You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 public_review_platform
【Schema】
# Table: Days
[
(day_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(day_of_week:TEXT, Examples: [Sunday, Monday, Tuesday])
]
# Table: Users
[
(user_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(user_yelping_since_year:INTEGER, Examples: [2012, 2010, 2009]),
(user_average_stars:TEXT, Examples: [4.0, 2.5, 4.5]),
(user_votes_funny:TEXT, Examples: [Low, None, Uber]),
(user_votes_useful:TEXT, Examples: [Low, Medium, Uber]),
(user_votes_cool:TEXT, Examples: [Low, Uber, None]),
(user_review_count:TEXT, Examples: [Medium, High, Low]),
(user_fans:TEXT, Examples: [Low, None, Medium])
]
# Table: Elite
[
(user_id:INTEGER, Primary Key, Examples: [3, 19, 33]),
(year_id:INTEGER, Primary Key, Examples: [2010, 2011, 2012])
]
# Table: Business_Attributes
[
(attribute_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(business_id:INTEGER, Primary Key, Examples: [2, 3, 13]),
(attribute_value:TEXT, Examples: [none, full_bar, beer_and_wine])
]
# Table: Compliments
[
(compliment_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(compliment_type:TEXT, Examples: [photos, cool, hot])
]
# Table: Business
[
(business_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(active:TEXT, Examples: [true, false]),
(city:TEXT, Examples: [Phoenix, Scottsdale, Glendale]),
(state:TEXT, Examples: [AZ, SC, CA]),
(stars:REAL, Examples: [3.0, 4.5, 4.0]),
(review_count:TEXT, Examples: [Low, Medium, High])
]
# Table: Business_Categories
[
(business_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(category_id:INTEGER, Primary Key, Examples: [8, 143, 18])
]
# Table: Reviews
[
(business_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(user_id:INTEGER, Primary Key, Examples: [36129, 40299, 59125]),
(review_stars:INTEGER, Examples: [2, 1, 5]),
(review_votes_funny:TEXT, Examples: [None, Low, Medium]),
(review_votes_useful:TEXT, Examples: [None, Low, Medium]),
(review_votes_cool:TEXT, Examples: [None, Low, Medium]),
(review_length:TEXT, Examples: [Medium, Short, Long])
]
# Table: Business_Hours
[
(business_id:INTEGER, Primary Key, Examples: [2, 3, 4]),
(day_id:INTEGER, Primary Key, Examples: [2, 3, 4]),
(opening_time:TEXT, Examples: [11AM, 12PM, 10AM]),
(closing_time:TEXT, Examples: [8PM, 9PM, 6PM])
]
# Table: Years
[
(year_id:INTEGER, Primary Key, Examples: [2005, 2006, 2007]),
(actual_year:INTEGER, Examples: [2005, 2006, 2007])
]
# Table: Attributes
[
(attribute_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(attribute_name:TEXT, Examples: [Alcohol, Waiter Service, Delivery])
]
# Table: Users_Compliments
[
(compliment_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(user_id:INTEGER, Primary Key, Examples: [3, 19, 45]),
(number_of_compliments:TEXT, Examples: [Medium, Low, High])
]
# Table: Checkins
[
(business_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(day_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(label_time_0:TEXT, Examples: [None, Low, Medium]),
(label_time_1:TEXT, Examples: [None, Low, Medium]),
(label_time_2:TEXT, Examples: [None, Low, Medium]),
(label_time_3:TEXT, Examples: [None, Low, Medium]),
(label_time_4:TEXT, Examples: [None, Low, Medium]),
(label_time_5:TEXT, Examples: [None, Low, Medium]),
(label_time_6:TEXT, Examples: [None, Low, Medium]),
(label_time_7:TEXT, Examples: [None, Low, Medium]),
(label_time_8:TEXT, Examples: [None, Low, Medium]),
(label_time_9:TEXT, Examples: [Low, None, Medium]),
(label_time_10:TEXT, Examples: [None, Low, Medium]),
(label_time_11:TEXT, Examples: [Low, None, Medium]),
(label_time_12:TEXT, Examples: [None, Low, Medium]),
(label_time_13:TEXT, Examples: [None, Low, Medium]),
(label_time_14:TEXT, Examples: [None, Low, Medium]),
(label_time_15:TEXT, Examples: [None, Low, Medium]),
(label_time_16:TEXT, Examples: [None, Low, Medium]),
(label_time_17:TEXT, Examples: [Low, None, Medium]),
(label_time_18:TEXT, Examples: [None, Low, Medium]),
(label_time_19:TEXT, Examples: [None, Low, Medium]),
(label_time_20:TEXT, Examples: [None, Low, Medium]),
(label_time_21:TEXT, Examples: [None, Low, Medium]),
(label_time_22:TEXT, Examples: [None, Low, Medium]),
(label_time_23:TEXT, Examples: [None, Low, Medium])
]
# Table: Tips
[
(business_id:INTEGER, Primary Key, Examples: [2, 3, 6]),
(user_id:INTEGER, Primary Key, Examples: [12490, 16328, 19191]),
(likes:INTEGER, Examples: [0, 1, 2]),
(tip_length:TEXT, Examples: [Medium, Short, Long])
]
# Table: Categories
[
(category_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(category_name:TEXT, Examples: [Active Life, Arts & Entertainment, Stadiums & Arenas])
]
【Foreign keys】
Elite.year_id=Years.year_id
Elite.user_id=Users.user_id
Business_Attributes.business_id=Business.business_id
Business_Attributes.attribute_id=Attributes.attribute_id
Business_Categories.category_id=Categories.category_id
Business_Categories.business_id=Business.business_id
Reviews.user_id=Users.user_id
Reviews.business_id=Business.business_id
Business_Hours.day_id=Days.day_id
Business_Hours.business_id=Business.business_id
Users_Compliments.user_id=Users.user_id
Users_Compliments.compliment_id=Compliments.compliment_id
Checkins.day_id=Days.day_id
Checkins.business_id=Business.business_id
Tips.user_id=Users.user_id
Tips.business_id=Business.business_id

    【Question】
    How many cities have businesses with active life category? Find the percentage of the city where the review count that is low in total review count. category refers to category_name; percentage = MULTIPLY(DIVIDE(SUM(category_name = 'Active Life'), SUM(review_count = 'LOW')), 1.0);

    【Evidence】
    category refers to category_name; percentage = MULTIPLY(DIVIDE(SUM(category_name = 'Active Life'), SUM(review_count = 'LOW')), 1.0);

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT SUM(CASE WHEN T2.category_name LIKE 'Active Life' THEN 1 ELSE 0 END) AS "num" , CAST(SUM(CASE WHEN T3.city LIKE 'Phoenix' THEN 1 ELSE 0 END) AS REAL) * 100 / ( SELECT COUNT(T3.review_count) FROM Business_Categories AS T1 INNER JOIN Categories AS T2 ON T1.category_id = T2.category_id INNER JOIN Business AS T3 ON T1.business_id = T3.business_id WHERE T3.review_count LIKE 'Low' ) FROM Business_Categories AS T1 INNER JOIN Categories AS T2 ON T1.category_id = T2.category_id INNER JOIN Business AS T3 ON T1.business_id = T3.business_id