Speed performance of sqlite3 queries looped in python

I have a table data stored in a database ships.db,
the data are informations of tracked ships hourly.
The table data looks like this.

time  |  shipId  |   longitude  |  latitude
00:00:00          1              xx.xxxx       yy.yyyy
00:00:00          2              xx.xxxx       yy.yyyy
00:00:00          3              xx.xxxx       yy.yyyy
00:00:00          4              xx.xxxx       yy.yyyy
01:00:00          2              xx.xxxx       yy.yyyy
01:00:00          4              xx.xxxx       yy.yyyy
     ...                 ...                 ...                ...
23:00:00          4              xx.xxxx       yy.yyyy

Splitting the whole earth to a grid of 5-degree width and length for each cell,
I would get the number of fetched records hourly per cell of that grid.

Note that the number of records are not the same each hour because some ships are not more live therefore not fetched.

I wrote this code in python, it works but it takes large time because the database has roughly 250000 records.
Is there another method or approach to make it better and faster in python?

My script:

import sqlite3

def writeToFile(string, file):
    with open(file,"a") as ouf:

output = "report.txt"

with sqlite3.connect("ships.db") as con:
    cur = con.cursor()
    #iterate over times from 0 to 23 (hours)
    for hour in range(0,24):  # hours: from 0 to 23
        #make each loop of time in this time format "hh:00:00"
        time = str(hour).zfill(2)+":00:00"
        #scan from longitude -180 (180 W) to +180 (180 E) each 5 degree of longitude
        for longitude in range(-180,180,5):
                #scan from latitude -90 (90 S) to +90 (90 N) each 5 degree of latitude
                for latitude in range(-90,90,5):
                    sql = f'''SELECT time, count(*) AS occurence FROM 'data'
                    WHERE time ="{time}"
                    AND latitude BETWEEN {latitude} AND {latitude+5}
                    AND longitude BETWEEN {longitude} AND {longitude+5}
                    GROUP BY time'''
                    data = cur.execute(sql).fetchone() #fetchone because group by time
                    if data != None:
                        time, occurence = data
                    else: #some cell of grid may have no ship at a hour therefore this else
                        occurence = None
                    result = (time, occurence, longitude, latitude)
                    #writing the result to output
                    writeToFile("t".join(result), output)