219 research outputs found
Crowd-Sourcing the Smart City: Using Big Geosocial Media Metrics in Urban Governance
Using Big Data to better understand urban questions is an exciting field with challenging methodological and theoretical problems. It is also, however, potentially troubling when Big Data (particularly derived from social media) is applied uncritically to urban governance via the ideas and practices of âsmart citiesâ. This essay reviews both the historical depth of central ideas within smart city governance âparticular the idea that enough data/information/knowledge can solve society problemsâbut also the ways that the most recent version differs. Namely, that the motivations and ideological underpinning behind the goal of urban betterment is largely driven by technology advocates and neoliberalism rather than the strong social justice themes associated with earlier applications of data to cities. Geosocial media data and metrics derived from them can provide useful insight and policy direction. But one must be ever mindful that metrics donât simply measure; in the process of deciding what is important and possible to measure, these data are simultaneously defining what cities are
A Tutorial on Event Detection using Social Media Data Analysis: Applications, Challenges, and Open Problems
In recent years, social media has become one of the most popular platforms
for communication. These platforms allow users to report real-world incidents
that might swiftly and widely circulate throughout the whole social network. A
social event is a real-world incident that is documented on social media.
Social gatherings could contain vital documentation of crisis scenarios.
Monitoring and analyzing this rich content can produce information that is
extraordinarily valuable and help people and organizations learn how to take
action. In this paper, a survey on the potential benefits and applications of
event detection with social media data analysis will be presented. Moreover,
the critical challenges and the fundamental tradeoffs in event detection will
be methodically investigated by monitoring social media stream. Then,
fundamental open questions and possible research directions will be introduced
A Bayesian-Based Approach for Public Sentiment Modeling
Public sentiment is a direct public-centric indicator for the success of
effective action planning. Despite its importance, systematic modeling of
public sentiment remains untapped in previous studies. This research aims to
develop a Bayesian-based approach for quantitative public sentiment modeling,
which is capable of incorporating uncertainty and guiding the selection of
public sentiment measures. This study comprises three steps: (1) quantifying
prior sentiment information and new sentiment observations with Dirichlet
distribution and multinomial distribution respectively; (2) deriving the
posterior distribution of sentiment probabilities through incorporating the
Dirichlet distribution and multinomial distribution via Bayesian inference; and
(3) measuring public sentiment through aggregating sampled sets of sentiment
probabilities with an application-based measure. A case study on Hurricane
Harvey is provided to demonstrate the feasibility and applicability of the
proposed approach. The developed approach also has the potential to be
generalized to model various types of probability-based measures
Capturing time in space : Dynamic analysis of accessibility and mobility to support spatial planning with open data and tools
Understanding the spatial patterns of accessibility and mobility are a key (factor) to comprehend the functioning of our societies. Hence, their analysis has become increasingly important for both scientific research and spatial planning. Spatial accessibility and mobility are closely related concepts, as accessibility describes the potential to move by modeling, whereas spatial mobility describes the realized movements of individuals. While both spatial accessibility and mobility have been widely studied, the understanding of how time and temporal change affects accessibility and mobility has been rather limited this far. In the era of âbig dataâ, the wealth of temporally sensitive spatial data has made it possible, better than ever, to capture and understand the temporal realities of spatial accessibility and mobility, and hence start to understand better the dynamics of our societies and complex living environment. In this thesis, I aim to develop novel approaches and methods to study the spatio-temporal realities of our living environments via concepts of accessibility and mobility: How people can access places, how they actually move, and how they use space. I inspect these dynamics on several temporal granularities, covering hourly, daily, monthly, and yearly observations and analyses. With novel big data sources, the methodological development and careful assessment of the information extracted from them is extremely important as they are increasingly used to guide decision-making. Hence, I investigate the opportunities and pitfalls of different data sources and methodological approaches in this work. Contextually, I aim to reveal the role of time and the mode of transportation in relation to spatial accessibility and mobility, in both urban and rural environments, and discuss their role in spatial planning. I base my findings on five scientific articles on studies carried out in: Peruvian Amazonia; national parks of South Africa and Finland; Tallinn, Estonia; and Helsinki metropolitan area, Finland. I use and combine data from various sources to extract knowledge from them, including GPS devices; transportation schedules; mobile phones; social media; statistics; land-use data; and surveys. My results demonstrate that spatial accessibility and mobility are highly dependent on time, having clear diurnal and seasonal changes. Hence, it is important to consider temporality when analyzing accessibility, as people, transport and activities all fluctuate as a function of time that affects e.g. the spatial equality of reaching services. In addition, different transport modes should be considered as there are clear differences between them. Furthermore, I show that, in addition to the observed spatial population dynamics, also natureâs own dynamism affects accessibility and mobility on a regional level due to the seasonal variation in river-levels. Also, the visitation patterns in national parks vary significantly over time, as can be observed from social media. Methodologically, this work demonstrates that with a sophisticated fusion of methods and data, it is possible to assess; enrich; harmonize; and increase the spatial and temporal accuracy of data that can be used to better inform spatial planning and decision-making. Finally, I wish to emphasize the importance of bringing scientific knowledge and tools into practice. Hence, all the tools, analytical workflows, and data are openly available for everyone whenever possible. This approach has helped to bring the knowledge and tools into practice with relevant stakeholders in relation to spatial planning
Conflating point of interest (POI) data: A systematic review of matching methods
Point of interest (POI) data provide digital representations of places in the
real world, and have been increasingly used to understand human-place
interactions, support urban management, and build smart cities. Many POI
datasets have been developed, which often have different geographic coverages,
attribute focuses, and data quality. From time to time, researchers may need to
conflate two or more POI datasets in order to build a better representation of
the places in the study areas. While various POI conflation methods have been
developed, there lacks a systematic review, and consequently, it is difficult
for researchers new to POI conflation to quickly grasp and use these existing
methods. This paper fills such a gap. Following the protocol of Preferred
Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we conduct a
systematic review by searching through three bibliographic databases using
reproducible syntax to identify related studies. We then focus on a main step
of POI conflation, i.e., POI matching, and systematically summarize and
categorize the identified methods. Current limitations and future opportunities
are discussed afterwards. We hope that this review can provide some guidance
for researchers interested in conflating POI datasets for their research
Social Media Analytics in Disaster Response: A Comprehensive Review
Social media has emerged as a valuable resource for disaster management,
revolutionizing the way emergency response and recovery efforts are conducted
during natural disasters. This review paper aims to provide a comprehensive
analysis of social media analytics for disaster management. The abstract begins
by highlighting the increasing prevalence of natural disasters and the need for
effective strategies to mitigate their impact. It then emphasizes the growing
influence of social media in disaster situations, discussing its role in
disaster detection, situational awareness, and emergency communication. The
abstract explores the challenges and opportunities associated with leveraging
social media data for disaster management purposes. It examines methodologies
and techniques used in social media analytics, including data collection,
preprocessing, and analysis, with a focus on data mining and machine learning
approaches. The abstract also presents a thorough examination of case studies
and best practices that demonstrate the successful application of social media
analytics in disaster response and recovery. Ethical considerations and privacy
concerns related to the use of social media data in disaster scenarios are
addressed. The abstract concludes by identifying future research directions and
potential advancements in social media analytics for disaster management. The
review paper aims to provide practitioners and researchers with a comprehensive
understanding of the current state of social media analytics in disaster
management, while highlighting the need for continued research and innovation
in this field.Comment: 11 page
Social Space and Social Media: Analyzing Urban Space with Big Data
This dissertation focuses on the key role that big data can play in minimizing the perceived disconnect between social theory and quantitative methods in the discipline of geography. It takes as its starting point the geographic concept of space, which is conceptualized very differently in social theory versus quantitative methodology. Contrary to this disparity, an examination of the disciplinary history reveals a number of historic precedents and potential pathways for a rapprochement, especially when combined with some of the new possibilities of big data. This dissertation also proposes solutions to two common barriers to the adoption of big data in the social sciences: accessing and collecting such data and, subsequently, meaningful analysis. These methods and the theoretical foundation are combined in three case studies that show the successful integration of a quantitative research methodology with social theories on space. The case studies demonstrate how such an approach can create new and alternative understandings of urban space. In doing so it answers three specific research questions: (1) How can big data facilitate the integration of social theory on space with quantitative research methodology? (2) What are the practical challenges and solutions to moving âbeyond the geotagâ when utilizing big data in geographical research? (3) How can the quantitative analysis of big data provide new and useful insight in the complex character of social space? More specifically, what insights does such an analysis of relational social space provide about urban mobility and cognitive neighborhoods
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