Modeling non-stationary urban growth: The SPRAWL model and the ecological impacts of development

McGarigal, Kevin; Plunkett, Ethan; Willey, Lisabeth L.; Compton, Bradley W.; DeLuca, William V.; Grand, Joanna

doi:10.1016/j.landurbplan.2018.04.018

Cited by 38 publications

(21 citation statements)

References 37 publications

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“…Due to urgent social and environmental issues resulting from rapid urbanization including overcrowding [4], urban heat island effects [5], air pollution [6], and ecosystem degradation [7], ample scholarly research has sought to understand the driving factors of urban growth for cities all over the world. Contemporary studies approach this topic from a number of methodological angles, most of which fall into one or both of two broad categories: (1) urban simulation models (e.g., [8][9][10][11][12][13]) and (2) empirical models (e.g., [14]).…”

Section: Introductionmentioning

confidence: 99%

“…Within this literature, regression-based methods are often used to explore and describe the empirical relationships that exist between a dependent variable (e.g., land type change) and a variety of independent variables, in order to characterize underlying factors of urban growth. Popular regression model specifications in such studies include the spatial general linear model (GLM) [17], geographically weighted regression (GWR) [13,18], and multi-level modeling techniques [19]. Compared to urban simulation models, regression models tend to be less computationally intensive, and their outputs are relatively easy to interpret, even by non-specialists [20,21].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, while empirical modeling therefore has utility for exploring socioeconomic factors that correlate with land type change in urbanizing areas, the preponderance of empirical analyses of urban growth are based on single cities with similar economic backgrounds and/or that are situated in singular political contexts [13,[21][22][23]. For this reason, a region like the U.S.-Mexico border offers a relatively novel arena in which to study spatial patterns of urbanization and land cover change.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to a conventional (global) regression model, GWR is able to identify spatial variation in relationships between dependent and independent variables, such as between land cover change and the associated driving factors. Moreover, research has shown that GWR can outperform global regression models with respect to residual spatial autocorrelation and model goodness of fit [13].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Global and Local Modeling of Land Use Change in the Border Cities of Laredo, Texas, USA and Nuevo Laredo, Tamaulipas, Mexico: A Comparative Analysis

Zhao

Jensen

Weaver

2020

Land

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This paper estimates global logistic regression and logistic geographically weighted regression (GWR) models of urban growth in the adjacent border cities of Laredo, Texas in the United States and Nuevo Laredo, Tamaulipas in Mexico, for two time periods from 1985 to 2014. Historical land use and land cover patterns were monitored through Landsat imagery from the United States Geological Survey to identify instances of urban growth through land type change. Data on socioeconomic variables related to urban growth were collected from various sources and used as independent variables. In both time periods, the logistic GWR was proven to be a complementary model for the global logistic regression to explore the urban growth effect. In addition, GWR outperformed the global logistic regression model with respect to goodness of fit. These results suggest that local models are complementary to global models to empirically analyze the determinants of urban growth in study areas that contain political borders, presumably because the relationships between socioeconomic factors and urban growth are characterized by spatial heterogeneity in such areas. The spatial variable of the relationship between urban growth and the neighborhood interactions and proximity effect present the idea of complexity and interconnections between the land use change and associated factors.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Global and Local Modeling of Land Use Change in the Border Cities of Laredo, Texas, USA and Nuevo Laredo, Tamaulipas, Mexico: A Comparative Analysis

Zhao

Jensen

Weaver

2020

Land

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“…Simulation generally takes into account the whole dataset; regional trends are averaged out, and this choice owns to the need to keep the complexity of data and algorithms under a manageable level. Alternatives to circumvent this problem consists of partitioning the map (Ke et al, 2016;Shu et al, 2017;McGarigal et al, 2008), or to structure the model in different scales (White and Engelen, 2000;Stevens and Dragićević, 2007).…”

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confidence: 99%

UFlow 1.0: A Computer Model for Projections of Urban Sprawl

Koscianski

2020

Preprint

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Abstract. Cities concentrate most of the world’s population and are the stage of difficult problems around logistics, economy, or quality of life, to enumerate just a few. As an object of research on itself, a urban agglomeration is difficult to characterize; it is both an ensemble of various disconnected heterogeneous elements, and the product of numerous actions and effects between those elements. Studies of the structure and the functioning of cities date back to one century ago, with an increased interest in the last decades on the phenomenon of expansion and all of its impacts. Models of city growth face the complex nature of this system and are approximative. Different representations seek to balance characteristics as data availability, level of detail of internal processes, or precision. The uflow model approaches the problem with the metaphor of an abstract field, which evolves over time and signals the conversion from empty to urban cells. The procedure for calibration adjusts parameters according to the history of the region under study, and is able to capture local conditions. The implementation takes advantage of parallel hardware, and the simulation can be performed in reverse mode, a feature that can be useful to verify the adaptation of the tool to a given scenario, or to compute approximations of the past state of a region. Tests confirmed the expected behaviour of the algorithms, and good agreement with actual data. The flexibility of the concept of intensity of urbanization is open to the integration of different data sources into the model, and the possibility of simulating their evolution over time.

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Spatiotemporal Analysis of Urban Form Change in Developing Africa: The Case of Addis Ababa City

Mamaru,

Shi,

Wong

2023

Appl. Spatial Analysis

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Modeling non-stationary urban growth: The SPRAWL model and the ecological impacts of development

Cited by 38 publications

References 37 publications

Global and Local Modeling of Land Use Change in the Border Cities of Laredo, Texas, USA and Nuevo Laredo, Tamaulipas, Mexico: A Comparative Analysis

Global and Local Modeling of Land Use Change in the Border Cities of Laredo, Texas, USA and Nuevo Laredo, Tamaulipas, Mexico: A Comparative Analysis

UFlow 1.0: A Computer Model for Projections of Urban Sprawl

Spatiotemporal Analysis of Urban Form Change in Developing Africa: The Case of Addis Ababa City

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