Towards a Collective Spatial Analysis: Proposal of a New Paradigm
for Supporting the Spatial Decision-Making from a Geoprospective
Approach
Juan Daniel Castillo Rosas
1,3
, María Amparo Núñez Andrés
2
, Josep María Monguet Fierro
1
and Alex Jiménez Vélez
1,4
1
Departament d'Expressió Gràfica a l'Enginyeria, Universitat Politècnica de Catalunya (UPC-BarcelonaTech),
Av. Diagonal, 647, Barcelona, Spain
2
Departament d'Enginyeria del Terreny, Cartogràfica i Geofísica, Universitat Politècnica de Catalunya
(UPC-BarcelonaTech), C. Jordi Girona, 1-3, Barcelona, Spain
3
Direccion General de Cartografía, SEDENA, Av. Industria Militar 261, Naucalpan de Juárez, Mexico
4
Universidad de las Fuerzas Armadas (ESPE), Av. Gral. Rumiñahui s/n, Sangolquí, Ecuador
Keywords: Spatial Analysis, Collective Intelligence, Complexity Theory, Spatio-Temporal, GIS, SDSS, Gisc,
Geoprospective.
Abstract: This paper presents the progress of a research work that seeks to establish prospective spatio-temporal
locations of goods, services or events in a given territory primarily through the application of concepts and/or
tools that combine Collective Intelligence (CI), Geographic Information Science (GISc) and Complexity
Theory. Relying on this notion, probable and plausible future scenarios could be projected to conduct various
studies within the context of the Geoprospective (an emerging field of research aimed at issues of territorial
forecasting), which might provide valuable alternatives in the decision-making process in order to carry out
anticipatory actions to achieve or avoid such scenarios. In the light of the above, it is suggested that this kind
of Collective Spatial Analysis (CSA) would provide a new paradigm about how to perform spatial analysis,
the same that is based on a cognitive approach of a multidisciplinary group of users who collectively
participate with their knowledge on an interdisciplinary basis, and not from a limited single user approach
that uses geometric, statistical or mathematical geoprocessing algorithms.
1 INTRODUCTION
The spatio-temporal dimension should be considered
transcendental in decision-making, specially when is
intended to plan, organise and use the territory and its
resources, since it is the geographical space in which
most of the human activities are conducted and will
take place. It is worth highlighting that the
geographical space must be considered as a complex
system; being understood by “complex” as something
that stands out from complicated, characterised by
nonlinearity, emergence and surprise, and that
involves uncertainties that must be taken into
account, particularly in strategic planning (Ratter
2006; O’Sullivan et al. 2006; Pitman 2005). From this
point of view, research related to the territory should
not be addressed from reductionist approach, splitting
up and then adding parts, as in most cases, but
through a set of interactions of its key components,
selected according to the topic to be approached to,
and bounded in time and space, as it is impossible to
consider the full range of its elements, interactions
and variants.
Given the complexity of the geographical space,
the need for an interdisciplinary study of it is also
manifested. Consequently, in this regard different
approaches have emerged to analyse it for planning
purposes; among others, the Territorial Intelligence
(Guzmán Peña 2013), and the Geoprospective
(Emsellem et al. 2012). In practice, both approaches
differ slightly, nevertheless, according to the purpose
of this work the Geoprospective approach will be
addressed.
The Geoprospective is a relatively emerging
research field whose concept emerged in 1968, and it
was boosted since the year 2000, with the mere
purpose of carrying out foreseeing tasks on territorial
planning by integrating different methods and
Castillo Rosas J., Amparo Núñez Andrés M., Monguet Fierro J. and Jiménez Vélez A..
Towards a Collective Spatial Analysis - Proposal of a New Paradigm for Supporting the Spatial Decision-making from a Geoprospective Approach.
DOI: 10.5220/0005469301850190
In Proceedings of the 1st International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM-2015), pages
185-190
ISBN: 978-989-758-099-4
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
participants to provide results based an
interdisciplinary approach. Under this approach, one
or more future possible or plausible scenarios are
intended to generate which will help early decision-
making to achieve the objectives pursued (Emsellem
et al. 2012; Godet et al. 2008; Houet & Gourmelon
2014).
As it is well known, so far this kind of scenarios
can be generated through spatial analysis by using
Geographic Information System (GIS), either using it
as an independent tool or widening its capabilities to
integrate (along with other conceptual,
methodological, and technological resources) a
Spatial Decision Support System (SDSS) (Buzai
2011; Densham & Goodchild 1994; Jankowski et al.
2014; Moon & Ashworth 1992; Sugumaran &
Degroote 2011). In this respect, it is worth remarking
that thanks to the introduction and proliferation of
these technologies (GIS and SDSS), nowadays the
term Spatial Analysis is mainly related to
computerised processes executed by one single user;
however, it should be pointed out that the Spatial
Analysis not only can be performed through one or
several geoprocessing functions, i.e., these types of
scenarios not only can be generated from the
available computational capabilities of the GIS to
manipulate and analyse spatial data and which are
"considerably influenced by the progress on
information technology” (Zhao et al. 2012).
That is why this document presents the research
progress that suggests the insertion of a new paradigm
into Spatial Analysis, whose purpose is to generate
spatio-temporal locations from the interdisciplinary
study of geographical space, perceiving such space
from the Complexity Theory perspective (Ratter
2006), and for that purpose, based on the Collective
Intelligence philosophy (Lévy 2010), as well as some
concepts and technologies of the recent Geographic
Information Science (Blaschke & Merschdorf 2014).
As a consequence of the conceptualisation of this
paradigm, a geotechnological tool has been
developed, which will lead to determine
geoprospective locations of goods, services and/or
events, in order to support spatial decision-making.
2 SPATIAL ANALYSIS
In general, Spatial Analysis can be understood as the
set of systematic procedures that allow studying the
characteristics of the complexity of geographical
space to draw conclusions, assumptions or solutions
to certain questions that will help to better understand
the world that surrounds us.
The concept of Spatial Analysis has also been
addressed and extended by other disciplines such as
Economics, Biology, and Ecology, nevertheless, it is
considered as an elemental part for studying
geographical space, especially from 1950’s decade
with the raise of the quantitative Geography, and later
due to its inseparable linkage with Geographic
Information Systems (Goodchild & Haining 2004),
thanks to which the term is widely associated as a
computerised process, and in consequence, it is usual
to notice that some studies used it as the equivalent of
Geoprocessing, Spatial Statistics and even Spatial
Data Analysis. Nonetheless, it must be specified that
the latter concept corresponds to diverse tools that
form different georeferenced data processings; and
alone or in combination by themselves will allow to
undertake a Spatial Analysis where the user’s
knowledge plays a crucial role (Fischer 2006b;
Fischer 2006a; Fischer & Getis 2010).
Nowadays, the Spatial Analysis is a very active
area of research in the field of Geographic
Information Science and it can be performed with
simple visual and interactive observation data,
systematically through GIS modelling (Longley et al.
2011), supported by software specially designed to
solve problems of statistics, using algorithms of
Computational Intelligence and Geostatistics (Fischer
& Getis 2010), and even by a combination of all of
them; therefore it is applied in multiple spatio-
temporal studies such as environment, security and
defence, hazards and risks health, education, energy,
communications, commerce, regional planning and
development, among others.
Table 1: Methods and description to Spatial Analysis
(Haller 2007).
Spatial analysis
method
Description
Queries
Retrieve information from
database.
Measurements
Numerical value that describes
geographic entities and relations
between geographic entities.
Transformations
Changing, combining or
comparing datasets.
Descriptive
summaries
Descriptive statistics applied in
GIS.
Optimisation
P-median problem – selecting
ideal locations according to well-
define rules.
Hypothesis
testing
Make generalisations about the
whole from a sample dataset.
Nevertheless, the user is the main component of
any Spatial Analysis, whether designing
geoprocessing algorithms and/or applying them to the
qualitative and/or quantitative characteristics of the
different layers. The user is who develops the
procedures, chooses the variables, confirms the
analysis and interprets the results by using his/her
knowledge, feelings and experiences (Gomez &
Jones 2010, p.32). Therefore, the same problem may
be approached differently according to the reasoning
of each user; and the potential to analyse and obtain
the corresponding knowledge will vary according to
the sort of data and methods to be used (Table 1 and
Figure 1).
Figure 1: Traditional Spatial Analysis, from a single
cognitive stance.
3 COLLECTIVE INTELLIGENCE
IN GIS
C
“Collective intelligence has existed for at least as long
as humans have”, and it can be used from different
perspectives (MIT 2012), for example when studying
the use and exchange of collective information in
insect colonies, (Franks et al. 2002); in the research
of fanatic feelings and emotions during a professional
football game (Trappey et al. 2014); in the
programming of Artificial Intelligence algorithms to
create a recommendation system that provides
filtered information from a great quantity of elements
in field of the modern medicine (Pérez Gallardo et al.
2013), or in public administration for establish
priorities in public health policy (Martì et al. 2014).
Due to this wide scope of applications, it is
difficult to define Collective Intelligence without
excluding some of its applications; hence, for the
purposes of this work, it is understood by IC: "The
capacity of human collectives to engage in
intellectual cooperation in order to create, innovate,
and invent" (Lévy 2010). It can be noted that this
philosophy is able to be applied from a reduced
number of individuals to the whole humankind.
Furthermore, it is necessary to identify the difference
between Collective Intelligence and Collaborative
Work (Patel et al. 2012), because while the first
intends to develop knowledge together, the second
only implies the interaction among the individuals to
work towards common goals.
With regard to the scope of Geographic
Information Science, some methods have been
extensively developed that allow using
geotechnologies, basically from a collaborative
approach. Among the most outstanding are the
Participatory Geographic Information Systems
(PGIS), whose purpose is to stimulate the
participation of society in collaborative research of its
own territory (Sieber 2006); subsequently the
capabilities of these have been extended to make up
Collaborative Geographic Information Systems
(CGIS) (Balram & Dragićević 2006), which may also
incorporate social network services and provide a
working platform to share georeferenced information
in real time as a Geocollaboration System (Chang &
Li 2013). On the other hand, the Spatial Decision
Support Systems (SDSS) thus constitutes another tool
sometimes made up for collaboration as a team, and
are designed to support decision makers to solve the
complex problems related to the space (Jankowski et
al. 1997; Jelokhani-niaraki and Malczewski 2015;
Sugumaran and Degroote 2011).
With the advent of Web 2.0, widespread
dissemination of internet portals was launched where
any person may contribute and look up in a simple
way (like Wikipedia), which currently is known as
Volunteered Geographical Information (VGI)
(Goodchild, 2007). This practice is usually associated
to the term Neogeography which defines the
democratisation of the information that is used and
uploaded by this sort of "non-expert" users (Hudson-
Smith et al. 2009). In the same vein, from the
collective production of geographical information,
recently the VGI has accurately been called as Spatial
Collective Intelligence (Spielman 2014). Albeit,
given the characteristics and nature of its production,
a whole debate regarding the quality and the
reliability of this information has also been created
(Flanagin and Metzger 2008; Spielman 2014).
4 DISCUSSION: COLLECTIVE
SPATIAL ANALYSIS (CSA)
Most of human activities are related to territory, and
therefore, they constitute complex space systems that
require an interdisciplinary study for proper planning
and management. In this regard, the necessary
analysis demands to locate and map out certain events
in a space-time through spatial analysis carried out in
a Geographic Information System; the same which is
used independently or as part of a Spatial Decision
Support System or a Collaborative Geographic
Information System. However, this spatial analysis is
invariably produced from a single cognitive stance,
since nowadays, there are no means that allow
collectively perform a Space Analysis for obtaining
an interdisciplinary result.
Concerning the work undertaken, it can be seen
that through Participatory Geographic Information
Systems the society actually participates only
providing local information for a specific purpose,
but is not involved in a process of spatial analysis. In
terms of Volunteered Geographic Information, it is
observed a similar case, with the difference that in
this exercise, the collective does not belong to the
same local community, nor has it been expressly
convened for that purpose. Yet, in both PGIS and the
CGI, it can be stated that an operation of Spatial
Collective Intelligence is performed (Spielman,
2014), since in both cases a collection of unique
spatial knowledge is created through intellectual
cooperation of a group, but emphasising that a task of
Collective Spatial Analysis is not carried out.
Figure 2: Collective Spatial Analysis, from a cognitive
stance of group.
In the light of the above-mentioned, a paradigm is
considered vital in the Spatial Analysis that supports
new lines of research for the study of geographical
space from the Collective Intelligence philosophy,
which could be coined as a Collective Spatial
Analysis, and is defined as the ability of a human
collective -that cooperates intellectually- to
investigate the complexity of geographical space in
order to create, innovate or draw conclusions,
assumptions or solutions to certain questions that will
make a contribution for a better understanding of the
world around us (Figure 2).
This aspect is crucial when is consider for
example, that in geoprospective studies in order to
support decision-making regarding planning or
prevention, it is necessary to devise future scenarios
within which space-time component of goods,
services and/or events is extremely important, and
also these scenarios will be more rational through the
interdisciplinary opinion of a group of experts.
5 CONCLUSIONS
This paper has presented the progress of research, that
seeking to establish geoprospective spatio-temporal
locations of goods, services or events in the territory,
has highlighted the need to open new lines of research
to analyse the geographical space adopting the
Collective Intelligence philosophy, because, as it can
be seen, the range of possibilities suggests thinking of
a new paradigm within the Spatial Analysis and the
Collective Spatial Analysis.
To validate this assumption and as an example of
possible applications, in this research, a Spatial
Decision Support System Group G-SDSS tool has
been developed, which is named Geospatial System
of Collective Intelligence (SIGIC for its acronym in
Spanish and Catalan), with which is intended to
determine spatio-temporal locations in an
interdisciplinary way (from the geoprospective
approach) through the geo-consensus (agreement on
territorial locations relative to different opinions) (Di
Zio and Pacinelli, 2011).
It is considered that those geospatial features of
the locations obtained through geo-consensus could
even be used as an input pattern*, so that from it, the
rest of the area under study is classified; for instance,
through Neural Networks which have produced
encouraging results in numerous geographical
problems (Painho et al., 2004), being able to even
employ this method of expert geo-consensus for
supervised classification of remote sensing.
This does represent a significant advantage over
the usual way of carrying out Spatial Analysis, if it is
considered situations where there are insufficient data
to perform geoprocessing, or in circumstances which
are characterised by uncertainty as in the case of
*
A pattern should be understood as an entity that is represented
by a set of measured properties, and the relationships
between them (Watanabe, 1985)
.
nonlinearity, emergency and surprise, and even, as
support to narrow, guide, verify and/or correct the
results of other Spatial Analysis alternatives.
Of course, questions remain unresolved, the fact
is that the spatial analysis currently done with GIS is
weak to support decision-making in situations like
those presented here, specially because they are not
designed for a group to develop in an interdisciplinary
way, a spatial analysis on complex spatial scenarios.
Moreover, it is not intended to imply, nor intended
this work to discredit the current way of doing spatial
analysis. But it does to raise awareness regarding the
necessity to consider new research lines in spatial
analysis that take into account the participation of
multidisciplinary groups to develop knowledge of
geographic space in an interdisciplinary way, with the
aim to refine what until now has been done; because
as Albert Einstein hinted: in order to obtain different
results, is imperative to do different things.
ACKNOWLEDGEMENTS
We specially thank our PhD colleague José J. Diez-
Rodríguez for his valuable collaboration in the
development of this work, as well as the National
Council of Science and Technology of Mexico
(CONACYT) for its support.
REFERENCES
Balram, S. & Dragićević, S., 2006. Collaborative
geographic information systems, United Kingdom: Idea
Group Publishing.
Blaschke, T. & Merschdorf, H., 2014. Geographic
information science as a multidisciplinary and
multiparadigmatic field. Cartography and Geographic
Information Science, 41(3), pp.196–213. Available at:
http://www.tandfonline.com/doi/abs/10.1080/1523040
6.2014.905755 [Accessed April 29, 2014].
Buzai, G.D., 2011. Modelos de localización-asignación
aplicados a servicios públicos urbanos: Análisis
espacial de Centros de Atención Primaria de Salud en
la ciudad de Luján, Argentina. Cuadernos de Geografía
- Revista Colombiana de Geografía, 20(2), pp.111–123.
Chang, Z.E. & Li, S., 2013. Geo-Social Model: A
Conceptual Framework for Real-time
Geocollaboration. Transactions in GIS, 17(2), pp.182–
205. Available at: http://doi.wiley.com/10.1111/j.1467-
9671.2012.01352.x [Accessed May 16, 2013].
Densham, P.J. & Goodchild, M.F., 1994. Spatial Decision
Support Systems, Santa Barbara, California, E.U.A.
Emsellem, K., Liziard, S. & Scarella, F., 2012. La
géoprospective: l’émergence d’un nouveau champ de
recherche? L’Espace géographique, 2(41), pp.154–168.
Available at: http://www.cairn.info/revue-espace-
geographique-2012-2-page-154.htm.
Fischer, M. M., 2006a. Spatial Analysis and
GeoComputation, Viena, Austria: Springer Berlin
Heidelberg.
Fischer, M. M., 2006b. Spatial Analysis in Geography. In
Spatial Analysis and GeoComputation.
Fischer, M. M. & Getis, A. eds., 2010. Handbook of
Applied Spatial Analysis. Software Tools, Methods and
Applications, Springer-Verlag Berlin Heidelberg.
Available at: http://www.springerlink.com/index/10.
1007/978-3-642-03647-7.
Flanagin, A.J. & Metzger, M.J., 2008. The credibility of
volunteered geographic information. GeoJournal, 72(3-
4), pp.137–148. Available at: http://link.springer.com/
10.1007/s10708-008-9188-y [Accessed May 21, 2013].
Franks, N.R. et al., 2002. Information flow, opinion polling
and collective intelligence in house-hunting social
insects. Philosophical transactions of the Royal Society
of London. Series B, Biological sciences,
357(October), pp.1567–1583.
Godet, M., Durance, P. & Gerber, A., 2008. Strategic
Foresight La Prospective Use and Misuse of Scenario
Building, Paris, France. Available at:
http://scholar.google.com/scholar?hl=en&btnG=Searc
h&q=intitle:Strategic+Foresight+La+Prospective+Use
+and+Misuse+of+Scenario+Building#2.
Gomez, B. & Jones, J.P., 2010. Research methods in
geography, United Kingdom: A John Wiley & Sons,
Ltd., Publication.
Goodchild, M.F., 2007. Citizens as sensors: the world of
volunteered geography. GeoJournal, 69, pp.211–221.
Goodchild, M.F. & Haining, R.P., 2004. GIS and spatial
data analysis: Converging perspectives. Papers in
Regional Science, 83(1), pp.363–385. Available at:
http://www.redalyc.org/articulo.oa?id=28900609.
Guzmán Peña, A. R., 2013. Proposal of a Model of
Territorial Intelligence. Journal of Technology
Management & Innovation, 8(ALTEC), pp.76–83.
Haller, E.A., 2007. Geospatial analysis framework. Brain:
Broad Research in Artificial Intelligence and
Neuroscience, 1(2), pp.166–171.
Houet, T. & Gourmelon, F., 2014. La géoprospective -
apport de la dimension spatiale aux démarches
prospectives. Cybergeo: European Journal of
Geography [En ligne], pp.1–9. Available at: http://
cybergeo.revues.org/26194.
Hudson-Smith, A. et al., 2009. NeoGeography and Web
2.0: concepts, tools and applications. Journal of
Location Based Services, 3(2), pp.118–145. Available
at: http://www.tandfonline.com/doi/abs/10.1080/174
89720902950366 [Accessed March 1, 2013].
Jankowski, P. et al., 1997. Spatial group choice: a SDSS
tool for collaborative spatial decision-making.
International Journal of Geographical Information
Science, 11(6), pp.577–602.
Jankowski, P., Fraley, G. & Pebesma, E., 2014. An
exploratory approach to spatial decision support.
Computers, Environment and Urban Systems, 45,
pp.101–113. Available at: http://linkinghub.elsevier.
com/retrieve/pii/S0198971514000246 [Accessed
November 14, 2014].
Jelokhani-niaraki, M. & Malczewski, J., 2015. A group
multicriteria spatial decision support system for parking
site selection problem: A case study. Land Use Policy,
42, pp.492–508. Available at:
http://dx.doi.org/10.1016/j.landusepol.2014.09.003.
Lévy, P., 2010. From social computing to reflexive
collective intelligence: the IEML research program.
Information Sciences, 180(1), pp.71–94. Available at:
http://linkinghub.elsevier.com/retrieve/pii/S00200255
09003478 [Accessed November 14, 2013].
Longley, P. et al., 2011. Geographic information systems &
science 3rd ed., Hoboken, NJ: Wiley. Available at:
http://cataleg.upc.edu/record=b1380997~S1*cat.
Martì, T. et al., 2014. Collective health policy making in the
Catalan Health System: applying Health Consensus to
priority setting and policy monitoring. In Collective
Intelligence Conference. Massachusetts, USA:
Massachusetts Institute of Technology (MIT), pp. 1–5.
Available at: http://humancomputation.com/ci2014/
papers/Active Papers%5CPaper 77.pdf.
MIT, 2012. What is collective intelligence? Handbook of
Collective Intelligence. Available at: http://scripts.
mit.edu/~cci/HCI/index.php?title=Main_Page#What_i
s_collective_intelligence.3F [Accessed November 10,
2014].
Moon, G. & Ashworth, M., 1992. Capabilities needed in
spatial decision support systems. In GIS/LIS. San Jose,
California: American Society of Photogrammetry and
Remote Sensing, pp. 594 – 600.
O’Sullivan, D. et al., 2006. Space, place, and complexity
science. Environment and Planning A, 38(4), pp.611–617
Painho, M. et al., 2004. Exploring spatial data through
computational intelligence: a joint perspective. Soft
Computing, 9(5), pp.326–331. Available at:
http://link.springer.com/10.1007/s00500-004-0411-6
[Accessed May 10, 2014].
Patel, H., Pettitt, M. & Wilson, J.R., 2012. Factors of
collaborative working: A framework for a collaboration
model. Applied Ergonomics, 43(1), pp.1–26. Available
at: http://dx.doi.org/10.1016/j.apergo.2011.04.009.
Pérez Gallardo, Y. et al., 2013. Collective intelligence as
mechanism of medical diagnosis: The iPixel approach.
Expert Systems with Applications, 40(7), pp.2726–
2737. Available at: http://dx.doi.org/10.1016/j.
eswa.2012.11.020 [Accessed April 22, 2013].
Pitman, A. J., 2005. On the role of Geography in Earth
System Science. Geoforum, 36, pp.137–148.
Ratter, B.M.W., 2006. Complexity theory and geography -
a contribution to the discussion on an alternative
perspective on systems. Mitteilungen der
Osterreichischen Geographischen Gesellschaft, 148,
pp.109–124.
Sieber, R., 2006. Public Participation Geographic
Information Systems: A Literature Review and
Framework. Annals of the Association of American
Geographers, 96(January), pp.491–507. Available at:
http://www.ingentaconnect.com/content/bpl/anna/200
6/00000096/00000003/art00003.
Spielman, S. E., 2014. Spatial collective intelligence?
Credibility, accuracy, and volunteered geographic
information. Cartography and Geographic Information
Science, 41(2), pp.115–124. Available at: http://www.
tandfonline.com/doi/abs/10.1080/15230406.2013.8742
00 [Accessed September 18, 2014].
Sugumaran, R. & Degroote, J., 2011. Spatial Decision
Support Systems: Principles and Practices, Boca Ratón,
Florida: CRC Press, Taylor & Francis Group.
Trappey, C. et al., 2014. Using the collective intelligence of
sports fans to improve professional football league
customer service. In Proceedings of the 2014 IEEE 18th
International Conference on Computer Supported
Cooperative Work in Design (CSCWD). IEEE, pp.
313–318. Available at: http://ieeexplore.ieee.org/
lpdocs/epic03/wrapper.htm?arnumber=6846861
[Accessed January 23, 2015].
Watanabe, S., 1985. Pattern recognition: human and
mechanical, New York, New York, USA: John Wiley
& Sons, Inc.
Zhao, P., Foerster, T. & Yue, P., 2012. The Geoprocessing
Web. Computers & Geosciences, 47, pp.3–12.
Available at: http://www.sciencedirect.com/science/art
icle/pii/S0098300412001446 [Accessed January 6,
2015].
Di Zio, S. & Pacinelli, A., 2011. Opinion convergence in
location: a spatial version of the delphi method.
Technological Forecasting and Social Change, 78(9),
pp.1565–1578. Available at:
http://dx.doi.org/10.1016/j.techfore.2010.09.010
[Accessed October 10, 2012].