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Application of Geographic Information and Big Data Analysis in Responding to Climate Change

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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Earth Science and Medical Geology".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 8374

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Special Issue Editors

Dr. Yuehong Chen

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Guest Editor

College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Interests: GIscience; geographic big data analysis; geospatial artificial intelligence; risk assessment; climate change

Dr. Jiawei Yi

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Guest Editor

State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: human’s digital footprints and geographic big data analysis; social perception and response to climate change and extreme events

Dr. Yi Liu

E-Mail Website
Guest Editor

College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Interests: hydrology; hydrological modeling; drought; drought monitoring and evaluation; climate change, extreme events

Special Issue Information

Dear Colleagues,

We would like to cordially invite you to consider publishing your manuscripts in our Special Issue on the topic of “Application of Geographic Information and Big Data Analysis in Responding to Climate Change” in the International Journal of Environmental Research and Public Health (IJERPH).

In past decades, global climate change has caused various extreme climate events and disasters that are significantly challenging to human living environments. These extreme climate events and disasters can lead to severe environmental, economic, and social consequences. Thus, disaster prevention and mitigation are greatly needed.

With the rapid development of Earth observation networks and geographic information techniques, a variety of geographic big data and new approaches have emerged. For instance, many satellites provide the potential to observe daily global dynamics for precipitation, land surface temperature, disasters, etc. Volunteered Geographic Information (VGI) platforms, mobile crowdsensing, and social media allow us to observe the daily and even hourly dynamics of human behavior. Artificial intelligence (AI) techniques have shown outstanding learning ability in multiple disciplines. These new data and approaches enable us to develop data-driven models and create smart solutions to respond to extreme climate events and disasters caused by climate change.

This Special Issue aims to provide an opportunity for scholars to share their latest research findings related to the topic of the application of geographic big data and approaches to respond to climate change. This Special Issue welcomes high-quality research papers that focus on spatiotemporal pattern analysis of extreme climate events and disasters, disaster prevention and mitigation, disaster susceptibility assessment, parameters (e.g., hazard, exposure, and vulnerability), generation of climate change models, collective responses to extreme climate events, social perception of climate change, etc.

Dr. Yuehong Chen
Dr. Jiawei Yi
Dr. Yi Liu
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. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly 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 2500 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

Geographic big data analysis
Geospatial artificial intelligence
Spatiotemporal pattern of disaster events
Disaster prevention and mitigation
Disaster susceptibility modeling
Parameters generation of climate change models
Social perception of climate change
Collective response to extreme events
Urban resilience to extreme disasters
Climate change

Published Papers (4 papers)

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Research

20 pages, 5378 KiB

Open AccessArticle

Study on Urbanization Sustainability of Xinjiang in China: Connotation, Indicators and Measurement

by Lei Kang and Siyou Xia

Int. J. Environ. Res. Public Health 2023, 20(3), 2535; https://doi.org/10.3390/ijerph20032535 - 31 Jan 2023

Cited by 2 | Viewed by 1932

Abstract

BACKGROUND: Current research about sustainability evaluations in urbanization pays limited attention to certain areas of the world, thus potentially leading to an incomplete portrayal of the rich connotation of sustainable development. In fact, the existing evaluation criteria used by researchers in this field may not be generalizable due to regional variations. This study evaluated urbanization sustainability in Xinjiang Province (China) taking into account different perspectives, such as security and stability, social integration, economic vitality, happiness and livability, and ecological health. The aim was to develop an urbanization sustainability evaluation system, resulting in a new Index customized to regional characteristics and local development needs. METHODS: A spatial clustering analysis methodology was adopted to reveal the prominence of 15 issues in different areas of Xinjiang. RESULTS: Overall, the results showed low urbanization sustainability in Xinjiang, with significant intra-regional variability. The dimensions of security and stability scored the lowest in the newly developed Index, indicating specific aspects of weakness in Xinjiang’s urbanization sustainability. Social integration scored highly in the new index, implying that this aspect plays a supporting role in the urbanization sustainability of the region. Nevertheless, economic vitality scored low, representing a limitation for the region’s urbanization sustainability, as well as the happiness and livability dimensions. On the contrary, the parameter of ecological health scored high, despite spatial variances. Urbanization sustainability within each prefecture was further categorized as high, balanced, or low, revealing the main challenges faced by each prefecture during urbanization. CONCLUSIONS: The purpose of this study was to divert attention to the urbanization sustainability in different regions of the world, considering their particularity and diversity, thereby providing a research paradigm for scientific evaluation of urbanization sustainability. Full article

(This article belongs to the Special Issue Application of Geographic Information and Big Data Analysis in Responding to Climate Change)

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18 pages, 2567 KiB

Open AccessArticle

The Time-Lag Effect of Climate Factors on the Forest Enhanced Vegetation Index for Subtropical Humid Areas in China

by Jushuang Qin, Menglu Ma, Jiabin Shi, Shurui Ma, Baoguo Wu and Xiaohui Su

Int. J. Environ. Res. Public Health 2023, 20(1), 799; https://doi.org/10.3390/ijerph20010799 - 1 Jan 2023

Cited by 3 | Viewed by 2065

Abstract

Forests represent the greatest carbon reservoir in terrestrial ecosystems. Climate change drives the changes in forest vegetation growth, which in turn influences carbon sequestration capability. Exploring the dynamic response of forest vegetation to climate change is thus one of the most important scientific questions to be addressed in the precise monitoring of forest resources. This paper explores the relationship between climate factors and vegetation growth in typical forest ecosystems in China from 2007 to 2019 based on long-term meteorological monitoring data from six forest field stations in different subtropical ecological zones in China. The time-varying parameter vector autoregressive model (TVP-VAR) was used to analyze the temporal and spatial differences of the time-lag effects of climate factors, and the impact of climate change on vegetation was predicted. The enhanced vegetation index (EVI) was used to measure vegetation growth. Monthly meteorological observations and solar radiation data, including precipitation, air temperature, relative humidity, and photosynthetic effective radiation, were provided by the resource sharing service platform of the national ecological research data center. It was revealed that the time-lag effect of climate factors on the EVI vanished after a half year, and the lag accumulation tended to be steady over time. The TVP-VAR model was found to be more suitable than the vector autoregressive model (VAR). The predicted EVI values using the TVP-VAR model were close to the true values with the root mean squares error (RMSE) < 0.05. On average, each site improved its prediction accuracy by 14.81%. Therefore, the TVP-VAR model can be used to analyze the relationship of climate factors and forest EVI as well as the time-lag effect of climate factors on vegetation growth in subtropical China. The results can be used to improve the predictability of the EVI for forests and to encourage the development of intensive forest management. Full article

(This article belongs to the Special Issue Application of Geographic Information and Big Data Analysis in Responding to Climate Change)

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16 pages, 2659 KiB

Open AccessArticle

Factors Associated with Housing Damage Caused by an EF4 Tornado in Rural Areas of Funing, China

by Peng Qiao, Wei Chen, Jun Zhao, Jingyi Gao and Guofang Zhai

Int. J. Environ. Res. Public Health 2022, 19(21), 14237; https://doi.org/10.3390/ijerph192114237 - 31 Oct 2022

Viewed by 1284

Abstract

Rural areas are vulnerable to natural disasters and tend to suffer severe losses. An EF4 tornado occurred in Funing on 23 June 2016, killing 99 people, injuring at least 846 people, and destroying more than 2000 houses. Using a multinomial logistic regression model, this study explored the influencing factors between housing damage and variables of building conditions, tornado intensity, and village environmental factors. The results show that 2-story houses and masonry houses were more likely to be slightly damaged or be in a dangerous state. Furthermore, the building area was positively related to houses in two categories: slight damage (SD) and dangerous and requiring immediate repair (DR), indicating that the larger or taller the house, the more severe the damage. In terms of tornado intensity, houses classified as SD were more likely to be hit by EF4 tornados than by EF3 tornados, and houses were damaged more by EF1 or EF2 tornados. This finding demonstrates that the level of housing damage was not strongly correlated with the tornado intensity. Slightly damaged houses exhibited the highest correlation with environmental factors. The proportion of slightly damaged houses was positively correlated with the water area in the village, unlike the proportion of houses in the DR and unable to be repaired (UR) categories. Moreover, the larger the water area of a village, the less housing damage it suffered. These findings provide new insights into minimizing housing damage in wind disasters to improve disaster prevention planning in rural areas. Full article

(This article belongs to the Special Issue Application of Geographic Information and Big Data Analysis in Responding to Climate Change)

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26 pages, 4553 KiB

Open AccessArticle

Spatial-Temporal Evolution Analysis of Carbon Emissions Embodied in Inter-Provincial Trade in China

by Tianrui Wang, Yu Chen and Leya Zeng

Int. J. Environ. Res. Public Health 2022, 19(11), 6794; https://doi.org/10.3390/ijerph19116794 - 1 Jun 2022

Cited by 13 | Viewed by 2037

Abstract

Under the support of Multi-Regional Input–Output (MRIO) analysis, this study constructs the Embodied Carbon Emission Transfer Network (ECETN) using the input–output tables of 42 sectors in 31 provinces of China in 2012, 2015, and 2017 and applies a series of complex network measurement indicators and analysis methods to describe its evolution features. The results show that the embodied carbon emission transfers between provinces generally narrow over time. With its high clustering coefficient and short average path length, ECETN has small-world characteristics and behaves sensitively, and changes in individual provinces can quickly spread and affect the entire system. In addition, the clustering effect and the spatial spillover structural properties of ECETN are explored based on the block model analysis. Finally, Quadratic Assignment Procedure (QAP) is used to analyze and quantify the contribution of provincial structural roles to ECETN, and it is found that spatial adjacency and differences in strength-in, strength-out, and betweenness centrality have significant positive effects, while differences in eigenvector centrality, clustering coefficient have significant negative effects. The restructuring of domestic trade can help achieve national emission reduction. These findings can provide more insights for the government to formulate future development directions and policies to reduce emissions further. Full article

(This article belongs to the Special Issue Application of Geographic Information and Big Data Analysis in Responding to Climate Change)

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