      0.847

    5. 0.839

      0.839

    6. 0.855

      0.855

    7. 0.839

      0.839

    8. 0.839

      0.839

    9. 0.855

      0.855

    10. 0.855

      0.855

    11. 12  Canada 0.890

      .885

    12. 12  Canada 0.890 16.

      .886

    13. 12  Canada 0.890

      0.887

    14. 12  Canada 0.890

      0.890

    15. 35  Italy 0.814

      .778

    16. 35  Italy 0.814

      .780

    17. 35  Italy 0.814

      .810

    18. 1  Australia 0.939

      .930

    19. 1  Australia 0.939

      .937

    20. 1  Australia

      .938

    21. 1  Australia 0.939

      .939

    22. 35  Italy 0.814 16.3 10.9 26

      .814

    1. Largest census metropolitan areas in Canada by population (2016 Census) viewtalkedit CMA Province Population CMA Province Population Toronto Ontario 5,928,040 London Ontario 494,069 Montreal Quebec 4,098,927 St. Catharines–Niagara Ontario 406,074 Vancouver British Columbia 2,463,431 Halifax Nova Scotia 403,390 Calgary Alberta 1,392,609 Oshawa Ontario 379,848 Ottawa–Gatineau Ontario–Quebec 1,323,783 Victoria British Columbia 367,770 Edmonton Alberta 1,321,426 Windsor Ontario 329,144 Quebec Quebec 800,296 Saskatoon Saskatchewan 295,095 Winnipeg Manitoba 778,489 Regina Saskatchewan 236,481 Hamilton Ontario 747,545 Sherbrooke Quebec 212,105 Kitchener–Cambridge–Waterloo Ontario

      5928040

    2. Largest census metropolitan areas in Canada by population (2016 Census) viewtalkedit CMA Province Population CMA Province Population Toronto Ontario 5,928,040 London Ontario 494,069 Montreal Quebec 4,098,927 St. Catharines–Niagara Ontario 406,074 Vancouver British Columbia 2,463,431 Halifax Nova Scotia 403,390 Calgary Alberta 1,392,609

      4098927

    3. Vancouver British Columbia 2,463,431

      2463431

    4. Largest census metropolitan areas in Canada by population (2016 Census) viewtalkedit CMA Province Population CMA Province Population Toronto Ontario 5,928,040 London Ontario 494,069 Montreal Quebec 4,098,927 St. Catharines–Niagara Ontario 406,074 Vancouver British Columbia 2,463,431 Halifax Nova Scotia 403,390 Calgary Alberta 1,392,609
    1. 1 Sydney NSW 5,131,326 11 Hobart Tas 224,462 BrisbanePerth 2 Melbourne Vic 4,850,740 12 Geelong Vic 192,393 3 Brisbane Qld 2,408,223 13 Townsville Qld 178,864 4 Perth WA 2,043,138 14 Cairns Qld 150,041 5 Adelaide SA 1,333,927

      5131326

    2. 2 Melbourne Vic 4,850,740

      4850740

    3. 4 Perth WA 2,043,138

      2043138

    4. 5 Adelaide SA 1,333,927

      1333927

    1. Region Capital Area (km2) Area (sq mi) Population Nominal GDP EURO billions (2016)[148] Nominal GDP EURO per capita(2016) [149] Abruzzo L'Aquila 10,763 4,156 1,331,574 32 24,100 Aosta Valley Aosta 3,263 1,260 128,298 4 34,900 Apulia Bari 19,358 7,474 4,090,105 72 17,800 Basilicata Potenza 9,995 3,859 576,619 12 20,600 Calabria Catanzaro 15,080 5,822 1,976,631 33 16,800 Campania Naples 13,590 5,247 5,861,529 107 18,300 Emilia-Romagna Bologna 22,446 8,666 4,450,508 154 34,600 Friuli-Venezia Giulia Trieste 7,858 3,034 1,227,122 37 30,300 Lazio Rome 17,236 6,655 5,892,425 186 31,600 Liguria Genoa 5,422 2,093 1,583,263 48 30,800 Lombardy Milan 23,844 9,206 10,002,615 367 36,600 Marche Ancona 9,366 3,616 1,550,796 41 26,600 Molise Campobasso 4,438 1,713 313,348 6 20,000 Piedmont Turin 25,402 9,808 4,424,467 129 29,400 Sardinia Cagliari 24,090 9,301 1,663,286 34 20,300 Sicily Palermo 25,711 9,927 5,092,080 87 17,200 Tuscany Florence 22,993 8,878 3,752,654

      3752654

    2. Liguria Genoa 5,422 2,093 1,583,263 48 30,800 Lombardy Milan 23,844 9,206 10,002,615 367 36,600

      1583263

    3. Region Capital Area (km2) Area (sq mi) Population Nominal GDP EURO billions (2016)[148] Nominal GDP EURO per capita(2016) [149] Abruzzo L'Aquila 10,763 4,156 1,331,574 32 24,100 Aosta Valley Aosta 3,263 1,260 128,298 4 34,900 Apulia Bari 19,358 7,474 4,090,105 72 17,800 Basilicata Potenza 9,995 3,859 576,619 12 20,600 Calabria Catanzaro 15,080 5,822 1,976,631 33 16,800 Campania Naples 13,590 5,247 5,861,529 107 18,300 Emilia-Romagna Bologna 22,446 8,666 4,450,508 154 34,600 Friuli-Venezia Giulia Trieste 7,858 3,034 1,227,122 37 30,300 Lazio Rome 17,236 6,655 5,892,425 186 31,600 Liguria Genoa 5,422 2,093 1,583,263 48 30,800 Lombardy Milan 23,844 9,206 10,002,615 367 36,600 Marche Ancona 9,366 3,616 1,550,796 41 26,600 Molise Campobasso 4,438 1,713 313,348 6 20,000 Piedmont Turin 25,402 9,808 4,424,467 129 29,400 Sardinia Cagliari 24,090 9,301 1,663,286 34 20,300 Sicily Palermo 25,711 9,927 5,092,080 87 17,200 Tuscany Florence 22,993 8,878 3,752,654 112 30,000 Trentino-Alto Adige/Südtirol Trento 13,607 5,254 1,055,934 42 39,755 Umbria Perugia 8,456 3,265 894,762 21 24,000 Veneto Venice 18,399 7,104 4,927,596 156 31,700

      4450508

    1. MontrealToronto Improve Data Improve Data Air Pollution Low 28.32 Low 33.55 Drinking Water Pollution and Inaccessibility Very Low 16.03 Very Low 18.92 Dissatisfaction with Garbage Disposal Low 23.64 Low 28.55 Dirty and Untidy Low 32.88 Low 35.83 Noise and Light Pollution Moderate 49.73 Moderate 44.97 Water Pollution Low 36.36

      36.36

    2. Water Pollution Low 36.36 Low 39.39

      39.39

    3. Quality of Green and Parks Very High 82.88 High 71.96

      71.96

    4. Very High 82.88 High 71.96

      82.88

    1. PerthMelbourne Improve Data Improve Data Air Pollution Very Low 19.20 Low 22.95 Drinking Water Pollution and Inaccessibility Very Low 14.96 Very Low 13.42 Dissatisfaction with Garbage Disposal Low 22.37 Low 24.18 Dirty and Untidy Low 25.56 Low 31.65 Noise and Light Pollution Low 35.80 Moderate 42.09 Water Pollution Low 32.50 Low 30.75 Dissatisfaction to Spend Time in the City Low 20.22 Very Low 19.68 Dissatisfaction with Green and Parks in the City Very Low 16.86 Very Low 16.74 Contributors:141227Last Update:August 2018August 2018 $(function() { $( "#first_air_pollution" ).progressbar({ value: 19.20 }); $( "#second_air_pollution" ).progressbar({ value: 22.95 }); $( "#first_drinking_water_pollution_and_inaccessibility" ).progressbar({ value: 14.96 }); $( "#second_drinking_water_pollution_and_inaccessibility" ).progressbar({ value: 13.42 }); $( "#first_dissatisfaction_garbage" ).progressbar({ value: 22.37 }); $( "#second_dissatisfaction_garbage" ).progressbar({ value: 24.18 }); $( "#first_dissatisfaction_clean_and_tidy" ).progressbar({ value: 25.56 }); $( "#second_dissatisfaction_clean_and_tidy" ).progressbar({ value: 31.65 }); $( "#first_noise_and_light_pollution" ).progressbar({ value: 35.80 }); $( "#second_noise_and_light_pollution" ).progressbar({ value: 42.09 }); $( "#first_water_pollution" ).progressbar({ value: 32.50 }); $( "#second_water_pollution" ).progressbar({ value: 30.75 }); $( "#first_uncomfotable_spend_time" ).progressbar({ value: 20.22 }); $( "#second_uncomfotable_spend_time" ).progressbar({ value: 19.68 }); $( "#first_dissatisfaction_green_and_parks" ).progressbar({ value: 16.86 }); $( "#second_dissatisfaction_green_and_parks" ).progressbar({ value: 16.74 }); }); Purity and Cleanliness Perth vs Melbourne PerthMelbourne Improve Data Improve Data Air quality Very High 80.80 High 77.05 Drinking Water Quality and Accessibility Very High 85.04 Very High 86.58 Garbage Disposal Satisfaction High 77.63 High 75.82 Clean and Tidy High 74.44 High 68.35 Quiet and No Problem with Night Lights High 64.20 Moderate 57.91 Water Quality High 67.50 High 69.25 Comfortable to Spend Time in the City High 79.78 Very High 80.32 Quality of Green and Parks Very High 83.14 Very High 83.26 Contributors:141227Last Update:August 2018August 2018 $(function() { $( "#first_air_quality" ).progressbar({ value: 80.80 }); $( "#second_air_quality" ).progressbar({ value: 77.05 }); $( "#first_drinking_water_quality_accessibility" ).progressbar({ value: 85.04 }); $( "#second_drinking_water_quality_accessibility" ).progressbar({ value: 86.58 }); $( "#first_garbage_disposal_satisfaction" ).progressbar({ value: 77.63 }); $( "#second_garbage_disposal_satisfaction" ).progressbar({ value: 75.82 }); $( "#first_clean_and_tidy" ).progressbar({ value: 74.44 }); $( "#second_clean_and_tidy" ).progressbar({ value: 68.35 }); $( "#first_noise_and_light_purity" ).progressbar({ value: 64.20 }); $( "#second_noise_and_light_purity" ).progressbar({ value: 57.91 }); $( "#first_water_quality" ).progressbar({ value: 67.50 }); $( "#second_water_quality" ).progressbar({ value: 69.25 }); $( "#first_comfortable_to_spend_time" ).progressbar({ value: 79.78 }); $( "#second_comfortable_to_spend_time" ).progressbar({ value: 80.32 }); $( "#first_green_and_parks_quality" ).progressbar({ value: 83.14 }); $( "#second_green_and_parks_quality" ).progressbar({ value: 83.26 }); }); (adsbygoogle = window.adsbygoogle || []).push({}); About In the News Newsletter API Copyright © 2009-2018 Numbeo. Your use of this service is subject to our Terms of Use and Privacy Policy !function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0],p=/^http:/.test(d.location)?'http':'https';if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=p+'://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js,fjs);}}(document, 'script', 'twitter-wjs'); (function(d, s, id) { var js, fjs = d.getElementsByTagName(s)[0]; if (d.getElementById(id)) return; js = d.createElement(s); js.id = id; js.src = "//connect.facebook.net/en_US/sdk.js#xfbml=1&version=v2.5&appId=182369865155656"; fjs.parentNode.insertBefore(js, fjs); }(document, 'script', 'facebook-jssdk')); var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www."); document.write(unescape("%3Cscript src='" + gaJsHost + "google-analytics.com/ga.js' type='text/javascript'%3E%3C/script%3E")); try { var pageTracker = _gat._getTracker("UA-10969791-1"); pageTracker._trackPageview(); } catch(err) {}

      32.5

    2. Water Pollution Low 32.50 Low 30.75

      30.75

    1. Water Pollution Low 27.20 Low 33.87

      33.87

    2. AdelaideSydney Improve Data Improve Data Air Pollution Very Low 12.25 Low 25.67 Drinking Water Pollution and Inaccessibility Low 20.05 Very Low 13.79 Dissatisfaction with Garbage Disposal Low 22.28 Low 24.65 Dirty and Untidy Low 25.26 Low 30.65 Noise and Light Pollution Low 26.72 Moderate 43.53 Water Pollution Low 27.20 Low 33.87 Dissatisfaction to Spend Time in the City Very Low 14.39 Low 21.40 Dissatisfaction with Green and Parks in the City Very Low 13.83 Low 23.78 Contributors:102153Last Update:July 2018August 2018 $(function() { $( "#first_air_pollution" ).progressbar({ value: 12.25 }); $( "#second_air_pollution" ).progressbar({ value: 25.67 }); $( "#first_drinking_water_pollution_and_inaccessibility" ).progressbar({ value: 20.05 }); $( "#second_drinking_water_pollution_and_inaccessibility" ).progressbar({ value: 13.79 }); $( "#first_dissatisfaction_garbage" ).progressbar({ value: 22.28 }); $( "#second_dissatisfaction_garbage" ).progressbar({ value: 24.65 }); $( "#first_dissatisfaction_clean_and_tidy" ).progressbar({ value: 25.26 }); $( "#second_dissatisfaction_clean_and_tidy" ).progressbar({ value: 30.65 }); $( "#first_noise_and_light_pollution" ).progressbar({ value: 26.72 }); $( "#second_noise_and_light_pollution" ).progressbar({ value: 43.53 }); $( "#first_water_pollution" ).progressbar({ value: 27.20 }); $( "#second_water_pollution" ).progressbar({ value: 33.87 }); $( "#first_uncomfotable_spend_time" ).progressbar({ value: 14.39 }); $( "#second_uncomfotable_spend_time" ).progressbar({ value: 21.40 }); $( "#first_dissatisfaction_green_and_parks" ).progressbar({ value: 13.83 }); $( "#second_dissatisfaction_green_and_parks" ).progressbar({ value: 23.78 }); }); Purity and Cleanliness Adelaide vs Sydney AdelaideSydney Improve Data Improve Data Air quality Very High 87.75 High 74.33 Drinking Water Quality and Accessibility High 79.95 Very High 86.21 Garbage Disposal Satisfaction High 77.72 High 75.35 Clean and Tidy High 74.74 High 69.35 Quiet and No Problem with Night Lights High 73.28 Moderate 56.47 Water Quality High 72.80 High 66.13 Comfortable to Spend Time in the City Very High 85.61 High 78.60 Quality of Green and Parks Very High 86.17 High 76.22 Contributors:102153Last Update:July 2018August 2018 $(function() { $( "#first_air_quality" ).progressbar({ value: 87.75 }); $( "#second_air_quality" ).progressbar({ value: 74.33 }); $( "#first_drinking_water_quality_accessibility" ).progressbar({ value: 79.95 }); $( "#second_drinking_water_quality_accessibility" ).progressbar({ value: 86.21 }); $( "#first_garbage_disposal_satisfaction" ).progressbar({ value: 77.72 }); $( "#second_garbage_disposal_satisfaction" ).progressbar({ value: 75.35 }); $( "#first_clean_and_tidy" ).progressbar({ value: 74.74 }); $( "#second_clean_and_tidy" ).progressbar({ value: 69.35 }); $( "#first_noise_and_light_purity" ).progressbar({ value: 73.28 }); $( "#second_noise_and_light_purity" ).progressbar({ value: 56.47 }); $( "#first_water_quality" ).progressbar({ value: 72.80 }); $( "#second_water_quality" ).progressbar({ value: 66.13 }); $( "#first_comfortable_to_spend_time" ).progressbar({ value: 85.61 }); $( "#second_comfortable_to_spend_time" ).progressbar({ value: 78.60 }); $( "#first_green_and_parks_quality" ).progressbar({ value: 86.17 }); $( "#second_green_and_parks_quality" ).progressbar({ value: 76.22 }); }); (adsbygoogle = window.adsbygoogle || []).push({}); About In the News Newsletter API Copyright © 2009-2018 Numbeo. Your use of this service is subject to our Terms of Use and Privacy Policy !function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0],p=/^http:/.test(d.location)?'http':'https';if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=p+'://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js,fjs);}}(document, 'script', 'twitter-wjs'); (function(d, s, id) { var js, fjs = d.getElementsByTagName(s)[0]; if (d.getElementById(id)) return; js = d.createElement(s); js.id = id; js.src = "//connect.facebook.net/en_US/sdk.js#xfbml=1&version=v2.5&appId=182369865155656"; fjs.parentNode.insertBefore(js, fjs); }(document, 'script', 'facebook-jssdk')); var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www."); document.write(unescape("%3Cscript src='" + gaJsHost + "google-analytics.com/ga.js' type='text/javascript'%3E%3C/script%3E")); try { var pageTracker = _gat._getTracker("UA-10969791-1"); pageTracker._trackPageview(); } catch(err) {}

      27.20

    3. Garbage Disposal Satisfaction High 77.72 High 75.35 Clean and Tidy High 74.74 High 69.35 Quiet and No Problem with Night Lights High 73.28 Moderate 56.47 Water Quality High 72.80 High 66.13 Comfortable to Spend Time in the City Very High 85.61 High 78.60 Quality of Green and Parks Very High 86.17 High 76.22 Contributors:102153Last Update:July 2018August 2018 $(function() { $( "#first_air_quality" ).progressbar({ value: 87.75 }); $( "#second_air_quality" ).progressbar({ value: 74.33 }); $( "#first_drinking_water_quality_accessibility" ).progressbar({ value: 79.95 }); $( "#second_drinking_water_quality_accessibility" ).progressbar({ value: 86.21 }); $( "#first_garbage_disposal_satisfaction" ).progressbar({ value: 77.72 }); $( "#second_garbage_disposal_satisfaction" ).progressbar({ value: 75.35 }); $( "#first_clean_and_tidy" ).progressbar({ value: 74.74 }); $( "#second_clean_and_tidy" ).progressbar({ value: 69.35 }); $( "#first_noise_and_light_purity" ).progressbar({ value: 73.28 }); $( "#second_noise_and_light_purity" ).progressbar({ value: 56.47 }); $( "#first_water_quality" ).progressbar({ value: 72.80 }); $( "#second_water_quality" ).progressbar({ value: 66.13 }); $( "#first_comfortable_to_spend_time" ).progressbar({ value: 85.61 }); $( "#second_comfortable_to_spend_time" ).progressbar({ value: 78.60 }); $( "#first_green_and_parks_quality" ).progressbar({ value: 86.17 }); $( "#second_green_and_parks_quality" ).progressbar({ value: 76.22 }); }); (adsbygoogle = window.adsbygoogle || []).push({}); About In the News Newsletter API Copyright © 2009-2018 Numbeo. Your use of this service is subject to our Terms of Use and Privacy Policy !function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0],p=/^http:/.test(d.location)?'http':'https';if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=p+'://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js,fjs);}}(document, 'script', 'twitter-wjs'); (function(d, s, id) { var js, fjs = d.getElementsByTagName(s)[0]; if (d.getElementById(id)) return; js = d.createElement(s); js.id = id; js.src = "//connect.facebook.net/en_US/sdk.js#xfbml=1&version=v2.5&appId=182369865155656"; fjs.parentNode.insertBefore(js, fjs); }(document, 'script', 'facebook-jssdk')); var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www."); document.write(unescape("%3Cscript src='" + gaJsHost + "google-analytics.com/ga.js' type='text/javascript'%3E%3C/script%3E")); try { var pageTracker = _gat._getTracker("UA-10969791-1"); pageTracker._trackPageview(); } catch(err) {}

      77.72

    1. 20,679,490Internet Users in Australia (2016*)Share of Australia Population: 85.1 % (penetration)Total Population : 24,309,330 Share of World Internet Users: 0.6 %Internet Users in the World: 3,424,971,237

      89

    2. 0,679,490Internet Users in Australia (2016*)Share of Australia Population: 85.1 % (penetration)Total Population : 24,309,330 Share of World Internet Users: 0.6 %Internet Users in the World: 3,424,971,237

      84

    3. 20,679,490Internet Users in Australia (2016*)Share of Australia Population: 85.1 % (penetration)Total Population : 24,309,330 Share of World Internet Users: 0.6 %Internet Users in the World: 3,424,971,237

      84

    4. 20,679,490Internet Users in Australia (2016*)Share of Australia Population: 85.1 % (penetration)Total Population : 24,309,330 Share of World Internet Users: 0.6 %Internet Users in the World: 3,424,971,237

      86

    1. Share of Italy Population: 65.6 % (penetration)

      66

    2. 39,211,518Internet Users in Italy (2016*)Share of Italy Population: 65.6 % (penetration)Total Population : 59,801,004

      60

    3. 39,211,518Internet Users in Italy (2016*)Share of Italy Population: 65.6 % (penetration)Total Population : 59,801,004 Share of World Internet Users: 1.1 %

      61

    4. 39,211,518Internet Users in Italy (2016*)Share of Italy Population: 65.6 % (penetration)Total Population : 59,801,004 Share of World Internet Users: 1.1 %

      70

    1. 32,120,519Internet Users in Canada (2016*)Share of Canada Population: 88.5 % (penetration)Total Population : 36,286,378 Share of World Internet Users: 0.9 %Internet Users in the World: 3,424,971,237

      87

    2. 32,120,519Internet Users in Canada (2016*)Share of Canada Population: 88.5 % (penetration)Total Population : 36,286,378 Share of World Internet Users: 0.9 %Internet Users in the World: 3,424,971,237

      90

    3. 32,120,519Internet Users in Canada (2016*)Share of Canada Population: 88.5 % (penetration)Total Population : 36,286,378 Share of World Internet Users: 0.9 %Internet Users in the World: 3,424,971,237

      85

    4. 32,120,519Internet Users in Canada (2016*)Share of Canada Population: 88.5 % (penetration)Total Population : 36,286,378 Share of World Internet Users: 0.9 %Internet Users in the World: 3,424,971,237

      92

    1. Quality of Green and Parks Very High 83.26 High 76.22

      76.22

    2. Quality of Green and Parks Very High 83.26 High 76.22 Contributors:227153Last Update:August 2018August 2018 $(function() { $( "#first_air_quality" ).progressbar({ value: 77.05 }); $( "#second_air_quality" ).progressbar({ value: 74.33 }); $( "#first_drinking_water_quality_accessibility" ).progressbar({ value: 86.58 }); $( "#second_drinking_water_quality_accessibility" ).progressbar({ value: 86.21 }); $( "#first_garbage_disposal_satisfaction" ).progressbar({ value: 75.82 }); $( "#second_garbage_disposal_satisfaction" ).progressbar({ value: 75.35 }); $( "#first_clean_and_tidy" ).progressbar({ value: 68.35 }); $( "#second_clean_and_tidy" ).progressbar({ value: 69.35 }); $( "#first_noise_and_light_purity" ).progressbar({ value: 57.91 }); $( "#second_noise_and_light_purity" ).progressbar({ value: 56.47 }); $( "#first_water_quality" ).progressbar({ value: 69.25 }); $( "#second_water_quality" ).progressbar({ value: 66.13 }); $( "#first_comfortable_to_spend_time" ).progressbar({ value: 80.32 }); $( "#second_comfortable_to_spend_time" ).progressbar({ value: 78.60 }); $( "#first_green_and_parks_quality" ).progressbar({ value: 83.26 }); $( "#second_green_and_parks_quality" ).progressbar({ value: 76.22 }); }); (adsbygoogle = window.adsbygoogle || []).push({}); About In the News Newsletter API Copyright © 2009-2018 Numbeo. Your use of this service is subject to our Terms of Use and Privacy Policy !function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0],p=/^http:/.test(d.location)?'http':'https';if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=p+'://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js,fjs);}}(document, 'script', 'twitter-wjs'); (function(d, s, id) { var js, fjs = d.getElementsByTagName(s)[0]; if (d.getElementById(id)) return; js = d.createElement(s); js.id = id; js.src = "//connect.facebook.net/en_US/sdk.js#xfbml=1&version=v2.5&appId=182369865155656"; fjs.parentNode.insertBefore(js, fjs); }(document, 'script', 'facebook-jssdk')); var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www."); document.write(unescape("%3Cscript src='" + gaJsHost + "google-analytics.com/ga.js' type='text/javascript'%3E%3C/script%3E")); try { var pageTracker = _gat._getTracker("UA-10969791-1"); pageTracker._trackPageview(); } catch(err) {}

      83.26

    1. Air quality80.80 Very High Drinking Water Quality and Accessibility85.04 Very High Garbage Disposal Satisfaction77.63 High Clean and Tidy74.44 High Quiet and No Problem

      77.63

    1. Garbage Disposal Satisfaction75.82 High Clean and Tidy68.35 High Quiet and No Problem with Night Lights57.91 Moderate Water Quality69.25 High Comfortable to Spend Time in the City80.32 Very High Quality of Green and Parks83.26 Very High

      75.82

    1. Air quality77.86 High Drinking Water Quality and Accessibility87.22 Very High Garbage Disposal Satisfaction82.85

      82.85

    1. (adsbygoogle = window.adsbygoogle || []).push({}); Crime > Australia > Sydney Crime in Sydney, Australia Compare Sydney with: jQuery(document).ready(function($) { jQuery('#city_selector_city_id1').autocomplete({ source: 'https://www.numbeo.com/common/CitySearchJson', minLength: 1, delay: 3, autoFocus: true, select: function(event, ui) { event.preventDefault(); $('#city_selector_city_id1').val(ui.item.label); $('#city_id1').val(ui.item.value); jQuery('#dispatch_form').submit(); }, focus: function(event, ui) { event.preventDefault(); } }) .keydown(function(e){ if (e.keyCode === 13){ if ($('#city_id1').val() == '') { e.preventDefault(); } var val = $('#city_selector_city_id1').val(); $('#name_city_id1').val(val); } }); // end autocompleter definition }); // end document ready Do you live in Sydney? Add data for Sydney Index Crime Index: 37.89 Safety Index: 62.11

      62.11

    1. Google also says location records stored in My Activity are used to target ads. Ad buyers can target ads to specific locations — say, a mile radius around a particular landmark — and typically have to pay more to reach this narrower audience. While disabling “Web & App Activity” will stop Google from storing location markers, it also prevents Google from storing information generated by searches and other activity. That can limit the effectiveness of the Google Assistant, the company’s digital concierge. Sean O’Brien, a Yale Privacy Lab researcher with whom the AP shared its findings, said it is “disingenuous” for Google to continuously record these locations even when users disable Location History. “To me, it’s something people should know,” he said.
    2. Google says that will prevent the company from remembering where you’ve been. Google’s support page on the subject states: “You can turn off Location History at any time. With Location History off, the places you go are no longer stored.” That isn’t true. Even with Location History paused, some Google apps automatically store time-stamped location data without asking. (It’s possible, although laborious, to delete it .)
    1. hus it becomes possible to see how ques-tions around data use need to shift from asking what is in the data, to include discussions of how the data is structured, and how this structure codifies value systems and social practices, subject positions and forms of visibility and invisi-bility (and thus forms of surveillance), along with the very ideas of crisis, risk governance and preparedness. Practices around big data produce and perpetuate specific forms of social engagement as well as understandings of the areas affected and the people being served.

      How data structure influences value systems and social practices is a much-needed topic of inquiry.

    2. Big data is not just about knowing more. It could be – and should be – about knowing better or about changing what knowing means. It is an ethico- episteme-ontological- political matter. The ‘needle in the haystack’ metaphor conceals the fact that there is no such thing as one reality that can be revealed. But multiple, lived are made through mediations and human and technological assemblages. Refugees’ realities of intersecting intelligences are shaped by the ethico- episteme-ontological politics of big data.

      Big, sweeping statement that helps frame how big data could be better conceptualized as a complex, socially contextualized, temporal artifact.

    3. Burns (2015) builds on this to investigate how within digital humanitarianism discourses, big data produce and perform subjects ‘in need’ (individuals or com-munities affected by crises) and a humanitarian ‘saviour’ community that, in turn, seeks answers through big data

      I don't understand what Burns is arguing here. Who is he referring to claims that DHN is a "savior" or "the solution" to crisis response?

      "Big data should therefore be be conceptualized as a framing of what can be known about a humanitarian crisis, and how one is able to grasp that knowledge; in short, it is an epistemology. This epistemology privileges knowledges and knowledge- based practices originating in remote geographies and de- emphasizes the connections between multiple knowledges.... Put another way, this configuration obscures the funding, resource, and skills constraints causing imperfect humanitarian response, instead positing volunteered labor as ‘the solution.’ This subjectivity formation carves a space in which digital humanitarians are necessary for effective humanitarian activities." (Burns 2015: 9–10)

    4. Crises are often not a crisis of information. It is often not a lack of data or capacity to analyse it that prevents ‘us’ from pre-venting disasters or responding effectively. Risk management fails because there is a lack of a relational sense of responsibility. But this does not have to be the case. Technologies that are designed to support collaboration, such as what Jasanoff (2007) terms ‘technologies of humility’, can be better explored to find ways of framing data and correlations that elicit a greater sense of relational responsibility and commitment.

      Is it "a lack of relational sense of responsibility" in crisis response (state vs private sector vs public) or is it the wicked problem of power, class, social hierarchies, etc.?

      "... ways of framing data and correlations that elicit a greater sense of responsibility and commitment."

      That could have a temporal component to it to position urgency, timescape, horizon, etc.

    5. In some ways this constitutes the production of ‘liquid resilience’ – a deflection of risk to the individuals and communities affected which moves us from the idea of an all-powerful and knowing state to that of a ‘plethora of partial projects and initiatives that are seeking to harness ICTs in the service of better knowing and governing individuals and populations’ (Ruppert 2012: 118)

      This critique addresses surveillance state concerns about glue-ing datasets together to form a broader understanding of aggregate social behavior without the necessary constraints/warnings about social contexts and discontinuity between data.

      Skimmed the Ruppert paper, sadly doesn't engage with time and topologies.

    6. Indeed, as Chandler (2015: 9) also argues, crowdsourcing of big data does not equate to a democratisation of risk assessment or risk governance:

      Beyond this quote, Chandler (in engaging crisis/disaster scenarios) argues that Big Data may be more appropriately framed as community reflexive knowledge than causal knowledge. That's an interesting idea.

      *"Thus, It would be more useful to see Big Data as reflexive knowledge rather than as causal knowledge. Big Data cannot help explain global warming but it can enable individuals and household to measure their own energy consumption through the datafication of household objects and complex production and supply chains. Big Data thereby datafies or materialises an individual or community’s being in the world. This reflexive approach works to construct a pluralised and multiple world of self-organising and adaptive processes. The imaginary of Big Data is that the producers and consumers of knowledge and of governance would be indistinguishable; where both knowing and governing exist without external mediation, constituting a perfect harmonious and self-adapting system: often called ‘community resilience’. In this discourse, increasingly articulated by governments and policy-makers, knowledge of causal connections is no longer relevant as communities adapt to the real-time appearances of the world, without necessarily understanding them."

      "Rather than engaging in external understandings of causality in the world, Big Data works on changing social behaviour by enabling greater adaptive reflexivity. If, through Big Data, we could detect and manage our own biorhythms and know the effects of poor eating or a lack of exercise, we could monitor our own health and not need costly medical interventions. Equally, if vulnerable and marginal communities could ‘datafy’ their own modes of being and relationships to their environments they would be able to augment their coping capacities and resilience without disasters or crises occurring. In essence, the imaginary of Big Data resolves the essential problem of modernity and modernist epistemologies, the problem of unintended consequences or side-effects caused by unknown causation, through work on the datafication of the self in its relational-embeddedness.42 This is why disasters in current forms of resilience thinking are understood to be ‘transformative’: revealing the unintended consequences of social planning which prevented proper awareness and responsiveness. Disasters themselves become a form of ‘datafication’, revealing the existence of poor modes of self-governance."*

      Downloaded Chandler paper. Cites Meier quite a bit.

    7. However, with these big data collections, the focus becomes not the individu-al’s behaviour but social and economic insecurities, vulnerabilities and resilience in relation to the movement of such people. The shift acknowledges that what is surveilled is more complex than an individual person’s movements, communica-tions and actions over time.

      The shift from INGO emergency response/logistics to state-sponsored, individualized resilience via the private sector seems profound here.

      There's also a subtle temporal element here of surveilling need and collecting data over time.

      Again, raises serious questions about the use of predictive analytics, data quality/classification, and PII ethics.

    8. Andrejevic and Gates (2014: 190) suggest that ‘the target becomes the hidden patterns in the data, rather than particular individuals or events’. National and local authorities are not seeking to monitor individuals and discipline their behaviour but to see how many people will reach the country and when, so that they can accommodate them, secure borders, and identify long- term social out-looks such as education, civil services, and impacts upon the host community (Pham et al. 2015).

      This seems like a terribly naive conclusion about mass data collection by the state.

      Also:

      "Yet even if capacities to analyse the haystack for needles more adequately were available, there would be questions about the quality of the haystack, and the meaning of analysis. For ‘Big Data is not self-explanatory’ (Bollier 2010: 13, in boyd and Crawford 2012). Neither is big data necessarily good data in terms of quality or relevance (Lesk 2013: 87) or complete data (boyd and Crawford 2012)."

    9. as boyd and Crawford argue, ‘without taking into account the sample of a data set, the size of the data set is meaningless’ (2012: 669). Furthermore, many tech-niques used by the state and corporations in big data analysis are based on probabilistic prediction which, some experts argue, is alien to, and even incom-prehensible for, human reasoning (Heaven 2013). As Mayer-Schönberger stresses, we should be ‘less worried about privacy and more worried about the abuse of probabilistic prediction’ as these processes confront us with ‘profound ethical dilemmas’ (in Heaven 2013: 35).

      Primary problems to resolve regarding the use of "big data" in humanitarian contexts: dataset size/sample, predictive analytics are contrary to human behavior, and ethical abuses of PII.