**6. Conclusions**

GISc is strongly connected Geography, as they equally analyze the same features of reality [65] using comparable outlooks. Thus, GISc is the "science behind the system" [5] mainly focused in computational and representation topics, while Geography aims to model and predict geographical phenomenon.

The dissatisfaction with traditional social physics (and geography) is comprehensible. They both looked for universal laws, which now, looking back, seems a little bit naıve to say the least. At the time, due to limited data and weak computation processing capacity, researcher in general and specifically geographers presumed homogeneity within physical and social systems. Doing that, we turn an exciting and data-rich environment, i.e., reality, into sterilized, amorphous, lifeless models. At their beginning, spatial analysis and GISc followed this approach despite having much more appealing representations, i.e., maps.

The new Geography, social physics, spatial analysis, and GISc are substantially different, as they are data and computation driven. The computer is the essential feature rather than an auxiliary one. When becoming more and more sophisticated, GISc assumes that generalization is possible although context is extremely important.

Lastly, instead of flattening geographic space into an insipid uniformity, GISc promotes heterogeneity as a key feature to understand how processes evolve and how to get better outcomes through a science-based policy. In addition, this continuous research can focus on the complexity of policy results. Social and social-physical systems are complex by nature and have particular dynamics with several feedback (positive or negative) loops. Some of these feedbacks are expected, like the mechanism that all living systems have for maintaining orderly conditions (i.e., homeostasis), and others are not, leading to the appearance of new system features (i.e., emergence). For that reason, it can be demanding (or even impossible) to evaluate the success of a policy intervention. Big data, and its related constant data assemblage, enable this to happen naturally, without constraints [3].

A policy intervention can result in numerous outcomes, positives or negatives, that will withstand for a while long [30]. The use of the actual time consuming deliberate experimental researches makes it problematic to explore all the amount of existing options [66]. Broader researchers' commitment to data-intensive analysis enables additional subtle, comprehensive, and profound approaches to complex

**13**

**Figure 6.**

*Introductory Chapter: Geographic Information Systems and Science*

capture prove that this a reality with no turning back [71].

measurement methods and technologies) that generate it [72].

geographic scales with, for instance indoor geospatial analysis.

ciplinary that characterizes GISc.

problems, refining the research and, at the same time, makes policies supported by science more understandable to the common people, e.g., climate change [67].

These progresses are still somewhat new [8], and GISc is just starting to methodically analyze whether these matters have (or not) the capability to leave a meaningful impact on society. Nonetheless, Big Data is probably the outmost important paradigm shift that, with more or less delay, will change GISc. The main question is the inherent and ever more spreading communicative status of geographic data [68, 69]. Web 2.0, VGI [70] and neogeography [15], and also the sensors that allow real time data

Nevertheless, one should not be blind by data. Theory is critical to get enlightenments about what data reveal and for handling big data [42]. To avoid being trapped in a kind of data dependency, we need to understand the processes (including

Blaschke and Merschdorf [7] recognized distinct trends in GISc. They systematized them into 10 items: (i) plenty of spatial data, (ii) thinking spatial, (iii) fuzzy analysis and turning into geographic nonspatial data, (iv) spatial computing, (v) ubiquitous computing, (vi) non-Cartesian measurements, (vii) spherical innovative spatial analysis, (viii) VGI, (ix) neogeography, and (x) geographic knowledge. From a citizen's and/or researcher's perspective, these 10 trends can be grouped into five main clusters: (i) big data and location analytics; (ii) the reborn of time geography with mobile users, mobile sensors, and trajectories; (iii) cognition, emotions, and other data unmeasurable in a straightforward manner; (iv) a more spatially aware society with geobrowers and/or virtual globes; and (v) the discovery of new

The future will certainly continue to include various research fields (**Figure 6**), and yet, it is inspiring to enable harmony among processes and patterns. Looking to the possible interactions, one can isolate three groups, i.e., location analytics and mapping, spatiotemporal modeling, and social media and citizens. This last one clearly includes the user's perspectives, and it is heavily connected with the interdis-

Thinking and spatial reasoning constitute a form of thinking grounded in the concept of space, in the tools of representation and in the process of reasoning [74], and are stimulated through the manipulation of geotechnologies [75, 76]. Researches should act with extreme caution; as recently Facebook and Google shown to the world, there is not a crisp line, but rather a very fuzzy one, between naive and not-so

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

*Cubic representation of GISc research perspectives [73].*

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

*Geographic Information Systems and Science*

and techniques for dealing with geographic data.

**6. Conclusions**

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

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,

GISc is strongly connected Geography, as they equally analyze the same features of reality [65] using comparable outlooks. Thus, GISc is the "science behind the system" [5] mainly focused in computational and representation topics, while

The dissatisfaction with traditional social physics (and geography) is comprehensible. They both looked for universal laws, which now, looking back, seems a little bit naıve to say the least. At the time, due to limited data and weak computation processing capacity, researcher in general and specifically geographers presumed homogeneity within physical and social systems. Doing that, we turn an exciting and data-rich environment, i.e., reality, into sterilized, amorphous, lifeless models. At their beginning, spatial analysis and GISc followed this approach despite

The new Geography, social physics, spatial analysis, and GISc are substantially different, as they are data and computation driven. The computer is the essential feature rather than an auxiliary one. When becoming more and more sophisticated, GISc assumes that generalization is possible although context is extremely important. Lastly, instead of flattening geographic space into an insipid uniformity, GISc promotes heterogeneity as a key feature to understand how processes evolve and how to get better outcomes through a science-based policy. In addition, this continuous research can focus on the complexity of policy results. Social and social-physical systems are complex by nature and have particular dynamics with several feedback (positive or negative) loops. Some of these feedbacks are expected, like the mechanism that all living systems have for maintaining orderly conditions (i.e., homeostasis), and others are not, leading to the appearance of new system features (i.e., emergence). For that reason, it can be demanding (or even impossible) to evaluate the success of a policy intervention. Big data, and its related constant data assemblage, enable this to happen

A policy intervention can result in numerous outcomes, positives or negatives, that will withstand for a while long [30]. The use of the actual time consuming deliberate experimental researches makes it problematic to explore all the amount of existing options [66]. Broader researchers' commitment to data-intensive analysis enables additional subtle, comprehensive, and profound approaches to complex

can study global patterns within geographic information networks [1].

Geography aims to model and predict geographical phenomenon.

having much more appealing representations, i.e., maps.

**12**

naturally, without constraints [3].

**Figure 6.** *Cubic representation of GISc research perspectives [73].*

problems, refining the research and, at the same time, makes policies supported by science more understandable to the common people, e.g., climate change [67].

These progresses are still somewhat new [8], and GISc is just starting to methodically analyze whether these matters have (or not) the capability to leave a meaningful impact on society. Nonetheless, Big Data is probably the outmost important paradigm shift that, with more or less delay, will change GISc. The main question is the inherent and ever more spreading communicative status of geographic data [68, 69]. Web 2.0, VGI [70] and neogeography [15], and also the sensors that allow real time data capture prove that this a reality with no turning back [71].

Nevertheless, one should not be blind by data. Theory is critical to get enlightenments about what data reveal and for handling big data [42]. To avoid being trapped in a kind of data dependency, we need to understand the processes (including measurement methods and technologies) that generate it [72].

Blaschke and Merschdorf [7] recognized distinct trends in GISc. They systematized them into 10 items: (i) plenty of spatial data, (ii) thinking spatial, (iii) fuzzy analysis and turning into geographic nonspatial data, (iv) spatial computing, (v) ubiquitous computing, (vi) non-Cartesian measurements, (vii) spherical innovative spatial analysis, (viii) VGI, (ix) neogeography, and (x) geographic knowledge. From a citizen's and/or researcher's perspective, these 10 trends can be grouped into five main clusters: (i) big data and location analytics; (ii) the reborn of time geography with mobile users, mobile sensors, and trajectories; (iii) cognition, emotions, and other data unmeasurable in a straightforward manner; (iv) a more spatially aware society with geobrowers and/or virtual globes; and (v) the discovery of new geographic scales with, for instance indoor geospatial analysis.

The future will certainly continue to include various research fields (**Figure 6**), and yet, it is inspiring to enable harmony among processes and patterns. Looking to the possible interactions, one can isolate three groups, i.e., location analytics and mapping, spatiotemporal modeling, and social media and citizens. This last one clearly includes the user's perspectives, and it is heavily connected with the interdisciplinary that characterizes GISc.

Thinking and spatial reasoning constitute a form of thinking grounded in the concept of space, in the tools of representation and in the process of reasoning [74], and are stimulated through the manipulation of geotechnologies [75, 76]. Researches should act with extreme caution; as recently Facebook and Google shown to the world, there is not a crisp line, but rather a very fuzzy one, between naive and not-so naive social investigation [77]. The protection of citizens' privacy and the way it interlinks with the increasing need for data is a key point in the future of GISc [78].
