Analyzing Personal Happiness from Global Survey and Weather Data: A Geospatial Approach
Analyzing Personal Happiness from Global Survey and Weather Data: A Geospatial Approach
Yi-Fan Peng 0 1 2
Jia-Hong Tang 0 2
Yang-chih Fu 2
I-chun Fan 0 2
Maw-Kae Hor 1 2
Ta- Chien Chan 0 2
0 Research Center for Humanities and Social Sciences , Academia Sinica, Taipei, Taiwan , Republic of China, 3 Institute of Sociology , Academia Sinica, Taipei, Taiwan , Republic of China, 4 Institute of History and Philology , Academia Sinica, Taipei, Taiwan , Republic of China, 5 School of Informatics, Kainan University , Taoyuan, Taiwan , Republic of China
1 Department of Computer Science, National Chengchi University , Taipei, Taiwan , Republic of China
2 Editor: Dongmei Li, University of Rochester , UNITED STATES
Past studies have shown that personal subjective happiness is associated with various macro- and micro-level background factors, including environmental conditions, such as weather and the economic situation, and personal health behaviors, such as smoking and exercise. We contribute to this literature of happiness studies by using a geospatial approach to examine both macro and micro links to personal happiness. Our geospatial approach incorporates two major global datasets: representative national survey data from the International Social Survey Program (ISSP) and corresponding world weather data from the National Oceanic and Atmospheric Administration (NOAA). After processing and filtering 55,081 records of ISSP 2011 survey data from 32 countries, we extracted 5,420 records from China and 25,441 records from 28 other countries. Sensitivity analyses of different intervals for average weather variables showed that macro-level conditions, including temperature, wind speed, elevation, and GDP, are positively correlated with happiness. To distinguish the effects of weather conditions on happiness in different seasons, we also adopted climate zone and seasonal variables. The micro-level analysis indicated that better health status and eating more vegetables or fruits are highly associated with happiness. Never engaging in physical activity appears to make people less happy. The findings suggest that weather conditions, economic situations, and personal health behaviors are all correlated with levels of happiness.
Funding: The authors have no support or funding to
Competing Interests: The authors have declared
that no competing interests exist.
Happiness is one of the most important human emotions and a key predictor of many
important life outcomes. Several studies have examined how factors in the external environment and
human behaviors affect happiness [
]. Those factors can be roughly divided into either
macro or micro effects. The former include weather effects, such as temperature , sunlight
], seasonal climate change , and the societal socio-economic environment [
], while the
latter include individuals’ demographic factors such as gender [
], age [
], and personal health
behaviors like smoking [
], exercise [
], and eating more fruits and vegetables [
have indicated that several factors simultaneously affect people’s happiness, and the variations
occur in different persons, cities, and countries. One previous study pointed out that climate
change might alter the distribution of happiness among countries [
]. The spatiotemporal
resolution of the weather data, however, might have deeply affected this inference.
To examine how such a spatiotemporal perspective may help advance our knowledge about
happiness, we integrate global survey data with various corresponding weather conditions. In
particular, we expand the spatial resolution from the country level to the city level, and the
temporal resolution from the monthly level to the daily level. Short-term weather effects also might
be correlated with happiness. While the preexisting world database of happiness archives
several research findings on the subjective enjoyment of life in the form of summarized statistics
for each country [
], this study focuses on individual-level survey data across 32 countries.
Such survey data facilitate multivariate analyses that control for personal factors and variations
within countries. Because happiness is such a complex issue linked to various personal and
societal circumstances, it would be even more revealing and challenging to further enrich such
individual-level analyses in light of macro-level factors.
In this study, we aim to examine the macro and micro effects on happiness from a global
perspective. To cover such different circumstantial factors, we integrated international social
survey data from the International Social Survey Programme (ISSP): Health and Health Care—
ISSP 2011 [
] and weather data called Global Summary of the Day (GSOD) from global
meteorological stations , from the National Oceanic and Atmospheric Administration
(NOAA). Using a geospatial method, we not only could connect these two highly different
types of data, but also incorporate other factors that may be linked to happiness, such as the
cities’ economic development and geo-information. By exploring how weather, economic
situations, and personal health behaviors are associated with happiness, our study also points to the
potential power and usefulness of combining two seemingly distant and divergent global
Materials and Methods
Ethics and Survey data
The ISSP survey data that we used are available to the public from the GESIS data archive
(https://dbk.gesis.org/dbksearch/download.asp?db=E&id=56356). None of the databases that
we used include identifiable personal information, thus informed consent was not necessary.
Because the datasets we used in this study are all publicly available, furthermore, no approval
from an institutional review board (IRB) was needed. The dataset was the 2011 ISSP module
on health and health care, conducted from 2011 to 2013 by 32 countries. Because the survey
data in three countries (Japan, Norway, and South Africa) lacked the specific dates of
interviews, which made it impossible to link the two global data sets for these countries, we
constructed the weather data to correspond to the survey data for the remaining 29 countries (see
S1 Table for the list of member countries).
The purpose of the module was to evaluate healthcare systems, personal health, and health
insurance in various national contexts. Among the survey topics, satisfaction with life was
measured by respondents’ degree of self-reported happiness. Using it as our main focus, we adopt
this measure of happiness as our dependent variable. In the original ISSP questionnaire, the
degree of happiness was self-reported on a 7-point scale of "completely happy," "very happy,"
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"fairly happy," "neither happy nor unhappy," "fairly unhappy," "very unhappy," and "completely
unhappy." The only exception was China, where happiness was measured on a 5-point scale
from "very happy," "quite happy," "I’m not sure, neither happy nor unhappy," and "quite
unhappy" to "very unhappy." Due to differences in the response scales, we decided to first use
data from 28 countries (all but China), then analyze the data from China separately. Using
these two different datasets enabled us to better validate and compare the results.
To elucidate the relationship between weather and happiness, we collected daily weather data
from meteorological stations around the world. There are 28,014 meteorological stations with
spatial coordinates throughout the world that can be accessed at the website (ftp://ftp.ncdc.
noaa.gov/pub/data/gsod) maintained by the National Oceanic and Atmospheric
Administration (NOAA). The daily Global Summary of the Day (GSOD) includes the temperature, dew
point, visibility, wind speed, and so on from each station. We extracted the meteorological data
from stations located in the cities with ISSP’s samples. This data-extraction procedure allowed
more precise matches between the survey samples and local weather conditions.
Administrative area data
When analyzing ISSP data, we used the city as our basic spatial unit. To compute the
geographic location of cities, we needed to identify the administrative boundaries around the
world. We used a global digital map from the publicly available GADM database of Global
Administrative Areas (http://www.gadm.org/) to locate such boundaries .
Our study relied heavily on both data processing and statistical analysis. Detailed data
processing allowed us to organize, filter, and compare both the survey data and weather data that we
retrieved. This procedure could be further divided into “data preprocess,” “geocoding process,”
“integration process,” and “data fetching.”
Data preprocess. To locate the cities where the survey respondents lived, and to identify
the corresponding meteorological stations in or close to these cities, we needed to geo-process
both the survey data and the meteorological data. The purpose of this step was to extract useful
information for geocoding. First, the names of all 32 countries in the ISSP data set were easily
identifiable. All respondents also reported the cities in which they lived at the time of the
interview. By geocoding those two variables, we obtained every respondent’s spatial information.
The same process was applied to the meteorological stations as well. In this study, we
selected four meteorological variables as our explanatory variables, including temperature, dew
point, visibility, and wind speed for the corresponding stations for the 2011–2013 study period.
In total, we used 1,517 meteorological stations in this study. Due to the different date of
establishment in each station, some weather records were not available for specific survey dates.
Although the NOAA provides the spatial coordinates of every station, no data linkage is readily
available between the stations and GSOD. As a result, we merged these two data sources based
on the unique station numbers.
Geocoding process. The ISSP data included 55,081 survey records covering 32 countries,
which needed to be geocoded. We used two steps to reduce the complexity of the geocoding
process. Based on city of residence, we first classified all records into 510 cities or regions. We
then used the Google Maps Geo-coding API [
] to derive most city coordinates. To validate
the geocoding accuracy, we checked the results manually. After correcting missing or
erroneous geocoding results, we managed to obtain the spatial coordinates for all survey records.
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The spatial information in the NOAA and the ISSP is stored in different formats. In the
NOAA dataset, weather stations were marked by latitude and longitude, without exact city
names. In contrast, the locations in the ISSP were presented by the cities’ names, some of
which were written in languages other than English. To minimize these discrepancies, we used
the international administrative area data from the GADM as our base map. We spatially
integrated these two different types of source data into one common map using the spatial join
function in ArcGIS (ArcMap, version10.2; ESRI Inc., Redlands, CA, USA). The integration
helped produce a map with the respondents and meteorological stations connected to each
city, allowing us to map the spatial distribution of happiness from the survey data at the city
level throughout the world. Those areas marked in red shown in Fig 1 are the cities that came
from survey data.
Integration process. We needed the exact date of the interview to integrate the survey
data with the weather data that we extracted from the GSOD. Due to this requirement, we had
to remove 4,866 records with incomplete survey dates in the ISSP survey data, including those
from Norway and South Africa, which only recorded month and year. We also excluded Japan
from our analysis, because all respondents in this country were coded with the same date.
Data fetching. To evaluate the short-term weather effects, we set three different periods of
the date and computed the average effects of weather variables in each period. The three
different periods of the date are 2 days, 4 days, and 8 days, which refer to the survey date and its past
1 day, 3 days, and 7 days, respectively. The three periods of average weather variables were
then analyzed into three separate models.
The models also take other macro-level factors into account. GDP, for example, was used to
represent the overall economic situation. The survey data in each country ended in different
years. Therefore, it was not suitable to use GDP data in one fixed year. To solve this problem, we
used the difference in GDP between the survey year and the previous year. In other words, we
focused on the increase or decrease of the GDP instead of the absolute value of the GDP itself.
Fig 2 shows the workflow of data processing and the number of records in every step. After
the first part of data processing, we obtained the filtered ISSP data and the corresponding
weather data and GDP data.
Fig 1. The spatial distribution of the cities derived from survey data.
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Fig 2. The workflow of data processing.
This study used quantitative methods that allowed a reliable inference of associations between
weather conditions and personal happiness. The software used in processing the statistical data
is the IBM SPSS Statistics version 20. The programming syntax used in SPSS is provided in S2
Table. The research question is whether and which weather conditions are significantly
correlated with personal happiness across countries and regions of the world. We treated happiness
as ordinal, under the assumption that degrees of happiness have a natural order (low to high),
with the quantitative difference between adjacent categories not exactly known. We chose
ordinal logistic regression analysis, because it could properly measure the dependent variable on an
ordinal scale. An ordinal logistic regression model can be seen as an extension of logistic
regression. While the latter evaluates binary dependent variables, ordinal logistic regression
models take into account dependent variables with more than two response categories ordered
in a logical sequence, e.g., from very unhappy to very happy.
In each of the models, we used the following parameters as the key weather variables: mean
temperature (°F), mean dew point (°F), mean visibility (miles), mean wind speed (knots), and
the elevation of the city (hectometers). To calculate each city’s elevation, we first located the
center coordinate of the city from the GADM by ArcGIS, followed by Google Maps Elevation
For individual-level variables from ISSP survey data, we included gender, age, self-reported
health status, the frequency of visits to a doctor in the past 12 months, smoking history, and
the frequencies of drinking alcohol, physical activity, and eating fresh fruits or vegetables.
After fitting a statistical model, it is important to determine whether all the necessary model
assumptions are valid before performing inferencial procedures. If there are any violations,
subsequent inferential procedures may be invalid, and if so, the conclusions would be faulty.
Therefore, it is crucial to perform appropriate model diagnostics. All relevant model diagnostic
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procedures for the ordinal logistic regressions used in this study are provided in greater detail
in the S1 File.
Fig 3 shows the distribution of happiness in different survey areas. Green indicates happier and
red means unhappier. Overall, residents in Europe were happier than those in other areas, as
shown in Fig 3a. Those living in northern Europe were happier than those in southern Europe
(Fig 3b). In China, however, there is not an obvious spatial pattern about happiness (Fig 3c).
The descriptive statistics of the macro and micro variables among the 28 countries and China
are listed in Tables 1 and 2, respectively. Among the macro variables, temperature ranged from
-17.41°F to 87.6°F in the 28 countries, and from 26.62°F to 90.32°F in China. The range of
visibility was from 0 mile to 31.76 miles in the 28 countries, and from 2 miles to 15.59 miles in
China. The values of wind speed were between 0 knot and 19.9 knots in the 28 countries, and
from 1.31 knots to 11.45 knots in China. City elevation ranged from -0.0658 hectometers to
21.0429 hectometers in the 28 countries, and from 0.05 hectometers to 22.5658 hectometers in
China. The minimum and maximum values of the dew point were -21.86°F and 77.25°F in the
28 countries and 4.19°F and 76.73°F in China. Most respondents (64%) in the 28 countries
experienced improving GDP. The distributions of age and gender are similar in both groups.
Compared to their counterparts in other countries, more residents in China appeared to be in
either fair or poor health and did not visit a doctor as often. The percentages of never smoking,
never drinking, and never doing physical activity were all higher in China, where people eat
fruits or vegetables more often than their counterparts in other countries.
Based on the ordinal logistic regression model, the results from the 28 countries are shown in
Table 3. Most macro background factors turned out to be highly relevant with happiness in
different periods. People were happier, for example, when and where temperature, visibility, and
wind speed were all higher, as measured by various intervals (mostly significant at the .01
level). Except for the dew point, which had negative effects (for example, 4 Days: -0.005 ; 8
Days: -0.005 ), all weather conditions and GDP showed some positive effects on happiness
(significant at the .001 level).
In addition to geospatial factors, some individual characteristics were also closely linked to
the extent of happiness. Male and younger respondents, for example, tended to be happier
(Gender: 2 Days: 0.07 ; 4 Days: 0.07 ; 8 Days: 0.071 ; Age: 2 Days: -0.004 ; 4 Days:
-0.004 ; 8 Days: -0.004 ). Self-reported health status was very strongly linked to happiness:
Compared to those in excellent shape, all others felt less happy (e.g., for the 8-day sensitivity
tests, Poor: -3.137 ; Fair: -2.11 ; Good: -1.304 ; Very good: -0.642 ). Somewhat
unexpectedly, such a strong association also existed between happiness and the habit of eating fresh
fruits and vegetables. Those who ate fruits and vegetables daily were significantly happier than
those eating fruits and vegetables less often (for the 8-day sensitivity tests, Never: -0.412 ;
Once a month or less often: -0.62 ; Several times a month: -0.38 ; Several times a week:
-0.16 ). Among other lifestyle characteristics, only doing physical activity played a partial
role in identifying who was happier: Although the frequency of doing physical activity made
little difference, those who skipped exercise altogether (never doing physical activity) were
significantly less happy (2 Days: -0.108 ; 4 Days: -0.108 ; 8 Days: -0.107 ). Other than that, the
extent of happiness did not vary by the frequencies of smoking, drinking alcohol, or visiting a
doctor (Table 3).
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Fig 3. The happiness map. (a) The happiness map of the world except China. (b) The happiness map of
Europe (c) The happiness map of China.
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To further examine whether the geospatial effects showed any temporal or regional
variations, we conducted similar analyses taking both seasons and climate zones into account
(Table 4). Because the vast majority of the countries in our study are located in the northern
hemisphere, the season variable needed to be adjusted only for Australia and Chile. Because
the number of countries with sufficient information was limited, we combined climate zones
into either Tropical/Subtropical or Temperate/Cold, with the latter being the reference group
in the statistical analyses.
In the spring, city elevation was positively associated with a greater level of happiness (2
Days: 0.023 ; 4 Days: 0.02 ; 8 Days: 0.023 ), while a lower dew point also helped. Those
living in tropical/subtropical zones were not as happy as their counterparts in temperate/cold
zones. The effect of dew point on happiness remained significantly negative in summer, but
other geospatial effects showed a very different pattern from that in spring (2 Days: -0.004 ;
4 Days: -0.005 ). Neither city elevation nor climate zone played a significant role in summer,
but the wind speed had a significantly positive effect on happiness.
The seasonal patterns also differed between autumn and winter. Even though a higher
dew point remained a negative factor in terms of residents’ happiness in winter (2 Days:
-0.006 ; 4 Days: -0.009 ; 8 Days: -0.009 ), better visibility emerged as a positive factor in
autumn (2 Days: 0.02 ; 4 Days: 0.025 ; 8 Days: 0.034 ), the only season when this
particular weather condition showed a significant effect. Furthermore, unlike its positive effect in
spring, higher city elevation turned out to be a negative factor for personal happiness in both
autumn and winter (In autumn: 2 Days: -0.031 ; In winter: 2 Days: -0.035 ; 8 Days: -0.038 ).
While residents in tropical/subtropical zones were not as happy as those living in temperate/
cold zones in spring, they became significantly happier than their counterparts from such
colder climate zones when autumn arrived (2 Days: 0.174 ; 4 Days: 0.197 ; 8 Days: 0.285 );
and this climate zone positive effect lasted into winter (2 Days: 0.687 ; 4 Days: 0.769 ; 8
Days: 0.67 ).
Overall, both seasonal and climate zone variations in the geospatial effects on personal
happiness were evident in these 28 countries. Such variations remained unknown in China,
because the Chinese interviews of the ISSP module were mainly completed in the summer.
Without taking season and climate zones into account, the analyses of geospatial factors and
individual characteristics for China showed some results very similar to the findings from the
28 countries. For example, higher temperature (2 Days: 0.017 ; 4 Days: 0.018 ; 8 Days:
0.021 ), lower dew point (2 Days: -0.019 ; 4 Days: -0.02 ; 8 Days: -0.022 ), and higher
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city elevation (2 Days: 0.032 ; 4 Days: 0.032 ; 8 Days: 0.031 ) were all positively
associated with happiness (Table 5). As in the other countries, furthermore, being in excellent
health and eating fruits or vegetables daily were both very strong indicators for being a
happier person in China.
(N = 25,441 Records)
China (N = 5,420
The uniqueness of this study is the integration of globally representative survey data (from the
ISSP 2011 module) with the corresponding weather data, geo-information about the city, and
the economic development index. Few previous studies have used large samples, long temporal
periods, and wide spatial coverage to elucidate the correlation between happiness and weather
factors. One of the most critical and challenging tasks for data integration lies in linking such
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macro and micro data at the city level by a geospatial method. The daily weather data and the
individual-level background factors in this study are detailed enough for examining short-term
weather effects on happiness, while controlling for possible confounders among respondents.
To our knowledge, this is the first paper to use both global survey data and world weather data
with an advanced geoprocessing method to address the macro and micro effects on happiness.
At the macro level, several factors were found to be strongly linked to happiness. A previous
] showed that weather conditions, such as humidity, wind speed, precipitation, and
sunshine, were not significantly associated with happiness, but temperature remained an
important factor that helped distinguish happiness among people. Other research has also
shown a positive correlation between the temperature in the coldest months and the degrees of
happiness from the world database of happiness [
]. In addition, people tend to feel less happy
as the dew point becomes higher [
]. According to yet another study, better visibility also had
a significant effect on happiness [
], which can also be found from our analyses.
With our unique approach, the analyses of seasonal variations further enrich discussions on
the geospatial effects on happiness. It appears that in the spring, people enjoy less humid
weather and higher elevation. It is also interesting to note that those living in temperate and
cold zones are happier than those living in tropical and subtropical zones. It is reasonable to
argue that when spring arrives, residents in temperate and cold zones experience a more drastic
temperature change from cold to warm weather than those living in tropical and subtropical
zones, which often brings about some excitement and surprises in everyday life. In contrast,
people living in tropical and subtropical zones are happier than their counterparts in temperate
and cold zones in both autumn and winter. The reason is similar: Residents in temperate and
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cold zones feel a larger temperature drop from warm to cold than those living in tropical and
subtropical zones. Also in both autumn and winter, higher elevations make it colder than lower
ones, which in turn impedes short-term subjective happiness. In short, the incorporation of
both season and climate zone into the models further enriches our geospatial approach to
studying personal happiness.
In addition to geospatial circumstances like weather conditions, a societal factor, GDP, also
exerted a significantly positive effect on happiness among residents in the 28 countries. Some
previous studies have shown a similar positive and significant association between growth in
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GDP per capita and happiness. Hagerty and Veenhoven [
], for example, found that GDP
was positively related to the number of “happy life years” in 14 of the 21 countries available in
their dataset. In a later study, Hagerty and Veenhoven [
] also observed a statistically
significant rise in happiness in 4 out of 8 high-income countries and 3 out of 4 low-income countries.
In light of these recent studies, the current paper reconfirms the positive impact of an increase
in GDP on personal happiness, while taking into account both geospatial factors and individual
characteristics from two large-scale global datasets.
At the individual level, whether in 28 countries or in China, two of the personal
characteristics and lifestyles, health status and the frequency of eating fresh fruits and vegetable, were
consistently linked to happiness. Health and well-being were always interconnected, with
wellbeing influencing health and health influencing well-being [
]. Good health was linked
with greater happiness, while setbacks in health, such as serious diseases or disability, had
negative effects on happiness. Likewise, it is widely known that eating fresh fruits and vegetables is
good for your health. Happiness represents a relatively new field of study that is nonetheless in
great demand today; but surprisingly, scarce attention has been given to people’s eating habits
in happiness studies. A recent study did conclude that a person’s mental well-being might be
associated with the consumption of fruits . With very similar research results about this
lifestyle effect, while also controlling for weather conditions and societal development, our
geospatial approach of happiness studies should further strengthen the significance of this
particular habit of food consumption.
Our geospatial approach also confirmed the linkage between exercise and happiness.
Respondents with no physical activity tended to experience significantly a lower happiness
level than those doing physical activity, regardless of the frequency. Similarly, a multi-country
analysis of the association between physical activity and happiness found that those who
engaged in daily physical activity were more likely to be happy [
]. Some previous studies
have found that unhealthy habits, such as smoking, drinking alcohol, and not exercising, were
closely linked to substantially lower happiness scores [
]. Although our study confirmed a
linkage between happiness and physical activity, neither smoking nor drinking alcohol showed
any significant, consistent relationship with happiness.
Compared to the overall findings from the global dataset, the analyses of the Chinese data
unveiled some interesting and contrasting results. Happiness, for example, was clearly linked
with gender in the 28 countries, where men were happier than women, but such a gender
difference was lacking in China. While younger people were happier than older people in the 28
countries, older people were happier than their younger counterparts in China.
According to relevant research, such an age effect may be due to cognitive processes,
particularly the processes that focus on and are related to remembering positive events, while leaving
behind negative ones. If such processes are indeed at work, they may help older people regulate
their emotions, letting them view life in a sunnier light [
]. In western culture, being
happy is seen more as a personal accomplishment. Under the influence of Confucianism,
however, Chinese people often regard interpersonal relationships as a key factor in defining
happiness . As a result, women in China may feel happier if they fulfill their role obligations in a
family, which in turn helps ensure family harmony, as well as prosperity. By contrast, men in
China tend to rely on social status and material achievement to a greater extent for their
Due to the nature of the approach and the data collected at different levels in different
countries and regions, however, several limitations and concerns deserve discussion in more details.
First, the ISSP survey data do not cover residents in the tropical zone, which presents some
representativeness problems if we want to discuss happiness in that climate zone individually.
Second, because the response scales of happiness differ between the 28 countries and China, we
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could not compile both survey datasets together. The split of the subsamples may have slightly
reduced the power for explaining happiness on a global level. As an alternative, we treated the
China data as our validation data. Third, we used the average weather variables within each
city. Because we did not know the exact location of every respondent, we could only average
the weather factors from all the stations within a city. This rough estimation may ignore the
variability of weather conditions within a city.
Fourth, social phenomena involving macro- and micro-level data can be better understood
with countries’ or regions’ dummy explanatory variables, or using multilevel analyses. Because
the survey data were collected from individuals, while the weather data were aggregate statistics
of the cities or areas where a number of these survey respondents lived, statistical analyses of
personal happiness should take into account how the repeated measures of the weather data
may affect the variation in personal happiness. The GDP was also aggregate data at the country
level. When we tried incorporating countries or regions as dummy variables, part of the
regression outcomes became somewhat unstable. The distribution of the 29 countries used in this
study is scarce and uneven compared with the total number of countries in different
geographical regions, as categorized by United Nations (S3 Table). Except for Eastern and Western
Europe, most geographical regions are under-represented. Further analyses controlling for
countries or regions would be more feasible and reliable in the future when survey data from
more countries become available.
Fifth, data compatibility remains a major issue in comparative studies, especially when the
comparisons involve subjective survey items. The ordering and wording of question items
and response categories, for example, need to be identical in different surveys for the data to
be compatible. Furthermore, sampling and interviewing need to be designed and
administered in the manner that data come from samples that well represent the populations. Any
incoherent designs and practices may cause potential biases that no advanced statistical
analyses can amend. Such biases may be particularly serious when comparing survey data across
different countries and cultures, which requires the continued invariant measurement of
]. Even when question items are well translated in different countries, respondents
from various cultural backgrounds may interpret them in a manner that deviates from the
original design. As a result, the differences found across countries could be methodological
artifacts rather than real differences. To prevent discovery of such artifacts, it is critical to
design and follow specific precautions in all survey stages to assess and ensure data
The global survey dataset we used, the 2010 ISSP module, was available through GESIS.
Started from 1984, the ISSP has been continuously conducting annual surveys on diverse
topics, covering various cultures around the globe [
]. To achieve data comparability, the
scientific committees of the ISSP compose and revise detailed project specifications for each
successive round of survey. All member teams are required to conduct and document fieldwork
comprehensively according to the same standard setups. Such a principle of equality applies to
sample selection, translation of the questionnaire, and all methods and processes associated
with data collection and processing . With such rigid requirements, the ISSP ensures
compatibility across countries and cultures in questionnaire design, sampling, fielding, and data
For data processing in the current study, we filtered out incomplete and unreasonable cases
to extract useful information for geocoding. To adjust for variations in the dates of interviews,
we further used dummy variables for season, climate zone, and GDP. Combined with the high
quality of the ISSP data, such solutions in data process should have enhanced data
comparability to the extent that each ISSP country made their best efforts in complying with the
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The current study has taken advantage of widely available global datasets that have emerged
and blossomed in recent years. While world-wide survey and weather data have facilitated
various studies and inspired research insights, our study moved a step further by combining
individual-level survey data with city-level weather data. The research findings based on the
geospatial approach shed new light on the substantive research subject.
The factors linked to personal happiness are quite complex. This study tried to integrate
international survey data with global weather data by a geospatial approach. With a macro
perspective, the ordinal logistic analyses indicated that people living under the conditions of
higher temperature, higher visibility, a little wind, lower dew point, and an improving
economic situation felt happier. In an effort to distinguish the effects of weather conditions on
happiness in different seasons and different climate zones, further analyses revealed significant
seasonal variations in these geospatial effects. At the individual level, the same ordinal logistic
analyses reconfirmed that better health condition and eating more fruits or vegetables were
both highly associated with happiness, while taking into account geospatial factors and societal
development. Never doing physical activity appeared to make people less happy. The findings
from these macro- and micro-level background factors suggest that weather conditions,
economic situations, and personal health behaviors all provide a better understanding of subjective
happiness at the individual level. Adding the geospatial approach to the regression analyses of
social survey data thus helps broaden and enhance happiness studies.
S1 File. The model diagnostic results.
S1 Table. List of ISSP member countries of the 2010 data.
S2 Table. The SPSS syntax used for ordinal logistic regression.
S3 Table. Number of countries in this study and in the UN data.
Conceived and designed the experiments: YCF ICF MKH TCC. Analyzed the data: YFP JHT
TCC. Wrote the paper: YFP JHT YCF TCC.
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