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# Motivating Question¶

What can we learn about the distribution of book checkouts in time over the course of a week?

## Problems with data¶

### First Problem¶

We notice that there are a very large number of checkouts in 1970 as compared to nearby years. This is the UNIX ephoc time and reflects a date stamp of 0 in the database. We should discard these because the checkout time does not reflect reality. The most data activity is past 2005 so we'll start most queries here. But it's important to exclude 1970.

Total checkouts by Year:

SELECT Year(checkout) AS year,
Count(*)
FROM   transactions
GROUP  BY year;

Year Checkouts
1970 936295
1987 4
1988 84
1989 119
1990 172
1991 204
1992 292
1993 519
1994 738
1995 880
1996 1053
1997 1426
1998 2001
1999 2703
2000 4023
2001 4470
2002 7970
2003 15758
2004 38982
2005 543324
2006 13029176
2007 13769669
2008 18427707
2009 19813291
2010 18535749
2011 16027444
2012 12969344
2013 13939772
2014 13080441
2015 12285802
2016 11418873
2017 10200509
2018 7442800
2019 803889

### Second Problem¶

We notice that when looking at the time of day, many checkout times are at 12:00 AM. This does not match intuition because checkouts should be most common while the library is open. So we should discard these records as well.

Checkouts by Hour from 1990 to present

SELECT Hour(sample.checkout) AS hr,
Count(*)
FROM   (SELECT checkout
FROM   transactions
WHERE  checkout >= '1990-01-01') sample
GROUP  BY hr;

hour Checkouts
0 102863
1 436
2 405
3 81
4 492
5 852
6 2248
7 26475
8 384043
9 617853
10 9196210
11 14524855
12 15952591
13 22242891
14 23524377
15 24859556
16 26992550
17 23490490
18 10684293
19 9649194
20 102756
21 2201
22 3187
23 8082

Notice that the entry for 0 is relatively large. There seem to be a lot of midnight checkouts. We can examine how the number of midnight checkouts has compares to a more recent sample:

Checkouts by Hour from 2015 to present:

SELECT Hour(sample.checkout) AS hr,
Count(*)
FROM   (SELECT checkout
FROM   transactions
WHERE  checkout >= '2005-01-01') sample
GROUP  BY hr;

hour Checkouts
0 21673
1 436
2 405
3 81
4 492
5 852
6 2248
7 26475
8 384043
9 617852
10 9196210
11 14524855
12 15952591
13 22242891
14 23524377
15 24859556
16 26992550
17 23490490
18 10684293
19 9649194
20 102756
21 2201
22 3187
23 8082

### Third Problem¶

These tables are almost exactly the same!

Let's take a closer look at how many yearly checkouts are logged at midnight.

Analysis of Midnight Checkouts from 1990 to present

SELECT
YEAR(checkout) AS yr,
COUNT(checkout),
SUM(CASE WHEN HOUR(checkout) = 0 THEN 1 ELSE 0 END)
FROM transactions
WHERE checkout > '1990-01-01'
GROUP BY YEAR(checkout);


Results

Year Total Checkouts Midnight Checkouts
1990 172 172
1991 204 204
1992 292 292
1993 519 519
1994 738 738
1995 880 880
1996 1053 1053
1997 1426 1426
1998 2001 2001
1999 2703 2703
2000 4023 4023
2001 4470 4469
2002 7970 7970
2003 15758 15758
2004 38982 38982
2005 543324 19164
2006 13029176 1635
2007 13769669 754
2008 18427707 80
2009 19813291 4
2010 18535749 25
2011 16027444 6
2012 12969344 2
2013 13939772 0
2014 13080441 3
2015 12285802 0
2016 11418873 0
2017 10200509 0
2018 7442800 0
2019 803889 0

### Conclusion¶

We should only consider data from 2007 to present in analyzing any timestamp information, or alternatively exclude timestamps which are at midnight.

## Components¶

Checkouts by Weekday

SELECT Weekday(sample.checkout) AS day,
Count(*)
FROM   (SELECT checkout
FROM   transactions
WHERE  checkout >= '1990-01-01'
LIMIT 10000) sample
GROUP  BY day;


Results

Weekday Checkouts (sample) Checkouts (whole table)
0 1802 28016941
1 2059 29150448
2 1847 29848679
3 1823 27792077
4 1387 22149467
5 799 30114174
6 283 15297195

(Monday is 0, weekends are days 5 & 6)

Checkouts by weekday & hour

USE spl_2016;

SELECT Count(*) AS qty, day, hr
FROM   (SELECT checkout,
Day(checkout)  AS day,
Hour(checkout) AS hr
FROM   transactions
WHERE  checkout > '1990-01-01'
LIMIT  1000000) sample
WHERE  hr != 0
GROUP  BY day, hr;


The query returned the following table showing the number of book checkouts grouped by day of the week, and the hour of the day the checkout happened.

Checkouts Day of week Hour of day
2 1 6
12 1 7
54 1 8
252 1 9
802 1 10
1083 1 11
912 1 12
... ... ...

Checkouts by Hour & weekday in 2D

SELECT
hr,
SUM(CASE WHEN day=0 THEN qty ELSE 0 end) Monday,
SUM(CASE WHEN day=1 THEN qty ELSE 0 end) Tuesday,
SUM(CASE WHEN day=2 THEN qty ELSE 0 end) Wednesday,
SUM(CASE WHEN day=3 THEN qty ELSE 0 end) Thursday,
SUM(CASE WHEN day=4 THEN qty ELSE 0 end) Friday,
SUM(CASE WHEN day=5 THEN qty ELSE 0 end) Saturday,
SUM(CASE WHEN day=6 THEN qty ELSE 0 end) Sunday
FROM   (SELECT Count(*) AS qty,
day,
hr
FROM   (SELECT checkout,
Weekday(checkout) AS day,
Hour(checkout)    AS hr
FROM   transactions
WHERE  checkout > '1990-01-01'
LIMIT  1000000) sample
WHERE  hr != 0
GROUP  BY day, hr)
AS hourly
GROUP  BY hr;

hr Monday Tuesday Wednesday Thursday Friday Saturday Sunday
2 0 0 4 0 2 4 0
3 0 0 0 7 1 0 0
4 0 0 1 0 0 0 0
5 0 0 0 6 2 0 0
6 0 0 0 2 0 0 0
7 109 227 83 99 49 0 2
8 937 1383 991 867 790 4 5
9 998 1500 1294 1504 755 92 16
10 2507 3232 9793 9660 10833 8616 85
11 3399 3117 14898 14454 15298 12487 21
12 3663 4549 16697 14316 17190 13467 18
13 16456 18632 17025 15990 16862 14612 6583
14 19996 19491 20247 17805 15976 15842 8335
15 20884 22581 22106 19530 18395 17071 7781
16 21980 23537 22225 19706 19207 14369 10309
17 19156 23397 21559 25296 21740 15428 227
18 14156 17648 17777 322 499 135 0
19 13831 17901 16210 13 17 1 0
20 172 320 211 0 4 0 0
21 0 0 4 0 0 0 0
22 3 6 0 0 0 0 0
23 5 0 0 0 0 0 0

Book Checkouts in 2006

SELECT
hr,
SUM(CASE WHEN day=0 THEN qty ELSE 0 end) Monday,
SUM(CASE WHEN day=1 THEN qty ELSE 0 end) Tuesday,
SUM(CASE WHEN day=2 THEN qty ELSE 0 end) Wednesday,
SUM(CASE WHEN day=3 THEN qty ELSE 0 end) Thursday,
SUM(CASE WHEN day=4 THEN qty ELSE 0 end) Friday,
SUM(CASE WHEN day=5 THEN qty ELSE 0 end) Saturday,
SUM(CASE WHEN day=6 THEN qty ELSE 0 end) Sunday
FROM   (SELECT Count(*) AS qty,
day,
hr
FROM   (SELECT checkout,
itemtype,
Weekday(checkout) AS day,
Hour(checkout)    AS hr
FROM   transactions,
itemType
WHERE  checkout BETWEEN '2006-01-01' AND '2007-01-01'
AND transactions.itemnumber = itemType.itemnumber) sample
WHERE  hr != 0
AND ( itemtype = 'acbk'
OR itemtype = 'arbk'
OR itemtype = 'bcbk'
OR itemtype = 'drbk'
OR itemtype = 'jcbk'
OR itemtype = 'jrbk' )
GROUP  BY day, hr) hourly
GROUP  BY hr;

hr Monday Tuesday Wednesday Thursday Friday Saturday Sunday
1 3 2 1 6 1 0 0
2 5 3 3 4 2 13 2
3 0 0 0 0 0 0 1
4 0 21 6 18 1 0 0
5 0 7 0 5 3 0 0
6 0 11 15 1 2 4 0
7 77 212 214 56 440 13 7
8 8590 7651 6922 7498 4930 83 59
9 9228 8908 6911 7105 7194 1022 232
10 15331 18580 74400 71409 74432 77946 343
11 22973 22365 111724 114829 104054 119854 1046
12 25436 26998 108180 94497 107172 129096 46520
13 107671 125027 112343 99118 111399 131384 87096
14 118640 133812 122332 109672 123475 140085 95457
15 123489 139373 136664 119841 137642 142563 95164
16 136972 150134 150010 135653 148246 139704 110763
17 131536 144595 146790 140494 170291 130034 17064
18 98925 112922 112647 81759 3700 1960 295
19 96666 110952 109427 75498 65 3 14
20 1478 1660 1669 1080 4 0 0
21 1 2 20 31 28 0 0
22 14 10 0 4 0 0 1
23 17 20 116 108 99 0 7

# Final Result¶

Expanding on these results, this SQL code collects the checkout totals in 15-minute time intervals, rather than hours. Here, it gathers information about DVDs in 2016:

This code takes about 3-5 minutes to run.

USE spl_2016;

SELECT
tslice,
SUM(CASE WHEN day=0 THEN qty ELSE 0 end) Monday,
SUM(CASE WHEN day=1 THEN qty ELSE 0 end) Tuesday,
SUM(CASE WHEN day=2 THEN qty ELSE 0 end) Wednesday,
SUM(CASE WHEN day=3 THEN qty ELSE 0 end) Thursday,
SUM(CASE WHEN day=4 THEN qty ELSE 0 end) Friday,
SUM(CASE WHEN day=5 THEN qty ELSE 0 end) Saturday,
SUM(CASE WHEN day=6 THEN qty ELSE 0 end) Sunday
FROM   (SELECT Count(*) AS qty,
day,
tslice
FROM   (SELECT checkout,
itemtype,
Weekday(checkout) AS day,
floor(Hour(checkout) * 4 + (Minute(checkout)/15)) AS tslice
FROM   transactions,
itemType
WHERE  checkout BETWEEN '2016-01-01' AND '2017-01-01'
AND transactions.itemnumber = itemType.itemnumber) sample
WHERE  tslice != 0
AND ( itemtype = 'acdvd'
OR itemtype = 'ardvd'
OR itemtype = 'bcbk'
OR itemtype = 'bcdvd'
OR itemtype = 'jcdvd'
OR itemtype = 'jrdvd'
OR itemtype = 'scmed')
GROUP  BY day, tslice) daytime
GROUP BY tslice;


This grid layout compares the checkout activity of books and dvds in 2006 and 2016. Each graph shows a heatmap of the checkout activity over the course of the year, for one item type (books or dvds).

** See Picture

## Analysis¶

In comparing these data, we notice a few trends:

1. Weekends became much more active over the 2006 - 2016 decade
2. Books overall are much more popular than DVDs
3. In 2006 the most active time / item was book checkouts on friday nights
4. Friday activity decreased dramatically for both books and DVDs
5. It seems the library hours may have changed over time. In 2006 there is almost no activity on Mon/Tue mornings or Fri / Sat evenings, but this is not true in 2016.