scholarly journals Modeling and Predicting Urban Growth of Nairobi City Using Cellular Automata with Geographical Information Systems

2007 ◽  
Vol 80 (12) ◽  
pp. 777-788 ◽  
Author(s):  
Charles N. MUNDIA ◽  
Masamu ANIYA
Keyword(s):  
Information Systems ◽  
Cellular Automata ◽  
Urban Growth ◽  
Geographical Information Systems ◽  
Geographical Information

scholarly journals Using building types and demographic data to improve our understanding and use of urban sprawl simulation

2019 ◽  
Vol 2 ◽  
pp. 1-8
Author(s):  
Mojtaba Eslahi ◽  
Rani El Meouche ◽  
Anne Ruas
Keyword(s):  
Cellular Automata ◽  
Urban Growth ◽  
Urban Areas ◽  
Geographical Information Systems ◽  
Dynamic Models ◽  
Urban Expansion ◽  
Demographic Data ◽  
Population Data ◽  
Geographical Information ◽  
Sleuth Model

<p><strong>Abstract.</strong> Many studies, using various modeling approaches and simulation tools have been made in the field of urban growth. A multitude of models, with common or specific features, has been developed to reconstruct the spatial occupation and changes in land use. However, today most of urban growth techniques just use the historical geographic data such as urban, road and excluded maps to simulate the prospective urban maps. In this paper, adding buildings and population data as urban fabric factors, we define different urban growth simulation scenarios. Each simulation corresponds to policies that are more or less restrictive of space considering what these territories can accommodate as a type of building and as a global population.</p><p>Among the urban growth modeling techniques, dynamic models, those based on Cellular Automata (CA) are the most common for their applications in urban areas. CA can be integrated with Geographical Information Systems (GIS) to have a high spatial resolution model with computational efficiency. The SLEUTH model is one of the cellular automata models, which match the dynamic simulation of urban expansion and could be adapted to morphological model of the urban configuration and fabric.</p><p>Using the SLEUTH model, this paper provides different simulations that correspond to different land priorities and constraints. We used common data (such as topographic, buildings and demography data) to improve the realism of each simulation and their adequacy with the real world. The findings allow having different images of the city of tomorrow to choose and reflect on urban policies.</p>

PROBLEMATIC ISSUES OF THE GEOGRAPHIC INFORMATION SYSTEMS USE IN SOCIO-HYGIENIC MONITORING AND RISK-BASED SUPERVISION

2019 ◽  
pp. 31-36 ◽  
Author(s):  
Е.М. Studenikina ◽  
Yu.I. Stepkin ◽  
O.V. Klepikov ◽  
I.V. Kolnet ◽  
L.V. Popova
Keyword(s):  
Information Systems ◽  
Technology Implementation ◽  
Geographical Information Systems ◽  
Geographical Information ◽  
Electronic Library ◽  
Information Portal ◽  
Software Products ◽  
Stage Of Development ◽  
Software Product ◽  
Information Systems Use

The paper considers the problematic issues of the geographical information systems (GIS) use in the sociohygienic monitoring (SHM). We analyzed scientific and practical publications on this subject that are freely available on the largest Russian information portal of scientific electronic library eLIBRARY.RU during 2014- 2018, which allowed us to formulate the principles of organization and requirements for effective operation of geographic and information systems in the socio-hygienic monitoring. An analysis of the implementation of these principles at the present stage of development for the socio-hygienic monitoring system is presented, the results of which were used in formulating priority tasks in the area of geographic and information technology implementation into socio-hygienic monitoring and risk-based planning of control and supervisory measures: to determine the necessary level of detail and an information list depicted on electronic maps for the implementation of risk-based control planning; to provide organizational and regulatory and methodological support for the hierarchical principle of GIS within Rospotrebnadzor operating on a single software product of domestic developers for organizations and institutions; to work out the need to combine GIS with similar systems of other departments involved in the data collection of social and hygienic monitoring (Rosstat, Roshydromet, Rosprirodnadzor, Ministry of Health, etc.) to enable automated data export and import; to solve staffing issues to ensure customization and subsequent GIS operation; to provide budget funding for the purchase of licensed software products for GIS in SHM, preferably of Russian developers.

scholarly journals Analyzing the Spatiotemporal Uncertainty in Urbanization Predictions

2021 ◽  
Vol 13 (3) ◽  
pp. 512
Author(s):  
Jairo Alejandro Gómez ◽  
ChengHe Guan ◽  
Pratyush Tripathy ◽  
Juan Carlos Duque ◽  
Santiago Passos ◽  
...  
Keyword(s):  
Urban Growth ◽  
Geographical Information Systems ◽  
Growth Models ◽  
A Priori ◽  
Remote Sensing Data ◽  
Public Utility ◽  
Town Planning ◽  
Geographical Information ◽  
New Town ◽  
Spatiotemporal Uncertainty

With the availability of computational resources, geographical information systems, and remote sensing data, urban growth modeling has become a viable tool for predicting urbanization of cities and towns, regions, and nations around the world. This information allows policy makers, urban planners, environmental and civil organizations to make investments, design infrastructure, extend public utility networks, plan housing solutions, and mitigate adverse environmental impacts. Despite its importance, urban growth models often discard the spatiotemporal uncertainties in their prediction estimates. In this paper, we analyzed the uncertainty in the urban land predictions by comparing the outcomes of two different growth models, one based on a widely applied cellular automata model known as the SLEUTH CA and the other one based on a previously published machine learning framework. We selected these two models because they are complementary, the first is based on human knowledge and pre-defined and understandable policies while the second is more data-driven and might be less influenced by any a priori knowledge or bias. To test our methodology, we chose the cities of Jiaxing and Lishui in China because they are representative of new town planning policies and have different characteristics in terms of land extension, geographical conditions, growth rates, and economic drivers. We focused on the spatiotemporal uncertainty, understood as the inherent doubt in the predictions of where and when will a piece of land become urban, using the concepts of certainty area in space and certainty area in time. The proposed analyses in this paper aim to contribute to better urban planning exercises, and they can be extended to other cities worldwide.

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