Spatiotemporal Characteristics and Risk Factors of the COVID-19 Pandemic in New York State: Implication of Future Policies
The Coronavirus disease 2019 (COVID-19) has been spreading in New York State since March 2020, posing health and socioeconomic threats to many areas. Statistics of daily confirmed cases and deaths in New York State have been growing and declining amid changing policies and environmental factors. Bas...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/10/9/627 |
| _version_ | 1797519002055999488 |
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| author | Anran Zheng Tao Wang Xiaojuan Li |
| author_facet | Anran Zheng Tao Wang Xiaojuan Li |
| author_sort | Anran Zheng |
| collection | DOAJ |
| description | The Coronavirus disease 2019 (COVID-19) has been spreading in New York State since March 2020, posing health and socioeconomic threats to many areas. Statistics of daily confirmed cases and deaths in New York State have been growing and declining amid changing policies and environmental factors. Based on the county-level COVID-19 cases and environmental factors in the state from March to December 2020, this study investigates spatiotemporal clustering patterns using spatial autocorrelation and space-time scan analysis. Environmental factors influencing the COVID-19 spread were analyzed based on the Geodetector model. Infection clusters first appeared in southern New York State and then moved to the central western parts as the epidemic developed. The statistical results of space-time scan analysis are consistent with those of spatial autocorrelation analysis. The analysis results of Geodetector showed that both temperature and population density were strong indications of the monthly incidence of COVID-19, especially in March and April 2020. There is a trend of increasing interactions between various risk factors. This study explores the spatiotemporal pattern of COVID-19 in New York State over ten months and explains the relationship between the disease transmission and influencing factors. |
| first_indexed | 2024-03-10T07:37:11Z |
| format | Article |
| id | doaj.art-d79a66bc96e7470e8843273a0cf52e69 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T07:37:11Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-d79a66bc96e7470e8843273a0cf52e692023-11-22T13:25:24ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-09-0110962710.3390/ijgi10090627Spatiotemporal Characteristics and Risk Factors of the COVID-19 Pandemic in New York State: Implication of Future PoliciesAnran Zheng0Tao Wang1Xiaojuan Li2College of Geospatial Information Science and Technology, Capital Normal University, North Road 105, Haidian District, Beijing 100048, ChinaCollege of Geospatial Information Science and Technology, Capital Normal University, North Road 105, Haidian District, Beijing 100048, ChinaCollege of Geospatial Information Science and Technology, Capital Normal University, North Road 105, Haidian District, Beijing 100048, ChinaThe Coronavirus disease 2019 (COVID-19) has been spreading in New York State since March 2020, posing health and socioeconomic threats to many areas. Statistics of daily confirmed cases and deaths in New York State have been growing and declining amid changing policies and environmental factors. Based on the county-level COVID-19 cases and environmental factors in the state from March to December 2020, this study investigates spatiotemporal clustering patterns using spatial autocorrelation and space-time scan analysis. Environmental factors influencing the COVID-19 spread were analyzed based on the Geodetector model. Infection clusters first appeared in southern New York State and then moved to the central western parts as the epidemic developed. The statistical results of space-time scan analysis are consistent with those of spatial autocorrelation analysis. The analysis results of Geodetector showed that both temperature and population density were strong indications of the monthly incidence of COVID-19, especially in March and April 2020. There is a trend of increasing interactions between various risk factors. This study explores the spatiotemporal pattern of COVID-19 in New York State over ten months and explains the relationship between the disease transmission and influencing factors.https://www.mdpi.com/2220-9964/10/9/627COVID-19GeodetectorNew York Statespatial autocorrelationspace-time scan statistics |
| spellingShingle | Anran Zheng Tao Wang Xiaojuan Li Spatiotemporal Characteristics and Risk Factors of the COVID-19 Pandemic in New York State: Implication of Future Policies ISPRS International Journal of Geo-Information COVID-19 Geodetector New York State spatial autocorrelation space-time scan statistics |
| title | Spatiotemporal Characteristics and Risk Factors of the COVID-19 Pandemic in New York State: Implication of Future Policies |
| title_full | Spatiotemporal Characteristics and Risk Factors of the COVID-19 Pandemic in New York State: Implication of Future Policies |
| title_fullStr | Spatiotemporal Characteristics and Risk Factors of the COVID-19 Pandemic in New York State: Implication of Future Policies |
| title_full_unstemmed | Spatiotemporal Characteristics and Risk Factors of the COVID-19 Pandemic in New York State: Implication of Future Policies |
| title_short | Spatiotemporal Characteristics and Risk Factors of the COVID-19 Pandemic in New York State: Implication of Future Policies |
| title_sort | spatiotemporal characteristics and risk factors of the covid 19 pandemic in new york state implication of future policies |
| topic | COVID-19 Geodetector New York State spatial autocorrelation space-time scan statistics |
| url | https://www.mdpi.com/2220-9964/10/9/627 |
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