Place Codes for South Sudan
Place Codes (Pcodes) are numbers that identify the location of items. They are used in libraries to provide the reference for each book. They are also used to provide unique reference codes to settlements whenever the names are not unique, like in Sudan. Experience has proven that the use of Pcodes can create a “common language” in countries with different ethnic groups or whenever a unique way to translate the names from different alphabets[1] does not exist.
This is very common in Sudan,
where one town may have several names and ways to spell it. This inconvenience
could be overcome by using relational databases that link 
the different names together. However, this is possible only if a central
database that would handle all the information is established. It becomes very
tricky whenever data should be shared and low-level data managers handle their
own datasets.
Fig. 1 Different names and way to spell
Bosasso,Somalia, from DIMU database.
Once the Pcodes are introduced, all the data referring to settlements or features belonging to a settlement will have a unique code to identify them, like schools and health posts in the same town. Therefore any organization can use data generated by different institutions with the surety that, at the same time, its own data will be easily utilized. This will also allow sharing of data by using software different from relational databases, like spreadsheets or word processors, because the code contains all the necessary information. Pcodes are related at the administrative areas officially endorsed by the Government of Somalia in 1986.
The information stored in the Pcode
The Pcodes created for South Sudan are composed by a unique number of 10 digits, which stores data about the region and district where the settlement is located, the source, data on the type of settlement and a progressive subset. Therefore, not only do the codes offer a tool to link different sets of data, but, most importantly, they make it possible to extract data related to different administrative areas or topics by querying subsets of the codes. This allows users who do not have GIS software and skills to extract and manipulate data according to spatial criteria. Although 10 digits could seem too long to be commonly used, the potential of desegregating data at any moment largely overcomes the annoying length of the codes. This peculiar way of storing data in a single code ensures the unique capacity to maintain a lot of information linked together to the settlement unique code, like a sort of DNA characteristic. Even dismembering the datasets in a single record, the “genetic code” is preserved.
Since the Pcodes contain geographical information, they can be translated in barcodes to be printed into lables. A hand bar code reader connected to a computer can detect the destination and support fast delivery.
Each code is composed of ten digits as follows:
Digit 1: Region code. The 6 regions have been numbered starting from the northwest down to the south. The relation between codes and regions is depicted in table 1. The following tables show the relations with the other subsets of digits.
Digit 2: County code. Each county has an incremental number that, like regions, begins from the northwest down to the south.
Digit 3-4: Payam code. It is compose by two-digit becouse in some county there are more than 10 payams
Digit 4-5: Source code. These two digits provide information on the source used to capture the information of the settlements. At present only one source has contributed to the database, but more will be available in the future from field surveys. The evisaged data sources are the following:
|
SOURCE |
YEAR OF DATA
ACQUISITION |
ESTIMATED
SPATIAL ACCURACY |
NUMBER OF
RECORDS |
MAIN
WEAKNESS |
|
Digital Chart of
the Word |
1980s |
Maximum error
encountered versus topographic maps 1.8 Km |
0 |
Poor spatial
accuracy |
|
Nima Gazetteer |
Nov 2000 |
Maximum error
encountered versus topographic maps 0.75 Km |
1121 |
Includes in the
dataset small entities like farms or nomadic huts that sometimes do not exist
anymore |
|
Topographic maps |
unknown |
Usual maximum
estimated error 150 m |
0 |
The dataset does
not include new settlements |
|
Field surveys |
future |
10 m |
0 |
|
Digit 6: Type of settlement. This digit includes information on the type of settlement, which is often linked to its size.
Digit 7-10: Incremental number. This subset identifies the unique code in a given Region, County and Payam. The number set starts with 000 for each regional or district town and proceeds in alphabetical order. To accommodate more settlements in the future and to maintain the alphabetical order the increment has been set to two.
How to extract data subsets
Although large collections of data should be handled with database software, the most common method used is to enter data into a spreadsheet. Therefore, the examples given will start with the use of the most popular one, which is Microsoft Excel.
Excel does not allow for data extraction. The easiest way to do so is to use the Data Sort tool from the menu. This changes the order of the rows in a way that is suitable for you. You can then copy and paste what you need into a new spreadsheet.
Unfortunately, Excel cannot sort by subset of numbers (even in form of string like the Pcodes are provided) in the same column. Therefore you have to desegregate the digits in the Pcode column according to the needs. To do so you need to use the function MID that is meant to do it. Its syntax is as follows:
MID(text, start_number, number_of_digits).
Let us assume that you would like to extract data related to the District of Iskushuban, in Bari Region. The scope of the exercise is to sort the data in a way that the information you want to copy will be grouped. The Pcodes are in the column C.
In a new column you will type in the first row (usually row 2):
=MID(A2,1,2)
That means that from the cell A2 you will need to extract, from the first digit, two digits. Copy this command and paste it in all the cells of the column. These are the first two digits that are related to the codes of the regions.
In the adjacent column type:
=MID(A2,3,2)
This extracts from the cell A2, from the third digit, two digits. Again, copy and paste it into the rest of the column. These are the two digits related to the districts.
Go to the Data menu and use the Sort tool, where in the Sort by window you will choose the columns with the code subsets you just created. Your data will be sorted and you will copy only the rows with the first digits equal to 1604 (16 is Bari and 04 is Iskushuban).
In this example you would like to extract the data related to the major towns in the Middle Shabelle region. As in the previous example, the Pcodes are in the column C.
In a new column you will type in the first row (usually row 2):
=MID(A2,1,2)
Like in the previous example you will get the codes for the regions.
In the adjacent column type:
=MID(A2,7,1)
This extracts part of the cell A2, from the seventh digit, only one digit. Again, copy and paste it into the rest of the column. You have isolated the digit related to the type of settlements.
Run the command Sort by including the two criteria and you will copy only the rows with the regional code equal to 21 (Middle Shabelle) and the type code equal to 2 (Regional town) and 3 (District town).
Is this too complex? Microsoft Access, a database software package, makes it easier, but it is included only in the Microsoft Office Professional Edition.
Access extracts data by running queries. To extract the information like in the first example, you go to the query section and type:
Left([Code],4)=”1604”
Where [Code] is the name of the field that the filter is to set on, 4 is the number of digits to check and 1604 is the part of the code related to Bari region and Iskushuban district that you want to match.
Pcodes maintenance
In April 2001, the Data and Information Management Unit (DIMU) of the United Nations Development Programme (UNDP) Somalia created Place Codes (Pcodes) for South Sudan. All the organizations using the Pcodes created by DIMU are encouraged to identify errors or possible amendments and to request DIMU to make the necessary corrections. This specifically concerns the digit related to information on major towns that are supposed to have a population greater than 5000 inhabitants.
For further information please contact:
DEPHA
P.O. Box 30552-00100
Nairobi, Kenya
Tel: 254-2-7624186/95
Fax: 254-2-7624315
e-mail: info.depha@unep.org
TABLES
Table 1: Region codes (digit 1)
CODE |
REGION |
|
1 |
Bahr El Ghazal |
|
2 |
Upper Nile |
|
3 |
Lakes |
|
4 |
Jonglel |
|
5 |
Western Eqautoria |
|
6 |
Eastern Equatoria |
Table 2: County codes (digit 2)
|
REGION |
CODE |
COUNTY |
|
Bahr El Ghazal |
1 |
Raga |
|
Bahr El Ghazal |
2 |
Awiel West |
|
Bahr El Ghazal |
3 |
Awiel East |
|
Bahr El Ghazal |
4 |
Abyei |
|
Bahr El Ghazal |
5 |
Twic |
|
Bahr El Ghazal |
6 |
Gogrial |
|
Bahr El Ghazal |
7 |
Wau |
|
Upper Nile |
1 |
Unity (Leech) |
|
Upper Nile |
2 |
Phou |
|
Upper Nile |
3 |
Shilluk Kingdom |
|
Upper Nile |
4 |
Sobat |
|
Upper Nile |
5 |
Renk |
|
Upper Nile |
6 |
Latjor |
|
Lakes |
1 |
Tonj |
|
Lakes |
2 |
Rumbek |
|
Lakes |
3 |
Yirol |
|
Jonglel |
1 |
Bor |
|
Jonglel |
2 |
Bieh |
|
Jonglel |
3 |
Pibor |
|
Western Eqautoria |
1 |
Tambura |
|
Western Eqautoria |
2 |
Yambio |
|
Western Eqautoria |
3 |
Maridi |
|
Western Eqautoria |
4 |
Mundri |
|
Eastern Equatoria |
1 |
Yei |
|
Eastern Equatoria |
2 |
Juba |
|
Eastern Equatoria |
3 |
Kajo Keji |
|
Eastern Equatoria |
4 |
Torit |
|
Eastern Equatoria |
5 |
Kapoeta |
Table 3: Payam codes (digit 3-4)
|
REGION |
COUNTY |
CODE |
PAYAM |
|
Bahr El Ghazal |
Raga |
01 |
Raga |
|
Bahr El Ghazal |
Awiel West |
01 |
Malual West |
|
Bahr El Ghazal |
Awiel West |
02 |
Malual Bai |
|
Bahr El Ghazal |
Awiel West |
03 |
Ariath |
|
Bahr El Ghazal |
Awiel West |
04 |
Gomjuer |
|
Bahr El Ghazal |
Awiel West |
05 |
Mariam |
|
Bahr El Ghazal |
Awiel West |
06 |
Manger Gier |
|
Bahr El Ghazal |
Awiel West |
07 |
Wathmuok |
|
Bahr El Ghazal |
Awiel East |
01 |
Yargot |
|
Bahr El Ghazal |
Awiel East |
02 |
Wunlang |
|
Bahr El Ghazal |
Awiel East |
03 |
Baau |
|
Bahr El Ghazal |
Awiel East |
04 |
Madhol |
|
Bahr El Ghazal |
Awiel East |
05 |
Malual East |
|
Bahr El Ghazal |
Abyei |
01 |
Abyei |
|
Bahr El Ghazal |
Twic |
01 |
Wunrok |
|
Bahr El Ghazal |
Twic |
02 |
Turalei |
|
Bahr El Ghazal |
Gogrial |
01 |
Akon |
|
Bahr El Ghazal |
Gogrial |
02 |
Lietnhom |
|
Bahr El Ghazal |
Gogrial |
03 |
Toch |
|
Bahr El Ghazal |
Gogrial |
04 |
Pathuon |
|
Bahr El Ghazal |
Gogrial |
05 |
Kwacjok |
|
Bahr El Ghazal |
Wau |
01 |
Udici |
|
Bahr El Ghazal |
Wau |
02 |
Marial Bai |
|
Bahr El Ghazal |
Wau |
03 |
Wau |
|
Bahr El Ghazal |
Wau |
04 |
Bazia |
|
Bahr El Ghazal |
Wau |
05 |
Kuarjina |
|
Upper Nile |
Unity (Leech) |
01 |
Mankien |
|
Upper Nile |
Unity (Leech) |
02 |
Nimne |
|
Upper Nile |
Unity (Leech) |
03 |
NhialDiu |
|
Upper Nile |
Unity (Leech) |
04 |
Wichok |
|
Upper Nile |
Unity (Leech) |
05 |
Guit |
|
Upper Nile |
Unity (Leech) |
06 |
Kock |
|
Upper Nile |
Unity (Leech) |
07 |
Leer |
|
Upper Nile |
Unity (Leech) |
08 |
Nyal |
|
Upper Nile |
Unity (Leech) |
09 |
Ganyliel |
|
Upper Nile |
Phou |
01 |
Pagum |
|
Upper Nile |
Phou |
02 |
Old Fangak |
|
Upper Nile |
Phou |
03 |
Atar |
|
Upper Nile |
Phou |
04 |
Haat |
|
Upper Nile |
Phou |
05 |
Pagil |
|
Upper Nile |
Phou |
06 |
Mogok |
|
Upper Nile |
Phou |
07 |
Ayod |
|
Upper Nile |
Shilluk Kingdom |
01 |
Malakal |
|
Upper Nile |
Sobat |
01 |
Sobat |
|
Upper Nile |
Renk |
01 |
Renk |
|
Upper Nile |
Latjor |
01 |
Nasir |
|
Lakes |
Tonj |
01 |
Warrap |
|
Lakes |
Tonj |
02 |
Akop |
|
Lakes |
Tonj |
03 |
Makuac |
|
Lakes |
Tonj |
04 |
Ananatak |
|
Lakes |
Tonj |
05 |
Thiet |
|
Lakes |
Rumbek |
01 |
Cueibel |
|
Lakes |
Rumbek |
02 |
Pagor |
|
Lakes |
Rumbek |
03 |
Maper |
|
Lakes |
Rumbek |
04 |
Matangai |
|
Lakes |
Rumbek |
05 |
Malek |
|
Lakes |
Rumbek |
06 |
Pacog |
|
Lakes |
Rumbek |
07 |
Akot |
|
Lakes |
Rumbek |
08 |
Wulu |
|
Lakes |
Yirol |
01 |
Yirol West |
|
Lakes |
Yirol |
02 |
Yirol East |
|
Lakes |
Yirol |
03 |
Aliap |
|
Jonglel |
Bor |
01 |
Duke Padiet |
|
Jonglel |
Bor |
02 |
Duke Payuel |
|
Jonglel |
Bor |
03 |
Kongor |
|
Jonglel |
Bor |
04 |
Jonglei |
|
Jonglel |
Bor |
05 |
Jalle |
|
Jonglel |
Bor |
06 |
Baidit |
|
Jonglel |
Bor |
07 |
Makuac (Bor) |
|
Jonglel |
Bor |
08 |
Anyidi |
|
Jonglel |
Bieh |
01 |
Langkien |
|
Jonglel |
Bieh |
02 |
Motot |
|
Jonglel |
Bieh |
03 |
Waat |
|
Jonglel |
Bieh |
04 |
Walgak |
|
Jonglel |
Bieh |
05 |
Yuai |
|
Jonglel |
Bieh |
06 |
Kaikuny |
|
Jonglel |
Bieh |
07 |
Akobo |
|
Jonglel |
Bieh |
08 |
Nyandit |
|
Jonglel |
Bieh |
09 |
Pochalla |
|
Jonglel |
Pibor |
01 |
Pibor |
|
Jonglel |
Pibor |
02 |
Boma |
|
Western Eqautoria |
Tambura |
01 |
Namutina |
|
Western Eqautoria |
Tambura |
02 |
Nagero |
|
Western Eqautoria |
Tambura |
03 |
Tambura |
|
Western Eqautoria |
Tambura |
04 |
Source Yuba |
|
Western Eqautoria |
Tambura |
05 |
Mupoi |
|
Western Eqautoria |
Tambura |
06 |
Ezo |
|
Western Eqautoria |
Tambura |
07 |
Yangiri |
|
Western Eqautoria |
Tambura |
08 |
Naadi |
|
Western Eqautoria |
Yambio |
01 |
Nandiagere |
|
Western Eqautoria |
Yambio |
02 |
Nzara |
|
Western Eqautoria |
Yambio |
03 |
Li-Rangu |
|
Western Eqautoria |
Yambio |
04 |
Bagasu |
|
Western Eqautoria |
Yambio |
05 |
Sakure |
|
Western Eqautoria |
Yambio |
06 |
Yambio |
|
Western Eqautoria |
Yambio |
07 |
Nariapai |
|
Western Eqautoria |
Maridi |
01 |
Maruko |
|
Western Eqautoria |
Maridi |
02 |
Kozi |
|
Western Eqautoria |
Maridi |
03 |
Mambe |
|
Western Eqautoria |
Maridi |
04 |
Maridi |
|
Western Eqautoria |
Maridi |
05 |
Ibba |
|
Western Eqautoria |
Mundri |
01 |
Mvolo |
|
Western Eqautoria |
Mundri |
02 |
Yeri |
|
Western Eqautoria |
Mundri |
03 |
Mundri |
|
Western Eqautoria |
Mundri |
03 |
Mundri |
|
Western Eqautoria |
Mundri |
04 |
Kediba |
|
Western Eqautoria |
Mundri |
05 |
Lozoh |
|
Western Eqautoria |
Mundri |
06 |
Bangolo |
|
Eastern Equatoria |
Yei |
01 |
Tore |
|
Eastern Equatoria |
Yei |
02 |
Yei |
|
Eastern Equatoria |
Yei |
03 |
Lainya |
|
Eastern Equatoria |
Yei |
04 |
Lasu/Otodo |
|
Eastern Equatoria |
Yei |
05 |
Morobo |
|
Eastern Equatoria |
Juba |
01 |
Tali |
|
Eastern Equatoria |
Juba |
02 |
Terekeka |
|
Eastern Equatoria |
Juba |
03 |
Mongala |
|
Eastern Equatoria |
Juba |
04 |
Juba |
|
Eastern Equatoria |
Juba |
05 |
Katigiri |
|
Eastern Equatoria |
Kajo Keji |
01 |
Ngepo |
|
Eastern Equatoria |
Kajo Keji |
02 |
Kangopo |
|
Eastern Equatoria |
Kajo Keji |
03 |
Lire |
|
Eastern Equatoria |
Kajo Keji |
04 |
Kajo Keji |
|
Eastern Equatoria |
Kajo Keji |
05 |
Liuslo |
|
Eastern Equatoria |
Torit |
01 |
Lirya |
|
Eastern Equatoria |
Torit |
02 |
Lafon |
|
Eastern Equatoria |
Torit |
03 |
Lopit West |
|
Eastern Equatoria |
Torit |
04 |
Lopit East |
|
Eastern Equatoria |
Torit |
05 |
Tohubak |
|
Eastern Equatoria |
Torit |
06 |
Ikotos |
|
Eastern Equatoria |
Torit |
07 |
Isoke |
|
Eastern Equatoria |
Torit |
08 |
Imotong |
|
Eastern Equatoria |
Torit |
09 |
Keyalla |
|
Eastern Equatoria |
Torit |
10 |
Magwe |
|
Eastern Equatoria |
Torit |
11 |
Pageri |
|
Eastern Equatoria |
Kapoeta |
01 |
Kimatong |
|
Eastern Equatoria |
Kapoeta |
01 |
Kimatong |
|
Eastern Equatoria |
Kapoeta |
02 |
Kapoeta |
|
Eastern Equatoria |
Kapoeta |
03 |
Riwoto |
|
Eastern Equatoria |
Kapoeta |
04 |
Naita |
|
Eastern Equatoria |
Kapoeta |
05 |
Mogos |
|
Eastern Equatoria |
Kapoeta |
06 |
Narus |
|
Eastern Equatoria |
Kapoeta |
07 |
Chukudum |
|
Eastern Equatoria |
Kapoeta |
08 |
Morukagkipi |
Table 4: source codes (digit 5-6)
|
CODE |
SOURCE |
|
01 |
Digital Chart of the Word |
|
02 |
NIMAGazetteer |
|
03 |
Topographic maps |
|
04 |
Field surveys |
Table 5: type of settlement codes (digit 7)
|
CODE |
TYPE OF SETTLEMENT |
|
1 |
Capital |
|
2 |
Regional town |
|
3 |
County town |
|
4 |
Payam and other major town |
|
9 |
Small settlement |