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

, Josep María Monguet Fierro

and Alex Jiménez Vélez

1,4

Departament d'Expressió Gràfica a l'Enginyeria, Universitat Politècnica de Catalunya (UPC-BarcelonaTech),

Av. Diagonal, 647, Barcelona, Spain

Departament d'Enginyeria del Terreny, Cartogràfica i Geofísica, Universitat Politècnica de Catalunya

(UPC-BarcelonaTech), C. Jordi Girona, 1-3, Barcelona, Spain

Direccion General de Cartografía, SEDENA, Av. Industria Militar 261, Naucalpan de Juárez, Mexico

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

 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

“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.

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