Data sources¶

ZČU open data¶

All ZČU data can be downloaded in formats xml, csv and json.

As discussed in further chapters there are certain complications with data sources not providing sufficient data granuality or amount. However there is a possibility that the data will in future contain more suitable datasets, and such should be at least acknowledged to some degree. However this is more of a topic for Prediction models, where it will be further discussed.

Table with faculties and room prefixes belonging to those faculties:

Faculty	Abbreviation	Room prefix
Fakulta strojní	FST	UV, UU, UK, UL, UP, UF, UT
Fakulta ekonomická	FEK	UV, UU, UK, UL, UP, UF
Fakulta elektrotechnická	FEL	EU, EK, EL, EP, ES, ET, EH, EZ
Fakulta designu a umění	FDU	LS
Fakulta filozofická	FF	JJ, RJ, RS, SP, SD, ST, SO
Fakulta pedagogická	FPE	CH, VC, KL
Fakulta ekonomická Cheb	FEK-CH ?	CD
Fakulta zdravotních studií	FZS	HJ, DD
Fakulta aplikovaných věd	FAV	UN, UC,US
Fakulta právnická	FPR	PC, PS

Table with "special" buildings:

Building name	Abbreviation	Room prefix
Centrum informatice a výpočetní techniky	CIV	UI
Univerzitní knihovna ZČU		UB
Rektorát ZČU		UR
Tylova 59 - currently incative		TS
Ředitelství PS a SKM		KO
Stavovská unie studentů		RS
Nové technologie - výzkumné centrum		T, TF, TG, TH
Regionální technologický institut		UH, UD, UX

Unassigned room prefixes: TY
Unknown location: STUD_PRA1, "Parkoviště"
How to assign "special" buidlings (knihovna, ředitelství) to faculties?

Thorough the data standard university tags are used, however in some cases there is no source to find out what they mean (for example "parkoviště" or "STUD-PRA1")

For university tags was used this source: https://ps.zcu.cz/strediska/budovy-plzen.html#kampus

Historical weather data¶

Link to data: http://opendata.zcu.cz/Energeticky-dispecink.html

Data contains:

• datum_a_cas - date and time, time at which the values were measured with hour accuracy
• teplota - average temperature in given time slot (°C)
• vitr - average wind speed in given time slot (m/s)
• dest - value signifying rain (1) and no rain (0)
• svetelnost - average value of luminance (k lux)

For further processing luminance will be translated to the terms "sunny", "overcast" and "cloudy". In the 2019 data are values between 0 and 83.2k lux.

Lux values can be understood using the following table:

Conditions	Value (lux)
Sunlight	107527
Full Daylight	10752
Overcast Day	1075
Very Dark Day	107
Twilight	10.8
Deep Twilight	1.08
Full Moon	0.108
Quarter Moon	0.0108
Starlight	0.0011
Overcast Night	0.0001

Source: https://www.engineeringtoolbox.com/light-level-rooms-d_708.html

However, upon comparing values in data with archived weather predictions it seems more like the following table would be appropriate:

Conditions	Value (k lux)
Direct sungligt	>60
Sunny	40-60
Overcast	20-40
Cloudy	10-20
Night	0

Used weather archive: https://www.in-pocasi.cz/archiv/archiv.php?historie=2019-12-01&region=9

JIS data¶

Link to data: http://opendata.zcu.cz/Snimace-JIS.html

Data contains:

• datum_a_cas - timestamp of JIS authentication (accuracy in milliseconds)
• pocet_logu - number of authentized users in given time
• popis_objektu - description of object according to standard ZČU tagging

On the linked page there is written that " ... Data about dorms, the entry to laboratories and other spaces with restricted access, informations about university canteen, checkouts in univeristy library, access to copy machines etc can be interesting for students ...". However not all of these places can be found in said data. In data from 2019 are present only 46 different places, and most of them are dorms, parking lots and buffets.

There is a possibility that in the future the number of logged places will increase, however it is also possible that the data was affected by GDPR and more detailed data now won't be provided for the public anymore.

Possible solution is to assign provided spaces to faculties.

Dorms and gyms
A1, A2-Hlavni vchod, A3	all	on Borská street
B3-LEVY	all	on Baarova street
M16, M14	all	on Máchova street
L1, L2, L1L2-vchod	all	on Bolevecká street
L-Posilovna	none	in Bolevecká dorm
KL-Posilovna	none	on Klatovská street

Parking lots
Zavora-FEL	FEL
Zavora-Kaplirova	all	on Kaplířova street
US 005 - závora vjezd, US 005 - mříž vjezd	FAV
Zavora-FDU	FDU
Parkoviste-vjezd, Parkoviste-vyjezd	?	?
Zavora-NTIS-vjezd, Zavora-NTIS-vyjezd	FAV
VC-VJEZD, VC-VYJEZD	FPE	on Veleslavínova street
KolaBory-vnejsi, KolaBory-vnitrni	all
EXT/kola	FST
EXT/kola-B	FAV
B3-kolarna	all	on Baarova street

Food courts
EP-BUFET	FEL
NTIS-BUFET	FAV
UV1-Bufet	FEK
MenzaKL-vydej	all
Menza4-kasa{x}	all	x in range <1, 5>
Menza1-kasa-l, Menza1-kasa-p	all

Study rooms
STUD_VC53	FPE
STUD_CHEB	FEK-CH
STUD_KL20, STUD_KL87	FPE
STUD_PRA1	FPR
STUD_UB113, STUD_UB211	all	in the on campus library
STUD_ST407	FF

WebAuth data¶

Link to data: http://opendata.zcu.cz/Autentizacni-system.html

Data contains:

• datum - date of access
• budova - building tag
• hodina_zacatek - start of lecture
• hodina_konec - end of lecture
• pocet_prihlaseni - number of successfull sign-ins to given computer in given lecture
• stroj_hostname - name of specific computer
• typ_objektu - type of object (classroom, laboratory, lecture room, other)
• ucebna_nazev - specific name of room
• vyucovaci_hodina - number of lecture (according to the timetable)

On the linked page there is written that "... Signing in using orion login and password can also help track sign-ins to computers at ZČU and corresponding activity in computer laboratories ..." however it seems quesstionable if really all computer logins are in this data. Since it contains only 106 different rooms for all of ZČU in data from the year 2019, which seems suspicious especially since some rooms that we know that they are equipped with computers and are being used (at least sometimes) are not present.

So, it would be possible to again assign those rooms to the appropriate buldings using the table at the beggining of ZČU open data chapter and go off the assumption that a similar set of students will be attending lessons in the same building (which is often the case at least with KIV lectures).

Occupancy data¶

Link to data: http://opendata.zcu.cz/Obsazeni-mistnosti.html

Data contains:

• rok_platnosti - year
• budova - building tag
• ucebna_nazev - room name
• typ_objektu - type of room (učebna/laboratoř/posluchárna/jiné)
• kapacita_objektu - maximum capacity of room
• obsazeni - number of students enlisted
• predmet - abbreviation of timetable action
• typ_akce - type of lecture (seminář/přednáška/cvičení)
• vyucovaci_hodina - lesson number (according to the timetable)
• hodina_zacatek - lesson beggining
• hodina_konec - lesson end
• semestr - semester (Letní semestr/Zimní semestr)
• tyden - week (S(even), L(odd), K(every),J(other))
• tyden_v_roce - week in the year
• datum - date

It seems possible that not all lessons that are taught on ZČU are included in this data. Data from 2019+2020 contains only 1202 unique lesson instances.
Also there are some instances without assigned building and room name, however this shouldn't be an issue since lessons are usually looked up by their abbrevation, not by room.
How to work with lessons that are not included in these datasets is rather a topic either for Prediction models or handling user input.

Weather data¶

Link to data: http://wttr.in/Plzen,czechia?format=j1

Data is in json file format and contains detailed weather prediction for Pilsen, CZ. For this application will be usefull mainly the following details:

Current weather:

• localObsDateTime - date and time
• cloudcover - amount of clouds (values in range <0-100>)
• weatherDesc - weather description
• temp_C - temperature (°C)
• precipMM - rainfall (mm)
• humidity - humidity (values in range <0, 100>)
• windspeedKmph - wind (km/h)

Prediction:

• avgtempC - average temperature (°C)
• date - date
  further contains hourly prediction for following information
• WindGustKmph - wind (km/h)
• chanceofrain - chance of rain (0-100%)
• chanceofsnow - chance of snow (0-100%)
• cloudcover - amount of clouds (values in range <0-100>)
• humidity - humidity (values in range <0-100>)

In current data precipMM and in prediction chance of rain specifies rain value. From cloudcover can be estimated values such as sunny/overcast and cloudy.