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<idAbs>&lt;div style='text-align:Left;'&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;&lt;span&gt;The FGS used a modeling technique called Weights of Evidence (WofE) that involves the combination of diverse spatial data to describe and analyze interactions and generate predictive models from which a map of favorability can be produced. The project began with a one year pilot study in Columbia, Hamilton, and Suwannee Counties, during which methodologies were developed in preparation to model the entire state. To train and validate the model, locations of sinkholes were required. Over two-and-a-half-years, field teams traversed the state investigating over 3,600 points of interest (potential sinkholes) and mapped 654 sinkholes. After evaluating fourteen different spatial data types, the three statistically strong spatial data layers were used to model the favorability of the State’s geology to sinkhole formation. The resulting map depicts four classes representing areas where the geology is least favorable to most favorable to sinkhole formation.&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;</idAbs>
<idPurp>The Florida Division of Emergency Management (DEM) contracted the Florida Geological Survey (FGS) to map the favorability of the State’s geology to sinkhole formation in response a large outbreak of sinkholes across the State following Tropical Storm Debby in late June, 2012. The project’s results are intended to bolster the State Hazard Mitigation Plan’s section on sinkhole hazards allowing for improved mitigation strategies. The three-and-a-half-year project was funded by the Federal Emergency Management Administration’ s Hazard Mitigation Grant Program (75%) and the State of Florida (25%).</idPurp>
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<useLimit>&lt;div style='text-align:Left;'&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;&lt;span&gt;The data collected and entered into the output model by the Florida Geological Survey (FGS), an office of the Florida Department of Environmental Protection (DEP), are provided solely as a general reference and are not intended to replace site-specific or use-specific investigations. Use of FGS data by an end-user for any purpose other than general reference shall free the DEP and the FGS from any and all liability for outcomes resulting from unintended or inappropriate applications of FGS data. Examples of such applications include but are not limited to the mapping of point data with respect to cadastral boundaries and the use of geologic data for engineering purposes, and the interpolation of results of laboratory analysis that may or may not be deemed geologically analogous. As with all data, coordinates and datasets are subject to inherent errors due to limitations in the resources and technology available to record data. The FGS makes every effort to provide the most up-to-date and accurate information possible, but cannot be held liable for the use of outdated data regardless of whether more current data have been obtained, analyzed, or made available by the FGS or DEP. Any modifications of FGS data by end-users must be acknowledged and noted appropriately in all subsequent products and analysis, and any FGS logos or author information must be removed from altered FGS products prior to distribution to any other individual or entity. All FGS data are distributed on an "as is" basis and end-users shall assume all risks concerning data quality, performance, and analytic results. The same shall also be true concerning the use of unpublished drafts of FGS products, which may not be distributed to other individuals or entities without prior FGS approval. Comments and inquiries regarding FGS data, projects, and other analysis are encouraged and the FGS asks to be notified of any documented errors or inaccuracies. As a professional courtesy, the FGS asks that due credit be given when data sets created and distributed by the FGS are utilized by other individuals or entities. Further, since some or all of the data yielded from each FGS project were developed using public funds, no proprietary rights may be attached to FGS products wholly or in part, nor may FGS products be sold to the U.S. Government or the Florida State Government as part of any procurement of products or services. By using FGS data for any purpose other than general reference, the end-user acknowledges all the aforementioned stipulations and absolves the FGS completely of any responsibility for unintended or inappropriate usage. For questions, comments, or observations regarding FGS datasets, including questions concerning appropriate uses for specific datasets, please contact the FGS.&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;</useLimit>
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<statement>The Florida Division of Emergency Management (DEM) contracted the Florida Geological Survey (FGS) to map the favorability of the State’s geology to sinkhole formation in response a large outbreak of sinkholes across the State following Tropical Storm Debby in late June, 2012. The project’s results are intended to bolster the State Hazard Mitigation Plan’s section on sinkhole hazards allowing for improved mitigation strategies. The three-and-a-half-year project was funded by the Federal Emergency Management Administration’ s Hazard Mitigation Grant Program (75%) and the State of Florida (25%).</statement>
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<stepDesc>A favorability map of the Weights of Evidence Model was generated using three evidential themes that showed the strongest association with the training point theme and therefore were considered the strongest for identifying areas with favorable geology for sinkhole formation. Those layers were overburden (figure 8), a categorical representation of closed topographic depressions (figure 11) and, a layer depicting the difference between the water-table surface and the top of limestone (figure 12). Each of the model evidential layers were calculated against the study area training sites. A calculated weights table was used to pick the break between areas that are associated with training sites and areas not associated with sites. Those breaks for each of the evidential data layers used in the model are displayed in figures 8, 11, and 12. Overburden thickness was calculated by taking the top of limestone surface and subtracting it from land surface. Values across the state ranged from approximately 505 meters (~1,650 ft.) thick in the extreme southern Florida to 0 meters (0 ft.) which occurs mostly in the lower lying areas along the major area rivers in the big bend area of Florida. Intersecting the training sites with this evidential layer revealed that training sites occurred in areas with 31 meters (103 ft.) or less of overburden. A second observed category is from 31 meters (103 ft) to 88 meters (288 ft ) of overburden. This second classification is weekly associated with the formation of sinkholes. Areas where the calculated overburden values exceed 88 meters displayed no association with observed collapse sinkholes in the field and therefore had no association. (figure 8). Closed topographic depressions are sourced from the United States Geological Survey (USGS) 1:24,000 topographic maps and are the hachured closed isolines on the map (figure 9). Since sinkholes tend to be highly circular, filtering by circularity index allows for the removal of closed topographic depressions that are highly linear (e.g., a drainage ditch or linear dune feature). The circularity index of a feature is the ratio of the area of a perfect circle with the same perimeter as the closed depression. The circularity value is displayed as a ratio where 1.0 is a perfect circle and lower values are more elongated (figure 10). The depression features were intersected with the United States National Grid to summarize feature statistics on a 1 kilometer basis. The resulting 1km grid of closed depressions was then queried to find the best fit with the known sinkhole occurrences that field staff identified. These were then filtered based on an index of circularity or circular index (Denizman, 2003). Ultimately, closed topographic features with a circularity index of 0.75 or greater and depth ranges greater than 5 feet coupled with the existence of multiple sinks depressions within a grid cell meeting that criteria had the strongest association with the training point sites (figure 10). This evidential layer is separated into two classes based on the selection criteria discussed and is displayed as being associated with the known sinkholes. In some instances, multiple layers can be combined into a single layer to select for complex interactions between layers. For example, the difference between the top of limestone layer and the top of the potentiometric surface are two layers that have been combined into a single evidential theme. The combined layer references the difference between water table surface and top of limestone. The complex layer helps reveal the areas in the state where the top of soluble rock is near the potentiometric surface. Presumably, this is a zone where the hydraulic pumping action of the aquifer is most pronounced, thereby actively flushing sediments from cavities within the underlying soluble limestone rock layers (figure 12). Top of limestone data points are used to create a layer depicting the surface of limestone that is susceptible to dissolution. The layer was subtracted from a groundwater level surface and then intersected with training sites to show areas that are associated with sinkholes. Red areas are more associated with the training sites and have groundwater levels that are generally 0-10 meters (0-33 ft.) from the top the limestone. Areas with values more than 10 meters (33 ft.) are not associated with training sites.</stepDesc>
<stepDateTm>2023-06-05T00:00:00</stepDateTm>
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<attrlabl Sync="TRUE">REL_FAV</attrlabl>
<attalias Sync="TRUE">REL_FAV</attalias>
<attrtype Sync="TRUE">String</attrtype>
<attwidth Sync="TRUE">50</attwidth>
<atprecis Sync="TRUE">0</atprecis>
<attscale Sync="TRUE">0</attscale>
</attr>
<attr>
<attrlabl Sync="TRUE">AREA_SQKM</attrlabl>
<attalias Sync="TRUE">AREA_SQKM</attalias>
<attrtype Sync="TRUE">Double</attrtype>
<attwidth Sync="TRUE">8</attwidth>
<atprecis Sync="TRUE">0</atprecis>
<attscale Sync="TRUE">0</attscale>
</attr>
<attr>
<attrlabl Sync="TRUE">Red</attrlabl>
<attalias Sync="TRUE">RED</attalias>
<attrtype Sync="TRUE">Integer</attrtype>
<attwidth Sync="TRUE">4</attwidth>
<atprecis Sync="TRUE">0</atprecis>
<attscale Sync="TRUE">0</attscale>
</attr>
<attr>
<attrlabl Sync="TRUE">Green</attrlabl>
<attalias Sync="TRUE">GREEN</attalias>
<attrtype Sync="TRUE">Integer</attrtype>
<attwidth Sync="TRUE">4</attwidth>
<atprecis Sync="TRUE">0</atprecis>
<attscale Sync="TRUE">0</attscale>
</attr>
<attr>
<attrlabl Sync="TRUE">Blue</attrlabl>
<attalias Sync="TRUE">BLUE</attalias>
<attrtype Sync="TRUE">Integer</attrtype>
<attwidth Sync="TRUE">4</attwidth>
<atprecis Sync="TRUE">0</atprecis>
<attscale Sync="TRUE">0</attscale>
</attr>
</detailed>
</eainfo>
<spdoinfo>
<rastinfo>
<rasttype Sync="TRUE">Pixel</rasttype>
<rowcount Sync="TRUE">7743</rowcount>
<colcount Sync="TRUE">8251</colcount>
<rastxsz Sync="TRUE">100.000000</rastxsz>
<rastysz Sync="TRUE">100.000000</rastysz>
<rastbpp Sync="TRUE">4</rastbpp>
<vrtcount Sync="TRUE">1</vrtcount>
<rastorig Sync="TRUE">Upper Left</rastorig>
<rastcmap Sync="TRUE">FALSE</rastcmap>
<rastcomp Sync="TRUE">LZ77</rastcomp>
<rastband Sync="TRUE">1</rastband>
<rastdtyp Sync="TRUE">pixel codes</rastdtyp>
<rastifor Sync="TRUE">FGDBR</rastifor>
<rastplyr Sync="TRUE">TRUE</rastplyr>
</rastinfo>
</spdoinfo>
<spref>
<horizsys>
<planar>
<planci>
<plance Sync="TRUE">row and column</plance>
<coordrep>
<absres Sync="TRUE">100.000000</absres>
<ordres Sync="TRUE">100.000000</ordres>
</coordrep>
</planci>
</planar>
</horizsys>
</spref>
</metadata>
