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Matthew Wheelwright

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ABSTRACT:

This dataset contains an amenity index for all Utah Census Places. Using Census data and other available datasets, an index for 'amenityness' was created. Following Ganning and Flint's previous research note, the index is calculated using physical amenities as well as socio-economic indicators (See: Ganning, Joanna Paulson, and Courtney G. Flint. “Constructing a Community-Level Amenity Index.” Society & Natural Resources 23, no. 12 (November 2, 2010): 1253–58. doi:10.1080/08941920903030132).

This index adds context to any study where an understanding of local, natural amenities is valued. It contains the following physical amenity variables: 1. Open Land 2. Open Water 3. Kapos Classification 4. Number of Recreation sites

It also includes key indicators which come from Census Data. 1. Employment Diversity 2. Median Household Income 3. Seasonal Housing 4. Population Growth 5. In-migration from out of state 6. In-migration from out of county 7. College education 8. New Housing Built 9. Housing Valued over $175,000 10. Median Rent 11. Median Housing Value 12. Employment in arts, entertainment, recreation, accommodation, and food services.

For more information on the calculations and variables, please read Ganning & Flint's research note and the ReadMe file linked to this dataset or the data dictionary within the excel spreadsheet as Tab 2.

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ABSTRACT:

This dataset contains event history for floods in Utah based on the NOAA weather database. It has been dis-aggregated to census place for alignment with other compatible databases in order to analyze local history and experience. The readme file elaborates on the methods of disaggregation. It includes data from 1997-2014, but more reliable data is separated for 2010-2014 after a change in NWS procedure and event recognition.

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ABSTRACT:

This dataset contains important categories (per an extensive literature review) in relation to vulnerability to water hazards within Utah at the Census Place level (i.e. cities). Although social and physical vulnerability to water hazards (i.e. flooding) data has been collected extensively in many coastal areas, this is a costly problem in Utah and many other non-coastal regions. The variables shown here are categorized by type and collection method. 1. General data is shown for all Census Places in Utah including County, Geocode, and Total Population. These are taken from the 2010 Census. 2. Literature suggests that there are various approaches which local governments take to mitigate the impacts of flood events. Indicators of these approaches are captured in the section entitled Web Survey. A web survey was conducted of each and every census place in Utah. The data includes evaluations of content including water hazard education, land use restrictions described in the code, freeboard requirements, and emergency operations plan implementations. 3. Information about the local government including their planning and building inspector staff was collected using a phone survey along with emails and website investigations. 4. FEMA data was consolidated from FEMA's website showing census places with current insurance premium discounts achieved by demonstrating compliance with certain federal requirements. It also includes data on policies and losses. 5. A social vulnerability index was created by our team for this project and details can be found here: http://repository.iutahepscor.org/dataset/social-vulnerability-at-the-census-place-level. This dataset includes summary findings from the SoVI index. 6. As housing is recognized in the literature as a contributer to natural hazard vulnerability, important housing statistics were defined and created from Census data. These include housing age, median value, and renter occupied statistics. A standardized rating of building code effectiveness is also included from a recent Utah Hazard Mitigation Report. 7. Event data is summarized for number of events and estimated monetary damages from another of our team's datasets found here: http://repository.iutahepscor.org/dataset/noaa-storm-events. This NOAA dataset helps us understand the nature of past experience and physical exposure to water hazards. 8. As this dataset is focused on water hazard exposure, two measurements were calculated for each census place reflecting the percentage area of the city included in the defined special flood hazard area.

Together these data paint a picture of Utah's vulnerabilities to flood hazards and potential exposure to other natural hazard events. County level statistics were also collected and add insight at that spatial scale. they can be found here: http://repository.iutahepscor.org/dataset/utah-s-counties-sensitivity-to-water-hazards. The variables are different as prescribed in the readme file there.

Further details of the data collection methods can be found in the data dictionary within the spreadsheet workbook or in the ReadMe file included as a resource here.

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ABSTRACT:

This dataset contains important categories (per an extensive literature review) in relation to vulnerability to water hazards within Utah at the County level. Although social and physical vulnerability to water hazards (i.e. flooding) data has been collected extensively in many coastal areas, this is a costly problem in Utah and many other non-coastal areas. The variables shown here are categorized by type and collection method. 1. General data is shown for all Counties in Utah. These are taken from the 2010 Census. 2. Literature suggests that there are various approaches which local governments take to mitigate the impacts of flood events. Indicators of these approaches are captured in the section entitled Web Survey. A web survey was conducted of each County. The data includes evaluations of content including water hazard education, land use restrictions described in the code, freeboard requirements, and emergency operations plan implementations. 3. A social vulnerability index as created by the University of South Carolina is shown here. More information can be found at their website. 4. Event data is summarized for number of events and estimated monetary damages. This NOAA dataset helps us understand the nature of past experience and physical exposure to water hazards. Utah's Hazard Mitigation Plan 2014 includes a flood vulnerability score. It is included here for reference but is not critiqued as part of this dataset. 5. Fema has modeling software known as HAZUS which can be used to estimate damages for certain hazards including flooding. A county level summary is included here with estimated of building damage and exposure. 6. Dams are a man-made structure which play a part on flood management and can also create additional exposure. 7. Much of social vulnerability and disaster management should consider those with special needs. Census and the Division of Hazard Mitigation of Utah help us understand more of this important context.

Together these data paint a picture of Utah's vulnerabilities to flood hazards and potential exposure to other natural hazard events. Place level statistics were also collected and add insight at that spatial scale. they can be found here: http://repository.iutahepscor.org/dataset/hazard-mitigation-and-capacity-in-utah-census-places. The variables are different as prescribed in the readme file there.

Further details of the data collection methods can be found in the data dictionary within the spreadsheet workbook or in the ReadMe file included as a resource here.

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ABSTRACT:

This dataset uses Census Data following published social vulnerability index literature to provide an index at the Place level.

The Corps of Engineers has chosen SoVI as the “foundational SVA (Social Vulnerability Analysis) method for characterizing social vulnerability….” (Dunning and Durden 2013) The University of South Carolina has provided extensive and historic data for this methodology. Susan Cutter and her team have published their methodology and continue to maintain their database. Thus it was chosen as the “primary tool for [Army] Corps SVA applications.” (ibid) The downside is that this method is complex and hard to communicate and understand at times. (S. Cutter, Boruff, and Shirley 2003) The Social Vulnerability Index (SoVI) for this study was constructed at the U.S. Census Place level for the state of Utah. We utilized the conventions put forth by Cutter (2011) as closely as possible using the five-year American Community Survey (ACS) data from 2008 to 2012. The ACS collects a different, more expansive set of variables than the Census Long Form utilized in Cutter et al. (2003), which required some deviation in variable selection from the original method. However, Holand and Lujala (2013) demonstrated that the SoVI could be constructed using regional contextually appropriate variables rather than the specific variables presented by Cutter et al. (2003). Where possible, variables were selected which matched with the Cutter et al. (2003) work. The Principle Components Analysis was conducted using the statistical software R version 3.2.3 (R 2015) and the prcomp function. Using the Cutter (2011) conventions for component selection, we chose to use the first ten principle components which explained 76% of the variance in the data. Once the components were selected, we assessed the correlation coefficients for each component and determined the tendency (how it increases or decreases) of each component for calculating the final index values. With the component tendencies assessed, we created an arithmetic function to calculate the final index scores in ESRI’s ArcGIS software (ESRI 2014). The scores were then classified using an equal interval classification in ArcGIS to produce five classes of vulnerability, ranging from very low to very high. The SoVI constructed for our study is largely consistent with previous indices published by Susan Cutter at a macro scale, which were used as a crude validation for the analysis. The pattern of vulnerability in the state is clustered, with the lowest vulnerability in the most densely populated area of the state, centered on Salt Lake City (see Figure [UT_SoVI.png]). Most of the state falls in the moderate vulnerability class, which is to be expected.

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Generic Generic
Social Vulnerability at the Census Place level
Created: July 22, 2016, 8:03 p.m.
Authors: Matthew Wheelwright

ABSTRACT:

This dataset uses Census Data following published social vulnerability index literature to provide an index at the Place level.

The Corps of Engineers has chosen SoVI as the “foundational SVA (Social Vulnerability Analysis) method for characterizing social vulnerability….” (Dunning and Durden 2013) The University of South Carolina has provided extensive and historic data for this methodology. Susan Cutter and her team have published their methodology and continue to maintain their database. Thus it was chosen as the “primary tool for [Army] Corps SVA applications.” (ibid) The downside is that this method is complex and hard to communicate and understand at times. (S. Cutter, Boruff, and Shirley 2003) The Social Vulnerability Index (SoVI) for this study was constructed at the U.S. Census Place level for the state of Utah. We utilized the conventions put forth by Cutter (2011) as closely as possible using the five-year American Community Survey (ACS) data from 2008 to 2012. The ACS collects a different, more expansive set of variables than the Census Long Form utilized in Cutter et al. (2003), which required some deviation in variable selection from the original method. However, Holand and Lujala (2013) demonstrated that the SoVI could be constructed using regional contextually appropriate variables rather than the specific variables presented by Cutter et al. (2003). Where possible, variables were selected which matched with the Cutter et al. (2003) work. The Principle Components Analysis was conducted using the statistical software R version 3.2.3 (R 2015) and the prcomp function. Using the Cutter (2011) conventions for component selection, we chose to use the first ten principle components which explained 76% of the variance in the data. Once the components were selected, we assessed the correlation coefficients for each component and determined the tendency (how it increases or decreases) of each component for calculating the final index values. With the component tendencies assessed, we created an arithmetic function to calculate the final index scores in ESRI’s ArcGIS software (ESRI 2014). The scores were then classified using an equal interval classification in ArcGIS to produce five classes of vulnerability, ranging from very low to very high. The SoVI constructed for our study is largely consistent with previous indices published by Susan Cutter at a macro scale, which were used as a crude validation for the analysis. The pattern of vulnerability in the state is clustered, with the lowest vulnerability in the most densely populated area of the state, centered on Salt Lake City (see Figure [UT_SoVI.png]). Most of the state falls in the moderate vulnerability class, which is to be expected.

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Generic Generic
Utah's Counties: Sensitivity to Water Hazards
Created: July 22, 2016, 8:17 p.m.
Authors: Matthew Wheelwright

ABSTRACT:

This dataset contains important categories (per an extensive literature review) in relation to vulnerability to water hazards within Utah at the County level. Although social and physical vulnerability to water hazards (i.e. flooding) data has been collected extensively in many coastal areas, this is a costly problem in Utah and many other non-coastal areas. The variables shown here are categorized by type and collection method. 1. General data is shown for all Counties in Utah. These are taken from the 2010 Census. 2. Literature suggests that there are various approaches which local governments take to mitigate the impacts of flood events. Indicators of these approaches are captured in the section entitled Web Survey. A web survey was conducted of each County. The data includes evaluations of content including water hazard education, land use restrictions described in the code, freeboard requirements, and emergency operations plan implementations. 3. A social vulnerability index as created by the University of South Carolina is shown here. More information can be found at their website. 4. Event data is summarized for number of events and estimated monetary damages. This NOAA dataset helps us understand the nature of past experience and physical exposure to water hazards. Utah's Hazard Mitigation Plan 2014 includes a flood vulnerability score. It is included here for reference but is not critiqued as part of this dataset. 5. Fema has modeling software known as HAZUS which can be used to estimate damages for certain hazards including flooding. A county level summary is included here with estimated of building damage and exposure. 6. Dams are a man-made structure which play a part on flood management and can also create additional exposure. 7. Much of social vulnerability and disaster management should consider those with special needs. Census and the Division of Hazard Mitigation of Utah help us understand more of this important context.

Together these data paint a picture of Utah's vulnerabilities to flood hazards and potential exposure to other natural hazard events. Place level statistics were also collected and add insight at that spatial scale. they can be found here: http://repository.iutahepscor.org/dataset/hazard-mitigation-and-capacity-in-utah-census-places. The variables are different as prescribed in the readme file there.

Further details of the data collection methods can be found in the data dictionary within the spreadsheet workbook or in the ReadMe file included as a resource here.

Show More
Generic Generic

ABSTRACT:

This dataset contains important categories (per an extensive literature review) in relation to vulnerability to water hazards within Utah at the Census Place level (i.e. cities). Although social and physical vulnerability to water hazards (i.e. flooding) data has been collected extensively in many coastal areas, this is a costly problem in Utah and many other non-coastal regions. The variables shown here are categorized by type and collection method. 1. General data is shown for all Census Places in Utah including County, Geocode, and Total Population. These are taken from the 2010 Census. 2. Literature suggests that there are various approaches which local governments take to mitigate the impacts of flood events. Indicators of these approaches are captured in the section entitled Web Survey. A web survey was conducted of each and every census place in Utah. The data includes evaluations of content including water hazard education, land use restrictions described in the code, freeboard requirements, and emergency operations plan implementations. 3. Information about the local government including their planning and building inspector staff was collected using a phone survey along with emails and website investigations. 4. FEMA data was consolidated from FEMA's website showing census places with current insurance premium discounts achieved by demonstrating compliance with certain federal requirements. It also includes data on policies and losses. 5. A social vulnerability index was created by our team for this project and details can be found here: http://repository.iutahepscor.org/dataset/social-vulnerability-at-the-census-place-level. This dataset includes summary findings from the SoVI index. 6. As housing is recognized in the literature as a contributer to natural hazard vulnerability, important housing statistics were defined and created from Census data. These include housing age, median value, and renter occupied statistics. A standardized rating of building code effectiveness is also included from a recent Utah Hazard Mitigation Report. 7. Event data is summarized for number of events and estimated monetary damages from another of our team's datasets found here: http://repository.iutahepscor.org/dataset/noaa-storm-events. This NOAA dataset helps us understand the nature of past experience and physical exposure to water hazards. 8. As this dataset is focused on water hazard exposure, two measurements were calculated for each census place reflecting the percentage area of the city included in the defined special flood hazard area.

Together these data paint a picture of Utah's vulnerabilities to flood hazards and potential exposure to other natural hazard events. County level statistics were also collected and add insight at that spatial scale. they can be found here: http://repository.iutahepscor.org/dataset/utah-s-counties-sensitivity-to-water-hazards. The variables are different as prescribed in the readme file there.

Further details of the data collection methods can be found in the data dictionary within the spreadsheet workbook or in the ReadMe file included as a resource here.

Show More
Generic Generic
NOAA Flood Events
Created: July 22, 2016, 10:11 p.m.
Authors: Matthew Wheelwright

ABSTRACT:

This dataset contains event history for floods in Utah based on the NOAA weather database. It has been dis-aggregated to census place for alignment with other compatible databases in order to analyze local history and experience. The readme file elaborates on the methods of disaggregation. It includes data from 1997-2014, but more reliable data is separated for 2010-2014 after a change in NWS procedure and event recognition.

Show More
Generic Generic
Amenity Index
Created: Aug. 6, 2016, 4:22 p.m.
Authors: Matthew Wheelwright

ABSTRACT:

This dataset contains an amenity index for all Utah Census Places. Using Census data and other available datasets, an index for 'amenityness' was created. Following Ganning and Flint's previous research note, the index is calculated using physical amenities as well as socio-economic indicators (See: Ganning, Joanna Paulson, and Courtney G. Flint. “Constructing a Community-Level Amenity Index.” Society & Natural Resources 23, no. 12 (November 2, 2010): 1253–58. doi:10.1080/08941920903030132).

This index adds context to any study where an understanding of local, natural amenities is valued. It contains the following physical amenity variables: 1. Open Land 2. Open Water 3. Kapos Classification 4. Number of Recreation sites

It also includes key indicators which come from Census Data. 1. Employment Diversity 2. Median Household Income 3. Seasonal Housing 4. Population Growth 5. In-migration from out of state 6. In-migration from out of county 7. College education 8. New Housing Built 9. Housing Valued over $175,000 10. Median Rent 11. Median Housing Value 12. Employment in arts, entertainment, recreation, accommodation, and food services.

For more information on the calculations and variables, please read Ganning & Flint's research note and the ReadMe file linked to this dataset or the data dictionary within the excel spreadsheet as Tab 2.

Show More