Center for Spatial Information Science, the University of Tokyo.

Analysis of Light Data on the DMPS/OLS Satellite Image Using
Existing Spatial Data for Monitoring Human Activity in Japan1)
Yuki Akiyama and Ryosuke Shibasaki
1email:aki@iis.u-tokyo.ac.jp

URL:http://shiba.iis.u-tokyo.ac.jp/member/akiyama/

INTRODUCTION
This study shows how much can be monitored various human activities using night light images by the DMSP/OLS from NOAA/NGDC. In Japan, various humanactivities can be monitored easily without satellite images because there are many kinds of detailed spatial dataset and statistics of Japan. On the other hand detailed
spatial data about human activities are not developed adequately in many countries especially in developing countries. It is expected that night light images canalternate such detailed data. Therefore we discuss how to use night light images of the DMSP/OLS for monitoring human activities. This study evaluates performanceof satellite images using other spatial data. Especially there are a few studies to evaluate performance of DMSP night light images using detailed spatial data.

ANALYSIS FLOW
Source data
Dependent variable (Y)STUDY AREA

Resampled intosame size grids (1)Step1:

A study area is whole area of Miyagi prefecture,
Japan. Sendai city, the largest city in the Tohoku
region and some small cities are located in thisprefecture.

Single regression analyses(2) Step2:
Multiple regression analysis

Night light image of DMSP
Independent variables (X)Step 1 shows relationships between lightimages with each factor of human activity.
Step2 evaluates differences of effect forthe dependent variable between eachfactor by a multiple regression analysis
using all factors.

Road length data (grid)
Building distribution data (point)

Fluid population data (grid)

Study area was classified into 3 zones, i.e.
Urban area(628 grids), suburban area(2,076grids) and rural area (5,022 grids) by the Digital
National Land Information of Japan in 2006.
Study area

Zonal classification

DATA DEVELOPMENT AND RESULT OF SINGLE REGRESSION ANALYSES (STEP1)
Building distribution data Fluid population data

Road length data

Resampled night light image

Road data were developed using
the grid data of road length fromthe Digital National Land
Information of Japan in 2010,
Building data were developed
using the digital residential maps
in 2008 (ZmapTOWN II,ZENRIN CO., LTD), andPopulation data were developed
using The Agoop Data in 2009
(fluid population data).
0.8

0.8

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0.7

0.7

0.7

0.6

0.6

0.6

0.5

0.5

0.5

0.4

0.4

0.4

0.3

0.3

0.3

0.2

0.2

0.2

0.1

0.1

0.1

0

Correlation
coefficient

Whole
area

Urban
area

Suburban
Rural area
area

0.70780.53340.5096

0Correlation
coefficient

0.468

Whole
area

Urban
area

0.63290.44610Suburban
Rural areaarea
0.437

Correlation coefficients

Correlation
coefficient

0.4892WholeareaUrban
area

Suburban
Rural area
area

0.30460.20290.4006RESULT OF MULTIPLE REGRESSION ANALYSIS (STEP2)
Impacts by road length are strong without relying
on areas. It means that lights from cars and night
illuminations on road affect the light intensity ofDMSP strongly. Impacts by building distribution
become strong in suburban and rural area. This isbecause lights from large-scale factories andwaterfront areas and large-scale commercial
facilities affect the light intensity. There are few
affects by fluid populations.

Using intercepts and coefficients,
estimated light intensities can becalculated in each grid.

y  Rx1  Bx2  Px3  I
y: Estimated light intensity
xn: Independent variables

Correlation coefficient (R )Coefficient of determination (R2)
Adjusted R2Standard deviation
The number of gridsIntercept(I)Road length (R)Coefficient
Building area (B)
Fluid population (P)Standard partial Road length
regression
Building areacoefficient
Fluid population

Road length data and building
distribution data have a strong
correlation with the night light. On
the other hand population has a poorcorrelation especially in urban areas.
In rural areas, an effect of building
distribution is especially strong.

0.386

0.6000Whole area

Urban area

0.74950.56180.5616
11.31937725
2.27680.001065
0.0001780.000505
0.50350.3791
0.11340.58080.33730.334214.8238628
32.85680.000468
0.000064
0.000131
0.41230.30490.0916

Suburbanarea
0.56490.31910.318113.84932076
7.07440.001064
0.0002040.001192
0.39770.34250.0834

Rural area
0.57700.33290.33255.45565022
0.18680.000615
0.000377
0.000851
0.32980.34220.0288

Standard partial regression
coefficientNight light images
were resampled into
1km square grid called“Japanesestandardregional mesh” (7,725grids). Other factors
were also resampled
into same grids.

0.50000.40000.30000.20000.10000.0000Road length
Building distribution
Fluid population

Whole
area
0.50350.3791
0.1134Urban
area
0.41230.30490.0916

Suburbanarea
0.39770.34250.0834Rural
area
0.32980.34220.0288

COMPARISON BETWEEN DMSP AND ESTIMATED LIGHT INTENSITIES
Estimated light intensity

Residual errors

Correlation70

Large residual errors
locate around cities
like ring shape.

Light intensity of DMSP/OLS

60
50This phenomenon isespecially prominent
around large cities forexample…
Sendai city
Ishinomaki city

403020y = 1.0585x - 0.4424
R = 0.887291
R2 = 0.787286

1000102030

4050

60

708090100110120

Estimated light intensity

Tendency of spatial distribution of the light intensity by the estimated result agrees rather well with tendency by DMSP. Howeverareas with strong light intensity by the estimated result are smaller than areas by DMSP in not only Sendai city but also other cities.
It is believed that phenomenon which the light intensity of DMSP was saturated over sensitive ranges in urban areas was happened.

CONCLUSION
This study showed the relationship between night light images of DMSP/OLS and human activities.
Analytical results show that automobile traffics have a major effect on the light intensity of DMSP.
On the other hand, the effect by building distribution becomes large in suburban and rural areas. It isalso shown that the light intensity of DMSP and accumulation of people do not necessarily accord.