<data>
<row _id="1"><city>Milano</city><regione>Lombardia</regione><Milano Scoreboard edition>2019</Milano Scoreboard edition><X1 = reputation of the city on Google>83,6</X1 = reputation of the city on Google><X2 = reputation on Google for business, industrial and fin_4>100</X2 = reputation on Google for business, industrial and fin_4><X3 = reputation on Google for shopping>100</X3 = reputation on Google for shopping><x1_anno>2018</x1_anno><x2_anno>2018</x2_anno><x3_anno>2018</x3_anno><x1_città/regione>Milano</x1_città/regione><x2_città/regione>Milano</x2_città/regione><x3_città/regione>Milano</x3_città/regione><x1_geo>city</x1_geo><x2_geo>city</x2_geo><x3_geo>city</x3_geo><x1_fonte>PTS Clas on Google Trends data</x1_fonte><x2_fonte>PTS Clas on Google Trends data</x2_fonte><x3_fonte>PTS Clas on Google Trends data</x3_fonte><x1_note>average number of queries on Google Trends about the city. Data are benchmarked to the city with the highest average</x1_note><x2_note>average number of queries on Google Trends under the categories "Business &amp; Industrial" and "Finance" with reference to the city. Data are benchmarked to the city with the highest average</x2_note><x3_note>average number of queries on Google Trends under the category "Shopping" with reference to the city. Data are benchmarked to the city with the highest average</x3_note><X1 indice (media=1)>1,2751677852349</X1 indice (media=1)><X2 indice (media=1)>1,39082058414465</X2 indice (media=1)><X3 indice (media=1)>1,45518044237485</X3 indice (media=1)><Score di dimensione (media X123 indicizzati)>1,37372293725147</Score di dimensione (media X123 indicizzati)></row>
<row _id="2"><city>Barcelona</city><regione>Cataluña</regione><Milano Scoreboard edition>2019</Milano Scoreboard edition><X1 = reputation of the city on Google>100</X1 = reputation of the city on Google><X2 = reputation on Google for business, industrial and fin_4>82,4</X2 = reputation on Google for business, industrial and fin_4><X3 = reputation on Google for shopping>86,9</X3 = reputation on Google for shopping><x1_anno>2018</x1_anno><x2_anno>2018</x2_anno><x3_anno>2018</x3_anno><x1_città/regione>Barcelona</x1_città/regione><x2_città/regione>Barcelona</x2_città/regione><x3_città/regione>Barcelona</x3_città/regione><x1_geo>city</x1_geo><x2_geo>city</x2_geo><x3_geo>city</x3_geo><x1_fonte>PTS Clas on Google Trends data</x1_fonte><x2_fonte>PTS Clas on Google Trends data</x2_fonte><x3_fonte>PTS Clas on Google Trends data</x3_fonte><x1_note>average number of queries on Google Trends about the city. Data are benchmarked to the city with the highest average</x1_note><x2_note>average number of queries on Google Trends under the categories "Business &amp; Industrial" and "Finance" with reference to the city. Data are benchmarked to the city with the highest average</x2_note><x3_note>average number of queries on Google Trends under the category "Shopping" with reference to the city. Data are benchmarked to the city with the highest average</x3_note><X1 indice (media=1)>1,52532031726663</X1 indice (media=1)><X2 indice (media=1)>1,14603616133519</X2 indice (media=1)><X3 indice (media=1)>1,26455180442375</X3 indice (media=1)><Score di dimensione (media X123 indicizzati)>1,31196942767519</Score di dimensione (media X123 indicizzati)></row>
<row _id="3"><city>Lyon</city><regione>Rhône-Alpes</regione><Milano Scoreboard edition>2019</Milano Scoreboard edition><X1 = reputation of the city on Google>36,5</X1 = reputation of the city on Google><X2 = reputation on Google for business, industrial and fin_4>44,7</X2 = reputation on Google for business, industrial and fin_4><X3 = reputation on Google for shopping>40,5</X3 = reputation on Google for shopping><x1_anno>2018</x1_anno><x2_anno>2018</x2_anno><x3_anno>2018</x3_anno><x1_città/regione>Lyon</x1_città/regione><x2_città/regione>Lyon</x2_città/regione><x3_città/regione>Lyon</x3_città/regione><x1_geo>city</x1_geo><x2_geo>city</x2_geo><x3_geo>city</x3_geo><x1_fonte>PTS Clas on Google Trends data</x1_fonte><x2_fonte>PTS Clas on Google Trends data</x2_fonte><x3_fonte>PTS Clas on Google Trends data</x3_fonte><x1_note>average number of queries on Google Trends about the city. Data are benchmarked to the city with the highest average</x1_note><x2_note>average number of queries on Google Trends under the categories "Business &amp; Industrial" and "Finance" with reference to the city. Data are benchmarked to the city with the highest average</x2_note><x3_note>average number of queries on Google Trends under the category "Shopping" with reference to the city. Data are benchmarked to the city with the highest average</x3_note><X1 indice (media=1)>0,556741915802318</X1 indice (media=1)><X2 indice (media=1)>0,621696801112656</X2 indice (media=1)><X3 indice (media=1)>0,589348079161816</X3 indice (media=1)><Score di dimensione (media X123 indicizzati)>0,58926226535893</Score di dimensione (media X123 indicizzati)></row>
<row _id="4"><city>München</city><regione>Bayern</regione><Milano Scoreboard edition>2019</Milano Scoreboard edition><X1 = reputation of the city on Google>77,4</X1 = reputation of the city on Google><X2 = reputation on Google for business, industrial and fin_4>93,6</X2 = reputation on Google for business, industrial and fin_4><X3 = reputation on Google for shopping>86,4</X3 = reputation on Google for shopping><x1_anno>2018</x1_anno><x2_anno>2018</x2_anno><x3_anno>2018</x3_anno><x1_città/regione>München</x1_città/regione><x2_città/regione>München</x2_città/regione><x3_città/regione>München</x3_città/regione><x1_geo>city</x1_geo><x2_geo>city</x2_geo><x3_geo>city</x3_geo><x1_fonte>PTS Clas on Google Trends data</x1_fonte><x2_fonte>PTS Clas on Google Trends data</x2_fonte><x3_fonte>PTS Clas on Google Trends data</x3_fonte><x1_note>average number of queries on Google Trends about the city. Data are benchmarked to the city with the highest average</x1_note><x2_note>average number of queries on Google Trends under the categories "Business &amp; Industrial" and "Finance" with reference to the city. Data are benchmarked to the city with the highest average</x2_note><x3_note>average number of queries on Google Trends under the category "Shopping" with reference to the city. Data are benchmarked to the city with the highest average</x3_note><X1 indice (media=1)>1,18059792556437</X1 indice (media=1)><X2 indice (media=1)>1,30180806675939</X2 indice (media=1)><X3 indice (media=1)>1,25727590221187</X3 indice (media=1)><Score di dimensione (media X123 indicizzati)>1,24656063151188</Score di dimensione (media X123 indicizzati)></row>
<row _id="5"><city>Stuttgart</city><regione>Baden-Württemberg</regione><Milano Scoreboard edition>2019</Milano Scoreboard edition><X1 = reputation of the city on Google>30,3</X1 = reputation of the city on Google><X2 = reputation on Google for business, industrial and fin_4>38,8</X2 = reputation on Google for business, industrial and fin_4><X3 = reputation on Google for shopping>29,8</X3 = reputation on Google for shopping><x1_anno>2018</x1_anno><x2_anno>2018</x2_anno><x3_anno>2018</x3_anno><x1_città/regione>Stuttgart</x1_città/regione><x2_città/regione>Stuttgart</x2_città/regione><x3_città/regione>Stuttgart</x3_città/regione><x1_geo>city</x1_geo><x2_geo>city</x2_geo><x3_geo>city</x3_geo><x1_fonte>PTS Clas on Google Trends data</x1_fonte><x2_fonte>PTS Clas on Google Trends data</x2_fonte><x3_fonte>PTS Clas on Google Trends data</x3_fonte><x1_note>average number of queries on Google Trends about the city. Data are benchmarked to the city with the highest average</x1_note><x2_note>average number of queries on Google Trends under the categories "Business &amp; Industrial" and "Finance" with reference to the city. Data are benchmarked to the city with the highest average</x2_note><x3_note>average number of queries on Google Trends under the category "Shopping" with reference to the city. Data are benchmarked to the city with the highest average</x3_note><X1 indice (media=1)>0,462172056131788</X1 indice (media=1)><X2 indice (media=1)>0,539638386648122</X2 indice (media=1)><X3 indice (media=1)>0,433643771827707</X3 indice (media=1)><Score di dimensione (media X123 indicizzati)>0,478484738202539</Score di dimensione (media X123 indicizzati)></row>
<row _id="6"><city>Milano</city><regione>Lombardia</regione><Milano Scoreboard edition>2018</Milano Scoreboard edition><X1 = reputation of the city on Google>71,7</X1 = reputation of the city on Google><X2 = reputation on Google for business, industrial and fin_4>88,2</X2 = reputation on Google for business, industrial and fin_4><X3 = reputation on Google for shopping>94,4</X3 = reputation on Google for shopping><x1_anno>2017</x1_anno><x2_anno>2017</x2_anno><x3_anno>2017</x3_anno><x1_città/regione>Milano</x1_città/regione><x2_città/regione>Milano</x2_città/regione><x3_città/regione>Milano</x3_città/regione><x1_geo>city</x1_geo><x2_geo>city</x2_geo><x3_geo>city</x3_geo><x1_fonte>Gruppo Clas on Google Trends data</x1_fonte><x2_fonte>Gruppo Clas on Google Trends data</x2_fonte><x3_fonte>Gruppo Clas on Google Trends data</x3_fonte><x1_note>average number of queries on Google Trends about the city. Data are benchmarked to the city with the highest average</x1_note><x2_note>average number of queries on Google Trends under the categories "Business &amp; Industrial" and "Finance" with reference to the city. Data are benchmarked to the city with the highest average</x2_note><x3_note>average number of queries on Google Trends under the category "Shopping" with reference to the city. Data are benchmarked to the city with the highest average</x3_note><X1 indice (media=1)>1,07689996996095</X1 indice (media=1)><X2 indice (media=1)>1,21487603305785</X2 indice (media=1)><X3 indice (media=1)>1,24966904951019</X3 indice (media=1)><Score di dimensione (media X123 indicizzati)>1,18048168417633</Score di dimensione (media X123 indicizzati)></row>
<row _id="7"><city>Barcelona</city><regione>Cataluña</regione><Milano Scoreboard edition>2018</Milano Scoreboard edition><X1 = reputation of the city on Google>100</X1 = reputation of the city on Google><X2 = reputation on Google for business, industrial and fin_4>78,3</X2 = reputation on Google for business, industrial and fin_4><X3 = reputation on Google for shopping>92,7</X3 = reputation on Google for shopping><x1_anno>2017</x1_anno><x2_anno>2017</x2_anno><x3_anno>2017</x3_anno><x1_città/regione>Barcelona</x1_città/regione><x2_città/regione>Barcelona</x2_città/regione><x3_città/regione>Barcelona</x3_città/regione><x1_geo>city</x1_geo><x2_geo>city</x2_geo><x3_geo>city</x3_geo><x1_fonte>Gruppo Clas on Google Trends data</x1_fonte><x2_fonte>Gruppo Clas on Google Trends data</x2_fonte><x3_fonte>Gruppo Clas on Google Trends data</x3_fonte><x1_note>average number of queries on Google Trends about the city. Data are benchmarked to the city with the highest average</x1_note><x2_note>average number of queries on Google Trends under the categories "Business &amp; Industrial" and "Finance" with reference to the city. Data are benchmarked to the city with the highest average</x2_note><x3_note>average number of queries on Google Trends under the category "Shopping" with reference to the city. Data are benchmarked to the city with the highest average</x3_note><X1 indice (media=1)>1,50195253829979</X1 indice (media=1)><X2 indice (media=1)>1,07851239669421</X2 indice (media=1)><X3 indice (media=1)>1,22716441620334</X3 indice (media=1)><Score di dimensione (media X123 indicizzati)>1,26920978373245</Score di dimensione (media X123 indicizzati)></row>
<row _id="8"><city>Lyon</city><regione>Rhône-Alpes</regione><Milano Scoreboard edition>2018</Milano Scoreboard edition><X1 = reputation of the city on Google>42</X1 = reputation of the city on Google><X2 = reputation on Google for business, industrial and fin_4>51,7</X2 = reputation on Google for business, industrial and fin_4><X3 = reputation on Google for shopping>49,7</X3 = reputation on Google for shopping><x1_anno>2017</x1_anno><x2_anno>2017</x2_anno><x3_anno>2017</x3_anno><x1_città/regione>Lyon</x1_città/regione><x2_città/regione>Lyon</x2_città/regione><x3_città/regione>Lyon</x3_città/regione><x1_geo>city</x1_geo><x2_geo>city</x2_geo><x3_geo>city</x3_geo><x1_fonte>Gruppo Clas on Google Trends data</x1_fonte><x2_fonte>Gruppo Clas on Google Trends data</x2_fonte><x3_fonte>Gruppo Clas on Google Trends data</x3_fonte><x1_note>average number of queries on Google Trends about the city. Data are benchmarked to the city with the highest average</x1_note><x2_note>average number of queries on Google Trends under the categories "Business &amp; Industrial" and "Finance" with reference to the city. Data are benchmarked to the city with the highest average</x2_note><x3_note>average number of queries on Google Trends under the category "Shopping" with reference to the city. Data are benchmarked to the city with the highest average</x3_note><X1 indice (media=1)>0,630820066085912</X1 indice (media=1)><X2 indice (media=1)>0,712121212121212</X2 indice (media=1)><X3 indice (media=1)>0,657929573735769</X3 indice (media=1)><Score di dimensione (media X123 indicizzati)>0,666956950647631</Score di dimensione (media X123 indicizzati)></row>
<row _id="9"><city>München</city><regione>Bayern</regione><Milano Scoreboard edition>2018</Milano Scoreboard edition><X1 = reputation of the city on Google>83,1</X1 = reputation of the city on Google><X2 = reputation on Google for business, industrial and fin_4>100</X2 = reputation on Google for business, industrial and fin_4><X3 = reputation on Google for shopping>100</X3 = reputation on Google for shopping><x1_anno>2017</x1_anno><x2_anno>2017</x2_anno><x3_anno>2017</x3_anno><x1_città/regione>München</x1_città/regione><x2_città/regione>München</x2_città/regione><x3_città/regione>München</x3_città/regione><x1_geo>city</x1_geo><x2_geo>city</x2_geo><x3_geo>city</x3_geo><x1_fonte>Gruppo Clas on Google Trends data</x1_fonte><x2_fonte>Gruppo Clas on Google Trends data</x2_fonte><x3_fonte>Gruppo Clas on Google Trends data</x3_fonte><x1_note>average number of queries on Google Trends about the city. Data are benchmarked to the city with the highest average</x1_note><x2_note>average number of queries on Google Trends under the categories "Business &amp; Industrial" and "Finance" with reference to the city. Data are benchmarked to the city with the highest average</x2_note><x3_note>average number of queries on Google Trends under the category "Shopping" with reference to the city. Data are benchmarked to the city with the highest average</x3_note><X1 indice (media=1)>1,24812255932713</X1 indice (media=1)><X2 indice (media=1)>1,37741046831956</X2 indice (media=1)><X3 indice (media=1)>1,3238019592269</X3 indice (media=1)><Score di dimensione (media X123 indicizzati)>1,31644499562453</Score di dimensione (media X123 indicizzati)></row>
<row _id="10"><city>Stuttgart</city><regione>Baden-Württemberg</regione><Milano Scoreboard edition>2018</Milano Scoreboard edition><X1 = reputation of the city on Google>36,1</X1 = reputation of the city on Google><X2 = reputation on Google for business, industrial and fin_4>44,8</X2 = reputation on Google for business, industrial and fin_4><X3 = reputation on Google for shopping>40,9</X3 = reputation on Google for shopping><x1_anno>2017</x1_anno><x2_anno>2017</x2_anno><x3_anno>2017</x3_anno><x1_città/regione>Stuttgart</x1_città/regione><x2_città/regione>Stuttgart</x2_città/regione><x3_città/regione>Stuttgart</x3_città/regione><x1_geo>city</x1_geo><x2_geo>city</x2_geo><x3_geo>city</x3_geo><x1_fonte>Gruppo Clas on Google Trends data</x1_fonte><x2_fonte>Gruppo Clas on Google Trends data</x2_fonte><x3_fonte>Gruppo Clas on Google Trends data</x3_fonte><x1_note>average number of queries on Google Trends about the city. Data are benchmarked to the city with the highest average</x1_note><x2_note>average number of queries on Google Trends under the categories "Business &amp; Industrial" and "Finance" with reference to the city. Data are benchmarked to the city with the highest average</x2_note><x3_note>average number of queries on Google Trends under the category "Shopping" with reference to the city. Data are benchmarked to the city with the highest average</x3_note><X1 indice (media=1)>0,542204866326224</X1 indice (media=1)><X2 indice (media=1)>0,617079889807163</X2 indice (media=1)><X3 indice (media=1)>0,541435001323802</X3 indice (media=1)><Score di dimensione (media X123 indicizzati)>0,566906585819063</Score di dimensione (media X123 indicizzati)></row>
<row _id="11"><city>Milano</city><regione /><Milano Scoreboard edition>2017</Milano Scoreboard edition><X1 = reputation of the city on Google>85,1</X1 = reputation of the city on Google><X2 = reputation on Google for business, industrial and fin_4>100</X2 = reputation on Google for business, industrial and fin_4><X3 = reputation on Google for shopping>100</X3 = reputation on Google for shopping><x1_anno>2016</x1_anno><x2_anno>2016</x2_anno><x3_anno>2016</x3_anno><x1_città/regione>Milano</x1_città/regione><x2_città/regione>Milano</x2_città/regione><x3_città/regione>Milano</x3_città/regione><x1_geo>city</x1_geo><x2_geo>city</x2_geo><x3_geo>city</x3_geo><x1_fonte>Gruppo Clas on Google Trends data</x1_fonte><x2_fonte>Gruppo Clas on Google Trends data</x2_fonte><x3_fonte>Gruppo Clas on Google Trends data</x3_fonte><x1_note>average number of queries on Google Trends about the city. Data are benchmarked to the city with the highest average</x1_note><x2_note>average number of queries on Google Trends under the categories "Business &amp; Industrial" and "Finance" with reference to the city. Data are benchmarked to the city with the highest average</x2_note><x3_note>average number of queries on Google Trends under the category "Shopping" with reference to the city. Data are benchmarked to the city with the highest average</x3_note><X1 indice (media=1)>1,17769166897315</X1 indice (media=1)><X2 indice (media=1)>1,20365912373616</X2 indice (media=1)><X3 indice (media=1)>1,2950012950013</X3 indice (media=1)><Score di dimensione (media X123 indicizzati)>1,22545069590353</Score di dimensione (media X123 indicizzati)></row>
<row _id="12"><city>Barcelona</city><regione /><Milano Scoreboard edition>2017</Milano Scoreboard edition><X1 = reputation of the city on Google>100</X1 = reputation of the city on Google><X2 = reputation on Google for business, industrial and fin_4>99,9</X2 = reputation on Google for business, industrial and fin_4><X3 = reputation on Google for shopping>96,5</X3 = reputation on Google for shopping><x1_anno>2016</x1_anno><x2_anno>2016</x2_anno><x3_anno>2016</x3_anno><x1_città/regione>Barcelona</x1_città/regione><x2_città/regione>Barcelona</x2_città/regione><x3_città/regione>Barcelona</x3_città/regione><x1_geo>city</x1_geo><x2_geo>city</x2_geo><x3_geo>city</x3_geo><x1_fonte>Gruppo Clas on Google Trends data</x1_fonte><x2_fonte>Gruppo Clas on Google Trends data</x2_fonte><x3_fonte>Gruppo Clas on Google Trends data</x3_fonte><x1_note>average number of queries on Google Trends about the city. Data are benchmarked to the city with the highest average</x1_note><x2_note>average number of queries on Google Trends under the categories "Business &amp; Industrial" and "Finance" with reference to the city. Data are benchmarked to the city with the highest average</x2_note><x3_note>average number of queries on Google Trends under the category "Shopping" with reference to the city. Data are benchmarked to the city with the highest average</x3_note><X1 indice (media=1)>1,38389150290617</X1 indice (media=1)><X2 indice (media=1)>1,20245546461242</X2 indice (media=1)><X3 indice (media=1)>1,24967624967625</X3 indice (media=1)><Score di dimensione (media X123 indicizzati)>1,27867440573161</Score di dimensione (media X123 indicizzati)></row>
<row _id="13"><city>Lyon</city><regione /><Milano Scoreboard edition>2017</Milano Scoreboard edition><X1 = reputation of the city on Google>48,1</X1 = reputation of the city on Google><X2 = reputation on Google for business, industrial and fin_4>68,4</X2 = reputation on Google for business, industrial and fin_4><X3 = reputation on Google for shopping>52,2</X3 = reputation on Google for shopping><x1_anno>2016</x1_anno><x2_anno>2016</x2_anno><x3_anno>2016</x3_anno><x1_città/regione>Lyon</x1_città/regione><x2_città/regione>Lyon</x2_città/regione><x3_città/regione>Lyon</x3_città/regione><x1_geo>city</x1_geo><x2_geo>city</x2_geo><x3_geo>city</x3_geo><x1_fonte>Gruppo Clas on Google Trends data</x1_fonte><x2_fonte>Gruppo Clas on Google Trends data</x2_fonte><x3_fonte>Gruppo Clas on Google Trends data</x3_fonte><x1_note>average number of queries on Google Trends about the city. Data are benchmarked to the city with the highest average</x1_note><x2_note>average number of queries on Google Trends under the categories "Business &amp; Industrial" and "Finance" with reference to the city. Data are benchmarked to the city with the highest average</x2_note><x3_note>average number of queries on Google Trends under the category "Shopping" with reference to the city. Data are benchmarked to the city with the highest average</x3_note><X1 indice (media=1)>0,665651812897869</X1 indice (media=1)><X2 indice (media=1)>0,823302840635532</X2 indice (media=1)><X3 indice (media=1)>0,675990675990676</X3 indice (media=1)><Score di dimensione (media X123 indicizzati)>0,721648443174692</Score di dimensione (media X123 indicizzati)></row>
<row _id="14"><city>München</city><regione /><Milano Scoreboard edition>2017</Milano Scoreboard edition><X1 = reputation of the city on Google>88,5</X1 = reputation of the city on Google><X2 = reputation on Google for business, industrial and fin_4>97,1</X2 = reputation on Google for business, industrial and fin_4><X3 = reputation on Google for shopping>96,4</X3 = reputation on Google for shopping><x1_anno>2016</x1_anno><x2_anno>2016</x2_anno><x3_anno>2016</x3_anno><x1_città/regione>München</x1_città/regione><x2_città/regione>München</x2_città/regione><x3_città/regione>München</x3_città/regione><x1_geo>city</x1_geo><x2_geo>city</x2_geo><x3_geo>city</x3_geo><x1_fonte>Gruppo Clas on Google Trends data</x1_fonte><x2_fonte>Gruppo Clas on Google Trends data</x2_fonte><x3_fonte>Gruppo Clas on Google Trends data</x3_fonte><x1_note>average number of queries on Google Trends about the city. Data are benchmarked to the city with the highest average</x1_note><x2_note>average number of queries on Google Trends under the categories "Business &amp; Industrial" and "Finance" with reference to the city. Data are benchmarked to the city with the highest average</x2_note><x3_note>average number of queries on Google Trends under the category "Shopping" with reference to the city. Data are benchmarked to the city with the highest average</x3_note><X1 indice (media=1)>1,22474398007196</X1 indice (media=1)><X2 indice (media=1)>1,16875300914781</X2 indice (media=1)><X3 indice (media=1)>1,24838124838125</X3 indice (media=1)><Score di dimensione (media X123 indicizzati)>1,21395941253367</Score di dimensione (media X123 indicizzati)></row>
<row _id="15"><city>Stuttgart</city><regione /><Milano Scoreboard edition>2017</Milano Scoreboard edition><X1 = reputation of the city on Google>39,6</X1 = reputation of the city on Google><X2 = reputation on Google for business, industrial and fin_4>50</X2 = reputation on Google for business, industrial and fin_4><X3 = reputation on Google for shopping>41</X3 = reputation on Google for shopping><x1_anno>2016</x1_anno><x2_anno>2016</x2_anno><x3_anno>2016</x3_anno><x1_città/regione>Stuttgart</x1_città/regione><x2_città/regione>Stuttgart</x2_città/regione><x3_città/regione>Stuttgart</x3_città/regione><x1_geo>city</x1_geo><x2_geo>city</x2_geo><x3_geo>city</x3_geo><x1_fonte>Gruppo Clas on Google Trends data</x1_fonte><x2_fonte>Gruppo Clas on Google Trends data</x2_fonte><x3_fonte>Gruppo Clas on Google Trends data</x3_fonte><x1_note>average number of queries on Google Trends about the city. Data are benchmarked to the city with the highest average</x1_note><x2_note>average number of queries on Google Trends under the categories "Business &amp; Industrial" and "Finance" with reference to the city. Data are benchmarked to the city with the highest average</x2_note><x3_note>average number of queries on Google Trends under the category "Shopping" with reference to the city. Data are benchmarked to the city with the highest average</x3_note><X1 indice (media=1)>0,548021035150844</X1 indice (media=1)><X2 indice (media=1)>0,601829561868079</X2 indice (media=1)><X3 indice (media=1)>0,530950530950531</X3 indice (media=1)><Score di dimensione (media X123 indicizzati)>0,560267042656485</Score di dimensione (media X123 indicizzati)></row>
</data>
