The tools described above are mostly ArcGIS Desktop tools with specific rules and regulations to be followed while implementation. There are certain other tools present for GIS which are listed and grouped in the following manner:.
GIS tools are useful in terms of getting the proper information regarding any geographical terrain with the blend of scientific research and usage of GIS tools which are useful in providing many information related to it.
The statistical analysis and manipulation of data using these tools perform many operations that are quite useful. This is a guide to GIS Tools. Here we discuss the introduction along with the different GIS tools for better understanding.
You may also have a look at the following articles to learn more —. Submit Next Question. By signing up, you agree to our Terms of Use and Privacy Policy. Forgot Password? If a black cell has less than 2 or more than 3 black neighbors it becomes white.
If a white cell has 3 black neighbors, it becomes black. Despite its simplicity, the system achieves an impressive diversity of behavior, fluctuating between apparent randomness and order.
One of the most apparent features of the Game of Life is the frequent occurrence of gliders , arrangements of cells that essentially move themselves across the grid.
It is possible to arrange the automaton so that the gliders interact to perform computations, and after much effort it has been shown that the Game of Life can emulate a universal Turing machine. Possibly because it was viewed as a largely recreational topic, little follow-up work was done outside of investigating the particularities of the Game of Life and a few related rules. In , however, German computer pioneer Konrad Zuse published his book Calculating Space , proposing that the physical laws of the universe are discrete by nature, and that the entire universe is just the output of a deterministic computation on a giant cellular automaton.
This was the first book on what today is called digital physics. In Stephen Wolfram published the first of a series of papers systematically investigating a very basic but essentially unknown class of cellular automata, which he terms elementary cellular automata see below. The unexpected complexity of the behavior of these simple rules led Wolfram to suspect that complexity in nature may be due to similar mechanisms.
Additionally, during this period Wolfram formulated the concepts of intrinsic randomness and computational irreducibility, and suggested that rule may be universal—a fact proved later with the help of Wolfram's research assistant Matthew Cook in the s. Wolfram left academia in the mid-late s to create Mathematica, which he then used to extend his earlier results to a broad range of other simple, abstract systems. In he published his results in the page text A New Kind of Science , which extensively argued that the discoveries about cellular automata are not isolated facts but are robust and have significance for all disciplines of science.
Despite much confusion in the press and academia, the book did not argue for a fundamental theory of physics based on cellular automata, and although it did describe a few specific physical models based on cellular automata, it also provided models based on qualitatively different abstract systems.
This used cellular automata as a model to explain how simple rules can generate complex results. The simplest nontrivial CA would be one-dimensional, with two possible states per cell, and a cell's neighbors defined to be the adjacent cells on either side of it.
These CAs are generally referred to by their Wolfram code, a standard naming convention invented by Wolfram which gives each rule a number from 0 to A number of papers have analyzed and compared these CAs, either individually or collectively [ dead link ]. The rule 30 and rule CAs are particularly interesting. The images below show the history of each when the starting configuration consists of a 1 in the center of each image surrounded by 0's.
Each pixel is colored white for 0 and black for 1. Rule 30 cellular automaton. Rule cellular automaton. Rule 30 generates exhibits class 3 behavior, meaning even simple input patterns such as that shown lead to chaotic, seemingly random histories.
Rule , like the Game of Life, exhibits what Wolfram calls class 4 behavior, which is neither completely random nor completely repetitive. Localized structures appear and interact in various complicated-looking ways.
In the course of the development of A New Kind of Science , as a research assistant to Stephen Wolfram back in , Matthew Cook proved that some of these structures were rich enough to support universality. This result is interesting because rule is an extremely simple one-dimensional system, and one which is difficult to engineer to perform specific behavior.
This result therefore provides significant support for Wolfram's view that class 4 systems are inherently likely to be universal. Cook presented his proof at a Santa Fe Institute conference on Cellular Automata in , but Wolfram blocked the proof from being included in the conference proceedings, as Wolfram did not want the proof to be announced before the publication of A New Kind of Science.
Rule has been the basis over which some of the smallest universal Turing machines have been built, inspired on the breakthrough concepts that the development of the proof of rule universality produced. A CA is said to be reversible if for every current configuration of the CA there is exactly one past configuration preimage.
If one thinks of a CA as a function mapping configurations to configurations, reversibility implies that this function is bijective. For one dimensional CA there are known algorithms for finding preimages, and any 1D rule can be proved either reversible or irreversible. For CA of two or more dimensions it has been proved that the reversibility is undecidable for arbitrary rules. The proof by Jarkko Kari is related to the tiling problem by Wang tiles.
Reversible CA are often used to simulate such physical phenomena as gas and fluid dynamics, since they obey the laws of thermodynamics. Such CA have rules specially constructed to be reversible. Such systems have been studied by Tommaso Toffoli, Norman Margolus and others.
For finite CAs that are not reversible, there must exist patterns for which there are no previous states. These patterns are called Garden of Eden patterns. In other words, no pattern exists which will develop into a Garden of Eden pattern. Several techniques can be used to explicitly construct reversible CA with known inverses. Two common ones are the second order technique and the partitioning technique , both of which involve modifying the definition of a CA in some way.
Although such automata do not strictly satisfy the definition given above, it can be shown that they can be emulated by conventional CAs with sufficiently large neighborhoods and numbers of states, and can therefore be considered a subset of conventional CA.
A special class of CAs are totalistic CAs. Conway's Game of Life is an example of an outer totalistic CA with cell values 0 and 1. A notation exists to describe rulesets of two-state totalistic CAs consisting of an initial indicating the neighbourhood of each cell and sums following the letters S for survival and B for birth for which those changes occur.
This notation has been extended for non-totalistic CAs, where a letter or letters follow each sum indicating what patterns of neighbours cause survival or birth events. Stephan Wolfram, in A New Kind of Science and in several papers dating from the mids, defined four classes into which cellular automata and several other simple computational models can be divided depending on their behavior.
While earlier studies in cellular automata tended to try to identify type of patterns for specific rules, Wolfram's classification was the first attempt to classify the rules themselves. In order of complexity the classes are:. These definitions are qualitative in nature and there is some room for interpretation.
According to Wolfram,. And so it is with cellular automata: there are occasionally rules Recently there has been a keen interest in building decentralized systems, be they sensor networks or more sophisticated micro level structures designed at the network level and aimed at decentralized information processing. The idea of emergent computation came from the need of using distributed systems to do information processing at the global level.
Melanie Mitchell who is Professor of Computer Science at Portland State University and also the Santa Fe Institute External professor [4] has been working on the idea of using self-evolving cellular arrays to study emergent computation and distributed information processing. In decentralized system, the information processing occurs in the form of global and local pattern dynamics. The inspiration for this approach comes from complex natural systems like insect colonies, nervous system and economic systems.
In order to model some of the features of these systems and study how they give rise to emergent computation, Mitchell and collaborators at the SFI have applied Genetic Algorithms to evolve patterns in cellular automata. They have been able to show that the GA discovered rules that gave rise to sophisticated emergent computational strategies.
The array can be thought of as a circle where the first and last cells are neighbors. The evolution of the array was tracked through the number of ones and zeros after each iteration. The optimal factors and the relative importance of the driving factors varied over time, thus, providing a valuable insight into the urban growth process. The developed model for Shannon catchment has been calibrated, validated, and used for predicting the future land use scenarios for the future time intervals , and By involving natural and socioeconomic variables, the developed Cellular automata CA model had proved to be able to reproduce the historical urban growth process and assess the consequence of future urban growth.
This paper presented as a novel application to the integrated CA-GIS model using a complicated land use dynamic system for Shannon catchment.
The major conclusion from this paper was that land use simulation and projection without GIS rasterization formats cannot perform a multi-class, multi factors analysis which makes high accuracy simulation is impossible.
Urbanization, land cover and land use transformation have been universal and important socioeconomic phenomena around the world. Urban growth has been accelerating with the significant increase in urban population Cohen ; DeFries et al. Although urbanization promotes socioeconomic development and improves quality of life, it is the most powerful and visible anthropogenic force that has caused the fundamental conversion from natural to artificial land cover in the cities around the world Clarke et al.
Rapid urban expansion has marked effects on environment and socio-economy, it usually happens at the expense of prime agricultural land, with the destruction of natural landscape and public open space such as: displacement of agriculture and forest Kueppers et al.
The spatio-temporal process of urban development and the social—environmental consequences of such development deserve meticulous study by urban geographers, planners, and policy makers because of the direct and profound impacts on human beings Sim and Balamurugan ; Simmie and Martin ; Chen et al.
In order to obtain better understanding of urban growth process, recent issues related to urban growth have attracted increasing attention in literature, ranging from spatial and temporal land cover patterns, the factors affecting the urban growth, to urban growth scenarios by using Land Cover maps, Geographic Information Systems GIS and different modelling techniques Li ; Pijanowski et al.
Cellular automata CA have gained popularity as modelling tools for urban process simulation. Since the pioneering work of Tobler , several approaches have been proposed for modifying standard Cellular automata CA in order to make them suitable for urban simulations White et al.
Cellular automata based models are a powerful tool for representing and simulating spatial processes underlying the spatial decisions due to their accuracy, simplicity, flexibility and intuitiveness. This paper investigates the implementation of an urban growth Cellular automata CA model in the River Shannon Basin area Gharbia et al. The focus is on the investigation of spatio-temporal dynamics of land cover change pattern from land cover maps and simulation of the urban growth.
The main objectives are to: 1 extract and compare the historical land cover information for the investigation area through the interpretation of land cover maps and the using of quantitative measures; 2 identify any strategies currently formulated by government to manage the extent and nature of urban growth in Ireland; 3 implement and evaluate the performance of the proposed integrated model between CA and GIS to predict future urban expansion; 4 quantify the future urban expansion in the River Shannon Basin area and investigating the spatio-temporal dynamics effects of the factors on urban growth to provide insight into how driving factors contribute to the urban growth.
The River Shannon, the focus of this study, is the largest transboundary river system and catchment in the island of Ireland and one of the most important water and power resources in the Republic of Ireland. However Cellular automata CA are models that simulate complex systems, they have been defined as very simple dynamic spatial systems Torrens ; Reinau ; Liu and He In CA the state of each cell in an array depends on the previous state of the cells within a neighbourhood, according to a set of transition rules White et al.
CA has a remarkable potential for modelling complex spatio-temporal processes Deutsch and Dormann ; Barredo et al. Many processes in nature and in social systems are somehow complicated process to be modeled through linear equations, therefore non-linear differential equations are needed in such cases.
This configuration defines a basic non-linear differential equation Barredo et al. These equations, although fully deterministic, can produce a very dynamic behaviour, from stable points and limit cycles to chaotic regimes strange attractors Wolfram ; May Moreover, the behaviour of non-linear differential equations may be indistinguishable from the one produced by a random process.
Wolfram stated that Cellular automata have been considered as spatial idealizations of partially differential equations with discrete space and time, thus it is not strange that CA show behaviours analogous to non-linear ordinary differential equations.
Therefore, it is not surprising that CA is capable of producing and simulating complex spatial processes showing non-linear dynamics such as some socio-spatial processes i. In these kinds of systems the behaviour depends on its own internal logic Barredo et al. In CA, cells are the basic and smallest spatial unit in a cellular space which must manifest some adjacency or proximity Li and Yeh They are typically represented by a regular two-dimensions grid usually composed of square cells, although some researchers have proposed hexagonal cells to obtain a more homogeneous neighbourhood Iovine et al.
Moreover, the regular cell can be modified by using irregular tessellations such as Voronoi polygons Shi and Pang The cells are characterised by the following:. Size the cell size is the area of the landscape each cell will cover. The use of cell resolution is either based on the availability of data or on the convenience for computation.
Different researcher used different cell size in their studies, which can be related to the different conditions of the study area White and Engelen ; Cho and Swartzlander ; Chen and Mynett State the cell state defines the attributes of the system. Each cell can take only one state from a set of states at any one time. In urban-based cellular automata models, the states of cells may represent the types of land use or land cover, such as urban or rural, or any specific type of land use; or it may be used to represent other features of the urban area, such as social categories of populations as was proposed by Portugali and Benenson The traditional neighbourhood types for two-dimensional raster based Cellar automata CA models are: Von Neumann neighbourhood and rectangular Moore neighbourhood Flache and Hegselmann ; Vezhnevets and Konouchine The Von Neumann neighbourhood consists of four cells which include the North, South, East, and West neighbours of a cell in question.
The Moore neighbourhood consists of eight cells which include the cells defined in the von Neumann neighbourhood as well as cells in the North-west, North-east, South-east, and South-west directions, which are commonly used in CA model applications Wu ; Lau and Kam ; Flache and Hegselmann ; Vezhnevets and Konouchine Neighbourhood size defines the extent of interactions between land use and the dynamics of the system Caruso et al.
In general, the effect of neighbourhood cells decreases with the increasing distance to the central cell Barredo et al. The definition of the transition rules of a CA model is the most important part to achieve realistic simulations of land use and land cover change Verburg et al. This is the key component of CA because these rules represent the process of the system being modelled, and are thus essential to the success of a good modelling practice White The traditional transition rules are dependent on the current cell state and its neighbourhood effects Jenerette and Wu ; Li et al.
In the context of urban growth, however, a variety of factors have significant impacts on urban growth, such as physical suitability for a specific land use, accessibility, socioeconomic factors, urban planning factors, and stochastic disturbance related to the complexity of human system. Consequently, the transition rules should consider various factors to allow for more realistic simulation Jokar Arsanjani et al.
In addition, traditional CA models employ only one uniform transition rule for different periods and sub-regions, while the urban growth process may vary over time and space, so it is necessary to apply different transition rules to the specific characteristics of each period and area. Spatial and temporal varying transition rules can be obtained by calibration Geertman et al. Urban development resembles the behaviour of a cellular automaton in many aspects.
The space of an urban area can be regarded as a combination of a number of cells, each cell taking a finite set of possible states representing the extent of its urban development. The state of each cell evolves in discrete time steps according to some local rules.
This section describes the details of preparation the data sets that are used in feeding the fuzzy constrained cellular automata model of urban development through GIS platform, which means cellular automata model of land use simulation uses fuzzy-sets and fuzzy logic approaches. It maps homogeneous landscape patterns, i. In Ireland, this nomenclature is a 3-level hierarchical classification system and has 34 classes at the third and most detailed level, as detailed in EPA The first iteration of the data series covered the reference year of with subsequent releases covering the years , and Through this baseline and subsequent updating of changes, CORINE has become a key data source for informing environmental and planning policy on a national and European level.
The land use modelling especially urban growth is a complex process which involves the interaction influence of various factors. According to data availability, the following set of variables representing natural, socioeconomic, spatial policies and neighbourhood factors were selected to design this study Table 1 :.
Physical factor a Digital Elevation Model DEM obtained from Eurosat at a spatial resolution of 25 m of the study areas was used to represent topography. Slope gradient was derived from the elevation surface using 3D analyst package toolbox in ArcMap. Socioeconomic factors The influence of the socioeconomic conditions in the region can be best characterized by the accessibility of that location to socioeconomic centres, which has a significant effect on urban growth pattern Li et al.
Transportation is an important factor in accelerating urban development and attracting new development. A good transportation network increases the accessibility of land Miller ; Couclelis Consequently, areas with good accessibility are more easily selected for urban development. Major roads motorways and national primary roads and minor roads national secondary roads and regional roads were considered in this study.
Because of the infrastructure construction, the traffic system changes all the time, therefore, in this study it was assumed that the roads dataset remained unchanged during one period — Population are one of the main drivers of urban growth.
More urban land will be required to satisfy further growth of urban population in the future. The population variable was represented by the population density of Electoral Divisions EDs administrative units. Spatial Policy factor Policy variables affect the urban growth by acting as constraints or incentives to development Buss ; Jantz et al.
Development Plans constitute the basic policy document of the land use and development system in Ireland. In this study, initial attempts were made to apply direct translation of the development plans into zoning maps for the CA model. Modelling geographical phenomena and processes using CA simulations is based on constructing regular spatial tessellation models. Therefore, applications of CA in geography are mostly integrated with a GIS, and consequently it can work at high spatial resolution with computational efficiency.
In this study, a cellular automata GIS based algorithm has been implemented in the GIS environment using raster data forms in order to simulate the urban expansion and land use trends in the Shannon River catchment, then the calibrated model has been used to produce future land use scenarios.
CA model expects all input maps to be strictly comparable; they must cover the same area and have the same resolution, various spatial analysis techniques in ArcGIS were applied to process the raw data and verify the quality and consistency of data collected from different sources. For input into the CA model, the 34 classes in the land use maps were reduced to five by aggregating related classes from the initial land use layer. This was required because it would be too computationally intensive to model each individual land use class separately.
The main five land cover classes were water bodies, wetlands, urban area, agricultural and forest Li as detailed in Table 2. The CA model requires the Land Cover maps to be in raster format. Using ArcGIS Following the maps classification, the spatial patterns and trends over time were examined using the Land Cover data, Continual and historical, information about the land cover change is essential for urban growth analysis, in which land cover information serves as one of the major input criteria for the model.
Change detection analysis was used to analyses patterns of Land Cover change during the study period. Change detection can be defined as the process of identifying differences in the states of an object by observing it at different times Singh There are several methods can be used for change detection, like Image differencing, principal component analysis and post classification detection Lu and Weng In this study post classification was selected as a change detection method to identify the changes in land covers over different time intervals for the River Shannon Basin Area.
The method involves independent classification results for each end of the time interval of interest, followed by a pixel by pixel comparison to detect land cover changes Coppin et al. The change detection analysis was conducted primarily through the use of ArcGIS by using the overlay tool. GIS has been applied widely to visualize the spatio-temporal process of physical changes Batty ; Clarke and Gaydos The main advantage of this method lies in the fact that each land use map is classified separately; moreover, the Land Cover matrix produced was also used to get the conversion rate from one land use to another between two periods.
The overall trend analysis and visualization of the data in ArcGIS demonstrate that the urban areas of River Shannon Basin area had significantly increased from 1.
The analysis showed that urban area cover increased at the expense of agricultural and forest cover. Whereas forest cover increased at the expense of wetlands and agricultural covers, as the trend toward forest regeneration on abandoned land continued. The analysis showed the forest cover had increased from Moreover, the agricultural cover had been increased as well from The wetlands had been reduced severally with a percentage of On the other hand, the urban area increased from 1.
The recession from to had highlighted the major impacts on the environment and society of a contraction in economic activities and development decisions, this might be one of the reasons that the urban expansion from to is less than urban expansion during — The forest area had been slightly increased from The agricultural cover had been reduced slightly from Also, the wetlands had witnessed a small reduction from 9.
Figures 1—3 show the Land Cover classification for the periods of , and One of the main input for CA urban expansion simulation is land suitability map. Land suitability map overlay generated by the mechanism overlaying the driving-factor maps as well as several restrictions, such as zoning, protected areas, slope, hazards, etc. Secondly, constrain maps should be prepared which are slope and protected areas in this study.
Transportation plays an important part in urban growth because a good transportation increases the accessibility of land and decreases the cost of construction Reilly et al.
The data map used for the major roads was for and for the minor roads was for Because of the infrastructure construction, the traffic system changes all the time, therefore, in this study it was assumed that the roads dataset remained unchanged; therefore, these transportation maps were entered for Land Cover assuming the transportation network were similar during that period.
The accessibilities were calculated as the Euclidean distance using the Spatial Analyst geo-processor. The Euclidean distance tools gives the distance from each cell in the raster to the closest source based on the straight-line distance.
Figures 4 and 5 show the Euclidean distance from major and minor road respectively. Population is one of the main drivers of urban growth Liu et al.
The data was obtained by Central Statistics Office Ireland CSO , which has been the official body in charge of the collection of the last five censuses in the Republic of Ireland in , , , , and For the Shannon River Basin area model population data for was required to use it as a factor in order to simulate the baseline period land use. The population data for year was not available, so the population for year was used instead.
To prepare the population density map to be input into the CA model, first the population density was calculated for all the Electoral Divisions for the year , and then the data were extracted into three maps: small population, medium population and high population maps, in order to present the population data in the form of fuzzy data.
Using this method the weighting parameter for the high population map would be higher than the medium population map, and the medium population map will have higher weighting parameter than the small population map, which basically means that the probability of the urban land expansion is higher around the highly populated area than it is in the area with medium population, also the probability of the urban expansion around the area with medium population is higher than the area with small population.
Figures 6—8 show the distance to the different pupation categories. The influence of the socioeconomic conditions in the region can be best characterized by the access that a location has to socioeconomic centres, which has a significant effect on urban growth pattern Verburg et al.
It is logical to think that new residential areas usually grow near or adjacent to existent residential areas, i. These centres can reflect the accessibility effect on land use development at different levels. Figures 9 and 10 show the distance raster maps to the existing communities. Suitability represents the degree of relevance of each cell to each land use type, according to a set of predefined criteria Wu and Webster ; Rounsevell et al.
Thus, land use suitability displays locations that fulfil suitability criteria defined for each land use class, therefore the slope was used as one of the constraints to the River Shannon Basin Area development, as the slope has been used as a constrain for the Greater Dublin Region project Shahumyan et al.
In the study of Shahumyan et al. Therefore, the same slope suitability values of the Greater Dublin Region were used, because the study is based in an Irish area and the topography might be similar. The slope values were divided into two categories, suitable and non-suitable for the urban development.
The CA understand these divisions in terms of 0 and 1, so 0 means the land use is not suitable for development and 1 is suitable. The slope map was initially converted into raster and then using the reclassify tool in spatial analyst tools in ArcGIS the divisions of 0 and 1 was created.
The slope constraint was then merged with the constraint from zoning calculated in the following section to create the constraint map in Fig. This study initial attempts were made to apply direct translation of the development plans into zoning maps for the CA model. These areas were defined as constraints, where the development was limited. The three maps were converted into raster format and then using the reclassify tool in spatial analyst tools in ArcGIS the divisions of 0 and 1 was created.
Where 0 represent all protected areas in which the development is restricted. Figure 11 shows all the constraint map. In this study a strong coupling approach was adopted to implement a CA model within a GIS environment. There are several reasons for using this approach, which are the following:. The main reason was that data were initially processed and stored in ArcGIS and then later converted into raster grid files, the simulated outputs of the model were also stored as raster grid files in ArcGIS.
This feature was especially advantageous during the model calibration process when simulation results were compared and fitted with data illustrating actual Land Cover.
The spatial visualization capability was a very useful feature. As all input data and output results were stored and processed within the same GIS environment, so the results could be easily visualized spatially using the data display and visualization capabilities of ArcGIS.
The design of a friendly graphic user interface made it possible to modify and calibrate the model quickly. This is the same size as the minimum area mapped in urban areas in the land use datasets. All the data sets including the driving and constraints factors were also processed as regular spatial grids at a spatial scale of m. These data sets are used to feed the model to simulate the urban growth in the River Shannon Basin area.
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