Evaluating water quality impacts on visitation to coastal recreation areas using data derived from cell phone locations.

Linking human behavior to environmental quality is critical for effective natural resource management. While it is commonly assumed that environmental conditions partially explain variation in visitation to coastal recreation areas across space and time, scarce and inconsistent visitation observations challenge our ability to reveal these connections. With the ubiquity of mobile phone usage, novel sources of digitally derived data are increasingly available at a massive scale. Applications of mobile phone locational data have been effective in research on urban-centric human mobility and transportation, but little work has been conducted on understanding behavioral patterns surrounding dynamic natural resources. We present an application of cell phone locational data to estimate the effects of beach closures, based on measured bacteria threshold exceedances, on visitation to coastal access points. Our results indicate that beach closures on Cape Cod, MA, USA have a significant negative effect on visitation at those beaches with closures, while closures at a sample of coastal access points elsewhere in New England have no detected impact on visitation. Our findings represent geographic mobility patterns for over 7 million unique coastal visits and suggest that closures resulted in approximately 1,800 (0.026%) displaced visits for Cape Cod during the summer season of 2017. We demonstrate the potential for human-mobility data derived from mobile phones to reveal the scale of use and behavior in response to changes in dynamic natural resources. Potential future applications of passively collected geocoded data to human-environmental systems are vast.

Introduction

Environmental degradation is increasingly recognized as having harmful social and economic consequences. Capturing the mode and magnitude of these consequences relies on measurement of human behavior, but the methods of measurement have constrained the ability to reveal the impacts of environmental quality [1, 2]. Environmental characteristics like aesthetics and weather are commonly associated with influencing the demand for coastal recreation; however, the spatial heterogeneity of water resources combined with the lack of visitor counts for coastal access points makes the quantification of visitor behavior especially challenging for this setting [3, 4].

Degraded coastal water quality is a pervasive issue and an important factor in the availability and quality of coastal visits. To protect water quality for swimming purposes, coastal recreation waters in the United States are monitored seasonally for bacterial contamination and subsequently closed to swimming when levels surpass established thresholds. It has been demonstrated that closures can lead to economic and social losses for coastal communities [5-8] and that coastal recreation is sensitive to physical characteristics and changes in climactic conditions [3, 9]. However, few studies have empirically demonstrated the impacts of environmental quality on recreational activity at high spatiotemporal resolution across an entire region. This is largely because of the lack of large-scale, consistent, and ongoing visitation data collections. While effective, visual counts and surveys can be time-consuming and expensive, leading to work that is necessarily constrained across space and time and limited in reproducibility [10-12].

Passively collected geocoded data derived from cell phones provide the digital footprints of human activity. Using this form of data to study the spatiotemporal dynamics of human mobility has garnered considerable attention in recent years and is promising as a measurement instrument to assess the distribution of populations in space and time [10, 11, 13–16]. Cell data from a single service provider can quickly accumulate the activities of millions of people, especially in densely populated urban areas [17]. Newer forms of cell data are increasingly robust, as most cell phones contain GPS units that track locational data far more frequently than calls and texts are performed. This means that location data, in the form of latitude and longitude, are generated each time a device interacts with a network, which happens when a device connects to WiFi, GPS, Bluetooth, and mobile applications, in addition to calls or texts. Given the near universal adoption of mobile phones, cell data presents a compelling data source for investigating and understanding human mobility at a global scale, with much potential for understanding human interaction with natural resources [14, 18–20].

Cell data has been extensively used to analyze transportation infrastructure and commuting patterns [10, 11], human mobility [14, 16, 21–24], transportation mode inference [25], and tourism dynamics and human behavior during special events [26-28]. Only a few studies have used cell data to understand human interaction with natural resources. Yu et al. [29] and Nyhan et al. [30-32] conducted some of the few examples of this type of study, using cell data to estimate exposure to ambient air pollution in urban areas. AirSage, in partnership with the U.S. Forest Service and National Park Service, piloted a project to assess monthly visitation to national forests and national parks using AirSage’s proprietary data [33]. Merrill et al. [13], Kubo et al. [34], and Monz et al. [35] provided the first uses of cell data to quantify visitation to natural areas across entire regions and for extensive timeframes. While these projects demonstrate the growing use of cell data for understanding general human mobility trends, there has yet to be research which employs cell data to analyze variations in human mobility patterns in response to changes in the quality of coastal resources. To date, no research has used cell data to investigate behavioral responses to water quality changes.

This paper presents an application of human-mobility data derived from mobile phone locations (hereafter referred to as cell data) to estimate the effects of beach closures bacteria exceedances on visitation to 565 coastal recreation areas in New England, USA, 465 of which are on Cape Cod, MA. Merrill et al. [13] demonstrated the viability of cell data to provide extensive and detailed visitation data to natural areas, showing how the data can replicate visitation estimates produced by observational counts, but with a much higher spatial and temporal resolution and larger geographic extent. Using a dataset derived from cell phone location data which estimates visitation to 565 coastal recreation areas in New England across the summer season of 2017 (June-September), combined with EPA data on beach closures from bacterial contamination, this paper estimates visitation totals and the behavioral impacts of closures for coastal recreation areas on Cape Cod, MA, USA, as well as across New England. Our work demonstrates the potential for cell data to reveal behavioral patterns in response to a dynamic natural resource.

Materials and methods

Study area

Cape Cod is a peninsular land mass that protrudes into the Atlantic Ocean from Massachusetts’ southeastern shoreline. The coastline is roughly 560 miles long and contains a range of water recreation areas from marine bathing beaches to estuarine waterways and inland ponds. These water recreation areas on Cape Cod are major attractions and provide significant ecosystem services for both visitors and residents alike [36]. The nature of water recreation in New England creates an element of significant seasonality; visitors flood to Cape Cod in the summer months, driving high rates of second-home ownership and residential dependence on a tourism-based economy. In 2015, the Cape Cod Commission estimated that roughly 5 million people visited Cape Cod, more than half of which were sometime between Memorial Day and Labor Day [37]. Given the significance of Cape Cod’s water resources to both its seasonal visitors and permanent residents, beach closures from bacterial contamination are a primary concern for local and state environmental managers. While our research is primarily focused on Cape Cod, we include an additional analysis on a smaller sample of coastal recreation areas across the New England states of Maine, New Hampshire, Massachusetts, Rhode Island, and Connecticut. The purpose of this additional analysis was to test how generalizable our results from Cape Cod were across a more diverse range of coastal recreation areas.

Data

Cell phone data

We purchased cell data to estimate visitation for a comprehensive set of 465 public coastal access points on Cape Cod (Fig 1) during the summer season of 2017 (June, July, August, and September). Our sample of 465 coastal access points on Cape Cod represents all public ways to water which includes monitored and unmonitored freshwater and saltwater recreation areas. These recreation areas vary in type from small inland ponds to large coastal bathing beaches. Monitored recreation areas refer to coastal access points that were routinely (often weekly) examined for bacterial contamination by beach managers, municipalities, or state departments of health. We selected this complete set of public access points to maximize the opportunity for understanding variations in visitation within a socially and economically significant region with water quality concerns and to have comprehensive information for an entire region. Additionally, we purchased data for a set of 100 coastal access points with water quality monitoring histories in the New England states of Maine, New Hampshire, Massachusetts, Rhode Island, and Connecticut. The sample across New England’s coastal states are a mix of saltwater beaches and public access points to saltwater areas that vary in size, type, recreational attributes, and water quality histories.

Fig 1

The comprehensive set of sampled water recreation areas on Cape Cod, Massachusetts.

This includes all public ways to water, from inland freshwater ponds, to estuarine inlets, to large marine swimming beaches. Many of the coastal marine beaches are monitored for bacterial contamination, while freshwater ponds are unmonitored. Closed beaches refer to any beach that was closed, even once, during the summer in 2017. Base map and data from OpenStreetMap and OpenStreetMap Foundation.

We purchased data from Airsage, Inc. Airsage is one of many companies that sells a range of data products derived from raw cell phone locations, which are collected by cellular service providers and application developers. The specific product provided to EPA by Airsage was developed using GPS locational information captured by smartphone applications. This data is then anonymized, cleaned, and packaged using proprietary methods to transform the raw cell data generated by smartphone application-level GPS information into anonymized estimates of visitation, which we aggregated into daily totals.

Through a comparison to a series of observational counts, Merrill et al. [13] determined that the cell data provided by Airsage overestimates the quantity of visitation to the specified geographic areas, especially when aiming to quantify visits that are uniquely recreational. Monz et al. [35] found that calibration of raw cell data was important for park areas in California, as Merrill et al. [13] did for water recreation areas in New England. The Airsage product used in this study was calibrated to observational visitation counts following the process described in Merrill et al. [13] to create daily visitation estimates to all coastal access areas for the duration of the study. Across the entire 2017 summer season, there were 7.5 million distinct visits to the 465 sampled coastal access points on Cape Cod (Fig 2), and 4.3 million visits to the sampled set of 100 coastal access points across New England.

Fig 2

Total visits to the 465 coastal access points on Cape Cod in 2017.

Beach closure data

In the United States, non-point source pollution is the most common cause of contaminated water [38]. The specific causes of impairments vary based on location and waterbody type, but most coastal impairment listings are the result of pathogens, specifically bacterial contamination [39]. Bacterial contamination is typically from fecal sources, both point and non-point. The pathways through which these organisms travel are often sewage (leaking sewer pipes, combined sewer overflows, leaching septic systems, etc.), stormwater runoff, or human and animal waste discharged directly into the water. The bacterial pathogens that cause contaminated waters are directly linked to waterborne illnesses that are harmful to humans (such as gastrointestinal illness or respiratory illness) [39]. Given the risk posed to humans who are exposed to these pathogens, federal and state regulations require many coastal recreation areas (mainly popular state saltwater bathing beaches and select freshwater ponds) to be monitored for potential contamination.

The EPA’s Beaches Environmental Assessment and Coastal Health (BEACH) Act of 2000 provides states with funding (through BEACH Act grants) to monitor their waterbodies for impairment [40]. The EPA is tasked with providing the data resulting from the states’ monitoring efforts to the public. To do this, the EPA developed the Beach Advisory and Closing Online Notification (BEACON) system as a publicly accessible database [41] that aggregates states’ beach monitoring data to a national scale. This resource provided the dataset that details 2017 beach closures in New England for our analysis. Beach closure data is readily available and reasonably consistent at a national scale, whereas other water quality measures are not.

On Cape Cod there were 173 water recreation areas monitored for bacterial contamination in 2017. Across the summer season, there were eight closure events at eight (5%) recreation areas resulting in 20 closure days. There were over 800 coastal access points monitored for bacterial contamination across New England in 2017, 251 (nearly 30%) of which were locations that either closed or posted advisories due to water quality testing results surpassing bacterial thresholds. The closures at these 251 locations resulted in 713 closure or advisory days across New England. Further detail on closures across New England is described in the supplementary information.

Weather data

Our model also included a single set of weather parameters collected by the National Oceanic and Atmospheric Administration (NOAA) at the Hyannis, Barnstable Municipal-Boardman Airport weather station which is located centrally on Cape Cod and representative of the weather conditions across our sample locations. NOAA provides daily summaries of precipitation, windspeed, and temperature, which we accessed and downloaded through NOAA’s online weather data download portal [42].

Model

To understand how closures due to impaired water quality affect visits to New England coastal recreation areas, we developed a model that explains the variation in daily visitation to our set of 565 New England coastal access points. Once a representative model was established, we could then interrogate what effect, if any, closures have on visitation to these coastal access points. The closure dataset was incorporated into the behavioral model to determine if closures were significant in explaining the daily variation in visitation across the 2017 summer season conditional on other factors influencing visitation.

The unique size and structure of the visitation data, which includes 51,511 daily visitation estimates, allowed us to apply a panel regression model to understand which factors explain coastal visitation. Taking advantage of the panel structure of our data (location of interest x days), we created a fixed effects regression model to estimate daily visitation as a function of a set of explanatory variables and a coastal access point specific constant: where,

Y—visits to coastal access point i on day t derived from cell data

α—intercept for each coastal access point i

—vector product of coefficients and daily weather conditions (temperature, precipitation, and rainy-day dummy variable)

—vector product coefficients and month dummy variables

—vector product of coefficients and day of the week dummy variables

—vector product of coefficients and dummy variables for weekends (including holiday weekends)

βC—product of coefficient β and dummy variable for each day t a beach i had a closure posted C

e—within beach error term

A fixed effect specification controls for any non-time-varying attributes of the coastal recreation areas and points of interest, such as site size, facilities and any other non-varying environmental and site quality features [43]. While the specification controls for these factors in estimating the other marginal effects of interest, it did not allow us to distinguish the individual effect of these non-time varying factors on visitation. Our water quality attribute of interest varied over time, as a time series of open or closed statuses for each coastal access point.

We specified different functional forms of the model: linear, log-linear and log-log. We inferred that all the covariates, such as weather or day of the week, would not explain visitation linearly across differently sized or types of locations, meaning the effect of changes in covariates were not additive but more likely multiplicative. Based on this logic and the structure of the errors post estimation, we chose a log-linear regression model, which fit the data best. A percent change in visitation (Y) resulting from a change in one of the explanatory variables, holding all others constant, was calculated as 100 • (). Therefore, the effect can be interpreted as the percent fewer people visiting the access point that day than would have been visiting with no closure.

We ran the regression described above for the set of coastal access points on Cape Cod (n = 465) [45]. In addition, considering the diverse physical and spatial characteristics of the access points in our sample, we elected to stratify the sample into categories (Fig 3) to test two hypotheses: 1. assuming variation in physical and spatial characteristics drives differences in visitor type (and therefore recreational use type), our model would likely explain visitation differently for various categories of water recreation areas; and 2. given the variation in visitation type, we would also expect to capture closures’ effect differently for these categories. So, in addition to running the regression on the sample set for Cape Cod, we also ran the regression on spatially large access points on Cape Cod (3rd quartile area m2, n = 185) and on only access points monitored by the Barnstable County Department of Health and Environment for bathing beach quality (bacteria) on Cape Cod (n = 174). Lastly, to investigate how generalizable the results are to access points across New England we also ran the regression on a set of 100 water recreation areas across New England (i.e., off-Cape access points).

Fig 3

The four groupings of access points by category.

We ran regressions on each set of coastal access points consisting of (clockwise from top left) all Cape Cod access points (n = 465), all large Cape Cod access points (3rd quartile area m2, n = 185), all off-Cape access points, and all monitored Cape access points (n = 174). Access points with closures in 2017 are indicated in red. Base map and data from OpenStreetMap and OpenStreetMap Foundation.

Results

Results from our models can be seen in Table 1 and in the expanded results in the supplementary information. Our initial regression included the entire set of 465 access points on Cape Cod across the summer season of 2017. Daily precipitation and temperature had a substantial effect on visitation, with one centimeter of precipitation reducing estimated visitation by 24 percent and an increase in one-degree centigrade resulting in an 8 percent increase. Daily average wind speed (wind), and dummies for months and days of the week were also significant in affecting visitation.

Table 1

Fixed effect regressions for beach closures.

Dependent variable: Log of visits
	All Cape Access Points	Big Cape Access Points	Monitored Cape Access Points	Off Cape Access Points
Temperature	0.080***	0.100***	0.097***	0.090***
	(0.001)	(0.002)	(0.001)	(0.002)
Wind	0.004***	-0.003	-0.001	0.005*
	(0.001)	(0.002)	(0.002)	(0.002)
Precipitation	-0.217***	-0.274***	-0.112***	-0.100***
	(0.010)	(0.020)	(0.003)	(0.004)
June	-0.217***	-0.274***	-0.263***	-0.167***
	(0.010)	(0.020)	(0.016)	(0.021)
July	-0.140***	-0.145***	-0.120***	-0.089***
	(0.011)	(0.023)	(0.019)	(0.024)
August	-0.226***	-0.238***	-0.230***	-0.192***
	(0.011)	(0.023)	(0.018)	(0.023)
Tuesday	-0.034***	-0.062***	-0.068***	-0.032*
	(0.009)	(0.018)	(0.014)	(0.019)
Wednesday	0.074***	0.170***	0.105***	0.116***
	(0.010)	(0.020)	(0.016)	(0.021)
Thursday	0.042***	0.028*	-0.003	0.018
	(0.009)	(0.017)	(0.014)	(0.018)
Friday	0.095***	0.103***	0.102***	0.131***
	(0.009)	(0.018)	(0.014)	(0.019)
Saturday	0.263***	0.273***	0.278***	0.263***
	(0.010)	(0.020)	(0.016)	(0.020)
Sunday	0.316***	0.415***	0.386***	0.447***
	(0.009)	(0.018)	(0.014)	(0.019)
Closed	-0.164 **	-0.253 **	-0.180 **	0.015
	(0.074)	(0.100)	(0.083)	(0.027)
Observations	29,141	8,565	13,360	8,196
R2	0.49	0.55	0.533	0.510
Adjusted R2	0.478	0.538	0.526	0.503
F Statistic	1,179.208*** (df = 23; 28701)	439.217*** (df = 23; 8437)	653.605*** (df = 23; 13164)	365.458*** (df = 23; 8073)

*p<0.1

**p<0.05

***p<0.01.

Note: Regressions include fixed effects for each beach. The effect of beach closures is significant for the Cape beaches and more-so for larger beaches over 56,926 m2 in size. Each fixed effect regression contains lags of visitation for 10 days to correct for serial correlation in the error term.

When executing the regression for all Cape Cod access points, spatially large access points on Cape Cod, and monitored access points on Cape Cod, the closure variable was negative and significant (p<0.01). According to our model, a closure at any Cape Cod access point would reduce visitation to that location by 18 percent for that day, while a closure at a large access point on Cape Cod would reduce visitation by 29 percent, and a closure at any monitored access point would reduce visits by 20 percent. In 2017, there were eight coastal access points that had closures from bacterial contamination resulting in 20 days closed to water-based activities. This resulted in approximately 1,800 lost visits for these access points across 20 closure days in the season, shown as the lighter shaded area below the dotted line in Fig 4.

Fig 4

Total visits to Cape Cod access points with at least 1 closure in 2017.

Red areas beneath the line represent date ranges that have at least one closed beach. We project the total visits if there was not a closure in light red, but the difference is quite small except for the closure in late July.

Closure events vary in duration and geographic effect. Looking at individual beaches illuminates the varying effects of closures. Certain events, like the closure at Dyer Street Beach (Fig 5), closed the recreation area for four days, but the closure was isolated to the single beach. Other closure events were minimal in duration, like the event on July 26 and 27, 2017, but affect groups of access points as opposed to isolated locations likely due to regionally high rainfall and runoff events (Fig 6).

Fig 5

Visits to Dyer Street beach in 2017.

Red area beneath the line shows the range of dates when the beach was closed. We project the total visits if there was not a closure (i.e., lost visits) in light red below the dotted line.

Fig 6

Seasonal total visits to 333 Commercial Beach, Atlantic Avenue Beach, Ryder Street Beach, and 593 Commercial Street Beach.

Highlighted in red is the July 26 and 27 closure that impacted all four of these beaches. These four simultaneous closure events resulted in the most displaced visits in 2017 of all closure events and were likely directly tied to the same specific rain event.

Despite the closures being significant and negative for coastal access points on Cape Cod, this result did not generalize to our sample of 100 coastal access points across New England. While there is certainly room to improve upon our model of visitation to more accurately estimate the variation in visitation at each beach, there are several other reasons why effects of closures may not have been detected when running the regression on that set of 100 coastal access points across New England. The beaches where we detected the effect of a closure historically close less frequently (0.4 days per year on average in the last five years for monitored beaches on Cape Cod). Locations that had closures in 2017 where closures were not detected as a significant driver of variation in visitation on average close more often (3.3 days per year on average in the last five years for the set of 100 coastal recreation areas across New England). Certain locations like Wollaston Beach, MA, had five-year closure averages surpassing 30 days annually. We hypothesize that for beaches where closures were detected as significant, the closure was a rarity and resulted in more disruption of assumed quality and water-based activities. For beaches where closures were more frequent, a closure might not have affected the plans of those individuals visiting because the intended activities were not water-based or did not involve direct water contact. Furthermore, those visiting beaches with historically frequent closures may have been aware of that beach’s closure reputation and planned their water-contact accordingly. In general, coastal recreation activities on Cape Cod may be more water contact based, where coastal recreation across greater New England may favor activities with little direct water contact. It is difficult to prove this using cell data alone, as there is no straightforward way to stratify visits by activity type. Regardless, these findings point to the limits of using a single general scale of an impact of a beach closure, out of sample, for places where we do not have visitation or recreational behavior information. The use of cell phone locational data allows for vastly more beach-specific visitation data in many more places, limiting the need for applying mean effects from different studies, regions, or beaches.

Another critical driver of our ability to detect the impact of water quality on visitation is the closure dataset itself. While the dataset provides a crucial indicator of water quality at a national scale, the nuances of beach closures vary at the regional, state, and municipal levels. Certain states (like Maine, New Hampshire, and Rhode Island) post “advisories” that are suggestions to beach-goers to avoid contact with water. These states’ laws contain provisions where beach postings are the responsibility of local jurisdictions [44, 45]. Other states (such as Massachusetts) have it written into law that swimming or bathing are prohibited when water quality does not meet requirements [46]. In New Hampshire and Maine, local beach managers and boards of health retain the right to keep a beach open or state a beach is closed using their own discretion [45, 47]. Across New England, beach closures and swimming advisories do not prohibit the use of a beach for land-based activities (walking, sports, etc.) [48]. Additionally, the methods of sampling and testing water quality often take at minimum 24 hours to process, creating a potential delay between the beach closure and the quality event.

The aggregation of this ambiguity in the mechanisms employed to measure, close a given beach, and communicate that closure to the public create practical challenges to using beach closures as a proxy for water quality. For example, the impact being measured in this paper is the impact of the whole chain of events from sample to implementation of the policy threshold to communication with the public, through to the closure announcement, signage, and enforcement. The effects being measured in this paper may be distinct from the impact of water quality alone in absence of a closure management system built around it. Other measures of coastal water quality exist (dissolved oxygen, chlorophyll a, Secchi depth, etc.) but they are not collected comprehensively near the recreational areas of interest at a fine time resolution and are not often communicated and shared with the public. These other metrics may also have less of a direct impact on people’s day to day decisions around beach water quality than bacteria conditions, given the health implications.

One of the benefits of using more comprehensive datasets of visitation to water recreation areas, as in this study, is quantifying the total use and the number trips protected by town, regional, and state health departments sampling and closure programs. For Cape Cod, the monitoring program was protective of 4.2 million trips in one summer alone. As coastal recreation continues to grow in its economic influence and cultural value, so should the efficacy of these methods and the funding that is allocated to the organizations responsible for performing them.

Discussion

This study used cell data to estimate the effects of beach closures from bacteria exceedances on visitation to coastal access points on Cape Cod and in New England, USA for the summer season of 2017. Using a model of daily visitation combined with the dataset on bacterial closures, we were able to detect the impact of closures for coastal access points on Cape Cod. Our findings represent geographic mobility patterns for over 7 million unique visits and suggest that beach closures resulted in approximately 1,800 displaced visits on Cape Cod beaches during the summer season of 2017. However, we were unable to detect this effect for our broader New England sample.

Although there has been significant progress made towards understanding the biological and physical implications of water quality degradation, capturing both market and non-market damages of pollution, especially for water quality degradation, has remained a challenge for researchers. Linking changes in environmental systems to socioeconomic, behavioral, and human health outcomes is a crucial step in valuing the benefits of scientific progress and assessing the damages of environmental degradation [49]. In order to accomplish this, it is necessary to develop methods that reveal the scale of use and behavior in response to changes in dynamic natural resources. As coastal recreation is increasingly recognized as a primary driver of economic activity both across New England and nationally [50], accurate visitation estimates are crucial in enabling novel research methodologies to advance [12].

Cell data offers a promising tool in approaching and resolving spatiotemporal mobility problems in the environmental sciences. Whereas the use of cell data for investigating human interaction with natural resources is still emerging as a body of literature, the last decade has seen substantial growth in volume and velocity of geographically coded data products [19]. With the growing availability of this type of data, many applications complementary to ours are appearing within social sciences research, urban planning, and public health [32, 33, 35]. As these data types become more accessible and accurate, so will the ability of researchers to open new lines of inquiry and derive novel understanding of human-environmental systems.

Despite its promise as an instrument for measuring human behavior around natural resources, cell data is not a panacea. The utility in a spatiotemporally resolved dataset like that provided by mobile devices is in its ability to understand a sample population’s behavior. However, it does little to help with understanding the motivations behind decision making. While cell data does well with estimating aggregate daily visitation, this estimate is based on a sample of cell phone users. Thus, we do not know the specific effect of closures on different demographic groups, or the implications of the demographics of the cell data sample on this specific measure of the impact of water quality and beach closures. To understand these nuanced implications, traditional field-based methods of social science are still required. Evaluated independently, cell data lack nuance and context, leading to premature and one-size-fits-all assumptions. Instead, employing methods of research that reveal motivations are more necessary than ever.

The Clean Water Act, beach monitoring, and what happens when a sample tests positive for contamination.

(DOCX)

Click here for additional data file.

Monitoring records by state for the 2017 bathing season.

(DOCX)

Click here for additional data file.

Comprehensive regression results for multiple subsets of the coastal recreation areas.

(DOCX)

Click here for additional data file.

Visits to Cahoon Hollow Beach.

The dark red area beneath the visitation line shows the date range when there was a closure event, and projected visitation (i.e. predicted visitation if there had not been a closure event) is shown in light red below the dotted line.

(TIFF)

Click here for additional data file.

Visits to Cross Street Beach.

The dark red area beneath the visitation line shows the date range when there was a closure event, and projected visitation (i.e. predicted visitation if there had not been a closure event) is shown in light red below the dotted line.

(TIFF)

Click here for additional data file.

(TIFF)

Click here for additional data file.

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Visits to Mayo/Indian Neck beach.

The dark red area beneath the visitation line shows the date range when there was a closure event, and projected visitation (i.e. predicted visitation if there had not been a closure event) is shown in light red.

(TIFF)

Click here for additional data file.

Visits to comprehensive set of recreation areas in New England in 2017.

Our sample consists of 465 recreation areas on Cape Cod, and 100 recreation areas across Connecticut, Rhode Island, Massachusetts (off Cape Cod), New Hampshire, and Maine. This figure shows visitation for all 565 recreation areas across the summer season.

(TIF)

Click here for additional data file.

12 Mar 2021

PONE-D-21-02353

A novel approach to evaluating water quality impacts on visitation to coastal recreation areas on Cape Cod using data derived from cell phone locations.

PLOS ONE

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Reviewer #1: Partly

Reviewer #2: Partly

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors use a basic fixed-effects panel regression technique to determine the effect of beach closures (a proxy for water quality) on beach visitation in Cape Cod, MA. They find that beach closures in Cape Cod negatively influence visitation rates, but results are insignificant when the sample is extended to the greater New England region. As the second paper to use cell phone data to assess water recreation visitation rates (Merrill et al., 2020), this paper expands the use of novel cell phone data to a practical application. The authors carefully explain the gaps in the literature and how they aim to fill them. They also clearly describe the study area and statistical approach. Finally, the present their results, discuss concerns readers may have (with a few notable exceptions), and describe ways in which limitations can be improved. This research has broad applications for benefit transfer ecosystem services valuations but should be used with caution.

Although this research is well formulated and the results are presented clearly in this paper, there are a couple of concerns that I believe are worth mentioning. First, I’m concerned about the ability of cell phone data to proxy visitation, as expressed in Merril et al. (2020).

Second, I have concerns about the use of beach closures as a proxy for water quality. Closures aren’t proxying water quality per se, instead they proxy water quality thresholds (i.e. the quality level the EPA has deemed too dangerous for direct use). I believe a longer time series of data is needed to accurately determine the effects of water quality on visitation and here is why:

1. This paper estimates the impact of beach closures due to extreme water quality degradation (above the EPA threshold) on beach visitation, not the impact of water quality on beach visitation. This sounds equivalent but they are not. The EPA threshold influences the results that water quality has on visitation. Visitors may not visit beaches with quality below the threshold because the beach is closed, not because they are avoiding poor water conditions. Change the threshold and the results will likely change. A better way is to include a water quality index and a beach closure dummy variable. i.e. below the water quality threshold., and an interaction. This would allow the authors to determine the impact of closure, i.e. the threshold, on visitation.

2. Beach visitors (especially local/ multiple use day visitors) may use average water quality from past experiences as a proxy for the recreational amenities of each beach. Stated differently, they have rational expectations for beach quality and only large unexpected deviations from mean water quality will influence their choice. One way to test this hypothesis would be to determine where the closure threshold lands on the distribution of water quality for each beach access point and then remove data points where the threshold is, say, 1-2 standard deviations from mean water quality. I suspect access points with poorer average water quality will not see a huge change in visitation rates from closures because visitors already consider the poor water quality while considering other options. I think using the threshold as a proxy underestimates the true impact of water quality on sites where direct water recreation makes up the majority of recreation. Where poor water quality has persisted at a site for a long time, the beach may have already adapted to cater to non-direct water-based recreation so changes in water quality will have little impact on visitation.

Third, the authors may have missed a reason for the difference in the statistical significance of closures as a predictor of visitation in Cape Cod and the greater New England region. The greater New England region (as a whole) probably has relatively little direct water recreation (e.g. swimming and boating) off the coast. I imagine these results would not transfer well to other regions where direct water reactional makes up a large portion of total recreation, which should be stated by the authors.

Finally, demographics likely play a role in the results. Demographics may be influencing the results if cell phone users are more or less likely to avoid beach closures due to poor water quality than non-cell phone users. For example, older individuals may be more reluctant to visit beaches with poor water quality than younger individuals and be less likely to have a cell phone. Thus, the research estimates the effects of beach closures on visitation by cell phone users, not the general public.

In all, I believe this is a well written paper that should be revised to account for the concerns I mentioned in this review.

I am available to view a revised version if necessary.

Reviewer #2: Author proposed a novel approach to evaluate the water quality impacts on visitation to coastal recreation areas. There are some significant improvements required as following:

Line 51-54, author needs to re-write again. It is quite difficult to understand "what the author wants to explain".

Datasets: There are a lot of Datasets used in this study, In line 87-89 authors mentioned about combining these datasets. However, there is a lack of explanation about “how they combine these datasets”. Either author used the graphical explanation or block diagram for the readers to understand easily.

Model: Authors proposed a novel approach is used in the title. But in this study, linear regression model is used or can say modified linear regression model used. If the author used the “novel approach” comparison is required with existing approaches or models.

Mathematical explanation is very weak. It should be improved.

Results: What are technical parameters, parameters for the linear regression model and computer specification for the reproducible of the results ? Results reproducible is very important for the future researchers that follows the same path of results or improves these results outcome.

**********

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22 Dec 2021

Response to Reviewers

Reviewer #1:

"First, I’m concerned about the ability of cell phone data to proxy visitation, as expressed in Merril et al. (2020)."

We largely share these concerns about the ability of cell phone data to proxy visitation. In our manuscript (line 136), we reference the Merrill et al. (2020) finding that the raw cell phone locational data “out of the package” overestimated visitation to the specified geographic areas. To account for this, Merrill et al. (2020) conducted a series of manual observational counts to calibrate the raw cell data and found that cell data is useful and accurate once rescaled.

We understand there still may be concerns with using cell phone data to proxy visitation. The proprietary process vendors use to take raw signal data and translate it into population-scale aggregates remains opaque. Considering this, we recommend examining raw data from vendors before assuming it is representative. This is one of the reasons why we chose to use a dataset calibrated to on-the-ground observations for the region (Merrill et al. 2020).

While the use of cell phone location data as a measure of recreational visitation to natural areas may still require caution, there are no other sources of visitation estimates that can provide the same spatial scale, temporal resolution, and demonstrated accuracy to pick up a water quality impact, even one such as this, based on a policy threshold.

"Second, I have concerns about the use of beach closures as a proxy for water quality. Closures aren’t proxying water quality per se, instead they proxy water quality thresholds (i.e. the quality level the EPA has deemed too dangerous for direct use). "

These water quality thresholds are implemented specifically to protect the public from exposure to harmful levels of pathogens during water recreation. While it may be a threshold-based measure of water quality (like DO criteria, as another example), it is the best mechanism available at the geographic scale, timeframe, and connection to people that we are interested in examining with the cell phone data.

Water quality data across an entire region (like Cape Cod, for example, or especially across a larger geographic area that spans municipalities or states like New England) is unavailable at consistent time intervals let alone daily. While it may be ideal to develop a water quality index that combines biochemical data with beach closure data, the biochemical data is currently too incomplete to use for our purposes. In the meantime, bacteria sampling and beach closures represent the closest thing to a daily water quality indicator that we have that is consistent across all of Cape Cod and New England (this sentiment is expressed in our manuscript in line 165) and is directly people relevant.

To address these concerns in the manuscript, we made it more clear the type of water quality metric (based on bacteria conditions and a policy threshold) we are using by changing wording in the introduction. We also added to qualifying the results on page 14 putting the bacteria sampling and closure program in context with other water quality monitoring data options so that the reader can appreciate this point that the impact of closures is due to a whole series of events including, but not limited to, measuring water quality.

“The aggregation of this ambiguity in the mechanisms employed to measure, close a given beach and communicate that to the public create practical challenges to using beach closures as a proxy for water quality. For example, the impact being measured in this paper is the impact of the whole chain of events from sample to implementation of the policy threshold to communication with the public, through the closure announcement, signage and enforcement. This effect being measured in this paper may be distinct from the impact of water quality alone in absence of a closure management system built around it. Other measures of coastal water quality exist (dissolved oxygen, chlorophyll a, Secchi depth, etc.) but they are not collected comprehensively near the recreational areas of interest at a fine time resolution and are not often communicated and shared with the public. These other metrics may also have less of a direct impact on people’s day to day decisions around beach water quality than bacteria conditions, given the health implications.”

"I believe a longer time series of data is needed to accurately determine the effects of water quality on visitation and here is why:

1. This paper estimates the impact of beach closures due to extreme water quality degradation (above the EPA threshold) on beach visitation, not the impact of water quality on beach visitation. This sounds equivalent but they are not. The EPA threshold influences the results that water quality has on visitation. Visitors may not visit beaches with quality below the threshold because the beach is closed, not because they are avoiding poor water conditions. Change the threshold and the results will likely change. A better way is to include a water quality index and a beach closure dummy variable. i.e. below the water quality threshold., and an interaction. This would allow the authors to determine the impact of closure, i.e. the threshold, on visitation."

It may be the case that changing the threshold would influence the impact of water quality on visitation, since the policy would change. However, developing a daily water quality index for every beach in our sample is not currently possible given dispersed and inconsistent water quality data. Our lab is currently working on this exact problem, but it is an ongoing effort that is outside the scope of this paper.

We agree that using a water quality index and beach closure dummy variable could be a better approach to disentangle a threshold versus a gradient of conditions So, the results of this paper ask if, and how much, visitation is sensitive to this water quality driven management threshold. We make this clear up front now in the abstract and introduction.

We added text on this important point throughout and in the results on Page 14. Text referenced in the response to the previous comment.

"2. Beach visitors (especially local/ multiple use day visitors) may use average water quality from past experiences as a proxy for the recreational amenities of each beach. Stated differently, they have rational expectations for beach quality and only large unexpected deviations from mean water quality will influence their choice. One way to test this hypothesis would be to determine where the closure threshold lands on the distribution of water quality for each beach access point and then remove data points where the threshold is, say, 1-2 standard deviations from mean water quality. I suspect access points with poorer average water quality will not see a huge change in visitation rates from closures because visitors already consider the poor water quality while considering other options. I think using the threshold as a proxy underestimates the true impact of water quality on sites where direct water recreation makes up the majority of recreation. Where poor water quality has persisted at a site for a long time, the beach may have already adapted to cater to non-direct water-based recreation so changes in water quality will have little impact on visitation."

This is a great idea and something we considered while writing the paper. We make reference to the idea of average closures impacting our ability to detect the effects on visitation in the conclusion (lines 271-285), and similarly discuss the idea of a beach site catering to non-direct water-based recreation based on historic water quality. We hypothesize this average condition and expectations as a potential reason why we did not find the effect of closures with this instrument off the Cape (on relatively dirtier beaches).

"Third, the authors may have missed a reason for the difference in the statistical significance of closures as a predictor of visitation in Cape Cod and the greater New England region. The greater New England region (as a whole) probably has relatively little direct water recreation (e.g. swimming and boating) off the coast. I imagine these results would not transfer well to other regions where direct water reactional makes up a large portion of total recreation, which should be stated by the authors."

We alluded to this hypothesis in the conclusion but have now more explicitly stated this idea in added lines 279-286. To clarify, though, I believe the reviewer may have reversed the implication in the comment here. The reviewer states: “...these results would not transfer well to other regions where direct water recreational makes up a large portion of total recreation,” but I think the inverse may be the case; our results may not transfer well to other regions where direct water recreation makes up a small portion of total recreation, but may transfer well for other regions similar to Cape Cod where water-contact is the dominant activity type.

We edited and added this text to page 12 and 13:

“Despite the closures being significant and negative for coastal access points on Cape Cod, this result did not generalize to our sample of 100 coastal access points across New England. While there is certainly room to improve upon our model of visitation to more accurately estimate the variation in visitation at each beach, there are several other reasons why effects of closures may not have been detected when running the regression on that set of 100 coastal access points across New England. The beaches where we detected the effect of a closure historically close less frequently (0.4 days per year on average in the last five years for monitored beaches on Cape Cod). Locations that had closures in 2017 where closures were not detected as a significant driver of variation in visitation on average close more often (3.3 days per year on average in the last five years for the set of 100 coastal recreation areas across New England). Certain locations like Wollaston Beach, MA, had five-year closure averages surpassing 30 days annually. We hypothesize that for beaches where closures were detected as significant, the closure was a rarity and resulted in more disruption of assumed quality and water-based activities. For beaches where closures were more frequent, a closure might not have affected the plans of those individuals visiting because the intended activities were not water-based or did not involve direct water contact. Furthermore, those visiting beaches with historically frequent closures may have been aware of that beach’s closure reputation and planned their water-contact accordingly. In general, coastal recreation activities on Cape Cod may be more water contact based, where coastal recreation across greater New England may favor activities with little direct water contact. It is difficult to prove this using cell data alone, as there is no straightforward way to stratify visits by activity type. Regardless, these findings point to the limits of using a single general scale of an impact of a beach closure for places where we do not have visitation or recreational behavior information. The use of cell phone locational data allows for vastly more beach-specific visitation estimates in many more places, limiting the need for applying mean effects from different studies, regions or beaches.”

"Finally, demographics likely play a role in the results. Demographics may be influencing the results if cell phone users are more or less likely to avoid beach closures due to poor water quality than non-cell phone users. For example, older individuals may be more reluctant to visit beaches with poor water quality than younger individuals and be less likely to have a cell phone. Thus, the research estimates the effects of beach closures on visitation by cell phone users, not the general public."

The manual counts observed by Merrill et al. (2020) were demographic agnostic and, therefore, our calibrated visitation counts should be corrected for any bias towards cell phone users in terms of estimating daily visitation. However, if the effect of the closure were to change this relationship between the cell phone users in the sample and visitation (say if demographics affected this impact) than this could be an issue.

We acknowledge this point now in the discussion on page 16:

“Despite its promise as an instrument for measuring human behavior around natural resources, cell data is not a panacea. The utility in a spatiotemporally resolved dataset like that provided by mobile devices is in its ability to understand a sample population’s behavior. However, it does little to help with understanding the motivations behind decision making. While cell data does well with estimating aggregate daily visitation, this estimate is based on a sample of cell phone users. Thus, we do not know the specific effect of closures on different demographic groups, or the implications of the demographics of the cell data sample on this specific measure of the impact of water quality and beach closures. To understand these nuanced implications, traditional field-based methods of social science are still required. Evaluated independently, cell data lack nuance and context, leading to premature and one-size-fits-all assumptions. Instead, employing methods of research that reveal motivations are more necessary than ever.”

Reviewer #2:

Line 51-54, author needs to re-write again. It is quite difficult to understand "what the author wants to explain".

Rewritten to (now in lines 52-54): “However, few studies have empirically demonstrated the impacts from changes in environmental quality on recreational activity at high spatiotemporal resolution across an entire region.”

Datasets: There are a lot of Datasets used in this study, In line 87-89 authors mentioned about combining these datasets. However, there is a lack of explanation about “how they combine these datasets”. Either author used the graphical explanation or block diagram for the readers to understand easily.

There are multiple data sources for this study (beach closures, weather data, cell-phone data), but the model relies on a single dataset which unites by a “date” variable. This dataset is publicly available in a Github repository (see below for link).

Model: Authors proposed a novel approach is used in the title. But in this study, linear regression model is used or can say modified linear regression model used. If the author used the “novel approach” comparison is required with existing approaches or models.

The novel part is combining cell phone data to estimate the effects of environmental condition. The linear regression is a common tool, but the data inputs and application are novel to the field. The literature review compares this to other applications to park and tourism management.

We changed the titled to “Evaluating water quality impacts on visitation to coastal recreation areas using data derived from cell phone locations”. We explain the novel contribution of the paper in the introduction and literature review.

Mathematical explanation is very weak. It should be improved.

As suggested above, linear regression is standard in many fields, so we kept the explanation to the inputs and specification of the model and not the mathematics of the regression in linear and log forms. The references provided explain the regression model specifics. We also include a code package for more model specifics and reproducibility.

Results: What are technical parameters, parameters for the linear regression model and computer specification for the reproducible of the results ? Results reproducible is very important for the future researchers that follows the same path of results or improves these results outcome.

We have built a Github repository (available at https://github.com/USEPA/Recreation_Benefits) that includes all technical parameters, computer specifications, and data used in this study.

Submitted filename: Responses to Reviewers.docx

Click here for additional data file.

25 Jan 2022

Evaluating water quality impacts on visitation to coastal recreation areas using data derived from cell phone locations.

PONE-D-21-02353R1

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Reviewer #2: Yes

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21 Feb 2022

PONE-D-21-02353R1

Evaluating water quality impacts on visitation to coastal recreation areas using data derived from cell phone locations.

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