**5. Insights into the future**

According to Haklay [37], GISc relies upon an inductive approach (**Figure 4**). Contrarily to deductive approach that start with a theory that can either be corroborated or not, depending on the results of the validation tests, i.e., observations, carried out, inductive approach comes to theory based on observations, trying to detect patterns that can lead to the formulation of the former.

Remler and Van Ryzin [40] propose a clear division between observational studies, natural experiments, and weak/strong quasi-experiments (**Figure 5**). This distinction is made in function of the sorting method, if it has some degree of control (latter two) or if the researcher has no control over it (former two). Observation, i.e., evidence based approach, is supported by longitudinal and experimental procedures that reinforce the randomness prerogatives inherit from cross-sectional methods [41]. The new methods for data gathering and the availability of open big data enable this kind of approach for studying human-physical, or simply human, complex systems [42].

**11**

*Introductory Chapter: Geographic Information Systems and Science*

The colossal and distinct data dissemination through a multitude of worldwide connected databases lead to an increasing expansion of complex data availability, built from different sources, and to its interaction with the existing procedures

The increasing growth of popularity in social media platforms, e.g., Facebook, Twitter, Instagram, or Flickr, etc., resulted in the availability of a large amount of VGI. Goodchild [45] defines VGI as geographic data produced by users usually with the backing of Web 2.0 capabilities [46]. Indeed, society changed from a web supported by documents to a web supported by databases, crowdsourced data and social networks [47], bent by social behavior [48] and giving access to both collabo-

The Web 2.0 and the omnipresence of the data radically changed not only the technical support of GIS but also of GISc, extending the potentials of people participation in administration and planning processes [50]. This holistic point of view allowed researchers to study social phenomenon using the digital traces and social

There are several examples of the VGI: potential to forest-fire mapping [52], crisis-maps [53, 54], geotagged (Flickr) photograph analysis for tourism management [55–57], or mapping the sense of place [58]. Twitter is also an important source of data [59] and Takhteyev, Gruzd, and Wellman [60] studied the social ties

Additionally, Crampton et al. [61] evaluated the possible influences of big data on critical geography using exploratory methods to overcome some of the limitations related to the usage of VGI, and Viana et al. [62] accessed the value of OpenStreetMap (OSM) data for land use land cover (LULC) cartography. One may state that neogeography is bringing cartographic and GIS expertise to the common citizens [19]. Nevertheless, VGI properties are very different from the ones of traditional data sources. This can turn possible a more complex and dynamic interpretation than the one that census data allowed [63], but implies further research in

ration tools and environments, allowing analytical visualizations [49].

interactions that individuals leave online [51].

*DOI: http://dx.doi.org/10.5772/intechopen.86121*

and behaviors [43, 44].

*Different approaches of experimental research [40].*

**Figure 5.**

between its users.

the field of GISc [46].

**Figure 4.** *Deductive (A) and inductive (B) workflows.*

*Introductory Chapter: Geographic Information Systems and Science DOI: http://dx.doi.org/10.5772/intechopen.86121*

#### **Figure 5.**

*Geographic Information Systems and Science*

amorphous field of research.

**5. Insights into the future**

complex systems [42].

being an original set of concepts and expertise with an extensive applicability, has captured the attention of several traditional sciences, e.g., physics, philosophy, mathematics, etc. (**Figure 3**). As follows, GISc is many times labeled as interdisciplinary, multidisciplinary, transdisciplinary, and multiparadigmatic. Despite having different meanings, these definitions usually are used as synonymous. But, why this debate of science, subscience, or multidisciplinary field remembering the outdated—or perhaps

Some researchers still say that GISc is just a response to GIS technology and not a state-of-the-art science, giving the example of critical GIS, today fully integrated in GISc but initially the focus of pronounced disagreement [37]. This bifurcated propensity is clear in two polemic and completely distinct understandings [37]: one defending a mathematical and formal perspective of GISc, mainly focusing on quantitative and computational aspects [38], and other antiessentialist and antiinterpretative [39]. The latest one, being a deflationary method, allows joining a role of essential ideologies. However, taken to the extreme, it can lead to a totally

According to Haklay [37], GISc relies upon an inductive approach (**Figure 4**). Contrarily to deductive approach that start with a theory that can either be corroborated or not, depending on the results of the validation tests, i.e., observations, carried out, inductive approach comes to theory based on observations, trying to

Remler and Van Ryzin [40] propose a clear division between observational studies, natural experiments, and weak/strong quasi-experiments (**Figure 5**). This distinction is made in function of the sorting method, if it has some degree of control (latter two) or if the researcher has no control over it (former two). Observation, i.e., evidence based approach, is supported by longitudinal and experimental procedures that reinforce the randomness prerogatives inherit from cross-sectional methods [41]. The new methods for data gathering and the availability of open big data enable this kind of approach for studying human-physical, or simply human,

not—discussion about if GIS and quantitative analysis are geography?

detect patterns that can lead to the formulation of the former.

**10**

**Figure 4.**

*Deductive (A) and inductive (B) workflows.*

*Different approaches of experimental research [40].*

The colossal and distinct data dissemination through a multitude of worldwide connected databases lead to an increasing expansion of complex data availability, built from different sources, and to its interaction with the existing procedures and behaviors [43, 44].

The increasing growth of popularity in social media platforms, e.g., Facebook, Twitter, Instagram, or Flickr, etc., resulted in the availability of a large amount of VGI. Goodchild [45] defines VGI as geographic data produced by users usually with the backing of Web 2.0 capabilities [46]. Indeed, society changed from a web supported by documents to a web supported by databases, crowdsourced data and social networks [47], bent by social behavior [48] and giving access to both collaboration tools and environments, allowing analytical visualizations [49].

The Web 2.0 and the omnipresence of the data radically changed not only the technical support of GIS but also of GISc, extending the potentials of people participation in administration and planning processes [50]. This holistic point of view allowed researchers to study social phenomenon using the digital traces and social interactions that individuals leave online [51].

There are several examples of the VGI: potential to forest-fire mapping [52], crisis-maps [53, 54], geotagged (Flickr) photograph analysis for tourism management [55–57], or mapping the sense of place [58]. Twitter is also an important source of data [59] and Takhteyev, Gruzd, and Wellman [60] studied the social ties between its users.

Additionally, Crampton et al. [61] evaluated the possible influences of big data on critical geography using exploratory methods to overcome some of the limitations related to the usage of VGI, and Viana et al. [62] accessed the value of OpenStreetMap (OSM) data for land use land cover (LULC) cartography. One may state that neogeography is bringing cartographic and GIS expertise to the common citizens [19]. Nevertheless, VGI properties are very different from the ones of traditional data sources. This can turn possible a more complex and dynamic interpretation than the one that census data allowed [63], but implies further research in the field of GISc [46].

Constantly, wide-ranging-data assembly allows to reverse engineer the events that stimulate the emergence of unexpected outcomes [27]. The complexity of geographic systems points for knowledge experimental analysis throughout the observation of processes [64]. The changing potential of big data is not about the size but instead about its spatiotemporal resolution, thematic coverage and omnipresence, and crossing analysis levels, from the single to the all [27, 42]. Now, we can study global patterns within geographic information networks [1].

Elwood, Goodchild, and Sui [46] define VGI as a "paradigmatic shift in how geographic information is created and shared" and reinforce the idea that further research is needed to develop new methods of spatial data analysis. Intrinsically, the advances we are seeing in the fields of VGI and/or big data open new research fields for GISc, especially regarding analytical capabilities. Simultaneously, GISc looks for a procedural background for dealing with the specific restrictions related to the usage of VGI and/or big data. Such challenges comprise the data quality and understanding in order to achieve a statistical valid sample, privacy issues and methods, and techniques for dealing with geographic data.
