Spatial Evolutionary Modeling (Spatial Information Systems)


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Again, network analysis works as a good example as the availability of longitudinal relational data generated the latest procedural and theoretic advances on network dynamics [ 41 ]. Big data and its influence on geographic research have to be interpreted in the context of the computational and algorithmic shift that is progressively influencing geography research methods. To fully understand such shift, one can make the distinction between two modelling approaches [ 45 ]: i the data modelling approach which assumes a stochastic data model and ii the algorithmic modelling approach that considers the data as complex and unknown.

The geographic sources of this spatial and temporal data embrace location-aware tools such as mobile phones, airborne e. There is in big data an enormous potential for innovative statistics [ 51 ]. Perhaps the upmost importance is the necessity for a distinct mind-set because big data points toward a paradigm shift, comprising an increased and improved use of modelling practices [ 52 , 53 ]. Spatial analysis is defined as a way of looking at the geographical patterns of data and analyzes the relationships between the entities. In spatial analysis, the tendency in the direction of local statistics, for example, geographically weighted regression [ 54 ] and local indicators of spatial association [ 9 ], characterizes a concession where the main rules of nomothetic geography can evolve in their own way across the geographic space.

Goodchild [ 55 ] sees GIS as a mix of both the nomothetic and idiographic characteristics, retained, respectively, on the software and algorithms, and within the databases. Hence, spatial analysis is some sort of modelling procedure that relates data features over a geographic space 2D , across several spaces 3D , and along time dimension 4D.

What is a model? Well, in a broad sense, a model is a simplification of the reality: thus, all models are wrong [ 56 ]. As one can understand, it is impractical or even functionally impossible to collect cartographic information using an exact match between the representation and the real objects; the elements generated would be a replica of the studied area and not a model.

The acquisition of information is therefore a numerical relationship between reality and the cartographic representation and, therefore, requires a semantic transfer, inseparable from the graphic and thematic generalization processes. This blank sheet of paper, with suggestions for navigation North, South, etc. Chorley and Haggett [ 58 ] mention that one of the approaches to model building can start with the simplification of a system to its essential and then start building an increasingly complex structure, by induction, a priori reasoning, and so on.

Hardly there may be a standard procedure for the construction of a system model never before modeled, but the suggestion of ways to address the problem given by the authors can help in a first approach to the problem. The original thought processes are difficult to understand and explain, and the solutions of the problems auto-suggest in strange shapes and times.

It is not expected that two researchers working on the same subject address two models in the same way. What is expected is that they start with a topic of interest and then try to model it their own way. All information is gathered at a certain range. This can be set, in a somewhat crude manner, as the number of real-world metrics units that correspond to a same unit in the spatial model. As one reduces the operating scale, the level of detail decreases according to the implicit generalization. However, before doing it, this option should be weighted because, in practice, it is not always possible to reduce and then enlarge a map, without such procedures will lead to a loss of information.

It is possible that, except if the Empire comes to decline, the next step would have been to represent each of the transformations of any details of that territory to the extent that it would be impossible to distinguish the importance between the representation and the object represented. These cartographers though believed achieving an increasingly better representation, through a perfect copy of the geometry of place, distorted, in inverse proportion, the ability of these maps to explain the territory of the Empire.


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Nowadays, spatial modelling and in a broad sense geography have shifted from a data-scarce to a data-rich environment. Contrary to the generalized idea, the critical change is not about the data volume, but relatively to the variety and the velocity at which georeferenced data can be taken. Data-driven geography is re emerging due to a massive georeferenced dataflow coming from sensors and people. The notion of data-driven science defends that the generation of hypothesis and theory creation is up to date by an iterative process where data is used inductively. Hence, it is possible to name a new category of big data research that leads to the creation of new knowledge [ 61 ].

One should note that the inductive process should not start in a theory-less void. Preexisting knowledge is used to outline the analytic engine in order to inform the knowledge discovery process, to originate valuable conclusions instead of detecting any-and-all possible relations [ 62 ]. Data-driven geography raises some issues that in fact have been long-lasting problems debated within the geographic community.

Introductory Chapter: Spatial Analysis, Modelling, and Planning

Just to name a few, one can point dealing with large data volumes the problem of samples versus populations, the data fuzziness, and the frictions between idiographic and nomothetic approaches. Yet, the conviction that location matters i. Models can have very distinct applications, from the conception of suitability, vulnerability, or risk indicators, to simulation to the assessment of planning scenarios. In a GIS framework, modelling can provide insights about the way real systems work with enough precision and accuracy to permit prediction and assertive decision-making.

Nowadays, two distinct cultures of modelling coexist [ 45 , 63 ]. By one side, one can start imaging a stochastic data model in what can be called a data modelling culture. The other one, the algorithmic modelling culture, assumes that the core of the model is complex and unidentified.

The former uses the model for both information and prediction after retrieving the parameter values from the data. In the latter, a shift exists from the data models to the algorithms properties. Putka and Oswald [ 64 ] indicate how geography could benefit by implementing the data algorithmic philosophy. The history of territories reveals cycles, both of progress and decline, if we consider only the opposites.

Each cycle mirrors, in scales, dimensions, and variable rhythms, the importance of political decisions. Planning the territory constitutes an instituted praxis from which the models of the desired evolution are derived.

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As a general rule, the models are drawn up on the basis of essentially qualitative assumptions. They establish themselves as models that transpose the dominant ideas resulting from the interpretation of the spirit of the laws and regulations, from the debate of the technical solutions, and from public participation. However, in the light of the recent theories on the territorial dynamics, there is the possibility to resort to quantitative models that reveal the self-organizing systems of the territories e. These models use intensively spatial modelling in a GIS environment and future scenarios simulation based on historical information of geographical changes.

On the contrary, it is to evaluate the potential of each other and make use of it to improve the technical efficiency in the moment of preparation, monitoring, and evaluation of the territorial management instruments. The legal systems and regulations of each country can be an opportunity to use geosimulation models, of quantitative root, to enrich the political and technical debate, about the planning of the territories in the future.

It was in this context that we captured the questions relating to the analysis and spatial modelling as fundamentals of urban planning and regional planning, which, as we know, are complex processes of geographic space organization. The difficulties to interpret and understand the territory, particularly with regard to the mixing of subsystems, inevitably require using the notion of complexity. Thus, it is essential to provide tools that could address complexity, linking both spatial organizations and the system of actors who make them evolve. Therefore, the systems approach presents itself as a paradigm capable of guiding the use and understanding of complex systems and as a prerequisite for more advanced modelling approaches.

Understanding social complexity requires the use of a large variety of computational approaches. For instance, the multiscale nature of social clusters comprises a countless diversity of organizational, temporal, and spatial dimensions, occasionally at once. Moreover, computation denotes several computer-based tools, as well as essential concepts and theories, varying from information extraction algorithms to simulation models [ 65 , 66 ].

Location models might embrace a descriptive methodology, but they can also be very operative as normative environments.

Coevolution of teaching ability and cooperation in spatial evolutionary games | Scientific Reports

Hence, spatial analysis overlaps typical data analytic methods such as statistics, network analysis, and several data science viewpoints, such as data mining and machine learning. Whereas there is an interesting discussion between statistics and machine learning researchers about the advantages and disadvantages of each method, it is unmistakable that the huge mainstream of quantitative analytical methods falls inside the concept of data modelling culture. This enables a profounder knowledge about the importance of spatial values in shaping the geographic space.

The spatial analysis overlapping with numerous fields of application leads to the coin of the designation spatial science [ 68 ], which seems to better represent its singularities. In addition to geography, spatial analysis has a clear linkage to regional science. Ever since its beginning, regional science has dealt with knowledge discovery adopting a neopositivism approach.

It embraces the emerging architype of geospatial data integration rooted in geographic information science [ 69 , 70 , 71 ] to analyze the complex systems and the spatiotemporal processes that make them. This proved to be not quite true, but presently big data opens, specially through data mining, new possibilities for spatial analysis research [ 27 ] and can extend the limits of quantitative approaches to a wide array of problems usually addressed qualitatively [ 27 , 28 ]. This is clearly a new paradigm shift in geography research methodologies: a fourth—data-intensive—paradigm [ 32 ].

The alleged spatially integrated social sciences intend to influence GIS in order to analyze the enormous amounts of available geocoded data [ 33 ]. Making sense of these data requires both computationally based analysis methods and the ability to situate the results [ 34 ] and brings together the risk of plunging traditional interpretative approaches [ 35 ].

The big data era calls for new capacities of synthesis and synergies between qualitative and quantitative approaches [ 36 ]. It is a similar case to the rebirth of social network theory and analysis where due to the growing availability of relational datasets covering human interactions and relationships, network researchers manage to implement a new set of theoretical techniques and concepts [ 41 ].

Surveys are an example of this new paradigm. This methodology is at a crisis because of the decline of response rates, sampling frames, and the narrow ability to record certain variables that are the core of geographical analysis, for example, accurate geographical location [ 42 ]. These limitations are still more pronounced if one considers two additional features: i the majority of social survey data is cross-sectionally deprived of a longitudinal temporal facet [ 44 ] and ii most social datasets are rough clusters of variables due to the limitations of what can be asked in self-reported approaches.

Big data is leading to advances on both aspects, shifting from static snapshots to dynamic recounting and from rough aggregations to high resolution, spatiotemporal, data. Here, what matters the most is the likelihood of an increased emphasis of geography on processes rather than structures. Again, network analysis works as a good example as the availability of longitudinal relational data generated the latest procedural and theoretic advances on network dynamics [ 41 ].

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Big data and its influence on geographic research have to be interpreted in the context of the computational and algorithmic shift that is progressively influencing geography research methods. To fully understand such shift, one can make the distinction between two modelling approaches [ 45 ]: i the data modelling approach which assumes a stochastic data model and ii the algorithmic modelling approach that considers the data as complex and unknown.

The geographic sources of this spatial and temporal data embrace location-aware tools such as mobile phones, airborne e. There is in big data an enormous potential for innovative statistics [ 51 ]. Perhaps the upmost importance is the necessity for a distinct mind-set because big data points toward a paradigm shift, comprising an increased and improved use of modelling practices [ 52 , 53 ].

Spatial analysis is defined as a way of looking at the geographical patterns of data and analyzes the relationships between the entities. In spatial analysis, the tendency in the direction of local statistics, for example, geographically weighted regression [ 54 ] and local indicators of spatial association [ 9 ], characterizes a concession where the main rules of nomothetic geography can evolve in their own way across the geographic space.

Goodchild [ 55 ] sees GIS as a mix of both the nomothetic and idiographic characteristics, retained, respectively, on the software and algorithms, and within the databases. Hence, spatial analysis is some sort of modelling procedure that relates data features over a geographic space 2D , across several spaces 3D , and along time dimension 4D.

What is a model? Well, in a broad sense, a model is a simplification of the reality: thus, all models are wrong [ 56 ]. As one can understand, it is impractical or even functionally impossible to collect cartographic information using an exact match between the representation and the real objects; the elements generated would be a replica of the studied area and not a model. The acquisition of information is therefore a numerical relationship between reality and the cartographic representation and, therefore, requires a semantic transfer, inseparable from the graphic and thematic generalization processes.

This blank sheet of paper, with suggestions for navigation North, South, etc. Chorley and Haggett [ 58 ] mention that one of the approaches to model building can start with the simplification of a system to its essential and then start building an increasingly complex structure, by induction, a priori reasoning, and so on. Hardly there may be a standard procedure for the construction of a system model never before modeled, but the suggestion of ways to address the problem given by the authors can help in a first approach to the problem.

The original thought processes are difficult to understand and explain, and the solutions of the problems auto-suggest in strange shapes and times. It is not expected that two researchers working on the same subject address two models in the same way. What is expected is that they start with a topic of interest and then try to model it their own way. All information is gathered at a certain range. This can be set, in a somewhat crude manner, as the number of real-world metrics units that correspond to a same unit in the spatial model.

As one reduces the operating scale, the level of detail decreases according to the implicit generalization. However, before doing it, this option should be weighted because, in practice, it is not always possible to reduce and then enlarge a map, without such procedures will lead to a loss of information. It is possible that, except if the Empire comes to decline, the next step would have been to represent each of the transformations of any details of that territory to the extent that it would be impossible to distinguish the importance between the representation and the object represented.

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These cartographers though believed achieving an increasingly better representation, through a perfect copy of the geometry of place, distorted, in inverse proportion, the ability of these maps to explain the territory of the Empire. Nowadays, spatial modelling and in a broad sense geography have shifted from a data-scarce to a data-rich environment. Contrary to the generalized idea, the critical change is not about the data volume, but relatively to the variety and the velocity at which georeferenced data can be taken.

Data-driven geography is re emerging due to a massive georeferenced dataflow coming from sensors and people. The notion of data-driven science defends that the generation of hypothesis and theory creation is up to date by an iterative process where data is used inductively. Hence, it is possible to name a new category of big data research that leads to the creation of new knowledge [ 61 ]. One should note that the inductive process should not start in a theory-less void.

Preexisting knowledge is used to outline the analytic engine in order to inform the knowledge discovery process, to originate valuable conclusions instead of detecting any-and-all possible relations [ 62 ]. Data-driven geography raises some issues that in fact have been long-lasting problems debated within the geographic community. Just to name a few, one can point dealing with large data volumes the problem of samples versus populations, the data fuzziness, and the frictions between idiographic and nomothetic approaches.

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Yet, the conviction that location matters i. Models can have very distinct applications, from the conception of suitability, vulnerability, or risk indicators, to simulation to the assessment of planning scenarios. In a GIS framework, modelling can provide insights about the way real systems work with enough precision and accuracy to permit prediction and assertive decision-making.

Nowadays, two distinct cultures of modelling coexist [ 45 , 63 ]. By one side, one can start imaging a stochastic data model in what can be called a data modelling culture. The other one, the algorithmic modelling culture, assumes that the core of the model is complex and unidentified.

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The former uses the model for both information and prediction after retrieving the parameter values from the data. In the latter, a shift exists from the data models to the algorithms properties. Putka and Oswald [ 64 ] indicate how geography could benefit by implementing the data algorithmic philosophy. The history of territories reveals cycles, both of progress and decline, if we consider only the opposites. Each cycle mirrors, in scales, dimensions, and variable rhythms, the importance of political decisions.

Planning the territory constitutes an instituted praxis from which the models of the desired evolution are derived. As a general rule, the models are drawn up on the basis of essentially qualitative assumptions. They establish themselves as models that transpose the dominant ideas resulting from the interpretation of the spirit of the laws and regulations, from the debate of the technical solutions, and from public participation.

However, in the light of the recent theories on the territorial dynamics, there is the possibility to resort to quantitative models that reveal the self-organizing systems of the territories e. These models use intensively spatial modelling in a GIS environment and future scenarios simulation based on historical information of geographical changes. On the contrary, it is to evaluate the potential of each other and make use of it to improve the technical efficiency in the moment of preparation, monitoring, and evaluation of the territorial management instruments.

The legal systems and regulations of each country can be an opportunity to use geosimulation models, of quantitative root, to enrich the political and technical debate, about the planning of the territories in the future. It was in this context that we captured the questions relating to the analysis and spatial modelling as fundamentals of urban planning and regional planning, which, as we know, are complex processes of geographic space organization.

The difficulties to interpret and understand the territory, particularly with regard to the mixing of subsystems, inevitably require using the notion of complexity. Thus, it is essential to provide tools that could address complexity, linking both spatial organizations and the system of actors who make them evolve. Therefore, the systems approach presents itself as a paradigm capable of guiding the use and understanding of complex systems and as a prerequisite for more advanced modelling approaches.

Spatial Evolutionary Modeling (Spatial Information Systems) Spatial Evolutionary Modeling (Spatial Information Systems)
Spatial Evolutionary Modeling (Spatial Information Systems) Spatial Evolutionary Modeling (Spatial Information Systems)
Spatial Evolutionary Modeling (Spatial Information Systems) Spatial Evolutionary Modeling (Spatial Information Systems)
Spatial Evolutionary Modeling (Spatial Information Systems) Spatial Evolutionary Modeling (Spatial Information Systems)
Spatial Evolutionary Modeling (Spatial Information Systems) Spatial Evolutionary Modeling (Spatial Information Systems)
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