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The Role of Geospatial Analytics in Advancing Sustainable, Healthy and Resilient Cities

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A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 12530

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Special Issue Editors

Dr. Dong Liu

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Guest Editor

Institute of Space and Earth Information Science & Institute of Future Cities, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong
Interests: urban sustainability; environmental health; travel behavior; physical activity; GIScience
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Mei-Po Kwan

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Guest Editor

Institute of Space and Earth Information Science & Department of Geography and Resource Management, The Chinese University of Hong Kong, New Territories, Sha Tin, Hong Kong
Interests: environmental health; human mobility; healthy cities; social justice; GIScience
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cities play a pivotal role in the pursuit of sustainable development, public health, and resilience in the face of dynamic challenges like climate change, (non-)communicable disease and food security. Geospatial analytics, an interdisciplinary field at the intersection of geography, data science, and urban planning, provides a powerful framework for understanding and addressing these critical urban issues. The importance of geospatial analytics in advancing sustainable, healthy, and resilient cities cannot be overstated. It enables researchers and policymakers to harness the power of spatial data and advanced analytics to gain insights into complex urban systems, inform evidence-based decision-making processes, and design effective interventions.

With this in mind, we are pleased to announce the launch of a Special Issue on ‘Geospatial Analytics for Sustainable, Healthy and Resilient Cities.’ This Special Issue aims to bring together cutting-edge research that utilizes geospatial analytics to address the diverse challenges faced by cities worldwide. It provides a unique platform for sharing innovative methodologies, empirical studies, and theoretical advancements that contribute to the advancement of urban sustainability, public health, and resilience. By highlighting the role of geospatial analytics in urban contexts, this Special Issue aims to foster a deeper understanding of the spatial and temporal dynamics shaping cities, while promoting the use of geospatial data and analytical techniques to inform urban policy, planning, and design.

This Special Issue invites researchers and practitioners from various disciplines to submit their original research articles and reviews, providing novel insights into topics focusing on, but not limited to, the following:

Sustainable urban mobility and accessibility;
Active and healthy communities;
Sustainable transport in cities;
Spatial-temporal analysis of noise and air pollution;
Urban heat monitoring and mitigation;
Geospatial analytics for travel behavior and physical activities;
Promotion of sustainable and equitable food access;
Environmental health inequality and spatial justice;
Spatial analysis of urban infrastructure;
Geospatial approaches to urban land use and urban growth modeling;
Spatial analysis of social vulnerability and equity in urban contexts;
Remote sensing for urban infrastructure and utilities;
Remote sensing for urban resiliency;
Geospatial analytics for equitable urban planning and governance.

We look forward to receiving your contributions.

Sincerely,

Dr. Dong Liu
Prof. Dr. Mei-Po Kwan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

geospatial analytics
urban mobility
accessibility
urban inequity
sustainable cities
healthy communities
urban resilience
remote sensing
environmental health

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Published Papers (5 papers)

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Research

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27 pages, 11213 KiB

Open AccessArticle

Spatiotemporal Evolution Mechanism and Dynamic Simulation of the Urban Resilience System in the Chengdu–Chongqing Economic Circle

by Huiqin Huang and Xia Yang

Sustainability 2025, 17(8), 3448; https://doi.org/10.3390/su17083448 - 12 Apr 2025

Viewed by 374

Abstract

The system subject, resilience capacity, and factor endowment are the core elements of resilient city construction. A rational assessment of urban resilience is crucial for transforming the urban governance paradigm. This study develops an analytical framework for resilient city system (RCS) grounded in the conceptual connotation and constituent elements of urban resilience. Using the strategically significant Chengdu–Chongqing Economic Circle (CCEC) as a case study, an urban resilience (UR) index system was proposed, encompassing economics, society, ecology, infrastructure, and organizational management. A panel dataset of urban resilience indicators was compiled using official data from national and local urban statistical yearbooks spanning 2012 to 2022. By analyzing the spatiotemporal evolution patterns of the CCEC, this study revealed the dominant factors influencing these patterns and dynamically simulated the urban resilience of the CCEC over the next 16 years. The findings indicate the following: (1) During 2012–2022, the urban resilience of the CCEC transitioned from a “single-core” model centered on Chongqing to a “dual-core” model featuring both Chongqing and Chengdu. The overall level of urban resilience in the study area exhibited an upward trend, characterized by a spatially divergent pattern with two prominent wings and a concave center. (2) Economic factors, social factors, and facility factors were identified as the dominant factors affecting urban resilience in the CCEC. (3) Projections for 2025–2035 suggest that the urban resilience level of the CCEC will continue to increase steadily at a moderate pace. These results provide valuable theoretical references for advancing the high-quality development of the CCEC and fostering a development pattern characterized by “two-wing drive and whole-area synergy”. Full article

(This article belongs to the Special Issue The Role of Geospatial Analytics in Advancing Sustainable, Healthy and Resilient Cities)

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15 pages, 3449 KiB

Open AccessArticle

From Individual Motivation to Geospatial Epidemiology: A Novel Approach Using Fuzzy Cognitive Maps and Agent-Based Modeling for Large-Scale Disease Spread

by Zhenlei Song, Zhe Zhang, Fangzheng Lyu, Michael Bishop, Jikun Liu and Zhaohui Chi

Sustainability 2024, 16(12), 5036; https://doi.org/10.3390/su16125036 - 13 Jun 2024

Cited by 3 | Viewed by 1704

Abstract

In the past few years, there have been many studies addressing the simulation of COVID-19’s spatial transmission model of infectious disease in time. However, very few studies have focused on the effect of the epidemic environment variables in which an individual lives on the individual’s behavioral logic leading to changes in the overall epidemic transmission trend at larger scales. In this study, we applied Fuzzy Cognitive Maps (FCMs) to modeling individual behavioral logistics, combined with Agent-Based Modeling (ABM) to perform “Susceptible—Exposed—Infectious—Removed” (SEIR) simulation of the independent individual behavior affecting the overall trend change. Our objective was to simulate the spatiotemporal spread of diseases using the Bengaluru Urban District, India as a case study. The results show that the simulation results are highly consistent with the observed reality, in terms of trends, with a Root Mean Square Error (RMSE) value of 0.39. Notably, our approach reveals a subtle link between individual motivation and infection-recovery dynamics, highlighting how individual behavior can significantly impact broader patterns of transmission. These insights have potential implications for epidemiologic strategies and public health interventions, providing data-driven insights into behavioral impacts on epidemic spread. By integrating behavioral modeling with epidemic simulation, our study underscores the importance of considering individual and collective behavior in designing sustainable public health policies and interventions. Full article

(This article belongs to the Special Issue The Role of Geospatial Analytics in Advancing Sustainable, Healthy and Resilient Cities)

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19 pages, 4267 KiB

Open AccessArticle

Sales in Commercial Alleys and Their Association with Air Pollution: Case Study in South Korea

by Khadija Ashraf, Kangjae Lee, Geunhan Kim and Jeon-Young Kang

Sustainability 2024, 16(2), 530; https://doi.org/10.3390/su16020530 - 8 Jan 2024

Viewed by 1670

Abstract

We investigate the dynamic interplay between air pollution (PM₁₀) and income and their joint association with quarterly sales in commercial alleys, focusing on the pre-COVID-19 (2018–2019) and COVID-19 (2020–2021) periods in Seoul, South Korea. The objective of this study is to identify how air pollution and income collectively influence consumer spending patterns by looking at the increase and decrease in sales in commercial alleys, with a focus on contrasting these effects before and during the COVID-19 pandemic, utilizing advanced machine learning techniques for deeper insights. Using machine learning techniques, including random forest, extreme gradient boosting, catboost, and lightGBM, and employing explainable artificial intelligence (XAI), this study identifies shifts in the significance of predictor variables, particularly PM₁₀, before and during the pandemic. The results show that before the pandemic, PM₁₀ played a notable role in shaping sales predictions, highlighting the sensitivity of sales to air quality. However, during the pandemic, the importance of PM₁₀ decreased significantly, highlighting the transformative indirect impact of external events on consumer behavior. This study also examines the joint association of PM₁₀ and income with sales, revealing distinctive patterns in consumer responses to air quality changes during the pandemic. These findings highlight the need for dynamic modeling to capture evolving consumer behavior and provide valuable insights for businesses and policymakers navigating changing economic and environmental conditions. While this study’s focus is on a specific region and time frame, the findings emphasize the importance of adaptability in predictive models and contribute to understanding the complex interplay between environmental and economic factors in shaping consumer spending behavior. Full article

(This article belongs to the Special Issue The Role of Geospatial Analytics in Advancing Sustainable, Healthy and Resilient Cities)

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26 pages, 14227 KiB

Open AccessArticle

How Did Urban Environmental Characteristics Influence Land Surface Temperature in Hong Kong from 2017 to 2022? Evidence from Remote Sensing and Land Use Data

by Zherong Wu, Xinyang Zhang, Peifeng Ma, Mei-Po Kwan and Yang Liu

Sustainability 2023, 15(21), 15511; https://doi.org/10.3390/su152115511 - 1 Nov 2023

Cited by 8 | Viewed by 3079

Abstract

Urbanization has led to environmental challenges, with the urban heat island effect being a prominent concern. Understanding the influence of urban environmental characteristics (UECs) on land surface temperature (LST) is essential for addressing this issue and promoting sustainable urban development. The spatiotemporal characteristics and influencing factors of LST have been investigated in past studies, but research that explicitly investigates the key factors and long-term spatial relationships affecting LST in city-scale urban areas is limited. Remote sensing techniques provide valuable insights into LST patterns and the relationship between urban environment and temperature dynamics. We utilized Landsat 8 images to derive the LST and six spectral indices from 2017 to 2022 in Hong Kong, a city characterized by high population density and rapid urban growth. We also acquired land use data to reflect Hong Kong’s dynamic urban landscape. The complex interactions between urban environment and LST were analyzed using various analytical techniques, including slope trend analysis, land use change detection, and correlation analysis. Finally, we constructed a random forest model to assess the importance of each environmental characteristic. Our findings provide three key insights for regions experiencing rapid urbanization. First, the LST showed an increasing trend in Hong Kong from 2017 to 2022, with the annual LST rising from 21.13 °C to 23.46 °C. Second, we identify negative relationships between LST and vegetation (−0.49)/water bodies (−0.49) and a positive correlation between LST and built-up areas (0.56) utilizing Pearson’s correlation. Third, the dominant influence of built-up areas was underscored, contributing as much as 53.4% to elevated LST levels, with specific attention to the substantial reclamation activities in Hong Kong. The insights from this study provide valuable guidance for policymakers, urban planners, and environmental researchers to formulate evidence-based strategies to achieve a resilient, livable urban future. Full article

(This article belongs to the Special Issue The Role of Geospatial Analytics in Advancing Sustainable, Healthy and Resilient Cities)

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26 pages, 3026 KiB

Open AccessReview

Data-Driven Net-Zero Carbon Monitoring: Applications of Geographic Information Systems, Building Information Modelling, Remote Sensing, and Artificial Intelligence for Sustainable and Resilient Cities

by Jilong Li, Sara Shirowzhan, Gloria Pignatta and Samad M. E. Sepasgozar

Sustainability 2024, 16(15), 6285; https://doi.org/10.3390/su16156285 - 23 Jul 2024

Cited by 11 | Viewed by 4149

Abstract

NZCCs aim to minimise urban carbon emissions for healthier cities in line with national and international low-carbon targets and Sustainable Development Goals (SDGs). Many countries have recently adopted Net-Zero Carbon City (NZCC) policies and strategies. While there are many studies available on NZCC cities’ definitions and policymaking, currently, research is rare on understanding the role of urban data-driven technologies such as Building Information Modelling (BIM) and Geographic Information Systems (GIS), as well as AI, for achieving the goals of NZCCs in relation to sustainable development goals (SDGs), e.g., SDGs 3, 7,11, 13, and 17. This paper aims to fill this gap by establishing a systematic review and ascertaining the opportunities and barriers of data-driven approaches, analytics, digital technologies, and AI for supporting decision-making and monitoring progress toward achieving NZCC development and policy/strategy development. Two scholarly databases, i.e., Web of Science and Scopus databases, were used to find papers based on our selected relevant keywords. We also conducted a desktop review to explore policies, strategies, and visualisation technologies that are already being used. Our inclusion/exclusion criteria refined our selection to 55 papers, focusing on conceptual and theoretical research. While digital technologies and data analytics are improving and can help in the move from net-zero carbon concepts and theories to practical analysis and the evaluation of cities’ emission levels and in monitoring progress toward reducing carbon, our research shows that these capabilities of digital technologies are not used thoroughly yet to bridge theory and practice. These studies ignore advanced tools like city digital twins and GIS-based spatial analyses. No data, technologies, or platforms are available to track progress towards a NZCC. Artificial Intelligence, big data collection, and analytics are required to predict and monitor the time it takes for each city to achieve net-zero carbon emissions. GIS and BIM can be used to estimate embodied carbon and predict urban development emissions. We found that smart city initiatives and data-driven decision-making approaches are crucial for achieving NZCCs. Full article

(This article belongs to the Special Issue The Role of Geospatial Analytics in Advancing Sustainable, Healthy and Resilient Cities)

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