ISO 24108-1:2025

International
Standard
ISO 24108-1
First edition
Grid square statistics and their
2025-10
applications —
Part 1:
Fundamental principle of grid
square statistics
Statistiques sur données carroyées et applications —
Partie 1: Principe fondamental des statistiques sur données
carroyées
Reference number
© ISO 2025
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ii
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Grid square statistics . 3
4.1 Basic concepts of grid square statistics .3
4.2 Categorical data and quantitative data .4
4.2.1 General .4
4.2.2 Categorical data .4
4.2.3 For quantitative data .4
5 Production process of grid square statistics . 5
5.1 General .5
5.2 Method of allocating from microdata .6
5.2.1 Using census data .6
5.2.2 Using sample data .7
5.2.3 Using register data .7
5.3 Method of allocating from disaggregated data .7
5.4 Method of allocating from both microdata and disaggregated data .8
6 Conversion between different grid square reference systems . 8
6.1 General .8
6.2 Method of conversion .9
6.2.1 Conversion method based on grid intersection .9
6.2.2 Conversion method based on a point approximation .10
Annex A (informative) Japanese national grid square code .11
Annex B (informative) World grid square code .18
Annex C (informative) Outline of the European reference grid: ETRS89-LAEA (INSPIRE) .24
Annex D (informative) Potential applications .25
Bibliography .27

iii
Foreword
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development.
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complete listing of these bodies can be found at www.iso.org/members.html.

iv
Introduction
Grid statistics are geospatial statistics produced by grids often with fairly high spatial resolution, which
enable the analysis of regional dependence on population and labour from economic and social activities.
They can help us to analyse demand and supply imbalances and can provide valuable insights to optimize a
strategic plan for commercializing new products and services that can expand worldwide.
However, currently, the grid definitions employed to grid statistics coexist in many countries and
organizations in different forms, which lacks controllability in data quality, reliability, and interoperability.
Therefore, it is highly appropriate to produce, exchange, and utilize them under a common understanding
based on international standards.
In order to promote a common international understanding not only of the formal description of spatial
information related to grid statistics, but also of its statistical utilization, this document takes the following
two points as its aim:
— Communication and decision-making requiring common understanding of grid statistics across multiple
sectors and organizations.
— Promoting to provide grid square statistics even for countries and areas not yet with grid statistics,
covering new services of business sector.

v
International Standard ISO 24108-1:2025(en)
Grid square statistics and their applications —
Part 1:
Fundamental principle of grid square statistics
1 Scope
This document specifies a fundamental principle on grid square statistics and includes the following:
— To define or specify a set of methods that can be recommended as the standard method to enable
policy decisions based on common recognition from grid statistics produced with different grid square
reference system standards in different countries and areas that can have different geographic coding
systems with different geometric shapes.
— To standardize methods to accommodate the conversion calculation among grid statistics with different
grid square reference systems having different geometric shapes by employing prorating method
based on the grid areas as well as the method to calculate the approximation errors, for exchanging the
converted grid statistics.
Spatial expressions other than grid square also exist, but this document does not apply to the spatial
expressions other than grid square.
NOTE Clause 4 and Clause 5 define grid square statistics and specify some methods to generate grid square
statistics. Clause 6 recommends conversion methods between grid square statistics generated based on different grid
square reference systems.
2 Normative references
The following documents are referred to in the text in such a way that some or all of their content constitutes
requirements of this document. For dated references, only the edition cited applies. For undated references,
the latest edition of the referenced document (including any amendments) applies.
ISO 3534-2, Statistics — Vocabulary and symbols — Part 2: Applied statistics
3 Terms and definitions
For the purposes of this document, the terms and definitions given in ISO 3534-2 and the following apply.
ISO and IEC maintain terminology databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https:// www .iso .org/ obp
— IEC Electropedia: available at https:// www .electropedia .org/
3.1
categorical data
variable with the measurement scale consisting of a set of categories
3.2
nominal data
variable with a nominal scale of measurement
[SOURCE: ISO 3534-2:2006, 1.1.6]

3.3
ordinal data
variable with an ordinal scale of measurement
[SOURCE: ISO 3534-2:2006, 1.1.7]
3.4
contingency table
tabular representation of categorical data (3.1), which shows frequencies for particular combinations of
values of two or more discrete random variables
3.5
grid
network composed of two or more sets of curves in which the members of each set intersect the members of
the other sets in an algorithmic way
Note 1 to entry: The curves partition a space into grid cells.
[9]
[SOURCE: ISO 19123-2:—, 3.1.3 , modified — Note 1 to entry has been rewritten.]
3.6
grid square
grid (3.5) whose shape is near to a square, which is not a hexagon or something else
Note 1 to entry: The centre position or area of a grid square can be expressed in longitude and latitude.
Note 2 to entry: Grids containing the north or south pole are exceptionally triangular.
3.7
grid square code
sequence of numbers, letters, or characters to uniquely identify a grid square (3.6)
3.8
grid square statistics
statistics computed by using grid square (3.6) as aggregation units, specifically appropriate summary
statistics of the quantitative data contained in each grid square (3.6)
Note 1 to entry: Mean and standard deviation are typical summary statistics both in interval and ratio scale data but
total is only valid in case of ratio scale data.
Note 2 to entry: The number is typical summary statistics for qualitative data in both nominal and ordinal data.
3.9
microdata
dataset comprised of records related to individual data principals
Note 1 to entry: Microdata are unit-level data obtained from sample surveys, censuses, and administrative systems.
They provide information about characteristics of individual people or entities
[13]
[SOURCE: ISO/IEC 20889:2018, 3.23 ]
3.10
register data
microdata (3.9) stored in administrative registers
3.11
disaggregated data
cross-section statistical tables (or summary data) that are tabulated from the microdata (3.9) of censuses,
statistical surveys, registers

3.12
georeferenced microdata
data obtained from the process of referencing data against a known geographic coordinate reference system
by matching to known points of reference in the coordinate system
Note 1 to entry: The georeferencing process unequivocally positions data to their precise and absolute location in the
digital environment – i.e. geographic coordinates (e.g. latitude, longitude and elevation; X, Y, Z) - according to a defined
geodetic reference system that enables to assign data to a unique geographic location (or geographical reference) on
the surface of the Earth with higher spatial accuracy.
3.13
grid coordinate system
coordinate system in which a position is specified relative to the intersection of curves
Note 1 to entry: Grid coordinate system defines the intersection of curves and its projection method for geographical
information under coordinate reference system
[11]
[SOURCE: ISO 19115-2:2019, 3.14 ]
3.14
grid square reference system
reference system to uniquely express grid squares (3.6) defined under a certain grid coordinate system (3.13)
by using grid square codes (3.7)
3.15
geocoding
translation of one form of location into another
Note 1 to entry: Geocoding is the process of transforming a description of the location or unreferenced location
information (e.g. address or name of a place) to the location’s measurable position on the earth’s surface” in the digital
environment (inspired by the GEOSTAT 4 and GSGF Europe terminology). Geocoding can be considered as a subset
concept of georeferencing.
Note 2 to entry: Geocoding is typically preceded by the data cleaning step of preprocessing and standardizing the
format of the data. Geocoding can be conducted through point and point-in-polygon operations from field data
collection or administrative data (direct location information), based on full physical address description (text strings
addressing indirect location information) and based on partial or incomplete physical address that can include only a
postcode, a locality name, administrative unit and/or region.
Note 3 to entry: Geocoding can be conducted by joining or linking location data with tabular/alphanumeric/nominal
data (i.e. non-geospatial data) within a database management system, a GIS environment (enterprise-owned software
or open source) or a service (web-based or API) by taking description location information (e.g., address) as input and
returning the respective geographic coordinates (point spatial objects) in digital format. The term of ‘geocoding’ is
commonly associated with the ‘geoenabling’ term depending on the application type and has been supported by the
advancement in geospatial technologies that increasingly improve and automate the matching process.
Note 4 to entry: Geocoding statistical data is considered a requirement within statistical production because it
facilitates data integration and enables (dis)aggregation by location into smaller or larger geographic units for
statistical analysis.
[12]
[SOURCE: ISO 19133:2005, 4.13 ]
4 Grid square statistics
4.1 Basic concepts of grid square statistics
Grid square statistics are geospatial statistics in terms of grid squares. grid squares are defined by a grid
coordinate system to indicate their positions under a certain projected coordinate reference system. Each
grid square is identified by a unique grid square code defined by a grid square reference system. There
are several types of grid squares in different spatial resolutions in a certain grid square reference system.
The grid square code is a text string corresponding with grid size under a certain projection coordinates
system. The objective of the grid codes is to generate unique identifiers for each grid square. Therefore, grid

square statistics depends on both grid coordinate system and grid square code. Hence, grid square reference
system consists of grid coordinate system that geographically expresses grid squares and grid square code
that uniquely identifies the grid squares.
4.2 Categorical data and quantitative data
4.2.1 General
Grid square statistics are expressed as matrix data or contingency table consisting of gird square codes and
statistics; each grid square code is a unique sequence consisting of numbers, letters, and characters. In the
case of the Japanese national grid square code, grid square codes are expressed as a numeric sequence. In
the case of the European Grid Grid_ETRS89-LAEA_1K, the identifiers have the coordinate reference system
[10] [1]
(CRS), the size of the grid and the X, Y coordinates. Also, the country prefix, see ISO 3166 (all parts) , is
another attribute of the grid statistics matrix, allowing identification when the country boundary intersects
a grid cell by using meta data about the grid square statistics (geometry and dataset types for discriminant
purpose).
There are grid square statistics for two types of data:
— Qualitative data (nominal or ordinal data) are expressed as frequency data contained in each grid.
— Quantitative data are appropriate grid square statistics of the data contained in each grid.
4.2.2 Categorical data
For categorical data, grid square statistics are produced by frequency of entities or events that are applicable
to a pre-defined condition included in each grid square. Such grid square statistics are expressed as a
matrix data consisting of multivariate frequency distribution computed from original qualitative data. Each
element is indicated by the grid square code and frequency conditioning on combination of items included in
the qualitative data.
Table 1 shows an example of grid square statistics by expressing three columns table for two types of
categorical data (the number of persons and the number of object A). Population and the number of objects A
are shown for each grid square code.
Table 1 — Population and number of object A by grid square
Grid square code Population Number of object A
2054111214 45 4
2054111215 341 5
2054111216 3 413 76
2054111217 415 1
2054111218 315 34
2054111219 641 14
2054111220 24 1
4.2.3 For quantitative data
For quantitative data, grid square statistics are produced as a set of representative statistic of each grid
square. The quantitative data are classified into the interval and ratio scales defined in ISO 3534-2. Examples
st rd
of the representative statistics may be mean, median, minimum value, maximum value, 1 quadrants, 3
quadrants, or percentiles. Grid square statistics of the ratio scale can compute for their ratio; however, grid
square statistics of the interval scales have no meaning for their ratio. Table 2 shows grid square statistics

expressed in a three-columned contingency matrix for quantitative data, as mean temperature and mean
elevation are shown for each grid square code.
NOTE Extensive variable (e.g. mass, volume) grid square statistics can be summed up to the total amount,
however for intensive variable (e.g. temperature, height) they result in meaningless value if summed up.
Table 2 — Mean temperature and elevation by grid square
Grid square code Mean temperature Mean elevation
°C m
2054111214 22,1 4,0
2054111215 23,1 8,0
2054111216 22,5 76,0
2054111217 22,2 10,0
2054111218 24,2 34,0
2054111219 25,3 14,0
2054111220 24,2 3,0
5 Production process of grid square statistics
5.1 General
The following indicates how to produce the standard grid square statistics regardless of the differences
that have caused by the coding systems employed in different organizations that may also use non-square
grid. This enables decision-makers to use grid square statistics based on mutual comprehension between
different organizations who want to exchange grid square statistics.
The following grid square reference systems should be adopted to produce grid square statistics. For
information, recommended grid square reference systems are listed below:
— Japanese national grid square code specified in Annex A.
— world grid square coding system: compatible extension to the Japanese national grid square code. The
world grid square code (RIWGS) is specified in Annex B.
— European reference grid: ETRS89-LAEA (INSPIRE, EU Commission) specified in Annex C.
— British ordnance survey national grid reference system specified in 5.1.
Grid square reference systems can be broadly divided into two types:
a) gridding-then-projection (e.g. Annex A and B);
b) projection-then-gridding (e.g. Annex C and the “British ordnance survey national grid reference system”
mentioned in 5.1).
Figure 1 shows a conceptual illustration to produce grid square statistics. Grid square statistics (key item 1)
can be allocated from georeferenced microdata (key item 2) and can be converted from disaggregated data
(key item 3) allocated from georeferenced microdata (see 5.2, 5.3 and 5.4). Gridded data (key item 4) is also
used to produce grid square statistics by applying conversion methods [see 6.2 (key item 8)]. There are four
paths to produce grid square statistics from georeferenced microdata and gridded data.
Statistical accuracy of grid square statistics depends on the production procedure. In general, the statistical
accuracy of allocation from microdata is better than the allocation from both microdata and disaggregated
data. The statistical accuracy of conversion from gridded data strongly depends on the ratio of representative
size of converted data to grid size of grid square to produce grid square statistics.

Key
1 grid square statistics
2 georeferenced microdata
3 disaggregated data
4 gridded data
5 allocating from microdata
6 allocating from disaggregated data
7 allocating from both microdata and disaggregated data
8 conversion methods
Figure 1 — Conceptual illustration to produce grid square statistics
Annex D provides further information about potential applications where grid square statistics are useful.
This method needs geographic location information such as the latitude and longitude or address for each of
housings and establishments to the corresponding grid square, which may require a computer file storing
the reference between the grid square codes and the locations.
5.2 Method of allocating from microdata
5.2.1 Using census data
Generally, to produce grid square statistics using georeferenced census microdata is the most accurate way
rather than using disaggregated data, which is applicable to automate the allocation task to identify grid
square statistics. Figure 2 shows the relation between grids and georeferenced census microdata.
If the georeferenced microdata contains nominal data (text strings) to represent locations (physical
addresses or postal code/zip code), it is necessary to obtain latitude and longitude coordinates from the
text strings by using geocoding. There are two major methods to obtain latitude and longitude coordinates:
One is to obtain them by using global positioning system (GPS) at census enumeration; and another is to
obtain them by using geocoding, that is, to transform each household address into latitude and longitude
coordinates.
Key
1 boundary of a grid square
2 boundary of a polygon
3 household
Figure 2 — Method of allocating by microdata
5.2.2 Using sample data
Aggregating the attributes from the geographic location information is necessary to calculate the grid
square statistics in quantitative or qualitative attributes. The multiplying factor is used if necessary for
obtaining estimates from the sampled observations, which differs from an exhaustive survey or a census.
If the sampling rate is uniform in space, the multiplying factor equals the inverse of the extraction rate,
namely sample size divided by population size.
5.2.3 Using register data
This method is the same as the above-mentioned census microdata if register data is regularly updated and
cover all population (or establishments). If not, the contents of the register data tend to be old. For example,
the registered information of establishment registration is often different from when the company was
established. In addition, a certain number of establishments are not registered usually.
5.3 Method of allocating from disaggregated data
A method to produce grid square statistics from census disaggregated data is an allocation of census
disaggregated data to each grid square based on area ratio. This method distributes census data to grid
squares proportionally, according to how much of the original area each grid square overlaps. Figure 3 and
Table 3 show the polygon blocks A, B, C, D, E, F, and G and statistics. In this case, the census disaggregated
data is aggregated by block. The blocks are the lowest level locality for tabulation, and are geographically
well-demarcated by road, river, etc. The statistical value of a block is allocated to each grid square in
proportion to the ratio of overlapping area.
For example, polygon block F shown in Figure 3 is included in four grid squares coded as 5336-25-10-1, 5336-
25-10-2, 2-05336-25-10-3, and 5336-25-10-4. As shown in Table 3, an area proportion in each grid square
is computed as 0,4, 0,3, 0,1, and 0,2, respectively. Each intersected area between each block and each grid
square can automatically be calculated by an intersection function of Geographic Information System (GIS),
if the digital boundary data of the blocks are available. While grid squares where it is clear that there is no
inhabitant or establishment should be excluded from this allocation.

Key
1 grid square boundary
2 block boundary
3 grid square code
4 block code 0900-02010
Figure 3 — Allocation by area ratio
Table 3 — Area proportion by grid square
Block code Area proportion Grid square code
0900-02010 0,4 5336-25-10-1
0900-02010 0,3 5336-25-10-2
0900-02010 0,1 5336-25-10-3
0900-02010 0,2 5336-25-10-4
5.4 Method of allocating from both microdata and disaggregated data
Microdata does not always have the variables of the latitude and longitude coordinates of each location
of all the buildings. While these variables are often available for buildings with a large statistical value
(population, number of establishments, etc.) such as government quarters, garrisons, hospitals, prisons,
large dormitories or municipal housings. In this case, firstly allocate these buildings to each grid square
by using the method described in 5.2 in advance. Secondly, subtract the statistical value of those buildings
from the statistical value of the census disaggregated data of each block. Finally, the subtracted statistical
value of each block is allocated to each grid square in proportion to the ratio of overlapping area by using the
method described in 5.3.
6 Conversion between different grid square reference systems
6.1 General
There are different types of grid square reference systems. National and international grid square reference
systems are addressed as follows:
— Japanese national grid square code: It is specified in Annex A.
— World grid square code: compatible extension to Japanese national grid square code, which is specified
in Annex B.
— European reference grid: ETRS89-LAEA (INSPIRE, EU Commission) shown in Annex C.
— British ordnance survey national grid reference system.

— Discrete global grid system (DGGS): ISO 19170-1.
Grids are used to convert geographical information (latitude and longitude) into ordered categorical data
(grid square code). However, since grid square statistics have irreversible properties, original data used to
produce the grid square statistics cannot be identified.
When users of grid square statistics want to exchange grid square statistics between two different grid
square reference systems, they need to convert grid square statistics on one grid square reference system
into those of the other grid square reference system.
6.2 Method of conversion
6.2.1 Conversion method based on grid intersection
Regarding interoperability of grid square statistics, it is necessary to define a method to convert grid
square statistics from one grid square reference system to another grid square reference system. Due to
irreversibility of grid square statistics, there is no exact conversion method for grid square statistics
produced between two different grid square reference systems, generally.
However, it is possible to approximately convert grid square statistics of one grid square reference system
into those of other grid square reference system by using proportionally dividing values based on area-rate
of one type of grid to other type of grid.
As shown in Figure 4, suppose that grid square code, e, and grid square statistics, x , are on one grid square
e
reference system and convert x into statistics y in grid square coded as w on other grid square reference
e w
system. Define a set Cover(e) of grid codes on other grid square reference system whose grid squares cover
grid squares coded as e in one grid square reference system. Assume that polygon V is a grid square coded
e
as e on one grid square reference system and that polygon W is a grid square coded as w on other grid
w
square reference system. Figure 4 shows a relationship between one grid square reference system and other
grid square reference system. Statistic y on other grid square reference system is computed as given by
w
Formula (1):
yW= ρ ,Vx (1)
()
w we′′e
∑
eC′∈ over()w
where
SW ∩V
()

we
wC∈ over()e

SV()
ρ WV, = ,
()
 e
we

0 wC∉ over e
()

where S(g) represents area of polygon g.
Grid size of one grid square reference system should be smaller than another grid square reference system.

Key
1 grid square coded as e on one grid square reference system
2 grid squares on other grid square reference system
3 cover(e)
4 V
e
Figure 4 — Grid to grid conversion
6.2.2 Conversion method based on a point approximation
[4]
Another type of conversion method is a point approximation method.
a) Grid squares on one grid coordinate system are transformed into points (centroid or central points of
grid cells).
b) Centroid or central point of each grid square is projected from CRS subjected to one grid coordinate
system to CRS subjected to another grid coordinate system.
c) Grid square codes on another grid square reference system are computed from projected centroid or
central point of each grid and added to values of grid square statistics.
d) Grid square statistics on another grid square reference system is computed by aggregating values of
grid square statistics in term of grid square codes on another grid square reference system.
e) Data description to express how to produce grid square statistics is added to meta data of grid square
statistics.
The grid cell size used as a starting point for the recast should be as small as possible for data accuracy of
converted grid square statistics.

Annex A
(informative)
1)
Japanese national grid square code
[14]
A.1 Introduction to JIS X0410:1976
This annex explains Japanese national grid square codes based on the latitude-longitude method when
data processing machines (referred to as machines) exchanging information between machines and
between machines and humans. Grid square is a small area roughly equivalent to a square, defined based
on geographical latitude and longitude coordinates throughout the Japanese territory, and used as a unit for
displaying the regional information.
The Japanese national grid square code, which is specified in this annex, is adopted to produce both official
and commercial grid square statistics in Japan. The standard for grid square statistics in Japan dates back
to the middle of 1960’s. In 1973, “Standard grid square and grid square code used for the statistics” was
made as the Announcement No. 143 of the Administrative Management Agency (AMA) that hierarchically
defines grid squares covering the entire land of Japan for an official statistics purpose. It has been adopted
as Japanese Industrial Standards (JIS) X0410 since 1976 for industrial purposes.
The Japanese national grid square codes are computed from latitude and longitude based on the GRS80
reference ellipsoid. There are three types of grid squares having geographically hierarchical structure with
different spatial resolutions.
This specification defines the following three types of grid squares: the basic grid square; the divided grid
square that a basic grid square has been divided; and the integrated grid square that some basic grid squares
have been integrated.
A.2 Types of grid square codes
A.2.1 Basic grid square
The basic grid squares are compiled according to the methods shown below:
— Compile the first level grid square partition by dividing the whole area of Japan into blocks measuring 1
degree of longitude by 2/3 degree of latitude;
— Divide the first level grid square partition into 64 (8 by 8) equal parts along longitude and latitude to
compile the second level grid square partitions; and
— Divide the second level grid square partition into 100 (10 by 10) equal parts along longitude and latitude
to compile the third level grid square partitions, which are equal to the basic grid squares.
A basic grid square code shall be a combination of numbers indicating the first level grid square partition,
second level grid square partition, and third level grid square partition, which has the structure shown in
Figure A.1.
1) Japanese national grid square code is specified in JIS X0410:1976 (JISC).

Key
1 number indicating a first level grid square partition
2 tenfold grid square code (the number indicating a second level grid square partition)
3 basic grid square code (the number indicating a third level grid square partition)
NOTE The "N" represents one of the Arabic numerals 0 to 9, that the "5" represents the Arabic numeral 5, and that
the small Arabic numerals above these indicate the number of digits from the most upper digit.
Figure A.1 — A structure of basic grid square code
The code indicating the first level grid square shall be a four-digit number obtained by combining a two-
digit number obtained by multiplying the southernmost latitude of a partition by 1,5 and a two-digit number
obtained by subtracting 100 from the westernmost longitude of the partition. An example of the first level
grid squares is shown in Figure A.2.
Figure A.2 — First level grid square partition
The code indicating the second level grid square shall be a two-digit number obtained by dividing the first
level grid square into eight equal parts along the latitude and longitude directions, and then assigning
numbers from 0 to 7 to each of the parts along the longitude direction from the south and to each of the
parts along the latitude direction from the west, and then combining these numbers, in that order as shown
in Figure A.3.
Figure A.3 — Tenfold grid square (Second level grid square partition)
The code indicating a third level grid square shall be a two-digit number obtained by dividing the second
level grid square into ten equal parts in the latitude and latitude directions, and then assigning numbers
from 0 to 9 to each of the parts along the longitude direction from the south and to each of the parts along the
latitude direction from the west, and then combining these numbers, in that order as shown in Figure A.4.
Figure A.4 — Basic grid square (Third level grid square partition)
A.2.2 Divided grid square
A divided grid square includes the following three types of grid squares: a one-half grid square that the side
length is one half of the basic grid square; a one-quarter grid square that the side length is one quarter of the
basic grid square; and the one-eighth grid square that the side length is one-eighth of the basic grid square.
Table A.1 shows a method to compile the divided grid squares from a basic grid square.

Table A.1 — Divided grid square partition
Type of grid square Method to divide a basic grid square
One-half grid square Divide a basic grid square into two equal parts in the longitude and
latitude directions. Refer to Figure A.4.
One-quarter grid square Divide a basic grid square into four equal parts in the longitude and
latitude directions. Refer to Figure A.5.
One-eighth grid square Divide a basic grid square into eight equal parts in the longitude and
latitude directions. Refer to Figure A.6.
The codes of divided grid squares shall have the structure shown in Figure A.5.
Key
1 the number indicating a first level grid square partition
2 tenfold grid square code (the number indicating a second level grid square partition)
3 basic grid square code (the number indicating a third level grid square partition)
4 divided grid square code (one-eighth level grid square code)
Figure A.5 — Structure of divided grid square code
The code indicating a one-half grid square shall be a nine-digit number which consists of an eight-digit
number to identify a basic grid square code by adding a one-digit number on to the end. This one-digit
number shall be obtained by dividing a basic grid square into two equal parts in the latitude and longitude
directions being numbered from 1 to 4 for the southwest, southeast, northwest, and northeast part in that
order as shown in Figure A.6.
Key
1 basic grid square
Figure A.6 — One-half grid square
Example of a one-half grid square code: NNNNNNNN1
The code indicating a one-quarter grid square shall be a ten-digit number which consists of a nine-digit
number to identify a one-half grid square by adding on a one-digit number to the end of the one-half grid
square code. This one-digit number shall be obtained by dividing a one-half grid square into two equal parts
in the latitude and longitude directions being numbered from 1 to 4 for the southwest, southeast, northwest,
and northeast part in that order as shown in Figure A.7.
Key
1 basic grid square
Figure A.7 — One-quarter grid square
example of a one-quarter grid square code: NNNNNNNN11
The code indicating a one-eighth grid square shall be an eleven-digit number which consists of a ten-digit
number to identify a one-quarter grid square by adding on a one-digit number to the end of the one-quarter
grid square code. This one-digit number shall be obtained by dividing a one-quarter grid square into two
equal parts in the latitude and longitude directions being numbered from 1 to 4 for the southwest, southeast,
northwest, and northeast part in that order as shown Figure A.8.

Key
1 basic grid square
Figure A.8 — One-eighth grid square
example of a one-eighth grid square code: NNNNNNNN111
A.2.3 Integrated grid square
An integrated grid square includes the following three types of grid squares: a double grid square that the
side length is double of the basic grid square; a fivefold grid square that the side length is quintuple of the
basic grid square; and a tenfold grid square that the side length is decuple of the basic grid square. Table A.2
shows a method to compile integrated grid squares from a basic grid square.
Table A.2 — Integrated grid square partition
Type of grid square Method of integration
Double grid square Divide a second level grid square into five equal parts in the longitude and
latitude directions. Refer to Figure A.7.
Fivefold grid square Divide a second level grid square into two equal parts in the longitude and
latitude directions. Refer to Figure A.8.
Tenfold grid square Equal to a second level grid square partition. Refer to Figure A.2.
The codes of integrated grid squares shall have the structure shown in Figure A.9.
Key
1 (integrated grid square code) tenfold grid square code (the number indicating a second level grid square
partition)
2 (integrated grid square code) fivefold grid square code
3 (integrated grid square code) double grid square code
Figure A.9 — Structure of integrated grid square code
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