6 datasets found
  1. Z

    First Street Community Risk Data V1.3

    • data.niaid.nih.gov
    • zenodo.org
    Updated Jun 17, 2024
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    First Street Foundation (2024). First Street Community Risk Data V1.3 [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_5711171
    Explore at:
    Dataset updated
    Jun 17, 2024
    Dataset authored and provided by
    First Street Foundation
    Description

    These datasets provide aggregated community risk scores for exposure to flooding using the First Street Foundation Flood Model (Version 1.3) at the county and zip code level. county_flood_score and zcta_flood_score provide the overall community risk score. county_flood_category_score and zcta_flood_category_score provide the risk score to specific categories of infrastructure. Each category; critical infrastructure, social infrastructure, residential properties, roads, and commercial properties, is a component of the overall community risk.

    If you are interested in acquiring First Street flood data, you can request to access the data here. More information on First Street's flood risk statistics can be found here and information on First Street's hazards can be found here.

    The following fields are in the overall risk datasets:

    Attribute

    Description

    county_id

    The county FIPS code

    count

    The count (#) of infrastructure facilities

    flood_score

    A score of 1, 2, 3, 4, or 5 is shown. Community risk rankings represent risk as Minimal, Minor (1), Moderate (2), Major (3), Severe (4) and Extreme (5). Minimal risk is a case where no facilities within a category have flood risk. County level risks are ranked based on how their total depths compare to counties across the country.

    The following fields are in the category risk datasets:

    Attribute

    Description

    FIPS

    County FIPS code

    ZIP_CODE

    ZIP code

    count

    The approximate length of roads (miles) within the geography of aggregation (i.e. ZIP Code, County)

    flood_score

    A score (Community Risk level) of 0, 1, 2, 3, 4, or 5 is shown. Community risk levels represent risk as Minimal (0), Minor (1), Moderate (2), Major (3), Severe (4) and Extreme (5). Minimal risk is a case where no facilities within a category have flood risk. ZIP Code and County level risks are assessed based on how their total depths compare to ZIP Codes and Counties across the country.

    risk_direction

    A score of 1, -1, or 0 is shown. These note if flood risk is expected to increase (1), decrease (-1), or remain constant (0) over the next 30 years.

    infrastructure_category_id

    1= critical infrastructure, 4 = social infrastructure , 6 = residential properties, 8 - roads, 9 = commercial properties

  2. Most dangerous cities in South Africa 2024

    • statista.com
    Updated Jun 23, 2025
    + more versions
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    Statista (2025). Most dangerous cities in South Africa 2024 [Dataset]. https://www.statista.com/statistics/1399565/cities-with-the-highest-crime-index-in-south-africa/
    Explore at:
    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2024
    Area covered
    South Africa
    Description

    In 2024, Pietermaritzburg in South Africa ranked first in the crime index among African cities, scoring **** index points. The six most dangerous areas on the continent were South African cities. Furthermore, Pretoria and Johannesburg followed, with a score of **** and **** points, respectively. The index estimates the overall level of crime in a specific territory. According to the score, crime levels are classified as very high (over 80), high (60-80), moderate (40-60), low (20-40), and very low (below 20). Contact crimes are common in South Africa Contact crimes in South Africa include violent crimes such as murder, attempted murder, and sexual offenses, as well as common assault and robbery. In fiscal year 2022/2023, the suburb of Johannesburg Central in the Gauteng province of South Africa had the highest number of contact crime incidents. Common assault was the main contributing type of offense to the overall number of contact crimes. Household robberies peak in certain months In South Africa, June, July, and December experienced the highest number of household robberies in 2023. June and July are the months that provide the most hours of darkness, thus allowing criminals more time to break in and enter homes without being detected easily. In December, most South Africans decide to go away on holiday, leaving their homes at risk for a potential break-in. On the other hand, only around ** percent of households affected by robbery reported it to the police in the fiscal year 2022/2023.

  3. f

    Descriptive statistics (mean & (sd)) for in situ neighbourhood judgements of...

    • plos.figshare.com
    xls
    Updated May 31, 2023
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    Rhiannon Corcoran; Rosie Mansfield; Christophe de Bezenac; Ellen Anderson; Katie Overbury; Graham Marshall (2023). Descriptive statistics (mean & (sd)) for in situ neighbourhood judgements of threat, trust, wealth and sentiment analysis scores. [Dataset]. http://doi.org/10.1371/journal.pone.0202412.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Rhiannon Corcoran; Rosie Mansfield; Christophe de Bezenac; Ellen Anderson; Katie Overbury; Graham Marshall
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Descriptive statistics (mean & (sd)) for in situ neighbourhood judgements of threat, trust, wealth and sentiment analysis scores.

  4. f

    Variance of 10-year and 30-year CVD risk scores explained by neighborhood...

    • figshare.com
    xls
    Updated Jun 9, 2023
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    Todd R. Sponholtz; Ramachandran S. Vasan (2023). Variance of 10-year and 30-year CVD risk scores explained by neighborhood membership, offspring cohort examination 7 (1998–2001)/generation 3 examination 1 (2002–2005). [Dataset]. http://doi.org/10.1371/journal.pone.0201712.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 9, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Todd R. Sponholtz; Ramachandran S. Vasan
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Variance of 10-year and 30-year CVD risk scores explained by neighborhood membership, offspring cohort examination 7 (1998–2001)/generation 3 examination 1 (2002–2005).

  5. f

    Characteristics of framingham offspring and generation 3 participants at...

    • plos.figshare.com
    xls
    Updated May 31, 2023
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    Todd R. Sponholtz; Ramachandran S. Vasan (2023). Characteristics of framingham offspring and generation 3 participants at offspring cohort examination 7 (1998–2001)/generation 3 examination 1 (2002–2005). [Dataset]. http://doi.org/10.1371/journal.pone.0201712.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Todd R. Sponholtz; Ramachandran S. Vasan
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Framingham
    Description

    Characteristics of framingham offspring and generation 3 participants at offspring cohort examination 7 (1998–2001)/generation 3 examination 1 (2002–2005).

  6. f

    Associations between types of groups and risk assessment areas.

    • plos.figshare.com
    • figshare.com
    xls
    Updated Jun 4, 2023
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    Robert Kaba Alhassan; Edward Nketiah-Amponsah; Nicole Spieker; Daniel Kojo Arhinful; Alice Ogink; Paul van Ostenberg; Tobias F. Rinke de Wit (2023). Associations between types of groups and risk assessment areas. [Dataset]. http://doi.org/10.1371/journal.pone.0142389.t004
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Robert Kaba Alhassan; Edward Nketiah-Amponsah; Nicole Spieker; Daniel Kojo Arhinful; Alice Ogink; Paul van Ostenberg; Tobias F. Rinke de Wit
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Source: WOTRO-COHEiSION Ghana Project (2014)Note: Spearman rank correlation test *p

  7. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

Share
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TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
First Street Foundation (2024). First Street Community Risk Data V1.3 [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_5711171

First Street Community Risk Data V1.3

Explore at:
Dataset updated
Jun 17, 2024
Dataset authored and provided by
First Street Foundation
Description

These datasets provide aggregated community risk scores for exposure to flooding using the First Street Foundation Flood Model (Version 1.3) at the county and zip code level. county_flood_score and zcta_flood_score provide the overall community risk score. county_flood_category_score and zcta_flood_category_score provide the risk score to specific categories of infrastructure. Each category; critical infrastructure, social infrastructure, residential properties, roads, and commercial properties, is a component of the overall community risk.

If you are interested in acquiring First Street flood data, you can request to access the data here. More information on First Street's flood risk statistics can be found here and information on First Street's hazards can be found here.

The following fields are in the overall risk datasets:

Attribute

Description

county_id

The county FIPS code

count

The count (#) of infrastructure facilities

flood_score

A score of 1, 2, 3, 4, or 5 is shown. Community risk rankings represent risk as Minimal, Minor (1), Moderate (2), Major (3), Severe (4) and Extreme (5). Minimal risk is a case where no facilities within a category have flood risk. County level risks are ranked based on how their total depths compare to counties across the country.

The following fields are in the category risk datasets:

Attribute

Description

FIPS

County FIPS code

ZIP_CODE

ZIP code

count

The approximate length of roads (miles) within the geography of aggregation (i.e. ZIP Code, County)

flood_score

A score (Community Risk level) of 0, 1, 2, 3, 4, or 5 is shown. Community risk levels represent risk as Minimal (0), Minor (1), Moderate (2), Major (3), Severe (4) and Extreme (5). Minimal risk is a case where no facilities within a category have flood risk. ZIP Code and County level risks are assessed based on how their total depths compare to ZIP Codes and Counties across the country.

risk_direction

A score of 1, -1, or 0 is shown. These note if flood risk is expected to increase (1), decrease (-1), or remain constant (0) over the next 30 years.

infrastructure_category_id

1= critical infrastructure, 4 = social infrastructure , 6 = residential properties, 8 - roads, 9 = commercial properties

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