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.

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.

TWEET DATA EXTRACTOR FOR CREATING A TWITTER TRAFFIC MAP MASHUP

2015 ◽  
Vol 75 (10) ◽  
Author(s):  
Amirul Afif Jasmi ◽  
Mohamad Hafis Izran Ishak ◽  
Nurul Hawani Idris
Keyword(s):  
Social Media ◽  
Big Data ◽  
Traffic Control ◽  
Transportation Planning ◽  
General Public ◽  
New Technologies ◽  
The Road ◽  
Current Information ◽  
Road Users ◽  
Use Of Social Media

Over recent years, there has been a growth of interest in the use of social media including Facebook and Twitter by the authorities to share and updates current information to the general public. The technology has been used for a variety of purposes including traffic control and transportation planning. There is a concern that the use of new technologies, including social media will lead to data abundance that requires effective operational resources to interpret the big data. This paper proposes a tweet data extractor to extract the traffic tweet by the authority and visualise the reports and mash up on top of online map, namely Twitter map. Visualisation of traffic tweet on a map could assist a user to effectively interpret the text based Twitter report by a location based map viewer. Hence, it could ease the process of planning itinerary by the road users. 

scholarly journals TOURISTS GEOVISUALIZATION ANALYSIS UTILIZING INSTAGRAM DATA IN CENTRAL JAVA PROVINCE AND SPECIAL REGION OF YOGYAKARTA

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 535-542
Author(s):  
A. Rofi’i ◽  
T. W. Wibowo ◽  
N. M. Farda ◽  
Keyword(s):  
Social Media ◽  
Big Data ◽  
Spatial Data ◽  
Visual Analysis ◽  
Data Extraction ◽  
Size Variation ◽  
Extraction Process ◽  
Tourist Attractions ◽  
Central Java ◽  
Special Region

Abstract. The growth of Indonesia‟s national economy in the sector of tourism has been improving constantly. Based on BPS data year 2015, tourism has contributed to PDB with the amount of 4,25%. Tourists is an important aspect because they play a role in the economic change in tourism. Along with social media development, tourists‟ distribution can be monitored using big data in social media. Instagram is one of the social media which is often used by tourists to share photos or videos while traveling. Issues that appeared when using big data Instagram to produce spatial data are size, variation, and a big time span. This research is aimed to extract big data Instagram to produce basic tourists’ spatial data and analyze tourist attractions using geovisualization hexagonal tessellation in West Java Province and Special Region of Yogyakarta. Generally, the methods used are divided into three which are data mining, graphic visual analysis and geovisualization classification hexagonal tessellation. The Instagram data extraction process uses a web-based software called Netlytic, while pre-processing data to produce tourists’ spatial data uses QGIS software. Hexagonal tessellation is tested by graphic visual to ensure the most effective measurement in the scale of 1 : 1.250.000. The most effective hexagonal tessellation measurement is used to classify tourist attractions. Based on the results, it shows that the most effective measurement of hexagonal tessellation is 2 Km. The most popular tourist attraction is Malioboro Yogyakarta based on hexagonal tessellation geovisualization analysis.

scholarly journals Machine Learning Based Mathematical Modelling for Prediction of Social Media Consumer behaviour using Big Data Analytics

2021 ◽  
Author(s):  
Kiran Chaudhary ◽  
Mansaf Alam ◽  
Mabrook S. Al-Rakhami ◽  
Abdu Gumaei
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Analytics ◽  
High Speed ◽  
Consumer Behaviour ◽  
Big Data Analytics ◽  
Quality Data ◽  
Volume Data ◽  
Process Data ◽  
Big Data Technology

Abstract Almost many consumers are inclined by social media to purchase the product and spend more money on purchasing. We got the data from social media to analyse the consumer behaviour. We have considered the consumer data from Facebook, Twitter, LinkedIn and YouTube. There is diversity and high-speed, high volume data is coming from social media, so we used big data technology. Big Data Technology is the recent technology is used in various field of research. In this paper we have used the concept of big data technology to process data and analyse to predict the consumer behaviour on social media. We have analysed the consumer behaviour based on certain parameter and criteria. we have analysed the consumer perception, attitude towards the social media. For doing the prediction we have pre-process the data to make the quality data so that we can take the quality decision based on outcome of our model. We have used the predictive big data analytics technique to analyse the consumer behaviour prediction in this paper.

scholarly journals Use of social media big data as a novel HIV surveillance tool in South Africa

PLoS ONE ◽  
2020 ◽  
Vol 15 (10) ◽  
pp. e0239304
Author(s):  
Alastair van Heerden ◽  
Sean Young
Keyword(s):  
South Africa ◽  
Social Media ◽  
Big Data ◽  
Hiv Surveillance ◽  
Use Of Social Media

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.

We Know Where You Are

2016 ◽  
Vol 7 (2) ◽  
pp. 61-75 ◽  
Author(s):  
Xining Yang ◽  
Xinyue Ye ◽  
Daniel Z. Sui
Keyword(s):  
Social Media ◽  
Spatial Data ◽  
Space And Place ◽  
Spatial Data Analysis ◽  
Food Services ◽  
Point Of Interest ◽  
Geographic Context ◽  
Retail Shopping ◽  
The City ◽  
Media Data

The convergence of social media and GIS provides an opportunity to reconcile space-based GIS and place-based social media. For this purpose, the authors conduct an empirical study in Columbus, Ohio, aiming to enrich both the spatial and platial context of geo-tagged data, using location-based social media Foursquare checkins as an example. An exploratory analytical approached is used to enrich the geographic context of social media data in both space and place. Specifically, exploratory spatial data analysis and point of interest matching are applied to analyze about 50,000 checkins crawled from social media feeds. It is found that checkins tend to be spatially clustered near the center of the city. Popular places related to food, services, and retail shopping venues are more likely to be reported by social media users. The authors also conducted platial analysis of the top 25 popular place venues in the study area.

scholarly journals Online social media usage of car dealerships in Gauteng Province, South Africa

2016 ◽  
Vol 14 (3) ◽  
pp. 601-607 ◽  
Author(s):  
Makgopa S. Sipho
Keyword(s):  
South Africa ◽  
Social Media ◽  
Qualitative Content Analysis ◽  
Research Approach ◽  
Marketing Communication ◽  
Gauteng Province ◽  
Motor Industry ◽  
Online Social Media ◽  
Social Media Platforms ◽  
Use Of Social Media

The concept of social media is top of the agenda for many organizations today. Decision makers, as well as marketers, try to identify ways in which organizations can make profitable use of social media platforms. The adoption of social media in marketing communication campaigns to carry the marketing communication message to the target audiences remains a challenge to organizations in the motor industry. The purpose of this paper was to establish an understanding of the online social media tools used by car dealerships in their marketing communication strategies and campaigns. In achieving the purpose of this paper, a qualitative research approach using semi-structured in-depth interviews with marketing personnel of different car dealerships in Gauteng province, South Africa was followed. In this paper, a qualitative content analysis was used to analyze primary data using Atlas ti version 10 computer software. The findings of this paper revealed that the use of social media platforms by car dealerships varied in terms of message content. Recommendations to stakeholders in the motor industry and future research directions are provided. Keywords: social media, marketing communications, communication channels, consumer-to-consumer communications, car dealerships. JEL Classification: M31, M37

Big Data Analytics of Geo-Social Media on Planning and Real-Time Decisions

2019 ◽  
Vol 16 (8) ◽  
pp. 3332-3337 ◽  
Author(s):  
S. Dhamodaran ◽  
G. Mahalakshmi ◽  
P. Harika ◽  
J. Refonaa ◽  
K. AshokKumar
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Big Data ◽  
Social Network ◽  
Real Time ◽  
Spatial Data ◽  
System Throughput ◽  
Future Planning ◽  
Efficient System ◽  
International Conference

The authorities are to be considered to make a real-time decision and future planning by various analyzing geo-social media posts in Geo-social Network. However, there are millions of Geo-social Network users who are producing overwhelming of data, called “Big Data” that is challenging to be analyzed and make the required real-time decisions. In our proposed system proposal of the efficient system for inquiring Geo-social Networks and harvesting the data as well as user’s location information (Dhamodaran, S. and Lakshmi, M., 2017. Design and Analysis of Spatial-Temporal Model Using Hydrological Techniques. IEEE International Conference on Computing of Power, Energy and Communication. pp.1–4). System architecture is proposed that processes an abundant amount of various social networks’ of data to monitor. Earth events, incidents, medical diseases, user trends, and views to make future real-time decisions and facilitate future planning (Dhamodarn, S., et al., Identification of User Poi in Spatial Data Using Android Application. International Conference on Computation of Power, Energy Information and Communication (ICCPEIC), IEEE. ISBN: 978-1-5090-0901-5). Twitter and Flickr have been analyzed using the proposed architecture in order to identify current events or disasters, such as earthquakes, snow, Ebola virus, and fires. The system is evaluated with respect to an efficiency of data while considering the system throughput.

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