A study on Analysis and Utilization of Crowd-sourced Spatio-temporal Contexts from Social Media

兵庫県立大学大学院201

Multidimensional Analysis to Understand Public Perception for a Health Initiative Project

Tõsiste vigastustega või surmaga lõppevate liiklusõnnetuste üks suurimatest põhjustajatest linnades on kiiruse ületamine. Sellega seoses käivitas Ühendkuningriigi valitsus tervisele orienteeritud transpordialgatuse mitmetes kohtades, tehes ettepaneku vähendada kiirusepiiranguid 20 miilini tunnis, et vähendada ohvrite arvu ja liiklusmahtu. Koos sellega paraneks ohutustunne ja suureneks inimeste füüsiline aktiivsus. On läbi viidud mitmeid uuringuid, et mõista kiiruse mõju õnnetustes ja liiklusohutuses. Tulemused on näidanud, et madalam kiiruspiirang linnades muudab liikluse ohutumaks. Bakalaurusetöös analüüsitakse sotsiaalmeedia andmeid, võttes aluseks Twitteri postitused, et mõista üldsuse taju kiirusepiirangu 20 miili tunnis suhtes. Andmed koosnevad Twitteri postitustest, mis on tehtud 2015.a veebruarist 2017.a märtsini. Analüüs viidi läbi hoiakute kaeve ja sotsiaalvõrgustiku analüüsi teel. Analüüsi tulemused näitasid, et inimesed reageerisid muudatusele positiivselt ja aktsepteerisid ohutuse ja tervise nimel uue kiirusepiirangu.Speeding is one of the major causes in road accidents in city areas that end in severe injuries or death. In this respect, the United Kingdom government launched a health oriented transport initiative across several sites by proposing reduction of speed limit to 20 miles per hour to achieve fewer casualties and to lower traffic volumes, leading to an improvement in the perception of safety and a subsequent increase in people’s physical activity. Various studies have been performed in the past to understand the impact of speed in accidents and road safety. Results have shown that lower speed limit in urban evironment makes traffic safer. In this work, social media data is analysed by taking Twitter as a use case to understand general public’s perception to 20 miles per hour speed limit. The data consists of tweets which span from February 2015 to March 2017. The analysis was performed using opinion mining and social network analysis. The results of the analysis indicated that people showed positive reaction to the change and were ready to accept the new speed limit for the benefit of safety and health

A study on Analysis and Utilization of Crowd-sourced Spatio-temporal Contexts from Social Media

兵庫県立大学大学院201

Understanding city dynamics: using geolocated social media in a problem-based activity as an investigative tool to enhance student learning

Research that tackles the pedagogical use of geolocated social media as an investigative tool for understanding cities in Geography and Urban Studies higher education programs has not been fully exploited. This study contributes by addressing the transferability of these sources as a research medium for enhancing student knowledge of urban phenomena. A collaborative problem-based learning activity was conducted in a third-year compulsory Urban Studies module of the Fundamentals in Architecture Degree at the University of Alicante. Two groups – Spanish (25 students) and English (34 students) language, participated in the activity. Foursquare and Twitter datasets were used as sources of information, and scaffolding in QGIS software, data analysis, and visualization tools were provided. Pre- and post- activity questionnaires as well as the work submitted by students gave an indication of the extent to which the activity was useful for achieving the set objective. Recurring approaches adopted by students and their “how-to” make sense of social media information enabled them to align spatiotemporal and social phenomena to the use and perception of city spaces. Students developed critical thinking and interpretative skills that are key transversal competencies for understanding the huge volume of data available in today’s digitalized world.This research was cofounded by the Vice-rectorate of Research and Knowledge Transfer of the University of Alicante, Spain (GRE18-19) and the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital. Generalitat Valenciana, Spain (GV/2021/177)

Crowdsourced Data Mining for Urban Activity: A Review of Data Sources, Applications and Methods

The penetration of devices integrated with location-based services and internet services has generated massive data about the everyday life of citizens and tracked their activities happening in cities. Crowdsourced data, such as social media data, POIs data and collaborative websites, generated by the crowd, has become fine-grained proxy data of urban activity and widely used in research in urban studies. However, due to the heterogeneity of data types of crowdsourced data and the limitation of previous studies mainly focusing on a specific application, a systematic review of crowdsourced data mining for urban activity is still lacking. In order to fill the gap, this paper conducts a literature search in the Web of Science database, selecting 226 highly related papers published between 2013 and 2019. Based on those papers, the review firstly conducts a bibliometric analysis identifying underpinning domains, pivot scholars and papers around this topic. The review also synthesises previous research into three parts: main applications of different data sources and data fusion; application of spatial analysis in mobility patterns, functional areas and event detection; application of socio-demographic and perception analysis in city attractiveness, demographic characteristics and sentiment analysis. The challenges of this type of data are also discussed in the end. This study provides a systematic and current review for both researchers and practitioners interested in the applications of crowdsourced data mining for urban activity.This research is funded by a scholarship from the China Scholarship Counci

Geo-Information Harvesting from Social Media Data

As unconventional sources of geo-information, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multi-perspective stream, but with unknown and diverse quality. Due to its complementarity to remote sensing data, geo-information from these sources offers promising perspectives, but harvesting is not trivial due to its data characteristics. In this article, we address key aspects in the field, including data availability, analysis-ready data preparation and data management, geo-information extraction from social media text messages and images, and the fusion of social media and remote sensing data. We then showcase some exemplary geographic applications. In addition, we present the first extensive discussion of ethical considerations of social media data in the context of geo-information harvesting and geographic applications. With this effort, we wish to stimulate curiosity and lay the groundwork for researchers who intend to explore social media data for geo-applications. We encourage the community to join forces by sharing their code and data.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin