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Applications of Geospatial Technologies on Urban, Environmental, and Public Health Sustainability

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainability in Geographic Science".

Deadline for manuscript submissions: 12 April 2026 | Viewed by 2815

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Dr. Kalliopi Kyriakou

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The School of Civil Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
Interests: geographic information systems (GISs); spatiotemporal data analysis; mix-method approaches to collect spatial; environmental and other data (PGIS, GPS, wearable sensors, INS, UAV); thematic chartography
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Special Issue Information

Dear Colleagues,

Geospatial technologies have become crucial for advancing sustainability across urban, environmental, and public health domains. The integration of geographic information systems (GIS), remote sensing, geoAI facilitates the systematic analysis of spatial patterns, enabling evidence-based decision making for sustainable development. In urban sustainability, these technologies support smart city initiatives, optimize land-use planning, and enhance resilience to climate change. In the environmental domain, these technologies enable the ability to monitor ecosystem dynamics, assess pollution levels, and evaluate the impacts of anthropogenic activities on natural resources. In public health, geospatial methodologies contribute to environmental health risk assessments, exposure assessments, and the spatial accessibility evaluation of healthcare services, thereby improving health equity and policy interventions. This Special Issue welcomes cutting-edge research on the application of geospatial technologies in these domains, emphasizing methodological advancements, innovative case studies, and interdisciplinary approaches that contribute to sustainability.

Dr. Kalliopi Kyriakou
Guest Editor

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Keywords

geographic information systems (GISs)
remote sensing
GeoAI
smart cities
urban environments
spatiotemporal data
environmental monitoring
public health
spatial analytics

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Published Papers (3 papers)

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Research

26 pages, 5200 KB

Open AccessArticle

A Spatial Analysis of Perceived Wellbeing During Large Urban Infrastructure Construction: The Case of the Flyover in Thessaloniki, Greece

by Kalliopi Kyriakou, Athina Maragkotidou, Aphrodite Polychroni and Konstantinos Lakakis

Sustainability 2026, 18(5), 2599; https://doi.org/10.3390/su18052599 - 6 Mar 2026

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Abstract

Large-scale urban infrastructure projects are essential, yet they often introduce prolonged disruptions that affect residents’ perceived wellbeing. Existing research has demonstrated temporary declines in wellbeing during construction periods, but often relies on aggregate indicators, longitudinal averages, aggregate indicators, or proximity-based measures, providing limited insight into neighbourhood-level spatial inequalities and local clustering. This study addresses this gap by developing a Perceived Wellbeing Indicator (PWI) and applying a place-based, spatially explicit framework to examine patterns of perceived wellbeing associated with the Thessaloniki Flyover project. A questionnaire survey captured residents’ experiences of stress, accessibility, and perceived air and noise pollution. Indicator weights were derived using a hybrid approach combining Principal Component Analysis and the Analytic Hierarchy Process. Exploratory Spatial Data Analysis techniques were applied to identify clusters, spatial outliers, and neighbourhood typologies of perceived wellbeing, which were further interpreted with child dependency ratios and perceived air pollution and noise annoyance. Results reveal pronounced spatial heterogeneity in perceived wellbeing. Low-wellbeing clusters are concentrated in Evosmos, Sykies, and Ano Toumpa, while higher wellbeing is observed in Efkarpia, Kato Toumpa, and Thermi. Lower PWI values are more frequent near the Flyover axis, indicating a spatial concentration of lower values in its vicinity, although similar patterns also appear in districts distant from the project. Overall, the findings demonstrate that perceived wellbeing is shaped by a combination of local environmental, socioeconomic, and neighbourhood conditions, including pre-existing spatial inequalities, rather than infrastructure proximity alone. By providing a spatially explicit understanding of wellbeing, this framework supports sustainable urban planning, enabling interventions that reduce environmental stress, promote social equity, and enhance community resilience during and after large-scale infrastructure development. Full article

(This article belongs to the Special Issue Applications of Geospatial Technologies on Urban, Environmental, and Public Health Sustainability)

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22 pages, 3115 KB

Open AccessArticle

Assessing Nonlinear Effects of Landscape Patterns on Habitat Quality in the Yellow River Basin: An Integrated Framework Combining Interpretable Machine Learning and Spatial Autocorrelation

by Faming Li, Kaiting Yang, Tianming Sun, Yuming Shao, Yanhong Huo and Yiqing Liu

Sustainability 2026, 18(4), 1779; https://doi.org/10.3390/su18041779 - 9 Feb 2026

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Abstract

In the context of accelerating worldwide urbanization and ecosystem decline, deciphering the interactions between landscape patterns and habitat quality is essential for biodiversity preservation, particularly within ecologically sensitive zones like the Yellow River Basin. This research investigates the spatiotemporal dynamics, spatial linkages, and nonlinear relationships connecting landscape patterns and habitat quality across the basin. Utilizing land use datasets spanning 1980–2023, we combined the InVEST model, landscape pattern indices, spatial autocorrelation analysis, the XGBoost algorithm, and SHAP interpretability methods. The results show that: (1) Landscape patterns underwent a clear transition around 1995, shifting from regularization and connectivity toward fragmentation and heterogeneity, evidenced by increases in PD, LSI, and SHEI, alongside decreases in LPI and CONTAG. (2) Mean habitat quality progressively declined, exhibited a spatial distribution characterized by “higher in the west, lower in the east.” Low-quality habitat areas expanded from 2.12% to 3.76%, whereas high-quality areas decreased from 23.12% to 22.45%, with better habitats largely maintained in western headwaters and the Qinling Mountains. (3) Significant spatial correlations were observed: LPI positively correlated with habitat quality, while PD, LSI, SHEI, and CONTAG showed negative correlations. Two dominant spatial aggregations emerged—namely “high connectivity–high quality” in the west and “high fragmentation–low quality” in the east. (4) CONTAG was identified as the dominant factor influencing habitat quality, with all landscape indices exhibiting distinct threshold effects. The proposed framework, which integrates spatial statistics, machine learning, and interpretability methods, offers a novel approach for deciphering complex ecological processes. Moreover, the identified thresholds and zonal management strategies offer a scientific foundation for ecological conservation and spatial planning in the Yellow River Basin and other vulnerable river systems worldwide. Full article

(This article belongs to the Special Issue Applications of Geospatial Technologies on Urban, Environmental, and Public Health Sustainability)

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25 pages, 1143 KB

Open AccessArticle

Extreme Precipitation and Flood Hazard Assessment for Sustainable Climate Adaptation: A Case Study of Diyarbakır, Turkey

by Berfin Kaya and Recep Çelik

Sustainability 2025, 17(20), 9339; https://doi.org/10.3390/su17209339 - 21 Oct 2025

Viewed by 1343

Abstract

This study investigates flood risk trends using rainfall data collected from 13 districts of Diyarbakır Province, Turkey, with a focus on supporting sustainability-oriented climate adaptation. Both annual and seasonal precipitation variations were examined, with particular emphasis on the role of maximum daily rainfall in driving flood potential. In addition, the analysis integrates extreme precipitation patterns with regional hazard characteristics to provide a more comprehensive flood risk assessment framework. Non-parametric statistical methods, including the Mann–Kendall trend test and Spearman’s Rho correlation, were applied to detect trends in annual and seasonal datasets. Flood magnitudes were estimated using the Generalized Extreme Value (GEV) and Peaks Over Threshold (POT) approaches. The dataset covers varying periods between 2009 and 2023, depending on station availability. The results show a statistically significant increase in both annual and winter precipitation at Bismil, and a significant winter increase at Çermik. Other stations displayed upward trends that were not statistically significant. Çüngüş, Lice, and Kulp were identified as particularly susceptible to extreme rainfall. Although the relatively short observation period poses a limitation, consistent patterns of intensified precipitation were detected. Previous studies in Turkey have demonstrated that such events often cause severe infrastructure damage and displacement of vulnerable communities. The findings of this study provide practical insights for national and regional authorities, including the Disaster and Emergency Management Authority (AFAD), the General Directorate of State Hydraulic Works (DSİ), and the Ministry of Environment, Urbanization, and Climate Change, to strengthen sustainable climate adaptation planning and disaster risk reduction strategies. Overall, this research highlights the importance of integrating extreme precipitation analysis into sustainable flood management, resilient infrastructure development, and long-term sustainability policies, thereby reinforcing the connection between hydrological risk assessment and sustainability science. Full article

(This article belongs to the Special Issue Applications of Geospatial Technologies on Urban, Environmental, and Public Health Sustainability)

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