An Integrative Approach to Assessing Property Owner Perceptions and Modeled Risk to Coastal Hazards

International Journal of

Geo-Information

Article

An Integrative Approach to Assessing Property

Owner Perceptions and Modeled Risk to

Coastal Hazards

Huili Hao *, Devon Eulie and Allison Weide

Department of Environmental Sciences, University of North Carolina, Wilmington, NC 28403, USA;

eulied@uncw.edu (D.E.); [email protected] (A.W.)

* Correspondence: haoh@uncw.edu

Received: 6 February 2020; Accepted: 16 April 2020; Published: 23 April 2020



 

Abstract:

Coastal communities are increasingly vulnerable to changes in climate and weather, as well

as sea-level rise and coastal erosion. The impact of these hazards can be very costly, and not

just in terms of property damage, but also in lost revenue as many coastal communities are also

tourism-based economies. The goal of this study is to investigate the awareness and attitudes of

full-time residents and second-home property owners regarding the impact of climate and weather

on property ownership and to identify the factors that most inﬂuences these attitudes in three coastal

counties (Brunswick, Currituck, and Pender) of North Carolina, USA. The majority of previous

studies have focused on only full-time residents’ risk perceptions. Given the fact that these coastal

communities have a high percentages of second homes, this study ﬁlls that research gap by including

second-home owners. This study integrates both social (survey data) and physical (geospatial coastal

hazards data) aspects of vulnerability into a single assessment to understand the determinants of

property owners’ risk perceptions and compare their perceived risks with their physical vulnerability.

The study also compared the utility of a global ordinary least square (OLS) model with a local

geographically weighted regression (GWR) model to identify explanatory variables in the dataset.

The GWR was found to be a slightly better ﬁt for the data with an R

of 0.248 (compared to 0.206 for

the OLS). However, this was still relatively low and indicated that this study likely did not capture

all of the factors that inﬂuence the perceptions of vulnerability in patterns of property ownership

(whether full-time residents or second-home owners). The geospatial variables used to determine

coastal vulnerability were found not to signiﬁcantly impact perceptions related property ownership,

but did provide additional insight in explaining spatial patterns of the response variable within

each county.

Keywords: coastal hazards; CVI; risk perceptions; climate change

1. Introduction

The coast of North Carolina is subject to severe weather events, erosion, and ﬂooding in the

low-lying coastal plain. This can be costly to state and federal governments and leave communities

with infrastructure and property damage. The National Oceanographic and Atmospheric Agency

(NOAA) annually calculates the cost of weather and climate related hazards (starting in 1980; [

]).

Overall, the number (and total cost) of billion-dollar disasters is consistently increasing over time [

This is due to a combination of changes in the intensity and frequency of hazard events, as well

as population growth in hazard-prone areas [

]. World-wide over 1 billion people live in these

high-risk zones and that number is expected to grow [

]. In 2016, Hurricane Matthew alone cost state

and federal governments approximately $97 million in ﬁnancial assistance to those aﬀected by the

ISPRS Int. J. Geo-Inf. 2020, 9, 275; doi:10.3390/ijgi9040275 www.mdpi.com/journal/ijgi

ISPRS Int. J. Geo-Inf. 2020, 9, 275 2 of 19

ﬂooding [

]. More recently, 2018 was classiﬁed by NOAA as the fourth most costly year on record

for hazards ($91 billion). This included Hurricane Florence that made landfall on the NC coast and

cost an estimated $24 billion [

]. Overall, the Atlantic and Gulf coasts have exhibited an increase in

tropical storm activity since the 1990s, leading to longer term impacts on both coastal communities and

ecosystems [

]. These environmental risks contrast with the attraction of NC’s coastal communities

to tourists and residents alike. This study seeks to ascertain what factors inﬂuence the perceptions of

full-time residents and secondary homeowners regarding the eﬀects that climate and weather risks

have on property ownership.

Research suggests the importance of community member perceptions of climatic and

weather-related risks, in addition to scientiﬁc knowledge and measurements of these risks [

This is due to risk perceptions associated with weather and climate inﬂuencing reactions to policy and

decisions made at the local, state, and federal levels. These include: readiness to evacuate in case of

storms, mitigation and preparation eﬀorts, and ﬂuctuations in property values.

This study targets three high-amenity, tourist-based coastal communities in North Carolina:

Brunswick, Currituck, and Pender counties (Figure 1). Because Brunswick, Currituck, and Pender

counties have largely tourism-based economies, vacation homes make up a signiﬁcant portion of

property ownership. For instance, in Currituck County, secondary home ownership is 43% of the

total home ownership in the county. Our study incorporates secondary homeowners into the sample

(accounting for 53% of respondents), whereas previous studies have failed to collect survey data on this

population. This represents a gap in many previous studies which have essentially not included almost

half of the homeowners in some of these counties. Second home development and associated tourism

also often impact decision making regarding the county’s economy, environment, and community

culture. From a physical hazard perspective, these counties also represent a range of coastal conditions

and morphologies across the state’s coastal zone. While weather and climate related risks can impact the

entire coastal zone, these counties represent a range of common topography, barrier island morphology,

and hydrologies, and therefore potential physical vulnerability to hazards [8].

ISPRS Int. J. Geo-Inf. 2020, 9, x FOR PEER REVIEW 2 of 21

cost an estimated $24 billion [1]. Overall, the Atlantic and Gulf coasts have exhibited an increase in

tropical storm activity since the 1990s, leading to longer term impacts on both coastal communities

and ecosystems [4,5]. These environmental risks contrast with the attraction of NC’s coastal

communities to tourists and residents alike. This study seeks to ascertain what factors influence the

perceptions of full-time residents and secondary homeowners regarding the effects that climate and

weather risks have on property ownership.

Research suggests the importance of community member perceptions of climatic and weather-

related risks, in addition to scientific knowledge and measurements of these risks [6,7]. This is due to

risk perceptions associated with weather and climate influencing reactions to policy and decisions

made at the local, state, and federal levels. These include: readiness to evacuate in case of storms,

mitigation and preparation efforts, and fluctuations in property values.

This study targets three high-amenity, tourist-based coastal communities in North Carolina:

Brunswick, Currituck, and Pender counties (Figure 1). Because Brunswick, Currituck, and Pender

counties have largely tourism-based economies, vacation homes make up a significant portion of

property ownership. For instance, in Currituck County, secondary home ownership is 43% of the

total home ownership in the county. Our study incorporates secondary homeowners into the sample

(accounting for 53% of respondents), whereas previous studies have failed to collect survey data on

this population. This represents a gap in many previous studies which have essentially not included

almost half of the homeowners in some of these counties. Second home development and associated

tourism also often impact decision making regarding the county’s economy, environment, and

community culture. From a physical hazard perspective, these counties also represent a range of

coastal conditions and morphologies across the state’s coastal zone. While weather and climate

related risks can impact the entire coastal zone, these counties represent a range of common

topography, barrier island morphology, and hydrologies, and therefore potential physical

vulnerability to hazards [8].

Figure 1.

Study area map of the three target coastal communities in North Carolina: Brunswick,

Currituck, and Pender counties.

ISPRS Int. J. Geo-Inf. 2020, 9, 275 3 of 19

1.1. Coastal Hazards

Coastal communities in NC are subject to multiple hazards that can impact local ecosystems,

infrastructure, and the economy. This study focused on severe weather, ﬂooding, and coastal erosion

as the primary, and speciﬁcally near-term (temporal), risks to these communities. Long-term sea level

rise was not addressed here due to the complexity of modeling those impacts on the coastal landscape.

Additionally, the focus of this risk vulnerability assessment was to determine the current, not projected,

vulnerability of coastal homeowners.

North Carolina coastal communities are subject to multiple tropical and extra-tropical storms

every year [

–

]. Tropical storms are recognized as the costliest hazard (over $900 billion since 1980)

and most deadly [

]. It is also the second most frequent hazard event. NC has experienced the second

highest number of billion-dollar tropical storm events in the country (second only to Florida) over

the last thirty years, costing over $400 billion in disaster recovery (adjusted costs; [

]). These impacts

are only expected to increase over the next several decades. Tropical storms primarily impact coastal

communities through ﬂooding, either storm surge or precipitation-driven inland ﬂooding. Both types

of ﬂooding were captured in the present study by utilizing storm surge models from NOAA and FEMA

ﬂood hazard maps. Storm surge ﬂooding primarily impacts ocean and estuarine shorelines and is

a relatively short-term, but high-impact event. Inland ﬂooding driven by precipitation can last for

weeks after the storm event and is often the most damaging and costly [

]. Additionally, ﬂooding

from both tropical and extra-tropical storms and other events has increased over recent decades in

North Carolina [4,5].

Coastal erosion due to human activities and wind-driven waves was also examined as part of this

study. Erosion rates from 0.5 to over eight meters per year has been documented across the east coast

of the United States and in the coastal zone of North Carolina [

–

]. The average rate of erosion for

estuarine shorelines in North Carolina is about 0.5 m yr

−1

while the average for some of our study area

(lower Cape Fear River) is about 0.2 m yr

−1

[8–10].

1.2. Public Perceptions

Public risk perceptions of climate change and weather events are the focus of a growing number

of studies ranging from climate science to sociology and real estate ﬁnance [

–

]. Much eﬀort has

gone into modeling the factors aﬀecting these risk perceptions in diﬀerent target groups, varying from

United States citizens, Floridian homeowners, university students, and residents with direct experience

of environmental hazards [

–

]. Six major factors inﬂuencing risk perceptions arise from previous

studies. These include: (1) socioeconomic status, (2) demographics, (3) direct experience, (4) individual

attitudes/beliefs, (5) the social context of respondents, and (6) location [

–

]. The factors and

perceptions of risk are most commonly assessed by distributing quantitative surveys [

] or by

conducting semi-structured interviews [

]. The inclusion of location-based variables, such as peak

wind gust contours [

] or proximity to coast and elevation in the 100-year ﬂoodplain [

], have been

modelled using geographic information systems (GIS) and compared with the respondent’s location.

The extra dimension of physical risk determined through GIS modelling is useful to relating perceived

risk with quantiﬁed risk.

Location serves an important role in risk perception. For one, it can relate back to the social

context of a respondent in the form of sense-of-place or their emotional and community ties to a

location. On the other hand, it can juxtapose respondent perceptions acquired through survey data

with mapped risks in their corresponding areas. Location-based factors, such as location in high

peak wind zones [

], the 100-year ﬂood plain, areas threatened by sea level rise, and distance to

coast [

], were often signiﬁcant in explaining respondent risk perceptions, though not always in

the expected direction. Although some of the measured environmental risks were associated with

higher risk perceptions, living in the ﬂoodplain was negatively associated with perceptions of risk [

Additionally, proximity to coast as a predicting factor is inconclusive in its signiﬁcance, having both

ISPRS Int. J. Geo-Inf. 2020, 9, 275 4 of 19

signiﬁcant and nonsigniﬁcant eﬀects on an individual’s risk perceptions [

]. Physical risks from

weather and climate generally impact the level of perceived risk.

This study is also unique in that it integrates both social and physical aspects of vulnerability into

a single assessment. The majority of previous work in the area of coastal vulnerability has focused on

either social or physical vulnerability, to the exclusion of the other. These studies also do not incorporate

the perceptions of a population regarding their potential vulnerability to coastal hazards. Some limited

work has been conducted in North Carolina that integrates homeowner perceptions with ﬁeld measures.

This was targeted to assess the relationship between perceptions of waterfront homeowners regarding

shoreline protection methods to actual measures of their eﬃcacy when impacted by hurricanes [20].

The goal of this study is to investigate the awareness and attitudes of property owners regarding

the impact of climate and weather on property ownership and identify the factors that most inﬂuence

these attitudes. This study integrates both social (survey data) and physical (geospatial coastal hazards

data) aspects of vulnerability into a single assessment to understand the determinants of property

owners’ risk perceptions and compare their perceived risks with their physical vulnerability. Figure 2

outlines the overall study methodology for integrating social and physical vulnerability and the

utilization of both OLS and GWR models.

Based on the discussion above, the following research questions are raised:

Question 1: What factors inﬂuence property owners’ perceptions of climate and weather’s positive

eﬀect on property ownership?

Question 2: Do spatial eﬀects exist in assessing property owners’ perceptions of climate and

weather’s positive eﬀect on property ownership?

Question 3: Does the GWR model produce more accurate prediction than the OLS model and

thus improve statistical ﬁt?

ISPRS Int. J. Geo-Inf. 2020, 9, x FOR PEER REVIEW 4 of 21

significant and nonsignificant effects on an individual’s risk perceptions [15]. Physical risks from

weather and climate generally impact the level of perceived risk.

This study is also unique in that it integrates both social and physical aspects of vulnerability

into a single assessment. The majority of previous work in the area of coastal vulnerability has

focused on either social or physical vulnerability, to the exclusion of the other. These studies also do

not incorporate the perceptions of a population regarding their potential vulnerability to coastal

hazards. Some limited work has been conducted in North Carolina that integrates homeowner

perceptions with field measures. This was targeted to assess the relationship between perceptions of

waterfront homeowners regarding shoreline protection methods to actual measures of their efficacy

when impacted by hurricanes [20].

The goal of this study is to investigate the awareness and attitudes of property owners regarding

the impact of climate and weather on property ownership and identify the factors that most influence

these attitudes. This study integrates both social (survey data) and physical (geospatial coastal

hazards data) aspects of vulnerability into a single assessment to understand the determinants of

property owners’ risk perceptions and compare their perceived risks with their physical

vulnerability. Figure 2 outlines the overall study methodology for integrating social and physical

vulnerability and the utilization of both OLS and GWR models.

Based on the discussion above, the following research questions are raised:

Question 1: What factors influence property owners’ perceptions of climate and weather’s

positive effect on property ownership?

Question 2: Do spatial effects exist in assessing property owners’ perceptions of climate and

weather’s positive effect on property ownership?

Question 3: Does the GWR model produce more accurate prediction than the OLS model and

thus improve statistical fit?

Figure 2. Study flow chart illustrating the integration of social and physical vulnerability variables

into OLS (Ordinary Least Square) and GWR (Geographically Weighted Regression) models.

Independent Variables (IV) were defined from the Factor Analysis and their impact on the Dependent

Variable (DV; effects on property ownership) was explored using both OLS and GWR models.

Figure 2.

Study ﬂow chart illustrating the integration of social and physical vulnerability variables into

OLS (Ordinary Least Square) and GWR (Geographically Weighted Regression) models. Independent

Variables (IV) were deﬁned from the Factor Analysis and their impact on the Dependent Variable (DV;

eﬀects on property ownership) was explored using both OLS and GWR models.

ISPRS Int. J. Geo-Inf. 2020, 9, 275 5 of 19

2. Materials and Methods

2.1. Survey Data Collection

A random sample of full-time resident and second-home property owners were selected from the

geographic information system (GIS) tax records from each county’s housing stock. The sample contains

7192 second-home property owners and 7395 full-time property owners. Second homeowners were

deﬁned as those who own property in the study area, but this property is not their primary residence.

The data collection was initiated by sending out an invitation letter to members of the sample

and inviting them to visit the project’s website, enter a designated code number, and complete the

survey. Participants also had the option of completing a hard copy of the questionnaire or answering

the survey through a telephone interview. Two reminder postcards and two reminder phone calls

were made to those who had not completed the survey, thus a total of ﬁve contacts were made during

the data collection period. Thirteen hundred and ninety-three (1393) usable questionnaires were

completed (53% second homeowners and 47% full-time residents). All data collection was conducted

to the standards of the Institutional Review Board for human subjects research.

The survey asked questions related to the six factors shown in the literature to inﬂuence risk

perceptions. Firstly, socioeconomic and demographic factors have repeatedly been found to have

signiﬁcant impact on a person’s perception of risks or willingness to address these risks [

Common predicting factors include: gender, race, and the economic status of the respondents. Higher

risk perceptions are consistently associated with: women, minority groups, lower economic statuses,

and lower education attainment levels [

]. However, additional studies also show an inverse

relationship between age and risk perceptions [

]. Incorporation of these factors into the model is

necessary for predicting risk perceptions.

Experiential factors in risk perception analysis refer to the direct exposure of an individual to

a target environmental hazard. The eﬀect of prior experience on risk perceptions is inconclusive.

In the case of one study, it was found to be non-signiﬁcant in their model [

], but in a study of

Floridian homeowners, experiencing hurricane damage was a signiﬁcant factor in predicting the level

of perceived risk [13].

One aspect of climate and weather-related risk perceptions that has been incorporated into recent

models is that of respondent attitudes and beliefs towards the environment and climate change.

Attitude towards the environment is a signiﬁcant predicting factor of environmental related risk

perceptions. In two studies, attitude was determined using the new ecological paradigm (NEP),

a measurement of an individual’s level of pro-ecological worldview, and respondent scores were

positively related with increased perceptions of climate or weather-related risks [15,16].

Similarly, this theme has been measured in a variety of other ways, from aﬀect, one’s preexisting

disposition to an idea, and values [

] to measures of an individual’s perceived personal eﬃcacy with

regards to climate change [

]. These have provided some measure of an individual’s disposition

towards climate change. In this study, attitude towards the environment is included through measures

of sustainable actions and values, which provide a quantiﬁable measure of the respondents’ views

towards sustainability and the environment.

The social context of respondents addresses knowledge, interest, and desire to seek out new

information as it is transferred among social circles, as well as place-based social-psychological

characteristics [

]. This has an impact on knowledge ﬂow and ties to the land and community,

which in turn inﬂuence people’s views of the land and risk associated with it [

]. Bonding social

networks limit knowledge ﬂow from outside sources and bridging networks support it. Increased

willingness to adapt to climate change is associated with lower bonding social capital and higher

bridging social capital [

]. This relationship is further explained as the interest of the respondent’s

social network is a signiﬁcant contributing factor to risk perceptions, as increased interest is related to

higher perceived climate change risk [15].

ISPRS Int. J. Geo-Inf. 2020, 9, 275 6 of 19

Sense-of-place and social-psychological place-based dependencies had signiﬁcant associations

with perceived climate resiliency [

]. However, community and place-based ties are often overlooked

in many of the climate change risk perception models. Other place-based factors, such as length of

residency [

], have been included in previous studies but the emotional and social- psychological

impact is not as universally addressed. This study incorporates sense-of-place factors into its model of

climate and weather risk perceptions. The responses to questions under these six factors were then

assessed using factor analysis.

2.2. Measurement and Factor Analysis

Property owners’ perceptions on how climate and weather aﬀect their property ownership

were captured in the survey by statements using a 5-point Likert-type scale (1 = strongly disagree;

2 = disagree; 3 = neither agree nor disagree; 4 = agree; 5 = strongly agree). These statements include:

(1) Weather and climate conditions were important in deciding to own property in this County, (2) I feel

the climate conditions here are ideal to attract new property owners, and (3) I feel I am adequately

prepared for a severe weather event (e.g., hurricanes, ﬂoods, heavy rainfall). Principal component

analysis (PCA) was conducted to assess the underlying dimension of the statements. One single

dimension emerged, which explained 56% of the variance (Table 1). The Kaiser–Meyer–Oklin (KMO)

measure of sampling adequacy statistic was high (0.73) and the Bartlett’s test was signiﬁcant (p = 0.000),

suggesting the PCA analyses were necessary and appropriate. An average scale was computed and

served as dependent variable.

Table 1.

PCA for Property Owners’ perceptions of climate and weather eﬀects on current

property ownership.

Dimension and Factored Items Factor Loadings

Cronbach’s Alpha: 0.518; KMO: 0.730; sig.: 0.000; VE: 56%)

Weather and climate conditions were important in deciding

to own property in this County

0.708

I feel the climate conditions here are ideal to attract new

property owners

0.718

I feel I am adequately prepared for a severe weather event

(e.g., hurricanes, ﬂoods, heavy rainfall)

0.447

KMO—Kaiser-Meyer-Oklin; VE—Variance Explained

Gender, age, duration of property ownership, annual household income, and education level data

helped to understand the demographic characteristics of the respondents. Age was categorized into

10-year intervals, with the exception of the ﬁrst two and the last groupings: 25 and under, 26 to 34,

and 75 and over. Education level contains six categories: (1) less than high school; (2) high school or

GED; (3) 2-year college or technical school; (4) some college, but no degree; (5) 4-year college; and

(6) postgraduate. Annual household income was coded into ten categories ranging from 1 (less than

$15,000) to 10 ($400, 000 and over). Duration of property ownership was determined based upon the

number of years a respondent resided (for full-time residents) or owned a second-home property (for

second-home owners) in the community.

The property owners’ community sense-of-place was measured by the following statements using

a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree): (1) I feel that I can

really be myself here; (2) I really miss it when I am away too long; (3) this is the best place to do the

things I enjoy. The KMO statistic was 0.703 and the Bartlett’s test was signiﬁcant (p = 0.000), suggesting

that the principal component analysis was necessary and appropriate. All of the three items have

factor loadings higher than 0.8 (Table 2). Reliability analysis produced a high Cronbach’s Alpha value

of 0.823. A sense-of-place score was then created from the three items based on the strong reliability

and used in the regression model.

ISPRS Int. J. Geo-Inf. 2020, 9, 275 7 of 19

Table 2. PCA for Property Owners’ Sense of Place eﬀects on current property ownership.

Dimension and Factored Items Factor Loadings

Cronbach’s Alpha: 0.823; KMO: 0.703; sig.: 0.000

I feel that I can really be myself here 0.795

I really miss it when I am away too long 0.859

This is the best place to do the things I enjoy 0.839

To examine property owners’ perceptions on how climate change considerations aﬀect property

values, the authors used a 5-point Likert-type scale ranging from 1 (not at all) to 5 (to a very great extent)

for the following statements: (1) changes in precipitation; (2) changes in temperature; (3) availability of

freshwater; (4) number and intensity of coastal storms; (5) sea level and coastal ﬂooding. Changes in

precipitation and the amount and intensity of freshwater are highly correlated with the other three

statements, hence they were later removed from the regression model.

Respondents were also asked their opinion regarding the importance of ﬁfteen sustainable actions

(See Table 3) on the future economic success of their community’s tourism industry using a 5-point

Likert-type scale ranging from 1 (not at all important) to 5 (very important). Sustainable actions were

derived from the literature [

–

]. PCA was conducted and revealed a single dimension among these

ﬁfteen items which explained 49% of the variance as shown in Table 3. The KMO statistic was 0.92 and

the Bartlett’s test was signiﬁcant (p = 0.000), suggesting that the principal component analysis was

necessary and appropriate. All of the ﬁfteen items have factor loadings higher than 0.5. Reliability

analysis produced a high Cronbach’s Alpha value of 0.92. A sustainable action score was then created

from the ﬁfteen items based on the strong reliability.

Table 3.

Principle Component Analysis for property owners’ perceptions of the importance of

sustainable development in their community.

Figure Factor Loadings

Cronbach’s Alpha: 0.920; KMO: 0.920; sig.: 0.000; VE*: 49%)

Reducing and managing greenhouse gas emissions 0.759

Managing, reducing, and recycling solid waste 0.735

Reducing consumption of freshwater 0.694

Managing wastewater 0.792

Being energy eﬃcient 0.826

Purchasing from companies with certiﬁed green practices 0.766

Training and educating employees on sustainability practices 0.753

Conserving the natural environment 0.725

Protecting our community’s natural environment for future generations 0.745

Protecting air quality 0.794

Protecting water quality 0.757

Reducing noise 0.601

Preserving culture and heritage 0.654

Providing economic beneﬁts from tourism to locals 0.447

Full access for everyone in the community to participate in tourism development decisions 0.557

Note: * VE means Variance Explained.

2.3. Coastal Vulnerability Index

An index of coastal vulnerability (CVI) was calculated in the software program ArcGIS 10.3.1 using

public domain data from several online sources. Index variables and rankings are synthesized in Table 4.

Figure 3 illustrates the work ﬂow for processing the spatial data layers used in the CVI calculation.

Storm surge data was sourced from the National Oceanographic and Atmospheric Agency’s (NOAA)

sea, lake, and overland surge (SLOSH) model. SLOSH uses a composite approach to eﬃciently provide

forecasts of possible storm surge risk. All outputs are referenced to the North American Vertical

Datum of 1988 (NAVD88) and represent water level above the reference datum. SLOSH basin outputs

are updated every few years. The reported accuracy for projected storm surge from SLOSH is 20%.

ISPRS Int. J. Geo-Inf. 2020, 9, 275 8 of 19

To assess the highest possible risk from storm surge, the maximum of the MEOWs (MOM; maximum

envelope of water (MEOW)) with landfall at high tide was selected for analysis. The MOM raster for

each category of hurricane (Categories 1–5) was downloaded for the Atlantic coast and clipped to

North Carolina in ArcGIS. Raster cells not inundated under any storm surge scenario were reclassiﬁed

to a value of zero (0). They were then combined to make a single combined storm surge layer. In this

raster, a ﬁeld was added to rank the extent of inundation on a scale from 1 (low) to 5 (high). Any coastal

area subject to ﬂooding in all storm categories (1–5) was ranked a 5, since these areas experience

ﬂooding during every hurricane, regardless of the hurricane’s strength. Areas that only experience

storm surge during the strongest storm (category 5) were ranked a 1, since these areas are farther

inland and do not ﬂood during weaker hurricanes.

Table 4.

Explanation of index values for storm surge, ﬂood, and erosion risk. Storm surge index values

derived from inundation by storm category. Flood risk index values derived from FEMA ﬂoodplain

zones. Erosion risk index values derived from the USGS Atlantic Coastal Vulnerability Index data layer.

Index Value Storm Surge Risk

Flood Risk

Erosion Risk

1 – Lowest risk

Cells only ﬂooded under category

5 conditions

Zone X – cells outside of 500 year ﬂoodplain 1

2 – Low risk

Cells ﬂooded under category 4 &

5 conditions

0.2% change – cells in the 500 year ﬂoodplain 2

3 – Medium risk

Cells ﬂooded under category

3–5 conditions

Zone A – cells with a 1% annual chance of ﬂooding

4 – High risk

Cells ﬂooded under category

2–5 conditions

Zone VE – cells in coastal areas with a 1% or

greater chance of ﬂooding; or at risk of storm

wavesOrZone AE – cells in the 100 year ﬂoodplain

5 – Highest risk

Cells ﬂooded under all storm

surge scenarios (categories 1–5)

Due to limitations of the source data, the ﬂood risk index only has values between one (1) and four (4).

ISPRS Int. J. Geo-Inf. 2020, 9, x FOR PEER REVIEW 10 of 21

Figure 3. Work flow diagram for calculation of the CVI and processing of individual spatial data

layers.

2.4. Ordinary Least Squares (OLS) Regression Model

Global regression models, such as ordinary least squares (OLS) regression, are the most

commonly used methods to examine factors influencing people’s risk perceptions. This paper

investigates the relationships between the dependent variable (property owners’ perceptions of

climate and weather’s positive effects on property ownership) and a range of independent variables

such as changes in temperature and/or humidity, availability of freshwater, sea level and flooding,

demographic characteristics, sense of place, sustainability perceptions, slosh, erosion, and flood

index. The study first uses OLS to explore the factors influencing property owners’ perceptions of

climate and weather’s positive impacts on property ownership and assess the global relationship

between the dependent variable and independent variables, then applies a geographically weighted

regression (GWR) to capture the local varying relationships across the study area. The global

regression models mask spatial heterogeneity in the relationships between the dependent variables

and a set of independent variables and fail to consider the existence of local variation due to spatial

autocorrelation [25]. To better understand the determinants of risk perceptions and how the

relationships can be affected by location variations, the geographically weighted regression (GWR)

model was adopted to investigate the effects of local factors on property owners’ risk perceptions.

The GWR model allows estimated parameters to vary across the study area to accommodate potential

spatial dependence [26].

OLS model performance is based on the following assumptions: 1) the relationships between

dependent and independent variables are linear; 2) the observations in the dependent variable are

independent of one another; 3) the data are normally distributed; 4) there is no multicollinearity

among independent variables; and 5) the variance of residuals is the same across all values of the

independent variables (homoscedasticity) [27]. Multicollinearity was assessed through the values of

variance inflation factor (VIF) statistics, which measure redundancy among explanatory variables.

Explanatory variables associated with VIF values larger than 7.5 suggest that they measure the same

concepts and provide similar information. These variables were removed one at a time from the

model based on VIF value until the multicollinearity problem disappeared. The examination of other

Figure 3.

Work ﬂow diagram for calculation of the CVI and processing of individual spatial data layers.

Data for the ﬂood layer came from the North Carolina Flood Risk Information System at

http://fris.nc.gov/fris/Download.aspx?ST=NC. This data consists of FEMA ﬂood maps for each county.

In the portal, you can select the county that you want data from and download as a shapeﬁle. Once the

data is unzipped, select V_E_FLD_HAZ_AR – this is the shapeﬁle that will have the ﬂood zone

ISPRS Int. J. Geo-Inf. 2020, 9, 275 9 of 19

information in it. The ﬁeld ZONE_LID_V contains the ﬂood zone information that you will use to

determine risk rating (1–3). Data was ranked from 1 to 4 based on chance of inundation, whereby 1 was

low risk (outside the 500-year ﬂoodplain), 2 was medium risk (within the 500-year ﬂoodplain), 3 was

medium-high risk (within the 100-year ﬂoodplain, no base ﬂood elevations calculated), and 4 was

high risk (coastal areas with base ﬂood elevations calculated and additional hazard associated with

storm waves). After merging all three county shapeﬁles, the feature class was dissolved by ﬂood zone

information (zone_lid_value). Then, a ﬁeld (Flood_Index) was added to rank the polygons and the

vector ﬁle converted to a raster using the cell size from the SLOSH data. Finally, a ﬂood zone ﬁeld was

added to keep the FEMA ﬂood zone data associated with the index ranking.

Erosion risk data was obtained from the Atlantic Coastal Vulnerability Index from USGS Digital

Data Series (https://pubs.usgs.gov/dds/dds68/htmldocs/data.htm). This coastal vulnerability index

already had erosion risk values from 1–5 associated with the shoreline, with 1 being the lowest risk of

erosion to 5 being the highest risk of erosion (ﬁeld: ERRRISK). Because this layer was at 1:2,000,000 it

was buﬀered to a smaller scale shoreline and joined to provide a more detailed layer of erosion risk.

It was then converted from a vector layer to a raster with a cell size of 100.

These three index variables: SLOSH, ﬂood risk, and erosion risk, were then combined using the

Raster Calculator tool in ArcGIS to calculate ﬁnal, unweighted, CVI values (Figure 3). The layers were

also utilized as independent variables in both an ordinary least squares regression model and a local

geographically weighted regression model.

2.4. Ordinary Least Squares (OLS) Regression Model

Global regression models, such as ordinary least squares (OLS) regression, are the most commonly

used methods to examine factors inﬂuencing people’s risk perceptions. This paper investigates

the relationships between the dependent variable (property owners’ perceptions of climate and

weather’s positive eﬀects on property ownership) and a range of independent variables such as changes

in temperature and/or humidity, availability of freshwater, sea level and ﬂooding, demographic

characteristics, sense of place, sustainability perceptions, slosh, erosion, and ﬂood index. The study

ﬁrst uses OLS to explore the factors inﬂuencing property owners’ perceptions of climate and weather’s

positive impacts on property ownership and assess the global relationship between the dependent

variable and independent variables, then applies a geographically weighted regression (GWR) to

capture the local varying relationships across the study area. The global regression models mask

spatial heterogeneity in the relationships between the dependent variables and a set of independent

variables and fail to consider the existence of local variation due to spatial autocorrelation [

]. To better

understand the determinants of risk perceptions and how the relationships can be aﬀected by location

variations, the geographically weighted regression (GWR) model was adopted to investigate the eﬀects

of local factors on property owners’ risk perceptions. The GWR model allows estimated parameters to

vary across the study area to accommodate potential spatial dependence [26].

OLS model performance is based on the following assumptions: (1) the relationships between

dependent and independent variables are linear; (2) the observations in the dependent variable are

independent of one another; (3) the data are normally distributed; (4) there is no multicollinearity

among independent variables; and (5) the variance of residuals is the same across all values of the

independent variables (homoscedasticity) [

]. Multicollinearity was assessed through the values of

variance inﬂation factor (VIF) statistics, which measure redundancy among explanatory variables.

Explanatory variables associated with VIF values larger than 7.5 suggest that they measure the same

concepts and provide similar information. These variables were removed one at a time from the

model based on VIF value until the multicollinearity problem disappeared. The examination of other

assumptions for the multivariate regression analysis showed: (1) linearity between dependent and

independent variables; (2) independence of observations in the dependent variables; and (3) normality.

The Koenker (BP) statistics (=144.86) is signiﬁcant (p < 0.01) indicating the relationships modeled

are not consistent, either due to non-stationary or heteroskedasticity. Hence, the assumption of

ISPRS Int. J. Geo-Inf. 2020, 9, 275 10 of 19

homoscedasticity was violated and a spatial model such as GWR is necessary. Regression models with

statistically signiﬁcant nonstationarity are good candidates for GWR analysis.

2.5. GWR

Under conditions of non-stationary in OLS modeling, GWR was adopted to potentially ﬁx the

spatial autocorrelation problems and improve the model ﬁt. Using the same dataset and the same

explanatory variables as the OLS model, a GWR model was developed and performed following

auto-calibration in ArcGIS. In contrast to estimations using a global model whereby one global R

was produced, the GWR local model produced a total of 1393 local regressions, wherein each of the

regressions corresponded to one of the 1393 sample points. The condition index was below 30 (range

from 23 to 26) for each local regression, indicating that the models did not have collinearity issues and

the results are reliable. Additionally, a check for spatial autocorrelation using the Moran’s Index test

was also conducted on the standard residuals for the GWR model. Moran’s index was signiﬁcant

(P < 0.05, z-score = 3.45, and Moran I = 0.33) and supported the reliability of using the local GWR model.

3. Results

3.1. Comparing Homeowner Perceptions with Physical CVI

Survey participants in three NC counties (Brunswick, Currituck, and Pender) were asked to

express their views on how climate change factors aﬀect their future property ownership. Almost

70% of the property owners felt that weather and climate conditions were important in deciding to

own property in their county. Approximately 76% of the respondents agreed or strongly agreed that

climate conditions in their county were ideal to attract new property owners. More than 70% of the

survey participants (72%) felt they were adequately prepared for a severe weather event. These results

indicate that overall, most property owners in the study counties felt comfortable with the current

climate and weather conditions and felt they were prepared for any severe weather that they might

encounter as residents of coastal NC.

The majority of land area in the three counties of interest was determined to fall in the Low

to Medium risk categories for the cumulative CVI (Table 5; Figure 4). For Brunswick and Pender

counties, over 80% (86.3% and 88.0%, respectively) of total land area was in the Low risk category.

In contrast, Currituck County had the highest percentage of land area (50.4%) in the Medium risk

category, indicating the county is potentially more vulnerable to coastal hazards (Table 5). Only a small

percentage of total land area in any of the counties was in the High to Highest categories. While only

0.1%–2.7% of these counties were determined to be highly vulnerable, these locations coincide with

high-density, high-value development, and tourist amenities, such as public beachfront or other water

access points. The majority of land are in these categories is concentrated along the barrier islands and

estuarine shoreline (Figure 4). This is best illustrated in Currituck county (Figure 4), where the greatest

land area in the High and Highest categories was determined (2.7%). The county also had the greatest

length of shoreline and most risk for erosion (Table 6; [

]). When examining the contribution of each

independent variable in the CVI (storm surge, ﬂooding, and erosion), Currituck had the most land

area in higher vulnerability categories for all three (Table 6; Figures 5–7). It was also the only county in

the study to have almost half of its land area (41.8%) fall in the Highest risk category for storm surge

(Table 6; Figure 5). This is signiﬁcant because of the extensive tourism present in the county. Currituck

also has the highest percentage (43%) of second home owners compared to the other counties in this

study, and a large number are present on the barrier island (part of the Outer Banks of North Carolina).

Pender County also had some land area in the Highest storm surge category, but it only accounted

for 2.9% of the county’s total land area (Table 6; Figure 5). However, like Currituck county, in Pender

(and Brunswick), the highest risk, especially for storm surge, is along the barrier islands which are

signiﬁcant for the local economy. The higher risk to Currituck is likely due to a combination of physical

ISPRS Int. J. Geo-Inf. 2020, 9, 275 11 of 19

variables, such as the amount of shoreline, average elevation, and the hydrodynamics associated with

storm tides [4].

Table 5. Percentage of area by county in each CVI risk classiﬁcation.

CVI Classiﬁcation Brunswick County Currituck County Pender County

Lowest (1–3) 86.3

17.0 88.0

Low (4–6) 5.5 29.9 6.9

Medium (7–9) 7.9 50.4

5.1

High (10–12) 0.2 2.0 0.1

Highest (13–14) 0.1 0.7 0

Greatest percentage of land area.

ISPRS Int. J. Geo-Inf. 2020, 9, x FOR PEER REVIEW 12 of 21

Figure 5). However, like Currituck county, in Pender (and Brunswick), the highest risk, especially for

storm surge, is along the barrier islands which are significant for the local economy. The higher risk

to Currituck is likely due to a combination of physical variables, such as the amount of shoreline,

average elevation, and the hydrodynamics associated with storm tides [4].

Table 5. Percentage of area by county in each CVI risk classification.

CVI Classification Brunswick County Currituck County Pender County

Lowest (1-3) 86.3

17.0 88.0

Low (4-6) 5.5 29.9 6.9

Medium (7-9) 7.9 50.4

5.1

High (10-12 0.2 2.0 0.1

Highest (13-14) 0.1 0.7 0

Greatest percentage of land area.

Figure 4. Cumulative Coastal Vulnerability Index (CVI) for Brunswick, Currituck, and Pender

counties, NC. Index values range from Lowest risk (1-3) to Highest risk (13-14).

Table 6. Summary of index variables by county. Reported as risk category with the greatest

percentage of land area (percentages noted in parentheses). NA designation under the Erosion risk

category represents the percentage of cells that were located inland (not shoreline coincident).

County Erosion Flood Storm Surge

Brunswick

NA (99.4%)

Medium (0.3%)

Lowest (62.6%) Lowest (6.9%)

Currituck

NA (96.5%)

Medium (2.1%)

Medium (58.3%)

Highest (41.8%)

Pender

NA (99.3%)

Medium (0.4%)

Lowest (63.8%) Highest (2.9%)

Figure 4.

Cumulative Coastal Vulnerability Index (CVI) for Brunswick, Currituck, and Pender counties,

NC. Index values range from Lowest risk (1–3) to Highest risk (13–14).

Table 6.

Summary of index variables by county. Reported as risk category with the greatest percentage

of land area (percentages noted in parentheses). NA designation under the Erosion risk category

represents the percentage of cells that were located inland (not shoreline coincident).

County Erosion Flood Storm Surge

Brunswick

NA (99.4%)

Medium (0.3%)

Lowest (62.6%) Lowest (6.9%)

Currituck

NA (96.5%)

Medium (2.1%)

Medium (58.3%) Highest (41.8%)

Pender

NA (99.3%)

Medium (0.4%)

Lowest (63.8%) Highest (2.9%)

The survey responses regarding climate change and the calculated physical vulnerability were

then utilized in a global OLS model to understand how each of these variables (perception of changes

in temperature & humidity, perception of sea-level rise & ﬂooding, SLOSH surge level, ﬂooding, and

erosion) inﬂuenced patterns of property ownership in the three NC counties. The physical vulnerability

variables were found to not inﬂuence the property owners’ perceptions and therefore patterns of

ownership. They responded that they were prepared for weather conditions and were satisﬁed with

climate characteristics. In contrast, the CVI results indicate that these counties have potentially high

ISPRS Int. J. Geo-Inf. 2020, 9, 275 12 of 19

vulnerability to coastal hazards, especially in Currituck County. However, while a direct inﬂuence was

not observed in the data, it is not unexpected to see that, when on average the physical vulnerability is

low, homeowners also exhibit less concern for their potential vulnerability.

ISPRS Int. J. Geo-Inf. 2020, 9, x FOR PEER REVIEW 13 of 21

Figure 5. Storm surge risk index for Brunswick, Pender, and Currituck counties, North Carolina.

Index values range from 1 (Lowest Risk) to 5 (Highest Risk). Values of zero (0; Not Included) indicate

areas of land found not to be at risk, or already existing bodies of water.

Figure 6. Flood risk index for Brunswick, Pender, and Currituck counties, North Carolina. Index

values range from 1 (Lowest Risk) to 4 (Highest Risk).

Figure 5.

Storm surge risk index for Brunswick, Pender, and Currituck counties, North Carolina. Index

values range from 1 (Lowest Risk) to 5 (Highest Risk). Values of zero (0; Not Included) indicate areas of

land found not to be at risk, or already existing bodies of water.

ISPRS Int. J. Geo-Inf. 2020, 9, x FOR PEER REVIEW 13 of 21

Figure 5. Storm surge risk index for Brunswick, Pender, and Currituck counties, North Carolina.

Index values range from 1 (Lowest Risk) to 5 (Highest Risk). Values of zero (0; Not Included) indicate

areas of land found not to be at risk, or already existing bodies of water.

Figure 6. Flood risk index for Brunswick, Pender, and Currituck counties, North Carolina. Index

values range from 1 (Lowest Risk) to 4 (Highest Risk).

Figure 6.

Flood risk index for Brunswick, Pender, and Currituck counties, North Carolina. Index values

range from 1 (Lowest Risk) to 4 (Highest Risk).

ISPRS Int. J. Geo-Inf. 2020, 9, 275 13 of 19

ISPRS Int. J. Geo-Inf. 2020, 9, x FOR PEER REVIEW 14 of 21

Figure 7. Erosion risk index for Brunswick, Pender, and Currituck counties, North Carolina. Index

values range from 1 (Lowest Risk) to 5 (Highest Risk). Values of zero (0; Not Included) indicate areas

of land not along the shoreline or already existing bodies of water.

The survey responses regarding climate change and the calculated physical vulnerability were

then utilized in a global OLS model to understand how each of these variables (perception of changes

in temperature & humidity, perception of sea-level rise & flooding, SLOSH surge level, flooding, and

erosion) influenced patterns of property ownership in the three NC counties. The physical

vulnerability variables were found to not influence the property owners’ perceptions and therefore

patterns of ownership. They responded that they were prepared for weather conditions and were

satisfied with climate characteristics. In contrast, the CVI results indicate that these counties have

potentially high vulnerability to coastal hazards, especially in Currituck County. However, while a

direct influence was not observed in the data, it is not unexpected to see that, when on average the

physical vulnerability is low, homeowners also exhibit less concern for their potential vulnerability.

3.2. Comparison of Model Results

3.2.1. Global OLS

A global OLS model was utilized to understand how the independent variables of the survey

responses and physical risk layers (CVI components of SLOSH, erosion, and flooding) influenced

patterns of property ownership in the three study counties. Only a few of the independent variables

were found to be statistically significant (P<0.05; Table 7). Property owners reported that changes in

temperature and humidity have a positive impact on property values so they will continue to own

and purchase property in the area. They also perceived that future sea-level rise and coastal flooding

could have a negative impact on their property values, indicating that they recognize that there is a

potential risk associated with owning property in the coastal zone. While not statistically significant,

it was also noted that respondents perceived that freshwater availability has a positive impact on

property values. Other variables, such as gender, age, and education, all showed a positive impact

on people’s perceptions of climate and weather effects on property ownership. Specifically, the

population demographics for the respondents of over 55 (almost 70%), educated (over 62% have a

college degree or above), and male (55%) recognized the role that climate and weather plays in

determining property ownership. An individual’s sense-of-place and their use of sustainable actions

also had a positive impact, so property owners who felt more attached to a place and saw the value

in sustainable practices were also more likely to recognize the impact of climate and weather on

Figure 7.

Erosion risk index for Brunswick, Pender, and Currituck counties, North Carolina. Index

values range from 1 (Lowest Risk) to 5 (Highest Risk). Values of zero (0; Not Included) indicate areas of

land not along the shoreline or already existing bodies of water.

3.2. Comparison of Model Results

3.2.1. Global OLS

A global OLS model was utilized to understand how the independent variables of the survey

responses and physical risk layers (CVI components of SLOSH, erosion, and ﬂooding) inﬂuenced

patterns of property ownership in the three study counties. Only a few of the independent variables

were found to be statistically signiﬁcant (P < 0.05; Table 7). Property owners reported that changes in

temperature and humidity have a positive impact on property values so they will continue to own

and purchase property in the area. They also perceived that future sea-level rise and coastal ﬂooding

could have a negative impact on their property values, indicating that they recognize that there is a

potential risk associated with owning property in the coastal zone. While not statistically signiﬁcant,

it was also noted that respondents perceived that freshwater availability has a positive impact on

property values. Other variables, such as gender, age, and education, all showed a positive impact on

people’s perceptions of climate and weather eﬀects on property ownership. Speciﬁcally, the population

demographics for the respondents of over 55 (almost 70%), educated (over 62% have a college degree

or above), and male (55%) recognized the role that climate and weather plays in determining property

ownership. An individual’s sense-of-place and their use of sustainable actions also had a positive

impact, so property owners who felt more attached to a place and saw the value in sustainable

practices were also more likely to recognize the impact of climate and weather on property ownership.

Finally, the physical index variables (slosh, erosion, ﬂooding) were not found to be signiﬁcant. So overall

perceptions of property owners play a direct role in patterns of property ownership, while actual

physical risk does not. This creates a potential disconnect whereby what people perceive is what

determines their actions, not what may actually be physically happening.

This study also compared the results between two potentially distinct population segments,

full-time residents and second-home property owners. The same global OLS model was run for both

populations. The results are reported in Table 8. A t-test was conducted to examine the diﬀerences

in full-time residents and second-home owners’ risk perceptions. The result showed a statistically

signiﬁcant diﬀerence between the risk perceptions of full-time residents and second-home owners.

ISPRS Int. J. Geo-Inf. 2020, 9, 275 14 of 19

This supports the need to survey both populations of coastal homeowners. To better understand these

signiﬁcant diﬀerences, the factors inﬂuencing these risk perceptions were investigated.

Table 7. OLS results for the 14 independent variables.

Variable Coeﬃcient SE p-Value

Intercept 1.664725 0.124927 0.0000*

Changes in temperature and humidity 0.048727 0.020279 0.016385*

Availability of fresh water 0.027319 0.019118 0.153237

sea level and coastal ﬂooding −0.048337 0.018573 0.009348*

Residential status 0.022583 0.043677 0.605219

Gender 0.080752 0.038613 0.036672*

Age 0.040757 0.014051 0.003791*

Length of owning property −0.000221 0.000338 0.513413

Income 0.007131 0.007045 0.311561

Education 0.043582 0.014569 0.002837*

Sense of Place 0.268724 0.0216 0.000000*

Sustainable Actions 0.125668 0.016534 0.000000*

Slosh Index −0.018467 0.014885 0.214937

Erosion Index −0.015688 0.033952 0.644129

Flood Index 0.007346 0.025332 0.77188

*indicate sig. at 0.05.

Table 8. OLS results for Full Time Residents and Second Home Owners.

Full Time Residents Secondhome Owners

Variable Coeﬃcient p-vlue Coeﬃcient p-value

Intercept 1.815 0.000 2.481 0.000*

Changes in temperature and humidity −0.018 0.500 0.042 0.121

Availability of fresh water 0.032 0.181 −0.005 0.849

sea level and coastal ﬂooding −0.024 0.303 −0.028 0.279

Gender 0.083 0.098 0.055 0.277

Age 0.082* 0.000 0.000 0.986

Length of owning property −0.005* 0.003 0.000 0.711

Income −0.008 0.576 −0.018 0.223

Education 0.022 0.243 0.049* 0.047

Sense of Place 0.258* 0.000 0.246* 0.000

Sustainable Actions 0.118* 0.004 0.069 0.136

Slosh Index −0.005 0.791 −0.010 0.610

Erosion Index −0.038 0.538 −0.011 0.794

Flood Index 0.008 0.817 −0.019 0.544

*indicate sig. at 0.05.

The OLS model for full-time residents shows that 21.2% of the variance in full-time residents’

risk perceptions were explained by the model (R

= 0.212, F = 9.684, sig. = 0.00). As shown in Table 8

age, sense-of-place and sustainable actions have signiﬁcantly positive relationships with full-time

residents’ risk perceptions. More speciﬁcally, older people and residents who felt more attached to a

place and saw the value in sustainable practices were also more likely to recognize the impact of climate

and weather on property ownership. Length of owning property has a negative relationship with

people’s perceptions of climate and weather eﬀects on property ownership. So the longer the residents

lived in the area, the less likely they were to recognize the impact of climate and weather on property

ownership. This may have implications related to storm evacuations or the level of preparedness

residence have for severe weather events.

The OLS model for second-home owners shows that 10.1% of the variance in second-home owners’

risk perceptions were explained by the model (R

= 0.101, F = 4.34, sig. = 0.00). The model was not as

a good ﬁt for explaining the relationship between risk perceptions and homeownership. As shown

in Table 8, education and sense-of-place have signiﬁcantly positive relationships with second-home

ISPRS Int. J. Geo-Inf. 2020, 9, 275 15 of 19

owners’ risk perceptions. That is, second-home owners who have higher education level and saw the

value in sustainable practices were more likely to recognize the impact of climate and weather on

property ownership. Overall, these results indicate a potential diﬀerence in both the perceived level of

risk and the response to that risk between full-time residents and second-home owners.

3.2.2. Local GWR

A local GWR was utilized (for the composite dataset including both full-time residents and

second-home owners) to ﬁnd a better ﬁt model for the data and examine geographic trends in

the response variable. The same independent variables from the global OLS model were utilized.

The overall results from the local GWR match those of the global OLS model, however GWR exhibited

greater sensitivity and a little better ﬁt. Property owner perceptions regarding temperature and humidity

changes had a similar positive relationship to ownership as seen in the OLS. However, there are

more negative coeﬃcient values for this variable then were seen in the OLS, particularly in Pender

County. This may be a function of the lower population density in this county. Results for homeowner

perceptions of freshwater availability were also similar to those of the OLS with the exception of

more negative values, primarily in Currituck County where almost 50% were negative coeﬃcients.

So, while on average homeowner perceptions of freshwater availability did not adversely impact

property ownership, in Currituck County there was more of a negative impact on ownership. This may

be due to localized variables not included in this analysis. Currituck County is well-connected to the

neighboring Virginia city of Norfolk, which has extensive problems with ﬂooding, salinization, storms,

and other coastal hazards. Norfolk is also considered a leader in proactive management of coastal

hazards and is home to signiﬁcant infrastructure to facilitate that management (i.e., a USACE regional

oﬃce and the Norfolk Naval Base). Property owners in Currituck are likely more aware of these issues

as a result of their proximity to Norfolk, as residents in much of the county receive all of their over-air

television and radio news from Norfolk. This relationship likely also explains the negative relationship

between homeowner perceptions of sea-level rise and ﬂooding on property ownership, which is in

contrast to the results from the OLS model.

There are also clear geographic trends across Currituck County with the physical variables

(Figure 8). The most negative coeﬃcient values for storm surge and erosion are concentration along

the southern shoreline of the mainland peninsula (Figure 8a,b), while the ﬂooding variable show a

positive inﬂuence on property ownership (Figure 8c). This may be due to the presence of Elizabeth

City, the largest population center in the county, at this location. This may indicate concern for the

impact of major storms, but some conﬁdence in the city’s ability to handle other types of ﬂooding.

Overall, the GWR results for the three physical (CVI) independent variables showed similar results to

the OLS model with the exception of Pender County where coeﬃcients for the three variables were

more negative.

ISPRS Int. J. Geo-Inf. 2020, 9, x FOR PEER REVIEW 17 of 21

(a) (b) (c)

Figure 8. Example GWR results for Currituck County, NC. Values interpolated from independent

variable coefficients: (a) storm surge coefficients; (b) erosion coefficients; and (c) flood coefficients.

3.2.3. Model Comparison

The comparison between the estimated results of the OLS model and the GWR was performed

for the full data set (both full-time residents and second-home owners included). Various diagnostic

parameters show the differences in performance between the OLS and GWR models. Table 9 shows

the comparison of the performance of the GWR model and the OLS. Comparing the fit of the global

OLS model assuming homogeneity of variables across the study area and local GWR model with no

assumption of homogeneity, we found that the global OLS model produced adjusted R

value of was

0.206. The local GWR adjusted R

was 0.248, which suggests that there has been some improvement

by using a local modeling approach. The other measure of model fit, AICc, gave a value of 2961 for

the global OLS model and 2915 for the local GWR model. The difference of 46 is a relatively strong

evidence of an improvement in the model fit to the data. Additionally, the problem of

heteroscedasticity that was noted in the OLS model was not observed in the GWR model. Further, in

the OLS global model, some predictors did not show any significant effects on property owners’ risk

perceptions of climate and weather effects on property values, but did significantly relate to the risk

perceptions in some specified area in GWR model, which indicates that the GWR model can achieve

better performance. In particular, coefficients for the three physical variables were found to vary by

county to a greater degree than was indicated in the OLS. Geographic patterns seen in these variables

indicate the GWR model both performed better and led to a more comprehensive interpretation of

the model results.

Table 9. OLS and GWR model comparison.

OLS Coefficients GWR Coefficients

Variable b SE p Minimum Mean Maximum Range

Intercept 1.664725 0.124927 0.000000* 1.390032 1.727093 2.372631 0.982599

Changes in temperature and humidity 0.048727 0.020279 0.016385* -0.021457 0.048518 0.105747 0.127204

Availability of fresh water 0.027319 0.019118 0.153237 -0.020698 0.030291 0.080383 0.101081

Sea level and coastal flooding -0.04834 0.018573 0.009348* -0.06447 -0.04723 0.011807 0.076277

Slosh index -0.01847 0.014885 0.214937 -0.05953 -0.00872 0.040956 0.100486

Erosion index -0.01569 0.033952 0.644129 -0.147627 -0.04787 0.032936 0.180563

Flood index 0.007346 0.025332 0.77188 -0.175644 0.021135 0.114225 0.289869

Adjusted R square 0.206 0.222606 0.252763 0.354725 0.132119

4. Discussion

Figure 8.

Example GWR results for Currituck County, NC. Values interpolated from independent

variable coeﬃcients: (a) storm surge coeﬃcients; (b) erosion coeﬃcients; and (c) ﬂood coeﬃcients.

ISPRS Int. J. Geo-Inf. 2020, 9, 275 16 of 19

3.2.3. Model Comparison

The comparison between the estimated results of the OLS model and the GWR was performed

for the full data set (both full-time residents and second-home owners included). Various diagnostic

parameters show the diﬀerences in performance between the OLS and GWR models. Table 9 shows

the comparison of the performance of the GWR model and the OLS. Comparing the ﬁt of the global

OLS model assuming homogeneity of variables across the study area and local GWR model with no

assumption of homogeneity, we found that the global OLS model produced adjusted R

value of was

0.206. The local GWR adjusted R

was 0.248, which suggests that there has been some improvement by

using a local modeling approach. The other measure of model ﬁt, AICc, gave a value of 2961 for the

global OLS model and 2915 for the local GWR model. The diﬀerence of 46 is a relatively strong evidence

of an improvement in the model ﬁt to the data. Additionally, the problem of heteroscedasticity that

was noted in the OLS model was not observed in the GWR model. Further, in the OLS global model,

some predictors did not show any signiﬁcant eﬀects on property owners’ risk perceptions of climate

and weather eﬀects on property values, but did signiﬁcantly relate to the risk perceptions in some

speciﬁed area in GWR model, which indicates that the GWR model can achieve better performance.

In particular, coeﬃcients for the three physical variables were found to vary by county to a greater

degree than was indicated in the OLS. Geographic patterns seen in these variables indicate the GWR

model both performed better and led to a more comprehensive interpretation of the model results.

Table 9. OLS and GWR model comparison.

OLS Coeﬃcients GWR Coeﬃcients

Variable b SE p Minimum Mean Maximum Range

Intercept 1.664725 0.124927 0.000000* 1.390032 1.727093 2.372631 0.982599

Changes in temperature and humidity 0.048727 0.020279 0.016385* −0.021457 0.048518 0.105747 0.127204

Availability of fresh water 0.027319 0.019118 0.153237 −0.020698 0.030291 0.080383 0.101081

Sea level and coastal ﬂooding −0.04834 0.018573 0.009348* −0.06447 −0.04723 0.011807 0.076277

Slosh index −0.01847 0.014885 0.214937 −0.05953 −0.00872 0.040956 0.100486

Erosion index −0.01569 0.033952 0.644129 −0.147627 −0.04787 0.032936 0.180563

Flood index 0.007346 0.025332 0.77188 −0.175644 0.021135 0.114225 0.289869

Adjusted R square 0.206 0.222606 0.252763 0.354725 0.132119

4. Discussion

This paper investigated factors inﬂuencing property owners’ perceptions of climate and weather’s

positive eﬀect on property ownership based on a sample of 1393 property owners in three North

Carolina coastal counties using both the OLS and GWR model. The OLS model suggested that,

in addition to common demographic variables (age, gender, education, etc.), respondent’s perceptions

of the climate (temperature and humidity), freshwater availability, sea-level rise and ﬂooding, sense of

place, and sustainability were all found to inﬂuence patterns of property ownership in the three study

counties. With the 14 available explanatory variables, the OLS model explained 20.6% of the variation

in future property ownership perceptions and the residuals of the OLS model showed signiﬁcant

spatial autocorrelation, which indicated the limitations of the OLS model in explaining the property

owners’ risk perceptions. In contrast to the global relationship established between the independent

variables and risk perceptions by the OLS model, the GWR model captured spatial heterogeneity in

explaining the distribution of property owners’ risk perceptions. The adoption of the GWR model

increased R

value from 0.206 to 0.248 and reduced the AICs value from 2961 to 2915, compared to

the OLS model. GWR model therefore performed better and showed a better statistical ﬁt for the

data than the OLS model. Additionally, separate OLS models were ﬁt to each group of property

owners, full-time residents and second-home owners. It was determined that full-time residents were

statistically signiﬁcantly diﬀerent from second-home owners. This supports the examination of both

segments of coastal populations in future studies. Only targeting one of these groups would not

provide a comprehensive dataset of the coastal population. It was also found that the OLS model

ISPRS Int. J. Geo-Inf. 2020, 9, 275 17 of 19

best ﬁt full-time residents, explaining 21.2% of the variance. In contrast, only 10.1% of the variance

was explained for second home owners, indicating that this study has not well captured the factors

inﬂuencing second home owner perceptions, as compared to residents.

The GWR model also revealed spatially explicit local relationships that explain property owners’

risk perceptions. Spatial patterns were found to impact property ownership as indicated by the results

of the local GWR. While the overall results of the OLS and GWR models were similar, the local GWR

model showed increased sensitivity for key independent variables. For instance, coeﬃcients for the

three physical variables, slosh, erosion, and ﬂood index, were found to vary by county to a greater

degree than was indicated in the OLS.

There are several limitations in this study. First, the R

value of both the OLS and GWR models

were relatively low, suggesting that there are other factors inﬂuencing property owners’ risk perceptions

have not been fully explored. Further research should examine the impact of other factors on risk

perceptions such as experience factors. Further inclusions of the role of past experience in storm events

and experienced damages [

] would be useful in determining the relationship between experience

and risk perceptions. Additionally, the geospatial data layers could be further reﬁned in future studies.

All three layers have potential issues with quality and accuracy that may have impacted study results.

Of particular note is the FEMA ﬂood map layer. This data set has been critiqued in recent years

following major ﬂood events that saw areas not even represented in the FEMA maps signiﬁcantly

inundated. However, there is currently no other comprehensive and publically available data set to

utilize. The current erosion data layer, while providing full coverage of the study area, was coarser in

resolution then was preferred. Work is currently underway to resolve this by utilizing updated aerial

imagery and a shoreline position layer created by the state of North Carolina to calculated rates of

coastal erosion at a much ﬁner resolution. Projections of local sea-level rise rates, as well as overland

wetland migration, are also in-progress to be utilized in the next version of the CVI.

The research demonstrated in this paper suggests that a spatially explicit local model using GWR

approaches to adjust for spatial autocorrelation and non-stationary can produce a better prediction

accuracy compared to OLS modeling of risk perceptions. A spatially explicit modeling technique

may be useful in decision and policy making. Our locally speciﬁc ﬁndings may assist developers,

elected oﬃcials, community planners, public managers, and property owners in high amenity and

tourist-based communities, by estimating, understanding, and managing the potential impacts of

climate and weather conditions such as storms and coastal ﬂooding on these communities at the local

level. The results are also intended to aid in eﬀective decision-making and to contribute to the long

term economic, environmental, and socio-cultural sustainability of these communities.

5. Conclusions

As coastal populations continue to increase, and hazards such as ﬂooding, erosion, and storms

increase, more property and people will be at risk. This study compared the property owners’ risk

perceptions with physical vulnerability in three coastal counties in North Carolina, USA. The study was

novel in that it integrated assessments of social and physical aspects of vulnerability, and compared

multiple statistical models utilized for data analysis. Overall, property owners reported that they felt

current weather and climate conditions were optimal and that they were prepared for severe weather

events. Their perceptions of weather and climate, freshwater availability, and factors such as sea-level

rise were all found to inﬂuence respondents’ patterns of property ownership. Physical variables such

as storm surge, ﬂooding, and erosion, were found to contribute to a range of vulnerability levels across

the three counties, but no signiﬁcant relationship between these variables and patterns of property

ownership were found. However, these variables, and the resulting coastal vulnerability index (CVI)

were useful in interpreting the statistical models. Both a global ordinary least squares (OLS) and a

local geographically weighted regression (GWR) were utilized and model ﬁts compared. While both

models were only able to explain around 20% of the variation seen in the response variable (property

ISPRS Int. J. Geo-Inf. 2020, 9, 275 18 of 19

ownership), the GWR model was a slightly better ﬁt (R

= 0.248). The GWR coeﬃcients for the physical

variables were especially useful in interpreting geographic patterns in Currituck County, NC.

This study also examined the diﬀerences in full-time residents and second-home property owners’

risk perceptions. A t-test was conducted that found risk perceptions of full-time residents were

statistically signiﬁcantly diﬀerent from those of second-home owners. To better understand these

signiﬁcant diﬀerences, the factors inﬂuencing these risk perceptions were investigated. The most

signiﬁcant ﬁnding was that the model best ﬁt full-time residents, versus second-home owners.

The results show that age, sense-of-place, and sustainable actions have statistically signiﬁcant

positive relationships with full time residents’ risk perceptions, whereas length of owning property

has a statistically signiﬁcant negative relationship with residents’ risk perceptions. Education and

sense-of-place have statistically signiﬁcant positive relationships with second homeowners’ risk

perceptions. There is a gap in the literature in that second-home owners are not usually included

in risk perception studies. However, these coastal communities have a high percentage of second

homes (vacation homes), which means previous work is missing a large portion of the population’s

perceptions. This study ﬁlled this research gap by including second-home owners and comparing their

risk perceptions with those of full-time residents. The study reported in this paper will also be used as

a baseline for comparison with new one that is in-progress. In the future, we also plan to expand the

study to examine perceptions of business owners, include more coastal counties, and reﬁne the coastal

vulnerability index with additional geospatial data, such as models of sea-level rise, wetland migration,

and higher resolution coastal erosion data.

Author Contributions:

Conceptualization, Huili Hao and Devon Eulie; methodology, Huili Hao, Devon Eulie,

and Allison Weide; formal analysis, Huili Hao and Devon Eulie; investigation, Huili Hao and Devon Eulie;

data curation, Huili Hao, Devon Eulie, and Allison Weide; writing—original draft preparation, Huili Hao and

Devon Eulie; writing—review and editing, Huili Hao and Devon Eulie; visualization, Huili Hao and Devon Eulie;

funding Huili Hao. All authors have read and agreed to the published version of the manuscript.

Funding: This research was funded by North Carolina Sea Grant, grant number 2010-0974-05.

Conﬂicts of Interest:

The authors declare no conﬂict of interest. The funders had no role in the design of the

study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to

publish the results.

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