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Article

Utilizing Mobility Data to Investigate Seasonal Hourly Visiting Behavior for Downtown Parks in Dallas

1
Department of Landscape Architecture & Urban Planning, College of Architecture, Texas A&M University, College Station, TX 77843, USA
2
College of Environment and Design, University of Georgia, Athens, GA 30602, USA
3
SWA Group, Houston, TX 77002, USA
*
Author to whom correspondence should be addressed.
Urban Sci. 2024, 8(2), 59; https://doi.org/10.3390/urbansci8020059
Submission received: 1 April 2024 / Revised: 5 May 2024 / Accepted: 15 May 2024 / Published: 30 May 2024

Abstract

:
Urban parks serve as vital spaces for leisure, social interaction, and nature engagement. At the same time, climate change disproportionately impacts densely populated megacities. While extensive research exists on climate change’s effects on mortality, agriculture, and economic activities, less is known about its impact on urban park usage. Understanding their temporal usage and how temperature changes affect park visitation is crucial for maximizing park benefits and building resiliency. This study analyzes long-term, hourly park visitation data on Dallas, Texas, using digital trace data from SafeGraph (San Francisco, CA, USA), which covers mobile records from approximately 10% of U.S. devices. We focus on five established parks in Dallas and examine their historical temperature data from 2018 to 2022. Descriptive statistics and scatter graphs are utilized to analyze temperature- and demographic-specific visitation patterns. The results of the study highlight the impact of climate change on park visitation and reveal how extreme temperatures influence visitation patterns across parks in Dallas. Additionally, this study explores the differences in visitation based on weekdays versus weekends and highlights demographic disparities. Notably, we examine the implications of nighttime park usage during extreme heat conditions. Our work is informative for urban planners seeking to improve park facilities and comfort amid climate change, ultimately enhancing the resilience and well-being of urban communities.

1. Introduction

1.1. Park Usage and Benefits

Urban parks, essential to city life, provide much more than just aesthetic appeal to urban landscapes [1]. As vital green spaces amid concrete jungles, they deliver essential ecological services, such as air purification and urban heat island mitigation [2]. These public spaces are not only havens for biodiversity [3] but also vital for social interaction and community building. Socially, parks serve as communal hubs, hosting diverse activities and events that meet the needs and preferences of various community members. They act as inclusive sanctuaries accommodating the diverse needs of residents [4], fostering social unity and a deeper sense of community belonging [5].
Meanwhile, parks provide accessible spaces for various activities, addressing the sedentary lifestyle common in urban settings. Engaging in physical activity in these spaces is critical for preventing health issues such as obesity, cardiovascular disease, and diabetes [6,7]. However, as climate change alters urban environments, understanding the evolving temporal dynamics of park usage, particularly under extreme weather conditions like heatwaves, is crucial [8]. Parks act as natural coolants in several ways. The vegetation in parks, through evapotranspiration, releases water into the air, aiding in lowering ambient temperatures [2]. Trees and plants also provide shade, significantly reducing the surface and air temperatures beneath their canopies. This shading effect cools adjacent areas and diminishes the heat absorbed by nearby buildings and sidewalks, reducing the overall urban heat load [9,10]. In the face of global warming and increasing urbanization, the role of parks in counteracting the urban heat island effect gains even greater importance [11].

1.2. Climate-Related Challenges to Park Utilization

As global temperatures continue to rise, this reality prompts an exploration of how these changes impact various aspects of urban life. One of the most concerning manifestations of climate change is the impact on human activity of the rising frequency and severity of heatwaves [12]. As temperatures continue to rise, these events transcend mere discomfort: they pose significant and immediate risks to human health, economies, societies, and ecosystems [5,13,14]. The consequences range from increased energy consumption for cooling to stress on water resources and impacts on agriculture [15]. The negative impact of heat is especially true for park users, who are often exposed to outdoor environments, where few climate control measures are available.
In summer, soaring temperatures, often uncomfortable due to the heat, prompt tourists to opt for cooler parks or to stay indoors. In this context, establishing cool parks becomes crucial. Remote sensing surface temperature data indicate that green spaces can reduce temperatures by 5–7 degrees Celsius, both by day and night [8]. Open green spaces and grass parks are particularly effective in cooling. Urban parks, utilizing cooling design principles, offer residents spaces that provide relief during extreme heat while also promoting physical activity and mental health benefits [16].

1.3. Research Gaps

The main methods for evaluating park usage are observational studies and surveys, which typically involve counting visitors at park entrances or distributing questionnaires to attendees [17,18,19]. However, these traditional approaches are labor-intensive and resource-demanding and often susceptible to implicit biases in the sampling and data collection. Consequently, most of the past research on park visitation factors has been short-term and limited to a few parks [20], raising questions about the long-term applicability and generalizability of its findings.
Investigations on heatwaves span diverse domains, including mortality, agriculture, and economic activities. Yet despite these comprehensive efforts, there remains a notable gap in our understanding of how climate change specifically influences human behaviors, such as its potential effects on the frequency and duration of park visits. Existing research, such as the 2022 Downtown Dallas Parks Analysis, primarily focuses on the design and measurement of cooling infrastructure in planned parks in the context of temperature changes [21].
However, there appears to be a gap in the considerations for nighttime park design. By emphasizing design efforts for nighttime park planning, parks can become more inclusive and functional around the clock, thereby maximizing their utility and role in urban environments. This approach also optimizes the benefits of public investments in parks.
The study goal is to discern how park-related engagements vary with temperature changes/seasonality. We also utilize emerging mobility datasets to offset the limitations of the traditional park usage methods, which may lead to greater granularity and the validity of our results. SafeGraph (San Francisco, CA, USA) is a data company that specializes in providing analytics and insights derived from geospatial data. Its data are characterized by enhanced accuracy and timely updates, with the source data revisited at least once a month [22]. Our study aims to optimize the design and functionality of urban parks in Dallas during heatwaves by (1) identifying the key demographic groups that frequently use the parks; (2) pinpointing the times during heatwaves when park usage peaks; and (3) evaluating potential improvements in park amenities based on observed user patterns. Additionally, we propose suggestions and interventions to enhance park usage during extreme heat events.

2. Methodology

The mobility data capture visits to points of interest (POIs) and consist of mobile phone records from users, representing around 10% of devices in the U.S. [23]. To ensure and enhance this accuracy, SafeGraph/Advan employs or maintains multiple types of truth sets, which are instrumental in measuring and improving the data’s reliability [23]. Moreover, in prioritizing ethical considerations, SafeGraph/Advan employs techniques such as the Laplacian noise algorithm to protect user privacy [24], which enhanced the study’s integrity.

2.1. Site Description

This study investigates the temporal patterns of park visitations in downtown Dallas and reveals the relationship between heatwaves and park visitation in Dallas, Texas. The analysis focuses on five specific parks in Dallas, all of which were established in the Dallas downtown area in the last ten years and were designed by landscape architects. The study examines the historical periods of heatwaves (with temperatures exceeding 90 degrees Fahrenheit) from 2018 to 2022.
As a central U.S. city, Dallas has witnessed a steady population increase. The Dallas–Fort Worth (DFW) region now houses 7.8 million residents, with an anticipated growth of 8.64 percent by 2023 [25]. However, with growth comes challenges. An Associated Press News article from August 2023 emphasized the city’s escalating heatwave issues, reporting temperatures reaching 101 degrees Fahrenheit, persisting for 55 days from June onward [26]. In response to these challenges, the Downtown Dallas 360 Plan was introduced in 2011. This strategic blueprint focuses on the development of, capital investment into, and the design ethos of downtown Dallas. Central to the plan are the enhancement of urban connectivity and the expansion of parks and public spaces [27]. The research aims to track the park usage of different demographic profiles among five downtown parks in Dallas, with their locations below (Figure 1):
In the heart of Dallas, these five parks stand out due to their distinctive characteristics and the specific demographics they cater to: 1. Spanning 0.4 acres, James W. Aston Park, also known as Pacific Plaza, began its development in April 2018. This park was thoughtfully designed to appeal to families with young children, literary enthusiasts seeking a tranquil reading spot, tourists, and local office workers. It features a modern playground, strategically placed seating areas, and a grove of mature trees. 2. John W. Carpenter Plaza, covering nearly 6 acres, was originally dedicated in 1981. Its renovation, which went through a design phase from 2013 to 2020, caters to city workers, art lovers, fitness enthusiasts, and families. The plaza includes landscaped gardens, public art installations, and a basketball court [28]. 3. Klyde Warren Park, encompassing 5.2 acres, began to be constructed in October 2009. As a lively hub, it attracts families, tourists, fitness buffs, and event-goers, with features like a children’s play area, an array of food trucks, dedicated yoga spaces, and a pet zone. 4. West End Square, which began development in January 2020, is a modern urban space designed for tech-savvy individuals, young professionals, and art enthusiasts. It boasts an “Outdoor Workroom,” curated art installations, and a unique “Porch” with swings. 5. Lastly, Main Street Garden, the construction of which started in November 2008, serves as a nexus for residents, employees, and visitors. It is equipped with advanced Wi-Fi, a sprawling lawn, a play area for toddlers, a dog run, fountains, the City Park Cafe, and cutting-edge public art installations [28].

2.2. Introduction to Mobility Data and the Role of SafeGraph and Advan in Data Acquisition

Our research utilized a comprehensive dataset called SafeGraph/Advan which maintains an extensive database of 22 million points of interest (POIs), encompassing a wide variety of locations, from parks and museums to stores and airports (SafeGraph, 2023). This representation includes every U.S. city, from major urban centers to serene towns. Mobility data are centered on the collection and analysis of human movement and spatial activities [29]. Utilizing mobile phone data, such as those from SafeGraph and Advan, is pivotal for urban planning, transportation enhancement, health research, and business assessments [30,31]. Regarding data accuracy, the SafeGraph and Advan dataset demonstrates notable precision at a large scale, covering states and counties, especially over prolonged durations (monthly/yearly). Additionally, the observed sampling rates in the panel align well with the expected rates from a randomized sample, confirming its reliability [23] (SafeGraph, 2023). Our research investigated the sampling bias in the SafeGraph panel, examining it across various geographic and demographic dimensions. These demographic dimensions are deduced from geographic metrics, primarily from census block groups (BGs) [32].
SafeGraph and Advan data include location visits, visitor demographics, weather conditions, and temporal patterns. The “Core Point of Interests (POI)” dataset encompasses 6.1 million U.S. locations, with a special focus on five city parks (SafeGraph, 2023). Meanwhile, the “Place Patterns” dataset provides insights into the hourly behaviors at each POI, including the distance traveled and visitor counts from specific census block groups (BGs). For privacy, BGs with fewer than five devices at a POI are excluded, and counts under four are adjusted to four. The Laplacian noise algorithm ensures user privacy, albeit with potential minor data variations [33]. This research, spanning five years from 2018 to 2022, collected 21,000 observations from 1235 BGs visiting various parks. With SafeGraph’s robust sampling rate, we delved deep into mobility trends, capturing metrics like hourly visits and demographics and integrating climatic and census data for a comprehensive analysis.

2.3. Measurement and Analysis

In our study, we consider both temporal variations across hours within months and variations among several demographic groups (DGs) in calculating the device-to-population ratio for each block group. Equation (1) is used to estimate the total count of actual visitors traveling from their home block group (BG) to a point of interest (POI).
V _ P O I _ B G i , a , t = ( S P H V _ S _ P O I _ B G i , a , t × T P ) / S D i , t
In Equation (1), “TP” represents the total population, “ S D i , t ” refers to the total smartphone users in BG i at time t, and “SPHV_S_POI_BGi,a,t” represents all the mobile phone users in our study at the POI for each individual block group. “ V _ P O I _ B G i , a , t ” refers to the real number of visitors from an individual BG i to a POI a during each month of the week t.
Our data analysis approach involves meticulous hourly data collection from the participating parks, which is then aggregated on a monthly basis, as shown in Equation (2). This thorough process allows us to detect and analyze fluctuations in usage patterns throughout the year. We particularly focus on monitoring the usage levels during the peak summer months when temperatures are highest. Our aim is not only to understand the impact of extreme heat on park visitation but also to compare these findings with the trends observed during other seasons in these specific parks.
P O I _ V a , t , p = ( i = 0 n V _ P O I _ B G i , a , t ) / NP
POI_Va,t,p: The total number of visitors to POI a (5 parks) from all the BGs during each month p at hour t; V _ P O I _ B G i , a , t refers to the number of visitors from BG i to POI a (five parks) during the hour t; n refers to the number of BGs that have traveled to POI a during the month p; NP refers to the number of hours during the month p.
For a comprehensive understanding, we also employed descriptive statistics to analyze both the temperature data recorded during these periods of temperature change and the demographic-specific data related to park visitation at our participating parks. To visualize the relationship between these two datasets, line graphs were crafted. These graphs effectively juxtapose the demographic park visitation data from these five parks with the corresponding temperature variation curves, offering a clear insight into how temperature fluctuations influence park attendance across different demographic groups at these renowned locations.

3. Result

3.1. User Demographic Analysis

Figure 2a illustrates the average proportion of park visitors with a bachelor’s degree, spanning several months, from July to December. The frequency of visitors holding a bachelor’s degree reaches its highest in August for most of the parks, with Main Street Garden (MSG), Klyde Warren Park (KW), and James W. Aston Park (JAP) registering the topmost percentages. Notably, JAP experienced the sharpest increase in August, indicating it might attract a demographic with a higher education level during this month. Subsequent to August, there is a widespread decline in this metric across all the parks, with WES being the exception, where a slight rise is observed.
The average percentage of visitors with children under 18 in each park from July to December shows the following results: the percentages of people with children are relatively high, with all the parks fluctuating around 60% (Figure 2b). JCP starts with a lower percentage in September and October, hits its highest point in November, and then falls. Lastly, JAP marks two significant peaks in August and October, indicating increased visitation by those with children under 18 during these times (Figure 2b). While other demographic groups display varied visitation percentages, the patterns for this particular group remain largely consistent, underscoring the heightened demand for parks by individuals with children compared to other demographics.
Figure 2c illustrates the average percentage of visitors below the poverty level at various Dallas parks from July to December. JCP experiences a notable surge in November. This situation might be attributed to specific facility improvements or seasonal variations. For instance, there has been an increase in private spaces and the addition of more comfortable seating options, such as chairs, tables, and benches, in JCP. MSG and JAP both show variations, with a gradual increase beginning in August; peaks for JAP and MSG both occur in September, while WES has a relatively higher percentage during the summer and maintains a steady trend, with a rise during October and a fall during November as the year ends (Figure 2c).
The line chart in Figure 2d outlines the average percentage of married couples visiting various Dallas parks from July to December. MSG sees a peak in August and then a decline, with a minor rise again at the year’s end. KW displays variability, peaking in September before descending towards winter. JAP has two peaks in August and October. JCP exhibits a notable rise in married couples visiting in November.

3.2. Overall Park Popularity (Yearly)

To assess the appeal and usage of various Dallas parks throughout the year, we illustrated the annual visitation and demographic trends in Figure 3. These visual representations enable us to discern patterns and preferences, as well as pinpoint opportunities for improvements, thus providing detailed insights into each park’s enduring performance. Our comprehensive analysis is primarily concentrated on JAP, JCP, KW, and WES, owing to the data availability restrictions for MSG, which was confined to the year 2022 due to access limitations.
Figure 3a displays the moving average of hourly visitation at JAP, illustrating the visitation trends across various years on a 24 h timeline. The visitor counts at JAP are consistently lowest during the early hours, from midnight to 6 a.m., followed by a marked rise starting at approximately 7 a.m., reaching a first peak in the early afternoon. The second peak of tourist visits is between 8 p.m. and 10 p.m. After this peak, the graph shows a steady decline up to midnight. The years 2020 and 2021 stand out as representing the higher overall visitor numbers, especially during the peak periods, with 2021’s peak being the most pronounced around 2 p.m.
The moving average of hourly visitor numbers at JCP over various years shows a discernible trend of increasing visitors in the morning, reaching a peak from around 8 a.m. to 10 a.m. While each year follows this general pattern, slight variations occur in the timing and intensity of the peak hours. Tourist activity at JCP is consistently minimal in the pre-dawn hours, with a noticeable uptick beginning around 6 a.m. and escalating through the morning. Visitation typically reaches its zenith between noon and early afternoon. The year 2018 is distinguished by an anomalous pattern, as the visitor numbers reach a second peak in the early afternoon. In 2020 and 2021, a pronounced peak is evident around midday (Figure 3b). Year-over-year variations in the timing and intensity of these peaks may reflect shifts in visitor behavior or external influences specific to each year, such as special events or construction that may have temporarily altered the accessibility of the plaza.
The moving average of hourly visitation at KW across multiple years is illustrated in Figure 3c. The visitor trends at KW show a clear pattern of low attendance during the late night to early morning hours, with a gradual increase commencing around 6 a.m. This rise becomes more pronounced as the morning progresses. Afternoon hours typically see the first peak in visitor numbers, with the data indicating the highest afternoon attendance between 2 p.m. and 3 p.m. A second increase in visitor numbers occurs during the evening, with the 2022 data revealing the most substantial evening turnout, from 7 p.m. to 10 p.m. Although 2018 saw fewer visitors, which could be attributed to various external factors, the overall trend across the years suggests a steady preference for afternoon visits to KW.
Figure 3d presents the moving average of hourly visitation at West End Square, illustrating fluctuations over several years. Visitor numbers begin to rise in the morning, reaching a peak around noon. The visitor data for WES reveal a consistent early morning uptick in attendance, beginning at 6 a.m. across all years. The peak hours fluctuate annually but generally span from late morning to early afternoon; notably, 2021 experienced particularly high peaks, potentially signifying special events or a surge in the square’s popularity. Despite a common post-peak decline as the day wears on, 2019 and 2021 exhibited a secondary visitor increase between 7 p.m. and 9 p.m. (Figure 3d).
Upon reviewing five years of data from various parks, the visitation at some parks peaks in the afternoon to evening, rather than from morning to midday. This pattern suggests that park activities and facilities are likely to be used during the latter half of the day, informing park management decisions related to programming, staffing, and resource allocation to accommodate the higher visitor volume during these hours.

3.3. Seasonal Park Popularity (Monthly)

In the warm season months of July, August, and September 2022, the temperature and visitor trends at the parks demonstrate distinct patterns. Across July, August, and September, the lowest daily temperatures typically occurred early in the morning between 7 a.m. and 9 a.m. The late afternoon, between 6 p.m. and 8 p.m., often saw the day’s peak temperatures. We see more extreme heat during July, as the peak temperatures in August and September have fewer instances of the thermometer climbing above 100 °F than in July (Figure 4). For the visitor trends, during the warmest part of the day in the late afternoon, KW and JAP exhibit a noticeable decline in the number of visitors, which aligns with the highest temperatures. This trend is less apparent in the other parks (Figure 4). Despite the slightly cooler conditions in August and September, there was not a significant uptick in visitation numbers when compared to July. The temperatures in August might still have been high enough to deter some visitors. Seasonal activities and schedules, such as the end of summer holidays and the beginning of the school year, could also have influenced park attendance during these months.
Figure 4e,f represents the cold season in 2022. The trend in the average hourly temperatures displays an afternoon peak at approximately 60 °F, with a substantial decline towards the night, reaching lows around 35 °F. This drop in temperature tends to coincide with a general decrease in park visitations when compared with the busier, warmer months. KW stands out, with consistently high visitor counts throughout the day, displaying a notable rise in foot traffic from the early morning. Contrary to the usual daytime peak, Klyde Warren Park sees its visitor numbers swell during the evening, with the highest attendance between 8:00 p.m. and 9:00 p.m., but WES presents a contrasting visitor pattern, with a marked increase during the early hours, achieving its visitor peak around noon, from 11:00 a.m. to 12:00 p.m., followed by a steady decline towards midnight (Figure 4e,f).
Overall, while the warmer months generally encourage outdoor activity, the very high temperatures observed in July seem to suppress the number of visitors in the late afternoon, particularly in parks like KW and JAP. The modest drop in temperatures in August and September does not markedly boost visitor numbers, suggesting that other factors, potentially including the start of the academic year, also play a role in influencing park visitation. The December figures suggest a correlation between temperature and visitor numbers, yet this is not entirely straightforward. While the average visitor numbers decrease with cooler evening temperatures, some locations like KW, MSG, and JCP buck this trend, experiencing a resurgence of visitors in the evening despite the chill.

4. Discussion

This research sought to present the relationship between temperature change, seasonality, and park visitation trends in downtown Dallas, Texas. A primary strength of this study is its use of SafeGraph and Advan’s expansive mobility database. This data source tracks visits to POIs through mobile phone records, enabling a detailed and nuanced analysis that can reveal hourly records for a long duration, which could not be achieved using traditional data collection methods [34]. Focusing on five specific Dallas parks allowed for a thorough examination, illuminating the unique visitation dynamics of each park, especially during temperature rises. This method proved especially useful in understanding the behavior of various demographic groups within urban environments. Another notable feature of this methodology is its scalability; although it concentrates on Dallas, the approach is flexible enough to be applied to other urban areas, offering a valuable tool for similar research in different contexts.

4.1. The Impact of Seasonality and Heat

4.1.1. Visitation Percentage by Demographic Groups

Analyzing the visitation patterns across demographics reveals distinct preferences in park usage over time. Our research effectively captures the perspectives and behaviors of underrepresented groups, enabling targeted strategies to protect these groups from the adverse effects of heatwaves.
Families with children under the age of 18 exhibit a higher propensity to visit parks throughout the year, with their visitation rates significantly exceeding those of other demographic groups (Figure 2a). This trend identifies them as the primary park users during high-temperature periods. Consequently, future park designs should consider incorporating additional fountains, water features, and shade provisions to accommodate the needs of parents and children.
Notable fluctuations in the visitation patterns are observed at John W. Carpenter Plaza (JCP) compared to other parks, with significant variations in the proportion of visitors, particularly those with a bachelor’s degree and those below the poverty line. JCP’s unique characteristics, including its location, amenities, and proximity to residential areas, contribute to these differences. Our findings also indicate potential variations in user profiles during winter. The visitation patterns of individuals with bachelor’s degrees and those below the poverty line are similar from July to October, but they diverge in November (Figure 2a,c). Further research is needed to explore the reasons behind these disparities, which currently remain unexplained.

4.1.2. Analyzing the Influence of Heat on Park Visitation

Our findings indicate that despite the amenities and shade provided by parks, high temperatures can lead to reduced visitation. This supports the hypothesis (made by Barnett-Itzhaki et al., 2023 [35]) that rising temperatures negatively impact overall park visitation. The data presented in Figure 4a–c) highlight that climate change and heatwaves exert a suppressive effect on park attendance. There is a marked decline in visitors during the peak summer months, particularly on the hottest days at parks like JAP and KW. This trend could be attributed to external climatic factors, as people may opt to avoid outdoor activities during the hottest parts of the day to escape discomfort and health risks.
Interestingly, even though temperatures slightly decreased in August and September compared to July, the data did not show a corresponding increase in park visitation. This observation suggests that even moderately high temperatures can deter park visitors, underscoring the significant role of heat as a factor influencing park attendance [35].

4.2. The Significance of Nighttime Use

Our analysis reveals the notable significance of park usage patterns from day to night during hot weather conditions. Figure 4 shows that during the warmer months (July, August, and September 2022), park usage increases at night. While the number of visitors decreases during the hottest hours of the afternoon, it increases again in the cooler evenings (Figure 4a–c). This shift was particularly pronounced in parks like KW and JAP, where the visitor numbers decreased during the afternoon’s peak heat but increased in the cooler evening hours. Extreme heat can cause discomfort and pose potential health risks [36], thus influencing the timing of park visits. Landscape designers should consider these changing patterns by enhancing park facilities for evening use, such as adding lighting and other amenities suitable for nighttime. Our findings suggest that visitors adjust their activities to avoid daytime heat, preferring the comfort of cooler nights.
In contrast, during the cooler months (October, November, and December), park usage at night is not only sustained but also exceeds the levels observed in the warmer months, as shown in Figure 4d–f. This trend, particularly evident in KW, indicates that certain attractions or activities retain visitor interest during the nighttime despite colder temperatures. To bridge the identified research gaps, strategies enhancing nighttime park use during cooler seasons need to be crafted. This should include the integration of design features conducive to nocturnal activities, while also considering environmental aspects like cooling effects. For instance, facilitating convenience for nighttime use in winter, organizing winter festivals, and hosting evening events can make parks more accessible and appealing. Adopting such measures will lead to more inclusive and diverse urban park planning, transforming parks into versatile, all-season community hubs.
The integration of documents from the 2022 “Downtown Dallas Parks Analysis” and the 2013 “Downtown Parks Master Plan Update Dallas, Texas” into our research reveals significant gaps in current policies and practices. These documents offer insights into park design, focusing on environmental sustainability and urban development factors like carbon sequestration, stormwater management, and heat island effects [21,37]. However, they notably lack considerations for nighttime park design. This absence underlines the research gap we have identified: the necessity for adaptive management strategies, inclusive of nighttime considerations, to ensure parks remain functional and inviting throughout different times, especially at night. This gap in Dallas’s park design guidelines and policies highlights the need for more inclusive and thorough urban park planning strategies.
Moreover, it was observed that certain parks are more amenable to nighttime usage due to their unique facilities and activities, and this trend is particularly evident in JAP and KW due to their distinctive lighting features (Figure 3a–c). The circular light installations at JAP not only improve visibility but also enhance the nocturnal experience, highlighting the critical role of strategic lighting in increasing the park’s attractiveness. KW features light fountains and a central open area, where events like the “Nancy Best Fountain Performance” are held from 6 to 7 p.m., leading to a rise in visitor numbers. This study supports the idea of improving park facilities and nighttime amenities to encourage their extended use in the evenings [38].

4.3. Implications for Urban Planning

In summarizing our analysis of seasonal park populations, several pivotal insights regarding park management and planning, especially in the context of climate change, could be proposed:
Park planning and design should cater to the specific needs of various age groups, creating a multi-generational space that is consistently welcoming and suitable for all. This study found that park usage varies with age, a disparity more pronounced in high-temperature environments. For example, a higher proportion of children under 18 frequent parks more often, requiring more facilities in both hot and high-temperature conditions. Park design should therefore consider the needs of children, including facilities tailored to them and resting areas for supervising parents. Additionally, cooling amenities and shade trees are vital to prevent health issues related to climate change.
The future planning of parks should also emphasize lighting design. Park lighting is crucial for creating ambiance, ensuring safety, and promoting environmental awareness. Strategically placed light installations can enhance the nocturnal park experience, attracting more visitors after dark. Future designs should prioritize sophisticated lighting strategies, like tiered lighting with varying intensities in different park areas, to set a new standard for nighttime park illumination [39]. The environmental impact of lighting should not be overlooked. Planners are encouraged to use energy-efficient solutions like LED lights, motion sensors, and downward-facing lights to reduce light pollution and its ecological consequences [40]. Incorporating educational elements about environmental conservation can increase public awareness.
Moreover, nighttime use of parks enriches social and cultural life. Park managers could work with external organizations to hold various events like open-air concerts, theater performances, and community gatherings, enriching the community’s cultural activities [38,41]. These events stimulate local economies and provide support for local vendors, artists, and performers.
In summation, the burgeoning trend of nighttime park visitation calls for a paradigm shift in how urban green spaces are conceptualized and managed. By emphasizing holistic design, adaptive management, and environmental stewardship, urban planners can sculpt parks that remain dynamic community epicenters, regardless of the hour.

4.4. Limitation

This study’s findings are subject to several limitations that must be acknowledged. First, the data collected during the COVID-19 pandemic may not accurately reflect typical park visitation patterns. The pandemic’s exceptional circumstances significantly altered outdoor recreational habits, and as such, these data may not align with regular visitor behavior. This anomaly is notable due to the pandemic’s extensive and unprecedented impact on public conduct [42].
Secondly, this study may contain potential biases due to not accounting for time-variant factors like special events. In the years 2018–2022, random events such as local festivals, sports events, political rallies, and natural occurrences may have affected park attendance [43]. These events, which may have greatly impacted visitor numbers, were not exhaustively recorded or analyzed in the study. This lack of comprehensive event data could result in inaccuracies in interpreting the true patterns of park visitation.
Lastly, the sample data from SafeGraph may not fully represent the entire population. Inherent biases, such as the overrepresentation of some demographic groups or the underrepresentation of others, might have distorted the findings and affected the study’s conclusions [44]. Moreover, the research’s concentration on particular years and parks in Dallas means that visitor trends outside of these parameters or in different locations were not examined, thus limiting the study’s breadth.

4.5. Conclusions

This study highlighted the interplay between temperature changes, seasonality, and park visitation in downtown Dallas, Texas. Utilizing SafeGraph and Advan’s mobility database, it offers a nuanced view of park usage, particularly during temperature rises. The study highlighted demographic preferences, revealing families with children as the primary users during hot periods. This observation underscores the need for park designs to be more family-friendly, such as by adding fountains for children’s play and shaded areas for parents to rest, enhancing the overall user experience. Notably, it uncovers a shift to nighttime park usage during hot weather, suggesting a need for improved facilities and lighting for evening activities. This trend extends to cooler months, with parks like KW showing increased night visitation, emphasizing the importance of adaptable park designs for all seasons. The lack of nighttime design considerations in Dallas park policies, as evident in the 2022 and 2013 documents, points to a gap in urban park planning. This study advocates for more inclusive and comprehensive strategies, recognizing the role of parks as year-round community assets. Future research could broaden its scope to include various types of parks, such as neighborhood or community parks, rather than focusing solely on city parks in downtown areas. Additionally, there is a need to implement regression models that estimate the causal impacts of lighting on park behaviors.

Author Contributions

Conceptualization, Y.S. and Z.G.; methodology, Y.S.; software, Y.S.; validation, Y.S., Z.G., N.W. and R.Y.; resources, Y.S. and Z.G.; data curation, Z.G.; writing—original draft preparation, Y.S.; writing—review and editing, Y.S.; visualization, R.Y. and N.W.; supervision, Y.S.; All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding..

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

In this study, SafeGraphic/Advan mobile phone data were used, and due to privacy and ethical restrictions, it is not convenient to share the data.

Acknowledgments

The authors thank SafeGraph Inc. and Advan Research Corporation for providing access to the Texas POI data.

Conflicts of Interest

Author Na Wang was employed by the company SWA Group. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Cranz, G.; Boland, M. Defining the Sustainable Park: A Fifth Model for Urban Parks. Landsc. J. 2004, 23, 102–120. [Google Scholar] [CrossRef]
  2. Feyisa, G.L.; Dons, K.; Meilby, H. Efficiency of Parks in Mitigating Urban Heat Island Effect: An Example from Addis Ababa. Landsc. Urban Plan. 2014, 123, 87–95. [Google Scholar] [CrossRef]
  3. Cornelis, J.; Hermy, M. Biodiversity Relationships in Urban and Suburban Parks in Flanders. Landsc. Urban Plan. 2004, 69, 385–401. [Google Scholar] [CrossRef]
  4. Song, Y.; Yang, R.; Lu, H.; Fernandez, J.; Wang, T. Why do we love the high line? A case study of understanding long-term user experiences of urban greenways. Comput. Urban Sci. 2023, 3, 18. [Google Scholar] [CrossRef]
  5. Zhang, B.; Song, Y.; Liu, D.; Zeng, Z.; Guo, S.; Yang, Q.; Wen, Y.; Wang, W.; Shen, X. Descriptive and Network Post-Occupancy Evaluation of the Urban Public Space through Social Media: A Case Study of Bryant Park, NY. Land 2023, 12, 1403. [Google Scholar] [CrossRef]
  6. Irvine, K.N.; Warber, S.L.; Devine-Wright, P.; Gaston, K.J. Understanding Urban Green Space as a Health Resource: A Qualitative Comparison of Visit Motivation and Derived Effects among Park Users in Sheffield, UK. Int. J. Environ. Res. Public Health 2013, 10, 417–442. [Google Scholar] [CrossRef]
  7. Hansen, J.; Kharecha, P.; Sato, M.; Masson-Delmotte, V.; Ackerman, F.; Beerling, D.J.; Hearty, P.J.; Hoegh-Guldberg, O.; Hsu, S.-L.; Parmesan, C.; et al. Assessing “Dangerous Climate Change”: Required Reduction of Carbon Emissions to Protect Young People, Future Generations and Nature. PLoS ONE 2013, 8, e81648. [Google Scholar] [CrossRef]
  8. Kraemer, R.; Kabisch, N. Parks Under Stress: Air Temperature Regulation of Urban Green Spaces Under Conditions of Drought and Summer Heat. Front. Environ. Sci. 2022, 10, 849965. [Google Scholar] [CrossRef]
  9. Armson, D.; Stringer, P.; Ennos, A.R. The Effect of Tree Shade and Grass on Surface and Globe Temperatures in an Urban Area. Urban For. Urban Green. 2012, 11, 245–255. [Google Scholar] [CrossRef]
  10. Georgi, N.J.; Zafiriadis, K. The Impact of Park Trees on Microclimate in Urban Areas. Urban Ecosyst. 2006, 9, 195–209. [Google Scholar] [CrossRef]
  11. Gago, E.J.; Roldan, J.; Pacheco-Torres, R.; Ordóñez, J. The City and Urban Heat Islands: A Review of Strategies to Mitigate Adverse Effects. Renew. Sustain. Energy Rev. 2013, 25, 749–758. [Google Scholar] [CrossRef]
  12. Mazdiyasni, O.; AghaKouchak, A. Substantial Increase in Concurrent Droughts and Heatwaves in the United States. Proc. Natl. Acad. Sci. USA 2015, 112, 11484–11489. [Google Scholar] [CrossRef] [PubMed]
  13. Guirguis, K.; Gershunov, A.; Tardy, A.; Basu, R. The Impact of Recent Heat Waves on Human Health in California. J. Appl. Meteorol. Climatol. 2014, 53, 3–19. [Google Scholar] [CrossRef]
  14. Marsha, A.; Sain, S.R.; Heaton, M.J.; Monaghan, A.J.; Wilhelmi, O.V. Influences of Climatic and Population Changes on Heat-Related Mortality in Houston, Texas, USA. Clim. Chang. 2018, 146, 471–485. [Google Scholar] [CrossRef]
  15. Callahan, C.W.; Mankin, J.S. Globally Unequal Effect of Extreme Heat on Economic Growth. Sci. Adv. 2022, 8, eadd3726. [Google Scholar] [CrossRef] [PubMed]
  16. Boyce, P.R.; Eklund, N.H.; Hamilton, B.J.; Bruno, L.D. Perceptions of Safety at Night in Different Lighting Conditions. Int. J. Light. Res. Technol. 2000, 32, 79–91. [Google Scholar] [CrossRef]
  17. Dallimer, M.; Davies, Z.G.; Irvine, K.N.; Maltby, L.; Warren, P.H.; Gaston, K.J.; Armsworth, P.R. What Personal and Environmental Factors Determine Frequency of Urban Greenspace Use? Int. J. Environ. Res. Public Health 2014, 11, 7977–7992. [Google Scholar] [CrossRef] [PubMed]
  18. Fernandez, J.; Song, Y.; Rezaeimalek, S.; Melcher, K.; Longnecker, D. Exploring rural community place assessment through mobility and social media data in Fort Gaines, Georgia. Reg. Sci. Policy Pract. 2023, 15, 425–446. [Google Scholar] [CrossRef]
  19. McCormack, G.R.; Rock, M.; Toohey, A.M.; Hignell, D. Characteristics of Urban Parks Associated with Park Use and Physical Activity: A Review of Qualitative Research. Health Place 2010, 16, 712–726. [Google Scholar] [CrossRef] [PubMed]
  20. Donahue, M.L.; Keeler, B.L.; Wood, S.A.; Fisher, D.M.; Hamstead, Z.A.; McPhearson, T. Using Social Media to Understand Drivers of Urban Park Visitation in the Twin Cities, MN. Landsc. Urban Plan. 2018, 175, 1–10. [Google Scholar] [CrossRef]
  21. Downtown Dallas Parks Department, City of Dallas. Downtown Dallas Parks Analysis. 2022. Available online: https://www.dallasparks.org/reports/2022-analysis (accessed on 30 October 2023).
  22. Precision|SafeGraph Docs. Available online: https://downtowndallasparks.org/wp-content/uploads/2022/10/PfDD_Final-Report_ABB-09.2022.pdf (accessed on 8 November 2023).
  23. SafeGraph: Places Accuracy. Available online: https://docs.safegraph.com/docs/accuracy (accessed on 11 November 2023).
  24. Groshen, E.L.; Goroff, D. Disclosure Avoidance and the 2020 Census: What Do Researchers Need to Know? Harv. Data Sci. Rev. 2022, 2, 31–32. [Google Scholar] [CrossRef]
  25. Amber, H. Dallas-Fort Worth Population Headed toward Jaw-Drop** Milestone by 2028—CultureMap Dallas. Available online: https://austin.culturemap.com/news/city-life/austin-to-become-the-fastest-growing-metro-area-in-5-years-new-report-says/ (accessed on 30 October 2023).
  26. Ken, M. Record Heat Recorded in Dallas as Scorching Summer Continues in the United States. Available online: https://apnews.com/article/heat-record-warning-dallas-dome-278c41cf39dd7a5bfe8916dc0fd03214 (accessed on 2 October 2023).
  27. Downtown Dallas 360. Available online: https://dallascityhall.com:443/departments/pnv/Pages/Downtown-Dallas-360.aspx (accessed on 2 October 2023).
  28. Dallas Parks and Recreation. Available online: https://dallasparks.org/ (accessed on 30 October 2023).
  29. Asgari, F.; Gauthier, V.; Becker, M. A Survey on Human Mobility and Its Applications. ar**v 2013, ar**v:1307.0814. [Google Scholar]
  30. Fernandez-Bou, A.S.; Ortiz-Partida, J.P.; Dobbin, K.B.; Flores-Landeros, H.; Bernacchi, L.A.; Medellín-Azuara, J. Underrepresented, Understudied, Underserved: Gaps and Opportunities for Advancing Justice in Disadvantaged Communities. Environ. Sci. Policy 2021, 122, 92–100. [Google Scholar] [CrossRef]
  31. Song, Y.; Lee, C.; Tao, Z.; Lee, R.J.; Newman, G.; Ding, Y.; Jessica, F.; Sohn, W. COVID-19 and campus users: A longitudinal and place-based study of university mobilities in Texas. Sustain. Cities Soc. 2023, 96, 104656. [Google Scholar] [CrossRef] [PubMed]
  32. Ryan, S. Quantifying Sampling Bias in SafeGraph Patterns. Available online: https://colab.research.google.com/drive/1u15afRytJMsizySFqA2EPlXSh3KTmNTQ#scrollTo=OqkjFI5pYwHm (accessed on 17 September 2023).
  33. Song, Y.; Lee, S.; Park, A.H.; Lee, C. COVID-19 Impacts on Non-Work Travel Patterns: A Place-Based Investigation Using Smartphone Mobility Data. Environ. Plan. B Urban Anal. City Sci. 2023, 50, 642–659. [Google Scholar] [CrossRef] [PubMed]
  34. Sun, P.; Liu, P.; Song, Y. Seasonal Variations in Urban Park Characteristics and Visitation Patterns in Atlanta: A Big Data Study Using Smartphone User Mobility. Urban For. Urban Green. 2024, 91, 128166. [Google Scholar] [CrossRef]
  35. Barnett-Itzhaki, Z.; Sar-Shalom, A.; Cohn, L.; Chen, L.; Steinitz, O. The Effect of Heatwaves on the Number of Visits to National Parks and Reserves. PLoS ONE 2023, 18, e0289201. [Google Scholar] [CrossRef] [PubMed]
  36. Harlan, S.L.; Chowell, G.; Yang, S.; Petitti, D.B.; Morales Butler, E.J.; Ruddell, B.L.; Ruddell, D.M. Heat-Related Deaths in Hot Cities: Estimates of Human Tolerance to High Temperature Thresholds. Int. J. Environ. Res. Public Health 2014, 11, 3304–3326. [Google Scholar] [CrossRef] [PubMed]
  37. Downtown Parks Master Plan Update 2013. In Hargreaves Associates. Downtown Parks Master Plan Update; City of Dallas Park and Recreation Department: Dallas, TX, USA, 2013; pp. 115–121.
  38. Marion, R.; Adam, E. Planning the Night-Time City—1st Edition—Marion Roberts—Adam Eld. Available online: https://www.routledge.com/Planning-the-Night-time-City/Roberts-Eldridge/p/book/9780415436182 (accessed on 11 November 2023).
  39. Groshong, L.; Wilhelm Stanis, S.A.; Kaczynski, A.T.; Hipp, J.A. Attitudes About Perceived Park Safety Among Residents in Low-Income and High Minority Kansas City, Missouri, Neighborhoods. Environ. Behav. 2020, 52, 639–665. [Google Scholar] [CrossRef]
  40. M. Kyba, C.C.; Hänel, A.; Hölker, F. Redefining Efficiency for Outdoor Lighting. Energy Environ. Sci. 2014, 7, 1806–1809. [Google Scholar] [CrossRef]
  41. Bianchini, F. Night Cultures, Night Economies. Plan. Pract. Res. 1995, 10, 121–126. [Google Scholar] [CrossRef]
  42. Fernandez, J.; Melcher, K.; Song, Y.; Rezaeimalek, S.; Liu, P.; Yang, R. Disparities in city-wide park use before and during the COVID-19 pandemic: A case study of Atlanta, Georgia. Sustain. Cities Soc. 2024, 101, 105148. [Google Scholar] [CrossRef]
  43. Bladen, C.; Kennell, J.; Abson, E.; Wilde, N. Events Management: An Introduction; Taylor & Francis: London, UK, 2022; ISBN 978-1-00-058909-2. [Google Scholar]
  44. Mathy, R.M.; Schillace, M.; Coleman, S.M.; Berquist, B.E. Methodological Rigor with Internet Samples: New Ways to Reach Underrepresented Populations. Cyberpsychol. Behav. 2002, 5, 253–266. [Google Scholar] [CrossRef]
Figure 1. Downtown Dallas parks: a comparative visualization of five key parks. Picture resource: Reprinted/adapted with permission from Ref. [28]. Copyright 2022, Dallas Parks and Recreation Department.
Figure 1. Downtown Dallas parks: a comparative visualization of five key parks. Picture resource: Reprinted/adapted with permission from Ref. [28]. Copyright 2022, Dallas Parks and Recreation Department.
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Figure 2. Seasonal demographic visitation percentage trends in downtown Dallas parks. (a) Bachelor’s Degree: Trends showing the visitation percentage for Bachelor’s Degree demographics from July to December; (b) Own Children Under 18: Trends showing the visitation percentage for demographics with own children under 18 from July to December; (c) Below Poverty Level: Trends showing the visitation percentage for below poverty level demographics from July to December; (d) Married Couple: Trends showing the visitation percentage for married couple demographics from July to December.
Figure 2. Seasonal demographic visitation percentage trends in downtown Dallas parks. (a) Bachelor’s Degree: Trends showing the visitation percentage for Bachelor’s Degree demographics from July to December; (b) Own Children Under 18: Trends showing the visitation percentage for demographics with own children under 18 from July to December; (c) Below Poverty Level: Trends showing the visitation percentage for below poverty level demographics from July to December; (d) Married Couple: Trends showing the visitation percentage for married couple demographics from July to December.
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Figure 3. Hourly park visitation counts by month in downtown Dallas during 2018–2022. (a) James W. Aston Park (JAP): Hourly visitation trends from 2018 to 2022; (b) John Carpenter Plaza (JCP): Hourly visitation patterns from 2018 to 2022; (c) Klyde Warren (KW): Hourly visitation trends from 2018 to 2022; (d) West End Square (WES): Hourly visitation trends from 2018 to 2022.
Figure 3. Hourly park visitation counts by month in downtown Dallas during 2018–2022. (a) James W. Aston Park (JAP): Hourly visitation trends from 2018 to 2022; (b) John Carpenter Plaza (JCP): Hourly visitation patterns from 2018 to 2022; (c) Klyde Warren (KW): Hourly visitation trends from 2018 to 2022; (d) West End Square (WES): Hourly visitation trends from 2018 to 2022.
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Figure 4. Hourly park visitation trends by month in downtown Dallas in 2022. (a) July visitation pattern: Displays the hourly visitation patterns for 5 parks during the month of July in 2022; (b) August visitation pattern: Displays the hourly visitation patterns for 5 parks during the month of August in 2022; (c) September visitation pattern: Displays the hourly visitation patterns for 5 parks during the month of September in 2022; (d) October visitation pattern: Displays the hourly visitation patterns for 5 parks during the month of October in 2022; (e) November visitation pattern: Displays the hourly visitation patterns for 5 parks during the month of November in 2022; (f) December visitation pattern: Displays the hourly visitation patterns for 5 parks during the month of December in 2022.
Figure 4. Hourly park visitation trends by month in downtown Dallas in 2022. (a) July visitation pattern: Displays the hourly visitation patterns for 5 parks during the month of July in 2022; (b) August visitation pattern: Displays the hourly visitation patterns for 5 parks during the month of August in 2022; (c) September visitation pattern: Displays the hourly visitation patterns for 5 parks during the month of September in 2022; (d) October visitation pattern: Displays the hourly visitation patterns for 5 parks during the month of October in 2022; (e) November visitation pattern: Displays the hourly visitation patterns for 5 parks during the month of November in 2022; (f) December visitation pattern: Displays the hourly visitation patterns for 5 parks during the month of December in 2022.
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Song, Y.; Guo, Z.; Yang, R.; Wang, N. Utilizing Mobility Data to Investigate Seasonal Hourly Visiting Behavior for Downtown Parks in Dallas. Urban Sci. 2024, 8, 59. https://doi.org/10.3390/urbansci8020059

AMA Style

Song Y, Guo Z, Yang R, Wang N. Utilizing Mobility Data to Investigate Seasonal Hourly Visiting Behavior for Downtown Parks in Dallas. Urban Science. 2024; 8(2):59. https://doi.org/10.3390/urbansci8020059

Chicago/Turabian Style

Song, Yang, Zipeng Guo, Ruiqi Yang, and Na Wang. 2024. "Utilizing Mobility Data to Investigate Seasonal Hourly Visiting Behavior for Downtown Parks in Dallas" Urban Science 8, no. 2: 59. https://doi.org/10.3390/urbansci8020059

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