Yosefa Ehrlich
Intro to GIS- Project 8
Extreme Storm Vulnerability in Cape Cod, MA
Project Goals:
Hazard assessments are common and important in areas that are susceptible to
natural disasters by virtue of their environment. This project will be an exercise in
vulnerability analysis. Vulnerability analysis answers the following questions:
1) Which groups of people are must vulnerable during a disaster?
2) What are the greatest environmental hazards in a community?
3) What areas are most vulnerable to disaster in a certain community given the prior
information?
Specific focus will be placed on Cape Cod, MA. As water levels rise and weather
conditions become more variable, islands and peninsulas will be at the greatest risk to
water induced disasters like hurricanes. This project will mirror typical vulnerability
analyses by addressing the same questions. It will also take the analysis one step further
by looking at accessibility to emergency services like hospitals and police stations.
Ultimately, I hope to compile two main categories of maps. The first category will
deal with socially vulnerable pockets. A series of maps will focus on a specific socially
vulnerable group based on age, income, background etc. Then a final social map will
compile risk assessment on a four item scale where 1 is low risk and 4 is high risk by
adding information from the previous series of maps.
The second will focus on infrastructure vulnerability by determining proximity to
important emergency resources (hospitals, fire stations, police stations etc.) where areas
that are further from the resources are more vulnerable. Again, information from a series
of maps will be collapsed into a four item scale to show which areas are most vulnerable
due to lack of infrastructure access.
A final map will be included that addresses the environmental layout of the region
by focusing on floodplains. This layer takes into account environmental conditions that
render an area vulnerable including elevation, land cover and proximity to water.
Methods:
Part I. Social Vulnerability Mapping
1) Add Massachusetts county and block group data layers from MassGIS under
Political_Boundaries and Census2000.
2) Select by location for Cape Cod counties (Barnstable) and block groups.
3) Export data layer and save the clip to use as the main template.
4) Insert relevant block group data layer from Census2000 (e.g. HH_Size_Family)
and join layer to block group using Logrecno.
5) Open properties  symbology  Fields ‘value’ and select the proper layer within
the household size family (F_HSHLD_7). Make sure it’s classified as natural
breaks with 4 classes.
6) Open attribute table and create a new field (under the ‘options’ button on the
bottom). Save as Fam_7_Plus.
7) Under the options tab click ‘select by attributes’ and set the field value
(F_HSHLD_7) to less than the lowest 25% of the natural breaks. For example, if
my the four classes break down to 1-5, 5-10, 10-15, 15-20 I would set
F_HSHLD_7 < 5.
8) Whichever fields in the attribute table that apply to that characterization will be
highlighted. Right click the new field- Fam_7_Plus, and click field calculator.
9) Set Fam_7_Plus = 1 to signify lowest vulnerability.
10) Repeat for the remaining 75% until you have classified all the data 1-4.
11) When finished, you can remove the join from the attribute table, so only the new
field remains.
12) Repeat for all census information.
13) To create total social vulnerability map use field calculator to add together all the
other fields created
14) Repeat above steps to divide information into quarters according to natural
breaks, select by attribute for each quarter and then assign it to label 1-4 using
field calculator. See field calculator image below.
Field Calculator: Used to compile
information across all
infrastructure/ social vulnerability
groups to form one big map of
vulnerability. Same technique was
used to create one overall
vulnerability map across social and
infrastructure.
Part II. Infrastructure Vulnerability
1) Insert relevant layer file from M/State/MA/MassGIS/Infrastrucute (e.g.
PoliceStations).
2) Select by location to the Cape Cod county block group (so that all the information
stays in the same attribute table) and export the clip. Save clip under Hdrive_Final
Project_ shp.file. (Cape_Cod_PoliceStation)
3) Add the layer and delete the original police station.
4) Activate spatial analyst by opening ‘tools’  ‘extensions’ and clicking ‘spatial
analyst’. You can activate the tool bar under ‘view’  ‘toolbars’.
5) Open spatial analyst ‘options’  ‘distance’  ‘straight line’
6) Under ‘distance to’ set it to Cape_Cod_PoliceStation and set the ‘cell size’ = 30
7) Open spatial analyst ‘options’  ‘zonal statistics’. Set ‘zone field’ = logrecno. Set
‘value raster’ = Distance to Cape_Cod_PoliceStation. Select ‘ignore NoData
caluclations’ and ‘join output table’. Unselect ‘chart’. Save as PoliceStation_Dist
(under zone_tables in H drive). Click OK
8) Open Cape_Cod_Blkgrp_LegAttrib attribute table. Under options (on bottom)
add new field ‘Poli_Mean’ and set ‘type’ = double (because numbers have
decimals).
9) Right click on the new field (Poli_Mean) and open field calculator. Set it = to
PoliceStation.Dist_Mean (at the bottom of the options) by double clicking it.
10) Back in the attribute table add another new field under ‘Poli_Dist’. Set the type =
short integer.
11) Back in data view right click Cape_Cod_Blkgrp_LegAttrib and open its
properties  symbology. Set field value to Cape_Cod_Blkgrp_LegAttrib. Poli.
Mean. Make sure its broken into 4 classes under natural breaks.
12) Return to attribute table for Cape_Cod_Blkgrp_LegAttrib and under options click
select by attribute. Set PoliceStationDist.Mean< 5 (as explained above it’s the
lowest 25%)
13) Left click new field Poli_Dist and select field calculator. Set it to = 1 to signify
lowest vulnerability.
14) Return to attribute table and click options- select by attribute again. This time set
Poli_Dist >=5 AND Poli_Dist < 10. \
15) Repeat the same procedure with field calculator only set it = 2.
16) Repeat for the remaining 75% until you have classified all the data 1-4.
17) When finished, you can remove the join from the attribute table, so only the new
field remains.
18) Repeat for all infrastructure information.
19) To create total infrastructure vulnerability map use field calculator to add together
all the other fields created.
20) Repeat above steps to divide information into quarters according to natural
breaks, select by attribute for each quarter and then assign it to label 1-4 using
field calculator. Refer to field calculator image above.
Part III. Floodplains
1) Insert floodplains layer as designated below.
2) Include SFHA layer (determines whether an area is in or out of the floodplain)
3) Select by attribute for ‘IN’
Data Layers (includes file location and year the data represents):
Social Vulnerability: (2000 census data)
% 65 +
# of Households with 7 + people
% Low Income
# of Non-English households
% 18 –
# of HsHlds built before 1959
% Minority
# of Male Population 25+ with
high school diploma
# of Female Population 25+ with
high school diploma
# of workers 16 + who commute to
work by car/ van/ or truck
Infrastructure:
Schools
Police stations
Hospitals
Airports
Fire stations
LegAttrib
LegAttrib
LegAttrib
LegAttrib
LegAttrib
CEN2K_BG_HOUS_STRUCT_AGE
LegAttrib
CEN2K_BG_ED_ATTAIN_GEN_AGE
 M_HSGRAD
CEN2K_BG_ED_ATTAIN_GEN_AGE
 F_HSGRAD
CEN2K_BG_TRNS_COM_MEANS 
CTV
M/State/MA/MassGIS/ Infrastructure
(December, 2007)
M/State/MA/MassGIS/ Infrastructure
(February, 2007)
M/State/MA/MassGIS/ Infrastructure
(January, 2004)
M/State/MA/MassGIS/ Infrastructure (June,
2006)
M/State/MA/MassGIS/
Infrastructure(February, 2007)
Environmental Vulnerability/ Land Use:
Flood zones
MassGISRegulated_AreasFEMA Q3
FloodSFHA (July 1997)
Difficulties and Limitations:
If a project of this sort were to be executed professionally where results would
bear real-life consequences, several further steps should be taken. First, accuracy of data
was never determined. Since a goal of this project is to develop a visually accurate map
of what areas are most vulnerable, accuracy is an important consideration. Assuming city
planners were to look at distance from certain emergency services, having inaccurate data
about the location of a hospital could lead to misplacement of roadblocks or
inappropriately cleared lanes in a highway. However, the census information does not
require positional accuracy. What it does require to be considered valid is current data.
All the census information is nearly 9 years out of date, as data was compiled from the
2000 Census. This can seriously mislead city planners. Most of the infrastructure
information is relatively current (within the last 4 years), but the floodplain data is nearly
12 years old. In this time period many more surfaces may have been paved for
development thereby affecting flood plain area limits. A more professional assessment
may want to look at more recent information.
Another logistical limitation of this project is that in determining distances from
various infrastructure sites, I used the spatial analyst ‘straight line’ feature. This tool
measures the distance from a given point and rates areas on a continuous scale based on
those distances. The concern here is that this distance is measured by a straight line from
the point of interest. Due to the triangular shape of Cape Cod, where the peninsula almost
curves in on itself, it would be impossible for residents of the northernmost tip to drive
straight down to the base of the peninsula without drowning. Clearly the straight-line
measurement is inadequate and underestimates actual distances.
A third limitation of this kind of project is that all the information was determined
by block group. Unfortunately, it is impossible to tell where specifically in the block
group people of a certain demographic are most concentrated, since the vulnerability
rating is spread across the entire block group area. In a real emergency situation this may
lead emergency service people astray in location hazard areas.
A more theoretical concern resulting from this project is that in assembling the
total vulnerability maps, all the variables were weighed evenly. However, in actuality
some variables contribute more heavily to vulnerability than others. For instance, low
level of attained education does not render someone as vulnerable as stark poverty. Many
people complete their education after high school and go on to live successful lives.
Living below the poverty line, though, makes all aspects of life extremely difficult,
especially in light of extreme storms or disasters. Considering these two variables equally
in determining the total social vulnerability map is doing a disservice to the people who
are at greater risk.
Another more abstract point is that some of the variables are of questionable
importance to vulnerability. For instance, populations who commute to work by
car/truck/van are in some ways more vulnerable and in others less. Spending a portion of
your day on the road makes you vulnerable in the event that an extreme storm hits during
rush hour while people are trapped in their cars behind lines of traffic. They are also more
likely to get into automobile accidents during sour weather conditions. Conversely,
assuming these commuters own cars places them at a greater advantage over some other
demographics. Prior to extreme storms, this pocket of society may have the foresight (and
capability) to drive themselves and their families out of the area until the weather calms.
This assessment presented this variable linearly and was incapable of expressing its
multi-faceted nature.
Conclusions and Future Research Directions:
By looking at the two total vulnerability maps it seems they are nearly mirror
images of one another. It would seem that the areas at the highest infrastructure risk are at
the lowest social risk and vice versa. Such a polarization of vulnerability may make it
difficult for city planners to isolate a particular area to fully service; however, it provides
them with the opportunity to address regions by their needs. In fact, this may prove more
efficient, since planners can allocate their resources very specifically between block
groups. For example, considering the “arm” seems at a low social vulnerability risk but at
an extremely high infrastructure vulnerability risk, during an emergency situation,
planners could ensure that an exit route is available for “arm” residents but that services
like clean water and English translators are provided for resents in the more southern/
central region of Cape Cod.
In the future, such an analysis may want to control for the logistical and
theoretical shortcomings and limitations of this assessment listed above. Perhaps
accuracy and currency should be determined and assessed prior to use of any data layers.
Further, variables should be weighed according to degree of vulnerability. Finally, future
research into this area may want to take advantage of statistical tools available through
GIS and SPSS or other statistics software programs. Determining percents of people at
risk may help in deciding which regions of Massachusetts are at highest risk and
therefore require the most aide and attention during an emergency. Ultimately, future
research should add more variables to attain a more complete understanding of
vulnerability so that in an emergency situation people are best helped at their level of
need.
Completed Maps:
Infrastructure Vulnerability Maps:
Social Vulnerability Maps:
Total Vulnerability (across social and infrastructural variables):
Floodplains: