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Yin C, Yan J, Wang J, Wang T, Li H, Wang Y, Wang H, Feng S, Liang Y. Spatial analysis of the prevalence of abdominal obesity in middle-aged and older adult people in China: exploring the relationship with meteorological factors based on gender differences. Front Public Health 2024; 12:1426295. [PMID: 39100945 PMCID: PMC11294229 DOI: 10.3389/fpubh.2024.1426295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Accepted: 07/03/2024] [Indexed: 08/06/2024] Open
Abstract
Background In recent years, the incidence of abdominal obesity among the middle-aged and older adult population in China has significantly increased. However, the gender disparities in the spatial distribution of abdominal obesity incidence and its relationship with meteorological factors among this demographic in China remain unclear. This gap in knowledge highlights the need for further research to understand these dynamics and inform targeted public health strategies. Methods This study utilized data from the 2015 China Health and Retirement Longitudinal Study (CHARLS) to analyze the incidence of abdominal obesity among the middle-aged and older adult population in China. Additionally, meteorological data were collected from the National Meteorological Information Center. Using Moran's I index and Getis-Ord Gi* statistical methods, the spatial distribution characteristics of abdominal obesity incidence were examined. The influence of various meteorological factors on the incidence of abdominal obesity in middle-aged and older adult males and females was investigated using the q statistic from the Geodetector method. Furthermore, Multi-Scale Geographically Weighted Regression (MGWR) analysis was employed to explore the impact of meteorological factors on the spatial heterogeneity of abdominal obesity incidence from a gender perspective. Results The spatial distribution of abdominal obesity among middle-aged and older adult individuals in China exhibits a decreasing trend from northwest to southeast, with notable spatial autocorrelation. Hotspots are concentrated in North and Northeast China, while cold spots are observed in Southwest China. Gender differences have minimal impact on spatial clustering characteristics. Meteorological factors, including temperature, sunlight, precipitation, wind speed, humidity, and atmospheric pressure, influence incidence rates. Notably, temperature and sunlight exert a greater impact on females, while wind speed has a reduced effect. Interactions among various meteorological factors generally demonstrate bivariate enhancement without significant gender disparities. However, gender disparities are evident in the influence of specific meteorological variables such as annual maximum, average, and minimum temperatures, as well as sunlight duration and precipitation, on the spatial heterogeneity of abdominal obesity incidence. Conclusion Meteorological factors show a significant association with abdominal obesity prevalence in middle-aged and older adults, with temperature factors playing a prominent role. However, this relationship is influenced by gender differences and spatial heterogeneity. These findings suggest that effective public health policies should be not only gender-sensitive but also locally adapted.
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Affiliation(s)
- Chaohui Yin
- School of Resources and Environment, Henan Agricultural University, Zhengzhou, Henan, China
| | - Jinlong Yan
- Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo, China
| | - Junqi Wang
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Tianyi Wang
- School of Management, Beijing University of Chinese Medicine, Beijing, China
| | - Hangyu Li
- School of Life Sciences, Beijing University of Chinese Medicine, Beijing, China
| | - Yuan Wang
- College of Acu-moxibustion and Massage, Shaanxi University of Chinese Medicine, Xianyang, China
| | - Haifeng Wang
- Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xianyang, China
| | - Shixing Feng
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Xianyang, China
- Centre France Chine de la Médecine Chinoise, Selles sur Cher, France
| | - Yafeng Liang
- School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing, China
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Li D, Gao D, Yamada M, Chen C, Xiang L, Nie H. Healthcare-seeking behavior and spatial variation of internal migrants with chronic diseases: a nationwide empirical study in China. GEOSPATIAL HEALTH 2024; 19. [PMID: 38804697 DOI: 10.4081/gh.2024.1255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 05/03/2024] [Indexed: 05/29/2024]
Abstract
Individuals migrating with chronic diseases often face substantial health risks, and their patterns of healthcare-seeking behavior are commonly influenced by mobility. However, to our knowledge, no research has used spatial statistics to verify this phenomenon. Utilizing data from the China Migrant Dynamic Survey of 2017, we conducted a geostatistical analysis to identify clusters of chronic disease patients among China's internal migrants. Geographically weighted regressions were utilized to examine the driving factors behind the reasons why treatment was not sought by 711 individuals among a population sample of 9272 migrant people with chronic diseases. The results indicate that there is a spatial correlation in the clustering of internal migrants with chronic diseases in China. The prevalence is highly clustered in Zhejiang and Xinjiang in north-eastern China. Hotspots were found in the northeast (Jilin and Liaoning), the north (Hebei, Beijing, and Tianjin), and the east (Shandong) and also spread into surrounding provinces. The factors that affect the migrants with no treatment were found to be the number of hospital beds per thousand population, the per capita disposable income of medical care, and the number of participants receiving health education per 1000 Chinese population. To rectify this situation, the local government should "adapt measures to local conditions." Popularizing health education and coordinating the deployment of high-quality medical facilities and medical workers are effective measures to encourage migrants to seek reasonable medical treatment.
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Affiliation(s)
- Dan Li
- United Graduate School of Agricultural Science, Tokyo University of Agriculture and Technology.
| | - Dawei Gao
- College of Economics and Management, Zhengzhou University of Light Industry, Henan Province.
| | - Masaaki Yamada
- Division of International Environmental and Agricultural Science, Institute of Agriculture, Tokyo University of Agriculture and Technology.
| | - Chuangbin Chen
- Business School, Shantou University, Shantou City, Guangdong Province.
| | - Liuchun Xiang
- United Graduate School of Agricultural Science, Tokyo University of Agriculture and Technology.
| | - Haisong Nie
- Division of International Environmental and Agricultural Science, Institute of Agriculture, Tokyo University of Agriculture and Technology.
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Zhang L, Jia X, Zhao Y, Wang H, Peng B, Zhang P, Zhao M. Spatio-temporal characteristics and driving mechanism of land degradation sensitivity in Northwest China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 918:170403. [PMID: 38307282 DOI: 10.1016/j.scitotenv.2024.170403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 12/31/2023] [Accepted: 01/22/2024] [Indexed: 02/04/2024]
Abstract
Northwest China has been experiencing severe land degradation for a long time due to various natural and social elements. Evaluating and analyzing the process of occurrence and driving mechanism of land degradation sensitivity in this area is crucial for enhancing the local ecological environment. In this study, 18 social and environmental elements were used to construct a land degradation sensitivity index (LDSI) evaluation system in the area from vegetation, climate, management, soil, and geomorphology five factors. The spatio-temporal characteristics of LDSI in Northwest China from 2000 to 2020 were evaluated on the basis of analyzing the developmental changes of each factor. Correlation analysis and multiscale geographical weighting regression (MGWR) were used to reveal the driving mechanism of land degradation sensitivity. The results indicated a high level of land degradation sensitivity in Northwest China, with >66 % of the area (190.96 × 104 km2) in the critical sensitive class from 2000 to 2020. But the land degradation sensitivity decreased in 18.52 % of the area (53.58 × 104 km2) from 2000 to 2020, the overall trend was weakening. The spatial distribution mainly showed stronger sensitivity in the northwest and weaker sensitivity in the southeast. By exploring the driving mechanism of land degradation sensitivity, it was found that vegetation and climate showed a strong correlation, with a correlation coefficient >0.8. Drought resistance played a strong role in the dynamic process of land degradation. The basic dynamic elements showed some spatial variability in land degradation in different regions. This study is of significance for land degradation prevention and sustainable development in Northwest China.
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Affiliation(s)
- Lei Zhang
- The School of Land Engineering, Key laboratory of Degraded and Unused Land Consolidation Engineering of the Ministry of Natural Resources, Shaanxi Key Laboratory of Land Consolidation, School of Water and Environment, Shaanxi Province Land Consolidation Engineering Technology Research Center, Chang'an University, Xi'an 710054, PR China
| | - Xia Jia
- The School of Land Engineering, Key laboratory of Degraded and Unused Land Consolidation Engineering of the Ministry of Natural Resources, Shaanxi Key Laboratory of Land Consolidation, School of Water and Environment, Shaanxi Province Land Consolidation Engineering Technology Research Center, Chang'an University, Xi'an 710054, PR China
| | - Yonghua Zhao
- The School of Land Engineering, Key laboratory of Degraded and Unused Land Consolidation Engineering of the Ministry of Natural Resources, Shaanxi Key Laboratory of Land Consolidation, School of Water and Environment, Shaanxi Province Land Consolidation Engineering Technology Research Center, Chang'an University, Xi'an 710054, PR China.
| | - Huanyuan Wang
- Shaanxi Provincial Land Consolidation Engineering Technology Research Center, Shaanxi Provincial Land Engineering Construction Group Co., Ltd, Xi'an 710075, PR China
| | - Biao Peng
- Shaanxi Provincial Land Consolidation Engineering Technology Research Center, Shaanxi Provincial Land Engineering Construction Group Co., Ltd, Xi'an 710075, PR China
| | - Peng Zhang
- The School of Land Engineering, Key laboratory of Degraded and Unused Land Consolidation Engineering of the Ministry of Natural Resources, Shaanxi Key Laboratory of Land Consolidation, School of Water and Environment, Shaanxi Province Land Consolidation Engineering Technology Research Center, Chang'an University, Xi'an 710054, PR China
| | - Ming Zhao
- The School of Land Engineering, Key laboratory of Degraded and Unused Land Consolidation Engineering of the Ministry of Natural Resources, Shaanxi Key Laboratory of Land Consolidation, School of Water and Environment, Shaanxi Province Land Consolidation Engineering Technology Research Center, Chang'an University, Xi'an 710054, PR China
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Luan G, Zhao F, Xia J, Huang Z, Feng S, Song C, Dong P, Zhou X. Analysis of long-term spatio-temporal changes of plateau urban wetland reveals the response mechanisms of climate and human activities: A case study from Dianchi Lake Basin 1993-2020. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169447. [PMID: 38141987 DOI: 10.1016/j.scitotenv.2023.169447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 08/16/2023] [Accepted: 12/15/2023] [Indexed: 12/25/2023]
Abstract
Wetlands serve many functions, including conserving water, providing habitats for animals and plants, and regulating climate change. Their unique ecological effects on the natural environment are indispensable in the whole ecosystem. Dianchi Lake Basin is located in Yunnan-Guizhou Plateau, China, and mainly in Kunming. It is a typical plateau urban wetland area. Based on spatio-temporal hotspot mining, spatio-temporal geographically weighted regression, and adaptive multidimensional grey prediction, we conducted correlation analyses of the wetland changes in Dianchi Lake Basin from 1993 to 2020 under the influence of human activities and natural conditions. The results show that (1) the active wetland change zone in Dianchi Lake Basin is mainly located around Dianchi Lake, and (2) the wetlands in some areas on the north and south of Dianchi Lake declined in the early 21st century, but under the protection policy in recent years, the wetlands in these areas gradually recovered. Meanwhile, the wetlands in most areas around Dianchi Lake showed a significant growth trend from 2018 to 2020. The results suggest that the wetland change in Dianchi Lake Basin is mainly related to the urbanization of Kunming, and it can be divided into five regions (strong negative correlation, weak negative correlation, weak correlation, weak positive correlation, and strong positive correlation) according to the different correlation of human activity intensity, among which the main factors affected by nature are different, but they are all related to temperature. This study shows that, although wetlands in plateau cities can be properly restored under proper protection, wetland protection should be kept in step with the development of plateau cities to support sustainable urban development and carbon neutrality.
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Affiliation(s)
- Guize Luan
- Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China; State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China.
| | - Fei Zhao
- School of Earth Sciences, Yunnan University, Kunming 650500, China; Technology Innovation Center for Natural Ecosystem Carbon Sink, Ministry of Natural Resources, Kunming, 650111, China; State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China.
| | - Jisheng Xia
- School of Earth Sciences, Yunnan University, Kunming 650500, China; Technology Innovation Center for Natural Ecosystem Carbon Sink, Ministry of Natural Resources, Kunming, 650111, China.
| | - Ziyu Huang
- School of Statistics, Lanzhou University of Finance and Economics, Lanzhou 730020, China.
| | - Siwen Feng
- Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China.
| | - Ci Song
- State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China.
| | - Pinliang Dong
- Department of Geography and the Environment, University of North Texas, Denton 76201, USA.
| | - Xiaolu Zhou
- Department of Geography, Texas Christian University, Fort Worth 76129, USA.
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Lord J, Odoi A. Determinants of disparities of diabetes-related hospitalization rates in Florida: a retrospective ecological study using a multiscale geographically weighted regression approach. Int J Health Geogr 2024; 23:1. [PMID: 38184599 PMCID: PMC10771651 DOI: 10.1186/s12942-023-00360-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 12/04/2023] [Indexed: 01/08/2024] Open
Abstract
BACKGROUND Early diagnosis, control of blood glucose levels and cardiovascular risk factors, and regular screening are essential to prevent or delay complications of diabetes. However, most adults with diabetes do not meet recommended targets, and some populations have disproportionately high rates of potentially preventable diabetes-related hospitalizations. Understanding the factors that contribute to geographic disparities can guide resource allocation and help ensure that future interventions are designed to meet the specific needs of these communities. Therefore, the objectives of this study were (1) to identify determinants of diabetes-related hospitalization rates at the ZIP code tabulation area (ZCTA) level in Florida, and (2) assess if the strengths of these relationships vary by geographic location and at different spatial scales. METHODS Diabetes-related hospitalization (DRH) rates were computed at the ZCTA level using data from 2016 to 2019. A global ordinary least squares regression model was fit to identify socioeconomic, demographic, healthcare-related, and built environment characteristics associated with log-transformed DRH rates. A multiscale geographically weighted regression (MGWR) model was then fit to investigate and describe spatial heterogeneity of regression coefficients. RESULTS Populations of ZCTAs with high rates of diabetes-related hospitalizations tended to have higher proportions of older adults (p < 0.0001) and non-Hispanic Black residents (p = 0.003). In addition, DRH rates were associated with higher levels of unemployment (p = 0.001), uninsurance (p < 0.0001), and lack of access to a vehicle (p = 0.002). Population density and median household income had significant (p < 0.0001) negative associations with DRH rates. Non-stationary variables exhibited spatial heterogeneity at local (percent non-Hispanic Black, educational attainment), regional (age composition, unemployment, health insurance coverage), and statewide scales (population density, income, vehicle access). CONCLUSIONS The findings of this study underscore the importance of socioeconomic resources and rurality in shaping population health. Understanding the spatial context of the observed relationships provides valuable insights to guide needs-based, locally-focused health planning to reduce disparities in the burden of potentially avoidable hospitalizations.
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Affiliation(s)
- Jennifer Lord
- Department of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, The University of Tennessee, Knoxville, TN, USA
| | - Agricola Odoi
- Department of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, The University of Tennessee, Knoxville, TN, USA.
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Li S, Zhang J, Moriyama M, Kazawa K. Spatially heterogeneous associations between the built environment and objective health outcomes in Japanese cities. INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH 2023; 33:1205-1217. [PMID: 35670499 DOI: 10.1080/09603123.2022.2083086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 05/18/2022] [Indexed: 06/15/2023]
Abstract
The built environment is a structural determinant of health. Here we reveal spatially heterogeneous associations of built environment indicators with objective health outcomes (morbidity) by combining a random forest (RF) approach and a multiscale geographically weighted (MGWR) regression method. Using data from six Japanese cities, we found that the ratio of morbidity has obvious spatial agglomerations. The mixed land-use diversity with 1000 m buffer, distance to hospital, proportion of park area with 300 m buffer, and house price with 2000 m buffer, negatively affect health outcomes at all locations. For most locations, high PM2.5 or high floor area ratio with 2000 m buffer are linked to a high ratio of morbidity. Our findings support the use of such data for long-term urban and health planning. We expect our study to be a starting point for further research on spatially heterogeneous associations of the built environment with comprehensive health outcomes.
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Affiliation(s)
- Shuangjin Li
- Mobilities and Urban Policy Lab, Graduate School for International Development and Cooperation, Hiroshima University, Higashihiroshima, Japan
| | - Junyi Zhang
- Mobilities and Urban Policy Lab, Graduate School for International Development and Cooperation, Hiroshima University, Higashihiroshima, Japan
- Graduate School of Advanced Science and Engineering, Hiroshima University, Japan
| | - Michiko Moriyama
- Division of Nursing Science, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Kana Kazawa
- Endowed Course, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
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Cadmus S, Taiwo OJ, Akinseye V, Cadmus E, Famokun G, Fagbemi S, Ansumana R, Omoluabi A, Ayinmode A, Oluwayelu D, Odemuyiwa S, Tomori O. Ecological correlates and predictors of Lassa fever incidence in Ondo State, Nigeria 2017-2021: an emerging urban trend. Sci Rep 2023; 13:20855. [PMID: 38012226 PMCID: PMC10682180 DOI: 10.1038/s41598-023-47820-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 11/18/2023] [Indexed: 11/29/2023] Open
Abstract
Lassa fever (LF) is prevalent in many West African countries, including Nigeria. Efforts to combat LF have primarily focused on rural areas where interactions between rodents and humans are common. However, recent studies indicate a shift in its occurrence from rural to urban areas. We analysed secondary data of reported LF outbreaks from 2017 to 2021 in Ondo State, Nigeria to identify the distribution pattern, ecological variations, and other determinants of disease spread from the ward level using nearest neighbour statistics and regression analysis. Data utilised include LF incidence, ecological variables involving population, nighttime light intensity, vegetation, temperature, market presence, road length, and building area coverage. ArcGIS Pro 3.0 software was employed for spatial analysis. Results revealed spatio-temporal clustering of LF incidents between 2017 and 2021, with an increasing trend followed by a decline in 2021. All wards in Owo Local Government Area were identified as LF hotspots. The ecological variables exhibited significant correlations with the number of LF cases in the wards, except for maximum temperature. Notably, these variables varied significantly between wards with confirmed LF and those without. Therefore, it is important to prioritise strategies for mitigating LF outbreaks in urban areas of Nigeria and other LF-endemic countries.
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Affiliation(s)
- Simeon Cadmus
- Department of Veterinary Public Health and Preventive Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria.
- Damien Foundation Genomics and Mycobacteria Research and Training Centre, University of Ibadan, Ibadan, Oyo State, Nigeria.
- Centre for Control and Prevention of Zoonoses, Faculty of Veterinary Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria.
| | | | - Victor Akinseye
- Damien Foundation Genomics and Mycobacteria Research and Training Centre, University of Ibadan, Ibadan, Oyo State, Nigeria
- Department of Chemical Sciences, Augustine University, Ilara-Epe, Lagos State, Nigeria
| | - Eniola Cadmus
- Department of Community Medicine, College of Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria
| | - Gboyega Famokun
- Department of Epidemiology and Disease Control, Ondo State Ministry of Health, Ondo State, Nigeria
| | - Stephen Fagbemi
- Department of Epidemiology and Disease Control, Ondo State Ministry of Health, Ondo State, Nigeria
| | - Rashid Ansumana
- School of Community Health Sciences, Njala University, Bo, Sierra Leone
| | | | - Adekunle Ayinmode
- Centre for Control and Prevention of Zoonoses, Faculty of Veterinary Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria
- Department of Veterinary Parasitology and Entomology, University of Ibadan, Ibadan, Oyo State, Nigeria
| | - Daniel Oluwayelu
- Centre for Control and Prevention of Zoonoses, Faculty of Veterinary Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria
- Department of Veterinary Microbiology, University of Ibadan, Ibadan, Oyo State, Nigeria
| | - Solomon Odemuyiwa
- Centre for Control and Prevention of Zoonoses, Faculty of Veterinary Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria
- Department of Veterinary Pathobiology, University of Missouri, Columbia, MO, USA
| | - Oyewale Tomori
- African Centre of Excellence for Genomics of Infectious Diseases, Redeemer's University, Ede, Osun State, Nigeria
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Xu J, Jing Y, Xu X, Zhang X, Liu Y, He H, Chen F, Liu Y. Spatial scale analysis for the relationships between the built environment and cardiovascular disease based on multi-source data. Health Place 2023; 83:103048. [PMID: 37348293 DOI: 10.1016/j.healthplace.2023.103048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 05/12/2023] [Accepted: 05/19/2023] [Indexed: 06/24/2023]
Abstract
To examine what built environment characteristics improve the health outcomes of human beings is always a hot issue. While a growing literature has analyzed the link between the built environment and health, few studies have investigated this relationship across different spatial scales. In this study, eighteen variables were selected from multi-source data and reduced to eight built environment attributes using principal component analysis. These attributes included socioeconomic deprivation, urban density, street walkability, land-use diversity, blue-green space, transportation convenience, ageing, and street insecurity. Multiscale geographically weighted regression was then employed to clarify how these attributes relate to cardiovascular disease at different scales. The results indicated that: (1) multiscale geographically weighted regression showed a better fit of the association between the built environment and cardiovascular diseases than other models (e.g., ordinary least squares and geographically weighted regression), and is thus an effective approach for multiscale analysis of the built environment and health associations; (2) built environment variables related to cardiovascular diseases can be divided into global variables with large scales (e.g., socioeconomic deprivation, street walkability, land-use diversity, blue-green space, transportation convenience, and ageing) and local variables with small scales (e.g., urban density and street insecurity); and (3) at specific spatial scales, global variables had trivial spatial variation across the area, while local variables showed significant gradients. These findings provide greater insight into the association between the built environment and lifestyle-related diseases in densely populated cities, emphasizing the significance of hierarchical and place-specific policy formation in health interventions.
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Affiliation(s)
- Jiwei Xu
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, PR China
| | - Ying Jing
- Business School, Ningbo Institute of Technology, Zhejiang University, Ningbo, 315100, PR China
| | - Xinkun Xu
- Fujian Provincial Expressway Information Technology Company Limited, Fuzhou, 350000, PR China
| | - Xinyi Zhang
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, PR China
| | - Yanfang Liu
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, PR China; Key Laboratory of Geographic Information System of Ministry of Education, Wuhan University, Wuhan, 430079, PR China; Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, 430079, PR China
| | - Huagui He
- Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou, 510060, PR China
| | - Fei Chen
- Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou, 510060, PR China
| | - Yaolin Liu
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, PR China; Key Laboratory of Geographic Information System of Ministry of Education, Wuhan University, Wuhan, 430079, PR China; Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, 430079, PR China.
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Gizamba JM, Wilson JP, Mendenhall E, Ferguson L. A review of place-related contextual factors in syndemics research. Health Place 2023; 83:103084. [PMID: 37437495 DOI: 10.1016/j.healthplace.2023.103084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 06/16/2023] [Accepted: 07/04/2023] [Indexed: 07/14/2023]
Abstract
This review investigates the extent to which a place-based approach has been used to conceptualize context, as well as the place-related contextual factors explored in studies that explicitly invoked a syndemic framework. The literature search focused on 29 peer-reviewed empirical syndemic studies. Only 11 studies used a place-based approach to define and measure contextual factors and the spatial context was denoted using administrative boundaries such as census tracts, counties, and countries. A narrow range of place-related contextual factors were explored and most of them were related to social and economic factors that were used to define a place. Methodological gaps like a paucity of multilevel studies and studies using a place-based approach to measure context were identified. Future syndemics research should leverage multidimensional geospatial approaches to decipher the role of place-related contextual factors in syndemic dynamics.
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Affiliation(s)
| | - John P Wilson
- Spatial Science Institute, University of Southern California, Los Angeles, USA
| | - Emily Mendenhall
- School of Foreign Service, Georgetown University, Washington, DC, USA
| | - Laura Ferguson
- Institute on Inequalities in Global Health, Keck School of Medicine, University of Southern California, Los Angeles, USA
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10
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Gómez MJ, Barboza LA, Vásquez P, Moraga P. Bayesian spatial modeling of childhood overweight and obesity prevalence in Costa Rica. BMC Public Health 2023; 23:651. [PMID: 37016373 PMCID: PMC10074779 DOI: 10.1186/s12889-023-15486-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 03/21/2023] [Indexed: 04/06/2023] Open
Abstract
BACKGROUND Childhood overweight and obesity levels are rising and becoming a concern globally. In Costa Rica, the prevalence of these conditions has reached alarming values. Spatial analyses can identify risk factors and geographical patterns to develop tailored and effective public health actions in this context. METHODS A Bayesian spatial mixed model was built to understand the geographic patterns of childhood overweight and obesity prevalence in Costa Rica and their association with some socioeconomic factors. Data was obtained from the 2016 Weight and Size Census (6 - 12 years old children) and 2011 National Census. RESULTS Average years of schooling increase the levels of overweight and obesity until reaching an approximate value of 8 years, then they start to decrease. Moreover, for every 10-point increment in the percentage of homes with difficulties to cover their basic needs and in the percentage of population under 14 years old, there is a decrease of 7.7 and 14.0 points, respectively, in the odds of obesity. Spatial patterns show higher values of prevalence in the center area of the country, touristic destinations, head of province districts and in the borders with Panama. CONCLUSIONS Especially for childhood obesity, the average years of schooling is a non-linear factor, describing a U-inverted curve. Lower percentages of households in poverty and population under 14 years old are slightly associated with higher levels of obesity. Districts with high commercial and touristic activity present higher prevalence risk.
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Affiliation(s)
- Mario J Gómez
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
| | - Luis A Barboza
- Centro de Investigación en Matemática Pura y Aplicada-Escuela de Matemática, Universidad de Costa Rica, San José, Costa Rica
| | - Paola Vásquez
- Centro de Investigación en Matemática Pura y Aplicada, Universidad de Costa Rica, San José, Costa Rica
| | - Paula Moraga
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
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Vorpe K, Hessinger S, Poth R, Miljkovic T. Clustering regions with dynamic time warping to model obesity prevalence disparities in the United States. J Appl Stat 2023. [DOI: 10.1080/02664763.2023.2192445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
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Yao P, Fan H, Wu Q, Ouyang J, Li K. Compound impact of COVID-19, economy and climate on the spatial distribution of global agriculture and food security. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 880:163105. [PMID: 36972878 PMCID: PMC10039698 DOI: 10.1016/j.scitotenv.2023.163105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 03/18/2023] [Accepted: 03/23/2023] [Indexed: 05/23/2023]
Abstract
As the COVID-19 pandemic continues to unfold around the world, the per unit area yield of the world's three major crops (i.e. maize, rice and wheat) decreased simultaneously for the first time in 20 years, and nearly 2.37 billion people faced food insecurity in 2020. Around 119-124 million people were pushed back into extreme poverty. Drought is one of the natural hazards that mostly affect agricultural production, and 2020 is one of the three warmest years on record. When the pandemic, economic recession and extreme climate change occur simultaneously, food crisis will often be exacerbated. Due to the limited research on the geographic modelling of crops and food security at the country level, we investigated the effects of the COVID-19 pandemic (COVID-19 incidence and mortality rate), economic (GDP and per capita GDP), climate (temperature change and drought), and their compound effects on three crop yields and food security in the world. On the basis of verifying the spatial autocorrelation, we used the global ordinary least squares model to select the explanatory variables. Then, geographically weighted regression (GWR) and multi-scale GWR (MGWR), were utilised to explore spatial non-stationary relationships. Results indicated that the MGWR was more efficient than the traditional GWR. On the whole, per capita GDP was the most important explanatory variable for most countries. However, the direct threats of COVID-19, temperature change and drought on crops and food security were small and localised. This study is the first to utilise advanced spatial methods to analyse the impacts of natural and human disasters on agriculture and food security in various countries, which can serve as a geographical guide for the World Food Organization, other relief agencies and policymakers to conduct food aid, health and medical assistance, financial support, climate change policy formulation, and anti-epidemic policy formulation.
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Affiliation(s)
- Peiwen Yao
- State Key Lab for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China.
| | - Hong Fan
- State Key Lab for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China.
| | - Qilong Wu
- School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, China.
| | - Jiani Ouyang
- State Key Lab for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China.
| | - Kairui Li
- State Key Lab for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China.
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Gao SJ, Mei CL, Xu QX, Zhang Z. Non-Iterative Multiscale Estimation for Spatial Autoregressive Geographically Weighted Regression Models. ENTROPY (BASEL, SWITZERLAND) 2023; 25:320. [PMID: 36832686 PMCID: PMC9954997 DOI: 10.3390/e25020320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 02/02/2023] [Accepted: 02/06/2023] [Indexed: 06/18/2023]
Abstract
Multiscale estimation for geographically weighted regression (GWR) and the related models has attracted much attention due to their superiority. This kind of estimation method will not only improve the accuracy of the coefficient estimators but also reveal the underlying spatial scale of each explanatory variable. However, most of the existing multiscale estimation approaches are backfitting-based iterative procedures that are very time-consuming. To alleviate the computation complexity, we propose in this paper a non-iterative multiscale estimation method and its simplified scenario for spatial autoregressive geographically weighted regression (SARGWR) models, a kind of important GWR-related model that simultaneously takes into account spatial autocorrelation in the response variable and spatial heterogeneity in the regression relationship. In the proposed multiscale estimation methods, the two-stage least-squares (2SLS) based GWR and the local-linear GWR estimators of the regression coefficients with a shrunk bandwidth size are respectively taken to be the initial estimators to obtain the final multiscale estimators of the coefficients without iteration. A simulation study is conducted to assess the performance of the proposed multiscale estimation methods, and the results show that the proposed methods are much more efficient than the backfitting-based estimation procedure. In addition, the proposed methods can also yield accurate coefficient estimators and such variable-specific optimal bandwidth sizes that correctly reflect the underlying spatial scales of the explanatory variables. A real-life example is further provided to demonstrate the applicability of the proposed multiscale estimation methods.
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Affiliation(s)
- Shi-Jie Gao
- Department of Finance and Statistics, School of Science, Xi’an Polytechnic University, Xi’an 710048, China
| | - Chang-Lin Mei
- Department of Finance and Statistics, School of Science, Xi’an Polytechnic University, Xi’an 710048, China
| | - Qiu-Xia Xu
- Department of Finance and Statistics, School of Science, Xi’an Polytechnic University, Xi’an 710048, China
| | - Zhi Zhang
- Department of Statistics, School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, China
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14
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Hu G, Feng K, Sun L. Multiscale Analysis of the Relationship between Toxic Chemical Hazard Risks and Racial/Ethnic and Socioeconomic Groups in Texas, USA. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:2019-2030. [PMID: 36693189 DOI: 10.1021/acs.est.2c04302] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Although quantitative environmental (in)justice research demonstrates a disproportionate burden of toxic chemical hazard risks among racial/ethnic minorities and people in low socioeconomic positions, limited knowledge exists on how racial/ethnic and socioeconomic groups across geographic spaces experience toxic chemical hazards. This study analyzed the spatial non-stationarity in the associations between toxic chemical hazard risk and community characteristics of census block groups in Texas, USA, for 2017 using a multiscale geographically weighted regression. The results showed that the percentage of Black or Asian population has significant positive associations with toxic risk across block groups in Texas, meaning that racial minorities suffered more from toxic risk wherever they are located in the state. By contrast, the percentage of Hispanic or Latino has a positive relationship with toxic risk, and the relationship varies locally and is only significant in eastern areas of Texas. Statistical associations between toxic risk and socioeconomic variables are not stationary across the state, showing sub-state patterns of spatial variation in terms of the sign, significant level, and magnitude of the coefficient. Income has a significant negative association with toxic risk around the Dallas-Fort Worth-Arlington Metropolitan Statistical Area. Proportions of people without high school diploma and the unemployment rate both have positive relationships with toxic risk in the eastern area of Texas. Our findings highlight the importance of identifying the spatial patterns of the association between toxic chemical hazard risks and community characteristics at the census block group level for addressing environmental inequality.
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Affiliation(s)
- Guangxiao Hu
- Department of Geographical Science, University of Maryland, College Park, Maryland20742, United States
| | - Kuishuang Feng
- Department of Geographical Science, University of Maryland, College Park, Maryland20742, United States
| | - Laixiang Sun
- Department of Geographical Science, University of Maryland, College Park, Maryland20742, United States
- School of Finance & Management, SOAS University of London, LondonWC1H 0XG, U.K
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15
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Wang Y, Cao J. Examining the Effects of Socioeconomic Development on Fine Particulate Matter (PM2.5) in China's Cities Based on Spatial Autocorrelation Analysis and MGWR Model. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:2814. [PMID: 36833511 PMCID: PMC9957249 DOI: 10.3390/ijerph20042814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 02/02/2023] [Accepted: 02/02/2023] [Indexed: 06/18/2023]
Abstract
Understanding the characteristics of PM2.5 and its socioeconomic factors is crucial for managing air pollution. Research on the socioeconomic influences of PM2.5 has yielded several results. However, the spatial heterogeneity of the effect of various socioeconomic factors on PM2.5 at different scales has yet to be studied. This paper collated PM2.5 data for 359 cities in China from 2005 to 2020, as well as socioeconomic data: GDP per capita (GDPP), secondary industry proportion (SIP), number of industrial enterprise units above the scale (NOIE), general public budget revenue as a proportion of GDP (PBR), and population density (PD). The spatial autocorrelation and multiscale geographically weighted regression (MGWR) model was used to analyze the spatiotemporal heterogeneity of PM2.5 and explore the impact of different scales of economic factors. Results show that the overall economic level was developing well, with a spatial distribution trend of high in the east and low in the west. With a large positive spatial correlation and a highly concentrated clustering pattern, the PM2.5 concentration declined in 2020. Secondly, the OLS model's statistical results were skewed and unable to shed light on the association between economic factors and PM2.5. Predictions from the GWR and MGWR models may be more precise than those from the OLS model. The scales of the effect were produced by the MGWR model's variable bandwidth and regression coefficient. In particular, the MGWR model's regression coefficient and variable bandwidth allowed it to account for the scale influence of economic factors; it had the highest adjusted R2 values, smallest AICc values, and residual sums of squares. Lastly, the PBR had a clear negative impact on PM2.5, whereas the negative impact of GDPP was weak and positively correlated in some western regions, such as Gansu and Qinghai provinces. The SIP, NOIE, and PD were positively correlated with PM2.5 in most regions. Our findings can serve as a theoretical foundation for researching the associations between PM2.5 and socioeconomic variables, and for encouraging the coequal growth of the economy and the environment.
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Affiliation(s)
- Yanzhao Wang
- College of Geography and Environment, Shandong Normal University, Jinan 250014, China
- Shandong Dongying Institute of Geographic Sciences, Dongying 257000, China
| | - Jianfei Cao
- College of Geography and Environment, Shandong Normal University, Jinan 250014, China
- Shandong Dongying Institute of Geographic Sciences, Dongying 257000, China
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16
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Chen Q, Cheng J, Tu J. Analysing the global and local spatial associations of medical resources across Wuhan city using POI data. BMC Health Serv Res 2023; 23:96. [PMID: 36709274 PMCID: PMC9883876 DOI: 10.1186/s12913-023-09051-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 01/09/2023] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND There is a sharp contradiction between the supply and demand of medical resources in the provincial capitals of China. Understanding the spatial patterns of medical resources and identifying their spatial association and heterogeneity is a prerequisite to ensuring that limited resources are allocated fairly and optimally, which, along with improvements to urban residents' quality of life, is a key aim of healthy city planning. However, the existing studies on medical resources pattern mainly focus on their spatial distribution and evolution characteristics, and lack the analyses of the spatial co-location between medical resources from the global and local perspectives. It is worth noting that the research on the spatial relationship between medical resources is an important way to realize the spatial equity and operation efficiency of urban medical resources. METHODS Localized colocation quotient (LCLQ) analysis has been used successfully to measure directional spatial associations and heterogeneity between categorical point data. Using point of interest (POI) data and the LCLQ method, this paper presents the first analysis of spatial patterns and directional spatial associations between six medical resources across Wuhan city. RESULTS (1) Pharmacies, clinics and community hospitals show "multicentre + multicircle", "centre + axis + dot" and "banded" distribution characteristics, respectively, but specialized hospitals and general hospitals present "single core" and "double core" modes. (2) Overall, medical resources show agglomeration characteristics. The degrees of spatial agglomeration of the five medical resources, are ranked from high to low as follows: pharmacy, clinic, community hospital, special hospital, general hospital and 3A hospital. (3) Although pharmacies, clinics, and community hospitals of basic medical resources are interdependent, specialized hospitals, general hospitals and 3A hospitals of professional medical resources are also interdependent; furthermore, basic medical resources and professional medical resources are mutually exclusive. CONCLUSIONS Government and urban planners should pay great attention to the spatial distribution characteristics and association intensity of medical resources when formulating relevant policies. The findings of this study contribute to health equity and health policy discussions around basic medical services and professional medical services.
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Affiliation(s)
- Qiao Chen
- grid.464325.20000 0004 1791 7587School of Tourism and Hospitality Management, Hubei University of Economics, Wuhan, 430205 China
| | - Jianquan Cheng
- grid.25627.340000 0001 0790 5329Department of Natural Sciences, Manchester Metropolitan University, Manchester, M1 5GD UK ,grid.411856.f0000 0004 1800 2274Centre for Health Geographic Information, Key Laboratory of Environmental Change and Resource Use in Beibu Gulf (Ministry of Education), Nanning Normal University, 175 MingxiuDonglu Road, 530051 Nanning, PR China
| | - Jianguang Tu
- grid.49470.3e0000 0001 2331 6153School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430070 PR China ,China Aero Geophysical Survey and Remote Sensing Center for Natural Resource, Beijing, 100083 China
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17
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Xu X, Shi K, Huang Z, Shen J. What Factors Dominate the Change of PM 2.5 in the World from 2000 to 2019? A Study from Multi-Source Data. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:ijerph20032282. [PMID: 36767646 PMCID: PMC9915345 DOI: 10.3390/ijerph20032282] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 01/21/2023] [Accepted: 01/25/2023] [Indexed: 05/30/2023]
Abstract
As the threat to human life and health from fine particulate matter (PM2.5) increases globally, the life and health problems caused by environmental pollution are also of increasing concern. Understanding past trends in PM2.5 and exploring the drivers of PM2.5 are important tools for addressing the life-threatening health problems caused by PM2.5. In this study, we calculated the change in annual average global PM2.5 concentrations from 2000 to 2020 using the Theil-Sen median trend analysis method and reveal spatial and temporal trends in PM2.5 concentrations over twenty-one years. The qualitative and quantitative effects of different drivers on PM2.5 concentrations in 2020 were explored from natural and socioeconomic perspectives using a multi-scale geographically weighted regression model. The results show that there is significant spatial heterogeneity in trends in PM2.5 concentration, with significant decreases in PM2.5 concentrations mainly in developed regions, such as the United States, Canada, Japan and the European Union countries, and conversely, significant increases in PM2.5 in developing regions, such as Africa, the Middle East and India. In addition, in regions with more advanced science and technology and urban management, PM2.5 concentrations are more evenly influenced by various factors, with a more negative influence. In contrast, regions at the rapid development stage usually continue their economic development at the cost of the environment, and under a high intensity of human activity. Increased temperature is known as the most important factor for the increase in PM2.5 concentration, while an increase in NDVI can play an important role in the reduction in PM2.5 concentration. This suggests that countries can achieve good air quality goals by setting a reasonable development path.
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Affiliation(s)
- Xiankang Xu
- Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China
- Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China
| | - Kaifang Shi
- Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China
- Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China
| | - Zhongyu Huang
- Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China
- Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China
| | - Jingwei Shen
- Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China
- Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China
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18
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Tong Z, Kong Z, Jia X, Zhang H, Zhang Y. Multiscale Impact of Environmental and Socio-Economic Factors on Low Physical Fitness among Chinese Adolescents and Regionalized Coping Strategies. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph192013504. [PMID: 36294085 PMCID: PMC9602530 DOI: 10.3390/ijerph192013504] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 10/16/2022] [Accepted: 10/17/2022] [Indexed: 05/26/2023]
Abstract
As low physical fitness in adolescents increases their risk of all-cause mortality in future adulthood as well as regional public health budgets, many scholars have studied the factors influencing physical fitness in adolescents. However, the spatial non-stationarity and scale between physical fitness and influencing factors in adolescents are often neglected. To rectify this situation, this study constructed a multi-scale geographically weighted regression model based on data from the China National Student Fitness Survey and the China Statistical Yearbook in 2018 to investigate the spatial patterns of factors influencing low physical fitness among adolescents. The results showed that the influencing factors for measuring the physical fitness of Chinese adolescents had significant spatial heterogeneity and multi-scale effects. The local R2 values were relatively low in the western region of China. Consideration should be given to increasing the lifestyle and ethnic and cultural characteristics of local residents when selecting influencing factors in the future. The physical fitness of men was mainly influenced by socio-economic factors, while that of women was influenced by natural environmental factors. According to the different spatial distribution patterns of MGWR, this study suggests that each region should develop regionalized strategies to cope with the low physical fitness of adolescents, including taking advantage of the natural environment to develop physical fitness promotion projects, accelerating the upgrading of industrial structures in the north-eastern and western regions, and the need to remain cautious of rapid urbanization in the east.
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Affiliation(s)
- Zihan Tong
- Key Laboratory of Exercise and Physical Fitness, Ministry of Education, Beijing Sport University, Beijing 100084, China
| | - Zhenxing Kong
- Key Laboratory of Exercise and Physical Fitness, Ministry of Education, Beijing Sport University, Beijing 100084, China
| | - Xiao Jia
- Key Laboratory of Exercise and Physical Fitness, Ministry of Education, Beijing Sport University, Beijing 100084, China
| | - Hanyue Zhang
- Institute of Physical Education, Northeast Normal University, Changchun 130024, China
| | - Yimin Zhang
- Key Laboratory of Exercise and Physical Fitness, Ministry of Education, Beijing Sport University, Beijing 100084, China
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19
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Spatial–Temporal Variations of Water Ecosystem Services Value and Its Influencing Factors: A Case in Typical Regions of the Central Loess Plateau. SUSTAINABILITY 2022. [DOI: 10.3390/su14127169] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Water resources provide indispensable ecosystem services, which are related to human well-being and sustainable social development. Accurately measuring the water ecosystem services value (WESV), and then grasping its changing characteristics, is particularly important for solving water problems. In this study, the typical area of the central Loess Plateau location is taken as the research area. Based on remote sensing images and statistical data, the direct market method combined with the equivalent factor method was used to calculate the WESV including groundwater and surface water, which is of greatest originality. The temporal and spatial variation characteristics in 2010, 2015 and 2020 were analyzed. Then, four WESV driving factors including per capita GDP, population density, proportion of water areas, and water consumption were selected, and the geographically weighted regression (GWR) model was used to analyze the spatial distribution pattern and temporal variation of WESV’s response to the influencing factors. The results showed that WESV experienced a process of first decreasing and then increasing, which was mainly caused by Yulin. For the composition of WESV, the proportion of provisioning services value has increased, which caused the proportion of regulating services value to decrease. The correlations between four factors and WESV were different. The distribution pattern of the influences was spatially heterogeneous, which showed regular variations over time. These results indicate the necessity of WESV’s independent research and provide a realistic basis for ecological compensation in the Yellow River Basin.
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20
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GIS-based spatio-temporal analysis and modeling of COVID-19 incidence rates in Europe. Spat Spatiotemporal Epidemiol 2022; 41:100498. [PMID: 35691655 PMCID: PMC8894707 DOI: 10.1016/j.sste.2022.100498] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 12/28/2021] [Accepted: 03/02/2022] [Indexed: 11/22/2022]
Abstract
The COVID-19 epidemic has emerged as one of the most severe public health crises worldwide, especially in Europe. Until early July 2021, reported infected cases exceeded 180 million, with almost 4 million associated deaths worldwide, almost a third of which are in continental Europe. We analyzed the spatio-temporal distribution of the disease incidence and mortality rates considering specific periods in this continent. Further, we applied Global Moran's I to examine the spatio-temporal distribution patterns of COVID-19 incidence rates and Getis-Ord Gi* hotspot analysis to represent high-risk areas of the disease. Additionally, we compiled a set of 40 demographic, socioeconomic, environmental, transportation, health, and behavioral indicators as potential explanatory variables to investigate the spatial variations of COVID-19 cumulative incidence rates (CIRs). Ordinary Least Squares (OLS), Spatial Lag model (SLM), Spatial Error Model (SLM), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) regression models were implemented to examine the spatial dependence and non-stationary relationships. Based on our findings, the spatio-temporal distribution pattern of COVID-19 CIRs was highly clustered and the most high-risk clusters of the disease were situated in central and western Europe. Moreover, poverty and the elderly population were selected as the most influential variables due to their significant relationship with COVID-19 CIRs. Considering the non-stationary relationship between variables, MGWR could describe almost 69% of COVID-19 CIRs variations in Europe. Since this spatio-temporal research is conducted on a continental scale, spatial information obtained from the models could provide general insights to authorities for further targeted policies.
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21
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Gaisie E, Oppong-Yeboah NY, Cobbinah PB. Geographies of infections: built environment and COVID-19 pandemic in metropolitan Melbourne. SUSTAINABLE CITIES AND SOCIETY 2022; 81:103838. [PMID: 35291308 PMCID: PMC8915450 DOI: 10.1016/j.scs.2022.103838] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/09/2022] [Accepted: 03/10/2022] [Indexed: 05/19/2023]
Abstract
This paper uses spatial statistical techniques to reflect on geographies of COVID-19 infections in metropolitan Melbourne. We argue that the evolution of the COVID-19 pandemic, which has become widespread since early 2020 in Melbourne, typically proceeds through multiple built environment attributes - diversity, destination accessibility, distance to transit, design, and density. The spread of the contagion is institutionalised within local communities and postcodes, and reshapes movement practices, discourses, and structures of administrative politics. We demonstrate how a focus on spatial patterns of the built environment can inform scholarship on the spread of infections associated with COVID-19 pandemic and geographies of infections more broadly, by highlighting the consistency of built environment influences on COVID-19 infections across three waves of outbreaks. A focus on the built environment influence seeks to enact visions of the future as new variants emerge, illustrating the importance of understanding geographies of infections as global cities adapt to 'COVID-normal' living. We argue that understanding geographies of infections within cities could be a springboard for pursuing sustainable urban development via inclusive compact, mixed-use development and safe public transport.
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Affiliation(s)
- Eric Gaisie
- Faculty of Architecture, Building and Planning, The University of Melbourne, Parkville, VIC 3010, Australia
- College of Engineering and Science, Victoria University, Footscray VIC 3011, Australia
| | - Nana Yaw Oppong-Yeboah
- Faculty of Architecture, Building and Planning, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Patrick Brandful Cobbinah
- Faculty of Architecture, Building and Planning, The University of Melbourne, Parkville, VIC 3010, Australia
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22
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Zhang Y, Li B. Detection of the Spatio-Temporal Differentiation Patterns and Influencing Factors of Wheat Production in Huang-Huai-Hai Region. Foods 2022; 11:foods11111617. [PMID: 35681364 PMCID: PMC9180555 DOI: 10.3390/foods11111617] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 05/23/2022] [Accepted: 05/27/2022] [Indexed: 02/04/2023] Open
Abstract
The stability of wheat production is closely related to national food security and agricultural sustainable development, and it has been a major policy concern for China. By analyzing the spatiotemporal factors and causes of wheat production, we can grasp the spatiotemporal distribution law of wheat production to rationally allocate agricultural resources. To this end, this study first conducted a quantitative analysis of the yield differentiation patterns in Huang-Huai-Hai (HHH) wheat based on the 2010–2020 wheat agricultural data, comprehensively using the Theil index and exploratory spatial data analysis. Second, to eliminate the spatial heterogeneity and multicollinearity of the modeling variables, a local model of SCA-GWR combining Spearman correlation analysis (SCA) and geographically weighted regression (GWR) was established. Compared with the traditional global regression model, the superiority and applicability of the SCA-GWR model are proved, and it is a simple and effective new method to detect spatial data nonstationarity. Finally, the factors influencing wheat production in the HHH region were detected based on the SCA-GWR local model, and relevant policy recommendations were put forward. The results show that: (1) The yield difference in different farming areas gradually narrowed, and the wheat production had a significant High-High aggregation trend. The center of gravity for wheat production lies in the southwest of the HHH region. (2) Wheat production still has a strong dependence on irrigation and fertilizer. Effective irrigated areas and temperature are the main driving forces for its production. The inhibitory effect of the proportion of nonagricultural employment on wheat production gradually weakened. Radiation and rainfall were only significantly positively correlated with wheat production in the central and southern HHH region. In response to the findings of the study, corresponding policy recommendations are made in terms of optimizing the allocation of resources, increasing investment in agricultural infrastructure, and vigorously researching and developing agricultural science and technology, and the results of the study can provide a basis for decision-making and management by relevant departments.
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Affiliation(s)
| | - Bingjun Li
- Correspondence: ; Tel.: +86-371-56990030
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23
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Murad A, Faruque F, Naji A, Tiwari A, Helmi M, Dahlan A. Modelling geographical heterogeneity of diabetes prevalence and socio-economic and built environment determinants in Saudi City - Jeddah. GEOSPATIAL HEALTH 2022; 17. [PMID: 35579244 DOI: 10.4081/gh.2022.1072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 04/21/2022] [Indexed: 06/15/2023]
Abstract
Type-2 diabetes is a growing lifestyle disease mainly due to increasing physical inactivity but also associated with various other variables. In Saudi Arabia, around 58.5% of the population is deemed to be physically inactive. Against this background, this study attempts explore the spatial heterogeneity of Type-2 diabetes prevalence in Jeddah and to estimate various socio-economic and built environment variables contributing to the prevalence of this disease based on modelling by ordinary least squares (OLS), weighted regression (GWR) and multi-scale geographically weighted (MGWR). Our OLS results suggest that income, population density, commercial land use and Saudi population characteristics are statistically significant for Type-2 diabetes prevalence. However, by the GWR model, income, commercial land use and Saudi population characteristics were significantly positive while population density was significantly negative in this model for 70.6%, 9.1%, 26.6% and 58.7%, respectively, out of 109 districts investigated; by the MGWR model, the corresponding results were 100%, 22%, 100% and 100% of the districts. With the given data, the corrected Akaike information criterion (AICc), the adjusted R2, the log-likelihood and the residual sum of squares (RSS) indices demonstrated that the MGWR model outperformed the GWR and OLS models explaining 29% more variance than the OLS model, and 10.2% more than the GWR model. These results support the development of evidence-based policies for the spatial allocation of health associated resources for the control of Type-2 diabetes in Jeddah and other cities in the Arabian Gulf.
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Affiliation(s)
- Abdulkader Murad
- Department of Urban and Regional Planning, Faculty of Architecture and Planning, King Abdulaziz University, Jeddah.
| | - Fazlay Faruque
- Department of Preventive Medicine, University of Mississipi, Jackson, MS.
| | - Ammar Naji
- Department of Urban and Regional Planning, Faculty of Architecture and Planning, King Abdulaziz University, Jeddah.
| | - Alok Tiwari
- Department of Urban and Regional Planning, Faculty of Architecture and Planning, King Abdulaziz University, Jeddah.
| | - Mansour Helmi
- Department of Urban and Regional Planning, Faculty of Architecture and Planning, King Abdulaziz University, Jeddah.
| | - Ammar Dahlan
- Department of Architecture, Faculty of Architecture and Planning, King Abdulaziz University, Jeddah.
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Akinwumiju AS, Oluwafemi O, Mohammed YD, Mobolaji JW. Geospatial evaluation of COVID-19 mortality: Influence of socio-economic status and underlying health conditions in contiguous USA. APPLIED GEOGRAPHY (SEVENOAKS, ENGLAND) 2022; 141:102671. [PMID: 35261415 PMCID: PMC8890982 DOI: 10.1016/j.apgeog.2022.102671] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 02/15/2022] [Accepted: 02/21/2022] [Indexed: 05/08/2023]
Abstract
Since its outbreak, COVID-19 disease has claimed over one hundred thousand lives in the United States, resulting to multiple and complex nation-wide challenges. In this study, we employ global and local regression models to assess the influence of socio-economic and health conditions on COVID-19 mortality in contiguous USA. For a start, stepwise and exploratory regression models were employed to isolate the main explanatory variables for COVID-19 mortality from the ensemble 33 socio-economic and health parameters between January 1st and 16th of September 2020. Preliminary results showed that only five out of the examined variables (case fatality rate, vulnerable population, poverty, percentage of adults that report no leisure-time physical activity, and percentage of the population with access to places for physical activity) can explain the variability of COVID-19 mortality across the Counties of contiguous USA within the study period. Consequently, we employ three global and two local regression algorithms to model the relationship between COVID-19 and the isolated socio-economic and health variables. The outcomes of the regression analyses show that the adopted models can explain 61%-81% of COVID-19 mortality across the contiguous USA within the study period. However, MGWR yielded the highest R2 (0.81) and lowest AICc values (4031), emphasizing that it is the most efficient among the adopted regression models. The computed average adjusted R2 values show that local regression models (mean adj. R2 = 0.80) outperformed the global regression models (mean adj. R2 = 0.64), indicating that the former is ideal for modeling spatial causal relationships. The GIS-based optimized cluster analyses results show that hotspots for COVID-19 mortality as well as socioeconomic variables are mostly delineated in the South, Mid-West and Northeast of contiguous USA. COVID-19 mortality exhibited positive and significant association with black race (0.51), minority (0.48) and poverty (0.34). Whereas, the percentage of persons that attended college was negatively associated with poverty (-0.51), obesity (-0.50) and diabetes (-0.45). Results show that education is crucial to improve socio-economic and health conditions of the Americans. We conclude that investing in people's standard of living would reduce the vulnerability of an entire population.
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Affiliation(s)
- Akinola S Akinwumiju
- Department of Remote Sensing and GIS, Federal University of Technology, Akure, Ondo State, Nigeria
| | - Olawale Oluwafemi
- Spatially Integrated Social Science Program, Department of Geography and Planning, University of Toledo, Toledo, OH, USA
| | | | - Jacob W Mobolaji
- Department of Demography and Social Statistics, Obafemi Awolowo University, Ile-Ife, Osun State, Nigeria
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25
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Li C, Managi S. Impacts of air pollution on COVID-19 case fatality rate: a global analysis. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:27496-27509. [PMID: 34982383 PMCID: PMC8724597 DOI: 10.1007/s11356-021-18442-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 12/28/2021] [Indexed: 05/22/2023]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic is still rapidly spreading globally. To probe high-risk cities and the impacts of air pollution on public health, this study explores the relationship between the long-term average concentration of air pollution and the city-level case fatality rate (CFR) of COVID-19 globally. Then, geographically weighted regression (GWR) is applied to examine the spatial variability of the relationships. Six air pollution factors, including nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), PM2.5 (particles with diameter ≤2.5 μm), PM10 (particles with diameter ≤10 μm), and air quality index (AQI), are positively associated with the city-level COVID-19 CFR. Our results indicate that a 1-unit increase in NO2 (part per billion, PPB), SO2 (PPB), O3 (PPB), PM2.5 (microgram per cubic meter, μg/m3), PM10 (μg/m3), AQI (score), is related to a 1.450%, 1.005%, 0.992%, 0.860%, 0.568%, and 0.776% increase in the city-level COVID-19 CFR, respectively. Additionally, the effects of NO2, O3, PM2.5, AQI, and probability of living with poor AQI on COVID-19 spatially vary in view of the estimation of the GWR. In other words, the adverse impacts of air pollution on health are different among the cities. In summary, long-term exposure to air pollution is negatively related to the COVID-19 health outcome, and the relationship is spatially non-stationary. Our research sheds light on the impacts of slashing air pollution on public health in the COVID-19 pandemic to help governments formulate air pollution policies in light of the local situations.
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Affiliation(s)
- Chao Li
- Urban Institute & School of Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan
| | - Shunsuke Managi
- Urban Institute & School of Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan.
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26
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Liu J, Yang L, Xiao L, Tao Z. Perceived Neighborhood Environment Impacts on Health Behavior, Multi-Dimensional Health, and Life Satisfaction. Front Public Health 2022; 10:850923. [PMID: 35359780 PMCID: PMC8964045 DOI: 10.3389/fpubh.2022.850923] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 02/16/2022] [Indexed: 11/23/2022] Open
Abstract
The impacts of perceived neighborhood environment on adults' health and life satisfaction have drawn increasing academic attention. However, previous studies usually examine multi-dimensional (physical, mental, and perceived) health and life satisfaction separately, and few studies dealt with them simultaneously. Moreover, limited research revealed the mechanisms behind the effects of perceived neighborhood environment on health and life satisfaction, as well as how such effects are moderated by socio-demographics. Therefore, employing the 2016 China Family Panel Study Dataset and using structural equation modeling, this study delves into the complicated relationships among perceived neighborhood environment, health behavior, health outcomes (i.e., body mass index, self-rated health status, and depression), and life satisfaction. Notably, it considers mediation and moderation simultaneously. It finds: (1) Better perceived neighborhood environment significantly promotes physical activity and reduces sedentary behavior, smoking, and drinking; (2) Health behavior fully mediates the effects of perceived neighborhood environment on health; (3) Perceived neighborhood environment significantly affects life satisfaction both directly and indirectly (through health behavior and health outcomes); (4) Socio-demographics moderate the above relationships. This study disentangles the complicated impacts of perceived neighborhood environment on adults' multi-dimensional health and life satisfaction, thus providing policy makers and practitioners with nuanced knowledge for intervention.
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Affiliation(s)
- Jixiang Liu
- Department of Urban Planning, School of Architecture and Civil Engineering, Xiamen University, Xiamen, China
- Faculty of Architecture, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Linchuan Yang
- Department of Urban and Rural Planning, School of Architecture, Southwest Jiaotong University, Chengdu, China
| | - Longzhu Xiao
- Department of Urban Planning, School of Architecture and Civil Engineering, Xiamen University, Xiamen, China
- Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, China
| | - Zhuolin Tao
- Faculty of Geographical Science, Beijing Normal University, Beijing, China
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27
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Spatial Patterns of the Spread of COVID-19 in Singapore and the Influencing Factors. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2022. [DOI: 10.3390/ijgi11030152] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Exploring the spatial patterns of COVID-19 transmission and its key determinants could provide a deeper understanding of the evolution of the COVID-19 pandemic. The goal of this study is to investigate the spatial patterns of COVID-19 transmission in different periods in Singapore, as well as their relationship with demographic and built-environment factors. Based on reported cases from 23 January to 30 September 2020, we divided the research time into six phases and used spatial autocorrelation analysis, the ordinary least squares (OLS) model, the multiscale geographically weighted regression (MGWR) model, and dominance analysis to explore the spatial patterns and influencing factors in each phase. The results showed that the spatial patterns of COVID-19 cases differed across time, and imported cases presented a random pattern, whereas local cases presented a clustered pattern. Among the selected variables, the supermarket density, elderly population density, hotel density, business land proportion, and park density may be particular fitting indicators explaining the different phases of pandemic development in Singapore. Furthermore, the associations between determinants and COVID-19 transmission changed dynamically over time. This study provides policymakers with valuable information for developing targeted interventions for certain areas and periods.
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28
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Tran DX, Pearson D, Palmer A, Lowry J, Gray D, Dominati EJ. Quantifying spatial non-stationarity in the relationship between landscape structure and the provision of ecosystem services: An example in the New Zealand hill country. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 808:152126. [PMID: 34863745 DOI: 10.1016/j.scitotenv.2021.152126] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 11/25/2021] [Accepted: 11/28/2021] [Indexed: 06/13/2023]
Abstract
Knowing how landscape structure affects the provision of ecosystem services (ES) is an important first step toward better landscape planning. Because landscape structure is often heterogenous across space, modelling the relationship between landscape structure and the provision of ES must account for spatial non-stationarity. This paper examines the relationship between landscape structure and the provision of ES using a hill country and steep-land case farm in New Zealand. Indicators derived from land cover and topographical data such as Largest Patch Index (LPI), Contrast Class Edge (CCE), Edge Density (ED), and Terrain slope (SLOPE) were used to examine the landscape's structure and pattern. Measures of pasture productivity, soil erosion control, and water supply were derived with InVEST tools and spatial analysis in a GIS. Multiscale Geographically Weighted Regression (MGWR) was used to evaluate the relationship between indicators of landscape structure and the provisioning of ES. Other regression models, including Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR), were carried out to evaluate the performance of MGWR. Results showed that landscape patterns significantly affect the supply of all mapped ES, and this varies across the landscape, dependent on the pattern of topographical features and land cover pattern and structure. MWGR outperformed other OLS and GWR in terms of explanatory power of the ES determinants and had a better ability to deal with the presence of spatial autocorrelation. Spatially and quantitatively detailed variations of the relationship between landscape structure and the provision of ES provide a scientific basis to inform the design of sustainable multifunctional landscapes. Information derived from this analysis can be used for spatial planning of farmed landscapes to promote multiple ES which meet multiple sustainable development objectives.
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Affiliation(s)
- Duy X Tran
- School of Agriculture and Environment, College of Sciences, Massey University, Palmerston North, New Zealand.
| | - Diane Pearson
- School of Agriculture and Environment, College of Sciences, Massey University, Palmerston North, New Zealand.
| | - Alan Palmer
- School of Agriculture and Environment, College of Sciences, Massey University, Palmerston North, New Zealand.
| | - John Lowry
- School of People, Environment and Planning, College of Humanities & Social Sciences, Massey University, Palmerston North, New Zealand.
| | - David Gray
- School of Agriculture and Environment, College of Sciences, Massey University, Palmerston North, New Zealand.
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29
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Research on the Temporal and Spatial Distributions of Standing Wood Carbon Storage Based on Remote Sensing Images and Local Models. FORESTS 2022. [DOI: 10.3390/f13020346] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Background and Objectives: It is important to understand the temporal and spatial distributions of standing wood carbon storage in forests to maintain ecological balance and forest dynamics. Such information can provide technical and data support for promoting ecological construction, formulating different afforestation policies, and implementing forest management strategies. Long-term series of Landsat 5 (Thematic Mapper, TM) and Landsat 8 (Operational Land Imager, OLI) remote sensing images and digital elevation models (DEM), as well as multiphase survey data, provide new opportunities for research on the temporal and spatial distributions of standing wood carbon storage in forests. Methods: The extracted remote sensing factors, terrain factors, and forest stand factors were analyzed with stepwise regression in relation to standing wood carbon storage to identify significant influential factors, build a global ordinary least squares (OLS) model and a linear mixed model (LMM), and construct a local geographically weighted regression (GWR), multiscale geographically weighted regression model (MGWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR). Model evaluation indicators were used to calculate residual Moran’s I values, and the optimal model was selected to explore the spatiotemporal dynamics of standing wood carbon storage in the Liangshui Nature Reserve. Results: Remote sensing factors, topographic factors (Slope), and stand factors (Age and DBH) were significantly correlated with standing wood carbon storage, and the constructed global models exhibited fitting effects inferior to those of the established local models. LMM is also used as a global model to add random effects on the basis of OLS, and R2 is increased to 0.52 compared with OLS. The local models based on geographically weighted regression, namely, GWR, MGWR, TWR, and GTWR, all have good performance. Compared with OLS, the R2 is increased to 0.572, 0.589, 0.643, and 0.734, and the fitting effect of GTWR is the best. GTWR can overcome spatial autocorrelation and temporal autocorrelation problems, with a higher R2 (0.734) and a more ideal model residual than other models. This study develops a model for carbon storage (CS) considering various influential factors in the Liangshui area and provides a possible solution for the estimation of long-term carbon storage distribution.
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Malacarne D, Handakas E, Robinson O, Pineda E, Saez M, Chatzi L, Fecht D. The built environment as determinant of childhood obesity: A systematic literature review. Obes Rev 2022; 23 Suppl 1:e13385. [PMID: 34859950 DOI: 10.1111/obr.13385] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 10/11/2021] [Indexed: 01/19/2023]
Abstract
We evaluated the epidemiological evidence on the built environment and its link to childhood obesity, focusing on environmental factors such as traffic noise and air pollution, as well as physical factors potentially driving obesity-related behaviors, such as neighborhood walkability and availability and accessibility of parks and playgrounds. Eligible studies were (i) conducted on human children below the age of 18 years, (ii) focused on body size measurements in childhood, (iii) examined at least one built environment characteristic, (iv) reported effect sizes and associated confidence intervals, and (v) were published in English language. A z test, as alternative to the meta-analysis, was used to quantify associations due to heterogeneity in exposure and outcome definition. We found strong evidence for an association of traffic-related air pollution (nitrogen dioxide and nitrogen oxides exposure, p < 0.001) and built environment characteristics supportive of walking (street intersection density, p < 0.01 and access to parks, p < 0.001) with childhood obesity. We identified a lack of studies that account for interactions between different built environment exposures or verify the role and mechanism of important effect modifiers such as age.
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Affiliation(s)
- Diego Malacarne
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Evangelos Handakas
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Oliver Robinson
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Elisa Pineda
- Centre for Health Economics & Policy Innovation (CHEPI), Imperial College Business School, and School of Public Health, Imperial College London, London, UK
| | - Marc Saez
- Research Group on Statistics, Econometrics and Health (GRECS), University of Girona, Girona, Spain
- CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Leda Chatzi
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Daniela Fecht
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
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31
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Yang R, Ren F, Ma X, Zhang H, Xu W, Jia P. Explaining the longevity characteristics in China from a geographical perspective: A multi-scale geographically weighted regression analysis. GEOSPATIAL HEALTH 2021; 16. [PMID: 34763415 DOI: 10.4081/gh.2021.1024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 11/05/2021] [Indexed: 06/13/2023]
Abstract
Longevity is a near-universal human aspiration that can affect moral progress and economic development at the social level. In rapidly developing China, questions about the geographical distribution and environmental factors of longevity phenomenon need to be answered more clearly. This study calculated the longevity index (LI), longevity index for females (LIF) and longevity index for males (LIM) based on the percentage of the long-lived population among the total number of elderly people to investigate regional and gender characteristics at the county level in China. A new multi-scale geographically weighted regression (MGWR) model and four possible geographical environmental factors were applied to explore environmental effects. The results indicate that the LIs of 2838 counties ranged from 1.3% to 16.3%, and the distribution showed obvious regional and gender differences. In general, the LI was high in the East and low in the West, and the LIF was higher than the LIM in 2614 counties (92.1%). The MGWR model performed well explaining that geographical environmental factors, including topographic features, vegetation conditions, human social activity and air pollution factors have a variable influence on longevity at different spatial scales and in different regions. These findings enrich our understanding of the spatial distribution, gender differences and geographical environmental effects on longevity in China, which provides an important reference for people interested in the variations in the associations between different geographical factors.
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Affiliation(s)
- Renfei Yang
- School of Resource and Environmental Science, Wuhan University, Wuhan.
| | - Fu Ren
- School of Resource and Environmental Science, Wuhan University, Wuhan; Key Laboratory of GIS, Ministry of Education, Wuhan University, Wuhan.
| | - Xiangyuan Ma
- School of Resource and Environmental Science, Wuhan University, Wuhan.
| | - Hongwei Zhang
- Electronic Information School, Wuhan University, Wuhan.
| | - Wenxuan Xu
- School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing.
| | - Peng Jia
- School of Resource and Environmental Science, Wuhan University, Wuhan; International Institute of Spatial Lifecourse Epidemiology (ISLE), Wuhan University, Wuhan.
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32
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Spatio-Temporal Evolution and Spatial Heterogeneity of Influencing Factors of SO2 Emissions in Chinese Cities: Fresh Evidence from MGWR. SUSTAINABILITY 2021. [DOI: 10.3390/su132112059] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In this study, based on the multi-source nature and humanities data of 270 Chinese cities from 2007 to2018, the spatio-temporal evolution characteristics of SO2 emissions are revealed by using Moran’s I, a hot spot analysis, kernel density, and standard deviation ellipse models. The spatial scale heterogeneity of influencing factors is explored by using the multiscale geographically weighted regression model to make the regression results more accurate and reliable. The results show that (1) SO2 emissions showed spatial clustering characteristics during the study period, decreased by 85.12% through pollution governance, and exhibited spatial heterogeneity of differentiation. (2) The spatial distribution direction of SO2 emissions’ standard deviation ellipse in cities was “northeast–southwest”. The gravity center of the SO2 emissions shifted to the northeast, from Zhumadian City to Zhoukou City in Henan Province. The results of hot spots showed a polarization trend of “clustering hot spots in the north and dispersing cold spots in the south”. (3) The MGWR model is more accurate than the OLS and classical GWR regressions. The different spatial bandwidths have a different effect on the identification of influencing factors. There were several main influencing factors on urban SO2 emissions: the regional innovation and entrepreneurship level, government intervention, and urban precipitation; important factors: population intensity, financial development, and foreign direct investment; secondary factors: industrial structure upgrading and road construction. Based on the above conclusions, this paper explores the spatial heterogeneity of urban SO2 emissions and their influencing factors, and provides empirical evidence and reference for the precise management of SO2 emission reduction in “one city, one policy”.
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Mollalo A, Tatar M. Spatial Modeling of COVID-19 Vaccine Hesitancy in the United States. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:9488. [PMID: 34574416 PMCID: PMC8467210 DOI: 10.3390/ijerph18189488] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 09/05/2021] [Accepted: 09/07/2021] [Indexed: 12/13/2022]
Abstract
Vaccine hesitancy refers to delay in acceptance or refusal of vaccines despite the availability of vaccine services. Despite the efforts of United States healthcare providers to vaccinate the bulk of its population, vaccine hesitancy is still a severe challenge that has led to the resurgence of COVID-19 cases to over 100,000 people during early August 2021. To our knowledge, there are limited nationwide studies that examined the spatial distribution of vaccination rates, mainly based on the social vulnerability index (SVI). In this study, we compiled a database of the percentage of fully vaccinated people at the county scale across the continental United States as of 29 July 2021, along with SVI data as potential significant covariates. We further employed multiscale geographically weighted regression to model spatial nonstationarity of vaccination rates. Our findings indicated that the model could explain over 79% of the variance of vaccination rate based on Per capita income and Minority (%) (with positive impacts), and Age 17 and younger (%), Mobile homes (%), and Uninsured people (%) (with negative effects). However, the impact of each covariate varied for different counties due to using separate optimal bandwidths. This timely study can serve as a geospatial reference to support public health decision-makers in forming region-specific policies in monitoring vaccination programs from a geographic perspective.
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Affiliation(s)
- Abolfazl Mollalo
- Department of Public Health and Prevention Science, School of Health Sciences, Baldwin Wallace University, Berea, OH 44017, USA
| | - Moosa Tatar
- Matheson Center for Health Care Studies, University of Utah, Salt Lake City, UT 84108, USA;
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Lak A, Sharifi A, Badr S, Zali A, Maher A, Mostafavi E, Khalili D. Spatio-temporal patterns of the COVID-19 pandemic, and place-based influential factors at the neighborhood scale in Tehran. SUSTAINABLE CITIES AND SOCIETY 2021; 72:103034. [PMID: 36570724 PMCID: PMC9761301 DOI: 10.1016/j.scs.2021.103034] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 05/09/2021] [Accepted: 05/18/2021] [Indexed: 05/13/2023]
Abstract
Since its emergence in late 2019, the COVID-19 pandemic has attracted the attention of researchers in various fields, including urban planning and design. However, the spreading patterns of the disease in cities are still not clear. Historically, preventing and controlling pandemics in cities has always been challenging due to various factors such as higher population density, higher mobility of people, and higher contact frequency. To shed more light on the spread patterns of the pandemic, in this study we analyze 43,000 confirmed COVID-19 cases at the neighborhood level in Tehran, the capital of Iran. To examine spatio-temporal patterns and place-based factors contributing to the spread of the pandemic, we used exploratory spatial data analysis and spatial regression. We developed a geo-referenced database composed of 12 quantitative place-based variables related to physical attributes, land use and public transportation facilities, and demographic status. We also used the geographically weighted regression model for the local examination of spatial non-stationarity. According to the results, population density (R2 = 0.88) and distribution of neighborhood centers (R2 = 0.59), drugstores (R2 = 0.64), and chain stores (R2 = 0.59) are the main factors contributing to the spread of the disease. Additionally, density of public transportation facilities showed a varying degree of contribution. Overall, our findings suggest that demographic composition and major neighborhood-level physical attributes are important factors explaining high rates of infection and mortality. Results contribute to gaining a better understanding of the role of place-based attributes that may contribute to the spread of the pandemic and can inform actions aimed at achieving Sustainable Development Goals, particularly Goals 3 and 11.
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Affiliation(s)
- Azadeh Lak
- Department of Planning and Urban Design, Faculty of Architecture and Urban Planning, Shahid Beheshti University, Tehran, Iran
| | - Ayyoob Sharifi
- Hiroshima University, Graduate School of Humanities and Social Science & Network for Education and Research on Peace and Sustainability (NERPS), Japan
| | - Siamak Badr
- Faculty of Architecture and Urban Planning, Shahid Beheshti University, Tehran, Iran
| | - Alireza Zali
- Functional Neurosurgery Research Center, Shohada Tajrish Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ali Maher
- School of Management and Medical Education, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ehsan Mostafavi
- Department of Epidemiology and Biostatistics, Research Centre for Emerging and Reemerging Infectious Diseases, Pasteur Institute of Iran, Tehran, Iran
| | - Davood Khalili
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Abstract
People migrate from rural to urban areas. In the meantime, the benefits of staying in greener areas are also known. People's preferences might be different by area that is composed of several land types. If so, the effect of particular land cover on human well-being is different spatially. The spatial analysis is required to formulate effective land-use policies. Here we show that urban land, water, and grassland are positively related to human well-being, whereas bare land is negatively associated in Japan. A 1 [Formula: see text] increase in the area of urban land per capita in a city is equivalent to an about 346 USD increase in the individual annual income of all the people in the city. Additionally, monetary values of areas of water, crops, and bare land per capita are 102, - 30, and - 268 [Formula: see text]. Furthermore, the spatial context matters to the relationship between land cover and human well-being. This paper investigates the monetary values of several land types and their spatial variability, which provides insights to make better usage for land cover.
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Affiliation(s)
- Chao Li
- Urban Institute and School of Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan
| | - Shunsuke Managi
- Urban Institute and School of Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan.
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36
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Forati AM, Ghose R. Geospatial analysis of misinformation in COVID-19 related tweets. APPLIED GEOGRAPHY (SEVENOAKS, ENGLAND) 2021; 133:102473. [PMID: 34103772 PMCID: PMC8176902 DOI: 10.1016/j.apgeog.2021.102473] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Revised: 04/26/2021] [Accepted: 05/03/2021] [Indexed: 05/10/2023]
Abstract
COVID-19 has emerged as a global pandemic caused by its highly transmissible nature during the incubation period. In the absence of vaccination, containment is seen as the best strategy to stop virus diffusion. However, public awareness has been adversely affected by discourses in social media that have downplayed the severity of the virus and disseminated false information. This article investigates COVID-19 related Twitter activity in May and June 2020 to examine the origin and nature of misinformation and its relationship with the COVID-19 incidence rate at the state and county level. A geodatabase of all geotagged COVID-19 related tweets was compiled. Multiscale Geographically Weighted Regression was employed to examine the association between social media activity and the spatial variability of disease incidence. Findings suggest that MGWR could explain 80% of the COVID-19 incidence rate variations indicating a strong spatial relationship between social media activity and spread of the Covid-19 virus. Discourse analysis was conducted on tweets to index tweets downplaying the pandemic or disseminating misinformation. Findings indicate that sites of Twitter misinformation showed more resistance to pandemic management measures in May and June 2020 later experienced a rise in the number of cases in July.
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Affiliation(s)
- Amir Masoud Forati
- Department of Geography, University of Wisconsin- Milwaukee, Milwaukee, WI 5321, USA
| | - Rina Ghose
- Department of Geography, University of Wisconsin- Milwaukee, Milwaukee, WI 5321, USA
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Forati AM, Ghose R, Mantsch JR. Examining Opioid Overdose Deaths across Communities Defined by Racial Composition: a Multiscale Geographically Weighted Regression Approach. J Urban Health 2021; 98:551-562. [PMID: 34231120 PMCID: PMC8260014 DOI: 10.1007/s11524-021-00554-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/09/2021] [Indexed: 11/28/2022]
Abstract
To provide data that can guide community-targeted practices, policies, and interventions in urban metropolitan areas, we used geospatial analysis to examine the community-level opioid overdose death determinants and their spatial variation across a study area. We obtained spatial datasets containing multiple, high-quality measures of socioeconomic conditions, public health status, and demographics for analysis and visualization in geographic information systems. We employed a multiscale modeling approach (multiscale geographically weighted regression; MGWR) to provide a comprehensive and robust analysis of opioid overdose death determinants, explain how geospatial patterns vary across scales across Milwaukee County in 2019, and examine the differential influence of factors locally, regionally, and globally. We subsequently examined how associations varied with the racial/ethnic composition of communities by dividing Milwaukee County into White-majority, Black-majority, and Hispanic-majority regions according to census data and conducting separate, independent modeling processes. Overall, the multiscale model explained 83% of opioid overdose death variability across neighborhoods in Milwaukee County using 12 selected variables. Statistical analysis and geovisualization of patterns, trends, and clusters using MGWR unveiled dramatic racialized health disparities in Milwaukee, showing how factors that influenced opioid overdose deaths varied across diverse communities in Milwaukee. The observed geographic variation in relationships included the impact of naloxone availability and incarceration rates on overdose deaths with pronounced differences between White communities and communities of color. Understanding, community-level factors that contribute to overdose risk should guide targeted community-level solutions. Overall, our findings demonstrate the value of precision epidemiology using MGWR analysis for defining and guiding responses to public health challenges.
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Affiliation(s)
- Amir M Forati
- Department of Geography, University Wisconsin-Milwaukee, Milwaukee, WI, 53211, USA
| | - Rina Ghose
- Department of Geography, University Wisconsin-Milwaukee, Milwaukee, WI, 53211, USA
| | - John R Mantsch
- Department of Pharmacology and Therapeutics and Institute for Health and Equity, Medical College of Wisconsin, Milwaukee, WI, 53206, USA.
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Geddes A, Fotheringham AS, Libby G, Steele RJC. Colorectal cancer screening participation: Exploring relationship heterogeneity and scale differences using multiscale geographically weighted regression. GEOSPATIAL HEALTH 2021; 16. [PMID: 34000795 DOI: 10.4081/gh.2021.967] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 04/03/2021] [Indexed: 06/12/2023]
Abstract
Scotland has an organised colorectal cancer screening programme; however, despite proactively offering screening opportunities free to the at-risk population, and also despite using a screening test which may be completed at home, screening participation levels are unequal. Understanding causal pathways linking participation with other population characteristics may be aided by identifying how relationships between the two patterns vary across different localities, and such knowledge may also inform decisions regarding geographical targeting of screening promotion efforts. In this analysis, models calibrated using multiscale geographically weighted regression enabled the assessment of spatial variations of determinants of screening participation levels. The models were calibrated for localities across west central Scotland (n=409), where participation levels were relatively low, using aggregated individual-level screening records within a two-year window (2009-2011). Area deprivation was found to have a strong negative impact on participation levels across the study area, and ethnic population concentration had a significant impact on male participation levels on localities within Glasgow city. Estimates of local intercepts pointed to a systemic difference in screening participation between the two health board regions in the study area. Overall the results suggest that work to increase screening participation was necessary. They also suggest that barriers to participation could be addressed locally, and that differences between health board regions required further investigation.
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Affiliation(s)
- Alistair Geddes
- School of Social Sciences, University of Dundee, Dundee, Scotland.
| | - A Stewart Fotheringham
- School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ.
| | - Gillian Libby
- School of Medicine, University of Dundee, Ninewells Hospital, Dundee, Scotland.
| | - Robert J C Steele
- School of Medicine, University of Dundee, Ninewells Hospital, Dundee, Scotland.
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Maiti A, Zhang Q, Sannigrahi S, Pramanik S, Chakraborti S, Cerda A, Pilla F. Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States. SUSTAINABLE CITIES AND SOCIETY 2021; 68:102784. [PMID: 33643810 PMCID: PMC7894099 DOI: 10.1016/j.scs.2021.102784] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 02/13/2021] [Accepted: 02/15/2021] [Indexed: 05/05/2023]
Abstract
Since December 2019, the world has witnessed the stringent effect of an unprecedented global pandemic, coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of January 29,2021, there have been 100,819,363 confirmed cases and 2,176,159 deaths reported. Among the countries affected severely by COVID-19, the United States tops the list. Research has been conducted to discuss the causal associations between explanatory factors and COVID-19 transmission in the contiguous United States. However, most of these studies focus more on spatial associations of the estimated parameters, yet exploring the time-varying dimension in spatial econometric modeling appears to be utmost essential. This research adopts various relevant approaches to explore the potential effects of driving factors on COVID-19 counts in the contiguous United States. A total of three global spatial regression models and two local spatial regression models, the latter including geographically weighted regression (GWR) and multiscale GWR (MGWR), are performed at the county scale to take into account the scale effects. For COVID-19 cases, ethnicity, crime, and income factors are found to be the strongest covariates and explain most of the variance of the modeling estimation. For COVID-19 deaths, migration (domestic and international) and income factors play a critical role in explaining spatial differences of COVID-19 deaths across counties. Such associations also exhibit temporal variations from March to July, as supported by better performance of MGWR than GWR. Both global and local associations among the parameters vary highly over space and change across time. Therefore, time dimension should be paid more attention to in the spatial epidemiological analysis. Among the two local spatial regression models, MGWR performs more accurately, as it has slightly higher Adj. R2 values (for cases, R2 = 0.961; for deaths, R2 = 0.962), compared to GWR's Adj. R2 values (for cases, R2 = 0.954; for deaths, R2 = 0.954). To inform policy-makers at the nation and state levels, understanding the place-based characteristics of the explanatory forces and related spatial patterns of the driving factors is of paramount importance. Since it is not the first time humans are facing public health emergency, the findings of the present research on COVID-19 therefore can be used as a reference for policy designing and effective decision making.
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Affiliation(s)
- Arabinda Maiti
- Geography and Environment Management, Vidyasagar University, West Bengal, India
| | - Qi Zhang
- Department of Earth and Environment, Boston University, Boston, MA, 02215, USA
- Frederick S. Pardee Center for the Study of the Longer-Range Future, Frederick S. Pardee School of Global Studies, Boston University, Boston, MA, 02215, USA
| | - Srikanta Sannigrahi
- School of Architecture, Planning and Environmental Policy, University College Dublin Richview, Clonskeagh, Dublin, D14 E099, Ireland
| | - Suvamoy Pramanik
- Center for the Study of Regional Development, Jawaharlal Nehru University, New Delhi, Delhi, 110067, India
| | - Suman Chakraborti
- Center for the Study of Regional Development, Jawaharlal Nehru University, New Delhi, Delhi, 110067, India
| | - Artemi Cerda
- Soil Erosion and Degradation Research Group, Department of Geography, Valencia University, Blasco Ibàñez, 28, 46010, Valencia, Spain
| | - Francesco Pilla
- School of Architecture, Planning and Environmental Policy, University College Dublin Richview, Clonskeagh, Dublin, D14 E099, Ireland
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Lee WD, Qian M, Schwanen T. The association between socioeconomic status and mobility reductions in the early stage of England's COVID-19 epidemic. Health Place 2021; 69:102563. [PMID: 33799134 PMCID: PMC9673007 DOI: 10.1016/j.healthplace.2021.102563] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 03/05/2021] [Accepted: 03/08/2021] [Indexed: 02/04/2023]
Abstract
This study uses mobile phone data to examine how socioeconomic status was associated with the extent of mobility reduction during the spring 2020 lockdown in England in a manner that considers both potentially confounding effects and spatial dependency and heterogeneity. It shows that socioeconomic status as approximated through income and occupation was strongly correlated with the extent of mobility reduction. It also demonstrates that the specific nature of the association of socioeconomic status with mobility reduction varied markedly across England. Finally, the analysis suggests that the spatial differentiation in the ability to restrict everyday mobility in response to a national lockdown is an important topic for future research.
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Affiliation(s)
- Won Do Lee
- Transport Studies Unit, School of Geography and the Environment, University of Oxford, UK.
| | | | - Tim Schwanen
- Transport Studies Unit, School of Geography and the Environment, University of Oxford, UK
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Raymundo CE, Oliveira MC, Eleuterio TDA, André SR, da Silva MG, Queiroz ERDS, Medronho RDA. Spatial analysis of COVID-19 incidence and the sociodemographic context in Brazil. PLoS One 2021; 16:e0247794. [PMID: 33647044 PMCID: PMC7920392 DOI: 10.1371/journal.pone.0247794] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 02/12/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Identified in December 2019 in the city of Wuhan, China, the outbreak of COVID-19 spread throughout the world and its impacts affect different populations differently, where countries with high levels of social and economic inequality such as Brazil gain prominence, for understanding of the vulnerability factors associated with the disease. Given this scenario, in the absence of a vaccine or safe and effective antiviral treatment for COVID-19, nonpharmacological measures are essential for prevention and control of the disease. However, many of these measures are not feasible for millions of individuals who live in territories with increased social vulnerability. The study aims to analyze the spatial distribution of COVID-19 incidence in Brazil's municipalities (counties) and investigate its association with sociodemographic determinants to better understand the social context and the epidemic's spread in the country. METHODS This is an analytical ecological study using data from various sources. The study period was February 25 to September 26, 2020. Data analysis used global regression models: ordinary least squares (OLS), spatial autoregressive model (SAR), and conditional autoregressive model (CAR) and the local regression model called multiscale geographically weighted regression (MGWR). FINDINGS The higher the GINI index, the higher the incidence of the disease at the municipal level. Likewise, the higher the nurse ratio per 1,000 inhabitants in the municipalities, the higher the COVID-19 incidence. Meanwhile, the proportional mortality ratio was inversely associated with incidence of the disease. DISCUSSION Social inequality increased the risk of COVID-19 in the municipalities. Better social development of the municipalities was associated with lower risk of the disease. Greater access to health services improved the diagnosis and notification of the disease and was associated with more cases in the municipalities. Despite universal susceptibility to COVID-19, populations with increased social vulnerability were more exposed to risk of the illness.
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Affiliation(s)
- Carlos Eduardo Raymundo
- Instituto de Estudos em Saúde Coletiva, Universidade Federal do Rio de Janeiro, Rio de Janeiro, State of Rio de Janeiro, Brazil
| | - Marcella Cini Oliveira
- Faculdade de Medicina, Universidade Federal do Rio de Janeiro, Rio de Janeiro, State of Rio de Janeiro, Brazil
| | - Tatiana de Araujo Eleuterio
- Instituto de Estudos em Saúde Coletiva, Universidade Federal do Rio de Janeiro, Rio de Janeiro, State of Rio de Janeiro, Brazil
- Departamento de Enfermagem em Saúde Pública, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, State of Rio de Janeiro, Brazil
| | - Suzana Rosa André
- Escola de Enfermagem Anna Nery, Universidade Federal do Rio de Janeiro, Rio de Janeiro, State of Rio de Janeiro, Brazil
| | - Marcele Gonçalves da Silva
- Faculdade de Medicina, Universidade Federal do Rio de Janeiro, Rio de Janeiro, State of Rio de Janeiro, Brazil
| | - Eny Regina da Silva Queiroz
- Instituto de Estudos em Saúde Coletiva, Universidade Federal do Rio de Janeiro, Rio de Janeiro, State of Rio de Janeiro, Brazil
| | - Roberto de Andrade Medronho
- Instituto de Estudos em Saúde Coletiva, Universidade Federal do Rio de Janeiro, Rio de Janeiro, State of Rio de Janeiro, Brazil
- Faculdade de Medicina, Universidade Federal do Rio de Janeiro, Rio de Janeiro, State of Rio de Janeiro, Brazil
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Shen SL, Cui JL, Wu XQ. A simple test for spatial heteroscedasticity in spatially varying coefficient models. J STAT COMPUT SIM 2020. [DOI: 10.1080/00949655.2020.1862112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Si-Lian Shen
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, People's Republic of China
| | - Jian-Ling Cui
- State Key Laboratory of Complex Electromagnetic Environment Effect on Electronic and Information System, Luoyang, People's Republic of China
| | - Xin-Qian Wu
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, People's Republic of China
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Feuillet T, Valette JF, Charreire H, Kesse-Guyot E, Julia C, Vernez-Moudon A, Hercberg S, Touvier M, Oppert JM. Influence of the urban context on the relationship between neighbourhood deprivation and obesity. Soc Sci Med 2020; 265:113537. [PMID: 33250318 DOI: 10.1016/j.socscimed.2020.113537] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 09/22/2020] [Accepted: 11/16/2020] [Indexed: 10/22/2022]
Abstract
BACKGROUND In middle- and high-income countries, obesity is positively associated with neighbourhood deprivation. However, the moderating effect of the broader urban residential context on this relationship remains poorly understood. METHODS In this study, we have examined the nonlinear and geographically varying relationship between neighbourhood deprivation and the likelihood of being a person with overweight among participants of the French NutriNet-Santé adult cohort study (n = 68,698), adjusted for age, gender and educational level. Ten urban residential contexts (e.g., suburbs, peri-urban or rural areas) were defined. We used a multilevel generalised additive modelling framework for analyses. RESULTS We found that the relationship between neighbourhood deprivation and overweight differed according to urban context, in terms of both linearity and intensity. Overall, the deprivation-overweight relationship was strongly positive (with a higher prevalence of overweight in deprived neighbourhoods) in suburban areas of Paris and of other large French cities, while weak or null in small towns and rural areas, and intermediate in inner cities. In addition, we observed in suburbs of Paris and in peri-urban belts of large cities that beyond a certain level of neighbourhood deprivation, the relationship with overweight plateaued. DISCUSSION In a French population from a high-income country, suburbs, as well as moderately deprived neighbourhoods of peri-urban areas of large cities, are potential targets for public health and urban planning policies aiming at preventing obesity. Our results emphasize the value of local analyses to better capture the complexity and contextual variations of socioeconomic determinants of non-communicable diseases such as obesity.
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Affiliation(s)
- T Feuillet
- University Paris 8, LADYSS, UMR 7533 CNRS, Saint-Denis, France; Sorbonne Paris Nord University, Inserm U1153, Inrae U1125, Cnam, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center - University of Paris (CRESS), Bobigny, France.
| | - J F Valette
- University Paris 8, LADYSS, UMR 7533 CNRS, Saint-Denis, France
| | - H Charreire
- Sorbonne Paris Nord University, Inserm U1153, Inrae U1125, Cnam, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center - University of Paris (CRESS), Bobigny, France; University Paris Est, Lab Urba, Créteil, France
| | - E Kesse-Guyot
- Sorbonne Paris Nord University, Inserm U1153, Inrae U1125, Cnam, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center - University of Paris (CRESS), Bobigny, France
| | - C Julia
- Sorbonne Paris Nord University, Inserm U1153, Inrae U1125, Cnam, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center - University of Paris (CRESS), Bobigny, France; Public Health Department, Avicenne Hospital (AP-HP), Bobigny, France
| | - A Vernez-Moudon
- Architecture, Landscape Architecture, and Urban Design and Planning, University of Washington, 1107 NE 45th St, Suite 535, Box 354802, Seattle, WA, 98195, USA
| | - S Hercberg
- Sorbonne Paris Nord University, Inserm U1153, Inrae U1125, Cnam, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center - University of Paris (CRESS), Bobigny, France; Public Health Department, Avicenne Hospital (AP-HP), Bobigny, France
| | - M Touvier
- Sorbonne Paris Nord University, Inserm U1153, Inrae U1125, Cnam, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center - University of Paris (CRESS), Bobigny, France
| | - J M Oppert
- Sorbonne Paris Nord University, Inserm U1153, Inrae U1125, Cnam, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center - University of Paris (CRESS), Bobigny, France; Sorbonne University, Department of Nutrition, Pitié-Salpêtrière Hospital (AP-HP), Institute of Cardiometabolism and Nutrition (ICAN), Paris, France
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Mollalo A, Vahedi B, Rivera KM. GIS-based spatial modeling of COVID-19 incidence rate in the continental United States. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020. [PMID: 30763849 DOI: 10.1016/j.scitotenv] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
During the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been reported, posing unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of disease incidence across the continental United States. We compiled a geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could explain the spatial variability of disease incidence. Further, we employed spatial lag and spatial error models to investigate spatial dependence and geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity. The results suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model, these models still represent a significantly poor performance compared to the local models. Moreover, MGWR could explain the highest variations (adj. R2: 68.1%) with the lowest AICc compared to the others. Mapping the effects of significant explanatory variables (i.e., income inequality, median household income, the proportion of black females, and the proportion of nurse practitioners) on spatial variability of COVID-19 incidence rates using MGWR could provide useful insights to policymakers for targeted interventions.
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Affiliation(s)
- Abolfazl Mollalo
- Department of Public Health and Prevention Sciences, School of Health Sciences, Baldwin Wallace University, Berea, OH, USA.
| | - Behzad Vahedi
- Department of Geography, University of California Santa Barbara (UCSB), Santa Barbara, CA, USA.
| | - Kiara M Rivera
- Department of Public Health and Prevention Sciences, School of Health Sciences, Baldwin Wallace University, Berea, OH, USA.
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Mollalo A, Vahedi B, Rivera KM. GIS-based spatial modeling of COVID-19 incidence rate in the continental United States. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 728:138884. [PMID: 32335404 PMCID: PMC7175907 DOI: 10.1016/j.scitotenv.2020.138884] [Citation(s) in RCA: 304] [Impact Index Per Article: 76.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Revised: 04/19/2020] [Accepted: 04/20/2020] [Indexed: 04/14/2023]
Abstract
During the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been reported, posing unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of disease incidence across the continental United States. We compiled a geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could explain the spatial variability of disease incidence. Further, we employed spatial lag and spatial error models to investigate spatial dependence and geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity. The results suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model, these models still represent a significantly poor performance compared to the local models. Moreover, MGWR could explain the highest variations (adj. R2: 68.1%) with the lowest AICc compared to the others. Mapping the effects of significant explanatory variables (i.e., income inequality, median household income, the proportion of black females, and the proportion of nurse practitioners) on spatial variability of COVID-19 incidence rates using MGWR could provide useful insights to policymakers for targeted interventions.
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Affiliation(s)
- Abolfazl Mollalo
- Department of Public Health and Prevention Sciences, School of Health Sciences, Baldwin Wallace University, Berea, OH, USA.
| | - Behzad Vahedi
- Department of Geography, University of California Santa Barbara (UCSB), Santa Barbara, CA, USA.
| | - Kiara M Rivera
- Department of Public Health and Prevention Sciences, School of Health Sciences, Baldwin Wallace University, Berea, OH, USA.
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Iyanda AE, Adeleke R, Lu Y, Osayomi T, Adaralegbe A, Lasode M, Chima-Adaralegbe NJ, Osundina AM. A retrospective cross-national examination of COVID-19 outbreak in 175 countries: a multiscale geographically weighted regression analysis (January 11-June 28, 2020). J Infect Public Health 2020; 13:1438-1445. [PMID: 32773211 PMCID: PMC7375316 DOI: 10.1016/j.jiph.2020.07.006] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 07/02/2020] [Accepted: 07/13/2020] [Indexed: 11/24/2022] Open
Abstract
Objective This study retrospectively examined the health and social determinants of the COVID-19 outbreak in 175 countries from a spatial epidemiological approach. Methods We used spatial analysis to examine the cross-national determinants of confirmed cases of COVID-19 based on the World Health Organization official COVID-19 data and the World Bank Indicators of Interest to the COVID-19 outbreak. All models controlled for COVID-19 government measures. Results The percentage of the population age between 15-64 years (Age15-64), percentage smokers (SmokTot.), and out-of-pocket expenditure (OOPExp) significantly explained global variation in the current COVID-19 outbreak in 175 countries. The percentage population age group 15-64 and out of pocket expenditure were positively associated with COVID-19. Conversely, the percentage of the total population who smoke was inversely associated with COVID-19 at the global level. Conclusions This study is timely and could serve as a potential geospatial guide to developing public health and epidemiological surveillance programs for the outbreak in multiple countries. Removal of catastrophic medical expenditure, smoking cessation, and observing public health guidelines will not only reduce illness related to COVID-19 but also prevent unecessary deaths.
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Affiliation(s)
| | - Richard Adeleke
- Department of Geography, University of Ibadan, Ibadan, Nigeria
| | - Yongmei Lu
- Department of Geography, Texas State University, San Marco, TX, United States
| | | | - Adeleye Adaralegbe
- Department of Rehabilitation and Health Services, University of North Texas, Denton, TX, United States
| | - Mayowa Lasode
- Department of Geography, Texas State University, San Marco, TX, United States
| | - Ngozi J Chima-Adaralegbe
- Department of Rehabilitation and Health Services, University of North Texas, Denton, TX, United States
| | - Adedoyin M Osundina
- Department of Internal Medicine, University College Hospital, University of Ibadan, Ibadan, Nigeria
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