Special Issue Information

Dear Colleagues,

Metropolitan areas are complex human–environmental systems consisting of urban agglomerations and their peri-urban hinterland. By 2050, two thirds of the population will live in cities. With this continuous increase in urban population and their footprint, the need to assess, map, and quantify urban sprawl with high spatial detail and the need for sustainable urban development also increase. Understanding transformations of metropolitan areas and navigating those transformations toward more sustainable and or resilient pathways is of utmost societal relevance. This Special Issue provides the possibility to contribute with studies dealing with the utilization of innovative geospatial data sources and state-of-the-art analysis methods. The aim of this Special Issue is, therefore, to generate new methodologic insights in GI science and generate knowledge to build a robust problem-solving capacity for urban sustainability research.

Prof. Dr. Andreas Rienow
Dr. Ahmed Mustafa
Guest Editors

Manuscript Submission Information

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Keywords

urban areas as socioecological systems
sustainable cities and communities
urban geosimulation
land consumption monitoring and projection
raster-based and vector-based cellular automata
artificial intelligence and open data
deprived areas mapping
spatial disaggregation methods
volunteered geographic information
impact of policy making

Published Papers (2 papers)

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Research

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15 pages, 4437 KiB

Open AccessArticle

A Cellular-Automaton Model for Population-Density and Urban-Extent Dynamics at the Regional Level: The Case of Ukrainian Provinces

by Mykhailo Lohachov and Nataliya Rybnikova

Geographies 2022, 2(2), 186-200; https://doi.org/10.3390/geographies2020013 - 02 Apr 2022

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Abstract

The efficient modeling of population-density and urban-extent dynamics is a precondition for monitoring urban sprawl and managing the accompanying conflicts. Currently, one of the most promising approaches in this field is cellular automata—spatial models allowing one to anticipate the behavior of unit areas (e.g., evolution or degradation) in response to the influence of their neighborhood. In the present study, the possibility of modeling the population-density and urban-extent dynamics via a cellular automaton with density-specific parameters is tested. Using an adaptive genetic algorithm, three key model parameters (the evolution and degradation thresholds of a cell and its impact upon the neighbors) are optimized to ensure minimal deviation of the model predictions from actual population dynamics data for 24 Ukrainian provinces during three subsequent time windows from 2010–2019. The performance of the obtained optimized models is assessed in terms of the ability to (1) predict population-density classes and (2) discriminate between urban and rural areas. Generally, the obtained optimized models show high performance for both population-density and urban-extent dynamics (with the average Cohen’s Kappa reaching ~0.81 and ~0.91, respectively). Rare cases with poor prediction accuracy usually represent politically and economically unstable Eastern Ukrainian provinces involved in the military conflict since 2014. Statistical analysis of the obtained model parameters reveals significant differences (p < 0.001) in all of them among population-density classes, arguing for the plausibility of the selected density-specific model architecture. Upon exclusion of the above-mentioned Eastern Ukrainian provinces, all model coefficients appear rather stable (p > 0.135) through the three analyzed time windows, indicating the robustness of the model. The ability of the model to discriminate between urban and rural areas depends on the population density threshold. The best correspondence between actual and predicted urban areas emerges upon the 3000 persons/km² population-density threshold. Further improvement of the model seems possible via extending its input beyond the population density data alone, e.g., by accounting for the existing infrastructure and/or natural boundaries—known factors stimulating or inhibiting urban sprawl. Full article

(This article belongs to the Special Issue Advanced GIS and Remote Sensing Applications in Urban Sprawl Monitoring)

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Other

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10 pages, 4318 KiB

Open AccessTechnical Note

Assessment of the Impact of Urbanization on Geoenvironmental Settings Using Geospatial Techniques: A Study of Panchkula District, Haryana

by Shruti Kanga, Suraj Kumar Singh, Gowhar Meraj, Anup Kumar, Ruby Parveen, Nikola Kranjčić and Bojan Đurin

Geographies 2022, 2(1), 1-10; https://doi.org/10.3390/geographies2010001 - 06 Jan 2022

Cited by 24 | Viewed by 4274

Abstract

Urbanization is an unavoidable process of social and economic growth in modern times. However, the speed with which urbanization is taking place produces complex environmental changes. It has affected the surface albedo and roughness of the soil, thereby modulating hydrological and ecological systems, which in turn has affected regional and local climate systems. In developing countries of South Asia, rampant and unplanned urbanization has created a complex system of adverse environmental scenarios. Similar is the case in India. The state of the urban environment across India is degrading so quickly that the long-term sustainability of its cities is endangered. Many metropolitan cities in India are witnessing the harmful impacts of urbanization on their land ecology. In this context, remote sensing and geographic information system (GIS) based assessments provide a comprehensive and effective analysis of the rate and the impact of urbanization. The present study focuses on understanding the spatiotemporal characteristics of urban growth and its implications on the geomorphology of the Panchkula District, Haryana, one of the fastest-growing urban centers in India. The study links the changes in land use/land cover (LULC) with the changing geomorphology of the study area using satellite remote sensing and GIS. The results showed that between 1980 and 2020, agricultural (+73.71%), built-up (+84.66%), and forest (+4.07%) classes of land increased in contrast to that of the fallow land (−76.80%) and riverbed (−50.86%) classes that have decreased in spatial extents. It has been observed that the hill geomorphological class had decreased in the area owing to conversion to industrial and built-up activities. Assessment of the environmental quality of cities involves multiple disciplines that call for a significant amount of scientific evaluation and strong decision making, and the present study shall lay down the baseline analysis of the impact of changing LULC on the geomorphological setup of the selected urban center. Full article

(This article belongs to the Special Issue Advanced GIS and Remote Sensing Applications in Urban Sprawl Monitoring)

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