Report of the Meeting for Promotion of the Use of Big Data No,03
June 2022

Validation of SDG Indicator 11.3.1(Ratio of land consumption rate to population growth rate)

FUJIMURA Hidenori
HAMAMOTO Ko
MIZUNO Takayuki
OCHIAI Osamu
SATO Aki-Hiro
TAKEUCHI Wataru

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(in alphabetical order)

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Geospatial Information Authority of Japan
Japan Aerospace Exploration Agency
National Institute of Informatics
School of Data Science, Yokohama City University
Institute of Industrial Science, the University of Tokyo

This report summarizes the results of surveys and research conducted by theauthors based on the discussions at the Industry-Government-Academia
Partnership Meeting for Promotion of the Use of Big Data and is intended to
be useful for various measures related to the development of official
statistics. The contents and opinions contained herein are those of theauthors alone and do not constitute official statements of the Ministry ofInternal Affairs and Communications.

Contents
1.

2.

3.

4.

5.

6.

7.

8.

Introduction
1.1.
Background and Objectives of Estimation and Verification of SDG11.3.1
1.2.
Flow of Estimation and Verification
Overview of SDG11.3.1
2.1.
Goal 11 “Sustainable cities and communities”, Target 11.3 and SDG11.3.1
2.2.
Overview of SDG11.3.1 Meta Data
2.3.
Calculation values by UN-Habitat
Datasets used
3.1.
Population data
3.2.
Land cover data
3.3.
Administrative district boundary data
Development of error evaluation method
4.1.
95% confidence interval of city population proportion
4.2.
95% confidence interval of LCR
4.3.
95% confidence interval of LCR considering classification error
4.4.
LCR definition by revised meta data
4.5.
Assessment/Verification
Estimation and verification by 3rd level mesh map
5.1.
3rd level mesh visualization application of SDG 11.3.1
5.2.
Collation analysis
5.3.
Nationwide estimation by 3rd level mesh leve
Estimation and verification for government ordinance-designated cities
6.1.
Calculation process
6.2.
Comparison of new/old definition of LCR
6.3.
Comparison and assessment of boundary condition
6.4.
Comparison of LCRPGR for two periods
Estimation and verification of Urbanization degree classification
7.1.
Calculation process
7.2.
Urbanization degree classification results and LCRPGR of each urbanization degreeclassification
Summary（Appendix）（Reference）

1

1. Introduction
1.1. Background and Objectives of Estimation and Verification of SDG11.3.1
The 2030 Agenda for Sustainable Development (hereinafter "2030 Agenda"), adopted by the United
Nations Summit in September 2015, is based on the concept of "Leave No One Behind" and expresses
the determination of the entire international community, including developed and developing countries, towork together to achieve sustainable development and resolve issues. The Sustainable Development
Goals (SDGs) have been set as the core of this agenda. The SDGs consist of 17 goals and 169 targets,with 248 indicators (231 excluding duplicates) to measure progress, and require high-quality, accessible,
timely, and disaggregated data.
Article 76 of the 2030 Agenda states that the use of a wide range of data, including Earth observation andgeospatial information, should be exploited. Special mention should be made of the fact that Japan insistedon the inclusion of Earth observation in the negotiation of this article, which was adopted. Earth observationand geospatial information include satellite, aircraft, ship, and ground observation data and model output
data, which have potential for monitoring at local, national, regional, and global levels and across sectors,
raising expectations for the integration of Earth observation data and geospatial information into statistics
by the UN and national statistical offices in various countries. Among the above, Earth observation satellite
data has the advantage of being able to monitor a wide area repeatedly with uniform quality, but it also has
issues such as huge data volume and complicated analysis. In addition, in order to use Earth observation
satellite data for the calculation of SDG global indicators, its accuracy must be verified and evaluated forits suitability for the purpose.
The UN Inter-Agency and Expert Group on SDG Indicators (IAEG-SDGs), for which the UN Statistics
Division serves as secretariat, is responsible for developing the indicator framework. One of its working
groups, the Working Group on Geospatial Information (WGGI), is considering the use of satellite data andgeospatial information for indicator calculation and provides advice and guidance to the IAEG-SDGs. The
Japan Aerospace Exploration Agency (JAXA) co-chairs the EO4SDG, an SDG initiative of the Group on
Earth Observation (GEO), with the National Aeronautics and Space Administration (NASA) and the
National Institute of Statistics and Geographic (INEGI) of Mexico. And JAXA has been invited by the United
Nations through the GEO to participate in the WGGI as a satellite data expert, together with NASA and theEuropean Space Agency (ESA), and is participating in the study on the application of Earth observation
satellite data to the assessment of SDG global indicators.
In Japan, the “Working Group (WG) on Verlidation of Using Observation Data” (Observation Data WG) has
been set up under the “Industry-Government-Academia Partnership Meeting for Promotion of Big Data”(MIC Big Data Meeting) held by Ministry of Internal Affairs and Communications (MIC). With thecooperation of the Transdisciplinary Federation of Science (an association of 34 academic societies,
including the Japan Statistical Society and the Remote Sensing Society of Japan) and Technology andrelated ministries and agencies, verification of indicators based on Earth observation data and geospatial
information is underway to examine whether they can be positioned as data for use in calculating thegovernment’s SDG global indicators.
SDG11.3.1 was developed and published by the United Nations Human Settlements Programme (UNHabitat) as the Custodian agency. According to the metadata published on the website of the UN Statistics
Division (revised March 1, 2021), SDG 11.3.1 is defined as the ratio of Land Consumption Rate to
Population Growth Rate (LCRPGR) [1-1], and the use of satellite data is also presented in calculating theindicator. In addition, estimates for representative regions in each country for SDG11.3.1 have been2

published by UN-Habitat [1-2], but in order to confirm the validity of the estimates and to use them inpolicies and measures for commercial and industrial activities, it is necessary to expand the calculation
range of the estimates and to evaluate the reliability of the calculated values in each country.
For this purpose, the Working Group (WG) on Verlidation of Using Observation Data conducted trial
calculation and validation of SDG 11.3.1 in FY2021 using data specific to Japan, including JAXA satellite
data. This report describes the content conducted in this work, as well as the method used to calculate thekey indicators included in SDG 11.3.1: Land Consumption Rate (LCR), Population Growth Rate (PGR),
and ratio of Land Consumption Rate to Population Growth Rate (LCRPGR), error evaluation method, error
evaluation results, estimated SDG 11.3.1 related indicator values, error estimates, and Japan-specific data
used are presented.
1.2.

Flow of Estimation and Verification

As shown in Figure/Table 1-1 (F/T 1-1), the process of estimation and verification of SDG11.3.1 is divided
into three parts: estimation of indicators (June to July 2021), evaluation of errors (August to November2021), and improvement of accuracy (November 2021 to January 2022), and carried out in parallel.
Figure/Table 1-1 (F/T 1-1) Schedule of Estimation and Verification of SDG11.3.1 Indicator
図表1-1

Process of Estimation and Verification

Accuracy
Improvement

Error
Evaluation

Indicator
Estimation

JUN

JUL

• Indicator calculations basedon population statistics bymultiple municipalities and
JAXA High-Resolution Land
Use and Land Cover Map• Development of LCR error
evaluation formula at 3rdlevel mesh
• Verification ofclassification error
correction at 3rd level mesh

AUG

SEP

OCT

NOV

• Indicator calculation at 3rd level mesh
• LCR calculation consideringclassification error• Classification of urbanization degreeby population density• LCR error evaluation at 3rd level mesh atseven major metropolitan with large
population concentrations• Four-Quadrant Classification of LCRPGR at
3rd level mesh
• Four-Quadrant Classification of LCRPGR ofseven major metropolitan with large
population concentrations#4 Observation Data WG

FEB• Collation analysis of land use land
classification maps with city correct
answer information and non-citycorrect answer information

MAR• Indicator
calculation of eachurbanization degreeclassification• Update of LCR error evaluation formula basedon the revision of Meta data
• Comparison of PGR calculations between
population mesh data and. population
statistical data
• LCR and PGR scatterplot evaluation forordinance-designated cities• Evaluation ofboundary
conditions• Making report of
MIC Big data
meeting

March 18th
#17 MIC Big Data Meeting#13 MIC Big Data Meeting

August 4th

JAN• Indicator calculation of sevenmajor metropolitan with large
population concentrations• Indicator calculation of ordinancedesignated cities• Impact evaluation of different land
cover map resolutions on the accuracyof city/non-city classification

June 30th

DEC

November 9th#5 Observation data WG

February 22nd
#6 Observation Data WG

2. Overview of SDG11.3.1
2.1. Goal 11 “Sustainable cities and communities”, Target 11.3 and SDG11.3.1
Under Goal 11 “Sustainable cities and communities”, aiming at participatory, integrated and sustainablehuman settlement planning and management for Target 11.3, Indicator 11.3.1 “the ratio of Land
Consumption Rate to Population Growth Rate (LCRPGR)” is set as follows:
Goal 11 “Sustainable cities and communities”
Make cities and human settlements inclusive, safe, resilient and sustainable3

Target 11.3
By 2030, enhance inclusive and sustainable urbanization and capacity for participatory, integrated andsustainable human settlement planning and management in all countries
SDG11.3.1
Ratio of land consumption rate to population growth rate
2.2.

Overview of SDG11.3.1 Meta Data

SDG11.3.1 of the Custodian agency is the United Nations Human Settlements Programme (UN-Habitat),and according to the metadata published by the UN Statistics Division (revised March 1, 2021) [1-1], theratio of Land Consumption Rate to Population Growth Rate (LCRPGR) is defined as the ratio of Land
Consumption Rate (LCR) to Population Growth Rate (PGR). As shown in Equation (Eq.) (2.1), 𝑃𝐺𝑅! isthe change of population in a defined region (country, city, etc.) per year over a given period, and expressedas the natural logarithm of the ratio of population at the end of the period to the population at the beginningof the period divided by the number of years in the period. While, as shown in Eq. (2.2), 𝐿𝐶𝑅! is thechange of land occupied by a city/urban area per year over a given period, and expressed as the ratio of
the change of the land occupied by a city/urban over the period to the land occupied by a city/urban at the
beginning of the period divided by the number of years in the period.
𝑃𝐺𝑅! ="#(%&'!"# ) * "#(%&'! )𝐿𝐶𝑅! =,-.!"# *,-.!

+

,-.!

/

×+

012

𝐿𝐶𝑅𝑃𝐺𝑅! = %32!

(2.1)
(2.2)
(2.3)

!

In Eq. (2.1) and Eq. (2.2), 𝑃𝑜𝑃! is the total population of the target region in the first or the past year,
𝑃𝑜𝑃!"# is the total population of the target region in the last or the present year, 𝑈𝑟𝑏! is the total area ofcity/urban area in the first or the past year, 𝑈𝑟𝑏!"# is the total area of city/urban area in the last or thepresent year and y is the number of years in the measurement period for population and the area of theregion. Where ln (𝑥) means the natural logarithm.
Calculation of 𝐿𝐶𝑅𝑃𝐺𝑅! in Eq. (2.3) requires population data and urban and city area data, and themetadata suggests the use of gridded population datasets and medium-to-high resolution satellite datasuch as land cover maps.
SDG indicator metadata and indicator formulas are reviewed and updated periodically. For SDG 11.3.1,
prior to the March 1, 2021 revision, the formula for 𝐿𝐶𝑅! in Eq. (2.3) was defined as Eq. (2.4) [2-2], thus
this paper also compares the old and revised versions in Japan.

𝐿𝐶𝑅! =$%('()!"# ) ,$% ('()! )
-

(2.4)

As shown in Eq. (2.3), since LCRPGR is the ratio of LCR to PGR, LCRPGR is positive both when 𝑃𝐺𝑅!
and 𝐿𝐶𝑅! are positive values and when 𝑃𝐺𝑅! and 𝐿𝐶𝑅! are negative values as shown in F/T 2-1.
Therefore, the assessment of each value of 𝑃𝐺𝑅! and 𝐿𝐶𝑅! is also necessary to compare and evaluate
𝐿𝐶𝑅𝑃𝐺𝑅! among multiple cities and over multiple periods.
4F/T 2-1 Four-Quadrant Classification of LCRPGR

2.3.

Calculation values by UN-Habitat

UN-Habitat releases LCR, PGR, and LCRPGR estimations for representative cities in each country,
including Tokyo, Osaka, Fukuoka, Okayama, and Yamaguchi for Japan [1-2]. As shown in F/T 2-2, theseestimates are based on a limited number of cities and a long calculation interval of 15 years. The time
resolution of the indicator and the number of target cities are considered insufficient for use in comparing
major Japanese cities, including government ordinance-designated cities, and for policy decision making.
Therefore, it is necessary to set the target cities and make calculations based on the situation in one’s own
country for the domestic use of Indicator 11.3.1.
F/T 2-2 LCRPGR Calculation values by UN-Habitat (Excerpts from Japanese Cities)

Cities

LCR_1990_ LCR_2000_ PGR_1990_ PGR_2000_ LCRPGR_1990 LCRPGR_2000
to_2000
to_2015
to_2000
to_2015_to_2000_to_2015

Tokyo

0.014851

0.01730.008484

0.00658

1.750382.62831Osaka

0.015277

0.005407

0.002587

0.00092

5.905655.88961

Fukuoka

0.030163

0.008188

0.003192

0.00032

9.44867

25.27458

Okayama

0.006747

0.005681

0.000163-

41.38004

Yamaguchi 0.017941

0.003232

-

-

-

-

3. Datasets used
3.1. Population data
Considering that the PGR is estimated by 3rd level mesh and by administrative district unit, the following
population 3rd level mesh statistical data and population administrative boundary statistical data were used
for PGR estimation.
Population 3rd level mesh statistical data:
The following 3rd level mesh (1km grid) population statistical data was retrieved from Portal Site of Official
Statistics of Japan, e-Stat of MIC/Statistics Map of Japan (Statistics GIS) [3-1] via API and used.

Ministry of Internal Affairs and Communications 2010 Population Census – World Geodetic System(1km mesh) Total Population by Sex and Total Households [3-2] (2010 Population Census
Population 3rd Level Mesh Statistical Data)
5

Ministry of Internal Affairs and Communications 2015 Population Census – World Geodetic System(1km mesh) No.1 Basic Population Data [3-3] (2015 Population Census Population 3rd Level Mesh
Statistical Data)

Population administrative boundary statistical data:
The following population statistical data by administrative district was retrieved from Portal Site of Official
Statistics of Japan, and the total population in 2010, 2015, and 2020 for government ordinancedesignated cities was used.

Ministry of Internal Affairs and Communications, Population Census / 2010 Population Census /Basic Complete Tabulation on Population and Households Japan [3-4] (2010 Population Census
Population Administrative Boundary Statistical Data)
Ministry of Internal Affairs and Communications, Population Census / 2015 Population Census /Basic Complete Tabulation on Population and Households Japan [3-5] (2015 Population Census
Population Administrative Boundary Statistical Data)
Ministry of Internal Affairs and Communications, Population Census / 2020 Population Census /Basic Complete Tabulation on Population and Households Japan [3-6] (2020 Population Census
Population Administrative Boundary Statistical Data)
3.2.

Land cover data

Data for the period (version) shown in F/T 3-1 of JAXA HRLULC Map: High-Resolution Land Use and Land
Cover Map [3-7] was used as the data for the city/urban area (ratio of city/urban area to the target region)
required for the LCR calculation. In the LCR estimation, the built-up and solar panel are regarded as urban
areas and cities in the classification categories of JAXA HRLULC Map.
F/T 3-1 JAXA High-Resolution Land Use and Land Cover Map List
PeriodResolution
Classification
Overall
Producer Accuracy(Version)classificationof Artificial Structure
accuracy of all
Classification*
classifications
2006-2011
10 m
10 categories (Water
78.0 %
96.5 %（ver.16.09）
bodies, Built-up, Paddy
field, Cropland, Grassland,
DBF (deciduous broad-leaf
forest), DNF (deciduous
needle-leaf forest), EBF(evergreen broad-leaf
forest), ENF (evergreen
needle-leaf forest), Bare
2014-2016
30 m
10 categories (same as
81.62 %
81.8 %（ver.18.03）
ver16.09)
2018-2020
10 m
12 categories (ver16.09 +88.85 %
95.3 %（ver.21.11）
Bamboo forest, Solar panel)*The percentage indicates the ratio of built-up verification data points actually classified as built-up (recall
ratio). It is calculated based on the confusion matrix of each version.

6

The accuracy of JAXA HRLULC Map was verified by comparing the land cover classification of the visually
verified data points with the land cover classification automatically classified from the satellite images, andthe results are organized and released in a confusion matrix shown in Appendix F/T A-1, F/T A-2, and F/T
A-3. [3-8][3-9][3-10]
Because JAXA HRLULC Map uses different satellite data for classification in each target period, theresolution and classification accuracy for built-up differ for each target period, as shown in F/T 3-1. In fact,
when the land cover maps were compared period by period, a case was identified where built-up finely
distributed in mountainous areas and agricultural land (paddy field) that were captured in the land covermaps of 2006-2011 (Ver. 16.09) and 2018-2020 (Ver. 21.11) were not captured in the 2014-2016 (Ver.
18.03). As a result, there is a possibility that errors in detection/non-detection of built-up may occur inmountainous and suburban areas due to differences in resolution, therefore, an error evaluation that takes
into account the accuracy of land cover map classification is necessary in estimating the LCR.
F/T 3-2 Comparison of JAXA HRLULC Maps

Four points (black) in the image indicate the four corners of the 3rd level mesh code “2052364018.”
No classification of built-up (red) only for 2014-2016 (Ver. 18.03) in the area enclosed by the dashedline (black).
3.3.

Administrative district boundary data

In the estimation of PGR and LCR for each administrative district, “Ministry of Land, Infrastructure,
Transport and Tourism, Digital National Land Information, Administrative Districts Boundary Data (2021)”[3-12] (2021 Administrative Districts Data) was retrieved from GIS website of Ministry of Land,
Infrastructure, Transport and Tourism (MLIT) [3-11] for extracting population 3rd level mesh statistical data
and land cover map for each administrative district.
4. Development of error evaluation method
4.1. 95% confidence interval of city population proportion
When the 3rd level mesh statistic of the city ratio in year t within the parcel indicated by the region wi is pt,i,and the area classified as city in year t from the definition of Eq. (2.4) is mt(wi), LCR between year t andyear t+n in the region wi is

𝐿𝐶𝑅(𝑤) =ln 0!"# (1$ ),ln 0! (1$ )
-

(4.1)

When the area of region wi is A(wi), the area mt(wi) classified as city within the region in year t can be
expressed by the city ratio pt,ii within wi as follows,7𝑚! (𝑤2 ) = 𝐴(𝑤2 )𝑝!,2(4.2)

Substituting Eq. (4.2) into Eq. (4.1), the LCR, originally defined as the logarithmic rate of increase in city
area, becomes

𝐿𝐶𝑅(𝑤) =ln 45(1$ )6!"#,$ 7,ln 45(1$ )6!,$ 7-=

ln 6!"#,$ ,ln 6!,$
-

(4.3)

Thus, it is evident that it is possible to calculate the LCR for region wi using the logarithm of the ratio of thecity ratios.
Furthermore, consider estimating the city population proportion pt,i by the sample city proportion 𝑝2!,2 =𝑛!,2 /𝑁!,2 , which is calculated by the total pixels Nt,i in region wi identified by the land cover map in year t
and the number of city identified pixels nt,I. Since the binomial distribution B(pt,i,Nt,i) can be approximatedby the normal distribution N(Nt,ipt,i,Nt,ipt,i(1-pt,i)) for sufficiently large Nt,i, the 95% confidence interval for thecity population proportion pt,i is given by

𝑝2!,2 <1 − 𝑝2!,2 =𝑝2!,2 <1 − 𝑝2!,2 =𝑝2!,2 − 1.96;≤ 𝑝!,2 ≤ 𝑝2!,2 + 1.96;
𝑁!,2
𝑁!,2
4.2.

95% confidence interval of LCR

Consider here how an error εx sufficiently small compared to x0 can cause error propagation for any twiceor more differentiable continuous function f(x). Let 𝑧8 = 𝑓(𝑥8 ) when there is a relation of transformation:;

z=f(x) and 𝑥 = 𝑥8 ± 𝜀9 . In this case, the error propagation of z is 𝑧 = 𝑧8 ± D D

𝜀 . If 𝑓(𝑥) = ln(𝑥),:9 9<9& 9

then

:
:9

=

ln(𝑥)= 9 , thus,ln(𝑥0 ± 𝜀𝑥 ) = ln(𝑥0 ) ±𝜀𝑥𝑥0Therefore, by setting 𝑝!"#,2 and 𝑝!,2 as the city population proportion with year t+n and year t,
respectively, and δαt+n,i and δαt,i as half of the 95% confidence interval width, respectively, the sample LCR
is expressed as

H (𝑤2 ) =𝐿𝐶𝑅

ln<𝑝!"#,2 ± 𝛿𝛼!"#,2 = − ln<𝑝!,2 ± 𝛿𝛼!,2 =ln<𝑝!"#,2 = − ln<𝑝!,2 = 1 𝛿𝛼!"#,2 𝛿𝛼!,2
=
± L
+
M
𝑦
𝑦
𝑦 𝑝!"#,2𝑝!,2Where by expressing the population LCR as

𝐿𝐶𝑅(𝑤2 ) =$%46!"#,$ 7,$%46!,$ 7-

the 95% confidence interval of the population LCR is calculated as

8

(4.4)

H (𝑤2 ) − = N@A!"#,$ + @A!,$O ≤ 𝐿𝐶𝑅(𝑤2 ) ≤ 𝐿𝐶𝑅
H (𝑤2 ) + = N@A!"#,$ + @A!,$ O
𝐿𝐶𝑅
- 6
6
- 6
6
!"#,$

!"#,$

𝑝𝑡+𝑛,𝑖 41−𝑝𝑡+𝑛,𝑖 5

𝑝 41−𝑝𝑡,𝑖 5

𝑁𝑡+𝑛,𝑖𝑡,𝑖Where 𝛿𝛼𝑡+𝑛,𝑖 = 1.96/

H (𝑤2 ) −𝐿𝐶𝑅!,$

, 𝛿𝛼𝑡,𝑖 = 1.96/ 𝑡,𝑖 𝑁

!,$

(4.5)

then

1.96
1 − 𝑝Q
1 − 𝑝R
1.96
1 − 𝑝Q
1 − 𝑝R
!"#,J
!,J
!"#,J
!,J
H (𝑤2 ) ++;+;P;
S ≤ 𝐿𝑅𝐶(𝑤2 ) ≤ 𝐿𝐶𝑅
P;
S𝑦𝑛!"#,2𝑛!,2𝑦𝑛!"#,2𝑛!,2Where the sample LCR is expressed by the total pixels Nt,i in the region wi identified by the land cover map
and the number of pixels nt,i identified as city

H (𝑤2 ) = $%4#!"#,$ /L!"#,$ 7 , $%4#!,$ /L!,$7
𝐿𝐶𝑅
4.3.

(4.6)

95% confidence interval of LCR considering classification error

Correction method by confusion matrix of classification error that had been used for SDG15.4.2 [4-1] isused. Correct classification error of city coverage ratios in the city/non-city binary classification problem by
calculating the conditional probability R(a|b) from a 2 × 2 confusion matrix for city (1)/non-city (0) binaryclassification. Calculate 95% confidence interval of LCR which is corrected the city/non-city classification
error for each 3rd level mesh.
F/T 4-1 Confusion matrix for city/non-city binary classification

validated b

Confusion matrix

classified aa/bcity (x=1)

non-city (x=0)

city (x=1)

TPFP

non-city (x=0)

FN

TN

Consider the confusion matrix for the binary classification, as shown in F/T 4-1. Let pt,i be the true city
population proportion of region wi in year t, and define 𝑅! (a|b) as the conditional probability that actual
land cover (validated) is b={1(city), 0(non-city), but land cover classification data (classified) is a={1(city),
0(non-city). From the conditional probability definition,

𝑅! (𝑎|𝑏) =6+,,! (M,))
6,,! ())

the point estimate by sample proportions of the conditional probability 𝑅! (a|b) is calculated as

𝑇𝑁!
𝐹𝑃!
, 𝑅! (1|0) =𝐹𝑃! + 𝑇𝑁!
𝐹𝑃! + 𝑇𝑁!
𝐹𝑁!
𝑇𝑃!
𝑅! (0|1) =, 𝑅! (1|1) =𝑇𝑃! + 𝐹𝑁!
𝑇𝑃! + 𝐹𝑁!
𝑅! (0|0) =

9

(4.7)

From Eq. (4.7),

𝑝5 (𝑎) = Z 𝑝5N (𝑎, 𝑏) = Z 𝑅! (𝑎|𝑏)𝑝N (𝑏)))is established, thus especially𝑝5 (1) = ∑)<8,= 𝑅! (𝑎|𝑏)𝑝N (𝑏) = 𝑅! (1|1)𝑝N (1) + 𝑅! (1|0)𝑝N (0)(4.8)

Let pt,i be the true sample city proportion of region wi in year t. Then 𝑝N (1) = 𝑝̂!,2 , 𝑝N (0) = 1 − 𝑝̂!,2 . Thus,
the sample city proportion 𝑝2!,2 = 𝑛!,2 /𝑁!,2 calculated in the situation where a classification error is
included in the observation is

𝑝2!,2 = 𝑅! (1|1)𝑝̂ !,2 + 𝑅! (1|0)(1 − 𝑝̂!,2 )Therefore, from
6O ,P (=|8)!,$!
𝑝̂ !,2 = P (=|=),P
(=|8)
!

(4.9)

!

it may be better to correct the true sample city proportion from the sample proportion estimated withclassification error. Here only the region that is 𝑝2!,2 ≥ 𝑅! (1|0) is subject to estimation.
In addition, estimation from the confusion matrix of Rt(a|b) is the sample proportion, so there areinterval estimates. 95% confidence interval is!"!
#$! %!"!
#$!
#$! %!"!
#"!
!$! %#"!
!$!
!$! %#"!

− 1.96U!"!
#$! %!"!

− 1.96U− 1.96U− 1.96U#$!
#$! %!"!
#"!

!$! %#"!
!$!
!$! %#"!

V1 −
V1 −
V1 −
V1 −

!"!
#$! %!"!

W /(𝐹𝑃& + 𝑇𝑁& ) ≤ 𝑅& (0|0) ≤#$!
#$! %!"!
#"!
!$! %#"!
!$!
!$! %#"!

W /(𝐹𝑃& + 𝑇𝑁& ) ≤ 𝑅& (1|0) ≤W /(𝑇𝑃& + 𝐹𝑁& ) ≤ 𝑅& (0|1) ≤W /(𝑇𝑃& + 𝐹𝑁& ) ≤ 𝑅& (1|1) ≤!"!
#$! %!"!
#$!
#$! %!"!
#"!
!$! %#"!
!$!
!$! %#"!

+ 1.96U!"!
#$! %!"!

+ 1.96U+ 1.96U+ 1.96U#$!
#$! %!"!
#"!

!$! %#"!
!$!
!$! %#"!

V1 −
V1 −
V1 −
V1 −

!"!
#$! %!"!

W /(𝐹𝑃& + 𝑇𝑁& )(4.10)

W /(𝐹𝑃& + 𝑇𝑁& )(4.11)

W /(𝑇𝑃& + 𝐹𝑁& )(4.12)

W /(𝑇𝑃& + 𝐹𝑁& )(4.13)#$!
#$! %!"!
#"!
!$! %#"!
!$!
!$! %#"!

By applying Eq.s (4.4), (4.11) and (4.13) to Eq. (4.9), the conditional probabilities calculated from thesample proportion and confusion matrix of the region wi in year t, respectively, from the error
propagation Eq. for the four arithmetic operations can be expressed as𝑎!,2 = 𝑝2!,2 , 𝑏! = 𝑅! (1|0), 𝑐! = 𝑅! (1|1)
and 𝛿 𝑎𝑡,𝑖 , 𝛿𝑏𝑡 , 𝛿𝑐𝑡 are half of the 95% confidence interval width of 𝑝2!,2 , 𝑅! (1|0), 𝑅! (1|1), respectively,
the confidence interval for the city population proportion pt,i of the region wi in period t is given by
M!,$ ,)!
]! ,)!

@M "@)@] "@)M ,)@M "@)@] "@)

!,$

!

!

!,$

!

L1 − N M !,$,,) ! + ]! ,) !OM ≤ 𝑝!,2 ≤ ]!,$,) ! L1 + N M!,$ ,) ! + ]! ,) ! OM
!

!

!

!

Eq.s (4.9) and (4.14) are applied to Eq.s (4.4) and (4.5), respectively. Here10!

(4.14)

𝑎!"#,2 − 𝑏!"#𝑐!"# − 𝑏!"#𝑎!,2 − 𝑏!
𝑝̂ !,2 =𝑐! − 𝑏!
𝑎𝑡+𝑛,𝑖 − 𝑏𝑡+𝑛 𝛿𝑎𝑡+𝑛,𝑖 + 𝛿𝑏𝑡+𝑛 𝛿𝑐𝑡+𝑛 + 𝛿𝑏𝑡+𝑛
𝛿𝛼𝑡+𝑛,𝑖 =+01𝑐𝑡+𝑛 − 𝑏𝑡+𝑛
𝑎𝑡+𝑛,𝑖 − 𝑏𝑡+𝑛
𝑐𝑡+𝑛 − 𝑏𝑡+𝑛
𝑎𝑡 − 𝑏𝑡 𝛿𝑎𝑡 + 𝛿𝑏𝑡 𝛿𝑐𝑡 + 𝛿𝑏𝑡
𝛿𝛼𝑡,𝑖 =+
01
𝑐𝑡 − 𝑏𝑡 𝑎𝑡 − 𝑏𝑡
𝑐𝑡 − 𝑏𝑡
𝑝̂!"#,2 =Thus, the 95% confidence interval for the sample LCR and population LCR is-

H (𝑤2 ) =
𝐿𝐶𝑅

./!"#
- ./!
_ , $%^ !,$_0!"# ./!"#
0! ./!

$%^ !"#,$(4.15)-

9 (𝑤! ) − " <$%!"#,%&$'!"# + $)!"#&$'!"# + $%!,%&$'! + $)!&$'!> ≤ 𝐿𝐶𝑅(𝑤! ) ≤ 𝐿𝐶𝑅
9 (𝑤! ) + " <$%!"#,%&$'!"# + $)!"#&$'!"# + $%!,%&$'! + $)!&$'!>
𝐿𝐶𝑅
#

4.4.

%!"#,% ('!"#)!"# ('!"#%!,% ('!

)! ('!

#%!"#,% ('!"#)!"# ('!"#%!,% ('!

)! ('!

(4.16)LCR definition by revised meta data

According to Eq. (2.2), which is the revised metadata [2-2], the LCR for region wi is defined in place of Eq.
(4.1) as

𝐿𝐶𝑅(𝑤2 ) =0!"# (1$),0! (1$)=×
0! (1$ )-(4.17)

where mt+n (wi) and mt(wi) are the region classified as city in year t+n and the area classified as urban inyear t in area wi. where y is the number of years between year t+n and year t. By substituting Eq. (4.2) into
Eq. (4.17),

𝐿𝐶𝑅(𝑤2 ) =5(1$ )6!"#,$ ,5(1$ )6!,$5(1$ )6!,$

=

×- =

6!"#,$ ,6!,$
6!,$

=

6

=

× - = N 6!"#,$ − 1O × !,$(4.18)

By applying Eq. (4.14) to Eq. (4.18), and also using the following error propagation Eq. for the ratio

𝛼 ± 𝛿𝛼 𝛼𝛿𝛼 𝛿𝛽= a1 ± N + Ob
𝛽 ± 𝛿𝛽 𝛽𝛼𝛽expressed as' (𝑤! ) ± 𝛿 = ."!"#,%±$%!"#,% − 11 × & = 3"!"#,% 31 ± .$"!"#,% + $"!,% 15 − 15 × & = 3(%!"#,%)*!"#) (,!)*!) 31 ± .$%!"#,%-$*!"# + $,!"#-$*!"# + $%!,%-$*! + $,!-$*!15 − 15 × &𝐿𝐶𝑅"!,% ±$%!,%

'

"!,%

"!"#,%"!,%'(,!"#)*!"#) (%!)*!)%!"#,% )*!"#,!"#)*!"#%!,% )*!

,!)*!

'(4.19)

Therefore, the 95% confidence interval for the LCR is given by'(%!"#,% )*!"#) (,!)*!)(,!"#)*!"#) (%!)*!)$%!"#,% -$*!"#'1 − *%!"#,% )*!"#+$% -$*(%)* ) (, )* )$%-$*$% -$*$,!"#-$*!"#$, -$*&$, -$*$, -$*&+ !,% ! + ! !,- − 1- × ≤ 𝐿𝐶𝑅(𝑤! ) ≤ ' !"#,% !"# ! ! '1 + * !"#,% !"# + !"# !"# + !,% ! + ! !,- − 1- ×,!"#)*!"#%!,% )*!
,!)*!
'(,!"#)*!"#) (%!,% )*!)%!"#,% )*!"#,!"#)*!"#%!,%)*!
,!)*!
'

11

(4.20)

4.5.

Assessment/Verification

Calculate the sample LCR and 95% confidence intervals for Eq.s (4.15) and (4.16) derived in section 4.3
using actual data. For this calculation, a binary classification confusion matrix for city and non-city wasderived based on the published confusion matrix values [3-8][3-9] for Ver 16.09 (2006-2011) and Ver 18.03(2014-2016) of the JAXA HRLULC Map [3-7], and from it, point estimates and 95% confidence interval
widths for the conditional probabilities 𝑅! (1|0) and 𝑅! (1|1) were calculated. F/T 4-2 shows theconditional probabilities for each period. F/T 4.3 also shows the calculation results of the sample LCR and
95% confidence interval widths for the N35E136 tile.
F/T 4-2 Point estimates and 95% confidence interval widths of conditional probabilities 𝑅! (1|0) and𝑅! (1|1) for (left) Ver 16.09 (2006-2011) and (right) Ver 18.03 (2014-2016)
Ver 16.09 (2006-2011)
Ver 18.03 (2014-2016)
Point estimate

𝑅! (1|0)
𝑅! (1|1)

0.005937235
0.9652174

95%
confidence
interval width
0.004385289
0.02368022

Point estimate

𝑅!"# (1|0) 0.01832061
𝑅!"# (1|1) 0.8178808

95%
confidence
interval width
0.005135234
0.04352868

F/T 4-3 3rd level mesh map of (left) sample LCR (corrected values) and (right) 95% confidence interval
width in eighth quartile display for N35E136 tile

5. Estimation and verification by 3rd level mesh map
5.1. 3rd level mesh visualization application of SDG 11.3.1
Following the calculation process shown in F/T 5-1, an algorithm was constructed to calculate LCR (3rdlevel mesh statistics) from the JAXA HRLULC Map [3-7] and PGR (3rd level mesh statistics) from the MIC
Population Census Population 3rd level Mesh Statistical Data [3-2][3-3]. An application incorporating thisalgorithm was developed on MESHSTATS, which is currently under prototype production in the JST MIRAIPROJECT, “Architecture Design and Actual Proof for Self-distributed World Grid Square Statistics
Platform”.

12F/T 5-1 Overview of LCRPGR (3rd level mesh statistics) calculation processThis application is available as open data and open source, along with calculated indicator values from the
Research Institute for World Grid Squares [5-1]. By using this application, LCRPGR indicators can becalculated and visualized at the 3rd level mesh statistical level, as shown in F/T 5-2, and can also be
aggregated by administrative division.
F/T 5-2

LCRPGR indicator visualization application

13

5.2.

Collation analysis

In order to verify the validity of the LCR calculated at the 3rd level mesh level, a collation analysis was
conducted with data on the age of land and buildings generated using different data collection methods.
The data used was statistical data on building age and area data by zip code derived from fire insurance
operations data provided by Mitsui Sumitomo Insurance[5-2]. F/T 5-3 shows a mesh map of the numberof buildings that started construction in the 2010s and the 3rd level mesh-level LCR (corrected value),obtained by approximate transformation of building age data by zip code derived from Mitsui Sumitomo
Insurance operations data to a 3rd level mesh.
F/T 5-3 (Left) Mesh map of 3rd level mesh data of number of buildings construction started in 2010sand (Right) 3rd level mesh data of LCR (corrected value)

F/T 5-4 Scatter plot of 3rd level mesh data of number of new buildings in 2010s and 3rd level meshdata of LCR (corrected values)

14

F/T 5-4 shows a scatter plot of 3rd level mesh data for the number of buildings construction started in the2010s and 3rd level mesh level LCR (corrected value), obtained from the building area age statistics data
provided by Mitsui Sumitomo Insurance. The scatter plot shows that 98.28% of the 3rd level mesh with
LCR (corrected value) greater than 0 have buildings newly constructed in the 2010s.
To calculate the correction and 95% confidence interval of the sample LRC accounting for classification
errors, the conditional probabilities calculated from the confusion matrix (Appendix F/T A-4, F/T A-5, and
F/T A-6), which is calculated from the collation analysis of the residential location coordinates from the
Ministry of Land, Infrastructure, Transport and Tourism (MLIT), National Spatial Planning and Regional
Policy Bureau, National Land Information Division, Land Market Value Publication Data (2010, 2015, 2020)[5-3] and the JAXA HRLULC Map (2006-2011, 2014-2016, and 2018-2020), is used.
Using the calculated values of 𝑅! (1|0) and 𝑅! (1|1) and their respective 95% confidence interval widths
shown in F/T 5-5, calculations were performed using Eq.s (4.15), (4.16) and (4.18) and (4.20).
F/T 5-5 𝑅! (1|0) and 𝑅! (1|1) calculated by the collation analysis s from the MLIT Land Market Value
Publication Data (2010, 2015, 2020) and the JAXA HRLULC Map (2006-2011, 2014-2016, and 20182020).
Point estimate
95% confidence interval width
2006-2011
0.00594
0.00439
𝑅! (1|0)0.89135
0.00364
𝑅! (1|1)
2014-2016
0.01832
0.00514
𝑅! (1|0)0.86368
0.00437
𝑅! (1|1)
2018-2020
0.00801
0.00369
𝑅! (1|0)0.94365
0.00279
𝑅! (1|1)
In the subsequent estimation calculations, 𝑅! (1|0) and 𝑅! (1|1), shown in F/T 5-5, were used as theconditional probabilities calculated from the confusion matrix used to correct for classification error.
5.3.

Nationwide estimation by 3rd level mesh level

Next, error evaluation of PGR and LCR at the 3rd level mesh is verified in 7 populated cities. F/T 5-6 shows
the visualization results of LCR and PGR for 3rd level meshes in Sapporo, Hokkaido; Sendai, Miyagi
Prefecture; Nagoya, Aichi Prefecture; Osaka, Osaka Prefecture; Hiroshima, Hiroshima Prefecture; andFukuoka, Fukuoka Prefecture, and the total values in these regions.
F/T 5-6 PGR and LCR at the 3rd level mesh and total values in seven major metropolitan areas(Sapporo, Hokkaido; Sendai, Miyagi Prefecture; Nagoya, Aichi Prefecture; Osaka, Osaka Prefecture;
Hiroshima, Hiroshima Prefecture; and Fukuoka, Fukuoka Prefecture)15

6. Estimation and verification for government ordinance-designated cities
6.1. Calculation process
In order to compare the LCRPGRs of major cities in Japan, the LCR, PGR, and LCRPGR of each city were
calculated according to the following procedure (F/T 6-1) using the data described in Chapter 3 for the 20
government ordinance-designated cities shown in Appendix F/T A-7 as major cities representing eachregion in Japan. The sample LCR and 95% confidence interval widths were calculated using Eq. (4.16)
and (4.20) and the values in F/T 5-5.
F/T 6-1 Conceptual Diagram of LCRPGR Calculation Process for Government-Designated Cities

16

Process (6-1) Create boundary data for each ordinance-designated city from the MLIT, Digital National Land

Information, Administrative Districts Boundary Data (2021) [3-12]
Process (6-2) Calculate the total population within the boundaries of each ordinance-designated city using
MIC 2010 Population Census Population 3rd level Mesh Statistical Data [3-2], 2015 Population
Census Population 3rd level Mesh Statistical Data[3-3] and the results of Process (6-1)
Process (6-3) Calculate PGR (administrative boundary statistics 3rd level mesh approximation) (One period:
2010-2015) using the results of Process (6-2)
Process (6-4) Calculate PGR (administrative boundary statistics) using the total population of ordinancedesignated cities acquired from MIC 2010 Population Census Population administrative
boundary statistical data [3-4], 2015 Population Census Population administrative boundary
statistical data [3-5] and 2020 Population Census Population administrative boundary
statistical data [3-6] (Two periods: 2010-2015, 2015-2020)
Process (6-5) Calculate the ratio of pixels of built-up (including the classification of solar panels in 2020) to
the total number of pixels within the ordinance-designated city boundaries using JAXAHRLULC Map [3-7] and the results of Process (6-1)
Process (6-6) Calculate LCR (administrative boundary statistics) using the results of Process (6-5) (Twoperiods: 2010-2015*, 2015-2020)*For the period 2010-2015, both the old and new LCR formulas were used.
Process (6-7) Calculate LCRPGR (administrative boundary statistics 3rd level mesh approximation) using
the results of Process (6-3) and Process (6-6)
Process (6-8) Calculate LCRPGR (administrative boundary statistics) using the results of Process (6-4) andProcess (6-6).
6.2.

Comparison of new/old definition of LCR

The results of the LCR (administrative boundary statistics) obtained in the calculation process (6-6) inSection 6.1 are plotted on a scatter plot using the two axes of LCR old definition - LCR new definition for
the LCR corrected values for representative cities, and are shown in F/T 6-3. The point estimates are
confirmed to be almost the same values for the LCR old and new definitions. Furthermore, for the 95%
confidence interval widths by the LCR old and new definitions, the value for the LCR new definition isslightly larger than that for the LCR old definition.
F/T 6-3 Scatter plot of LCR old definition - LCR new definition for representative cities (2010-2015)

Error bars indicate 95% confidence interval widths for the old and new LCR definitions.
17

6.3.

Comparison and assessment of boundary condition

Two different boundary conditions, A and B shown in F/T 6-4, are considered when calculating LCR(administrative boundary statistics 3rd level mesh approximation) and PGR (administrative boundary
statistics 3rd level mesh approximation) for administrative units such as ordinance-designated city. A is amethod that uses pixels or grids within an administrative block region to approximate polygon aggregation.
B is a method that approximates the polygon aggregate using pixels or grids that overlap with theadministrative block region. The actual aggregate value is always between these values.
F/T 6-4 Comparison of boundary conditions for PGR

In the estimation and verification of this paper, the LCR (administrative boundary statistics 3rd level meshapproximation) and PGR (administrative boundary statistics 3rd level mesh approximation) obtained in
Section 5.1 were calculated using boundary condition B, while the LCR (administrative boundary statistics
3rd level mesh approximation) and PGR (administrative boundary statistics 3rd level mesh approximation)
obtained in Section 6.1 were calculated using boundary condition A. The results of these calculations arecompared for Kanagawa and Fukuoka prefectures as representative cities, as shown in F/T 6-5.
F/T 6-5 Comparison results of boundary conditions (Left: Kanagawa Prefecture, Right: Fukuoka
Prefecture)

Kanagawa

Population Census 2010
Population Census 2015

Fukuokahttps://www.e-stat.go.jp/stat-search/filedownload?statInfId=000012660889&fileKind=0
https://www.e-stat.go.jp/stat-search/filedownload?statInfId=000031495550&fileKind=0

Population Census 2010https://www.e-stat.go.jp/stat-search/filedownload?statInfId=000012660889&fileKind=0

Population Census 2015

https://www.e-stat.go.jp/stat-search/filedownload?statInfId=000031495550&fileKind=0

Boundary
conditions

LCR
correction
value

LCR
minimumvalue

LCR
maximumvalue

PGR

LCRPGRLCRPGR
correction minimum
value
value

LCRPGR
maximum
value

Boundary
conditions

LCR
correction
value

A

0.01996499

0.008768204

0.03116177

0.001705654

11.70518

5.140671

18.26969

A-0.01215291 -0.03382473 -0.003805619 0.001194026 -15.75776-28.3283-3.187215

B

0.01920.00770.03080.001810.4989

4.178816.8191B-0.0202-33.7963-4.0483

Population in Kanagaw：9048000 (2010), 9126000 (2015), PGR =0.001716749

LCR
minimum
value-0.0361

LCR
maximumvalue-0.0043

PGR

LCRPGRLCRPGR
correction minimum
value
value

0.0011-18.9223

LCRPGR
maximum
value

Population in Fukuoka：5072000 (2010), 5102000 (2015), PGR =0.001179481

Boundary
conditions

pixel_count_beforebuilt.up_cou pixel_countnt_before_afterbuild.up_co
unt_after

population_
beforepopulation_
after

Boundary
conditions

pixel_count_beforebuilt.up_cou pixel_countnt_before_afterbuild.up_co
unt_after

population_
beforepopulation_
after

A

34527477

11380055

3836380

1374150

89508879027549A

69694845

123701107743857

129093650070245037006B

37860000

11717771

4207000

141371893568549442973B

76395000

12501814

84888501304583

50992685126575

B is aggregated by mesh included in prefectural boundaries using values aggregated by 3rd level mesh.

B is aggregated by mesh included in prefectural boundaries using values aggregated by 3rd level mesh.

As shown in F/T 6-5, the LCRPGR correction values, minimum LCRPGR values, and maximum LCRPGR
values are slightly different due to different methods of calculating the boundary conditions, but they areof roughly the same order of values. Since two different programs were implemented and output thecalculation results for the A and B boundary conditions, it was determined that there were no errors in thecalculation logic for either method.

18

6.4.

Comparison of LCRPGR for two periods

The results for the two periods (2010-2015 and 2015-2020) of the PGR (administrative boundaries
statistics) and LCR (administrative boundaries statistics) corrected values obtained in the calculation
process (6-4) and (6-6) in section 6.1 are indicated in F/T 6-6, a scatter plot using two axes of LCR-PGR.
(List of Calculation results is indicated in Appendix F/TA-8, F/TA-9.)
F/T 6-6 Scatter plot of LCR-PGR of Government-Designated Cities(Top: 2010-2015, Bottom: 2015-2020)

Error bars indicate 95% confidence interval widths for LCR correction values.
19

As shown in F/T 6.6, the trend of change in cities indicating positive LCR and negative PGR was confirmed:
only Sakai City was applicable in 2010-2015, while four cities (Niigata, Hamamatsu, Shizuoka, andKumamoto) were applicable in 2015-2020.
In order to understand the change during two periods, the difference between LCR (administrative
boundary statistics) correction values and the difference between PGR (administrative boundary statistics)
correction values for the two periods are indicated in F/T 6-7, a scatter plot of the two axes of LCR-PGR.
F/T 6-7 Scatter plot of Difference of LCR – Difference of PGR of Government-Designated Cities(“2015-2020” - “2010-2015”)

As shown in F/T 6-7, it was confirmed that it is possible to compare the trend of changes in LCR and PGR
for each ordinance-designated city, and that no ordinance-designated city shows a trend of increasing both
LCR and PGR correction values.
7. Estimation and verification of Urbanization degree classification
7.1. Calculation process
In SDG 11.3.1 metadata [1-1], it is proposed to use a method of setting up city region using population
density at the 3rd level mesh, rather than only a single administrative district boundary for the LCRPGR
calculation scope [7-1]. In this paper, the following procedure (F/T 7-1) was used to calculate the LCRPGR
after performing an urbanization degree classification for all of Japan. For LCR calculation, the sample
LCR and 95% confidence interval widths were calculated using Eq. (4.20) and the values in F/T 5-5.

20

F/T 7-1

Diagram of LCRPGR Calculation Process for Government-Designated Cities

Process (7-1) Create a national population map of 3rd level mesh resolution using MIC 2010 Population

Census Population 3rd level Mesh Statistical Data [3-2] and 2015 Population Census
Population 3rd level Mesh Statistical Data [3-3]Process (7-2) Classify the national population map (2015) obtained Process (7-1) into three categories:
1. Urban centre:
Contiguous population grid cells (using 4 adjacent) with a density of at least 1,500
inhabitants/km2 and a total population of at least 50,000 in the target region
2. Urban cluster:Contiguous population grid cells with a density of at least 300 inhabitants/km2 and a
total population of at least 5,000 in the target region (including the urban centre region)
3. Rural grid cells:
Remaining population grid cells not belonging to the Urban cluster
Process (7-3) Classify municipalities across the country into three categories based on the following
conditions, using the urbanization degree classification map obtained in Process (7-2) andmunicipal administrative district boundaries MLIT, Digital National Land Information,
Administrative Districts Boundary Data (2021) [3-12]”
1. Cities (or densely populated areas):
Total population of the Urban centre grid is more than 50% of the total population within
its boundaries
2. Towns and semi-dense areas (or intermediate density areas):
Total population of urban centers is less than 50% of the total population within theboundaries, and total population of Rural grid cells is less than 50% of the total
population within the boundaries.
3. Rural areas (or thinly populated areas):
Total population of Rural grid cells is more than 50% of the total population within theboundaries
21

Process (7-4)
Process (7-5)
Process (7-6)
Process (7-7)
Process (7-8)

7.2.

Calculate total population of municipalities classified by Cities and Towns and semi-denseareas using the results of Process (7-1) and (7-3).
Calculate PGR (Urbanization degree classification statistics 3rd level meshapproximation) using the result of Process (7-4).
Calculate the ratio of coverage of built-up in municipalities classified as Cities and Towns
and semi-dense areas using JAXA HRLULC Map [3-7] and the results of Process (7-3).
Calculate LCR (urbanization degree classification statistics) using the results of Process(7-6).
Calculate LCRPGR (Urbanization degree classification statistics 3rd level meshapproximation) using the results of Process (7-5) and Process (7-7).

Urbanization degree classification results and LCRPGR of each urbanization degreeclassification

F/T 7-2 shows the urbanization degree classification map (3rd level mesh resolution), and F/T 7-3 shows
the urbanization degree classification results for municipalities.
F/T 7-2 Urbanization degree classification Map (3rd level Mesh Resolution)

Red indicates Urban center, orange indicates Urban cluster, green indicates Rural grid cells, and white
indicates 3rd level mesh with population of 0.
22

F/T 7-3 Urbanization degree classification Results for Municipalities

Red indicates Cities (or densely populated areas), green indicates Towns and semi-dense areas (orintermediate density areas), green indicates Rural areas (or thinly populated areas).
As F/T 7-2 shows, 3rd level meshes of Urban centers and Urban clusters are often distributed around large
cities such as government-designated cities. On the other hand, as shown in F/T 7-3, in the classification
by municipality, the most urbanized category, Cities (or densely populated areas), is distributed mostly
around ordinance-designated cities, but is also found in rural areas. In addition, Towns and semi-denseareas (or intermediate density areas) were found to be widely distributed around Cities (or densely
populated areas) as well as in rural areas.
Next, the values of the LCR (urbanization degree classification statistics) and PGR (urbanization degreeclassification statistics 3rd level mesh approximation) are plotted together with the LCR (administrative
boundary statistics) and PGR (administrative boundary statistics 3rd level mesh approximation) for
government-designated cities for the period 2010-2015 obtained in Section 6.1 on a scatter plot with twoaxes of LCR-PGR, and the results are shown in F/T 7 -4. List of Calculation results is indicated in Appendix
F/TA-10.
23

F/T 7-4 Scatter plot of LCR-PGR of LCRPGR of Each Urbanization degree classification and LCRPGR
of Each Government-Designated City (2010-2015)

Error bars indicate 95% confidence interval widths for LCR correction values.
As F/T 7-4 shows, while the urbanization degree classification, Cities shows average values for ordinancedesignated cities, the urbanization degree classification, Towns and semi-dense areas shows large
negative values for both LCR and PGR. These trends are consistent with those observed in F/T 7-3, that
Cities are distributed mostly in the surrounding areas of government-designated cities, while Towns andsemi-dense areas are distributed mostly in relatively rural areas.
8.

Summary

In the estimation and verification of this paper, it was confirmed that the ratio of Land Consumption Rate
(LCR) to Population Growth Rate (LCRPGR) can be estimated for each 3rd level mesh, governmentdesignated city, and urbanization degree classification using JAXA High-Resolution Land Use and Land
Cover Map of Japan, MIC, 3rd level mesh population data, and population statistics information based on
metadata before and after the March 2021 revision [2-2].
For the indicator estimation, the error evaluation formulas to calculate 95% confidence intervals forstatistical errors in the city population proportion and land cover map classification errors were developed,
24and it was confirmed that the corrected values for LCR, PGR, and LCRPGR, as well as the minimum andmaximum 95% confidence interval values can be calculated.
In order to narrow the 95% confidence interval of the conditional probability of city/non-city and to narrow
the 95% confidence interval of the LCR, the number of samples were increased by collation of city correct
answer data and land cover map. As a result, it was possible to narrow the 95% confidence interval of theconditional probability of city/non-city.
Comparing the calculation results of the indicators before and after the revision of the metadata, it was
confirmed that the point estimates were almost the same values in the LCR new and old definitions.
Furthermore, it was confirmed that the 95% confidence interval widths for the new and old LCR definitions
were slightly larger than those for the old LCR definition.
Although the calculation results of LCR and PGR change depending on the handling of boundary
conditions, it was confirmed that they output values of the same order. In addition, the calculation resultsof PGR change depending on used population data (Population administrative boundary statistical data
and Population 3rd level mesh statistical data), it was confirmed that they output values of the same order.
Furthermore, from the viewpoint of computing resources, it was to confirm the usefulness of calculating
approximate indicators using 3rd level mesh data, for example, when expanding the scope of indicator
estimation verification and calculations.
The LCRPGR values released by UN-Habitat for Japan are limited to the target regions and cities, andthere is no information on the spatial distribution of LCR and PGR that covers the entire area of the target
region. Therefore, it was not sufficient to understand the situation of population and land use in Japan fromthe perspective of either bird's eye comparison among cities or local analysis within cities. This estimationand verification allowed us to calculate the values of LCR, PGR, and LCRPGR at the 3rd level mesh foralmost all inhabitable land with point estimates and 95% confidence interval estimates, using data fromJapan's own Earth observation satellites, geospatial information, and 3rd level mesh population data. This
enables us to understand the demographic and land use status of any given administrative and commercial
area, and shows the possibility of using the reliability of the LCR, PGR, and LCRPGR values in decision
making for policy and commercial activities.
Future issues include the continuous creation of high-resolution land use land cover maps and thecollection and updating of land cover map validation data (city/non-city correct data) in order tocontinuously calculate indicators and improve their accuracy.

25

Appendix
F/T A-1

Confusion Matrix of JAXA High-Resolution Land Use and Land Cover Map 2006-2011(ver.16.09)
Validation

Classified

User's accuracy

1

2

3

4

5

6

7

8

9

10 Total

1

193

1

1

0

0

0

0

0

0

2

197

98.0

2

2

222

2

1

0

0

0

0

0

2

229

96.9

（％）

3

1

2

260

18

6

2

0

1

0

1

291

89.3

4

1

2

28

76

41

4

1

9

1

5

168

45.2

5

0

0

10

14

42

4

2

8

0

1

81

51.9

6

0

1

2

8

5

74

13

15

13

0

131

56.5

7

0

0

0

0

1

1

11

0

1

0

14

78.6

8

0

0

0

0

1

5

1

32

16

0

55

58.2

9

1

0

0

1

0

11

4

30

167

0

214

78.0

10

0

2

1

1

3

0

0

0

0

22

29

75.9

198

230

304

119

99

101

32

95

198

33

1409

Total
Producer's

97.5

accuracy (%)

96.5

85.5

63.9

42.4

73.3

34.4

33.7

84.3

66.7

78.0
City
Non-city

F/T A-2

Confusion Matrix of JAXA High-Resolution Land Use and Land Cover Map 2014-2016(ver.18.03)
Validation

Classified

2

3

4

5

6

7

8

9

10 Total

1

276

1

1

0

0

0

0

1

3

0

282

97.9

2

3

247

2

7

1

0

0

0

0

35

295

83.7

3

0

5

284

5

1

1

1

0

0

1

298

95.3

1

3

31

218

26

4

3

1

1

6

294

74.1

5

0

2

6

14

240

14

0

8

0

5

289

83.0

6

0

0

0

0

9

236

29

13

11

0

298

79.2

7

0

0

0

1

4

24

252

4

14

0

299

84.3

8

0

1

0

1

2

15

7

207

49

0

282

73.4

9

0

0

0

0

1

6

4

24

264

0

299

88.3

10

15

43

6

14

23

8

3

6

7

161

286

56.3

295

302

330

260

307

308

299

264

349

208

2922

93.6

accuracy (%)

（％）

4

Total
Producer's

User's accuracy

1

81.8

86.1

83.8

78.2

76.6

84.3

78.4

75.6

77.4

81.6
City
Non-city

F/T A-3

Confusion Matrix of JAXA High-Resolution Land Use and Land Cover Map 2018-2020(ver.21.11)
Validation

Classified

Total
Producer's
accuracy (%)

User's accuracy

1

2

3

4

5

6

7

8

9

10

11

12 Total

1

153

0

0

0

0

0

0

0

0

1

0

0

154

99.35

2

0

243

1

2

0

0

1

0

0

8

1

5

261

93.10

（％）

3

0

0

235

17

3

0

0

0

0

0

0

0

255

92.16

4

0

0

11

188

16

2

1

0

0

5

1

0

224

83.93

5

0

0

0

19

202

2

1

1

0

4

1

4

234

86.32

6

0

0

0

2

5

193

39

6

3

0

8

0

256

75.39

7

0

0

0

0

0

3

162

0

0

0

0

0

165

98.18

8

0

0

0

1

1

0

0

126

10

0

4

0

142

88.73

9

0

0

0

1

0

9

7

16

214

1

12

0

260

82.31

10

3

11

0

6

0

0

0

0

0

199

0

3

222

89.64

11

0

0

0

1

0

1

0

17

0

0

137

1

157

87.26

12

0

1

0

0

0

0

0

0

0

0

0

171

172

99.42

184

2502

156
98.08

255
95.29

247
95.14

237
79.32

227
88.99

210
91.90

211
76.78

166
75.90

227
94.27

218
91.28

164
83.54

92.93

88.85
City
Non-city

26

F/T A-4 Confusion Matrix from Collation Analysis of MLIT, National Spatial Planning and Regional
Policy Bureau, National Land Information Division, Land Market Value Publication Data (2010) and the
JAXA HRLULC Map (2006-2011)
Validation

Classified

User's accuracy

1

2

3

4

5

6

7

8

9

10 Total

1

193

108

1

0

0

0

0

0

0

2

304

63.5

2

2

24988

2

1

0

0

0

0

0

2

24995

100.0

3

1

1269

260

18

6

2

0

1

0

1

1558

16.7

4

1

1224

28

76

41

4

1

9

1

5

1390

5.5

5

0

20

10

14

42

4

2

8

0

1

101

41.6

6

0

158

2

8

5

74

13

15

13

0

288

25.7

7

0

30

0

0

1

1

11

0

1

0

44

25.0

8

0

72

0

0

1

5

1

32

16

0

127

25.2

9

1

53

0

1

0

11

4

30

167

0

267

62.5

10

0

112

1

1

3

0

0

0

0

22

139

15.8

198

28034

304

119

99

101

32

95

198

33

29213

Total
Producer's

97.5

accuracy (%)

89.1

85.5

63.9

42.4

73.3

34.4

33.7

84.3

（％）

66.7

88.5
City
Non-city

User'sValidation
City
Classified City

accuracy

Non-city Total

24988

7

（％）
Point

24995 99.97199

95% confidence

estimate interval width

Non-city

3046

1172

4218 27.78568

R(1|0)

0.00594

0.00439

Total

28034

1179

29213

R(1|1)

0.89135

0.00364

Producer's

89.13462 99.40628

accuracy (%)

89.54917

F/T A-5 Confusion Matrix from Collation Analysis of MLIT, National Spatial Planning and Regional
Policy Bureau, National Land Information Division, Land Market Value Publication Data (2015) and the
JAXA HRLULC Map (2014-2016)
Validation

Classified

User's accuracy

1

2

3

4

5

6

7

8

9

10 Total

1

276

49

1

0

0

0

0

1

3

0

330

83.6

2

3

20439

2

7

1

0

0

0

0

35

20487

99.8

3

0

890

284

5

1

1

1

0

0

1

1183

24.0

4

1

1602

31

218

26

4

3

1

1

6

1893

11.5

5

0

63

6

14

240

14

0

8

0

5

350

68.6

6

0

111

0

0

9

236

29

13

11

0

409

57.7

7

0

60

0

1

4

24

252

4

14

0

359

70.2

8

0

22

0

1

2

15

7

207

49

0

303

68.3

9

0

12

0

0

1

6

4

24

264

0

311

84.9

10

15

417

6

14

23

8

3

6

7

161

660

24.4

295

23665

330

260

307

308

299

264

349

208

26285

Total
Producer's
accuracy (%)

93.6

86.4

86.1

83.8

78.2

76.6

84.3

78.4

75.6

（％）

77.4

85.9
City
Non-city

User'sValidation
City
Classified City

accuracy

Non-city Total

20439

48

（％）
Point

20487 99.76571

95% confidence

estimate interval width

Non-city

3226

2572

5798 44.36012

R(1|0)

0.01832

0.00514

Total

23665

2620

26285

R(1|1)

0.86368

0.00437

Producer's
accuracy (%)

86.36805 98.16794

87.54423

27

F/T A-6 Confusion Matrix from Collation Analysis of MLIT, National Spatial Planning and Regional
Policy Bureau, National Land Information Division, Land Market Value Publication Data (2020) and the
JAXA HRLULC Map (2018-2020)
Validation

Classified

User's accuracy

1

2

3

4

5

6

7

8

9

10

11

12 Total

1

153

0

0

0

0

0

0

0

0

1

0

0

154

99.35

2

0

24769

1

2

0

0

1

0

0

8

1

5

24787

99.93

3

0

100

235

17

3

0

0

0

0

0

0

0

355

66.20

4

0

1043

11

188

16

2

1

0

0

5

1

0

1267

14.84

5

0

31

0

19

202

2

1

1

0

4

1

4

265

76.23

6

0

83

0

2

5

193

39

6

3

0

8

0

339

56.93

7

0

10

0

0

0

3

162

0

0

0

0

0

175

92.57

8

0

9

0

1

1

0

0

126

10

0

4

0

151

83.44

9

0

27

0

1

0

9

7

16

214

1

12

0

287

74.56

10

3

102

0

6

0

0

0

0

0

199

0

3

313

63.58

11

0

35

0

1

0

1

0

17

0

0

137

1

192

71.35

12

0

39

0

0

0

0

0

0

0

0

0

171

210

81.43

156

26248

247

237

227

210

211

166

227

218

164

184

28495

Total
Producer's
accuracy (%)

98.08

94.37

95.14

79.32

88.99

91.90

76.78

75.90

94.27

91.28

83.54

（％）

92.93

93.87
City
Non-city

User'sValidation
City
Classified City

accuracy

Non-city Total

24769

18

（％）
Point

24787 99.92738

95% confidence

estimate interval width

Non-city

1479

2229

3708 60.11327

R(1|0)

0.00801

0.00369

Total

26248

2247

28495

R(1|1)

0.94365

0.00279

Producer's
accuracy (%)

94.36528 99.19893

94.74645

F/T A-7

List of Ordinance-designated Cities *CityTransition
Date

Cabinet Order for
Designation

Osaka

1956/9/1

Nagoya1956/9/1

Kyoto

1956/9/1

Yokohama

1956/9/1

Kobe

1956/9/1

Kitakyushu

1963/4/1

Sapporo1972/4/1

Kaw asaki

1972/4/1

Fukuoka

1972/4/1

Hiroshima

1980/4/1

Article 237 of the Cabinet
Order, 1979

Sendai1989/4/1

Article 261 of the Cabinet
Order, 1988

Chiba

1992/4/1

Article 324 of the Cabinet
Order, 1991

Saitama

2003/4/1

Article 319 of the Cabinet
Order, 2002

Shizuoka

2005/4/1

Article 322 of the Cabinet
Order, 2004

Sakai

2006/4/1

Article 323 of the Cabinet
Order, 2005

Niigata

2007/4/1

Hamamatsu

2007/4/1

Okayama

2009/4/1

Article 315 of the Cabinet
Order, 2008

Sagamihara

2010/4/1

Article 251 of the Cabinet
Order, 2009

Kumamoto

2012/4/1

Article 323 of the Cabinet
Order, 2011

Article 254 of the Cabinet
Order, 1956

Article 10 of the Cabinet
Order, 1963

Article 276 of the Cabinet
Order, 1971

Article 338 of the Cabinet
Order, 2006

* Excerpt from the list of Ordinance-designated Cities by the Ministry of Internal Affairs andCommunications [6-1]28

F/T A-8 LCRPGR (administrative boundary statistics) Estimation value of Ordinance-designated Cities (2010-2015)b u ilt-u p _ratio_b eforeb u ilt-u p _ratio_afterLC R

L C R _calib rated

L C R _m in

L C R _m ax

L C R _95% _in tervalp op u lation _b efore

p op u lation _after

PG R

LC R P G R

L C R P G R _calib rated

L C R P G R _1

L C R P G R _2

L C R P G R _m in

L C R P G R _m ax

S ap p oro

0.22166501

0.216543723-0.004620744-0.007523352-0.021071283

0.006024578

0.027095861

1913545

1952356

0.004015861-1.15062347-1.873409481-5.247014731

1.50019577-5.247014731

S en d ai

0.191863194

0.185581224-0.006548385-0.011555003-0.026607938

0.003497932

0.03010587

1045986

1082159

0.006799628-0.963050487-1.69935814-3.91314624

0.51442996-3.91314624

0.51442996

S aitam a

0.512127305

0.588538623

0.029840751

0.035967973

0.02606424

0.045871706

0.019807466

1222434

1263979

0.006684146

4.464407636

5.381087455

3.899412294

6.862762616

3.899412294

6.862762616

0.313186737-0.013384361-0.012665428-0.023376486

0.002096179-6.385122352-6.042149311-11.15194986-0.932348763-11.15194986-0.932348763

C h ib a0.335648972-0.00195437

0.021422116

9617499718821.50019577

K aw asaki

0.716120131

0.777380188

0.017108877

0.023887894

0.015739719

0.032036068

0.016296348

142551214752130.006854268

2.496091184

3.485112368

2.296338546

4.67388619

2.296338546

Y okoh am a

0.654411308

0.724609962

0.021453986

0.028147221

0.019836395

0.036458047

0.016621652

368877337248440.001946218

11.02342441

14.46252294

10.19227859

18.73276729

10.19227859

18.73276729

S agam ih ara

0.192972892

0.215299373

0.023139499

0.020610244

0.003525085

0.037695402

0.034170317

7175447207800.000899938

25.71233055

22.90185236

3.917031976

41.88667274

3.917031976

41.88667274

N iigata

0.23314282

0.234099305

0.000820514-0.001061433-0.014609872

0.012487005

0.027096877

811901810157-0.000430071-1.907855853

2.468040995

33.97082817-29.03474618-29.03474618

33.97082817

S h izu oka

0.068697135

0.065113582-0.010432905-0.043812565-0.076422457-0.011202674

0.065219783

716197704989-0.003154614

3.307189253

13.88840842

24.22561399

3.551202849

3.551202849

24.22561399

0.009965868-0.011268674

79798055.08426508-23.87003228

H am am atsu

0.087129557

0.091471165

0.057006704

80086615.6071164-23.87003228

4.67388619-0.039772026

0.017234678-0.000722022-13.80272894

N agoya

0.771455495

0.783363755

0.003087219

0.009341438

0.002069073

0.016613803

0.01454473

22638942295638

0.002784892

1.108559532

3.354326405

0.742963244

5.965689567

0.742963244

5.965689567

K yoto0.149819743

0.166764157

0.022619734

0.016115725-0.003588345

0.035819795

0.039408141

147401514751830.000158416

142.786967

101.7304381-22.65141399

226.1122901-22.65141399

226.1122901

55.08426508

O saka0.829237854

0.812036539-0.004148705

0.001944189-0.004972028

0.008860405

0.013832433

266531426911850.001931949-2.147419874

1.006335693-2.57358172

4.586253106-2.57358172

4.586253106

S akai

0.647325698

0.700091336

0.016302655

0.02266009

0.014105246

0.031214934

0.017109687

841966839310-0.000631902-25.79935976-35.86015957-22.32190533-49.39841382-49.39841382-22.32190533

0.007777295

0.023841682

1544200

1537272-0.000899312

17.86297476-8.648049661-8.648049661

17.86297476

O kayam a

0.165601394

0.169818073

0.005092565-0.001239488-0.018619866

0.01614089

0.034760756

7095847194740.002768301

1.839599143-0.447743247-6.726097961

5.830611468-6.726097961

5.830611468

H irosh im a

K ob e0.169687299

0.282205777

0.145581539

0.276630501-0.028411979-0.003951213-0.037203172-0.004143546-0.052396687-0.016064387-0.022009656

0.030387031

1173843

1194034

0.003410902-8.329755989

4.393595547-10.90713673

4.60746255-15.36153515-6.45273831-15.36153515-6.45273831

K itakyu sh u

0.36214055

0.33880103-0.012889758-0.011535023-0.02152279-0.001547256

0.019975533

976846961286-0.003211409

4.013739558

3.591888894

6.701977918

0.48179987

0.48179987

6.701977918

F u ku oka

0.450218278

0.417839883-0.014383421-0.011632388-0.020557147-0.002707629

0.017849518

1463743

15386810.009985741-1.440396031-1.164899914-2.058650255-0.271149573-2.058650255-0.271149573

K u m am oto

0.309185376

0.26459949-0.028840876-0.029879374-0.040338031-0.019420717

0.020917314

7344747408220.001721157-16.75668172-17.36005364-23.43658145-11.28352583-23.43658145-11.28352583

F/T A-9 LCRPGR (administrative boundary statistics) Estimation value of Ordinance-designated Cities (2015-2020)
b u ilt-u p _ratio_b eforeb u ilt-u p _ratio_afterLC R

L C R _calib rated

L C R _m in

L C R _m ax

L C R _95% _in tervalp op u lation _b efore

p op u lation _after

PG R

LC R P G R

L C R P G R _calib rated

L C R P G R _m in

L C R P G R _m ax

S ap p oro

0.216543723

0.203070016-0.01244433-0.022182076-0.034073566-0.010290587

0.023782979

1952356

1973395

0.002143712-5.805037411-10.34750626-15.89465439

S en d ai

0.185581224

0.193002507

0.007997882-0.000141889-0.014947327

0.014663549

0.029610876

10821591096704

0.00267024

2.995192499-0.053137123-5.59774727-4.800358128
5.491473024

S aitam a

0.588538623

0.611196896

0.007699842-0.008850392-0.015936882-0.001763902

0.01417298

1263979

1324025

0.009282332

0.829515975-0.953466501-1.716905076-0.190027926

C h ib a

0.313186737

0.475120353

0.103410264

0.086257332

0.072157302

0.100357361

0.028200058

9718829749510.000630563

163.9966973

136.794134

114.4331214

159.1551467

K aw asaki

0.777380188

0.76948482-0.002031276-0.018723193-0.024669539-0.012776847

0.011892692

147521315382620.008370164-0.242680528-2.236896689-2.947318343-1.526475034

Y okoh am a

0.724609962

0.713787618-0.002987081-0.019429102-0.025328254-0.01352995

0.011798305

37248443777491

0.002807012-1.064149666-6.92163077-9.023207758-4.820053782

S agam ih ara

0.215299373

0.223241338

0.007377602-0.002554306-0.016141643

0.01103303

0.027174673

7207807254930.001303493

5.659871149-1.959585816-12.38337527

8.464203636

N iigata

0.234099305

0.261601674

0.023496327

0.012367527-0.00061074

0.025345795

0.025956535

810157789275-0.005222651-4.498927465-2.368055593-4.853051836

0.11694065

S h izu oka

0.065113582

0.079500655

0.044190698

0.076074247

0.0241175

0.128030994

0.103913493

704989693389-0.003318206-13.3176469-22.92631729-38.5844001-7.268234486-16.74968875

H am am atsu

0.091471165

0.115201991

0.051887008

0.064791474

0.030625604

0.098957345

0.068331741

797980790718-0.001828428-28.37792993-35.43561262-54.1215365

N agoya

0.783363755

0.808392098

0.006389967-0.010951167-0.016890991-0.005011342

0.01187965

2295638

2332176

0.003158187

2.023302083-3.467548049-5.348318277-1.58677782

K yoto0.166764157

0.165407783-0.001626698-0.008398718-0.024089658

0.007292222

0.03138188

1475183

1463723-0.001559772

1.042907688

5.38458054-4.675184559

15.44434564

O saka0.812036539

0.834356994

0.005497402-0.011869187-0.017744474-0.005993901

0.011750573

269118527524120.0044992

1.221862004-2.63806598-3.943917264-1.332214696

S akai0.700091336

0.670375325-0.008489181-0.024441541-0.030578414-0.018304668

0.012273746

839310826161-0.003158091

2.688073481

7.739339721

5.796117566

9.682561876

K ob e0.276630501

0.289626819

0.009396157-0.002994007-0.014010785

0.00802277

0.022033554

15372721525152-0.001583068-5.935409731

1.891268986-5.067861844

8.850399815

O kayam a

0.169818073

0.1797195

0.011661217

0.004809513-0.011419676

0.021038702

0.032458378

7194747246910.001444994

8.0700824

3.328397435-7.902925333

14.5597202

H irosh im a

0.145581539

0.163603288

0.024758289

0.02093097

0.001458761

0.040403178

0.038944417

1194034

12007540.001122441

22.05755143

18.64773167

1.299633217

35.99583012

K itakyu sh u

0.33880103

0.359223453

0.012055703-0.001969664-0.011682269

0.007742942

0.019425212

961286939029-0.004685122-2.573188861

0.42040818-1.652666193

2.493482553

F u ku oka

0.417839883

0.460202483

0.020276954

0.004525682-0.004370993

0.013422358

0.017793351

15386811612392

0.009358647

2.16665438

0.483582972-0.46705397

1.434219914

K u m am oto

0.26459949

0.310914432

0.035007582

0.022249243

0.009571494

0.034926993

0.025355498

740822

738865-0.000529031-66.173011-42.05658765-66.0206776-18.0924977

29

F/T A-10 LCRPGR (administrative boundary statistics 3rd level mesh approximation) Estimation value of Ordinance-designated Cities (2010-2015)
and LCRPGR (urbanization degree classification statistics 3rd level mesh approximation) of Urbanization degree classification (2010-2015)b u ilt-u p _ratio_b eforeb u ilt-u p _ratio_afterLC R

L C R _calib rated

L C R _m in

L C R _m ax

L C R _95% _in tervalp op u lation _b efore

p op u lation _after

PG R

LC R P G R

L C R P G R _calib rated

L C R P G R _m in

L C R P G R _m ax

S ap p oro

0.22166501

0.216543723-0.004620744-0.007523352-0.021071283

0.006024578

0.027095861

1905922

19446100.004019112-1.149692695-1.871894021-5.242770256

1.498982215

S en d ai

0.191863194

0.185581224-0.006548385-0.011555003-0.026607938

0.003497932

0.03010587

1033824

1068891

0.006671423-0.981557476-1.732014788-3.988345363

0.524315786

S aitam a

0.512127305

0.588538623

0.029840751

0.035967973

0.02606424

0.045871706

0.019807466

12039531243604

0.006480661

4.604584379

5.550046782

4.021848898

7.078244665

C h ib a0.335648972

0.313186737-0.013384361-0.012665428-0.023376486-0.00195437

0.021422116

9469569579940.002317777-5.774654126-5.464472021-10.0857352-0.843208843

K aw asaki

0.716120131

0.777380188

0.017108877

0.023887894

0.015739719

0.032036068

0.016296348

14098491454173

0.00619095

2.763530434

3.858518533

2.542375655

5.174661411

Y okoh am a

0.654411308

0.724609962

0.021453986

0.028147221

0.019836395

0.036458047

0.016621652

3689004

3726553

0.002025435

10.59228571

13.89687718

9.793646953

18.00010741

S agam ih ara

0.192972892

0.215299373

0.023139499

0.020610244

0.003525085

0.037695402

0.034170317

6848466880430.000931468

24.84196638

22.12662307

3.784440173

40.46880597

N iigata

0.23314282

0.234099305

0.000820514-0.001061433-0.014609872

0.012487005

0.027096877

805146804181-0.000239852-3.420918788

4.42536987-52.06132767

60.91206741

S h izu oka

0.068697135

0.065113582-0.010432905-0.043812565-0.076422457-0.011202674

0.065219783

698489689126-0.002699061

3.865383864

16.23252426

4.150582607

28.31446592

H am am atsu

0.087129557

0.091471165

0.009965868-0.011268674-0.039772026

0.017234678

0.057006704

800716797865-0.000713383-13.96986137

15.79609754-24.15906619

55.75126126

N agoya

0.771455495

0.783363755

0.003087219

0.009341438

0.002069073

0.016613803

0.01454473

22439142276979

0.00292558

1.055250051

3.19302032

0.70723491

5.67880573

K yoto0.149819743

0.166764157

0.022619734

0.016115725-0.003588345

0.035819795

0.039408141

1469291

1471087

0.000244322

92.58151563

65.96090905-14.68693035

146.6087484

O saka0.829237854

0.812036539-0.004148705

0.001944189-0.004972028

0.008860405

0.013832433

26511082678946

0.002089154-1.985830456

0.930610773-2.379924402

4.241145947

S akai

0.647325698

0.700091336

0.016302655

0.02266009

0.014105246

0.031214934

0.017109687

830187826797-0.000818355-19.92123975-27.68978933-38.14349094-17.23608772

K ob e0.282205777

0.276630501-0.003951213-0.004143546-0.016064387

0.007777295

0.023841682

1534597

1527617-0.00091176

4.33361003

4.544557119-8.529978318

17.61909256

O kayam a

0.165601394

0.169818073

0.005092565-0.001239488-0.018619866

0.01614089

0.034760756

7037727142820.00296468

1.717744924-0.418084935-6.28056426

5.444394389

H irosh im a

0.169687299

0.145581539-0.028411979-0.037203172-0.052396687-0.022009656

0.030387031

11710021191603

0.003487933-8.145791871-10.66625071-15.02227296-6.310228456

K itakyu sh u

0.36214055

0.33880103-0.012889758-0.011535023-0.02152279-0.001547256

0.019975533

968611953460-0.003153123

4.087934207

3.658285562

0.490706021

6.825865103

F u ku oka

0.450218278

0.417839883-0.014383421-0.011632388-0.020557147-0.002707629

0.017849518

1416260

14892710.010053429-1.430698051-1.157056809-2.044789657-0.269323961

K u m am oto

0.309185376

0.26459949-0.028840876-0.029879374-0.040338031-0.019420717

0.020917314

7245087305110.001650297-17.47617221-18.10545143-24.44289032-11.76801254

1: C ities

0.167794176

0.170812905

0.003598133-0.002644769-0.018776391

0.013486852

0.032263243

93731182

94360751

0.001338859

2.687462549-1.975390613-14.02417529

10.07339407

0.038797722

0.036075007-0.014035436-0.086810454-0.13718531-0.036435598

0.100749712

26239578

25346613-0.006924754

2.02684976

12.53625096

5.26164509

19.81085683

0.082540424

0.081765057-0.001878755-0.026503972-0.054129763

0.001121819

0.055251582

119970760

119707364-0.000439583

4.273947959

60.29343353-2.552007299

123.1388744

2: T ow n s an dsem i-d en se areas
1+ 2

30

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32