Spatial Relationships between Socio-economic Indicators and Floodplain Maps in North Carolina: Are Digital Flood Insurance Rate Maps a Good Answer for Flood Policy? My proposal is to examine the spatial relationships between socio-economic indicators (parcel costs of property and population counts) as a function of the distance / elevation from a river. Currently floodplain zones, which are used as the basis for flood control policies and mitigation are derived through a model that looks at historic precipitation / stage flow and elevation change, without taking anything else into account. I am not sure that is the best method for establishing who needs to have flood insurance or needs to protect their home from floods. This proposal wants to determine how effective the new DFIRMS are and whether or not there is a better way to establish a flood control policy zone. Lauren Patterson December 6, 2006 Geography 593

Overview of Hazards in US ECONOMIC COSTS OF HAZARDS 90% of natural disasters in US are floods Shift from rural to urban Increasing costs through time Economic costs of hazards – 21% is flooding and another 19% is hurricanes – which typically involves extensive flooding 90% of natural hazards in the US are the result of floods Shift in last 15 years of floods occurring in rural areas to them occurring in urban areas – partially due to floodplain development (people are moving into flood areas), increasing hydrologic energy from climate change – especially in the southeast of the US, where it is supposed to get wetter, and urbanization of the landscape – flashier floods $6 - $10 Billion = all hazards $2.3 - $4 Billion = floods

U.S. Flood Control Policies Government Policy: Structural Control National Flood Insurance Program Mitigation and Floodplain Zoning 100 year floodplain concept Hurricane Floyd Source: Jackson, 2002 Flood control policy in the United States has been developing over the past 90 years in three main stages: structural control (dams and levees), the National Flood Insurance Program, and mitigation, largely by moving people out of floodplains. NFIP and mitigation both rely on this concept of the 100 year floodplain. The wisdom of using the 100 year floodplain has been drawn into question because: a) don’t have appropriate spatial or temporal resolution to discern in most areas the extent of the 100 year flood, b) flood maps are recreated every 10 to 20 years – urbanization and effects from global change might be happening at a faster rate, c) could be increasing vulnerability because only people inside the 100 year zone need insurance/mitigation – so it is possible that along the boundary of the 100 year floodplain is being rapidly urbanized with no preparedness. An example of the consequences of poor floodplain zones would be Hurricane Floyd in 1999. After the hurricane it was found that over 80% of flood damaged homes were outside of the 100 and 500 year floodplain boundaries. Much of this was attributable to having more floodplain maps and this has led to NC being the first partner with FEMA to create DFIRMS.

Research Questions What is the geographic relationship of people and property inside and outside of the 100 and 500 year floodplains? Specifically, is there a large increase in people and property directly outside the 100 year floodplain boundary, where regulations cease? This sets up the research questions I would like to address in this proposal. During a hazard, the number of lives lost and direct economic damage due to flooding is examined. I would like to take parcel data and population counts and graph them compared to elevation, distance, and land – use type within the 100 and 500 year floodplains. I would then like to take a cross-section of these attributed and plot them against one another to determine how socio-economic indicators change moving away from the floodplain. This would be an averaged value and a summed value. The integrity of the model could then be tested by taking random points throughout the county and seeing where they plot on the graph. The end product would be to determine how far from the river the insurance / mitigation policies should take effect based on the socio-economic locations within each county. 2) How do the DFIRM values for the 100 and 500 year floodplain compare with the original FIRM values?

Study Area and Data Availability Selected Counties Study Sites: 1) Wake County: Piedmont 2) Craven County: Coastal Plains 3) Buncombe County: Mountains The study area for this project will be three counties in North Carolina, each with a different topographic profile. The counties were selected based on whether both FIRM and parcel data were available. All of the data is freely available from the internet, so not a problem. COUNTY NLCD DEM POPULATION PARCEL DFIRMS TOPOGRAPHY Wake YES Piedmont Buncombe NO Mountain Craven Coastal

Methodology: Database Management Data structure is important to establish and maintain throughout the program in order to only have to change the workspace directory to run the program on any computer.

Methodology: Form 1 – Data Preparation NLCD, DEM, Population, Parcel COUNTY LEVEL NLCD, DEM, Population, Parcel NLCD DEM Population Data Parcel Data Project to NC State Plane (m) Mask to County Flow charts for preparing the data NC Counties Extract County

Methodology: Form 1 – Data Preparation Run a portion of the code as demo File = Workspace & "\Projected\" & County & "County.shp" If gp.Exists(File) Then MsgBox "The County file has already been clipped." Else Call ClipCounty(County, Workspace & "\Projected\County.shp", File) End If Dim ClipFile As String ClipFile = File File = Workspace & "\Projected\" & County & "DFIRMS.shp" MsgBox "The DFIRM file has already been clipped." Call ClipDFIRMS(Workspace & "\Projected\DFIRMS.shp", File, ClipFile) Dim gp As Object Set gp = CreateObject("esriGeoprocessing.GPDispatch.1") 'Check to see if the file already exists File = Workspace & "\Projected\Pop" & County & "00.shp" If gp.Exists(File) Then MsgBox "The population data has already been projected." Else Call ProjectFeatures(Workspace & “Raw\Population\" + County + "00.shp", File) End If Run portion of the code here for Craven County Population.

Methodology: Form 1 – Data Layers Created DEM NLCD DFIRM PARCEL 2000 POP FIRM These are the data files used to run the entire program. All of the raster data will eventually be resampled to the lidar 6.09 by 6.09 pixel cells for analysis.

Methodology: Form 2 – Creating Floodplain Layers 100 yr 500 yr Run Code for DFIRMS For the FIRMS and DFIRMS simply select by attribute 100 yr = A, AEFW, AFW while 500 year = Shaded X Extract to create a new shapefile

Methodology: Form 2 – Creating Analysis Layers 1 Herbaceous Wetlands Woody Wetlands 500 CultivatedCrops Pasture/Hay Grasslands Shrub Mixed Forest Evergreen Forest Deciduous Forest Barren Land 20 50000 High Density Residential 10 Commercial, Industrial High Density Developed 7 5000 Low Density Residential Water Population Building Tax NLCD Type The next portion was to redistribute parcel tax data and population data by block groups smartly. Used the NLCD to push people and building tax value into developed areas. This is useful in large population blocks and large parce data. So when people say the flood waters went right up to my porch – the building was not damaged and people were not affected. So large assumption that NLCD is accurate. So reclassified NLCD to put buildings in developed areas. People are more extreme in that during a flood situation – most likely they are taking shelter within in a structure of some sort.

Methodology: Form 2 – Creating Analysis Layers TaxSurface = FinParLand + FinParBuild Figure 4A: Tax Surface for Craven County Process for Reclassifying NLCD Process for Creating Land Tax Surface Process for Creating Building Tax Surface Process for Creating Total Tax Surface First, to create the land raster tax value. Rasterize the parcel blocks to the land value and divide the land value by the sum of the pixels in each parcel block. This evenly distributes the land value for each parcel block within that block boundary. Secondly, to create the building tax value – each reclassified pixel value is divided by the sum of the pixels within each parcel block group to get the percent of attraction of building tax to each pixel cell. This coefficient file then ranges between 0 and 1 with the sum of each parcel block = 1. The NLCD percent file is then multiplied by the rasterized building file to create the building tax surface. Lastly, the total tax surface is simply the sum of the land and building tax surfaces. The difference in the raster sum to the feature sum of tax value is less than 1%, so it is an acceptable method.

Methodology: Form 2 – Creating Analysis Layers The population surface is calculated in the same manner as the building – whereby the percent likelihood of each pixel is calculated by dividing the reclassified NLCD by the sum of the reclassified NLCD values in each block group. This file is then multiplied by the rasterized population file to create the population surface. Run Population Code

Methodology: Form 3 - Spatial Analysis 100 Yr DFIRM 100 Yr FIRM 100 /500 Yr Flood # People % Land Cover Type Property Value NLCD, DEM, Population Surface, Tax Surfaces Zonal Stats as a Table 500 Yr DFIRM 500 Yr FIRM 500 DFIRM/FIRM # People Affected Amt Land Cover Type Affected Parcel Money Affected Maximum DEM Height 100 DFIRM/FIRM - Socio-Economic Differences between Floodplains = Maps will be created to illustrate the spatial distribution of parcels, people, land cover, and elevation as they relate to the 100, 500, and the difference between the two floodplain zones. Secondly, data used to create the graphs above will be exported as a .dbf and placed into EXCEL for data analysis. The results expected is to find a relationship between vulnerability and physical flood hazards related to the geographic disposition of the area.

Methodology: Form 3 - Spatial Analysis Run Code for 1st Button Agree FIRM Differ DFIRM Differ Model builder – walk through zonal stats and maks. Walk through differences and intersection.

Results: Craven County Figure 9: Notice the increase in develpoment density between the 100 and 500 year floodplains. Notice also that the 100 year is below county average while the 500 year is above county average. This could reflect that floodplain zonation was having the intended effect of moving people out of the 100 year floodplain. Unfortunately, it might have also had the unintended effect of encouraging development directly outside the 100 year boundary.

Results: Wake County Population Tax This graph shows a small increase in development density between the DFIRMS and a larger increase between the FIRMS – which is what development has been based on until the last year or so. Notice that development is still less than the county average – which makes sense, since it is the piedmont and the majority of Wake county is suitable for development. Furthermore, due to the hills present in the piedmont, it restricts floodplain area.

Results: Buncombe County Population Tax No DFIRMS are available yet for western NC, so only the FIRMS were used. Still, you can see a large increase in development density between the 100 and 500 year floodplain. However, since it is a mountainous area, both densities are greater than the county density.

TOPOGRAPHIC SIGNATURE   N 2000 Pop % N Tax Val % N Area % N Pop Dens % N Tax Dens % Buncombe FIRM 100 - MOUNT 5.14 3.10 3.57 144 87 Wake FIRM 100 - PIEDMONT 2.47 2.81 9.16 27 31 Craven FIRM 100 - FLOODPLAIN 15.20 19.55 28.53 53 69 Buncombe FIRM 500 - MOUNT 2.20 0.46 0.23 941 197 Wake FIRM 500 - PIEDMONT 0.61 0.63 0.72 85 Craven FIRM 500 - FLOODPLAIN 4.03 3.40 1.15 352 297 N Tax Dense % Wake DFIRM 100 - PIEDMONT 2.17 2.55 8.80 25 29 Craven DFIRM 100 - FLOODPLAIN 11.76 15.78 22.54 52 70 Wake DFIRM 500 – PIEDMONT 0.49 0.59 1.58 37 Craven DFIRM 500 - FLOODPLAIN 4.50 3.22 1.75 258 184 The Area correlates well with the topographic signature – floodplain with little elevation change has an increased amount of area present within the DFIRM and FIRM boundaries. There is a 7 fold increase in population and 4 fold increase in property density between the 100 and 500 year floodplain for Craven county – reflecting a substantial increase in population and tax density outside of the 100 year floodplain boundary. Buncombe – mountainous has the smallest area within the floodplain boundaries as normalized to the county level – reflects the steep changes in topography. Due to the steep topography – it limits areas suitable for development, so greater development is occurring in flatter areas, such as the floodplains. This is illustrated by the high population and tax density. Also note the 7 fold increase in population and 2 fold increase in property value between the 100 and 500 year FIRM boundary – reflecting a substantial increase in development density outside the 100 year floodplain boundary. Wake – with a piedmont topography lies in between the coastal and mountain areas. There is lower development within the floodplain because the floodplain area is a smaller percent of the total county area and there is less development within the floodplain because the topography allows more area suitable for development. However, there is still a 3 fold increase in development density outside the 100 year floodplain. The DFIRM boundaries are remarkably similar to the FIRM boundaries – except that less people are protected in the 100 year floodplain and more people are located within the 500 year floodplain. Not what I would have expected in trying to improve floodplain management and safety from more disasters like Hurricane Floyd.

CONCLUSIONS Increased Development Density outside the 100 yr Floodplain Indicates floodplain boundaries are working? Indicates development directly outside regulation? Differentiation between different topographies influences floodplain boundaries and the extent of development within those boundaries. More people and property were protected in FIRMS than in the new DFIRMS The programming of this process enables rapid analysis of additional counties. Summing it all up: Easy to run more counties to determine trends in headwaters versuses floodplains. Can do for any county where data exists. Can also run more coastal, piedmont, and mountainous counties to determine if topographic singature is real.

QUESTIONS??? These pictures illustrate why it seems as though it should be important to include socio-economic indicators into the picture, because each one has a different level and type of development. QUESTIONS???

Results: DFIRM Versus FIRM Agreement 100 yr floodplain 500 yr floodplain WAKE CRAVEN These graphs show that for population and tax density – there is more disagreement than agreement between FIRMS and DFIRMS in the 100 year floodplain, but more agreement than disagreement between the 500 year floodplains.