Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis

ABSTRACT

The spatial pattern of urban growth determines how the physical, socio-economic and environmental characteristics of urban areas change over time. Monitoring urban areas for early identification of spatial patterns facilitates assuring their sustainable growth. In this paper, we assess the use of spatio-temporal metrics from land-use/land-cover (LULC) maps to identify growth patterns. We applied LULC change models to simulate different scenarios of urban growth spatial patterns (i.e., expansion, compact, dispersed, road-based and leapfrog) on various baseline urban forms (i.e., monocentric, polycentric, sprawl and linear). Then, we computed the spatio-temporal metrics for the simulated scenarios, selected the most informative metrics by applying discriminant analysis and classified the growth patterns using clustering methods. Two metrics, Weighted mean expansion and Weighted Euclidean distance, which account for the densification, compactness and concentration of urban growth, were the most efficient for classifying the five growth patterns, despite the influence of the baseline urban form. These metrics have the potential to identify growth patterns for monitoring and evaluating the management of developing urban areas.

KEYWORDS:

• Spatio-temporal metrics
• urban form
• urban simulation
• land-use/land-cover change model
• growth pattern

Acknowledgments

This study was partially funded by the Spanish Ministerio de Economía y Competitividad and European Regional Development Fund, in the framework of the project CGL2016-80705-R.

Data and codes availability statement

The data and codes that support the findings of this study are available with a DOI at https://doi.10.6084/m9.figshare.c.4853124.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Funding

This work was supported by the the Spanish Ministerio de Economía y Competitividad and FEDER [CGL2016-80705-R].

Notes on contributors

Marta Sapena

Marta Sapena, engineer in Geodesy and Cartography is completing her Ph.D. in Geomatics Engineering at the Polytechnic University of Valencia, Spain. Her main research is focused on the development of methodologies that allow relating the spatial structure of urban areas and their evolution to geographic, demographic, and socio-economic variables. She is currently working at the German Remote Sensing Data Center (DFD) of the German Aerospace Center (DLR), assessing the exposure and vulnerability to landslides in informally urbanized areas through remote sensing.

Luis A. Ruiz

Luis A. Ruiz is a professor of remote sensing at the Department of Cartographic Engineering, Geodesy and Photogrammetry of the Polytechnic University of Valencia, Spain. His research at the Geo-Environmental Cartography and Remote Sensing group (http://cgat.webs.upv.es/) is focused on the application of image processing, point cloud data and spatio-temporal metrics analysis to urban growth characterization and relation to socio-economic variables, land-use/land-cover change monitoring and database updating, and exploring forest structure using airborne laser scanning data.