scholarly journals Travel and us: The impact of mode share on sentiment using geo-social media and GIS

2021 ◽  
Author(s):  
Greg Rybarczyk ◽  
Syagnik Banerjee ◽  
Melissa D. Starking-Szymanski ◽  
Richard Ross Shaker
Keyword(s):  
Social Media ◽  
Spatial Data ◽  
Spatial Regression ◽  
Negative Influence ◽  
Spatial Data Analysis ◽  
Spatial Autoregression ◽  
Spatial Error ◽  
Factors Affecting ◽  
Pedestrian Travel ◽  
The Impact

Commute stress is a serious health problem that impacts nearly everyone. Considering that microblogged geo-locational information offers new insight into human attitudes, the present research examined the utility of geo-social media data for understanding how different active and inactive travel modes affect feelings of pleasure, or displeasure, in two major U.S. cities: Chicago, Illinois and Washington D.C. A popular approach was used to derive a sentiment index (pleasure or valence) for each travel Tweet. Methodologically, exploratory spatial data analysis (ESDA) and global and spatial regression models were used to examine the geography of all travel modes and factors affecting their valence. After adjusting for spatial error associated with socioeconomic, environmental, weather, and temporal factors, spatial autoregression models proved superior to the base global model. The results showed that water and pedestrian travel were universally associated with positive valences. Bicycling also favorably influenced valence, albeit only in D.C. A noteworthy finding was the negative influence temperature and humidity had on valence. The outcomes from this research should be considered when additional evidence is needed to elevate commuter sentiment values in practice and policy, especially in regards to active transportation.

scholarly journals Travel and us: The impact of mode share on sentiment using geo-social media and GIS

2021 ◽  
Author(s):  
Greg Rybarczyk ◽  
Syagnik Banerjee ◽  
Melissa D. Starking-Szymanski ◽  
Richard Ross Shaker
Keyword(s):  
Social Media ◽  
Spatial Data ◽  
Spatial Regression ◽  
Negative Influence ◽  
Spatial Data Analysis ◽  
Spatial Autoregression ◽  
Spatial Error ◽  
Factors Affecting ◽  
Pedestrian Travel ◽  
The Impact

Commute stress is a serious health problem that impacts nearly everyone. Considering that microblogged geo-locational information offers new insight into human attitudes, the present research examined the utility of geo-social media data for understanding how different active and inactive travel modes affect feelings of pleasure, or displeasure, in two major U.S. cities: Chicago, Illinois and Washington D.C. A popular approach was used to derive a sentiment index (pleasure or valence) for each travel Tweet. Methodologically, exploratory spatial data analysis (ESDA) and global and spatial regression models were used to examine the geography of all travel modes and factors affecting their valence. After adjusting for spatial error associated with socioeconomic, environmental, weather, and temporal factors, spatial autoregression models proved superior to the base global model. The results showed that water and pedestrian travel were universally associated with positive valences. Bicycling also favorably influenced valence, albeit only in D.C. A noteworthy finding was the negative influence temperature and humidity had on valence. The outcomes from this research should be considered when additional evidence is needed to elevate commuter sentiment values in practice and policy, especially in regards to active transportation.

SPATIAL AUTOREGRESSIVE (SAR) MODEL WITH ENSEMBLE LEARNING-MULTIPLICATIVE NOISE WITH LOGNORMAL DISTRIBUTION (CASE ON POVERTY DATA IN EAST JAVA)

2021 ◽  
Vol 14 (1) ◽  
pp. 89-97
Author(s):  
Dewi Retno Sari Saputro ◽  
Sulistyaningsih Sulistyaningsih ◽  
Purnami Widyaningsih
Keyword(s):  
Regression Model ◽  
Ensemble Learning ◽  
Spatial Data ◽  
Lognormal Distribution ◽  
Multiplicative Noise ◽  
Spatial Regression ◽  
Spatial Error ◽  
Spatial Regression Model ◽  
Sar Model ◽  
Spatial Autoregressive

The regression model that can be used to model spatial data is Spatial Autoregressive (SAR) model. The level of accuracy of the estimated parameters of the SAR model can be improved, especially to provide better results and can reduce the error rate by resampling method. Resampling is done by adding noise (noise) to the data using Ensemble Learning (EL) with multiplicative noise. The research objective is to estimate the parameters of the SAR model using EL with multiplicative noise. In this research was also applied a spatial regression model of the ensemble non-hybrid multiplicative noise which has a lognormal distribution of cases on poverty data in East Java in 2016. The results showed that the estimated value of the non-hybrid spatial ensemble spatial regression model with multiplicative noise with a lognormal distribution was obtained from the average parameter estimation of 10 Spatial Error Model (SEM) resulting from resampling. The multiplicative noise used is generated from lognormal distributions with an average of one and a standard deviation of 0.433. The Root Mean Squared Error (RMSE) value generated by the non-hybrid spatial ensemble regression model with multiplicative noise with a lognormal distribution is 22.99.

How does mobile social media affect knowledge sharing under the “Guanxi” system?

2020 ◽  
Vol 24 (6) ◽  
pp. 1343-1367 ◽  
Author(s):  
Xi Zhang ◽  
Jiaxin Tang ◽  
Xin Wei ◽  
Minghui Yi ◽  
Patricia Ordóñez
Keyword(s):  
Social Media ◽  
Knowledge Sharing ◽  
New Product Development ◽  
Negative Impact ◽  
Negative Influence ◽  
Content Type ◽  
Close Cooperation ◽  
The One ◽  
Explicit And Implicit Knowledge ◽  
The Impact

Purpose The purpose of this paper is to explore the impact of mobile social media functions on explicit and implicit knowledge sharing under the “Guanxi” system based on the framework of stimulus–organism–response (SOR). Design/methodology/approach Combined with Guanxi theory, this paper designs an experiment to collect data from the new product development (NPD) teams. Findings Interestingly, the results show that the effect of social media communication function on employees is greater than the impact of collaboration on employees. Specifically, on the one hand, the more employees communicate in social media, the better their feelings will be, the less they will share knowledge. On the other hand, the collaboration function has a significantly negative impact on the psychological factors of employees. Excessively close cooperation and contact may instead create a contradiction between the employees, which is not conducive to the occurrence of knowledge sharing. Originality/value This paper extends SOR framework by combining Guanxi theory to examine the relationship between social media functions and knowledge sharing behavior (KSB). In practical, companies should pay attention to the frequency of employee using social media when it is introduced for NPD teams to control the negative influence of social media functions on employee KSB.

scholarly journals Spatial-Temporal Characteristics in Grain Production and Its Influencing Factors in the Huang-Huai-Hai Plain from 1995 to 2018

2020 ◽  
Vol 17 (24) ◽  
pp. 9193
Author(s):  
Chunshan Zhou ◽  
Rongrong Zhang ◽  
Xiaoju Ning ◽  
Zhicheng Zheng
Keyword(s):  
Influencing Factors ◽  
Spatial Data ◽  
Spatial Data Analysis ◽  
Grain Production ◽  
Temporal Characteristics ◽  
Spatial Panel ◽  
Geographically Weighted Regression Model ◽  
Northern Areas ◽  
Spatial Panel Data ◽  
The Impact

The Huang-Huai-Hai Plain is the major crop-producing region in China. Based on the climate and socio-economic data from 1995 to 2018, we analyzed the spatial–temporal characteristics in grain production and its influencing factors by using exploratory spatial data analysis, a gravity center model, a spatial panel data model, and a geographically weighted regression model. The results indicated the following: (1) The grain production of eastern and southern areas was higher, while that of western and northern areas was lower; (2) The grain production center in the Huang-Huai-Hai Plain shifted from the southeast to northwest in Tai’an, and was distributed stably at the border between Jining and Tai’an; (3) The global spatial autocorrelation experienced a changing process of “decline–growth–decline”, and the area of hot and cold spots was gradually reduced and stabilized, which indicated that the polarization of grain production in local areas gradually weakened and the spatial difference gradually decreased in the Huang-Huai-Hai Plain; (4) The impact of socio-economic factors has been continuously enhanced while the role of climate factors in grain production has been gradually weakened. The ratio of the effective irrigated area, the amount of fertilizer applied per unit sown area, and the average per capita annual income of rural residents were conducive to the increase in grain production in the Huang-Huai-Hai Plain; however, the effect of the annual precipitation on grain production has become weaker. More importantly, the association between the three factors and grain production was found to be spatially heterogeneous at the local geographic level.

scholarly journals Spatial data analysis and the use of maps in scientific health articles

2016 ◽  
Vol 62 (4) ◽  
pp. 336-341
Author(s):  
Luciana Bertoldi Nucci ◽  
Patrick Theodore Souccar ◽  
Silvia Diez Castilho
Keyword(s):  
Spatial Analysis ◽  
Spatial Data ◽  
Geographical Location ◽  
Epidemiological Studies ◽  
Spatial Data Analysis ◽  
Careful Evaluation ◽  
Analysis Techniques ◽  
Impact Index ◽  
Scientific Papers ◽  
The Impact

Summary Introduction: Despite the growing number of studies with a characteristic element of spatial analysis, the application of the techniques is not always clear and its continuity in epidemiological studies requires careful evaluation. Objective: To verify the spread and use of those processes in national and international scientific papers. Method: An assessment was made of periodicals according to the impact index. Among 8,281 journals surveyed, four national and four international were selected, of which 1,274 articles were analyzed regarding the presence or absence of spatial analysis techniques. Results: Just over 10% of articles published in 2011 in high impact journals, both national and international, showed some element of geographical location. Conclusion: Although these percentages vary greatly from one journal to another, denoting different publication profiles, we consider this percentage as an indication that location variables have become an important factor in studies of health.

scholarly journals ANTESEDEN DAN KONSEKUEN PENGGUNAAN MEDIA SOSIAL BAGI UMKM: ANALISIS PERSPEKTIF ORGANISASI

2018 ◽  
Vol 19 (3) ◽  
pp. 12
Author(s):  
Anissa Hakim Purwantini ◽  
Friztina Anisa
Keyword(s):  
Social Media ◽  
Customer Service ◽  
Partial Least Square ◽  
Least Square ◽  
Media Technology ◽  
Factors Affecting ◽  
Media Usage ◽  
Internal Operations ◽  
Use Of Social Media ◽  
The Impact

Utilization of social media technology for business interests has been widely done both in largecompanies and MSMEs (Micro, Small and Medium Enterprise). Utilization of social media forMSMEs is very important to face the competition in this globalization era. This study empiricallyexamines the antecedents of social media usage and its impact on MSMEs performance basedon the Technology-Organization-Environment framework and Resource Based View theory. Thesurvey method by distributing questionnaires was conducted to MSMEs from various industriesin Magelang. Analysis with SEM-Partial Least Square indicates that customer pressure andmobile environment are significant factors affecting the use of social media. Furthermore, thedimensions of the impact on internal operations, sales, marketing and customer service aresignificant and make the value of social media usage for MSMEs. Technological competenceand competitive pressure does not affect the social media usage for MSMEs.Keywords: social media, SMEs, organization perspective, TOE, RBV

scholarly journals Marketing to Youth in the Digital Age: The Promotion of Unhealthy Products and Health Promoting Behaviours on Social Media

2016 ◽  
Vol 4 (3) ◽  
pp. 35-49 ◽  
Author(s):  
Sally Dunlop ◽  
Becky Freeman ◽  
Sandra C. Jones
Keyword(s):  
Social Media ◽  
Health Behaviours ◽  
Negative Influence ◽  
Lessons Learned ◽  
Current Evidence ◽  
Future Research ◽  
Policy And Practice ◽  
Media Campaigns ◽  
Health Promoting ◽  
The Impact

The near-ubiquitous use of social media among adolescents and young adults creates opportunities for both corporate brands and health promotion agencies to target and engage with young audiences in unprecedented ways. Traditional media is known to have both a positive and negative influence on youth health behaviours, but the impact of social media is less well understood. This paper first summarises current evidence around adolescents’ exposure to the promotion and marketing of unhealthy products such as energy dense and nutrient poor food and beverages, alcohol, and tobacco on social media sites such as Facebook, Twitter, Instagram and YouTube. We explore emerging evidence about the extent of exposure to marketing of these harmful products through social media platforms and potential impacts of exposure on adolescent health. Secondly, we present examples of health-promoting social media campaigns aimed at youth, with the purpose of describing innovative campaigns and highlighting lessons learned for creating effective social media interventions. Finally, we suggest implications for policy and practice, and identify knowledge gaps and opportunities for future research.

scholarly journals The Impact of Social Networking Sites on Study Habits and Interpersonal Relationships among Vietnamese Students

2021 ◽  
pp. 221-233
Author(s):  
Vu Van Tuan
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Interpersonal Relationships ◽  
Social Networking Sites ◽  
Pearson Correlation ◽  
Negative Influence ◽  
Study Habits ◽  
Quantitative Methodology ◽  
Human Beings ◽  
The Impact

Social media has a profound influence on every aspect of human beings nowadays. This study investigated the impact of social networking sites on study habits and interpersonal relationships at the tertiary level. A total of 125 college students from different universities in Hanoi were chosen through a convenience sampling technique. Quantitative methodology was employed for the research instrument and a descriptive survey design was adopted for this study. The researchers designed questionnaires with Cronbach's alpha reliability coefficients of at least 0.84 to collect data for the study. Analysis of the data was carried out using frequencies, percentages, means, t-tests, and Pearson correlation statistics at the 0.05 alpha level. The findings revealed that students’ level of using social networking sites had a negative influence on their study habits and their interpersonal relationships. Based on the findings, it was recommended that regular orientations should be given to students on how and when to use social media to enhance their study habits or to spend time improving their interpersonal relationships with their families, friends, and teachers.

scholarly journals GIS-based spatial modeling to identify factors affecting COVID-19 incidence rates in Bangladesh

2020 ◽  
Author(s):  
Md. Hamidu Rahman ◽  
Niaz Mahmud Zafri ◽  
Fajle Rabbi Ashik ◽  
Md Waliullah
Keyword(s):  
Health Workers ◽  
Disease Incidence ◽  
Spatial Regression ◽  
Ordinary Least Squares ◽  
Incidence Rates ◽  
Related Factors ◽  
Spatial Error ◽  
Factors Affecting ◽  
Spatial Lag Model ◽  
Lag Model

The outbreak of the COVID-19 pandemic is an unprecedented shock throughout the world which leads to generate a massive social, human, and economic crisis. However, there is a lack of research on geographic modeling of COVID-19 as well as identification of contributory factors affecting the COVID-19 in the context of developing countries. To fulfill the gap, this study aimed to identify the potential factors affecting the COVID-19 incidence rates at the district-level in Bangladesh using spatial regression model (SRM). Therefore, data related to 32 demographic, economic, weather, built environment, health, and facilities related factors were collected and analyzed to explain the spatial variability of this disease incidence. Three global (Ordinary least squares (OLS), spatial lag model (SLM) and spatial error model (SEM)) and one local (geographically weighted regression (GWR)) SRMs were developed in this study. The results of the models showed that four factors significantly affected the COVID-19 incidence rates in Bangladesh. Those four factors are urban population percentage, monthly consumption, number of health workers, and distance from the capital. Among the four developed models, the GWR model performed the best in explaining the variation of COVID-19 incidence rates across Bangladesh with a R square value of 78.6%. Findings from this research offer a better insight into the COVID-19 situation and would help to develop policies aimed to prevent the future epidemic crisis.

scholarly journals TWEETS AND FACEBOOK POSTS, THE NOVELTY TECHNIQUES IN THE CREATION OF ORIGIN-DESTINATION MODELS

2016 ◽  
Vol XLI-B2 ◽  
pp. 555-562
Author(s):  
H. K. Malema ◽  
W. Musakwa
Keyword(s):  
South Africa ◽  
Social Media ◽  
Big Data ◽  
Spatial Data ◽  
Transportation Planning ◽  
High Speed ◽  
Spatial Data Analysis ◽  
Gauteng Province ◽  
Distribution Studies ◽  
Network Patterns

Social media and big data have emerged to be a useful source of information that can be used for planning purposes, particularly transportation planning and trip-distribution studies. Cities in developing countries such as South Africa often struggle with out-dated, unreliable and cumbersome techniques such as traffic counts and household surveys to conduct origin and destination studies. The emergence of ubiquitous crowd sourced data, big data, social media and geolocation based services has shown huge potential in providing useful information for origin and destination studies. Perhaps such information can be utilised to determine the origin and destination of commuters using the Gautrain, a high-speed railway in Gauteng province South Africa. To date little is known about the origins and destinations of Gautrain commuters. Accordingly, this study assesses the viability of using geolocation-based services namely Facebook and Twitter in mapping out the network movements of Gautrain commuters. Explorative Spatial Data Analysis (ESDA), Echo-social and ArcGis software were used to extract social media data, i.e. tweets and Facebook posts as well as to visualize the concentration of Gautrain commuters. The results demonstrate that big data and geolocation based services have the significant potential to predict movement network patterns of commuters and this information can thus, be used to inform and improve transportation planning. Nevertheless use of crowd sourced data and big data has privacy concerns that still need to be addressed.

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