7 datasets found
  1. Data from: FCE Redlands Flood Zones, Miami-Dade County, South Florida

    • dataone.org
    Updated Feb 16, 2024
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    Jeff Onsted (2024). FCE Redlands Flood Zones, Miami-Dade County, South Florida [Dataset]. https://dataone.org/datasets/https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fmetadata%2Feml%2Fknb-lter-fce%2F1182%2F4
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    Dataset updated
    Feb 16, 2024
    Dataset provided by
    Long Term Ecological Research Networkhttp://www.lternet.edu/
    Authors
    Jeff Onsted
    Variables measured
    FID, AREA, ELEV, ZONE, Shape, PERIMETER, PFLOODZN_, ZONE_ELEV, PFLOODZN_I
    Description

    Urban growth models have increasingly been used by planners and policy makers to visualize, organize, understand, and predict urban growth. However, these models reveal a wide disparity in their attention to policy factors. Some urban growth models capture few if any specific policy effects (e.g.,as model variables), while others integrate certain policies but not others. Since zoning policies are the most widely used form of land use control in the United States, their conspicuous absence from so many urban growth models is surprising. This research investigated the impacts of zoning on urban growth by calibrating and simulating a cellular automaton urban growth model, SLEUTH, under two conditions in a South Florida location. The first condition integrated restrictive agricultural zoning into SLEUTH, while the other ignored zoning data. Goodness of fit metrics indicate that including the agricultural zoning data improved model performance. The results further suggest that agricultural zoning has been somewhat successful in retarding urban growth in South Florida. Ignoring zoning information is detrimental to SLEUTH performance in particular, and urban growth modeling in general.

  2. Flood Zones - MDC_FEMAFloodZone

    • data.wu.ac.at
    • datadiscoverystudio.org
    xml
    Updated Sep 21, 2017
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    NSGIC Local Govt | GIS Inventory (2017). Flood Zones - MDC_FEMAFloodZone [Dataset]. https://data.wu.ac.at/schema/data_gov/MTI2ZDU1M2EtMGM4Yy00ZTM0LWExNzktZGEwMTcyYTNmYWUy
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    xmlAvailable download formats
    Dataset updated
    Sep 21, 2017
    Dataset provided by
    National States Geographic Information Council
    Area covered
    38f9a457bdf73733fe136288470c7984c1e20677
    Description

    Polygon feature class of Federal Emergency Management Agency (FEMA) Flood Hazard Zones for Miami-Dade County.

  3. a

    County Flood Criteria 2022 - Raster

    • hub.arcgis.com
    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • +1more
    Updated Dec 28, 2023
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    Miami-Dade County, Florida (2023). County Flood Criteria 2022 - Raster [Dataset]. https://hub.arcgis.com/documents/68ee47dfc3af4b8daa7d63ebdbb0d165
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    Dataset updated
    Dec 28, 2023
    Dataset authored and provided by
    Miami-Dade County, Florida
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    A raster dataset of the county flood criteria boundaries within Miami-Dade County. The purpose of the Miami-Dade County Flood Criteria Map is to determine the minimum ground surface elevation of developed properties, crown/grade of roads, and secondary canal banks based on a 10-year, 24-hour storm event, 2060 scenario with SLR, and the minimum top elevation of seawalls, unless higher elevations are required by other regulatory applicable standards. Available for review and comment October 22, 2021 through December 22, 2021.Download County Flood Criteria Raster

  4. a

    County Flood Criteria 2022

    • hub.arcgis.com
    Updated Dec 31, 2022
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    Miami-Dade County, Florida (2022). County Flood Criteria 2022 [Dataset]. https://hub.arcgis.com/maps/MDC::county-flood-criteria-2022
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    Dataset updated
    Dec 31, 2022
    Dataset authored and provided by
    Miami-Dade County, Florida
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    A polygon feature class of the county flood criteria boundaries within Miami-Dade County. The purpose of the Miami-Dade County Flood Criteria Map is to determine the minimum ground surface elevation of developed properties, crown/grade of roads, and secondary canal banks based on a 10-year, 24-hour storm event, 2060 scenario with SLR, and the minimum top elevation of seawalls, unless higher elevations are required by other regulatory applicable standards.Available for review and comment October 22, 2021 through December 22, 2021.Updated: Every 10 yrs The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

  5. Building damage costs due to flood risk in the U.S. 2022, by metro area

    • statista.com
    Updated Aug 8, 2023
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    Statista (2023). Building damage costs due to flood risk in the U.S. 2022, by metro area [Dataset]. https://www.statista.com/statistics/1291811/structural-damage-costs-due-to-flood-in-buildings-by-metro-area-us/
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    Dataset updated
    Aug 8, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2021
    Area covered
    United States
    Description

    In 2022, Miami, FL was the U.S. metropolitan area with the highest structural damage costs due to flood risk across office, retail, and multi-residential buildings. At the time, costs to repair damages or replace buildings at flood risk at this metro area were estimated at over one billion U.S. dollars. Trailing second was New York's metro area, with an estimated expense of 582 million dollars. New York was also the U.S. metropolitan area with the highest number of buildings at flood risk, with a over 30 thousand retail, office, and multi-residential buildings.

  6. a

    Cut Fill Basin

    • hub.arcgis.com
    Updated Feb 17, 2017
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    Miami-Dade County, Florida (2017). Cut Fill Basin [Dataset]. https://hub.arcgis.com/maps/MDC::cut-fill-basin
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    Dataset updated
    Feb 17, 2017
    Dataset authored and provided by
    Miami-Dade County, Florida
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    A polygon feature class of specific special drainage basins within Miami-Dade County. A Cut and Fill Review is required for flood protection for projects located in flood prone areas and within special drainage basins. The special drainage basins are Bird Drive Basin, North Trail Basin and Basin B. The cut and fill review determines the amount of land, which must be set aside to retain the runoff from the design (100 years-3 days) storm. The owner of a parcels that have been continuously four and one half (4.5) acres or less of gross area since September 30, 1997 is eligible to contribute to the Miami-Dade County Stormwater Compensation Trust Fund instead. The amount of said contribution is found in the Miami-Dade County Environmental Resources Management Department Fee Schedule.Updated: Annually The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

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Email
Click to copy link
Link copied
Close
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Jeff Onsted (2024). FCE Redlands Flood Zones, Miami-Dade County, South Florida [Dataset]. https://dataone.org/datasets/https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fmetadata%2Feml%2Fknb-lter-fce%2F1182%2F4
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Data from: FCE Redlands Flood Zones, Miami-Dade County, South Florida

Related Article
Explore at:
Dataset updated
Feb 16, 2024
Dataset provided by
Long Term Ecological Research Networkhttp://www.lternet.edu/
Authors
Jeff Onsted
Variables measured
FID, AREA, ELEV, ZONE, Shape, PERIMETER, PFLOODZN_, ZONE_ELEV, PFLOODZN_I
Description

Urban growth models have increasingly been used by planners and policy makers to visualize, organize, understand, and predict urban growth. However, these models reveal a wide disparity in their attention to policy factors. Some urban growth models capture few if any specific policy effects (e.g.,as model variables), while others integrate certain policies but not others. Since zoning policies are the most widely used form of land use control in the United States, their conspicuous absence from so many urban growth models is surprising. This research investigated the impacts of zoning on urban growth by calibrating and simulating a cellular automaton urban growth model, SLEUTH, under two conditions in a South Florida location. The first condition integrated restrictive agricultural zoning into SLEUTH, while the other ignored zoning data. Goodness of fit metrics indicate that including the agricultural zoning data improved model performance. The results further suggest that agricultural zoning has been somewhat successful in retarding urban growth in South Florida. Ignoring zoning information is detrimental to SLEUTH performance in particular, and urban growth modeling in general.

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