1. WISCONSIN DEPARTMENT OF HEALTH SERVICES
Mapping Flood Hazard in the
Upper Fox River Basin:
Vulnerable Populations and Negative Health Outcomes
Hanson, Angelina M
12/20/2015
Objective: The aim of this project is to evaluate the flood hazard in Wisconsin’s Upper Fox River Basin and to review
the negative health outcomes related to flooding events. It seeks to find the location and extent of flooding along the
Fox River in the Upper Fox River Valley, assess the socioeconomic vulnerability of the communities at risk to flooding,
and investigate the negative health outcomes related to flooding events. Methods: The Federal Emergency
Management Agency’s (FEMA) Hazus-MH software was used to generate a 100 year and 500 year flood scenario and
give estimates on building damage and economic loss. ArcGIS was used to analyze the flood hazard by conducting an
Overlay Analysis to evaluate the magnitude of flooding by identifying the number of buildings affected by the flood
and a Drive Time Analysis as a way of evaluating access to healthcare. The 2010 U.S. Census Bureau data was used to
assess the socioeconomic vulnerability of the populations living in areas at risk to flooding. Conclusions: The new
alternative method of generating the 100 year and 500 year flood scenario was successful in capturing the floodplain
and reporting estimates on building damage and economic loss. The flood risk in the Upper Fox River Valley was
identified, but not without caveats. In particular the spatial accuracy of the points layers was inherently flawed. More
time and additional methods to increase the spatial accuracy should be adopted in further study or studies of this
nature. A review of the negative health outcomes related to flooding are wide ranging from drowning and injury to
psychological effects and malnutrition. The continuation of studies that seek methods to predict and prepare for
negative health outcomes related to flooding should continue to be supported and used as background information at
the State and Local Level to improve the overall health and safety of those communities.
Bureau of Environmental and Occupational Health
Technical Advisor:
Shane Hubbard
Assistant Researcher
Space Science and Engineering Center

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INTRODUCTION
Background
This project is supported by the Wisconsin Department of Health Services (DHS) through the Bureau of Occupational and
Environmental Health. It is funded through the Centers for Disease Control and Prevention (CDC) Building Resilience
Against Climate Effects (BRACE) grant awarded to the State of Wisconsin in 2014. The primary goal of the grant is to
improve the state-wide capacity of the DHS to predict, prepare for, assess, and effectively respond to extreme weather
and climate events thereby reducing or preventing negative health effects to Wisconsin’s citizens (WI Climate and
Health Profile, 2014). The project is also assisted by the Wisconsin Emergency Management (WEM) who provided data
on facilities with hazardous materials on site and helped define what other useful information should be included in the
flood hazard analysis. Technical assistance and training on Hazus-MH was given by Shane Hubbard, Assistant Researcher
with the Space Science and Engineering Center.
Project Area
The Upper Fox River Valley is a water rich basin with a history of flooding. It covers 2,090 square miles and includes all of
Marquette County and portions of Calumet, Columbia, Green Lake, Fond du Lac, Waushara, and Winnebago Counties.
The basin has 15 watersheds that are drained by 1,257 miles of rivers and streams; it has 154 lakes that are greater than
10 acres in size, and 145,428 acres of wetlands (WI DNR). The Basin experienced 39 days of flooding* between January,
1996 and June, 2015. The most active year, 2004, experienced 10 days of flooding. The cumulative damage caused by
these events was an estimated $46.5 million in property damage and $183.0 million in crop damage (NCDC Storm Events
Database).
Research Question
The objective of this project is to evaluate the flood hazard in Wisconsin’s Upper Fox River Basin and analyze the
negative health effects related to flooding events. It relies on peer-reviewed journal articles to address the negative
health outcomes related to flooding events and FEMA’s Hazus-MH software to generate a 100 year and 500 year flood
scenario along the Fox River for analyzing the flood hazard.
CONCEPTUALIZATION: Step 1 – Creation of the 100 year and 500 year Flood Scenarios
The process of evaluating the flood hazard in the Upper Fox River Basin begins with using FEMA’s Hazus-MH software to
generate a 100 year and 500 year flood scenario along the Fox River. FEMA’s Hazus-MH software is a standardized
methodology that estimates losses from earthquakes, hurricanes, and riverine and coastal flooding. It uses Geographic
Information Systems (GIS) to map and display hazard data and provides damage and economic loss estimates caused by
these types of disasters (FEMA, 2012). Essentially, Hazus-MH is a toolkit that extends the ‘out of the box’ ArcGIS to
generate a disaster scenario and evaluate the potential effects. A step-by-step process guides the user in defining their
project area and copies all the data into a file that is opened in ArcGIS. Once the shape file is opened in ArcGIS, the user

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goes through a series of tasks to, in this case, generate the riverine network and create the flood depth grid. The flood
depth grid (flood scenario) is created by subtracting, cell by cell, the ground elevation contained in the United States
Geological Survey Digital Elevation Model (DEM) from the flood elevation (FEMA’s 100 year flood boundary).
CONCEPTUALIZATION: Step 2 – Analyzing the Flood Hazard
Analyzing the flood hazard is conducted through a series of methods. First, the spatial accuracy of the 100 year and 500
year floodplain is determined by comparing it’s boundaries to the Digital Flood Insurance Rate Map (DFIRM) and by
visual observation. Second, an overlay analysis determines the number of buildings in either floodplain as well as those
within 500 feet of either floodplain. Third, to evaluate the socioeconomic vulnerability of the communities living in areas
at risk to flooding, block level U.S. Census Bureau data is used to identify populations of interest: the young, the old, and
the poor. Fourth, building damage is provided by Hazus-MH in its Global Summary Report. This information is then
mapped for visual comparison between the two flood scenarios. Finally, a Drive-Time Analysis is conducted to
determine the time and distance of hospitals to census blocks that intersect the 100 year floodplain and have high
percentages of young, old, and poor populations.
CONCEPTUALIZATION: Step 3 – Literature Review (Flooding Events and Negative Health Outcomes)
The purpose of the literature review is to identify the negative health outcomes related to flooding events. This
information is especially useful to local clinics, hospitals, public health offices, and emergency management in
responding to and preparing for future flooding events.
The literature review is based on articles retrieved from two sources: those collected through WebMD and others
already reviewed through BRACE. All articles are peer-reviewed and limited by search criterion that specifies that they
contain information on health outcomes related to flooding.
IMPLEMENTATION: Step 1 – How to Create a Flood Scenario
Creating the flood scenario took several months of experimentation
and ultimately a new method needed to be developed. In the
original method, Hazus-MH is opened and a new project region is
created through a series of steps. Digital Elevation Data (DEM) is
downloaded and clipped to the project region. The stream network
is built and finally the hydrology and hydraulics of the riverine
scenario are completed. However, the flood scenario created from
this method does not accurately portray the floodplain. Therefore,
the following alternative method is used.
Additional BFE Lines extend over Butte des Morts

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The Digital Flood Insurance Rate Map (DFIRM) is downloaded from FEMA and a copy of the DFIRM around Fox River is
added to the map. Using the editing tool bar, additional Base Flood Elevation (BFE) lines are added using the guidance of
Flood Insurance Reports (FIS) which have profile data of the river. With BFE lines along the Fox River and its lakes, new
vertices are added and converted to points. The points layer is then converted into a raster and subtracted from the
DEM. This layer is then imported back into Hazus-MH to run the flood scenario.
IMPLEMENTATION: Step 2 – Analyzing the Flood Hazard
GATHERING AND GEOCODING ADDITIONAL DATA LAYERS
During the early stages of this project, the Wisconsin Department of Health Services and Wisconsin Emergency
Management (WEM) discussed how to improve the analysis of the flood hazard by suggesting that additional data be
added and an overlay analysis be conducted to identify which large employers, major banks, facilities with hazardous
materials, hospitals and clinics, and day care facilities are located within the floodplain. The only data from this list not
added to the map is information on day cares.
Additional Data Layers: Data Sources
 Hospitals and Clinics Point Layer: provided by the WI Department of Health Services
 Facilities with Hazardous Materials: provided by WEM and David S. Liebl, UW-Madison, SHWEC
 Large Employers Point Layer: provided by the Wisconsin Regional Planning Commission
 Major Banks Point Layer: address data on major banks collected using Google Maps
All layers are clipped to the project area.
After collecting the data, layers that were not already geocoded were Major Banks and Facilities with Hazardous
Materials. Major Banks are geocoded by address and hazardous facilities are geocoded by coordinates using a streets
file in ArcGIS. Using Centrus, a program available to employees at the DHS, these data layers were re-geocoded to
increase spatial accuracy. Instructions on this process are in Appendix I.
OVERLAY ANALYSIS: TWO APPROACHES
Once point data on large employers, major banks, Facilities with hazardous materials, and clinics are gathered and
geocoded to the map, two different approaches to an overlay analysis are conducted. The objective of the first approach
is to identify which, if any, of the points intersected either the 100 year or 500 year floodplain. The objective of the
second approach is to identify all points within a 500 foot buffer from both the 100 year and 500 year floodplain. A
tabulation of the locations within the floodplain and those in the 500 foot buffer is tracked and saved in an excel file.
The accuracy of the point locations is cross-referenced by comparing a satellite imagery base layer to a Google Map
search of the address.

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How To Guide:
 Click on Selection on the top Tool Bar
 Click on Select by Location
a. Selection method: = “select features from”
b. Check the box next to layer(s) of interest
c. Source Layer: = “100 year flood”
d. Spatial selection method for target layer feature(s): = “intersect the source layer feature”
 Click Apply
 Click Okay
 Repeat process for “500 year flood”
Repeat the same steps for the 500 foot buffer, except for the following parameters:
 Spatial selection method for target layer feature(s): = “are within a distance of the source layer feature”
 Check the box – Apply a search distance
 Type in 500 and set the unit to feet
DRIVE TIME ANALYSIS
A Drive Time Analysis is used to analyze accessibility to healthcare for a subset of the population. Using 2010 US Census
Bureau data, the socioeconomic background of the populations of interest are the young (<16 yrs), the old (>65 yrs), and
the poor (Households with an annual income of $20,000 or less). Therefore, the criterion used to select census blocks for
the first Drive Time Analysis are (a) census block has > 48% of young and old populations and (b) census block intersects
the 100 year floodplain. The second Drive Time Analysis selects census blocks that (a) have at least one household with
an income of less than $20,000 a year and (b) intesect the 100 year floodplain.
Using the ArcGIS Network Analyst extension and an ESRI Street Map dataset, the closest hospital to the selected census
blocks is found. The results of the analysis show both the time (in minutes) and distance (in miles) from the centroid of
the census block to the nearest hospital.
In preparing for the Drive Time Analysis, census blocks with a high percentage of young and old populations are selected
and exported as a new data layer. A points layer is then generated from this data using the Feature to Point tool. The
time and distance from the point (centroid of each selected census block) to the nearest hospital in the project region is
calculated using a Drive Time Analysis. This process is repeated for census blocks having at least one household with an
annual income of $20,000 or less a year.
How To Guide (first steps):
1. Creating a new polygon layer showing socioeconomic variation
a. Open 100yrCenIntersectFP.shp and export data
b. Title new layer as PerUnder16Over65.shp
i. This layer contains only census blocks that intersect the 100 year floodplain

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c. Creating New Fields
i. Right Click and Open Attributes Table
ii. Left Click on Table Options and select Add Field and title it PercentPop
iii. Right Click on new field and open Field Calculator
1. Select PercentPop field from Fields
a. Use the formula: PercentPop = ([PopAgeLess + PopAgeOver])
b. Click OK
c. Close Attribute Table
d. Click on Selection then Select by Attribute
i. Select PerUnder16Over65 as the specified Layer
ii. Select Population
iii. Type search criteria “Population = 0”
iv. Click OK
e. Right Click on the PerUnder16Over65 layer and Open Attributes Table
i. Click on Switch Selection
ii. Close Attributes Table
f. Open PerUnder16Over65 layer Properties
i. Click on the Symbology tab
1. Click on Quantities and select Graduated Colors
2. Click the down arrow for Value and select PercentPop
a. Use a suitable color ramp
ii. Left Click on the word Label
1. Click on Format Labels
2. Select Percentage as the category
a. The number represents a fraction. Adjust it to show a percentage.
b. Click OK
iii. Click OK to close the Properties window
2. Select subset of population demographic data
a. Click on Select and Select by Attributes
i. Select PerUnder16Over65 as the specified Layer
ii. Use formula: "PercentPop" > 0.48148148148100001
1. This selects the census blocks having more than 48% of its population as young or old.
2. Click OK to exit the Select by Attributes window
iii. Export selected features as a new layer titled HighPerCentroids.shp
b. Open ArcToolbox

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i. Open Data Management Tools
1. Open Features
a. Open Feature to Point
b. Select HighPerCentroids.shp
c. The new points layer will be added to the map
How To Guide (Closest Facility):
1. Add ESRI StreetMap Dataset to the map
a. This data was provided by the WI Department of Health Services and was found on their shared drive.
2. Click on Customize on the top Tool Bar
a. Click on Extensions
i. Check the box next to Network Analyst
3. Open the Network Analyst Window
a. Click on the Closest Facility Method
b. Click on Analyst Properties
c. Right Click on Facilities and click on Load Locations
i. Click on Browse and select HospitalsProjectRegion.shp
1. This layer was provided by the WI Dept. of Health Services and clipped to the project
area. It contains 5 hospital locations.
ii. Click Okay
d. Right Click on Incidents and click on Load Locations
i. Click on Browse and select HighPerCentroids.shp
1. This layer contains 189 centroids generated from the polygon layer having only census
blocks with over 48% of its population as young and old.
ii. Click Okay
4. Click Solve on the Network Analyst toolbar to run the current analysis
5. Export the results as a new line layer and title it CB16_65DriveTime.shp
a. This layer contains the time and distance (miles) to nearest hospital
6. Right click on HighPerCentroids.shp layer and Open Attribute Table
a. Click on Table Properties and Add Field, title it Join_ID
b. Use the Field Calculator to populate the Join_ID column with info from the FID column
i. The FID number is the same as the Incident_ID number in the CB16_65DriveTime.shp
c. Close Attribute Table
7. Right click on Routes, scroll to Join and Relates, click on Join
a. Join Incident_ID from Routes to Join_ID from HighPerCentroids.shp

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i. The time and distance calculations are now in the HighPerCentroids.shp
ii. This is a Table Join
8. Right click on PerUnder16Over65.shp, scroll to Join and Relates, click on Join data from another layer based on
spatial location
a. Choose HighPerCentroids to join
b. Select the second option each polygon will be given all the attributes…
c. Name the output file CB16_65DriveTime.shp
d. This is a Spatial Join
9. Add new layer to map
a. This final layer shows drive time and distance calculations from census blocks that intersect the 100 year
floodplain that also have over 48% of its population being young or old.
10. Repeat the same process for all census blocks having at least one household having with an annual income of
$20,000 or less.
ASSESSING BUILDING DAMAGE
Upon completion of the 100 year and 500 year flood scenario, Hazus-MH produces a Global Summary Report (GSR)
which contains information about building damage by listing the number of damaged buildings and their respective
severity of damage as a percent. 0% means no damage while 100% means the building was completely destroyed.
One piece of information provided by the GSR is damage estimates to Essential Facilities. Essential Facilities are
emergency response facilities, schools, and hospitals. Hazus-MH classifies them by the services they provide. For
example Fire Stations, Police Stations, and Emergency Operations Centers are all emergency response facilities. Schools
serve as temporary shelter in the event of a disaster and hospitals are accounted for by the number of beds they
provide. It is important for hazard mitigation planning to know if these facilities are in the floodplain so that disruption
of these services after a disaster can be avoided.
Building damage by count and percentage of severity is also available in the attributes table of the building damage
shape file that can be imported into the map. The two maps contained in this report show the 100 year flood scenario
and 500 year flood scenario and their respective estimates on flood damage to buildings. See Appendix III and IV.
How To Guide:
1. Import the Damaged Buildings shape file from 100 year flood scenario results
2. Right click on the layer, Open Attribute Table
a. Right click on the TotalBuild column and Sort Descending
b. Select all non-zero entries
i. This selects all census blocks with building inventory
c. Close Attribute Table

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3. Right Click on Data Layer and go to Data
a. Click on Export Data
b. Name the file BuildingDamageALL100.shp
c. Add it to the map as a New Layer
4. Right click on the new layer and Open Attribute Table
a. Click on Table Options and Add Field
b. Title the new field Damaged and select long-integer
c. Click OK to exit
5. Right click on the Damaged field and open the Field Calculator
a. Click Yes
b. Type in the following formula: “[PctDmg1to1] + [PctDmg11to] + [PctDmg21to] + [PctDmg31to] +
[PctDmg41to] + [PctDmg51to] + [PctDmg61to] + [PctDmg71to] + [PctDmg81to] + [PctDmg91to]”
i. The number populated in the Damaged field represents all damaged buildings for that particular
census block. The buildings are categorized based on a percentage of damage, a calculation
produced by Hazus-MH. It is possible that there are buildings in the census block with 0%
damage. Those records have a 0 in the Damaged field.
6. Right Click on the BuildingDamageALL100.shp and click on Properties
a. Click on the Symbology tab
i. Under Show, Select Quantities > Graduated colors
ii. Under Fields, click the down arrow next to Value and select Damaged
iii. Select an appropriate color scheme
b. Click Okay
ANALYZING THE SOCIOECONOMIC VULNERABILITY OF THE COMMUNITIES LIVING IN THE FLOODPLAIN
Selecting a subset of the population—the young, the old, and the poor—was part of an overall objective to incorporate
Social Determinants of Health (SODH) into the project. In the CDC’s Healthy People 2020 report, SDOH are conditions in
the environments in which people live, learn, work, play, worship, and age that affect a wide range of health,
functioning, and quality-of-life outcomes and risks. (CDC, 2015). A subset of young and old populations were sought
because they have limited ability to get out of harm’s way and are generally seen as vulnerable populations for which
special care is needed. A subset of the poor was sought for analysis because these households will likely face financial
difficultly when recovering from a flooding event (e.g. they may not have flood insurance or lack the funds to rebuild
their homes). All subsets are a percentage of the overall population of interest within the project area. For example, the
project region has a population of 28,795 who are less than 16 years of age. The census blocks that intersect the 100
year floodplain contain 3,600 people who are in the same age category. Therefore, 3600/28795 = 0.125021…* 100 =
12.5% rounded to 13%.

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RESULTS AND DISCUSSION
RESULTS: GENERATING THE 100 YEAR AND 500 YEAR FLOOD SCENARIO (FLOODPLAIN)
When I started this project, a flood depth-grid for this region was not available. A flood depth-grid indicates the level of
flooding by which it can produce estimates on the severity of damage to buildings and estimates for the economic loss
based on the replacement value of those buildings. Therefore, Hazus-MH was used generate a flood depth-grid to
produce those damage and economic loss estimates.
In April 2015 the first attempt to generate the 100 year flood scenario failed to accurately capture the floodplain. Its
boundary was inconsistent with the DFIRM from FEMA. A second attempt using Hazus-MH’s Enhanced Quick Look also
failed to accurately capture the floodplain. The accuracy of the flood depth-grid could have a significant impact on the
results. Therefore, an alternative method developed by Shane Hubbard was attempted in June and July, 2015.
The third attempt was successful in creating a 100 year flood scenario. The flood-depth grid closely aligned with the
DFIRM. The process was replicated for the 500 year flood scenario.
RESULTS: ESTIMATES OF BUILDING DAMAGE AND ECONOMIC LOSS
Each flood scenario generated by Hazus-MH produces a Global Summary Report (GSR). Building damage contained in
the GSR includes the number of damaged buildings and their respective damage as a percent. The information is listed
by their occupancy type (e.g. residential) and construction material (e.g. concrete). It also includes estimates for
economic loss on replacement value of the damaged buildings.
The results from the 100 year flood scenario
estimate that 335 buildings will be at least
moderately damaged. Only five of those are
commercial or government; the other 330 are
residential. Of the residential buildings, Hasuz-MH
estimates that 138 households will need to be
temporarily relocated based on a threshold of 41%
damage. The estimated cost to repair the damage
and replace the contents of the buildings is $174,
730,000. Additional results can be found in
Appendix V.
The 500 year flood scenario results estimate that
504 buildings will be at least moderately damaged. There were no government or commercial building damages,
meaning all the estimated building damages in this scenario are residential. Of this number, households in 257

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residential buildings will need to be temporarily relocated. This is a 54% increase from the 100 year scenario. The
estimated cost to repair the damage and replace the contents of the buildings is $254,320,000. Additional results can be
found in Appendix VI.
RESULTS: ASSESSING SOCIOECONOMIC VULNERABLITY
The 2010 US Census Bureau census block data indicates a total population of 139,174 for the Upper Fox River Basin.
Looking at the census blocks that intersect the 100 Year Floodplain, there are 19,365 people living in areas at risk to
flooding. The breakdown of populations of interest can be seen in the pie chart. The socioeconomic vulnerability results
from the 500 Year Floodplain show an increase of 1% in each category. Therefore, there is an increase in the number of
people under the age of 16, over the age of 65, and in households with an annual income of $20,000 or less.
Percentages are derived from census blocks that intersect the floodplain. This aggregated data does not show where
individual houses or buildings are located; therefore, the percentages listed do not necessarily conclude that these
subgroups of the population are living within the floodplain, but rather that they are at risk to flooding.
RESULTS: OVERLAY ANALYSIS: FIRST APPROACH
The objective of the first approach to the overlay analysis is to investigate
which points from the additional data layers are located in 100 year or
500 year floodplain. These layers are major banks, large employers,
clinics, and Facilities with hazardous materials. There are two layers with
facilities having hazardous materials: WEM and David S. Liebl. To meet the
criterion for selection, all data points are located in the project area and
intersect one or both floodplains. This process is repeated twice, first for
the 100 year floodplain and the second for the 500 year floodplain.
 In summary: It can be revealed by using a Select by Location query that 11 data points are found in the 100 year
floodplain and 12 data points are found in the 500 year floodplain.
 The breakdown:
o Major Banks: 1/50 is found in both floodplains
o Large Employers: 2/117 are found in both floodplains
 The number of employees is shown to the right of the address.
o Clinics 2/129 are found in both floodplains.
 The number of beds at these locations is to the right of the address.
o Hazardous Materials (Data from David S. Liebl): 5/562 data points are located in the 100 year floodplain
and 6/562 are found in the 500 year floodplain.
o Hazardous Materials (Data from WEM): 3/562 are found in either floodplains
100 Year Flood Scenario
1099 Census Blocks intersect the floodplain
14% of the Total Population
16% of all Households
13% of all people under 16 years old
17% of all people 65 and older
15% of Households earning $20K or less annually
500 Year Flood Scenario
1221 Census Blocks intersect the floodplain
15% of the Total Population
17% of all Households
14% of all people under 16 years old
18% of all people 65 and older
16% of Households earning $20K or less annually

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 Interestingly, only one data point is found to intersect both floodplains from both sets of facility
data.
RESULTS: OVERLAY ANALYSIS: SECOND APPROACH
The second approach examines which, if any large employers, major banks, Facilities with hazardous materials, hospitals
and clinics were within 500 feet of either floodplain. In summary, three large employers, 20 major banks, 1 hospital, 14
clinics, and 12 Facilities with hazardous materials are located in the 500 foot buffer. A complete list can be reviewed in
Appendix II.
RESULTS: DRIVE TIME ANALYSIS
The first Drive Time Analysis calculates the time (in minutes) and distance (in miles) from 486 census blocks that have at
least 48% of its population < 16 years old or > 65 years old and intersect the 100 year floodplain. From this analysis the
average time to get to a hospital is 16 minutes from an estimated 10 miles away. The longest drive time is 50 minutes
from an estimated 33 miles away, while the shortest distance is less than one minute from less than one mile away. Out
of the second Drive Time Analysis which selected 327 census blocks that had at least one household with an annual
income of $20,000 or less and intersected the 100 year floodplain it is found that the mean is also 16 minutes from an
estimated 10 miles away and the max is also 50 minutes from an estimated 33 miles away.
DISCUSSION: DRIVE TIME ANALYSIS
In future research, additional analysis of the results from the Drive Time Analyses could be conducted. In particular it
would be beneficial to know if the results produce any spatial clustering, something to be analyzed visually in ArcGIS.
These results may indicate why some areas seem to be more isolated from health care facilities. Furthermore, a
calculation of road damaged by flooding would help develop more realistic drive times in a disaster scenario.

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DISCUSSION: OVERLAY ANALYSIS AND SPATIAL ACCURACY
The results from the overlay analysis inherently have errors because the project relies on the precision of the data
points. As with any GIS project that uses layers from different sources, many of the layers are in different geographic
coordinate systems. For example, the hazardous facility data is in GCS_WGS_1984 while the hospital data is in
GCS_North_American_1983_HARN.
Another way spatial accuracy affects the results of the overlay analysis is that some of the data points are geocoded to
the street address rather than to the center of the building. For example, upon cross-validation of the clinics layer it can
be determined that one of the clinics found in the floodplain through an ArcGIS ‘Select by Location’ query is actually not
in the floodplain. The clinic building is clearly outside the floodplain, but the data point is geocoded to a street address
that is in the floodplain.
Another source of error relates to the Facilities with hazardous
materials data. The data given by David S. Liebl and WEM is
geocoded by geographic coordinates. It can be discovered that
some facilities have different coordinates from one data set to
the other. Ideally, if the data point represents the same
company, the points should be on top one another.
In the future, if working on a project similar to this a margin of
error on the spatial accuracy should be defined. Furthermore,
additional time should be alloted to proceed with methods that would increase the spatial accuracy of the points layers.
For example, a points layer for parcel data could be used in conjunction with arial photography and the editing toolbar
to extact the precise number of buildings in the floodplain.
DISCUSSION: LITERARY REVIEW
Flood is one of the most common and severe forms of natural disasters worldwide. Flooding—“the condition that occurs
when water overflows the natural or artificial confines of a stream, river, or other body of water, or accumulates by
drainage over low-lying areas” (Du et al., 2010)—occurs in developing and developed countries. Managing the negative
health outcomes from these events relies on extensive knowledge about the health risks related to flooding and the
capacity for clinics, hospitals, and emergency operation centers to respond to the event.
Out of this literature review it was clear that the negative health outcomes from flooding are well known. They range
widely from drowning and injury to psychological effects and malnutrition. They can be categorized as immediate
(during) and secondary (post-flooding). The many factors contributing to these outcomes make managing and
responding to them difficult. To summarize these factors are: (1) characteristics of the flood, (2) geography of the
location, (3) the built environment, and (4) the socioeconomic background of the population affected by the flood. Flood
David
WEM

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type (i.e. flash flood or gradual inundation) and severity influence various health outcomes. The geography of the
location, including topography, existing water networks, and land cover type can all increase or reduce negative health
impacts. The built environment including but not limited to the construction standards, efficient drainage systems,
availability of shelter, and location of healthcare facilities also impact negative health outcomes. We live in a world of
diversity, therefore the demographics of the population affected by the flood also influences negative health outcomes.
One study found “During floods, females, elderly and children appear to be at greater risk of psychological and physical
health effects, while males between 10 to 29 years may be at greater risk of mortality.” (Lowe et al., 2013).
As mentioned above, negative health outcomes can be categorized by event phase. Common immediate (during) health
effects are drowning, injuries, burns, and hypothermia. Drowning often occurs (a) when individuals underestimate the
depth of the water and the strength of its current, (b) when high rising water traps individuals in buildings, or (c) when
individuals are swept away in the evacuation process. Injuries can be caused by the collapse of buildings, debris in fast
moving water, electrical injuries, and restoration of housing and other buildings. Burns may occur due to downed power
lines and fires that spread across the water after flammable liquids on the surface of the water have ignited. Because
most flood water is cooler than our body temperature, hypothermia can occur in any season with or without complete
submersion (Du et al., 2010). Common secondary (post-flooding) health effects are communicable diseases, chemical
contamination, carbon monoxide poisoning, and respiratory illnesses. Reasons for increased risk of communicable
diseases after flooding relate to crowded and unsanitary living conditions, lack of clean water, and vector-borne diseases
such as malaria and dengue fever that can merge when stagnant waters allow mosquitos to breed. Carbon monoxide
poisoning can result from using unventilated gas-powered electrical generators and cooking equipment. Increased
respiratory illness, such as asthma, is related to restoration and cleanup of homes, work places, and other affected
buildings.
Some of the most lasting negative health outcomes are mental health symptoms (e.g. psychological distress, anxiety,
depression, and post-traumatic stress disorder). Mental health symptoms are related to the overall experience of the
event and its severity. Individuals may have experienced injury or illness, death of a loved one, or loss of their prized
possessions as their home was destroyed. These experiences have a strong impact on an individual’s mental health;
“People who have experienced a flood have been shown to have a fourfold higher risk of psychological distress than do
those not exposed to flood, and a suicide rate 13.8% higher than pre-disaster rates.”(Du W., et al., 2010). How should
communities and those who respond to the event be evaluated on their understanding about subsequent mental health
symptoms and how can they prepare for these negative health outcomes?
Two studies can provide some insight into that question. Post-traumatic stress disorder (PTSD) is of particular concern
because it can persist long after the flooding event [one study found that PTSD had persisted for 13 years after the flood
(Hu S., et al., 2015)]. In a study concerning PTSD after the 1998 flood in China’s Hunan providence, researchers identified
“determinants of PTSD and developed a risk score model to predict PTSD among flood victims.” (Huang P., et al,. 2010).
In this study researchers categorized the flood type as soaked flood, collapsed embankment flood, and flash flood, and

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flood severity as mild, intermediate, and severe as a way of identifying the characteristics of the flood. Researchers then
conducted face-to-face interviews and asked specific questions about the flood victims experience such as “Was your
home damaged by the flood?”, “Was this your first experience of floods?”, and “Were you trapped and waited for
rescue during the flood?” As a result of their study, 9.2% (2336) of the 25,478 study subjects were diagnosed with
probable PTSD and they confirmed that a simple risk score can be used to predict PTSD among flood victims. Results
from another study that examined patterns and predictors of mental health services after two natural disasters in
Australia concluded that creating ‘flexible referral pathways (beyond a General Practitioner referral)’ significantly
increased access to care and that the demand for mental healthcare services was dependent on the disaster type.
(Reifels L., et al., 2015).
CONCLUSION
The new alternative method of generating the 100 year and 500 year flood scenario is successful in capturing the
floodplain and reporting estimates on building damage and economic loss. The flood risk in the Upper Fox River Valley is
evaluated, but not without caveats. In particular the spatial accuracy of the points is flawed. More time and additional
methods to increase the spatial accuracy should be adopted in further study or studies of this nature. A review of the
negative health outcomes related to flooding found wide ranging effects from drowning and injury to psychological
damage and malnutrition. The continuation of studies that seek methods to predict and prepare for the negative
outcomes from flooding should continue to be supported and used as background information at the State and Local
level.
REFERENCE
1. Lowe D., Ebi K.L., Forsberg B. Factors Increasing Vulnerability to Health Effects before, during and after Floods.
Environmental Research and Public Health. 2013; 10: 7015-7067.
2. Du W., FitzGerald G.J., Clark M., et al. Health Impacts of Floods. Prehospital and Disaster Medicine. May – June
2010: 265-272.
3. Greene G., Paranjothy S., Palmer S.R. Resilience and Vulnerability to the Psychological Harm from Flooding: The
Role of Social Cohesion. Research and Practice. 2015; 105(9): 1792-1795.
4. Huang P., Tan H., Liu A., et al. Prediction of posttraumatic stress disorder among adults in flood district. BMC
Public Health. 2010; 10(207): 1471-2458.
5. Reifels L., Bassilios B., Spittal M.J., et al. Patterns and Predictors of Primary Mental Health Service Use Following
Bushfire and Flood Disasters. Disaster Medicine and Public Health Preparedness.
6. Wisconsin Department of Natural Resources. (2001) The Upper Fox Basin. Retrieved from
http://dnr.wi.gov/water/basin/upfox/upfox_flyer.pdf
7. Federal Emergency Management Agency. (2012) Hazus-MH: Know Your Risk. Retrieved from
http://www.fema.gov/media-library-data/20130726-1629-20490-9057/hz_overview_flyer_feb2012.pdf

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8. Wisconsin Department of Health Services (DHS). (2014) Wisconsin Climate and Health Profile. Retrieved from
https://www.dhs.wisconsin.gov/publications/p0/p00709.pdf
9. National Climatic Data Center (NCDC), Storms Events Database. (2015) Flooding Results for Adams, Calumet,
Columbia, Fond du Lac, Green Lake, Marquette, Washara, and Winnebago counties. Retrieved from
http://www.ncdc.noaa.gov/stormevents/
10. Centers for Disease Control and Prevention. (2015) Social Determinants of Health: Know What Affects Health.
Retrieved from http://www.cdc.gov/socialdeterminants/faqs/index.htm

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Appendix I. Geocoding Methods
GEOCODING ADDITIONAL DATA LAYERS
1. Open excel file in ArcMap
2. Export file as a database or .dbf file and save
3. Click on File
o Scroll down to Add Data
o Scroll over to Geocoding
4. Click on Geocode Addresses
o Click Add
o Select the Street_Address.loc (or locator file)
o Add X and Y columns to Match Fields
o Once completed, go back to File, Add Data, Geocoding and click on Review/Rematch
5. Review Match Results
o Look for match percent, rematch addresses that have a viable match
o Note the number that could not be matched
To increase spatial accuracy of the geocoding process, a second method was used.
1. Open Centrus Desktop
2. On Tables Tab:
a. Click on Browse and add your .csv file
b. Click on the In-Place Update
3. On Address Coding Tab:
a. Review to make sure the field names match up; e.g. Name, Street, City, State, Zip
b. On the Lower Right – Address Elements
c. Click on Longitude, Latitude, Match Code, Location Code, Result Code
d. Move these over by clicking on the >> new button
4. On the top Tool Bar click on Batch Process Task
a. Ignore the first message, click okay
b. This will take a few minutes
5. Reopen Centrus Desktop
a. Open the Centrus file *this is the summary report
b. Results will be in the file with codes on which level in the hierarchy the address was geocoded to

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LARGE EMPLOYER
NAME STREET CITY STATE ZIP
Tw Design & Mfg Llc 33 West St Montello WI 53949
Grede Wisconsin Subsidiaries Llc 242 South Pearl Street Berlin WI 54923
City Of Berlin 108 N Capron St Berlin WI 54923
MAJOR BANKS
NAME STREET CITY STATE ZIP
Us Bank 212 West Edgewater St Portage WI 53901
Us Bank 238 West Wisconsin St Portage WI 53901
National Exchange Bank And Trust 24 West Street Montello WI 53949
1st NATIONAL BANK 408 Main Street Montello WI 53949
Uw Oshkosh Credit Union 90 Wisconsin St Oshkosh WI 54901
Us Bank 111 North Main St Oshkosh WI 54901
Health Care Credit Union 600 South Main St Oshkosh WI 54902
First Business Bank 230 Ohio Street Oshkosh WI 54902
Fnb Fox Valley 400 North Koeller St Oshkosh WI 54902
Choice Bank 2450 Witzel Ave Oshkosh WI 54904
M&I Bank 2100 Omro Rd Oshkosh WI 54904
Bmo Harris Bank 2060 Omro Rd Oshkosh WI 54904
Anchor Bank 240 Broadway Street Berlin WI 54923
Health Care Credit Union 2700 West 9th Ave Oshkosh WI 54904
First National Bank 120 Alder Avenue Omro WI 54963
Horicon Bank 515 Hill Street Green Lake WI 54941
Citizens Bank 124 East Main Street Omro WI 54963
American Bank 200 West Main Street Omro WI 54963
Us Bank 102 South Pearl Street Princeton WI 54968
Citizens Bank 124 West Main Street Winneconne WI 54986
HOSPITAL
NAME STREET CITY STATE ZIP
Mercy Medical Center Of Oshkosh 500 S Oakwood Rd OSHKOSH WI 54904
MEDICAL CLINICS
NAME STREET CITY STATE ZIP
Catholic Charities Central 230 Central Ave MONTELLO WI 53949
Accurate Imaging 2895 Algoma Blvd OSHKOSH WI 54901
Evergreen Garden Place 1130 N Westfield Street OSHKOSH WI 54901
Arborview Manor 1520 Arboretum Dr OSHKOSH WI 54901
Evergreen Health Center 1130 N Westfield St OSHKOSH WI 54902
Evergreen Sharehaven Home 1095 N Westfield St OSHKOSH WI 54902
Garden Heights Cbrf 1130 N Westfield St OSHKOSH WI 54902
Lss Adult Day Services 200 N Campbell Rd OSHKOSH WI 54902
Brookdale Oshkosh 190 Lake Pointe Dr OSHKOSH WI 54904
Azura Memory Care Of Oshkosh 2220 Brookview Ct OSHKOSH WI 54904
Fmc Oshkosh 2700 W 9th Ave Ste 101a OSHKOSH WI 54904
American House Of Berlin 123 S Pearl St BERLIN WI 54923
Ccls Mound Street 284 Mound St BERLIN WI 54923
Marthas Inc 404 W Water St PRINCETON WI 54968
Appendix II. 500 Foot Buffer Results by Data Layer

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WEM FACILITIES WITH HAZARDOUS MATERIALS
NAME STREET CITY STATE ZIP
Grede-Berlin 242 South Pearl Street BERLIN WI 54923
Frontier Communications 19 West Street MONTELLO WI 53949
At&T-Pl0308 215 South Webster OMRO WI 54963
Time Warner Cable 490 N. Campbell Rd. OSHKOSH WI 54902
Oshkosh Wastewater Treatment Plant 233 N. Campbell Road OSHKOSH WI 54902
Brunswick Corp-Mercury Marine Plant 33 505 Marion Road OSHKOSH WI 54901
Sonoco Protective Solutions, Inc. 109 Lynch Street PARDEEVILLE WI 53954
Portage Municipal Garage 616 Washington Street PORTAGE WI 53901
Henry G. Meigs Llc 1220 Superior Street PORTAGE WI 53901
Crawford Propane - Bulk Plant #1 904 Superior St. PORTAGE WI 53901
Crawford Oil Company-Bulk Plant #1 904 Superior Street PORTAGE WI 53901
Tank Technology, Incorporated 500 River Road PRINCETON WI 54968
DAVID S. LIEBL FACILITIES WITH HAZARDOUS MATERIALS
NAME STREET CITY STATE ZIP
Crawford Oil Company-Bulk Plant #1 904 Superior Street PORTAGE WI 53901
Crawford Propane - Bulk Plant #1 904 Superior St. PORTAGE WI 53901
Henry G. Meigs Llc 1220 Superior Street PORTAGE WI 53901
Portage Municipal Garage 616 Washington Street PORTAGE WI 53901
Berlin Well #4 W. Cumberland Street BERLIN WI 54923
Grede-Berlin 242 South Pearl Street BERLIN WI 54923
Tank Technology, Incorporated 500 River Road PRINCETON WI 54968
Montello Wastewater Treatment Pl. 399 5th Street MONTELLO WI 53949
Advanced Disposal Services Midwest 250 Alder Avenue OMRO WI 54963
At&T-Pl0308 215 South Webster OMRO WI 54963
Brunswick Corp-Mercury Marine Plant 33 505 Marion Road OSHKOSH WI 54901
Mercy Medical Center 500 S Oakwood Road OSHKOSH WI 54903
Oshkosh Transit System 926 Dempsey Trail OSHKOSH WI 54902
Oshkosh Wastewater Treatment Plant 233 N. Campbell Road OSHKOSH WI 54902
Pioneer Resort And Marina 1100 Pioneer Dr OSHKOSH WI 54904
Sjs International, Llc 5691 Courtney Plummer Road WINNECONNE WI 54986
Time Warner Cable 490 N. Campbell Rd. OSHKOSH WI 54902

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Appendix III.

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Appendix IV.

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Appendix V. 100 Year Flood Scenario - Building and Economic Loss Estimates
100 Year Flood Scenario
General Building Stock Damage
 335 buildings at least moderately damaged
 28 buildings completely destroyed
 330 of the buildings are residential
o 138 are 41%-100% damaged
 4 of the buildings are commercial
 1 is government
Expected Damage to Essential Facilities
Hazus-MH estimates 0% damage to:
 39 Fire Stations
 5 Hospitals
 18 Police Stations
 103 Schools
Building-Related Economic Losses
 Direct Building Losses - “Estimated costs to repair or replace the damage cause to the building and its
contents.” Estimated Loss – $174,730,000
 Business Interruption Losses - “The business interruption losses are the losses associated with inability
to operate a business because of the damage sustained during the flood. Business interruption losses
also include the temporary living expenses for those people displaced from their homes because of the
flood.” Estimated Loss – $750,000

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Appendix VI. 500 Year Flood Scenario – Building and Economic Loss
500 Year Flood Scenario
General Building Stock Damage
 504 buildings will be at least moderately damaged
 61 buildings completely destroyed
 All of the buildings are residential
o 257 are 41%-100% damaged
Expected Damage to Essential Facilities
Hazus-MH estimates 0% damage to:
 39 Fire Stations
 5 Hospitals
 18 Police Stations
 103 Schools
Building-Related Economic Losses
 Direct Building Losses- “Estimated costs to repair or replace the damage cause to the building and its
contents.” Estimated Loss – $254,320,000
 Business Interruption Losses- “The business interruption losses are the losses associated with inability
to operate a business because of the damage sustained during the flood. Business interruption losses
also include the temporary living expenses for those people displaced from their homes because of the
flood.” Estimated Loss – $970,000

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Appendix VII.

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Appendix VIII.