Modeling urban growth in Atlanta using logistic regression

Taking Yucheng, a typical agricultural county in Shandong Province as a case, this study applied Logistic regression models to spatially identify factors affecting farmland changes. Using two phases of high resolution imageries in 2001 and 2009, the study obtained the land use and farmland change data in [2001][2002][2003][2004][2005][2006][2007][2008][2009]. It was found that the farmland was reduced by 5.14% in the period, mainly due to the farmland conversion to forest land and built-up land, although part of forest land and unused land was converted to farmland. The results of Logistic regressions indicated that location, population growth and farmer income were main factors affecting the farmland conversion, while soil types and pro-curvature were main natural factors controlling the distribution of farmland changes. Regional differences and temporal-spatial variables of farmland changes affected fitting capability of the Logistic regression models. The ROC fitting test indicated that the Logistic regression models gave a good explanation of the regional land-use changes. Logistic regression analysis is a good tool to identify major factors affecting land use change by quantifying the contribution of each factor.

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“…These considerations are different from similar studies (e.g. Hu et al, 2007;Huang et al, 2007;Liu et al, 2007;Zeng et al, 2009;Yu et al, 2010).…”

Section: Discussioncontrasting

confidence: 41%

Farmland changes and the driving forces in Yucheng, North China Plain

Zhao

Fan

2012

J. Geogr. Sci.

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“…As an empirical estimation method, logistical regression has been used in deforestation analysis [19].…”

Section: Land Change Modeler and Logistic Regressionmentioning

confidence: 99%

“…Interpretation of statistical models is desirable for gaining knowledge of the processes driving the change of spatial patterns [19].…”

Section: Driving Forcesmentioning

confidence: 99%

“…The research identify the driving factors from two sources, first one by reviewing related literature, [19] [22] [24] [23]. The second one by analyzed the official detailed plan and the experience of researcher with the study area after that using LCM's Test and selection of site and driver variable module was used to test the potential power of explanatory variables Only variables with values higher than 0.15 in Cramer's V Test were used.…”

Section: B Chooseing the Driving Forcesmentioning

confidence: 99%

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Analyses the Driving Forces for Urban Growth by Using IDRISI®Selva Models Abouelreesh - Aswan as a Case Study

Hamdy¹,

Zhao²,

Salheen³

et al. 2017

IJET

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Abstract-Urban growth is very dynamic and complex processes, It has many factors affects on the directions and value of urban extensions, the factors that drive urban development must be identified and analyzed, The study of relative research shows that the driving forces which lead and redirect the urban sprawl, and try to classified these driving forces based on the value of its effectives on urban growth, Achieving the classification of driving forces needs using statistical method, our study used the logistic regressions to analysis and classes the driving forces for urban sprawl, Identifying the driving forces is the most important step to prediction the urban growth in future by using the cellular automata models so that the research try to prepare this step to complete the procedure to expect the urban extensions, This study takes Aswan area as a case study in period from 2001 to 2013 by analysis the official detailed plan and google earth historical imagery, Almost data prepared to logistic regressions analysis using ArcGIS software and IDRISI®Selva. We studied historical imagery of the area using Google Earth to examine changes in urban growth in 2001 and 2013, over a span of 12 years. The results showed urbanization in risk areas to be 59.79 % in 2001, then rising to 65.45 % in 2013, by the end of this paper it can be classified the effect of the driving forces in study area.

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“…These models usually classify to raster based and vector based method (Hu and Lo 2007). Raster based method are so popular due to simplicity of the definition of their environment.…”

Section: Urban Sprawl Modelingmentioning

confidence: 99%

Urban sprawl assessment and modeling using landsat images and GIS

Mohammady

Delavar

2016

Model. Earth Syst. Environ.

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Nowadays, urban sprawl phenomenon have been seen in many of cities in the developing and developed countries. Urban sprawl is considered as a particular kind of urban growth which comes up with a lot of negative effects. Thus, monitoring, analyzing and modeling of this phenomenon seem to be unavoidable. This paper assess urban sprawl in Tehran Metropolis as the capital of Iran and models urban sprawl in this mega city utilizing artificial neural networks and adaptive neuro-based fuzzy inference system methods with remote sensing data and geospatial information systems spatial analyses and modeling capabilities to simulate Tehran urban growth. The results confirm that this city has experienced sprawl and sprawl has an increasing rate. Three Landsat imageries from TM and ETM? sensors taken in 1988, 1999 and 2010 and seven predictor variables include distance to road, distance to green space, slope, elevation, distance to fault, distance to developed area and number of urban cells in 3 by 3 neighborhoods have been used for urban sprawl assessment and modeling. Relative operating characteristics (ROC) and sensitivity analyses have been used to evaluate simulation results. In this research, two evaluation steps have been implemented using ROC and on the both of them, ANFIS presented the best performance vs two different proposed ANN structures.

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Modeling urban growth in Atlanta using logistic regression

Cited by 449 publications

References 23 publications

Farmland changes and the driving forces in Yucheng, North China Plain

Farmland changes and the driving forces in Yucheng, North China Plain

Analyses the Driving Forces for Urban Growth by Using IDRISI®Selva Models Abouelreesh - Aswan as a Case Study

Urban sprawl assessment and modeling using landsat images and GIS

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