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】
    Which city has the highest number of businesses in the food industry whose number of reviews is high? highest number of businesses refers to MAX(business_id); food industry refers to category_name = 'Food'; number of reviews is high refers to review_count = 'High';

    【Evidence】
    highest number of businesses refers to MAX(business_id); food industry refers to category_name = 'Food'; number of reviews is high refers to review_count = 'High';

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

SQL: SELECT T1.city FROM Business AS T1 INNER JOIN Business_Categories ON T1.business_id = Business_Categories.business_id INNER JOIN Categories AS T3 ON Business_Categories.category_id = T3.category_id WHERE T1.review_count LIKE 'High' AND T3.category_name LIKE 'Food' GROUP BY T1.city