Using the SLOSH Data Model to Create a Vulnerability Index Based off Poverty in Nassau County, New York

Nicholas Manzione

SUNY College of Environmental Science and Forestry

Introduction

Storm surge due to hurricanes have caused many direct and indirect deaths. Powerful storms such as Hurricane Katrina, which was responsible for approximately 1,500 deaths directly and indirectly from storm surge [1], have also contributed to destroying major structures as well. This was also the case with Super Storm Sandy, as drowning, which was caused by the surge generated by the hybrid storm, was the main cause of death according to the morbidity and mortality report from the Center of Disease Control and Prevention [2]. However, deaths such as these can be avoided with better evacuation plans, advanced warning systems [2] and overall better preparation. One example of the lack of preparation that came during Super Storm Sandy was seen in New York City, as the flood maps were not updated since 1983 [3]. Although this is one example of unpreparedness some surrounding areas faced, the larger concern surrounds around the idea that coastal areas are not adapted to handle these stronger storms. A New York Times article outlines this as a concern, especially for New York City, but it was estimated that about $10.4 Billion dollars would have to be spent to put the necessary measures to protect these areas that could be inundated by a tropical weather event [4].

Considering the monetary value that could be spent to adapt to these events structurally, government officials need to look into areas that are vulnerable from a socioeconomic standpoint. Poverty is one example of vulnerability, as people who are impoverished, according to Alice Fothergill, may be less likely to perform the necessary actions to mitigate the effects of these hazardous events [5]. Due to this as well as other disadvantages living under or on the poverty line, serious damage to a living space or even death could occur. The purpose of this project is to understand how potential hurricane events could affect Nassau County, which according to the US census, has the third highest per capita income of New York state at $42,949[6], but what town or city in Nassau County has the highest population that is under the poverty line that would be severely affected by a hurricane event.

Materials

After Super Storm Sandy, NOAA updated flood maps for all coastal areas on the eastern seaboard. This model is known as Sea, Lakes and Overland Surges from Hurricanes or SLOSH [7]. Based on different environmental factors, physics equations based on waves, historical data and simulations based on hurricane size, strength and weather patterns, NOAA developed this vector model with the intention of showing the probability of where inland inundation would most occur. Based on these simulations, the inundation stops at category 4 (figure 1).

Nassau County Tax parcel data and a Nassau County Town Boundary vector data set from Bowne Management Systems [8], a private data management firm located in Mineola, New York was acquired with consent from the Nassau County GIS operation. Based on the projected coordinate system NAD_1983_StatePlane_New_York_Long_Island_FIPS_3104_Feet and the GCS_North_American_1983 geographic coordinate system, this projection will serve the basis for any un-projected data.

The last dataset used for this analysis is the 2014 5 - year American Community Survey found on American Fact Finder [9]. From the metadata, Age and Sex, Poverty and Income were chosen. The fields used and the metadata explanation for each of these data sets are shown below (Table 1). The minority groups chosen were based on ACS 2014 data for the top minorities in Nassau County.

Figure 1: SLOSH data model clipped to the Nassau County Boundary

Table 1: Data used from the ACS

Income Age and Sex PovertyB1903e1 – Median Household Income in the past 12 months(2014 adjusted inflation dollars) : total households (Estimate)

B01001e1 - SEX BY AGE: Total: Total population -- (Estimate)

B17001e2 - POVERTY STATUS IN THE PAST 12 MONTHS BY SEX BY AGE: Income in the past 12 months below poverty level: Population for whom poverty status is determined -- (Estimate)

B01001Be1 - SEX BY AGE (BLACK OR AFRICAN AMERICAN ALONE): Total: Black or African American alone -- (Estimate)

B17001Be2 - POVERTY STATUS IN THE PAST 12 MONTHS BY SEX BY AGE (BLACK OR AFRICAN AMERICAN ALONE): Income in the past 12 months below poverty level: Black or African American alone population for whom poverty status is determined -- (Estimate)

B01001De1 - SEX BY AGE (ASIAN ALONE): Total: People who are Asian alone -- (Estimate)

B17001De2 - POVERTY STATUS IN THE PAST 12 MONTHS BY SEX BY AGE (ASIAN ALONE): Income in the past 12 months below poverty level: Asian

alone population for whom poverty status is determined -- (Estimate)

B01001He1- SEX BY AGE (WHITE ALONE, NOT HISPANIC OR LATINO): Total: White alone, not Hispanic or Latino population -- (Estimate)

B17001He2 - POVERTY STATUS IN THE PAST 12 MONTHS BY SEX BY AGE (WHITE ALONE, NOT HISPANIC OR LATINO): Income in the past 12 months below poverty level: White alone, not Hispanic or Latino population for whom poverty status is determined -- (Estimate)

B01001Ie1 - SEX BY AGE (HISPANIC OR LATINO): Total: Hispanic or Latino population -- (Estimate)

B17001Ie2 - POVERTY STATUS IN THE PAST 12 MONTHS BY SEX BY AGE (HISPANIC OR LATINO): Income in the past 12 months below poverty level: Hispanic or Latino population for whom poverty status is determined -- (Estimate)

Methods

The analysis started with finding the parcels that were inundated by the SLOSH data model. Although this could have been a spatial join, it was soon discovered that multiple duplicates of the parcels were found. This was solved by querying the SLOSH model starting with category 4 and adding a short integer field to the parcel data set called “storm_cat.” After clicking on the field calculator and typing in storm_cat = 4, this process was repeated three more times. After completing the reverse selection, this data was spatially joined by location twice to the Nassau County Town Boundary to get the inundated parcel count for a category one and category four hurricane.

After finding the necessary metadata for the information listed in table 1 and clipping out the ACS 2014 shapefile, each table was joined by attribute to its respective census tract by GEO_ID. Each information set was then exported as its own polygon data set. Then this was changed to a point data set in order to avoid nulls and or duplicates when joining by location. This was different for the category 1 inundated town boundary, as the inundated parcels queried out to category one were used instead to reflect those points that were within that particular category. The Boundary for Long Beach, New York was used as a guide, as every census tract was inundated by a category one hurricane. After editing each point file and then exporting them after a select by location in the category one inundated parcels, these ACS points were then spatially joined with the Nassau County Town Boundary inundated at category one. Category four was an easier join, as the SLOSH model for a category four hurricane covered all of the ACS points. After these joins were done, all of the data was intersected with each other to create one Nassau County Boundary polygon file. An index based on the count data for inundated boundaries and the count for B17001e2 were conducted, with 4 being the highest and assigned to numbers in the tens to mid thousands via the field calculator and kept going down until the index reached 0, which reflected a low risk. Another field titled areas of interest was added to each intersected boundary, then with the field calculator, poverty and inundated boundaries that contained the parcel data were added together to create the vulnerability index. After maps reflecting the inundation of boundaries, inundation of poverty and the vulnerability index, parcels that scored 7 or 8’s were further analyzed based on race to see which race was affected by each hurricane category.

Results

Based off these models, the top three towns that received the highest scores for the vulnerability index based on a category one hurricane were Freeport, Long Beach and Oceanside. Freeport and Long Beach also had the highest total of people below the poverty line affected by the category one model (Table 2). Inwood had the third most affected people below the poverty line

Table 2: Highest total of people below the poverty line affected by the category one SLOSH model

Town Index Total PeopleFreeport 8 3471Long Beach 8 2814Inwood 6 1584

Based off these numbers divided by the respective short name (ex:B17001He1) Hispanics would be the most affected by a category one hurricane in Freeport, taking approximately 70 percent of the total, White no Hispanic would be affected the most in Long Beach at approximately 37 percent and Hispanics would be the most affected in Inwood coming in at approximately 54 percent.

In the category four model, Freeport remained the highest total of people below the poverty line followed by Valley Stream and the Roosevelt (Table 3).

Table 3: Highest total of people below the poverty line affected by the category four SLOSH model

Town Index Total PeopleFreeport 7 6139Valley Stream 8 3152Roosevelt 6 2835

Applying the same metrics as before, Freeport had the most Hispanics at approximately 68 percent, Valley Stream had the most Hispanics as well at approximately 44 percent and Roosevelt had the most African Americans at approximately 58 percent.

Below are the maps that were created based on parcel inundation, poverty inundation and the vulnerability index (Figures 2 – 7).

Figure 2: inundation of category 1

Figure 3: inundation of category 4

Figure 4: Poverty inundated by category 1

Figure 5: Poverty inundated by category 4

Figure 6: Vulnerability index for category 1

Figure 7: Vulnerability index for category 4

Discussion/Considerations

Poverty based off the figures and tables can be a good indicator of what government officials can do regarding helping the people of these areas come up with an evacuation plan. This can range from pamphlets showing these people evacuation routes, shelters and other necessary human resources, public meetings to inform them about hurricane safety and even increasing government assistance during these hurricane events. Some considerations that could be taken into account would be writing emergency pamphlets in English and Spanish, so both groups would have reading material based on their native language. This could help in areas such as Freeport, where there are many Hispanics below the poverty line living in these areas.

Although poverty can be a good indicator, there are other ACS data sets that could be useful for this vulnerability index, such as age and disability. This could have been done, but the metadata was vague and caused a lot of issues with what should be suitable for the analysis, as there are many subgroups within the American Community Survey that includes households, age and sex and families. This can make searching for the right data much harder. This occured with the median income, as when the data was summated, it was off the value found on the US Census for each of the towns in Nassau County, which although made sense for the category one dataset, as it was smaller, it did not check out with the US Census number for median income when many boundaries were completely covered by the SLOSH model. The median income in this case, was used more as a guide to see if there was any consistency with the inundated poverty maps. However, it appeared that an array of high median income areas as well as low median income areas were equally inundated by both the category one and category four hurricane projections. This was not thought of earlier and in this case should be looked at for a better analysis of these vulnerable areas.

Another issue with the data was the differences between the un-projected American Community Survey data and the Nassau County Town Boundary. Before changing the ACS data to a point file, one failed method was spatially joining the census tracts to the Town Boundaries, which in this case, created sliver polygons and caused for unwanted data to get into the analysis.

Conclusion

Although poverty could help indicate a possible inundated area, the more data that factors in such as senior citizens and people with disabilities or in group homes can make this analysis a lot more efficient and could give a better indication if these vulnerabilities exist in these inundated areas. If they do, necessary measures should be taken by the government, in the form of man-made or natural structures that levee the storm surge from hurricanes. This can not only save money, but could save lives of people in the future.

References

[1] N. (n.d.). Storm Surge Overview. Retrieved May 03, 2016, from http://www.nhc.noaa.gov/surge/ [2] Noe, R. S., Chamblee, G. A., Murti, M., Yard, E., Wolkin, A., & Casey-Lockyer, M. Deaths Associated with Hurricane Sandy: October–November 2012.[3] Rice, D., & Dastagir, A. E. (2013, October 29). One year after Sandy, 9 devastating facts. Retrieved April 12, 2016, from http://www.usatoday.com/story/news/nation/2013/10/29/sandy-anniversary-facts-devastation/3305985 / [4] Navarro, M. (2012, September 10). New York Is Lagging as Seas and Risks Rise, Critics Warn. Retrieved April 12, 2016, from http://www.nytimes.com/2012/09/11/nyregion/new-york-faces-rising-seas-and-slow-city-action.html?_ r=1 [5] Fothergill, A., & Peek, L. A. (2004). Poverty and disasters in the United States: A review of recent sociological findings. Natural hazards, 32(1), 89-110.[6] U. (2014). Nassau County Quick Facts. Retrieved May 03, 2016, from http://www.census.gov/quickfacts/table/LND110210/36059 [7] N. (2014, June 1). Sea, Lake, and Overland Surges from Hurricanes (SLOSH). Retrieved April 12, 2016, from http:// www.nhc.noaa.gov/surge/slosh.php

[8] L.B (August 2014) Nassau County GIS Geodatabase, Retrieved January 19th, 2016 [9] A.F.F (2014) American Community Survey 5 year, Tract 36, New York, Retrieved April 24th, 2016 http://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml