scholarly journals Modelling Land Use Changes at the Peri-Urban Areas using Geographic Information Systems and Cellular Automata Model

2011 ◽  
Vol 4 (6) ◽  
Author(s):  
Naimah Samat ◽  
Rosmiyati Hasni ◽  
Yasin Abdalla Eltayeb Elhadary
Keyword(s):  
Land Use ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Cellular Automata ◽  
Urban Areas ◽  
Land Use Changes ◽  
Geographic Information ◽  
Cellular Automata Model

scholarly journals The Application of Geographic Information Systems Cellular Automata Based Models to Land Use Change Modelling of Lagos, Nigeria

2021 ◽  
Author(s):  
◽  
Onuwa Honey Stephen Okwuashi
Keyword(s):  
Land Use ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Cellular Automata ◽  
Geographic Information ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Artificial Neural

<p><b>The urban expansion of Lagos continues unabated and calls for urgent concern. This thesis explored the use of both the conventional and unconventional techniques for modelling land use change. Two conventional methods (ordinary least squares and geographically weighted regression) were based on geographic information systems, while four unconventional methods (logistic regression, artificial neural networks, and two proposed types of support vector machine) were based on cellular automata. These techniques were evaluated using three land use epochs: 1963-1978, 1978-1984, and 1984-2000.</b></p> <p>The conventional methods make quite strong statistical assumptions, some of which are shown not to be met by the land use data at hand. Despite this, these methods do exhibit substantial agreement between observed and the predicted maps. The non cellular automata and cellular automata modelling were then implemented with the logistic regression, artificial neural network, support vector machine, and fuzzy support vector machine models, with model parameters set by k-fold cross-validation. The cellular automata predicted maps were more accurate than those of the non cellular automata.</p> <p>The cellular automata modelling results from the proposed support vector machine and fuzzy support vector machine were compared with those from the geographic information systems based geographically weighted regression, logistic regression, and artificial neural network. The results from the geographic information systems based geographically weighted regression were the best, followed by those from the support vector machine and fuzzy support vector machine, followed by the artificial neural network, and logistic regression. This research demonstrated that the proposed support vector machine and fuzzy support vector machine based cellular automata models are promising tools for land use change modelling.</p>

scholarly journals Pengaruh Perubahan Penggunaan Lahan Terhadap Insiden Penyakit Tuberkulosis Paru: Studi Di Provinsi Lampung

2017 ◽  
Vol 5 (1) ◽  
pp. 71
Author(s):  
Rita Rosari ◽  
Samsul Bakri ◽  
Trio Santoso ◽  
Dyah W.S.R Wardani
Keyword(s):  
Land Use ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Healthy Lifestyle ◽  
Land Use Changes ◽  
Geographic Information ◽  
Forest Plantation ◽  
Community Forest ◽  
Gis Software ◽  
Density Population

Deforestation and land conversion is one of the effects of high nativity rates and urbanizationthat affect the ecological situation.  The imbalance of ecological system become a factor ofincreasing pulmunary Tuberkulosis incidence (TB).  TB is a disease of pulmunary infectionsthat caused by the bacterium Mycobacterium tuberculosis, and it is spread directly.  Thisresearch was conducted to determine the contribution of land use changes incidence of TB inthe Lampung Province.  Land use changes be resultant through landsat imegeryinterpretation utilize Geographic Information Systems (GIS) software.  Parameter usedstatistical software, used the F test on the real level of 10%.  The result showed that therewere several factors that have real influence, namely; community forest with a coefficient of 1.0314(Pvalue=0.040), Clean and Healthy Lifestyle (PHBS) coefficient of -0.3691 (Pvalue=0.042), density population coefficient of 0.011661 (Pvalue=0.008) and the percentage of poorresident coefficient of 0.6641 (Pvalue=0.006).  While forest, plantation, developed land, healthfacility and healthy house did not have significant effect toward incidence of TB in Lampung Province.Keywords : deforestation, geographic information systems(GIS), incidence of TB, land use.

scholarly journals SPATIAL SIMULATION BASED ON GEOGRAPHIC INFORMATION SYSTEM (GIS) AND CELLULAR AUTOMATA (CA) FOR LAND USE CHANGE MODELING IN SINGARAJA CITY AND ITS SURROUNDING AREA

2019 ◽  
Author(s):  
Nyoman Arto Suprapto
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Land Use Change ◽  
Urban Areas ◽  
Agricultural Land ◽  
Land Use Changes ◽  
Geographic Information ◽  
Spatial Simulation ◽  
Land Use Change Modeling ◽  
The City

Singaraja is the second largest city after Denpasar in Bali. The magnitude of the potential of the region both trade and services, agriculture and tourism in Buleleng Regency has given a very broad impact not only on the economy but also the use of land. Economic development in the city of Singaraja cause some effects such as population growth, an increasing number of facilities (social, economic, health, and others), as well as changes in land use.Changes in land use have a serious impact on the environment in the city of Singaraja. The development of urban areas of Singaraja has given the excesses of increasing the land conversion. Suburb dominated by wetland agriculture has now turned into buildings to meet the needs of shelter, trade and services as well as urban utilities. This study was conducted by mean to determine how changes in land use from agricultural land into build up land during twelve years (period of 2002 - 2014) and the prediction of land use within the next 12 years (period of 2020 and 2026). Prediction of land use changes will be done using spatial simulation method which is integrating Cellular Automata (CA) and Geographic Information Systems (GIS) which analyzed based on land requirement, the driving variable of land use changes (population and road) and the inhabiting variable of land use change (slope steepness and rivers).Keywords : Land Use Change, Land Use Change Modeling, Celullar Automata, GIS

scholarly journals The Application of Geographic Information Systems Cellular Automata Based Models to Land Use Change Modelling of Lagos, Nigeria

2021 ◽  
Author(s):  
◽  
Onuwa Honey Stephen Okwuashi
Keyword(s):  
Land Use ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Cellular Automata ◽  
Geographic Information ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Artificial Neural

<p><b>The urban expansion of Lagos continues unabated and calls for urgent concern. This thesis explored the use of both the conventional and unconventional techniques for modelling land use change. Two conventional methods (ordinary least squares and geographically weighted regression) were based on geographic information systems, while four unconventional methods (logistic regression, artificial neural networks, and two proposed types of support vector machine) were based on cellular automata. These techniques were evaluated using three land use epochs: 1963-1978, 1978-1984, and 1984-2000.</b></p> <p>The conventional methods make quite strong statistical assumptions, some of which are shown not to be met by the land use data at hand. Despite this, these methods do exhibit substantial agreement between observed and the predicted maps. The non cellular automata and cellular automata modelling were then implemented with the logistic regression, artificial neural network, support vector machine, and fuzzy support vector machine models, with model parameters set by k-fold cross-validation. The cellular automata predicted maps were more accurate than those of the non cellular automata.</p> <p>The cellular automata modelling results from the proposed support vector machine and fuzzy support vector machine were compared with those from the geographic information systems based geographically weighted regression, logistic regression, and artificial neural network. The results from the geographic information systems based geographically weighted regression were the best, followed by those from the support vector machine and fuzzy support vector machine, followed by the artificial neural network, and logistic regression. This research demonstrated that the proposed support vector machine and fuzzy support vector machine based cellular automata models are promising tools for land use change modelling.</p>

scholarly journals Monitoring Land Use Changes and Their Future Prospects Using GIS and ANN-CA for Perak River Basin, Malaysia

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2286
Author(s):  
Muhammad Talha Zeshan ◽  
Muhammad Raza Ul Mustafa ◽  
Mohammed Feras Baig
Keyword(s):  
Land Use ◽  
Information Systems ◽  
Geographic Information Systems ◽  
River Basin ◽  
Land Use Changes ◽  
Geographic Information ◽  
Kappa Coefficient ◽  
Urban Land Use ◽  
Lulc Changes ◽  
The Impact

Natural landscapes have changed significantly through anthropogenic activities, particularly in areas that are severely impacted by climate change and population expansion, such as countries in Southeast Asia. It is essential for sustainable development, particularly efficient water management practices, to know about the impact of land use and land cover (LULC) changes. Geographic information systems (GIS) and remote sensing were used for monitoring land use changes, whereas artificial neural network cellular automata (ANN-CA) modeling using quantum geographic information systems (QGIS) was performed for prediction of LULC changes. This study investigated the changes in LULC in the Perak River basin for the years 2000, 2010, and 2020. The study also provides predictions of future changes for the years 2030, 2040, and 2050. Landsat satellite images were utilized to monitor the land use changes. For the classification of Landsat images, maximum-likelihood supervised classification was implemented. The broad classification defines four main classes in the study area, including (i) waterbodies, (ii) agricultural lands, (iii) barren and urban lands, and (iv) dense forests. The outcomes revealed a considerable reduction in dense forests from the year 2000 to 2020, whereas a substantial increase in barren lands (up to 547.39 km2) had occurred by the year 2020, while urban land use has seen a rapid rise. The kappa coefficient was used to assess the validity of classified images, with an overall kappa coefficient of 0.86, 0.88, and 0.91 for the years 2000, 2010, and 2020, respectively. In addition, ANN-CA simulation results predicted that barren and urban lands will expand in the future at the expense of other classes in the years 2030, 2040, and 2050. However, a considerable decrease will occur in the area of dense forests in the simulated years. The study successfully presents LULC changes and future predictions highlighting significant pattern of land use change in the Perak River basin. This information could be helpful for land use administration and future planning in the region.

scholarly journals Geographic Information Systems (GIS) in Urban Planning

2015 ◽  
Vol 1 (1) ◽  
pp. 85
Author(s):  
Sonila Xhafa ◽  
Albana Kosovrasti
Keyword(s):  
Sustainable Development ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Urban Planning ◽  
Urban Space ◽  
Spatial Data ◽  
Urban Areas ◽  
Geographic Information ◽  
Social Infrastructure ◽  
Urban Centers

Geographic information systems can be defined as a intelligent tool, to which it relates techniques for the implementation of processes such as the introduction, recording, storage, handling, processing and generation of spatial data. Use of GIS in urban planning helps and guides planners for an orderly development of settlements and infrastructure facilities within and outside urban areas. Continued growth of the population in urban centers generates the need for expansion of urban space, for its planning in terms of physical and social infrastructures in the service of the community, based on the principles of sustainable development. In addition urbanization is accompanied with numerous structural transformations and functional cities, which should be evaluated in spatial context, to be managed and planned according to the principles of sustainable development. Urban planning connects directly with land use and design of the urban environment, including physical and social infrastructure in service of the urban community, constituting a challenge to global levels. Use of GIS in this field is a different approach regarding the space, its development and design, analysis and modeling of various processes occurring in it, as well as interconnections between these processes or developments in space.

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