Table Of ContentContributions to Regional Science
Eric Vaz Editor
Geography
of Happiness
A Spatial Analysis of Subjective
Well-Being
Contributions to Regional Science
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Eric Vaz
Editor
Geography of Happiness
A Spatial Analysis of Subjective Well-Being
Editor
Eric Vaz
Toronto Metropolitan University
Toronto, ON, Canada
Contributions to Regional Science
ISBN 978-3-031-19870-0 ISBN 978-3-031-19871-7 (eBook)
https://doi.org/10.1007/978-3-031-19871-7
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Contents
Happiness Geography: Defining the Field ............................ 1
Eric Vaz
Regional Challenges
A Spatial Analysis of the Instagram Hashtag #happy:
An Assessment of Toronto .......................................... 11
Eric Vaz
The Subjective Well-Being in North Africa and the Impacts
on Agriculture and Urban Land .................................... 33
Azzeddine Bellout, Eric Vaz, Antonia Bousbaine,
and Christopher R. Bryant
Territorial Challenges
Researching Quality of Life in Old Age: Some Conceptual
and Methodological Principles ...................................... 51
José de São José
Peripheral Retail Expansion: Social Implications and Spatial
Inequalities the Case of the Île-de-France Region ..................... 73
André Torre and Océane Peiffer-Smadja
Locational Challenges
Sustainable Cities, Quality of Life, and Mobility-Related Happiness .... 103
Karima Kourtit, Peter Nijkamp, and Marina Toger
v
vi Contents
Tourism, Climate Change and Well-Being: The Products’ Diversity
as an Opportunity ................................................. 121
André Samora-Arvela, Eric Vaz, Jorge Ferreira,
and Thomas Panagopoulos
Tourism, Senses and Well-Being .................................... 161
Dora Agapito
Happiness Geography: Defining the Field
Eric Vaz
1 Introduction
Recent advances in spatial analysis have led to a growing interest in applying
geocomputation methods to complex issues (Du et al., 2020), often defined over
large datasets framing socioeconomic variables (Ekbia et al., 2015) and the natural
environment (Bryan et al., 2011). Much of this work has been carried out to optimize
decision-making and lead to a better quality of life, economic growth and social
stability (Higgins et al., 2014; Rosu et al., 2015; Ahlfeldt et al., 2020). Thanks to the
advances of computational power, spatial analysis has reached a new stage, where
nonlinear modelling approaches combined with stochastic modelling allow for a
better understanding of the geographical environment (Silva et al., 2020) and there-
fore foster more accurate decision-making (Carver, 2019). This decision-making has
focused predominantly on sustainable development, leading to better life quality,
where spatial information on the natural environment has brought a key role, partic-
ularly in ecology, to understand the present and offer a more sustainable future.
From an anthropocentric perspective, wellbeing is the relation of humankind’s social,
economic, and environmental stability, maximizing the opportunities for sustainable
development while leading to better life quality.
This holistic approach of wellbeing closely reminds a much deeper concept found
in Aristotelian Ethics. From a positivistic posture, understanding what we define
as happiness is subjective and explores individual wellbeing within space and time
(Houlden et al., 2019; Kosanic & Petzold, 2020; Shekhar et al., 2019). It is thus highly
uncertain, difficult to measure socially, and even harder to quantify and explain.
Nevertheless, the advances in complex system science and the exponential growth of
big data and the possibility of tracking individual data have led to a resounding interest
B
E. Vaz ( )
Department of Geography and Environmental Studies, Toronto Metropolitan University, Toronto,
ON, Canada
e-mail: [email protected]
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 1
E. Vaz (ed.), Geography of Happiness, Contributions to Regional Science,
https://doi.org/10.1007/978-3-031-19871-7_1
2 E.Vaz
in the integration of complex systems to define a spatial understanding of happiness.
The centre stage where the action of happiness plays is geography (Brereton, 2008;
Stanca, 2010; Ballas, 2020; Mavruk et al., 2021). The key players are surrounded
by the myriad of data sources that intertwine with computational methods that elab-
orate on defining the field of happiness geography. The choice of variables related
to wellbeing is chosen in three fundamental dimensions: Social, economic and envi-
ronmental, and adapted to the spatial dimension of administrative boundaries that
lead to the geography of happiness (David et al., 2014). The combination and spatial
dynamics of the patterns found in the analysis of spatial wellbeing, and mostly, the
potential of harnessing individual data and ongoing spatial and temporal response
rates, leads us a step closer to quantifying happiness at a regional scale (Morrison,
2021). Such a field may well be defined as a novel contribution to the existing
ideations of subjective happiness, where a new kind of spatial rationale leads to
optimizing the geographical and planning environment of future regions and cities
(Wang & Wang, 2016).
2 On Happiness as a Consequence of Sustainable
Development
In detriment to economic growth, much of our landscape has changed dramatically
over the last decades. With the unprecedented effects of urbanization and popu-
lation increase, human beings have significantly jeopardized the natural environ-
ment, heritage landscapes, and the stability of itself as a keystone species. The
increasing competition and economic growth have led to a neglect of the natural
environment, and since Brundtland raised the need for strategies for common goals
of sustainable development. However, there is no consensus on what this sustainable
development entails, particularly given the complexity of the very notion of what
could be discarded and what should be kept for future generations (O’Neill & Kahn,
2000). Scholars agree, however, that sustainable development is crucial for the very
survival of our species and that humankind must find a solution to readjust itself to
its surrounding environment (Garibaldi & Turner, 2004). One of the culprits of envi-
ronmental and landscape degradation has been the excessive growth human being
has generated (Vaz, 2016). From an economic standpoint, more significant asymme-
tries between social strata lead to loss of resources from a Schumpeterian perspective
(Puscaciu et al., 2016). In other words, society has taught us that only a small nucleus
of people remains and holds wealth, while the large majority of humankind lives in
challenging misery. There is a clear pattern of poverty, crime, and wealth at the spatial
level, clearly traceable to the root of hubs of economic growth, such as urban areas.
Therefore, urbanization processes are a logical consequence of economic prosperity,
and while cities grow, infrastructures spoil the already environmentally depleted land
use, where natural resources become scarce. This sinks process has led to new tech-
niques that enable better monitoring and understanding of the spatial dimension,
HappinessGeography:DefiningtheField 3
supporting the planning of urban and rural areas, and defining the socioeconomic
strata from a geographic perspective, leading to more sustainable and sounder envi-
ronments. Wellbeing is closely related to this very concept, as it assumes social and
economic stability and integration of awareness and harmony with the natural envi-
ronment. The Aristotelian notion of happiness furthermore expands on the idea that
happiness is an absolute objective for humankind, only reflected in the existence
of a common goal. From a sustainable development perspective, this common goal
may become the attempt to transmit to future generations our social, economic, and
natural environment, without any negative externalities that create dramatic changes,
but offer a stable system of growth to the generations to come.
3 Spatial Information for Complex Decision-Making
With the recent technological development witnessed in the last decades, computa-
tional systems have increased dramatically their ability to cope with large amounts
of data and offering more complex analysis of data sets. This has brought signif-
icant advances in geocomputation, where the assembly, editing, and manipulation
of data allow at present much more elaborate computational tasks than a decade
ago. A large volume of geostatistical and spatial analysis methodologies has arisen
from the possibility of understanding complex interactions, leading to advances in
nonlinear modelling approaches that follow out of the traditional body of knowl-
edge of statistical toolsets mainly available until the nineties. These new methods
embed the possibility to observe behaviours and predict motion over space and time.
It is only natural that the traditionally applied visions such as von Neuman and the
scholarly work found in genetics have become interesting paths for the advances in
spatial analysis and observation of empirical outcomes. One of these methods links to
cellular automata, which have primarily been used to prospecting spatial phenomena
such as land-use change. The very nature of land-use change modelling utilizing
stochastic processes enabled in cellular automata shows that combined method-
ologies of geographical analysis embed well into the stream of complex systems,
converging to the advances in the field of geographic information science.
The cellular automata itself derives from a straightforward concept. A single cell
iterates its state given the fact that growth is the produce of its adjacency. The anachro-
nism further explores this simple growth mechanism known as the cell iteration over
time that the future state of the cell is represented by a transition probability, in other
words, the mechanics on how plausible it is for the cell to grow adjacently, given the
probability of any other cell becoming similar to its adjacent type. Adding different
categories to this, the cells take action to form certain determinants of change over
space and time. In all its simplicity, this model represents a compelling concept of
nonlinearity that has been widely used for planning purposes in the field of spatial
analysis. It is therefore intriguing that the usability of this method has not yet been
applied to other fields of social science, and can only be a result of the little explo-
ration still at hand of complex systems modelling in line with fields such as the
