倉田奨励金研究報告書 第 51 集｜2020 年度（第 52 回）助成
自然科学・工学研究部門：1 年助成■受領№1445

夜間光データを用いた物流発生集中量推定モデルの構築とその応用
代表研究者

川崎智也

東京大学講師

Estimating freight production/attraction using nighttime
satellite imagery
Principal Researcher

Tomoya Kawasaki,
The University of Tokyo Lecturer

本研究は、夜間光データがゾーン別貨物発⽣集中量を推定するための重要な指標となり得るか検証するものである。分析の結果、夜間光データは GRP や⼈⼝などの⼀般的な社会経済指標よりも⾼精度に貨物発⽣集中量を推定できることが分かった。可住地⾯積を⽤いて夜間光を補正すると、さらに精度が向上することが⽰された。これらの知⾒は、特に統計データが限られている国や地域において、貨物の発⽣集中量の推定に有益なものとなることが考えられる。
This study aims to examine whether nighttime light intensity (NLI) data can be a significant indicator forestimating zonal freight production (FP) and attraction (FA) through a case study in Japan. As a result ofthe analysis, NLI exhibits better estimates than the popular indicators such as GRP and population. The
use of habitable area for the estimation of total NLI provides the highest accuracy among the four types of
proposed NLIs. Through the analysis, NLI data is sufficiently applicable for estimating zonal FP and FA,
relative to other important socio-economic indicators. These findings could help estimating FP/FA
particularly in countries and regions with limited statistical data available.

1．研究⽬的significant indicator for estimating the zonal FP
isand FA compared with other socio-economic

human-induced light emission data and the open

indicators and to examine the types of NLIs thatalternative data used for estimating a variety ofcan accurately estimate the FP and FA. The casesocio-economic

study

Nighttimelightintensity

activities

(NLI)

and

data

geographicisconductedinJapan,

wherebothconditions. Freight volume could be determinedinternational and domestic FP and FA are

by the economics of production and consumption;

available at the prefectural level. In the case study,

based on this, NLI is possibly useful for FP and FA

we first retrieved monthly unit NLI data for 47

estimations. However, the applicability of NLI

prefectures in Japan using the Google Earthdata for estimating freight volume has not been

Engine and proposed three total NLIs for eachclarified in the existing studies. Therefore, thisprefecture. As explanatory variables for estimating

study aims to examine whether NLI can be a

FP and FA, 196 socio-economic indicators were倉田奨励金研究報告書 第 51 集｜2020 年度（第 52 回）助成
自然科学・工学研究部門：1 年助成

obtainedfrom

thecentralandmunicipaleliminated compared with total NLI (H). Thus,governments of Japan. Thereafter, these indicators,

total NLI (M) was not significant in the FP model.

including NLI, were used as explanatory variables

Asto develop FP and FA models by using an elastic

socio-economic variables, total NLIs are identified

net, enabling the sorting of significant andas relatively useful data for estimating freightinsignificant variables. In addition, we developed

volume at the prefectural level in Japan.

single regression models using each type of NLI
and popular explanatory variables for the purposeofaccuracy

comparisonfor

theFP/FAestimations.

2．研究内容The parameter estimation of the elastic net foreach FP and FA is shown in Table 1. We obtain 21
and 27 explanatory variables for the FP and FAmodel, when 𝜆 of elastic net is 29,364 and 59,687,
respectively, as shown in Table 1. As a result, total
NLIs were selected as significant variables, while
the unit NLI was not selected in either model, as
shown in Table 2. Since the explanatory variables
are standardized, the relative impact between thevariables can be discussed based on the magnitudeof the coefficients. The total NLI (H) has a higher
impact than the other total NLIs; in particular, it is
only significant for the FP model. Althoughstraightforward comparisons are difficult because
the model structures for FP and FA are different,
the relative importance of NLIs to the othervariables is higher in the FA model. This isbecause areas with brighter lights are theconsumption areas in many cases, whereas
factories and other logistics-related facilities wherehigh volume of cargo is produced are located in
the suburb areas, not necessarily located in brightareas. Total NLI (M) was significant in the FAmodel for the same reason. In total NLI (M)calculation,

thedarker

area

wasdrasticallyaresult,

incomparison

withother倉田奨励金研究報告書 第 51 集｜2020 年度（第 52 回）助成
自然科学・工学研究部門：1 年助成

Table 2 Result of parameter estimation of elastic net (selected variables only)
Explanatory variable (xi)

Coefficients
Production

Total NLI
Total NLI (H)Attraction6,826

13,300328,113

Total NLI (M)161,291

Total population

9,576

Day and night population ratio-71,415

GRP (agriculture)-21,434

GRP (forestry)-30,282

GRP (mining)

117,211

GRP (electricity, gas, water, waste processing industry)

103,276

Prefectural income per capita

26,449-45,259

Number of establishments (fishery)-11,593

Number of establishments (secondary industry)

16,991

Number of employees (manufacturing industry)

11,181

Number of employees (secondary industry)
Product shipment amount

182,73676

326,846

Manufacturing industry added value

242,957
187,844

Total amount of industrial water

178,052

78,021

Amount of industrial water (fresh water)

2,303

5,668

Amount of industrial water (seawater)

47,931

511

Air freight volume-86,050

Passenger transported by ship

-84,322

Sales (private sector) (manufacturing industry)

468,534

241,292

Sales (private sector) (electricity, gas, heat supply)

24,954

3,072

Residential area

66,781

Number of pollution complaints (typical 7 pollution)

178,932

104,573

Number of traffic accidents

71,676

215,864

Number of casualties by traffic accidents

76,35644,106

Number of fatalities by traffic accidents

153,073

Number of injured by traffic accidents

76,46054,726

Number of driver's license holders (large vehicle)

183,205

101,271

Number of driver's license holders (middle vehicle)

184,867

Number of driver's license holders (regular vehicle)

30,289Traffic accident matters389,528

170,795

Sample size (N)4747Adjustment parameter (α)

0.669

0.512

Regularization parameter (λ)

29,363.82

59,686.55

倉田奨励金研究報告書 第 51 集｜2020 年度（第 52 回）助成
自然科学・工学研究部門：1 年助成

In many countries, especially developing countries,that tend to underestimate the observed values in

it is often not possible to implement analysis withprefectures that have relatively higher freights. For

multiple variables, as described in the previous

example, the actual FP volume of Aichi Prefecture

section, since statistical data are not always rich.

is approximately 10 million tons per month, while

Therefore, in this section, we develop single

the estimated value is 3 million tons, a discrepancy

regression models using the key variables that areof 7 million tons. The estimated values forimportant and frequently used variables for theHokkaido were approximately half of the actual

estimation of FP and FA as the explanatory

values. This implies that these prefectures have avariables. The estimated values from the single

low NLI compared with their cargo production.

regression models were compared with theThe areas of these prefectures are large, withobserved values to examine the relative accuracy

many dark areas, thus conversion to total NLI canfor the estimation of the FP and FA based on each

mitigate this effect. Therefore, the results based

explanatory variable. We compare the models

on the total NLI improve the accuracy ofusing four types of NLIs (i.e., unit NLI, total NLI,estimation results, particularly in these prefectures.

total NLI (H), and total NLI (M)) and discuss

In addition, habitable areas are more effective

which NLI accurately estimates the FP and FA.

adjustment factor than the other total areas for theUsing each indicator as an explanatory variable ofestimation of FP and FA. In the elastic net analysis

the single regression model, the theoretical values

shown in Table 1, unit NLI was not selected as aof FP and FA are estimated and subsequently

significant variable for estimating FP and FA;

compared with the observed values. The results

however, R2 with respect to the unit NLI model

are presented in Figure 1. Due to the spaceexceeds 0.7 which indicates calculating FP and FA

limitation, we did not provide all statistics of single

by unit NLI at the prefecture level is also feasible.

regression model but it is noted that all single
regression models provided a coefficient ofdetermination of more than 0.79, and all theestimated parameters satisfied more than the 5%
significance level. R2 in Figure 1 and 2 indicate
multiple correlation coefficients in terms of y = x(observed and estimated values). From Figure 1,
the estimated values based on the total NLI (H)and the observed values have the highest R2 that is
consistent with the result of the analysis based onelastic net. The accuracy of the estimation resultof the FA model was higher than that of the FP
model in all models. The slope of regression line
between observed and estimated values for unit
NLI is the smallest by 0.45 (FP) and 0.51 (FA)

倉田奨励金研究報告書 第 51 集｜2020 年度（第 52 回）助成
自然科学・工学研究部門：1 年助成

12Unit NLI
R² = 0.711
Total NLI
R² = 0.823
Total NLI (H) R² = 0.883
Total NLI (M) R² = 0.858

10Estimated FA (million tons/month)

Estimated FP (million tons/month)

12864
Aichi

2

Unit NLI
R² = 0.732
Total NLI
R² = 0.823
Total NLI (H) R² = 0.905
Total NLI (M) R² = 0.875

10864

Aichi

2Hokkaido

00246810Observed FP (million tons/month)

12Hokkaido

002
468
10Observed FA (million tons/month)(a) FP12(b) FA

Figure 1 Estimated and observed freight volumes based on four types of NLIs
*Note: The multiple correlation coefficient (R2) shown in the figure is R2 with respect to yregression

better utilization of nightlight imaginary data on

models with the significant variables identified byestimating cargo volume particularly for freightelastic net (i.e., “sales” and “product shipment”)

attraction.

and compared them with the model developed byshipment” and “sales” models, the “sales” model

NLI (H) that exhibited the highest accuracy

received a higher accuracy in terms of R2; inamong the four NLIs in Figure 1. Similar to theparticular, Tokyo is well-estimated based on themodels of NLIs, all estimated parameters exceed“sales” model, while estimations by the models of

the 5% significance level and 0.75 of thethecoefficient of determination in single regression

Consequently, it is shown that the “sales” model

models with each “sales” and “product shipment.”

has the highest accuracy in terms of R2. For theIn terms of R2 with respect to y = x for FP models,model of NLI (H), it can be concluded that it is

the models with sales and product shipment afford

useful enough for FP/FA estimation because its

better estimation results than the total NLI (H), as

accuracy is nearly equal to the models with theshown in Figure 2.

most significant variables (“product shipment”

We

additionallydeveloped

singleotherIn

twothecomparison

variables

areoflessand “sales”) identified by elastic net.
InAichiprefecture,theNLI(H)modelunderestimates the cargo volume in both the FP
and FA models, whereas the models with “sales”
and “product shipment” overestimate those values.
This brings the regression line of “sales” and“product shipment” closer to y = x for FP model.
Therefore, R2 increases in the FP models with“sales” and “product shipment”. However, FA
model indicates slightly better estimation result on
NLI (H) than product shipment. This also shows“productaccurate.

倉田奨励金研究報告書 第 51 集｜2020 年度（第 52 回）助成
自然科学・工学研究部門：1 年助成

14

R² = 0.883
Total NLI (H)R² = 0.934
Sales
Product shipment R² = 0.909

12Estimated FA (million tons/month)

Estimated FP (million tons/month)

14108Tokyo6Aichi

42TokyoR² = 0.905
Total NLI (H)Sales
R² = 0.936
Product shipment R² = 0.897

1210

Tokyo8Aichi

642

Tokyo

0002

4
68
10
12
Observed FP (million tons/month)14(a) FP02

4
6
8
10
12Observed FA (million tons/month)(b) FA

Figure 2 Estimated and observed freight volumes by NLI (H) and “sales”/ “product shipment”*Note: The multiple correlation coefficient (R2) shown in the figure is R2 with respect to y = x.

2．発表（研究成果の発表）
国内外の学会誌、学会講演会等における発表があれば５件程度記載。
記載内容：⽒名、題⽬、誌名、巻、号、⾴（年次）、学会名（場所、年次）
Kawasaki, T., Nakanishi, W., Hyodo, S., Namba, Y.,
Mori, H., Kishi, H.: Estimating freight production/
attraction

usingnighttimesatellite

imagery,Communications in Transportation Research, Vol.2,
100067, 2022.
Namba, Y., Kawasaki, T., Oka, H.: Estimation of
freight trip distributions using spatial-temporal
aggregated freight vehicle data, T-LOG 2022,
Incheon, South Korea, 2022.9.
⽣本⼒丸・川崎智也・兵頭知：夜間光データを⽤いた地域⽣活圏レベルの発⽣集中貨物量の推計，第66回⼟⽊計画学研究発表会，琉球⼤学，2022.11．

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