Exploring income and racial inequality in preparedness for Hurricane Ida (2021): insights from digital footprint data
Preparedness for adverse events is critical to building urban resilience to climate-related risks. While most extant studies investigate preparedness patterns based on survey data, this study explores the potential of big digital footprint data (i.e. population visits to points of interest (POI)) to...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2023-01-01
|
| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/ad08fa |
| _version_ | 1797629542876053504 |
|---|---|
| author | Qingchun Li Anu Ramaswami Ning Lin |
| author_facet | Qingchun Li Anu Ramaswami Ning Lin |
| author_sort | Qingchun Li |
| collection | DOAJ |
| description | Preparedness for adverse events is critical to building urban resilience to climate-related risks. While most extant studies investigate preparedness patterns based on survey data, this study explores the potential of big digital footprint data (i.e. population visits to points of interest (POI)) to investigate preparedness patterns in the real case of Hurricane Ida (2021). We further investigate income and racial inequality in preparedness by combining the digital footprint data with demographic and socioeconomic data. A clear pattern of preparedness was seen in Louisiana with aggregated visits to grocery stores, gasoline stations, and construction supply dealers increasing by nearly 9%, 12%, and 10% respectively, representing three types of preparedness: survival, mobility planning, and hazard mitigation. Preparedness for Hurricane Ida was not seen in New York and New Jersey states. Inequality analyses for Louisiana across census block groups (CBGs) demonstrate that CBGs with higher income have more (nearly 8% greater) preparedness in visiting gasoline stations, while CBGs with a larger percentage of the white population have more preparedness in visiting grocery stores (nearly 12% more) in the lowest income groups. The results indicate that income and racial inequality differ across different preparedness in terms of visiting different POIs. |
| first_indexed | 2024-03-11T10:55:53Z |
| format | Article |
| id | doaj.art-12a9e58b29cb4d8e8d70231ce7562248 |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-11T10:55:53Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-12a9e58b29cb4d8e8d70231ce75622482023-11-13T09:09:32ZengIOP PublishingEnvironmental Research Letters1748-93262023-01-01181212402110.1088/1748-9326/ad08faExploring income and racial inequality in preparedness for Hurricane Ida (2021): insights from digital footprint dataQingchun Li0Anu Ramaswami1https://orcid.org/0000-0002-0476-2315Ning Lin2https://orcid.org/0000-0002-5571-1606Urban Nexus Lab, Department of Civil & Environmental Engineering, Princeton University , Princeton, NJ, United States of AmericaUrban Nexus Lab, Department of Civil & Environmental Engineering, Princeton University , Princeton, NJ, United States of AmericaDepartment of Civil & Environmental Engineering, Princeton University , Princeton, NJ, United States of AmericaPreparedness for adverse events is critical to building urban resilience to climate-related risks. While most extant studies investigate preparedness patterns based on survey data, this study explores the potential of big digital footprint data (i.e. population visits to points of interest (POI)) to investigate preparedness patterns in the real case of Hurricane Ida (2021). We further investigate income and racial inequality in preparedness by combining the digital footprint data with demographic and socioeconomic data. A clear pattern of preparedness was seen in Louisiana with aggregated visits to grocery stores, gasoline stations, and construction supply dealers increasing by nearly 9%, 12%, and 10% respectively, representing three types of preparedness: survival, mobility planning, and hazard mitigation. Preparedness for Hurricane Ida was not seen in New York and New Jersey states. Inequality analyses for Louisiana across census block groups (CBGs) demonstrate that CBGs with higher income have more (nearly 8% greater) preparedness in visiting gasoline stations, while CBGs with a larger percentage of the white population have more preparedness in visiting grocery stores (nearly 12% more) in the lowest income groups. The results indicate that income and racial inequality differ across different preparedness in terms of visiting different POIs.https://doi.org/10.1088/1748-9326/ad08fahurricane preparednessdigital footprint dataHurricane Idaurban resilienceincome and racial inequality |
| spellingShingle | Qingchun Li Anu Ramaswami Ning Lin Exploring income and racial inequality in preparedness for Hurricane Ida (2021): insights from digital footprint data Environmental Research Letters hurricane preparedness digital footprint data Hurricane Ida urban resilience income and racial inequality |
| title | Exploring income and racial inequality in preparedness for Hurricane Ida (2021): insights from digital footprint data |
| title_full | Exploring income and racial inequality in preparedness for Hurricane Ida (2021): insights from digital footprint data |
| title_fullStr | Exploring income and racial inequality in preparedness for Hurricane Ida (2021): insights from digital footprint data |
| title_full_unstemmed | Exploring income and racial inequality in preparedness for Hurricane Ida (2021): insights from digital footprint data |
| title_short | Exploring income and racial inequality in preparedness for Hurricane Ida (2021): insights from digital footprint data |
| title_sort | exploring income and racial inequality in preparedness for hurricane ida 2021 insights from digital footprint data |
| topic | hurricane preparedness digital footprint data Hurricane Ida urban resilience income and racial inequality |
| url | https://doi.org/10.1088/1748-9326/ad08fa |
| work_keys_str_mv | AT qingchunli exploringincomeandracialinequalityinpreparednessforhurricaneida2021insightsfromdigitalfootprintdata AT anuramaswami exploringincomeandracialinequalityinpreparednessforhurricaneida2021insightsfromdigitalfootprintdata AT ninglin exploringincomeandracialinequalityinpreparednessforhurricaneida2021insightsfromdigitalfootprintdata |