Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data

With rapid advancement in location-based services (LBS), their acquisition has become a powerful tool to link people with similar interests across long distances, as well as connecting family and friends. To observe human behavior towards using social media, it is essential to understand and measure the check-in behavior towards a location-based social network (LBSN). This check-in phenomenon of sharing location, activities, and time by users has encouraged this research on the frequency of using an LBSN. In this paper, we investigate the check-in behavior of several million individuals, for whom we observe the gender and their frequency of using Chinese microblog Sina Weibo (referred as “Weibo”) over a period in Shanghai, China. To produce a smooth density surface of check-ins, we analyze the overall spatial patterns by using the kernel density estimation (KDE) by using ArcGIS. Furthermore, our results reveal that female users are more inclined towards using social media, and a difference in check-in behavior during weekday and weekend is also observed. From the results, LBSN data seems to be a complement to traditional methods (i.e., survey, census) and is used to study gender-based check-in behavior

Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data

With rapid advancement in location-based services (LBS), their acquisition has become a powerful tool to link people with similar interests across long distances, as well as connecting family and friends. To observe human behavior towards using social media, it is essential to understand and measure the check-in behavior towards a location-based social network (LBSN). This check-in phenomenon of sharing location, activities, and time by users has encouraged this research on the frequency of using an LBSN. In this paper, we investigate the check-in behavior of several million individuals, for whom we observe the gender and their frequency of using Chinese microblog Sina Weibo (referred as “Weibo”) over a period in Shanghai, China. To produce a smooth density surface of check-ins, we analyze the overall spatial patterns by using the kernel density estimation (KDE) by using ArcGIS. Furthermore, our results reveal that female users are more inclined towards using social media, and a difference in check-in behavior during weekday and weekend is also observed. From the results, LBSN data seems to be a complement to traditional methods (i.e., survey, census) and is used to study gender-based check-in behavior

Using VGI and Social Media Data to Understand Urban Green Space: A Narrative Literature Review

Volunteered Geographical Information (VGI) and social media can provide information about real-time perceptions, attitudes and behaviours in urban green space (UGS). This paper reviews the use of VGI and social media data in research examining UGS. The current state of the art is described through the analysis of 177 papers to (1) summarise the characteristics and usage of data from different platforms, (2) provide an overview of the research topics using such data sources, and (3) characterise the research approaches based on data pre-processing, data quality assessment and improvement, data analysis and modelling. A number of important limitations and priorities for future research are identified. The limitations include issues of data acquisition and representativeness, data quality, as well as differences across social media platforms in different study areas such as urban and rural areas. The research priorities include a focus on investigating factors related to physical activities in UGS areas, urban park use and accessibility, the use of data from multiple sources and, where appropriate, making more effective use of personal information. In addition, analysis approaches can be extended to examine the network suggested by social media posts that are shared, re-posted or reacted to and by being combined with textual, image and geographical data to extract more representative information for UGS analysis

Using social media data to understand the urban green space use before and after a pandemic

Urban green spaces (UGSs) are essential components of urban ecosystems that provide considerable benefits to residents, including recreational opportunities, improved air and water quality, and mental and physical health benefits. The COVID-19 pandemic and related restriction measures have affected people's daily lives in numerous ways, such as remote working and learning, online shopping, social distancing, travel restrictions, and outdoor activities. During the COVID-19 pandemic, UGSs have become the main places for outdoor activities. Understanding human-environment interactions in UGSs is an important research field that has broad implications for improving policies in response to a social crisis and informing urban planning strategies. The main challenges of investigating human-environment interactions lie in effectively collecting research datasets that can reflect or reveal human behaviour patterns within UGSs. Volunteered Geographical Information (VGI) and social media can provide better information about real-time perceptions, attitudes and behaviours than traditional datasets such as surveys and questionnaires. This provides great opportunities to investigate human-environment interactions in UGS in real-time. Additionally, Twitter is one of the most popular social networks, and it can provide more comprehensive and unbiased datasets through a new academic research Application Programming Interface (API). The overall aim of this thesis is to evaluate the contributions of UGS to human well-being, during a time of crisis, by investigating the characteristics and spatial-temporal patterns of UGS use across three periods: pre-, during- and after the COVID-19 pandemic. The thesis will document the process of examining spatial-temporal changes in UGS use associated with COVID-19 related pandemic, by using Twitter datasets incorporating approaches including text mining, topic modelling and spatial-temporal analysis. This is the first study to examine social media data over consistent time period before, during and after the lockdown in relation to UGS. The results show that the findings and method can potentially inform policy makers in their management and planning of UGS, especially in a period of social crisis like the COVID-19 pandemic. This research has great potential to help improve urban green space planning and management in urban areas