scholarly journals Interdependencies of Urban Behavioral Dynamics Whilst COVID-19 Spread

2021 ◽  
Vol 13 (17) ◽  
pp. 9910
Author(s):  
Sanghyeon Ko ◽  
Dongwoo Lee
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
Analysis Data ◽  
Transportation Systems ◽  
Transportation Policy ◽  
Spatial Behavior ◽  
County Level ◽  
Learning Approaches ◽  
Population Mobility ◽  
Novel Coronavirus

The outbreak of novel coronavirus disease 2019 (COVID-19) caused many consequences in almost all aspects of our lives. The pandemic dramatically changes people’s behavior in urban areas and transportation systems. Many studies have attempted to analyze spatial behavior and to present analysis data visually in the process of spreading COVID-19 and provided limited temporal and geographical perspectives. In this article, the behavioral changes in urban areas and transportation systems were analyzed throughout the U.S.A. while the COVID-19 spread over 2020. Specifically, assuming the characteristics are not repetitive over time, temporal phases were proposed where spikes or surges of confirmed cases are noticed. The interdependencies between population, mobility, and additional behavioral data were explored at the county level by adopting the machine learning approaches. As a result, interdependencies with the COVID-19 cases were identified differently by phase. It appeared to have a solid relationship with population size at all phases. Furthermore, it revealed racial characteristics, residential types, and vehicle mile traveled ratio in the urban and rural areas had a relationship with confirmed cases with different importance by phase. Although other short-term analyses were also conducted in terms of the COVID-19, this article is considered more legitimate as it provides dynamic relationships of urban elements by Phase at the county level. Moreover, it is expected to be encouraging and beneficial in terms of phase-driven transportation policy preparedness against a possible forthcoming pandemic crisis.

scholarly journals Healthy lifestyle behaviours and all-cause and cardiovascular mortality among 0.9 million Chinese adults

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Xingyi Zhang ◽  
Jiapeng Lu ◽  
Chaoqun Wu ◽  
Jianlan Cui ◽  
Yue Wu ◽  
...  
Keyword(s):  
Alcohol Use ◽  
Cardiovascular Mortality ◽  
Rural Areas ◽  
Urban Areas ◽  
Healthy Lifestyle ◽  
Healthy Diet ◽  
County Level ◽  
Negatively Associated ◽  
Lifestyle Behaviours ◽  
Average Annual Temperature

Abstract Background Healthy lifestyle behaviours are effective means to reduce the burden of diseases. This study was aimed to fill the knowledge gaps on the distribution, associated factors, and potential health benefits on mortality of four healthy lifestyle behaviours in China. Methods During 2015–2019, participants aged 35–75 years from 31 provinces were recruited by the China PEACE Million Persons Project. Four healthy lifestyle behaviours were investigated in our study, including non-smoking, none or moderate alcohol use, sufficient leisure time physical activity (LTPA), and healthy diet. Results Among 903,499 participants, 74.1% were non-smokers, 96.0% had none or moderate alcohol use, 23.6% had sufficient LTPA, 11.1% had healthy diet, and only 2.8% had all the four healthy lifestyle behaviours. The adherence varied across seven regions; the highest median of county-level adherence to all the four healthy lifestyle behaviours was in North China (3.3%) while the lowest in the Southwest (0.8%) (p < 0.05). Participants who were female, elder, non-farmers, urban residents, with higher income or education, hypertensive or diabetic, or with a cardiovascular disease (CVD) history were more likely to adhere to all the four healthy lifestyle behaviours (p < 0.001). County-level per capital Gross Domestic Product (GDP) was positively associated with sufficient LTPA (p < 0.05 for both rural and urban areas) and healthy diet (p < 0.01 for urban areas), while negatively associated with none or moderate alcohol use (p < 0.01 for rural areas). Average annual temperature was negatively associated with none or moderate alcohol use (p < 0.05 for rural areas) and healthy diet (p < 0.001 for rural areas). Those adhering to all the four healthy lifestyle behaviours had lower risks of all-cause mortality (HR 0.64 [95% CI: 0.52, 0.79]) and cardiovascular mortality (HR 0.53 [0.37, 0.76]) after a median follow-up of 2.4 years. Conclusions Adherence to healthy lifestyle behaviours in China was far from ideal. Targeted health promotion strategies were urgently needed.

scholarly journals Passive Wi-Fi monitoring in the wild: a long-term study across multiple location typologies

2020 ◽  
Author(s):  
Miguel Ribeiro ◽  
Nuno Nunes ◽  
Valentina Nisi ◽  
Johannes Schöning
Keyword(s):  
Public Transportation ◽  
Rural Areas ◽  
Urban Areas ◽  
Large Scale ◽  
Tracking System ◽  
Human Mobility ◽  
Transportation Systems ◽  
Mobility Patterns ◽  
Systematic Analysis ◽  
Long Term Study

Abstract In this paper, we present a systematic analysis of large-scale human mobility patterns obtained from a passive Wi-Fi tracking system, deployed across different location typologies. We have deployed a system to cover urban areas served by public transportation systems as well as very isolated and rural areas. Over 4 years, we collected 572 million data points from a total of 82 routers covering an area of 2.8 km2. In this paper we provide a systematic analysis of the data and discuss how our low-cost approach can be used to help communities and policymakers to make decisions to improve people’s mobility at high temporal and spatial resolution by inferring presence characteristics against several sources of ground truth. Also, we present an automatic classification technique that can identify location types based on collected data.

scholarly journals Global combustion sources of organic aerosols: model comparison with 84 AMS factor-analysis data sets

2016 ◽  
Vol 16 (14) ◽  
pp. 8939-8962 ◽  
Author(s):  
Alexandra P. Tsimpidi ◽  
Vlassis A. Karydis ◽  
Spyros N. Pandis ◽  
Jos Lelieveld
Keyword(s):  
Factor Analysis ◽  
Atmospheric Chemistry ◽  
Rural Areas ◽  
Urban Areas ◽  
Model Comparison ◽  
Climate Model ◽  
Organic Aerosol ◽  
Analysis Data ◽  
Chemical Transformations ◽  
Data Set

Abstract. Emissions of organic compounds from biomass, biofuel, and fossil fuel combustion strongly influence the global atmospheric aerosol load. Some of the organics are directly released as primary organic aerosol (POA). Most are emitted in the gas phase and undergo chemical transformations (i.e., oxidation by hydroxyl radical) and form secondary organic aerosol (SOA). In this work we use the global chemistry climate model ECHAM/MESSy Atmospheric Chemistry (EMAC) with a computationally efficient module for the description of organic aerosol (OA) composition and evolution in the atmosphere (ORACLE). The tropospheric burden of open biomass and anthropogenic (fossil and biofuel) combustion particles is estimated to be 0.59 and 0.63 Tg, respectively, accounting for about 30 and 32 % of the total tropospheric OA load. About 30 % of the open biomass burning and 10 % of the anthropogenic combustion aerosols originate from direct particle emissions, whereas the rest is formed in the atmosphere. A comprehensive data set of aerosol mass spectrometer (AMS) measurements along with factor-analysis results from 84 field campaigns across the Northern Hemisphere are used to evaluate the model results. Both the AMS observations and the model results suggest that over urban areas both POA (25–40 %) and SOA (60–75 %) contribute substantially to the overall OA mass, whereas further downwind and in rural areas the POA concentrations decrease substantially and SOA dominates (80–85 %). EMAC does a reasonable job in reproducing POA and SOA levels during most of the year. However, it tends to underpredict POA and SOA concentrations during winter indicating that the model misses wintertime sources of OA (e.g., residential biofuel use) and SOA formation pathways (e.g., multiphase oxidation).

scholarly journals Association between county-level risk groups and COVID-19 outcomes in the United States: a socioecological study

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Sadiya S. Khan ◽  
Amy E. Krefman ◽  
Megan E. McCabe ◽  
Lucia C. Petito ◽  
Xiaoyun Yang ◽  
...  
Keyword(s):  
United States ◽  
Rural Areas ◽  
Urban Areas ◽  
Latent Class ◽  
Communicable Disease ◽  
Local Level ◽  
Healthcare Access ◽  
The United States ◽  
County Level ◽  
Health Factors

Abstract Background Geographic heterogeneity in COVID-19 outcomes in the United States is well-documented and has been linked with factors at the county level, including sociodemographic and health factors. Whether an integrated measure of place-based risk can classify counties at high risk for COVID-19 outcomes is not known. Methods We conducted an ecological nationwide analysis of 2,701 US counties from 1/21/20 to 2/17/21. County-level characteristics across multiple domains, including demographic, socioeconomic, healthcare access, physical environment, and health factor prevalence were harmonized and linked from a variety of sources. We performed latent class analysis to identify distinct groups of counties based on multiple sociodemographic, health, and environmental domains and examined the association with COVID-19 cases and deaths per 100,000 population. Results Analysis of 25.9 million COVID-19 cases and 481,238 COVID-19 deaths revealed large between-county differences with widespread geographic dispersion, with the gap in cumulative cases and death rates between counties in the 90th and 10th percentile of 6,581 and 291 per 100,000, respectively. Counties from rural areas tended to cluster together compared with urban areas and were further stratified by social determinants of health factors that reflected high and low social vulnerability. Highest rates of cumulative COVID-19 cases (9,557 [2,520]) and deaths (210 [97]) per 100,000 occurred in the cluster comprised of rural disadvantaged counties. Conclusions County-level COVID-19 cases and deaths had substantial disparities with heterogeneous geographic spread across the US. The approach to county-level risk characterization used in this study has the potential to provide novel insights into communicable disease patterns and disparities at the local level.

scholarly journals Impact of Culture on Rural Built Environment

2019 ◽  
Vol 8 (10S) ◽  
pp. 78-84
Keyword(s):  
Built Environment ◽  
Natural Resources ◽  
Rural Areas ◽  
Human Activities ◽  
Urban Areas ◽  
Large Scale ◽  
Future Generation ◽  
Transportation Systems ◽  
Rural Regions ◽  
The Common

The term “built environment” refers to the human made or modified physical surroundings in which people live, work and play. These places and spaces include our homes, communities, schools, workplaces, parks/recreations areas, business areas and transportation systems, and vary in size from large-scale urban areas to smaller rural developments. Based on human activities, the environment was created to obtain the basic needs of people. The regular human activities for many generations to prepare their needs are considered as culture. Hence based on culture, the environment was built and maintained for future generation. Regions are separated into two types based on production occurs in rural area and trading developed in urban. In olden days, most of the places are rural because of the undevelopment in transport system. The activities involved in preparing food, shelter and other needs are the common factors to build rural environment. Natural resources are the basic factor that decides the build environment and culture of human in rural regions. By analyzing the natural resources, the cultural impacts are determined based on building environment in rural areas

scholarly journals Global combustion sources of organic aerosols: Model comparison with 84 AMS factor analysis data sets

2016 ◽  
Author(s):  
A. P. Tsimpidi ◽  
V. A. Karydis ◽  
S. N. Pandis ◽  
J. Lelieveld
Keyword(s):  
Factor Analysis ◽  
Rural Areas ◽  
Urban Areas ◽  
Model Comparison ◽  
Climate Model ◽  
Organic Aerosol ◽  
Analysis Data ◽  
Chemical Transformations ◽  
Computationally Efficient ◽  
Formation Pathway

Abstract. Emissions of organic compounds from biomass, biofuel and fossil fuel combustion strongly influence the global atmospheric aerosol load. Some of the organics are directly released as primary organic aerosol (POA). Most are emitted in the gas phase and undergo chemical transformations (i.e., oxidation by hydroxyl radical) and form secondary organic aerosol (SOA). In this work we use the global chemistry climate model EMAC with a computationally efficient module for the description of organic aerosol (OA) composition and evolution in the atmosphere (ORACLE). The tropospheric burden of open biomass and anthropogenic (fossil and biofuel) combustion particles is estimated to be 0.59 Tg and 0.63 Tg, respectively, accounting for about 30 % and 32 % of the total tropospheric OA load. About 30 % of the open biomass burning and 10 % of the anthropogenic combustion aerosols originate from direct particle emissions while the rest is formed in the atmosphere. A comprehensive dataset of aerosol mass spectrometer (AMS) measurements along with factor-analysis results from 84 field campaigns across the Northern Hemisphere are used to evaluate the model results. Both the AMS observations and the model results suggest that over urban areas both POA (25–40 %) and SOA (60–75 %) contribute substantially to the overall OA mass while further downwind and in rural areas the POA concentrations decrease substantially and SOA dominates (80–85 %). EMAC does a reasonable job in reproducing POA and SOA levels during most of the year. However, it tends to underpredict POA and SOA concentrations during winter indicating that the model misses a wintertime source of OA (e.g., residential biofuel use) and a SOA formation pathway (e.g., multiphase oxidation).

scholarly journals The Relationship Between Employment Opportunity and the Standard of Living as a Result of Population Mobility

2017 ◽  
Vol 1 (4) ◽  
pp. 537
Author(s):  
Rahma Mutiya Sari ◽  
Ekawati Sri Wahyuni ◽  
Dina Nurdinawati
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
Employment Opportunity ◽  
Standard Of Living ◽  
Research Use ◽  
Population Mobility ◽  
Job Status ◽  
Job Opportunities ◽  
The Relationship ◽  
Quantitative And Qualitative Methods

Indonesian labor  market continues to develop, it is proved by the increase in the number of jobs and the growth of open unemployment. Job opportunities not only in urban areas but also in the rural areas. Employment opportunities in rural areas could be open due to many  factors, one of them  for their arrivals to the region. The purpose of this research is to analyze the relationship between  employment opportunity with the standard of living because of population mobility.  Employment opportunity divided in to job sectors, job types, and job status. The standard of living divided in to primer, secunder, and tertiary. This research use quantitative and qualitative methods. The result shows that  employment opportunity open because the population mobility become an opportunity for people to improve the standard of living.  Keyword: Employment Opportunity, population mobility, the standard of living=============================================== ABSTRAKPasar tenaga kerja Indonesia terus mengalami perkembangan, hal ini terbukti dengan peningkatan jumlah pekerjaan dan penurunan angka pengangguran terbuka. Kesempatan kerja teryata tidak hanya terjadi di perkotaan, tetapi juga terjadi di pedesaan. Kesempatan kerja yang terbuka di pedesaan dapat terjadi karena banyak faktor, salah satunya karena adanya pendatang ke suatu wilayah. Tujuan dari penelitian ini adalah untuk menganalisis hubungan antara kesempatan kerja dengan taraf hidup masyarakat karena adanya pendatang. Kesempatan kerta terbagi menjadi tiga, sektor/lapangan pekerjaan, jenis/jabatan pekerjaan, dan status pekerjaan. Taraf  hidup telah dikelompokkan menjadi taraf hidup primer, sekunder, dan tersier. Penelitian ini dilakukan dengan pendekatan kuantitatif dan kualitatif. Hasil dari penelitian ini menunjukkan bahwa terbukanya kesempatan kerja karena adanya pendatang menjadi peluang bagi masyarakat guna meningkatkan taraf hidup.Kata kunci: gerak penduduk, kesempatan kerja, taraf hidup

scholarly journals Connections between migrations and commuting in Vojvodina

2012 ◽  
pp. 615-624
Author(s):  
Vesna Lukic
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
Labour Force ◽  
Significant Proportion ◽  
Correlation Method ◽  
Population Mobility ◽  
Rural Settlements ◽  
Economic Activities ◽  
Commuting Flows ◽  
The Relationship

Various forms of population mobility are usually studied independently and thus lacking information if there is a connection between migration processes and if so, whether it is positive or negative. Based on the correlation method, this paper presents a study of the relationship between migration and commuting by the example of Vojvodina. Population and economic activities in Vojvodina are unevenly distributed and mainly concentrated to certain areas. Considering that the majority of rural population is no longer engaged in agriculture and that there are not enough local jobs in rural areas, numerous rural residents commute to work to cities. So, the majority of commuting flows in Vojvodina are flows of labour force from rural to urban areas. Results of statistical analysis, based on data about migrations to rural settlements of Vojvodina and data about divergent commuters, have shown a positive correlation between the two types of population mobility for all observed settlements (defined as addition), and that a significant proportion of migration variations can be explained by variations in the scope of commuting flows.

Spatial Assessment of Commuting Patterns in India's National Capital Region

2019 ◽  
Vol 45 (4) ◽  
pp. 507-522 ◽  
Author(s):  
Manisha Jain ◽  
Robert Hecht
Keyword(s):  
Spatial Data ◽  
Public Transport ◽  
Rural Areas ◽  
Urban Areas ◽  
Transportation Policy ◽  
National Capital Region ◽  
Rail Network ◽  
Rural And Urban Areas ◽  
Commuting Distances ◽  
Commuting Patterns

Contemporary urbanization as experienced in India is characterized by urban sprawl, which increases commuting distances and promotes private individual transport. This article takes India's largest region as a case study and uses data from the Census of India on commuting, the population, socio-economic and infrastructural factors as well as spatial data on urban and rural administrative boundaries to understand commuting patterns. This article has two major objectives: first, to map spatially commuting patterns (distances to work and modes of travel); second, to estimate the effect of people-based (minorities, illiteracy rate, household facilities) variables and place-based (basic amenities, road and rail network densities, etc.) variables on commuting. The research findings are as follows: short trips are prevalent in urban areas, while intermediate and long trips are prevalent in rural areas. Short trips are common in areas with a high share of minorities as well as illiteracy rates. Long trips are undertaken by public transport such as trains and buses, intermediate trips by two-wheelers and buses, and short trips on foot and by bicycle. Areas with high prevalence of long trips have a better provision of basic amenities. The paper recommends the following measures to reduce motorization and long commuting distances: (i) government initiatives to reduce private transport and promote transitbased transportation; (ii) the integration of rural and urban areas through public transport; (iii) the establishment of a unified regional transportation authority to integrate regional transportation; and (iv) the introduction of subsidies to reduce private transportation and the implementation of transportation policy proposals.

Abstract 17355: Rural-Urban Differences in the Association of Change in County-Level Physician Density With Cardiovascular Disease Mortality

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Jacob B Pierce ◽  
Lucia Petito ◽  
Sadiya Khan
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
Primary Care Physicians ◽  
Health Resources ◽  
County Level ◽  
Physician Density ◽  
Rural Counties ◽  
Rural And Urban ◽  
Icd 10 ◽  
Cvd Mortality

Introduction: Rural areas face greater challenges in access to cardiovascular care related to lower physician density compared with urban areas. However, whether the association between recent changes in primary care physicians (PCP) and cardiologist density and contemporary CVD mortality differs by county-level urbanization is unknown. Hypothesis: Decreases in county-level physician density per capita between 2011-2017 will be associated with higher CVD mortality in rural compared with urban counties in 2017-18 in the US. Methods: Death certificates from CDC WONDER with CVD (ICD-10 codes I00-09, I11, I13, I20-51) listed as the underlying cause of death between 2017-2018 were queried to calculate age-adjusted mortality rate (AAMR) at the county-level. Changes in county-level PCPs (2011-2017) and cardiologists (2011-2017) per 100,000 population were derived from the Area Health Resources Files. We performed univariable and multivariable linear regression to determine the association between change in physician density and county-level CVD AAMR adjusted for county-level demographic and socioeconomic characteristics and baseline physician density. Results: PCP and cardiologist density was lower in rural counties compared with urban counties (Table). Between 2011 and 2017, PCP density decreased in rural (-2.2 [SD 15.8]) and increased in urban counties (0.8 [9.1]). Conversely, cardiologist density increased in rural (0.1 [1.8]) and decreased in urban counties (-0.1 [2.1]). Increases in PCP density were associated with lower CVD AAMR in both rural and urban counties (p<0.01). However, changes cardiologist density were not associated with lower CVD AAMR in multivariable analyses. Conclusion: Cardiologist density in rural counties was less than one-third that of urban counties and was not associated with CVD mortality. Increases in PCP density between 2011-17 was significantly associated with lower CVD AAMR in 2017-18 in both rural and urban counties.

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