What Human Activity Has Been Linked to Increased Earthquake Activity in the United States?
Int J Environ Res Public Health. 2019 Aug; 16(sixteen): 2836.
Daily Wheel and Pedestrian Activity equally an Indicator of Disaster Recovery: A Hurricane Harvey Case Study
Annie Doubleday
1Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, WA 98195, USA
Youngjun Choe
twoDepartment of Industrial and Systems Applied science, University of Washington, Seattle, WA 98195, Us
Scott Miles
3Department of Human Centered Blueprint and Engineering, University of Washington, Seattle, WA 98195, United states
Nicole A. Errett
oneDepartment of Environmental and Occupational Wellness Sciences, School of Public Health, Academy of Washington, Seattle, WA 98195, USA
4Department of Health Services, School of Public Wellness, University of Washington, Seattle, WA 98195, U.s.
Received 2019 Jun 18; Accepted 2019 Jul 27.
Abstruse
Changes in levels and patterns of physical activity might be a machinery to appraise and inform disaster recovery through the lens of wellbeing. However, few studies have examined disaster impacts on physical activity or the potential for concrete activity to serve as an indicator of disaster recovery. In this exploratory study, nosotros examined daily bike and pedestrian counts from 4 public bicycle/pedestrian trails in Houston, before and subsequently Hurricane Harvey landfall, to appraise if physical activeness returned to pre-Harvey levels. An interrupted time serial analysis was conducted to examine the firsthand impact of Harvey landfall on concrete activeness; t-tests were performed to assess if trail usage returned to pre-Harvey levels. Hurricane Harvey was found to accept a significant negative touch on on daily pedestrian and bicycle counts for three of the iv trails. Daily pedestrian and bicycle counts were found to return to pre-Harvey or higher levels at six weeks post-landfall at all locations studied. Nosotros discuss the potential for further research to examine the trends, feasibility, validity, and limitations of using bicycle and pedestrian apply levels as a proxy for disaster recovery and wellbeing amid afflicted populations.
Keywords: wellbeing, physical activeness, disaster recovery
1. Introduction
Disasters have short and long-term impacts on concrete and mental health, too as healthcare infrastructure [1]. Several studies have examined clinical indicators of physical and mental health afterward disasters, including post-traumatic stress disorder, injuries, gastrointestinal infections, and diabetes-related complications [2,3,4]. Flooding-related disasters, in particular, have been shown to result in negative impacts to health [4]. Nevertheless, at that place has been express inquiry on the bear upon of disasters on concrete action, which could act as an indicator of wellbeing.
According to the Centers for Illness Command and Prevention, wellbeing "includes the presence of positive emotions and moods…, the absence of negative emotions..., satisfaction with life, fulfillment and positive functioning" [five]. Existing studies have largely leveraged survey methods to appraise disaster-related wellbeing impacts. For instance, following the 2010–2011 Canterbury earthquakes, the Canterbury Earthquake Recovery Say-so surveyed the afflicted individuals to assess and runway wellbeing [6]. Quality of life (QOL) scores have besides been used to determine the wellbeing of survivors after a disaster [7,8]. Wu et al. surveyed survivors of a 1999 Taiwanese convulsion, and found that QOL scores decreased more for the group with worse pre-existing mental health [7], and Ardalan et al. found lower QOL scores amidst elderly survivors of the Bam earthquake in Iran five years after the issue compared to the pre-earthquake scores [viii]. While these studies provide insights near the impact of disasters on wellbeing, conducting surveys to assess wellbeing post-obit a disaster is time and resource intensive and often does not provide pre-event measurements to serve equally a comparison. Thus, existing or secondary information streams that are updated continuously provide opportunities to monitor changes to wellbeing subsequently a disaster. Farther, these case studies highlight the wellbeing impacts of tsunami and earthquake events, but limited literature exists for hurricane and overflowing-related events, highlighting the demand for additional research post-obit meteorological disasters of differing magnitudes and impacts.
Substantial evidence has linked physical action with improved wellbeing beyond dissimilar populations, and has shown sedentary activity to be associated with a reduction in overall wellbeing [9,10,eleven,12]. Despite known associations between physical activity and improved mental health and wellbeing [9,10,11,12,13,fourteen,15], there is limited research on the impacts of disasters on physical activity. A few relevant studies have focused on physical activity impacts to children/adolescents or older adults and have leveraged primary data collection. For example, 8 months after Hurricane Ike, hurricane exposure and stress from recovery were associated with symptoms of mail service-traumatic stress and increased sedentary activity among children in Texas [sixteen]. Another report examined the physical activity levels in children and adolescents before and after the 2011 Great E Nippon earthquake. They constitute a subtract in physical activeness later the convulsion among the children and adolescents who survived [17]. In a third report, Tsuji et al. surveyed older survivors of the same earthquake and found lower depression scores among those who participated in group exercise activities or daily walking [18].
In recent years, daily counts of bicycles and pedestrians on particular trails or roadways take been collected and made publicly available, due east.one thousand., in New York Urban center and Los Angeles [19,20]. The Texas Department of Transportation consolidates daily wheel and pedestrian count data collected by various entities in Houston and in other cities across the state [21]. Daily wheel and pedestrian information have not been used to assess disaster impacts to physical action, but due to such programs, these information can exist speedily and continuously accessed and provide an opportunity to assess disaster impacts to physical activity, as well every bit notice recovery trends. These data do non rely on self-reported information and might be able to serve as a faster and more attainable indicator of community wellbeing in disaster recovery settings.
In an effort to understand the potential to utilize continuously collected, and publicly available bicycle and pedestrian data to appraise the physical activity impacts of disasters and monitor trends during recovery, nosotros conducted a instance study in Houston, TX, USA, following the 2017 tempest, Hurricane Harvey. Hurricane Harvey made landfall on the Texas Coast on 25 August 2017, and moved slowly beyond southeast Texas, raining heavily in Houston and Harris County through 27 Baronial. Harvey then slowly moved offshore, continuing to rain heavily throughout 29 and xxx August, resulting in over 40 inches of rain in some areas in Houston over this period [22]. The massive rainfall resulted in severe flooding across Houston, forcing tens of thousands of residents to evacuate [22], and resulting in closures of many bicycle and pedestrian trails for several days following landfall [23].
2. Materials and Methods
We received pedestrian and bicycle count data from the Texas Department of Transportation. The data received were at xv-min intervals from 2013 to 2018 and covered 8 counter stations located on pedestrian and bicycle trails across Houston. The coverage over the six-year menstruation varied substantially by station within Houston, with only 4 of the viii stations covering both the calendar month before and afterwards the Harvey landfall. The iv stations included in the assay were: Brays Bayou Greenway Trail, Columbia Tap Trail, Heights Trail, and White Oak Bayou Trail (Effigy ane). We aggregated the count data past day and counter station to yield daily bike and pedestrian counts by station within Houston. Nosotros and then mapped the station locations to visualize their spatial coverage and proximity, and daily counts were plotted over time by station to assess for seasonality and for a lagged effect.
To assess the firsthand affect of Hurricane Harvey on pedestrian and bicycle trail use, nosotros analyzed the daily count information using an interrupted time series assay (ITSA) approach. A linear regression model was fitted to the daily pedestrian and bicycle count data with a fourth dimension variable to point pre- and post-Harvey landfall, which was divers as 25 August 2017. The pre-landfall period was defined as the month before Harvey landfall (25 July 2017–24 August 2017), and the postal service-landfall flow was defined every bit the 2-calendar week menses post-obit Harvey landfall (25 August 2017–7 September 2017). The regression lines were plotted past station, separately for pedestrian and wheel counts, to visualize the change in daily counts earlier and after landfall. These plots were overlaid onto the daily counts, with a LOESS (locally estimated scatterplot smoothing) smoother for visualization of the count trend over time.
To evaluate the recovery of the pedestrian and wheel trail utilize, we used Welch two-sample t-tests. Specifically, nosotros assessed the statistical significance of the difference between the boilerplate daily counts in the half dozen weeks before Harvey landfall and the average daily counts in the 6 weeks afterward Harvey landfall, excluding the two-week catamenia immediately post-landfall, during which flooding remained widespread. The t-tests allowed us to formally determine whether the post-event counts returned to the pre-event level (or higher) in 6 weeks, excluding the first two weeks. During this two-week menstruum, some of the trails in Houston were airtight, impeding the trail access every bit a issue [23]. An employee at the Houston Parks Board informed us of the degree of damage and extent of closures for several trails that were included in our assay. We used this information to define the mail-landfall period in the recovery analysis. Recovery was categorized by a return to pre-issue counts (no significant difference in counts) or an increase in counts in the six-week catamenia following landfall, compared to the half dozen-calendar week catamenia prior to landfall. The average daily counts in the vi weeks before and after the Harvey landfall were and so plotted by station and mode of transportation (i.e., bicycle or pedestrian utilise), for comparison. All analyses were performed using R iii.5.2 (R Cadre Team, Vienna, Austria). Effigy 1 was prepared using ArcMap ten.5.one (Esri, Redlands, CA, USA).
iii. Results
Figure 1 displays the location of the four stations in our analysis, and includes the maximum mail-Harvey alluvion depth, averaged across each census tract.
Figure 2 and Figure 3 brandish the results of the interrupted time-serial analysis. All stations except the Heights Trail station saw a significant decrease (p < 0.05) in bicycle counts immediately following Harvey landfall (Figure 2). Both the Columbia Tap Trail station and the White Oak Bayou Trail station as well saw a significant change in slope (p < 0.05), when comparison the month before landfall to the ii-week period post-obit landfall. Both stations saw a subtract in counts at the time of landfall, followed by an increment in counts, yielding the significantly steeper slopes in the post-landfall menstruation.
Interrupted time-series analysis of wheel counts by station. The vertical dashed red line corresponds to Harvey landfall (25 August 2017). The solid red lines correspond to the interrupted fourth dimension-series regression lines. The solid black line corresponds to the LOESS smoother and the grey shading corresponds to a 30% smoothing bridge around the LOESS smoother line.
Interrupted time-serial analysis of the pedestrian counts past station. The vertical dashed red line corresponds to the Harvey landfall (25 August 2017). The solid red lines correspond to the interrupted time-serial regression lines. The solid black line corresponds to the LOESS smoother, and the greyness shading corresponds to a thirty% smoothing span effectually the LOESS smoother line.
All stations except the Heights Trail station saw a pregnant subtract (p < 0.05) in pedestrian counts immediately following Harvey landfall (Figure 3). The same three stations too saw a significant change in slope (p < 0.05), when comparing the calendar month before landfall to the two-week menses following landfall. All three stations saw a subtract in counts at the time of landfall, followed by an increase in counts, yielding the significantly steeper slopes in the mail-landfall flow.
Table 1 shows the results of the recovery analysis using Welch two-sample t-tests. All four stations saw an increment in the mean daily counts in the 6 weeks after landfall (excluding the 2-week period after landfall), as compared to the 6 weeks prior to landfall for both pedestrian and bicycle counts, except for pedestrian counts at Brays Bayou Greenway Trail.
Table one
Results of the t-test for counts six weeks before and after Harvey landfall.
| Station | Pedestrian | Bike | ||||
|---|---|---|---|---|---|---|
| Pre-Harvey Average 1 | Mail-Harvey Average 2 | Difference in Means | Pre-Harvey Boilerplate i | Postal service-Harvey Average ii | Difference in Means | |
| Brays Bayou Greenway Trail | 36 | thirty | p = 0.14 | lxx | 74 | p = 0.56 |
| Columbia Tap Trail | 72 | 143 | p < 0.001 | 105 | 124 | p = 0.04 |
| Heights Trail | 268 | 360 | p < 0.001 | 566 | 660 | p = 0.15 |
| White Oak Bayou Trail | 243 | 308 | p = 0.06 | 208 | 231 | p = 0.48 |
Figure 4 and Effigy v show the boilerplate daily cycle and pedestrian counts in the six weeks earlier and after Harvey landfall, excluding the two-week flow after landfall. All trails experienced an increase in boilerplate daily counts or render to pre-landfall boilerplate daily counts in the post-landfall period (with the exception of the statistically insignificant decrease in pedestrian counts for the Brays Bayou Greenway Trail).
Averages of the daily cycle counts, past station, in the 6 weeks before and after landfall, excluding the 2-week period after landfall. The vertical dashed cherry line corresponds to the Harvey landfall (viii/25/2017). The red lines correspond to the average daily bicycle counts. The solid blackness line corresponds to the LOESS smoother, and the grayness shading corresponds to a 30% smoothing span around the LOESS smoother line.
Averages of daily pedestrian counts, past station, in the 6 weeks before and after landfall, excluding the two-week period after landfall. The vertical dashed reddish line corresponds to the Harvey landfall (25 August 2017). The cherry lines represent to the average daily pedestrian counts. The solid blackness line corresponds to the LOESS smoother, and the grey shading corresponds to a thirty% smoothing bridge around the LOESS smoother line.
4. Word
To the best of our noesis, this exploratory study is the first of its kind to compare pre-event and post-event bicycle and pedestrian activity trends, and it is the commencement to use publicly available wheel and pedestrian activity information to practise so. Virtually existing studies examine physical action patterns during a much longer post-issue period and practice not compare the pre- and postal service-event physical action levels [16,17]. Equally such, this work provides unique insights into both the immediate disaster impacts on physical activity, too equally the potential for such publicly available data to exist used to monitor recovery progress following extreme events.
The results from the interrupted time-series analysis signal a significant decrease in daily counts in the time-period immediately following Harvey landfall at most all stations included in the study. This might be explained by the fact that the corresponding trails in our analysis were underwater for several days following landfall. While this might be seen every bit an inevitable misreckoning factor in the analysis, it provides insight most the potential for this publicly attainable data to be used every bit a tool for post-event reconnaissance. Given these data are continuously tracked and could be made bachelor in near real-time, they have the potential to be an important data source for emergency managers and decision makers if information technology is used as a spatial marker of infrastructure impairment or as an indicator of wellbeing. Additional enquiry is necessary to correlate the observed impacts with other traditional indicators of recovery (e.g., disquisitional infrastructure restoration) and wellbeing impacts (e.g., measured through QOL scores or other validated instruments) to determine the utility of bike and pedestrian trail data every bit a near real-time indicator for recovery and wellbeing, respectively.
The results from the recovery assay indicate an increase in the average daily counts in the 6-week flow later on landfall (excluding the initial 2-week menses) compared to the 6-week catamenia prior to landfall among most stations, although only some stations saw a statistically meaning increase. This result differs from other studies, which accept constitute no testify for return to normal or an increase in physical action levels after a disaster [16,17]. Okazaki et al. [17] reported accelerometer-adamant daily steps in adolescents each year for three years following a 2011 earthquake, and constitute a significantly lower stride count at three years compared to 1 twelvemonth post-obit the earthquake. Similarly, Lai et al. [16] reported sedentary activeness in children 8 months later Hurricane Ike. Both of these studies followed children's physical activeness for long periods later on disasters, and did non find evidence for return to normal levels of concrete activity. These findings are not consistent with our results in the recovery analysis examining the affect of Harvey landfall on physical activity up to six weeks after landfall. Nosotros examined a significantly shorter time menstruation than those studied in Okazaki et al. [17] and Lai et al. [sixteen], and as such, it is possible the trends observed here might not continue in the months and years post-obit Harvey landfall. Examination of longer time-periods may yield dissimilar conclusions well-nigh mail service-event physical activity trends on bike and pedestrian trails.
The lack of individual user data impedes the ability to attribute the modify in activity to changes in use past any individual. Information technology is possible that the trail users chose alternate locations to walk or bike in the immediate aftermath of the outcome, or that different people accessed the trails for different reasons before and after the upshot. For instance, as many equally 1 million cars were damaged post-obit Hurricane Harvey [24] and it is possible that increased trail usage following the effect could stand for an increment in temporary or permanent agile transportation users. As such, additional enquiry is necessary to understand disaster-related impacts on private-level concrete activity patterns and their determinants. Prospective research should utilize data sources that provide private metrics of concrete action (e.g., through personal wellness monitoring devices) and surveys to assess the utilise of culling modes of activeness (e.g., gym usage) and determinants of pre- and post-result physical activity.
While physical action has been associated with wellbeing in non-disaster settings [10,11,12], we did not explicitly correlate cycle and pedestrian activity with markers of wellbeing. Additionally, as noted above, the lack of private user information further impedes our ability to attribute changes in trail utilize with disaster-related changes in individual wellbeing. Additional information collection, including through surveys (eastward.grand., using the Centers for Disease Command and Prevention's Customs Cess for Public Health Emergency Response (CASPER) epidemiologic technique [25]) and interviews, might provide more insights on the part and association of physical action and physical activity-promoting infrastructure on wellbeing in a post-effect setting. Prior to use of action data as an indicator of postal service-event wellbeing, enquiry should explore the association, if whatsoever, of concrete action changes with mail-result wellbeing indicators. As noted higher up, there are many potential reasons for changes in physical action post-obit a disaster that might derange the utilize of activity information as an indicator of post-consequence wellbeing.
Finally, communities with wheel and pedestrian infrastructure might exist systematically dissimilar from those without admission to such community amenities, and thus our results might not be generalizable. A report associating zoning code requirements with concrete activity data from the 2011 Behavioral Risk Factor Surveillance Organisation found 58% college odds of biking and 52% higher odds of vigorous biking, as well equally five% (non-pregnant) college odds of pedestrian activity, among communities with zoning-based bike and pedestrian infrastructure [26]. Moreover, communities with zoning provisions for bike/pedestrian infrastructure take reported significant, albeit pocket-sized, increases in rates of agile transportation to work [27]. As such, it is possible that members of communities with bicycle and pedestrian infrastructure are more than likely to exist physically active even earlier disaster events. Given this, bike and pedestrian activeness data should exist used and interpreted with caution, peculiarly in post-effect and recovery controlling. These information might disproportionately detect changes in those who are physically active prior to an event, and miss important impacts experienced by communities who are not physically active, or whose physical activity is not well-represented by cycle and pedestrian activeness data.
5. Conclusions
This exploratory assay is the commencement of its kind to compare physical activity trends before and after a flood-related disaster using publicly bachelor bicycle and pedestrian activity data. This analysis provides important insights regarding physical activity impacts of a disaster and explores the potential for using cycle and pedestrian activity data equally an indicator of disaster recovery. This enquiry plant that cycle and pedestrian activity decreased immediately post-obit Hurricane Harvey landfall, with a return to pre-event levels in half dozen weeks post-event. All the same, this exploratory analysis was limited by the number of counter stations, length of the assay period, lack of additional variables, and possible lack of applicability in other communities and following other disaster events. Information technology may not be feasible to employ bicycle and pedestrian action data afterwards an upshot with more extensive infrastructure damage, or in communities without wheel and pedestrian infrastructure and counters or without loftier levels of infrastructure usage. Additional research is necessary to explore the potential to use bike and pedestrian action data in other communities and following other types of disaster events with different levels of impacts, as well as to assess longer-term physical activity patterns. Finally, external factors that may affect post-outcome physical activity trends are non accounted for here, including level of impairment and flood depth by location, temperature, humidity, and air quality. As such, additional research should be conducted to explore physical activity levels and their environmental determinants in post-issue settings, including secondary and concurrent hazards. Further research is necessary to understand the feasibility, validity, and limitations of using individual physical action data for tracking recovery and wellbeing after disasters. This includes a demand for larger prospective studies with private-level data and those post-obit disasters acquired by different hazards, with different levels of damage.
Acknowledgments
We thank Bonnie Sherman at the Texas Department of Transportation and our colleagues at the Texas A&M Transportation Constitute for supplying the cycle and pedestrian count data from the Bicycle and Pedestrian Count Commutation and for providing technical assistance.
Author Contributions
Conceptualization, Y.C., Due south.K., and N.A.E.; Data curation, A.D., Y.C., and N.A.E.; Formal analysis, A.D. and Y.C.; Funding acquisition, Y.C., S.One thousand., and North.A.E.; Methodology, A.D. and Y.C.; Supervision, Y.C. and North.A.E.; Visualization, A.D., and Y.C...; Writing–Original Draft, A.D.; Writing–Review & Editing, A.D., Y.C., S.Yard., and N.A.E.
Conflicts of Interest
The authors declare no disharmonize of interest. The funders had no role in the design of the written report; in the collection, analyses, or interpretation of information; in the writing of the manuscript, or in the determination to publish the results.
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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6721727/
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