COVID-19 Impact on Residential Preferences in the Early-Stage Outbreak in South Korea.

In the early stage of the COVID-19 pandemic in South Korea, public fear or social scaring of urban living was observed, which caused people to change their daily routines. This study examines how the COVID-19 pandemic affected residential choice and perceptions of urban living. We analyzed self-reported survey data collected from 2000 participants in Seoul, Daegu, and Kyeongbuk in South Korea between 3-6 August 2020, targeting the relatively controlled period after the first COVID-19 outbreak. Logistic regression models were used to examine concerns of urban living and residence relocation consideration. Those who were aged 30 or older, regularly commuting, not feeling healthy, with a household size of two, and living in a low-rise condominium were more likely to be concerned with urban living. Those who were aged 40 or older and living in a townhouse or a single-detached house were more likely to consider moving to a less dense area. People perceived that their daily routine changed substantially after the pandemic. Certain participant groups showed concerns of urban living and relocation consideration, suggesting housing policy implications.

Developing Sidewalk Inventory Data Using Street View Images.

(1) Background: Public sidewalk GIS data are essential for smart city development. We developed an automated street-level sidewalk detection method with image-processing Google Street View data. (2) Methods: Street view images were processed to produce graph-based segmentations. Image segment regions were manually labeled and a random forest classifier was established. We used multiple aggregation steps to determine street-level sidewalk presence. (3) Results: In total, 2438 GSV street images and 78,255 segmented image regions were examined. The image-level sidewalk classifier had an 87% accuracy rate. The street-level sidewalk classifier performed with nearly 95% accuracy in most streets in the study area. (4) Conclusions: Highly accurate street-level sidewalk GIS data can be successfully developed using street view images.

Identifying street design elements associated with vehicle-to-pedestrian collision reduction at intersections in New York City.

OBJECTIVE: We evaluated associations between the installation of eleven street design elements, between 2007 and 2015, and subsequent changes in vehicle-to-pedestrian collisions in New York City. METHODS: Collision data were from Accident Location Information System in the New York State Department of Transportation. Safety improvement projects at 118 intersections were reviewed and their implemented street design elements were identified. First, we assessed potential regression-to-the-mean effects using historic trends of pedestrian collision count at the intersection project locations. Second, we used a two-group pretest-posttest design to assess individual element's associations with pedestrian collision reduction after installations. Pedestrian collision count and pedestrian- and vehicle-based pedestrian collision rates were examined. Third, regression trees were used to classify the intersections with design elements as independent variables for the target variables of collision outcomes, to identify street design element combinations associated with pedestrian collision reductions. RESULTS: Treatments with pedestrian refuge island or pedestrian plaza had reductions in pedestrian collision count and pedestrian-based collision rate while their comparisons had no changes. Treatments with pedestrian refuge island had a larger reduction in pedestrian collision when combined with lane removal or narrowing. Treatment with curb extension or pedestrian plaza had reductions in vehicle-based pedestrian collision rate while their comparisons had no changes. Other studied elements showed no, small, or insignificant associations with post-project pedestrian collision reductions.

Increased Walking's Additive and No Substitution Effect on Total Physical Activity.

PURPOSE: We assessed the associations between a change in time spent walking and a change in total physical activity (PA) time within an urban living adult sample to test for additive or substitution effects. METHODS: Participants living in the greater Seattle area were assessed in 2008-2009 and again 1-2 yr later (2010-2011). At each time point, they wore accelerometers and GPS units and recorded trips and locations in a travel diary for seven consecutive days. These data streams were combined to derive a more objective estimate of walking and total PA. Participants also completed the International Physical Activity Questionnaire to provide self-reported estimates of walking and total PA. Regression analyses assessed the associations between within-participant changes in objective and self-reported walking and total PA. RESULTS: Data came from 437 participants. On average, a 1-min increase in total walking was associated with an increase in total PA of 1 min, measured by objective data, and 1.2-min, measured by self-reported data. A similar additive effect was consistently found with utilitarian, transportation, or job-related walking, measured by both objective and self-reported data. For recreational walking, the effect of change was mixed between objective and self-reported results. CONCLUSION: Both objective and self-reported data confirmed an additive effect of utilitarian and total walking on PA.

Differences in Behavior, Time, Location, and Built Environment between Objectively Measured Utilitarian and Recreational Walking.

OBJECTIVES: Utilitarian and recreational walking both contribute to physical activity. Yet walking for these two purposes may be different behaviors. We sought to provide operational definitions of utilitarian and recreational walking and to objectively measure their behavioral, spatial, and temporal differences in order to inform transportation and public health policies and interventions. METHODS: Data were collected 2008-2009 from 651 Seattle-King County residents, wearing an accelerometer and a GPS unit, and filling-in a travel diary for 7 days. Walking activity bouts were classified as utilitarian or recreational based on whether walking had a destination or not. Differences between the two walking purposes were analyzed, adjusting for the nested structure of walking activity within participants. RESULTS: Of the 4,905 observed walking bouts, 87.4% were utilitarian and 12.6% recreational walking. Utilitarian walking bouts were 45% shorter in duration (-12.1 min) and 9% faster in speed (+0.3km/h) than recreational walking bouts. Recreational walking occurred more frequently in the home neighborhood and was not associated with recreational land uses. Utilitarian walking occurred in areas having higher residential, employment, and street density, lower residential property value, higher area percentage of mixed-use neighborhood destinations, lower percentage of parks/trails, and lower average topographic slope than recreational walking. CONCLUSION: Utilitarian and recreational walking are substantially different in terms of frequency, speed, duration, location, and related built environment. Policies that promote walking should adopt type-specific strategies. The high occurrence of recreational walking near home highlights the importance of the home neighborhood for this activity.

Comparisons of Physical Activity and Walking Between Korean Immigrant and White Women in King County, WA.

Immigrant and minority women are less physically active than White women particularly during leisure time. However, prior research demonstrates that reported household physical activity (PA) and non-leisure time walking/biking were higher among the former. Using accelerometers, GPS, and travel logs, transport-related, home-based, and leisure time PA were measured objectively for 7 days from a convenience sample of 60 first-generation Korean immigrant women and 69 matched White women from the Travel Assessment and Community Project in King County, Washington. Time spent in total PA, walking, and home-based PA was higher among Whites than Korean immigrants regardless of PA type or location. 58 % of the White women but only 20 % of the Korean women met CDC's PA recommendations. Socio-economic status, psychosocial factors, and participants' neighborhood built environmental factors failed to account for the observed PA differences between these groups.

How far from home? The locations of physical activity in an urban U.S. setting.

UNLABELLED: Little is known about where physical activity (PA) occurs, or whether different demographic groups accumulate PA in different locations. METHOD: Objective data on PA and location from 611 adults over 7days were collected in King County, WA in 2008-2009. The relative amounts of time spent in sedentary-to-low and moderate-to-vigorous PA (MVPA) were quantified at three locations: "home" (<125m from geocoded home locations); "near" home (125-1666m, defining the home neighborhood); and "away" from home (>1666m). Differences in MVPA by demographics and location were examined. The percent of daily time in MVPA was estimated using a mixed model adjusted for location, sex, age, race/ethnicity, employment, education, BMI, and income. RESULTS: Most MVPA time occurred in nonhome locations, and disproportionately "near" home; this location was associated with 16.46% greater time in MVPA, compared to at-home activity (p<0.001), whereas more time spent at "away" locations was associated with 3.74% greater time in MVPA (p<0.001). Location was found to be a predictor of MVPA independent of demographic factors. CONCLUSION: A large proportion of MVPA time is spent at "near" locations, corresponding to the home neighborhood studied in previous PA research. "Away" locations also host time spent in MVPA and should be the focus of future research.

Relation between higher physical activity and public transit use.

OBJECTIVES: We isolated physical activity attributable to transit use to examine issues of substitution between types of physical activity and potential confounding of transit-related walking with other walking. METHODS: Physical activity and transit use data were collected in 2008 to 2009 from 693 Travel Assessment and Community study participants from King County, Washington, equipped with an accelerometer, a portable Global Positioning System, and a 7-day travel log. Physical activity was classified into transit- and non-transit-related walking and nonwalking time. Analyses compared physical activity by type between transit users and nonusers, between less and more frequent transit users, and between transit and nontransit days for transit users. RESULTS: Transit users had more daily overall physical activity and more total walking than did nontransit users but did not differ on either non-transit-related walking or nonwalking physical activity. Most frequent transit users had more walking time than least frequent transit users. Higher physical activity levels for transit users were observed only on transit days, with 14.6 minutes (12.4 minutes when adjusted for demographics) of daily physical activity directly linked with transit use. CONCLUSIONS: Because transit use was directly related to higher physical activity, future research should examine whether substantive increases in transit access and use lead to more physical activity and related health improvements.

Emerging technologies for assessing physical activity behaviors in space and time.

Precise measurement of physical activity is important for health research, providing a better understanding of activity location, type, duration, and intensity. This article describes a novel suite of tools to measure and analyze physical activity behaviors in spatial epidemiology research. We use individual-level, high-resolution, objective data collected in a space-time framework to investigate built and social environment influences on activity. First, we collect data with accelerometers, global positioning system units, and smartphone-based digital travel and photo diaries to overcome many limitations inherent in self-reported data. Behaviors are measured continuously over the full spectrum of environmental exposures in daily life, instead of focusing exclusively on the home neighborhood. Second, data streams are integrated using common timestamps into a single data structure, the "LifeLog." A graphic interface tool, "LifeLog View," enables simultaneous visualization of all LifeLog data streams. Finally, we use geographic information system SmartMap rasters to measure spatially continuous environmental variables to capture exposures at the same spatial and temporal scale as in the LifeLog. These technologies enable precise measurement of behaviors in their spatial and temporal settings but also generate very large datasets; we discuss current limitations and promising methods for processing and analyzing such large datasets. Finally, we provide applications of these methods in spatially oriented research, including a natural experiment to evaluate the effects of new transportation infrastructure on activity levels, and a study of neighborhood environmental effects on activity using twins as quasi-causal controls to overcome self-selection and reverse causation problems. In summary, the integrative characteristics of large datasets contained in LifeLogs and SmartMaps hold great promise for advancing spatial epidemiologic research to promote healthy behaviors.

Walking objectively measured: classifying accelerometer data with GPS and travel diaries.

PURPOSE: This study developed and tested an algorithm to classify accelerometer data as walking or nonwalking using either GPS or travel diary data within a large sample of adults under free-living conditions. METHODS: Participants wore an accelerometer and a GPS unit and concurrently completed a travel diary for seven consecutive days. Physical activity (PA) bouts were identified using accelerometry count sequences. PA bouts were then classified as walking or nonwalking based on a decision-tree algorithm consisting of seven classification scenarios. Algorithm reliability was examined relative to two independent analysts' classification of a 100-bout verification sample. The algorithm was then applied to the entire set of PA bouts. RESULTS: The 706 participants' (mean age = 51 yr, 62% female, 80% non-Hispanic white, 70% college graduate or higher) yielded 4702 person-days of data and had a total of 13,971 PA bouts. The algorithm showed a mean agreement of 95% with the independent analysts. It classified PA into 8170 walking bouts (58.5 %) and 5337 nonwalking bouts (38.2%); 464 bouts (3.3%) were not classified for lack of GPS and diary data. Nearly 70% of the walking bouts and 68% of the nonwalking bouts were classified using only the objective accelerometer and GPS data. Travel diary data helped classify 30% of all bouts with no GPS data. The mean ± SD duration of PA bouts classified as walking was 15.2 ± 12.9 min. On average, participants had 1.7 walking bouts and 25.4 total walking minutes per day. CONCLUSIONS: GPS and travel diary information can be helpful in classifying most accelerometer-derived PA bouts into walking or nonwalking behavior.