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2022 ◽  
Vol 17 (s1) ◽  
Author(s):  
Yucheng Wang ◽  
Thomas C. Tsai ◽  
Dustin Duncan ◽  
John Ji

With people restricted to their residences, neighbourhood characteristics may affect behaviour and risk of coronavirus disease 2019 (COVID-19) infection. We aimed to analyse whether neighbourhoods with higher walkability, public transit, biking services and higher socio-economic status were associated with lower COVID-19 infection during the peak of the COVID-19 pandemic in Massachusetts. We used Walk Score®, Bike Score®, and Transit Score® indices to assess the walkability and transportation of 72 cities in Massachusetts, USA based on availability of data and collected the total COVID-19 case numbers of each city up to 10 April 2021. We used univariate and multivariate linear models to analyse the effects of these scores on COVID-19 cases per 100,000 in each city, adjusting for demographic covariates and all covariates, respectively. In the 72 cities studied, the average Walk Score, Transit Score and Bike Score was 48.7, 36.5 and 44.1, respectively, with a total of 426,182 COVID-19 cases. Higher Walk Score, Transit Score, and Bike Score rankings were negatively associated with COVID-19 cases per 100,000 persons (<0.05). Cities with a higher proportion of Hispanic population and a lower median household income were associated with more COVID-19 cases per 100,000 (P<0.05). Higher Walk Score, Transit Score and Bike Score were shown to be protective against COVID-19 transmission, while socio-demographic factors were associated with COVID-19 infection. Understanding the complex relationship of how the structure of the urban environment may constrain commuting patterns for residents and essential workers during COVID-19 would offer potential insights on future pandemic preparedness and response.


2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Chien-Yu Lin ◽  
Mohammad Javad Koohsari ◽  
Yung Liao ◽  
Kaori Ishii ◽  
Ai Shibata ◽  
...  

AbstractWorkplace settings—both internal and external—can influence how workers are physically active or sedentary. Although research has identified some indoor environmental attributes associated with sitting at work, few studies have examined associations of workplace neighbourhood built-environment attributes with workplace sitting time. We examined the cross-sectional associations of perceived and objective workplace neighbourhood built-environment attributes with sitting time at work and for transport among desk-based workers in Japan. Data were collected from a nationwide online survey. The Abbreviated Neighborhood Environment Walkability Scale (n = 2137) and Walk Score® (for a subsample of participants; n = 1163) were used to assess perceived and objective built-environment attributes of workplace neighbourhoods. Self-reported daily average sitting time at work, in cars and in public transport was measured using a Japanese validated questionnaire. Linear regression models estimated the associations of workplace neighbourhood built-environment attributes with sitting time. All perceived workplace neighbourhood built-environment attributes were positively correlated with Walk Score®. However, statistically significant associations with Walk Score® were found for sitting for transport but not for sitting at work. Workers who perceived their workplace neighbourhoods to be more walkable reported a longer time sitting at work and in public transport but a shorter sitting time in cars. Our findings suggest that walkable workplace neighbourhoods may discourage longer car use but have workplaces where workers spend a long time sitting at work. The latter finding further suggests that there may be missed opportunities for desk-based workers to reduce sitting time. Future workplace interventions to reduce sitting time may be developed, taking advantage of the opportunities to take time away from work in workplace neighbourhoods.


2021 ◽  
Vol 21 ◽  
pp. 101005
Author(s):  
Lawrence D. Frank ◽  
Bruce S. Appleyard ◽  
Jared M. Ulmer ◽  
James E. Chapman ◽  
Eric H. Fox
Keyword(s):  

2021 ◽  
Vol 55 (1) ◽  
pp. 1-2
Author(s):  
José Devezas

Entity-oriented search has revolutionized search engines. In the era of Google Knowledge Graph and Microsoft Satori, users demand an effortless process of search. Whether they express an information need through a keyword query, expecting documents and entities, or through a clicked entity, expecting related entities, there is an inherent need for the combination of corpora and knowledge bases to obtain an answer. Such integration frequently relies on independent signals extracted from inverted indexes, and from quad indexes indirectly accessed through queries to a triplestore. However, relying on two separate representation models inhibits the effective cross-referencing of information, discarding otherwise available relations that could lead to a better ranking. Moreover, different retrieval tasks often demand separate implementations, although the problem is, at its core, the same. With the goal of harnessing all available information to optimize retrieval, we explore joint representation models of documents and entities, while taking a step towards the definition of a more general retrieval approach. Specifically, we propose that graphs should be used to incorporate explicit and implicit information derived from the relations between text found in corpora and entities found in knowledge bases. We also take advantage of this framework to elaborate a general model for entity-oriented search, proposing a universal ranking function for the tasks of ad hoc document retrieval (leveraging entities), ad hoc entity retrieval, and entity list completion. At a conceptual stage, we begin by proposing the graph-of-entity, based on the relations between combinations of term and entity nodes. We introduce the entity weight as the corresponding ranking function, relying on the idea of seed nodes for representing the query, either directly through term nodes, or based on the expansion to adjacent entity nodes. The score is computed based on a series of geodesic distances to the remaining nodes, providing a ranking for the documents (or entities) in the graph. In order to improve on the low scalability of the graph-of-entity, we then redesigned this model in a way that reduced the number of edges in relation to the number of nodes, by relying on the hypergraph data structure. The resulting model, which we called hypergraph-of-entity, is the main contribution of this thesis. The obtained reduction was achieved by replacing binary edges with n -ary relations based on sets of nodes and entities (undirected document hyperedges), sets of entities (undirected hyperedges, either based on cooccurrence or a grouping by semantic subject), and pairs of a set of terms and a set of one entity (directed hyperedges, mapping text to an object). We introduce the random walk score as the corresponding ranking function, relying on the same idea of seed nodes, similar to the entity weight in the graph-of-entity. Scoring based on this function is highly reliant on the structure of the hypergraph, which we call representation-driven retrieval. As such, we explore several extensions of the hypergraph-of-entity, including relations of synonymy, or contextual similarity, as well as different weighting functions per node and hyperedge type. We also propose TF-bins as a discretization for representing term frequency in the hypergraph-of-entity. For the random walk score, we propose and explore several parameters, including length and repeats, with or without seed node expansion, direction, or weights, and with or without a certain degree of node and/or hyperedge fatigue, a concept that we also propose. For evaluation, we took advantage of TREC 2017 OpenSearch track, which relied on an online evaluation process based on the Living Labs API, and we also participated in TREC 2018 Common Core track, which was based on the newly introduced TREC Washington Post Corpus. Our main experiments were supported on the INEX 2009 Wikipedia collection, which proved to be a fundamental test collection for assessing retrieval effectiveness across multiple tasks. At first, our experiments solely focused on ad hoc document retrieval, ensuring that the model performed adequately for a classical task. We then expanded the work to cover all three entity-oriented search tasks. Results supported the viability of a general retrieval model, opening novel challenges in information retrieval, and proposing a new path towards generality in this area.


2021 ◽  
Author(s):  
Anna Racovali

This paper explores alternative methods in which an urban walkability score may be determined. Walk Score is a popular urban accessibility index which determines the walkability of a neighbourhood or specific address by measuring the proximity of the location to nearby services and amenities. Traditional walkability scores, such as Walk Score, are limited because of their inability to vary the importance of being in proximity to certain services and amenities. Multi-criteria decision analysis (MCDA) techniques, specifically simple additive weighting (SAW) and ordered weighted averaging (OWA), provide a geographic approach to determining the walkability of an area and allow users to determine the weights of importance of all services and amenities. MCDA-based walkability scores were calculated and compared to one another and to Walk Score. Both SAW and OWA methods created similar walkability indexes for dissemination areas throughout Toronto. However, the MCDA results could not be directly compared to Walk Score, as there was a significant difference between the value ranges of the scores. Thus, the 140 Toronto neighbourhoods were ranked from most to least walkable for the MCDA-based methods and Walk Score, based upon each method’s respective scores. Upon comparison, it was evident that both Walk Score’s methodology and the MCDA-based methodologies resulted in similar outcomes of walkability rankings for Toronto neighbourhoods


2021 ◽  
Author(s):  
Anna Racovali

This paper explores alternative methods in which an urban walkability score may be determined. Walk Score is a popular urban accessibility index which determines the walkability of a neighbourhood or specific address by measuring the proximity of the location to nearby services and amenities. Traditional walkability scores, such as Walk Score, are limited because of their inability to vary the importance of being in proximity to certain services and amenities. Multi-criteria decision analysis (MCDA) techniques, specifically simple additive weighting (SAW) and ordered weighted averaging (OWA), provide a geographic approach to determining the walkability of an area and allow users to determine the weights of importance of all services and amenities. MCDA-based walkability scores were calculated and compared to one another and to Walk Score. Both SAW and OWA methods created similar walkability indexes for dissemination areas throughout Toronto. However, the MCDA results could not be directly compared to Walk Score, as there was a significant difference between the value ranges of the scores. Thus, the 140 Toronto neighbourhoods were ranked from most to least walkable for the MCDA-based methods and Walk Score, based upon each method’s respective scores. Upon comparison, it was evident that both Walk Score’s methodology and the MCDA-based methodologies resulted in similar outcomes of walkability rankings for Toronto neighbourhoods


2021 ◽  
Vol 13 (10) ◽  
pp. 5632
Author(s):  
Seigo Mitsutake ◽  
Tatsuro Ishizaki ◽  
Yuri Yokoyama ◽  
Mariko Nishi ◽  
Mohammad Javad Koohsari ◽  
...  

Our study examined the associations between neighborhood walkability, frailty, and the incidence of long-term care insurance (LTCI) service needs using a prospective cohort survey in a suburban town in Japan. The final sample for analyses comprised 2867 community-dwelling older adults (mean age: 73.0 years). Neighborhood walkability was measured using the Walk Score®. A total of 387 participants (13.5%) exhibited frailty. The odds of frailty, adjusted for the covariates (sex, age, educational status, marital status, residential status, employment status, subjective economic status) among participants who lived in somewhat walkable/very walkable areas, was 0.750 (95% Confidence Interval, CI: 0.597–0.943) versus those who lived in car-dependent areas. During the 23-month follow-up, 102 participants needed LTCI services (19.0 per 1000 person-years), 41 of whom (21.0 per 1000 person-years) lived in car-dependent areas, and 61 of whom (17.9 per 1000 person-years) lived in somewhat walkable/very walkable areas. As compared with participants who lived in car-dependent areas, the incidence of LTCI service needs was not significantly lower than that of those who lived in somewhat walkable/very walkable areas. Walk Score® can provide the critical information for the strategies to improve walkability and prevent older adults’ frailty in less walkable areas, contributing to achieving the United Nation’s Sustainable Development Goals (SDGs).


2021 ◽  
Author(s):  
Maria Chiara Gallotta ◽  
Giovanna Zimatore ◽  
Lavinia Falcioni ◽  
Silvia Migliaccio ◽  
Massimo Lanza ◽  
...  

Abstract Background: The prevalence of overweight and obesity in childhood is increasing at an alarming rate worldwide, particularly in industrialized countries. Walkability measurements can be collected using the free open software Walk Score® that permit the measure of estimating neighbourhood walkability in many geographic locations. This study was aimed i) to investigate whether differences between rural and urban settings in the North, Centre and South of Italy could influence body-weight status, motor competence and physical activity (PA) level in school-age children; ii) to analyse the walkability of different school areas, and iii) to examine the relationship of motor competence, PA level, geographical areas, living setting, and neighbourhood walkability with children’s body-weight status. Methods: We assessed anthropometric parameters, gross motor coordination and PA level in 1549 children aged between 8 and 13 year. Three geographical areas (North, Centre, South of Italy), two settings (urban and rural) and neighbourhoods’ walkability (Walk Score®) were considered in the analysis. Results: The prevalence of overweight and obesity was 22.0% and 9.9%, respectively; 47.9% of the total sample showed motor impairments and 29.0% was inactive. Central children had higher BMI than Northern and Southern children. Northern children showed the highest MQ and PA level, followed by Southern and Central children. Children from the South of Italy attended schools located in neighbourhoods with the highest Walk Score®. Urban children attended schools located in neighbourhoods with a higher Walk Score® than rural children. Lower MQ, lower PA level, living in rural setting and in a car-dependent neighbourhood were associated with a higher relative risk for obesity. Being a girl was associated with a lower relative risk for obesity.Conclusions: The alarming high percentage of overweight and obesity in children as well as motor coordination impairments revealed the urgent need of targeted PA interventions in paediatric population.


Author(s):  
Faysal Kabir Shuvo ◽  
Soumya Mazumdar ◽  
S. M. Labib

Background: The existing environment literature separately emphasizes the importance of neighborhood walkability and greenness in enhancing health and wellbeing. Thus, a desirable neighborhood should ideally be green and walkable at the same time. Yet, limited research exists on the prevalence of such “sweet spot” neighborhoods. We sought to investigate this question in the context of a large metropolitan city (i.e., Sydney) in Australia. Methods: Using suburb level normalized difference vegetative index (NDVI), percentage urban greenspace, Walk Score® (Walk Score, Seattle, WA, USA), and other data, we explored the global and local relationships of neighborhood-level greenness, urban green space (percent park area) with walkability applying both non-spatial and spatial modeling. Results: We found an overall negative relationship between walkability and greenness (measured as NDVI). Most neighborhoods (represented by suburbs) in Sydney are either walkable or green, but not both. Sweet spot neighborhoods that did exist were green but only somewhat walkable. In addition, many neighborhoods were both less green and somewhat walkable. Moreover, we observed a significant positive relationship between percentage park area and walkability. These results indicate walkability and greenness have inverse and, at best, mixed associations in the Sydney metropolitan area. Conclusions: Our analysis indicates an overall negative relationship between greenness and walkability, with significant local variability. With ongoing efforts towards greening Sydney and improving walkability, more neighborhoods may eventually be transformed into becoming greener and more walkable.


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