Urban Form Lab Archives - Page 3 of 10

What County-level Factors Influence Covid-19 Incidence in the United States? Findings from the First Wave of the Pandemic

Wang, Lan; Zhang, Surong; Yang, Zilin; Zhao, Ziyu; Moudon, Anne Vernez; Feng, Huasen; Liang, Junhao; Sun, Wenyao; Cao, Buyang. (2021). What County-level Factors Influence Covid-19 Incidence in the United States? Findings from the First Wave of the Pandemic. Cities, 118.

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Abstract

Effective control of the COVID-19 pandemic via appropriate management of the built environment is an urgent issue. This study develops a research framework to explore the relationship between COVID-19 incidence and influential factors related to protection of vulnerable populations, intervention in transmission pathways, and provision of healthcare resources. Relevant data for regression analysis and structural equation modeling is collected during the first wave of the pandemic in the United States, from counties with over 100 confirmed cases. In addition to confirming certain factors found in the existing literature, we uncover six new factors significantly associated with COVID-19 incidence. Furthermore, incidence during the lockdown is found to significantly affect incidence after the reopening, highlighting that timely quarantining and treating of patients is essential to avoid the snowballing transmission over time. These findings suggest ways to mitigate the negative effects of subsequent waves of the pandemic, such as special attention of infection prevention in neighborhoods with unsanitary and overcrowded housing, minimization of social activities organized by neighborhood associations, and contactless home delivery service of healthy food. Also worth noting is the need to provide support to people less capable of complying with the stay-at-home order because of their occupations or socio-economic disadvantage.

Keywords

Pandemics; Covid-19; Covid-19 Pandemic; Infection Prevention; Stay-at-home Orders; Structural Equation Modeling; United States; Communicable Disease Prevention; Influential Factors; Lockdown; Structural Equation Modeling (sem); Prevalence; Disease; Healthy Food; Social Activities; Counties; Neighborhoods; Housing; Built Environment; Prevention; Minimization; Socioeconomic Factors; Intervention; Health Care; Vulnerability; Occupations; Coronaviruses; Food Service; Disease Transmission; United States--us

Obesity and Supermarket Access: Proximity or Price?

Drewnowski, Adam; Aggarwal, Anju; Hurvitz, Philip M.; Monsivais, Pablo; Moudon, Anne V. (2012). Obesity and Supermarket Access: Proximity or Price? American Journal Of Public Health, 102(8), e74 – e80.

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Abstract

Objectives. We examined whether physical proximity to supermarkets or supermarket price was more strongly associated with obesity risk. Methods. The Seattle Obesity Study (SOS) collected and geocoded data on home addresses and food shopping destinations for a representative sample of adult residents of King County, Washington. Supermarkets were stratified into 3 price levels based on average cost of the market basket. Sociodemographic and health data were obtained from a telephone survey. Modified Poisson regression was used to test the associations between obesity and supermarket variables. Results. Only 1 in 7 respondents reported shopping at the nearest supermarket. The risk of obesity was not associated with street network distances between home and the nearest supermarket or the supermarket that SOS participants reported as their primary food source. The type of supermarket, by price, was found to be inversely and significantly associated with obesity rates, even after adjusting for individual-level sociodemographic and lifestyle variables, and proximity measures (adjusted relative risk = 0.34; 95% confidence interval = 0.19, 0.63) Conclusions. Improving physical access to supermarkets may be one strategy to deal with the obesity epidemic; improving economic access to healthy foods is another. [ABSTRACT FROM AUTHOR]; Copyright of American Journal of Public Health is the property of American Public Health Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Keywords

Natural Foods; Obesity Risk Factors; Surveys; Cluster Analysis (statistics); Confidence Intervals; Correlation (statistics); Food Service; Geographic Information Systems; Poisson Distribution; Population Geography; Research Funding; User Charges; Residential Patterns; Socioeconomic Factors; Relative Medical Risk; Statistical Models; Descriptive Statistics; Economics; Washington (state)

How Far from Home? The Locations of Physical Activity in an Urban US Setting

Hurvitz, Philip M.; Moudon, Anne V.; Kang, Bumjoon; Fesinmeyer, Megan D.; Saelens, Brian E. (2014). How Far from Home? The Locations of Physical Activity in an Urban US Setting. Preventive Medicine, 69, 181 – 186.

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Abstract

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 7 days 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 (1666 m). 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. (C) 2014 Elsevier Inc All rights reserved.

Keywords

Accelerometer Data; Built Environment; United-states; Neighborhood Walkability; Exercise Intensity; Time Use; Land-use; Walking; Health; Behavior; Physical Activity; Objective Measurement; Gps; Accelerometry; Gis

Spatial Energetics Integrating Data from GPS, Accelerometry, and GIS to Address Obesity and Inactivity

James, Peter; Jankowska, Marta; Marx, Christine; Hart, Jaime E.; Berrigan, David; Kerr, Jacqueline; Hurvitz, Philip M.; Hipp, J. Aaron; Laden, Francine. (2016). Spatial Energetics Integrating Data from GPS, Accelerometry, and GIS to Address Obesity and Inactivity. American Journal Of Preventive Medicine, 51(5), 792 – 800.

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Abstract

To address the current obesity and inactivity epidemics, public health researchers have attempted to identify spatial factors that influence physical inactivity and obesity. Technologic and methodologic developments have led to a revolutionary ability to examine dynamic, high-resolution measures of temporally matched location and behavior data through GPS, accelerometry, and GIS. These advances allow the investigation of spatial energetics, high-spatiotemporal resolution data on location and time-matched energetics, to examine how environmental characteristics, space, and time are linked to activity-related health behaviors with far more robust and detailed data than in previous work. Although the transdisciplinary field of spatial energetics demonstrates promise to provide novel insights on how individuals and populations interact with their environment, there remain significant conceptual, technical, analytical, and ethical challenges stemming from the complex data streams that spatial energetics research generates. First, it is essential to better understand what spatial energetics data represent, the relevant spatial context of analysis for these data, and if spatial energetics can establish causality for development of spatially relevant interventions. Second, there are significant technical problems for analysis of voluminous and complex data that may require development of spatially aware scalable computational infrastructures. Third, the field must come to agreement on appropriate statistical methodologies to account for multiple observations per person. Finally, these challenges must be considered within the context of maintaining participant privacy and security. This article describes gaps in current practice and understanding and suggests solutions to move this promising area of research forward. (C) 2016 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Keywords

Physical-activity Levels; Built Environment; Activity Monitors; Travel Behavior; Health Research; Neighborhood; Exposure; Validation; Children; Design

Capturing Fine-Scale Travel Behaviors: A Comparative Analysis between Personal Activity Location Measurement System (PALMS) and Travel Diary

Kang, Mingyu; Moudon, Anne V.; Hurvitz, Philip M.; Saelens, Brian E. (2018). Capturing Fine-Scale Travel Behaviors: A Comparative Analysis between Personal Activity Location Measurement System (PALMS) and Travel Diary. International Journal Of Health Geographics, 17(1).

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Abstract

BackgroundDevice-collected data from GPS and accelerometers for identifying active travel behaviors have dramatically changed research methods in transportation planning and public health. Automated algorithms have helped researchers to process large datasets with likely fewer errors than found in other collection methods (e.g., self-report travel diary). In this study, we compared travel modes identified by a commonly used automated algorithm (PALMS) that integrates GPS and accelerometer data with those obtained from travel diary estimates.MethodsSixty participants, who made 2100 trips during seven consecutive days of data collection, were selected from among the baseline sample of a project examining the travel behavior impact of a new light rail system in the greater Seattle, WA (USA) area. GPS point level analyses were first conducted to compare trip/place and travel mode detection results using contingency tables. Trip level analyses were then performed to investigate the effect of proportions of time overlap between travel logs and device-collected data on agreement rates. Global performance (with all subjects' data combined) and subject-level performance of the algorithm were compared at the trip level.ResultsAt the GPS point level, the overall agreement rate of travel mode detection was 77.4% between PALMS and the travel diary. The agreement rate for vehicular trip detection (84.5%) was higher than for bicycling (53.5%) and walking (58.2%). At the trip level, the global performance and subject-level performance of the PALMS algorithm were 46.4% and 42.4%, respectively. Vehicular trip detection showed highest agreement rates in all analyses. Study participants' primary travel mode and car ownership were significantly related to the subject-level mode agreement rates.ConclusionsThe PALMS algorithm showed moderate identification power at the GPS point level. However, trip level analyses found lower agreement rates between PALMS and travel diary data, especially for active transportation. Testing different PALMS parameter settings may serve to improve the detection of active travel and help expand PALMS's applicability in geographically different urbanized areas with a variety of travel modes.

Keywords

Transportation Planning; Public Health; Accelerometers; Global Positioning System; Voyages & Travels; Cycling; Algorithms; Accelerometer; Automated Algorithm; Gis; Gps; Places; Trips; Global Positioning Systems; Physical-activity; Data-collection; Health Research; Gps Data; Accelerometry; Validity

A Cross-Sectional Analysis of Physical Activity and Weight Misreporting in Diverse Populations: The Seattle Obesity Study III

Buszkiewicz, James; Rose, Chelsea; Gupta, Shilpi; Ko, Linda K.; Mou, Jin; Moudon, Anne, V; Hurvitz, Philip M.; Cook, Andrea; Aggarwal, Anju; Drewnowski, Adam. (2020). A Cross-Sectional Analysis of Physical Activity and Weight Misreporting in Diverse Populations: The Seattle Obesity Study III. Obesity Science & Practice, 6(6), 615 – 627.

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Abstract

Background: In-person assessments of physical activity (PA) and body weight can be burdensome for participants and cost prohibitive for researchers. This study examined self-reported PA and weight accuracy and identified patterns of misreporting in a diverse sample. Methods: King, Pierce and Yakima county residents, aged 21-59 years (n= 728), self-reported their moderate-to-vigorous PA (MVPA) and weight, in kilograms. Self-reports were compared with minutes of bout-level MVPA, from 3 days of accelerometer data, and measured weights. Regression models examined characteristics associated with underreporting and overreporting of MVPA and weight, the potential bias introduced using each measure and the relation between perceived and measured PA and weight. Results: MVPA underreporting was higher among males and college educated participants; however, there was no differential MVPA overreporting. Weight underreporting was higher among males, those age 40-49 years and persons with obesity. Weight overreporting was higher among Hispanic participants and those reporting stress, unhappiness and fair or poor health. The estimated PA-obesity relation was similar using measured and self-reported PA but not self-reported weight. Perceived PA and weight predicted measured values. Conclusion: Self-reported PA and weight may be useful should objective measurement be infeasible; however, though population-specific adjustment for differential reporting should be considered.

Keywords

Self-reported Weight; Sedentary Behavior; Validation; Accuracy; Height; Adults; Health Disparity; Obesity; Physical Activity; Self-reported Outcomes

Associations of Cannabis Retail Outlet Availability and Neighborhood Disadvantage with Cannabis Use and Related Risk Factors Among Young Adults in Washington State

Rhew, Isaac C.; Guttmannova, Katarina; Kilmer, Jason R.; Fleming, Charles B.; Hultgren, Brittney A.; Hurvitz, Philip M.; Dilley, Julia A.; Larimer, Mary E. (2022). Associations of Cannabis Retail Outlet Availability and Neighborhood Disadvantage with Cannabis Use and Related Risk Factors Among Young Adults in Washington State. Drug & Alcohol Dependence, 232.

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Abstract

Background: This study examined associations of local cannabis retail outlet availability and neighborhood disadvantage with cannabis use and related risk factors among young adults. Methods: Data were from annual cross-sectional surveys administered from 2015 to 2019 to individuals ages 18-25 residing in Washington State (N = 10,009). As outcomes, this study assessed self-reported cannabis use at different margins/frequencies (any past year, at least monthly, at least weekly, at least daily) and perceived ease of access to cannabis and acceptability of cannabis use in the community. Cannabis retail outlet availability was defined as the presence of at least one retail outlet within a 1-kilometer road network buffer of one's residence. Sensitivity analyses explored four other spatial metrics to define outlet availability (any outlet within 0.5-km, 2-km, and the census tract; and census tract density per 1000 residents). Census tract level disadvantage was a composite of five US census variables. Results: Adjusting for individual- and area-level covariates, living within 1-kilometer of at least one cannabis retail outlet was statistically significantly associated with any past year and at least monthly cannabis use as well as high perceived access to cannabis. Results using a 2-km buffer and census tract-level metrics for retail outlet availability showed similar findings. Neighborhood disadvantage was statistically significantly associated with at least weekly and at least daily cannabis use and with greater perceived acceptability of cannabis use. Conclusions: Results may have implications for regulatory and prevention strategies to reduce the population burden of cannabis use and related harms.

Keywords

Outlet Stores; Young Adults; Neighborhoods; Older People; Sensitivity Analysis; Washington (state); Cannabis; Cannabis Retail Outlets; Neighborhood Disadvantage; Alcohol-use; Marijuana Use; Density; Proximity; Health; Norms

Home Versus Nonhome Neighborhood: Quantifying Differences in Exposure to the Built Environment

Hurvitz, Philip M.; Moudon, Anne Vernez. (2012). Home Versus Nonhome Neighborhood: Quantifying Differences in Exposure to the Built Environment. American Journal Of Preventive Medicine, 42(4), 411 – 417.

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Abstract

Background: Built environment and health research have focused on characteristics of home neighborhoods, whereas overall environmental exposures occur over larger spatial ranges. Purpose: Differences in built environment characteristics were analyzed for home and nonhome locations using GPS data. Methods: GPS data collected in 2007-2008 were analyzed for 41 subjects in the Seattle area in 2010. Environmental characteristics for 3.8 million locations were measured using novel GIS data sets called SmartMaps, representing spatially continuous values of local built environment variables in the domains of neighborhood composition, utilitarian destinations, transportation infrastructure, and traffic conditions. Using bootstrap sampling, CIs were estimated for differences in built environment values for home (1666 m) GPS locations. Results: Home and nonhome built environment values were significantly different for more than 90% of variables across subjects (p < 0.001). Only 51% of subjects had higher counts of supermarkets near than away from home. Different measures of neighborhood parks yielded varying results. Conclusions: SmartMaps helped measure local built environment characteristics for a large set of GPS locations. Most subjects had significantly different home and nonhome built environment exposures. Considering the full range of individuals' environmental exposures may improve understanding of effects of the built environment on behavior and health outcomes. (Am J Prev Med 2012;42(4):411-417) (C) 2012 American Journal of Preventive Medicine

Keywords

Built Environment; Public Health Research; Individual Differences; Neighborhoods; Environmental Exposure; Health Of Homeless People; Global Positioning System; Data Analysis; Quantitative Research; Seattle (wash.); Washington (state); Geographic Information-systems; Global Positioning Systems; Physical-activity; Health Research; Urban Form; Land-use; Associations; Transportation; Availability; Walkability

Relation between Higher Physical Activity and Public Transit Use

Saelens, Brian E.; Moudon, Anne Vernez; Kang, Bumjoon; Hurvitz, Philip M.; Zhou, Chuan. (2014). Relation between Higher Physical Activity and Public Transit Use. American Journal Of Public Health, 104(5), 854 – 859.

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Abstract

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.

Keywords

Transportation; Analysis Of Covariance; Analysis Of Variance; Chi-squared Test; Comparative Studies; Confidence Intervals; Geographic Information Systems; Research Funding; Statistics; Walking; Data Analysis; Accelerometry; Cross-sectional Method; Exercise Intensity; Physical Activity; Diary (literary Form); Descriptive Statistics; Washington (state); Work; Car; Impact

Physical Activity and the Built Environment in Residential Neighborhoods of Seoul and Seattle: An Empirical Study Based on Housewives’ GPS Walking Data and Travel Diaries

Park, Sohyun; Choi, Yeemyung; Seo, Hanlim; Moudon, Anne Vernez; Bae, C. -h. Christine; Baek, So-ra. (2016). Physical Activity and the Built Environment in Residential Neighborhoods of Seoul and Seattle: An Empirical Study Based on Housewives’ GPS Walking Data and Travel Diaries. Journal Of Asian Architecture And Building Engineering, 15(3), 471 – 478.

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Abstract

This paper is based on a collaborative pilot-study to ascertain the characteristic walking patterns and neighborhood features in residential areas of Seoul, Korea and Seattle, USA. As for sample sites, four case neighborhoods were selected: two from Seoul and two from in and outside of the Seattle-Shoreline areas. As for participants, thirty Korean housewives in Seoul and thirty Korean-American housewives in the Seattle area were selected respectively, and their socio-demographic characteristics, GPS records, and travel diary data for seven days were collected and analyzed. Considering the typical rainy seasons in the two cities, data collections, including the physical activity assessment by GPS devices, were carried out from May to June and from September to October in Seoul, and from July to October in Seattle during the year 2010. Noteworthy research findings include the following: Korean participants in Seoul walk about 2.6 km on average per day, while Korean-American participants in Seattle walk about 400m on average per day. In the case sites of Seoul, 75% of grocery shopping activities happen within the neighborhood by walking, while only 17% of those activities on foot happen in the case sites of Seattle. As for the most walking activity, about 70% of total walking amounts are related to utilitarian walking in Seoul sites, while 50% of total walking are related to recreational walking in Seattle sites. Recreational walking and utilitarian walking occur separately in Seattle sites, while the two walking types are often combined in Seoul sites, which also contribute to more walking amounts and farther walking distances in Seoul sites. This paper empirically confirms the widely held assumptions in part that residents in Seoul, a relatively high-density and high mixed-use city, walk more than those in Seattle, a relatively low-density and low mixed-use city. This paper also recognizes that in the case of both cities, more walking activities occur in the neighborhood built environment, where finely-grained street networks, small lots and blocks, various pedestrian destinations, public transit access, etc are provided in close connection. The amount and frequency of walking activities, as well as the fineness of neighborhood features, however, are remarkably different in the two cities, whose implications deserve in-depth exploration in further studies.

Keywords

Urban Design; Physical Activity; Neighborhood Environment; Objective Measures; Gps Walking Data; International Comparative Study