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Journal ArticleDOI

Park and Neighborhood Attributes Associated With Park Use: An Observational Study Using Unmanned Aerial Vehicles:

01 Jun 2020-Environment and Behavior (SAGE PublicationsSage CA: Los Angeles, CA)-Vol. 52, Iss: 5, pp 518-543
TL;DR: In this article, Park-use data collected by unmanned aerial vehicles (UAVs) were used to identify the most popular places for physical and social activities in neighborhood parks in urban areas.
Abstract: As the world becomes more urbanized, neighborhood parks are becoming an increasingly important venue where people engage in physical and social activities. Using park-use data collected by unmanned...

Summary (4 min read)

Introduction

  • While the positive role that built environments play in promoting walking or physical activity is well documented in the literature (Ewing & Cervero, 2010; McCormack et al., 2010) , urban form factors are rarely accounted for in studies of park use.
  • To address these research needs, this paper raises the following question: First, this paper establishes a conceptual framework of park use while claiming that studies have overlooked the importance of surrounding built environmental factors.

Literature Review

  • A review of the literature on park use indicates that park attributes, temporal factors, and neighborhood characteristics are related to the number of park users.
  • Travel behavior research refers to the built environment factors as D variables.
  • A recent nationwide study of the influences of the built environment on travel behavior in the United States showed similar findings-that walking trips depend on density, land use diversity, and intersection density (Ewing et al., 2014) .
  • One-quarter mile is a threshold for accessing public services within a walkable neighborhood (Duany, Plater-Zyberk, & Alminana, 2003; Kelbaugh, 1989) , including neighborhood parks (Loukaitou-Sideris & Sideris, 2009; Nicholls, 2001; Wolch, Wilson, & Fehrenbach, 2005) .
  • As the literature review reveals, park attributes, neighborhood physical and socio-demographic characteristics, and temporal factors explain the number of park users.

Method Study Sites

  • This study selected 30 neighborhood parks in Salt Lake County, Utah, based on their diversity in park attributes (e.g., size, park type) and neighborhood characteristics.
  • Focusing on the use of neighborhood parks, not regional parks, which might exhibit different use patterns and user characteristics, this study limited the park size to between 2 and 20 acres (Cohen et al., 2016b) .
  • Park-use patterns in an affluent White-dominant neighborhood could be much different from those in a poor non-White-dominant neighborhood.
  • The researcher gathered neighborhood socio-demographic data at the census tract level from the most recent American Community Survey (2015 five-year estimates) and assigned them to the onequarter mile buffers based on the relative areas of the census tracts (i.e., the spatial apportioning technique).
  • Thirty parks are located within nine municipalities in Salt Lake County, Utah .

Observation Process

  • The observations entail the use of a UAV, DJI Phantom 4 Professional carrying a fully stabilized 4K video camera.
  • In the initial phase of the research, the researchers developed and tested an observational method using UAVs (Park and Ewing, 2017) .

Figure 3. UAV observation process

  • The flight height was set to 30-40 feet (about 10-15 meters), considering a tradeoff between data accuracy and flight safety (Park and Ewing, 2017) , while allowing for slight adjustments depending on the presence of obstacles.
  • The reliability and validity of this observation tool were tested by Park and Ewing (2017) .
  • To understand park-use patterns across different times, each park was observed six times on various days of the week (one weekday and one weekend) and times of a day (morning: 8-10 AM, early afternoon: 12 noon-2 PM, late afternoon: 4-6 PM).
  • For the neighborhood parks of this study, ranging from 2 to 20 acres, every park was observed in a single UAV flight.
  • SOPARC is an observation tool for assessing park and recreation areas, especially park users' physical activity levels, gender, and age groupings.

Secondary Data Collection

  • The conceptual framework included three dimensions that predicted park use: park, neighborhood, and temporal factors.
  • For the crime safety variable, this study used Esri®'s 2016 Crime Index (http://doc.arcgis.com/en/esri-demographics/data/crime-indexes.htm), an assessment of the relative risk of major crime types at the block-group scale, based on the FBI Uniform Crime Report (UCR).
  • To capture the built environment characteristics of park neighborhoods, the authors computed the five D variables and included them in the models.
  • The average population density (1,000 residents per square mile) was computed as the number of people within the buffer divided by the total area of residential parcels whose centroids fell within the buffer.
  • For street design, the authors measured the proportion of four-way intersections over all intersections within a buffer.

Factor Analysis

  • Rather than relying on multiple, correlated variables to represent the built environment and socio-demographic condition surrounding the parks, the authors chose to reduce a large number of correlated variables to only a few factors.
  • Factor analysis can help find the underlying, though not always observable or readily measurable, dimensions of the neighborhood environments (Cervero & Kockelman, 1997) .
  • Using the factanal function (stats package) in R 3.4.2, the authors ran exploratory factor analyses separately for the neighborhood built environment variables and the socio-demographic variables, as done in a station-area context (Rodríguez et al., 2009) (Table 3 ).
  • Bartlett's test of sphericity and the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy were used to test the suitability of the data for structure detection.
  • Both tests indicate that a factor analysis may be useful with the data used in this study, in both sets of variables.

"Table 3. Oblique-rotated factor loadings: built environment and socio-demographic variables"

  • For the built environment variables, the first factor included positive loadings for the FAR, population density, the percentage of four-way intersections, WalkScore®, stop density, and the percentage of commercial uses and negative loadings for the percentage of public and residential uses.
  • The authors also interpreted another extracted built environment factor, land-use diversity, with positive loadings for the entropy index and the percentage of commercial and public uses and negative loadings for residential uses.
  • These two built environment factors accounted for 54% of the variance among the nine variables and their standardized Cronbach's alphas, a reliability measure, are higher than the acceptable level of .7.
  • For the socio-demographic variables, the first factor, comprised of positive loadings for the percentage of the minority population, including Hispanics, Blacks, and other races, the percentage of households on public assistance, and the Crime Index and a negative loading for median household income, were interpreted as a socio-demographic minority.
  • In addition, the authors tested for multicollinearity and the highest variance inflation factor (VIF) values are lower than the standard ceiling value of 10.0 in all models (Hair et al., 1995) .

Total number of park users

  • Table 4 shows the negative binomial model of the number of park users based on the two neighborhood-level factors and park attributes.
  • The IRR is the incidence rate ratio (or exponentiated coefficient) and shows the percentage change in the expected number of park users, given one-unit change in the independent variable.
  • An IRR of 1.50, for example, can be interpreted to mean that a one-unit increase in the associated independent variable increases the rate of the dependent variable count by 50%.

"Table 4. Negative binomial regression model of the total number of park users"

  • At the observation level, the time of day is significant while the day of the week is not.
  • Among the park attributes, park size, playground, creek/pond, and park maintenance show expected positive signs.
  • The use of factor analysis helps to see the associations between the outcome variable and underlying factors (e.g., neighborhood compactness, socio-demographic status), which is not always directly measured, while simplifying models.

Number of park users by user group

  • Table 5 lists the models according to user groups: gender, age, and activity level.
  • Across the user categories, morning time is negatively associated with park-user counts in most models, except the senior model (p=.09).
  • Organized activity, playground, and high-quality maintenance, however, exhibit a positive relationship with park use in all models, except the playground variable in the senior model (IRR=0.44, p=.56) .
  • While two gender-specific models share several commonly significant variables such as organized activities, playground, maintenance, and land-use diversity factors, only the female user group is negatively associated with the sports field and socio-demographic minority factors.
  • Only the male user group is positively associated with the tennis court, creek/pond, neighborhood age composition, and neighborhood compactness factor.

"Table 5. Incident rate ratio (IRR) of negative binomial models by user groups "

  • An expected finding is that the number of children is most strongly associated with playground provision (IRR = 18.79) and moderately with high-quality maintenance and adjacent land use diversity.
  • Compared to other models, the senior model exhibits a different pattern:.
  • Among the park attributes, the number of elderly people is negatively associated with the tennis court and trail factor and positively related to the connectivity to other parks, the total number of facilities, and high-quality maintenance factors.
  • Two activity-level models differ mainly in terms of surrounding neighborhood attributes.
  • Moderate-to-vigorous activity in a park decreases with the socio-demographic minority factor and increases with the neighborhood compactness factor.

Discussion and Conclusions

  • By modeling park-use levels according to user types, this paper identifies factors with strong relationships to promoting park use.
  • While Cohen et al. (2012) found that parks in residential areas attract more users than those in commercial areas, this study reveals an opposite finding: park use increases with the land use diversity factor, which incentivizes commercial and public uses and penalizes residential uses.
  • Regarding the detailed park attributes, this study, although consistent with the literature in several ways, was inconsistent in others.
  • The use of UAVs in park monitoring could foster a more consistent and comprehensive measurement of park use and enable comparative park studies.
  • The authors conclude by acknowledging two main limitations of this study -external validity and reliability.

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1
Title: Park and Neighborhood Attributes Associated with Park Use:
An Observational Study Using Unmanned Aerial Vehicles (UAVs)
Author
Keunhyun Park, Department of Landscape Architecture and Environmental Planning, Utah State
University, 4005 Old Main Hill, Logan, UT, USA 84322-4005, keunhyun.park@usu.edu
Abstract
As the world becomes more urbanized, neighborhood parks are becoming an increasingly
important venue where people engage in physical and social activities. Using park-use data
collected by unmanned aerial vehicles, the aim of this study is to account for park use in light of
park attributes and neighborhood conditions. The role of the built environment near a park
receives particular attention, as it is under-studied in the literature. A regression model shows
that neighborhood park utilization is positively associated with park attributes (i.e., larger area, a
playground, a creek/pond, quality maintenance, and organized activities) and neighborhood
attributes (i.e., lower minority/low-income population, higher density, more commercial and
public uses, and a well-connected street network). The statistical significance of the factors
varies by user types. This study provides insights into the role of neighborhood compactness and
mixed land use, which calls for interdisciplinary collaboration among urban planners/designers,
landscape architects, and park programmers.
Keywords: Park utilization, park-based physical activity, neighborhood park, direct observation,
Unmanned Aircraft Systems
This manuscript is accepted by Environment & Behavior journal and currently in press.

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Introduction
Urban parks are essential to the livability and sustainability of cities, providing various benefits,
including health, environmental, social, and economic gains (Chiesura, 2004; Konijnendijk et al.,
2013; Lee & Maheswaran, 2011; Yang et al., 2005). Such benefits, however, can be enjoyed
only if people use the parks. As it is more vulnerable to crime, an underused park could become
a dead zone. Therefore, a number of studies have examined factors promoting park use. Among
these factors are proximity to a park, the size of a park, the quality and quantity of facilities and
their maintenance, programming and outreach, and the characteristics of surrounding
neighborhoods (Akpinar, 2016; Baran et al., 2014; Cohen et al., 2010, 2012; Floyd et al., 2008;
Giles-Corti et al., 2005; Grow et al., 2008; Kaczynski et al., 2008; Kemperman & Timmermans,
2006; Koohsari et al., 2013; Leslie et al., 2010; Loukaitou-Sideris & Sideris, 2009; McCormack,
2010; Mowen et al., 2007; Özgüner, 2011; Parra et al. 2010; Ries et al., 2009; Schipperijn et al.,
2010; Wendel et al., 2012; Westley et al., 2013).
In spite of the popularity of studying park visitation or park-based physical activity, studies have
not reached a consensus on the roles that various factors, especially neighborhood conditions,
play. For example, while the positive role that built environments play in promoting walking or
physical activity is well documented in the literature (Ewing & Cervero, 2010; McCormack et
al., 2010), urban form factors are rarely accounted for in studies of park use. The literature either
does not take the built environment characteristics into consideration (Banda et al., 2014;
Loukaitou-Sideris & Sideris, 2009; Rung et al., 2011; Slater et al., 2016; Van Hecke et al., 2016)
or includes only population density as a confounding variable (Cohen et al., 2012, 2013, 2014,
2016a, 2016b). This lack of understanding would end up with a separation of two interrelated

3
tasks in city management – designing parks and designing surrounding communities, which
results in what we see in American cities the underutilization of neighborhood parks (Cohen et
al., 2016b).
To address these research needs, this paper raises the following question: Which factors have the
strongest relationship to promoting park use in light of both park attributes and neighborhood
conditions? In particular, how are the built environment characteristics near a park related to
park use? First, this paper establishes a conceptual framework of park use while claiming that
studies have overlooked the importance of surrounding built environmental factors. Then, with
park use data collected from an observational tool using unmanned aerial vehicles (UAVs; Park
and Ewing, 2017), this study develops multilevel negative binomial models that estimate the
number of park users by gender, age group, and activity level. An understanding of park-use
dynamics with regard to park and neighborhood design attributes could prompt planners and
government officials to collaborate to formulate more effective park plans, policies, and
programs that promote park utilization and park-based physical activity.
Literature Review
A review of the literature on park use indicates that park attributes, temporal factors, and
neighborhood characteristics are related to the number of park users. Each factor is examined
here to establish a conceptual framework that identifies the key factors and their relationships.
First, substantial evidence suggests that diverse park characteristics predict the level of park use.
A larger park is associated with higher levels of park use (Baran et al., 2014; Cohen et al., 2012,
2013, 2016b; Loukaitou-Sideris & Sideris, 2009; Van Dyck et al., 2013). In addition, types of
areas (e.g., open spaces, trails, playgrounds, sports facilities, picnic areas, water facilities) attract

4
diverse populations (Banda et al., 2014; Baran et al., 2014; Cohen et al., 2014; Rung et al., 2011;
Van Hecke et al., 2016). The number of facilities that entail a particular activity is also strongly
correlated with the level of park use (Cohen et al., 2013; Loukaitou-Sideris & Sideris, 2009).
In addition to design factors, program- or activity-related factors also constitute park attributes.
These factors include organized or supervised activities (Cohen et al., 2012, 2013, 2016b, 2016a;
Loukaitou-Sideris & Sideris, 2009) and the number of park employees (Cohen et al., 2012). Park
maintenance is also related to park use (Loukaitou-Sideris & Sideris, 2009; Rung et al., 2011;
Slater et al., 2016). Interestingly, Cohen et al. (2016a) found that the presence of homeless
individuals is associated with a higher number of park users while the presence of intoxicated
individuals is associated with lower numbers.
In addition to park attributes, temporal factors such as time, day, season, or weather are also
associated with park use. The probability of park use is generally higher on weekends than on
weekdays and higher in the afternoons and evenings than in the mornings or at noon (Banda et
al., 2014; Chung-Do et al., 2011; Cohen et al., 2012, 2013, 2014; Van Dyck et al., 2013; Van
Hecke et al., 2016). After other factors are controlled for, rainy days lead to fewer users (Van
Dyck et al., 2013).
Neighborhood characteristics, which include both socio-demographic and built environments,
are associated with park use. For one, the poverty level of the neighborhood exhibits a strong
negative correlation with the number of park users (Baran et al., 2014; Cohen et al., 2012, 2016b;
Van Dyck et al., 2013). In addition, Baran et al. (2014) found that racial heterogeneity is
negatively associated with park use, as is neighborhood crime (Baran et al., 2014; Slater et al.,
2016).

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Unlike studies in the urban planning and travel behavior literature, those pertaining to park use
have not fully accounted for urban form factors. Cohen and her research teams (2012, 2013,
2014, 2016a, 2016b) included only a population density variable (e.g., total population within a
one-mile radius) in their predictions of park use. One of their studies (Cohen et al., 2012) added
another built environment variable, dominant land-use type, and found that parks in residential
areas attract more users than do those in commercial areas. Van Dyck et al. (2013) used a
walkability index developed by Frank et al. (2010) and consisting of several built environment
factors such as residential density, land use mix, and intersection density and found that parks in
high-walkable neighborhoods were likely to attract more park users. Baran et al. (2014) included
three neighborhood urban form variables—the lengths of sidewalks, the number of intersections,
and the density of cul-de-sacs—and found that sidewalks and street intersections generally
boosted park use. Among the 13 studies reviewed here, only two (Baran et al., 2014; Van Dyck
et al. 2013) incorporated diverse built environment factors in their work, and none accounted for
transit-related factors or destination accessibility, both strong predictors of walking in travel
behavior research (Ewing & Cervero, 2010; Ewing et al., 2014).
Considering this lack of attention to urban form elements in park use studies, the researcher for
this paper consulted the travel behavior literature to determine which built environment factors
generate more walking trips, especially those for exercise or recreation. Travel behavior research
refers to the built environment factors as D variables. The original “three Ds,” developed by
Cervero and Kockelman (1997), are density, diversity, and design, which were followed later by
destination accessibility and distance to transit (Ewing & Cervero, 2001). Through a meta-
analysis, Ewing and Cervero (2010) found a strong relationship between walking and measures

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  • ...Furthermore, many more park features have been shown to be associated with park use that were not found in this study, such as playgrounds, table tennis tables, basketball courts, ponds and trees (Baran et al., 2014; Edwards et al., 2015; Park, 2019; Veitch et al., 2016)....

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TL;DR: The Logic of Hierarchical Linear Models (LMLM) as discussed by the authors is a general framework for estimating and hypothesis testing for hierarchical linear models, and it has been used in many applications.
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Abstract: Problem: Localities and states are turning to land planning and urban design for help in reducing automobile use and related social and environmental costs. The effects of such strategies on travel demand have not been generalized in recent years from the multitude of available studies. Purpose: We conducted a meta-analysis of the built environment-travel literature existing at the end of 2009 in order to draw generalizable conclusions for practice. We aimed to quantify effect sizes, update earlier work, include additional outcome measures, and address the methodological issue of self-selection. Methods: We computed elasticities for individual studies and pooled them to produce weighted averages. Results and conclusions: Travel variables are generally inelastic with respect to change in measures of the built environment. Of the environmental variables considered here, none has a weighted average travel elasticity of absolute magnitude greater than 0.39, and most are much less. Still, the combined effect o...

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"Park and Neighborhood Attributes As..." refers background or methods in this paper

  • ...Through a meta-analysis, Ewing and Cervero (2010) found a strong relationship between walking and measures of land-use diversity, intersection density, and destination accessibility....

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  • ...…only two (Baran et al., 2014; Van Dyck et al., 2013) incorporated diverse built environment factors in their work, and none accounted for transit-related factors or access to other amenities, both strong predictors of walking in travel behavior research (Ewing & Cervero, 2010; Ewing et al., 2014)....

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  • ...For example, while the positive role that built environments play in promoting walking or physical activity is well documented in the literature (Ewing & Cervero, 2010; McCormack, Rock, Toohey, & Hignell, 2010), urban form factors are rarely accounted for in studies of park use....

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  • ...…use, this study consulted the travel behavior research and included five D variables—density, diversity, design, distance to transit, and destination accessibility (Ewing & Cervero, 2010)—which are then aggregated into two factors—neighborhood compactness and land-use diversity—in the analyses....

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  • ...Factor analysis can help find the underlying, though not always observable or readily measurable, dimensions of the neighborhood environments (Cervero & Kockelman, 1997)....

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  • ...The original “three Ds,” 522 Environment and Behavior 52(5) developed by Cervero and Kockelman (1997), are density, diversity, and design, which were followed later by destination accessibility and distance to transit (Ewing & Cervero, 2001)....

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  • ...Urban parks are essential to the livability and sustainability of cities, providing various benefits, including health, environmental, social, and economic gains (Chiesura, 2004; Konijnendijk, Annerstedt, Nielsen, & Maruthaveeran, 2013; Lee & Maheswaran, 2011; Yang, McBride, Zhou, & Sun, 2005)....

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"Park and Neighborhood Attributes As..." refers background in this paper

  • ...The original “three Ds,” 522 Environment and Behavior 52(5) developed by Cervero and Kockelman (1997), are density, diversity, and design, which were followed later by destination accessibility and distance to transit (Ewing & Cervero, 2001)....

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Frequently Asked Questions (1)
Q1. What have the authors contributed in "Title: park and neighborhood attributes associated with park use: an observational study using unmanned aerial vehicles (uavs) author" ?

Using park-use data collected by unmanned aerial vehicles, the aim of this study is to account for park use in light of park attributes and neighborhood conditions. The role of the built environment near a park receives particular attention, as it is under-studied in the literature. This study provides insights into the role of neighborhood compactness and mixed land use, which calls for interdisciplinary collaboration among urban planners/designers, landscape architects, and park programmers.