Remote sensing of environment and disaster laboratory

Institute of Industrial Science, the University of Tokyo, Japan

Development of global livability index (GLI)
for evaluating global cities
Naoki KATAYAMA and Wataru TAKEUCHI
Institute of Industrial Science, the University of Tokyo, Japan
In this study, three factors were considered to evaluate livability in global cities; “environment”,“safety” and “economy”. “Environment” consists of air quality and amount of green space, and they
are estimated by aerosol optical depth (AOD) and normalized difference vegetation index (NDVI)
respectively. “Safety” is composed of natural disaster risks and peace level, and they are estimated by
Global Risk Data Platform by UNISDR and the Global Peace Index. “Economy” is represented byeconomic activity and country development level, and they were estimated by nighttime light exposure
from Visible Infrared Imager Radiometer Suite (VIIRS) day/night band and the Human Development
Index. These six factors were weighted by least-squares method using 97 cities of training data. The

objective variable was set to RepTrak, which is the indicator of reputation of each city based onquestionnaire survey by Reputation Institute. The representativeness of training data was confirmed
by comparing histogram between cities of training data and all urban areas in the world. As a result,global livability index (GLI) was originally developed and it was found that “safety” was the key
factor of livability, whose proportion was 50.2%. GLI enabled cities where questionnaire surveys were
not conducted to evaluate livability, and the most livable city in the world was Alta in Norway. By
comparing GLI and population density, it was found that the more people were living in the less livableareas. GLI is expected to be used for policy planning by local government.

INTRODUCTIONMETHODOLOGY

There are some previous studies evaluating livability of global cities. However, since they arebased on statistical data, only selected cities can be evaluated in these studies. Therefore, for spatialcompleteness, remote sensing data was combined with statistical data. The objective of this study is toevaluate livability of cities spatially homogeneously on the global scale by the combination of remotesensing data and statistical data.
Model	Questionnaire
Global Livability Index
Fig. 1 shows the image of thisHybrid	study. An indicator by questionnaire Subjective, Psychological	
survey in selected cities was spatialRemote sensing	ly extended by combining remote
Statistical data	
sensing data with statistical data, andObjective, Physical	
Global Livability Index (GLI) wasoriginally developed as a hybrid inLimited cities	
Global	dicator evaluate global cities.
Fig 1. Image of this study

RESULTS AND DISCUSSTIONS

Livability
EnvironmentAir
pollution

Aerosol
opticaldepth (AOD)

SafetyRichness

Green

Peace

Naturaldisaster

EconomyDevelopment

NDVIGlobalPeaceIndex(GPI)

Mortality Risk
from naturaldisaster(UNDP)

Nighttimelight(VIIRS DNB)

HumanDevelopment
Index(HDI)

Population(LandScan)

※

※

※

※

※

※

※Data	  extrac)on	  and	  normaliza)on	  	City
Reputation(RepTrak)

Modeling

Global Livability Index(GLI)
Mapping

GLI Map

Fig 2. A flowchart of this study

In this study, three factors were considered toevaluate livability; “environment”, “safety” and “economy”. Each factor is composed of two indicators; airand green for environment, peace and disaster for safety, economy and development for richness. These factors were evaluated by remote sensing data or statistical
data. As questionnaire data, RepTrak surveyed by Reputation Institute was used. This ranks world 100 cities
based on questionnaire collected by more than 20,000
persons worldwide. Six factors were weighted by leastsquares method using 97 cities of training data, and theobjective variable was set to RepTrak. Global livability
index (GLI) was originally developed in this method,and it was visualized as Figure 3 shows.

GLI：97.6

Most livable city：Alta（Norway）

New York（GLI：73.2）

GLI：22.2

Tokyo（GLI：83.5）

Paris（GLI：84.6）

Dhaka
ダッカ（GLI：34.1）

Kolkataコルカタ（GLI：27.8）

Sao Paulo（GLI：74.8）

Global Livability Index（GLI）
40

50

60

70

High8090Richne.5%
t 27enm

Figure 4 shows the result of weighting. Among
three categories, “safety” accounts for the greatest proportion of livability, which is 50.2%. “Economy” factor
in “richness” was absorbed by “development”, which iscountry-specific data.
For verifying the representativeness of 97 cities oftraining data, histogram of each category was comparedbetween world urban area (about 100km2) and 97 citiesof training data. FigEnv.3%
iro22
ure 5 shows comparinAirss6.9%
son of the histograms.
Development
22.3%
As a result, the peak
Green
20.6%
position and shape of
Livability
these two histograms
Peace
were closely fit and the22.6%
Disasterrepresentativeness of
27.6%
training data was conSaf
ety 5 0.2 %firmed.
Fig 4. Result of weighting
for livability model

Least livable city ：Khartoum（Sudan）

Livability
Environment
SafetyRichness

Frequency (Urban area 100km2)

Low
30

Johannesburg（GLI：63.2）(a) In global urban area

Richness

Environment

SafetyLivability

Frequency (Supervised 97 cities)

Mexico City（GLI：64.3）

マニラ
Manila（GLI：52.6）

Fig.3 GLI Map（Non-urbanareasHabitability
are masked in enlargedFig
3. Global
Map view）

CONCLUSION
In this study, questionnaire survey was extended spatially by combining with remote sensing dataand statistical data, and global livability index (GLI) was developed for evaluating livability of global
urban area. By comparing histogram in cities of training data and that in global urban area, the representativeness of training data was confirmed.
GLI enables cities where questionnaire surveys are not conducted to evaluate livability. According
to GLI, world most livable city is Alta in Norway, where GLI is 97.6, and least livable city is Khartoumin Sudan, where GLI is 22.2. In addition, it is easy to evaluate cities continually with this method, andGLI is expected to be used for policy planning by local government.

REFERENCES(b) In 97 cities of training dataIndex value

Fig 5. Comparison of histograms oflivability and each category.

[1] Baugh, Kimberlu, Hsu, Feng Chi, Elvidge, Chris, Zhizhin, Mikhail, 2013. Nighttime Lights Compositing Using the VIIRS Day-Night Band: Preliminary Results,Proceedings of the Asia-Pacific Advanced Network, 35, pp.70-86.
[2] Economist Intelligence Unit: Global Liveability Ranking and Report August 2013, Economist Intelligemce Unit, 2013.
[3] Engel-Cox, Jill A., Holloman, Christopher H., Coutant, Basil W., Hoff, Raymond M., 2004. Qualitative and quantitative evaluation of MODIS satellite sensor
data for regional and urban scale air quality, Atomospheric Environment, 38, pp.2495-2509.
[4] Institute for Economics and Peace: Global Peace Index 2013, Institute for Economics and Peace, 2013.
[5] Justice, C.O., Townshend, J.R.G., Vermote, E.F., Masuoka, E., Wolfe R.E., Saleous N., Roy D.P., Morisette J.T., 2002. An overview of MODIS Land data processing and product status, Remote Sensing of Environment, 83(1-2), pp.3-15, 2002.
[6] Levy, R. C., Remer, L. A., Mattoo, S., Vermote, E. F., and Kaufman, Y. J.: Second-generation operational algorithm: retrieval of aerosol properties overland from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance, Journal of Geophysical Research, 112, D13211, doi:10.1029/
2006JD007811.
[7] Peduzzi, P., Dao, H., Herold, C., Mouton, F., 2009. Assessing global exposure and vulnerability towards natural hazard: the Disaster Risk Index, Natural Hazards and Earth System Sciences, 9, pp.1149-1159.
[8] Reputation Institute: 2013 City RepTrak, Reputation Institute, 2013

For further details, contact: Naoki Katayama, Ce-506, 6-1, Komaba 4-chome, Meguro, Tokyo 153-8505 JAPAN (URL: http://wtlab.iis.u-tokyo.ac.jp/ E-mail: knaoki@iis.u-tokyo.ac.jp)