scholarly journals EXPLORING POTENTIAL OF CROWDSOURCED GEOGRAPHIC INFORMATION IN STUDIES OF ACTIVE TRAVEL AND HEALTH: STRAVA DATA AND CYCLING BEHAVIOUR

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1357-1361 ◽  
Author(s):  
Y. Sun
Keyword(s):  
Social Media ◽  
Spatial Patterns ◽  
Large Scale ◽  
Active Travel ◽  
Geographic Information ◽  
Sustainable Transportation ◽  
Large Spatial Scale ◽  
Fine Grained ◽  
Gps Traces ◽  
Hand Cycling

In development of sustainable transportation and green city, policymakers encourage people to commute by cycling and walking instead of motor vehicles in cities. One the one hand, cycling and walking enables decrease in air pollution emissions. On the other hand, cycling and walking offer health benefits by increasing people’s physical activity. Earlier studies on investigating spatial patterns of active travel (cycling and walking) are limited by lacks of spatially fine-grained data. In recent years, with the development of information and communications technology, GPS-enabled devices are popular and portable. With smart phones or smart watches, people are able to record their cycling or walking GPS traces when they are moving. A large number of cyclists and pedestrians upload their GPS traces to sport social media to share their historical traces with other people. Those sport social media thus become a potential source for spatially fine-grained cycling and walking data. Very recently, Strava Metro offer aggregated cycling and walking data with high spatial granularity. Strava Metro aggregated a large amount of cycling and walking GPS traces of Strava users to streets or intersections across a city. Accordingly, as a kind of crowdsourced geographic information, the aggregated data is useful for investigating spatial patterns of cycling and walking activities, and thus is of high potential in understanding cycling or walking behavior at a large spatial scale. This study is a start of demonstrating usefulness of Strava Metro data for exploring cycling or walking patterns at a large scale.

scholarly journals Predicting and Analyzing Language Specificity in Social Media Posts

2019 ◽  
Vol 33 ◽  
pp. 6415-6422 ◽  
Author(s):  
Yifan Gao ◽  
Yang Zhong ◽  
Daniel Preoţiuc-Pietro ◽  
Junyi Jessy Li
Keyword(s):  
Social Media ◽  
Computational Linguistics ◽  
Large Scale ◽  
Pearson Correlation ◽  
Absolute Error ◽  
Fine Grained ◽  
Argumentation Mining ◽  
Sentence Level ◽  
Prediction Systems ◽  
Mental Health Factors

In computational linguistics, specificity quantifies how much detail is engaged in text. It is an important characteristic of speaker intention and language style, and is useful in NLP applications such as summarization and argumentation mining. Yet to date, expert-annotated data for sentence-level specificity are scarce and confined to the news genre. In addition, systems that predict sentence specificity are classifiers trained to produce binary labels (general or specific).We collect a dataset of over 7,000 tweets annotated with specificity on a fine-grained scale. Using this dataset, we train a supervised regression model that accurately estimates specificity in social media posts, reaching a mean absolute error of 0.3578 (for ratings on a scale of 1-5) and 0.73 Pearson correlation, significantly improving over baselines and previous sentence specificity prediction systems. We also present the first large-scale study revealing the social, temporal and mental health factors underlying language specificity on social media.

scholarly journals The Spatial-Comprehensiveness (S-COM) Index: Identifying Optimal Spatial Extents in Volunteered Geographic Information Point Datasets

2020 ◽  
Vol 9 (9) ◽  
pp. 497
Author(s):  
Haydn Lawrence ◽  
Colin Robertson ◽  
Rob Feick ◽  
Trisalyn Nelson
Keyword(s):  
Social Media ◽  
Data Quality ◽  
Spatial Scales ◽  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Quality Metric ◽  
Fine Grained ◽  
Multi Scale ◽  
Feasible Study

Social media and other forms of volunteered geographic information (VGI) are used frequently as a source of fine-grained big data for research. While employing geographically referenced social media data for a wide array of purposes has become commonplace, the relevant scales over which these data apply to is typically unknown. For researchers to use VGI appropriately (e.g., aggregated to areal units (e.g., neighbourhoods) to elicit key trend or demographic information), general methods for assessing the quality are required, particularly, the explicit linkage of data quality and relevant spatial scales, as there are no accepted standards or sampling controls. We present a data quality metric, the Spatial-comprehensiveness Index (S-COM), which can delineate feasible study areas or spatial extents based on the quality of uneven and dynamic geographically referenced VGI. This scale-sensitive approach to analyzing VGI is demonstrated over different grains with data from two citizen science initiatives. The S-COM index can be used both to assess feasible study extents based on coverage, user-heterogeneity, and density and to find feasible sub-study areas from a larger, indefinite area. The results identified sub-study areas of VGI for focused analysis, allowing for a larger adoption of a similar methodology in multi-scale analyses of VGI.

Age-class distribution and spatial patterns in an old-growth hemlock–hardwood forest

1994 ◽  
Vol 24 (9) ◽  
pp. 1939-1947 ◽  
Author(s):  
Lee E. Frelich ◽  
Lisa J. Graumlich
Keyword(s):  
Spatial Patterns ◽  
Residence Time ◽  
Large Scale ◽  
Sugar Maple ◽  
Temporal Stability ◽  
Grid Cell ◽  
Quasi Equilibrium ◽  
Residence Times ◽  
Old Growth ◽  
Fine Grained

The frequency of canopy disturbance over the past 150 years was reconstructed on a 5-ha study area dominated by a patchy mosaic of old-growth sugar maple (Acersaccharum Marsh.) and eastern hemlock (Tsugacanadensis (L.) Carr.) forest in the Sylvania Wilderness Area in western Upper Michigan. The study area was divided into a 10-m grid system and one tree was cored near the center of each grid cell so that the spatial patterns of tree cohorts could be examined. The canopy turnover rate, averaged over all species and 150 years was 5.4% per decade, with a corresponding canopy residence time of 186 years. Canopy-residence times do not vary much between sugar maple (170 years) and hemlock (167 years), but yellow birch has a much longer canopy-residence time (232 years). Canopy-residence times calculated for individual decades over the last 150 years varied from 81 to 556 years. The spatial pattern of gaps of various ages is caused by disturbances in light intensity (2–12% canopy removal) that occur nearly every decade, each of which creates several to many small gaps scattered across the study area. As a result, the study area has a fine-grained random spatial mixture of age-classes at all distance classes from 5 m to >100 m. This mixture is stable throughout the mesic forest in the study area. None of the cohorts resulting from disturbance correspond spatially to patches dominated by either hemlock or sugar maple. Apparently, the dynamics of patch formation by gap-creating disturbances operate independently from the dynamics of the much larger mono-dominant patches. In forests such as the northern hardwood–hemlock type, where several tree lifetimes pass between any two large-scale catastrophic disturbances, spatial and temporal stability of the patch-dynamic processes (quasi equilibrium) may exist for periods of several decades in areas of <1 ha, and several thousand years for landscapes >10 000 ha in size.

Detecting online expressional anomie and its evolutions in social media

2019 ◽  
Vol 37 (4) ◽  
pp. 703-721
Author(s):  
Qingqing Zhou ◽  
Ming Jing
Keyword(s):  
Social Media ◽  
Design Methodology ◽  
Supervised Classification ◽  
Large Scale ◽  
Local Conditions ◽  
Content Type ◽  
Fine Grained ◽  
Rapid Transmission ◽  
National Literacy ◽  
Microblog Mining

Purpose Expressional anomie (e.g. obscene words) can hinder communications and even obstruct improvements of national literacy. Meanwhile, the borderless and rapid transmission of the internet has exacerbated the influences. Hence, the purpose of this paper is detecting online anomic expression automatically and analyzing dynamic evolution processes of expressional anomie, so as to reveal multidimensional status of expressional anomie. Design/methodology/approach This paper conducted expressional anomie analysis via fine-grained microblog mining. Specifically, anomic microblogs and their anomic types were identified via a supervised classification method. Then, the evolutions of expressional anomie were analyzed, and impacts of users’ characteristics on the evolution process were mined. Finally, expressional anomie characteristics and evolution trends were obtained. Findings Empirical results on microblogs indicate that more effective and diversified measures need to be used to address the current large-scale anomie in expression. Moreover, measures should be tailored to individuals and local conditions. Originality/value To the best of the authors’ knowledge, it is the first research to mine evolutions of expressional anomie automatically in social media. It may discover more continuous and universal rules of expressional anomie, so as to optimize the online expression environment.

scholarly journals Spatiality Preservable Factored Poisson Regression for Large-Scale Fine-Grained GPS-Based Population Analysis

2019 ◽  
Vol 33 ◽  
pp. 1142-1149 ◽  
Author(s):  
Masamichi Shimosaka ◽  
Yuta Hayakawa ◽  
Kota Tsubouchi
Keyword(s):  
Poisson Regression ◽  
Large Scale ◽  
Population Analysis ◽  
Empirical Evaluation ◽  
Fine Grained ◽  
Point Of Interest ◽  
Proposed Model ◽  
Gps Traces ◽  
Relationship Of ◽  
The Relationship

With the wide use of smartphones with Global Positioning System (GPS) sensors, the analysis of the population from GPS traces has been actively explored in the last decade. We propose herein a brand new population prediction model to capture the population trends in a fine-grained point of interest (POI) densely distributed over large areas and understand the relationship of each POI in terms of spatiality preservation. We propose a new framework, called Spatiality Preservable Factorized Regression (SPFR), to realize this model. The SPFR is inspired by the success of the recently proposed bilinear Poisson regression and the concept of multi-task learning with factorization approach and the graph proximity regularization. Given that the proposed model is written simply in terms of optimization, we achieve scalability using our model. The results of our empirical evaluation, which used a massive dataset of GPS logs in the Tokyo region over 32 M count logs, show that our model is comparable to the stateof-the-art methods in terms of capturing the population trend across meshes while retaining spatial preservation in finer mesh areas.

scholarly journals Social Media Big Data Mining and Spatio-Temporal Analysis on Public Emotions for Disaster Mitigation

2019 ◽  
Vol 8 (1) ◽  
pp. 29 ◽  
Author(s):  
Tengfei Yang ◽  
Jibo Xie ◽  
Guoqing Li ◽  
Naixia Mou ◽  
Zhenyu Li ◽  
...  
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Big Data ◽  
Temporal Analysis ◽  
Geographic Information ◽  
Disaster Mitigation ◽  
Emotional Information ◽  
Fine Grained ◽  
Big Data Mining ◽  
Spatio Temporal

Social media contains a lot of geographic information and has been one of the more important data sources for hazard mitigation. Compared with the traditional means of disaster-related geographic information collection methods, social media has the characteristics of real-time information provision and low cost. Due to the development of big data mining technologies, it is now easier to extract useful disaster-related geographic information from social media big data. Additionally, many researchers have used related technology to study social media for disaster mitigation. However, few researchers have considered the extraction of public emotions (especially fine-grained emotions) as an attribute of disaster-related geographic information to aid in disaster mitigation. Combined with the powerful spatio-temporal analysis capabilities of geographical information systems (GISs), the public emotional information contained in social media could help us to understand disasters in more detail than can be obtained from traditional methods. However, the social media data is quite complex and fragmented, both in terms of format and semantics, especially for Chinese social media. Therefore, a more efficient algorithm is needed. In this paper, we consider the earthquake that happened in Ya’an, China in 2013 as a case study and introduce the deep learning method to extract fine-grained public emotional information from Chinese social media big data to assist in disaster analysis. By combining this with other geographic information data (such population density distribution data, POI (point of interest) data, etc.), we can further assist in the assessment of affected populations, explore emotional movement law, and optimize disaster mitigation strategies.

Change of Attitude, Technology and Practice: Identifying the Change for Increased Value Creation with Customer Co-creation

2017 ◽  
Vol 5 (1) ◽  
pp. 70-82
Author(s):  
Soumi Paul ◽  
Paola Peretti ◽  
Saroj Kumar Datta
Keyword(s):  
Social Media ◽  
New Product Development ◽  
Value Creation ◽  
Large Scale ◽  
Customer Orientation ◽  
Customer Relationships ◽  
Future Research ◽  
Management Competencies ◽  
Business Decisions ◽  
Prime Concern

Building customer relationships and customer equity is the prime concern in today’s business decisions. The emergence of internet, especially social media like Facebook and Twitter, changed traditional marketing thought to a great extent. The importance of customer orientation is reflected in the axiom, “The customer is the king”. A good number of organizations are engaging customers in their new product development activities via social media platforms. Co-creation, a new perspective in which customers are active co-creators of the products they buy and use, is currently challenging the traditional paradigm. The concept of co-creation involving the customer’s knowledge, creativity and judgment to generate value is considered not only an upcoming trend that introduces new products or services but also fitting their need and increasing value for money. Knowledge and innovation are inseparable. Knowledge management competencies and capacities are essential to any organization that aspires to be distinguished and innovative. The present work is an attempt to identify the change in value creation procedure along with one area of business, where co-creation can return significant dividends. It is on extending the brand or brand category through brand extension or line extension. This article, through an in depth literature review analysis, identifies the changes in every perspective of this paradigm shift and it presents a conceptual model of company-customer-brand-based co-creation activity via social media. The main objective is offering an agenda for future research of this emerging trend and ensuring the way to move from theory to practice. The paper acts as a proposal; it allows the organization to go for this change in a large scale and obtain early feedback on the idea presented. 

scholarly journals Strategic resources and smallholder performance at the bottom of the pyramid

2019 ◽  
Vol 22 (3) ◽  
pp. 365-380 ◽  
Author(s):  
Matthias Olthaar ◽  
Wilfred Dolfsma ◽  
Clemens Lutz ◽  
Florian Noseleit
Keyword(s):  
Large Scale ◽  
Business Environment ◽  
Agricultural Economics ◽  
Local Market ◽  
Bottom Of The Pyramid ◽  
Fine Grained ◽  
Strategic Resources ◽  
Relative Contribution ◽  
The Relationship ◽  
Resource Based Theory

In a competitive business environment at the Bottom of the Pyramid smallholders supplying global value chains may be thought to be at the whims of downstream large-scale players and local market forces, leaving no room for strategic entrepreneurial behavior. In such a context we test the relationship between the use of strategic resources and firm performance. We adopt the Resource Based Theory and show that seemingly homogenous smallholders deploy resources differently and, consequently, some do outperform others. We argue that the ‘resource-based theory’ results in a more fine-grained understanding of smallholder performance than approaches generally applied in agricultural economics. We develop a mixed-method approach that allows one to pinpoint relevant, industry-specific resources, and allows for empirical identification of the relative contribution of each resource to competitive advantage. The results show that proper use of quality labor, storage facilities, time of selling, and availability of animals are key capabilities.

scholarly journals Public Awareness: What Climate Change Scientists Should Consider

2020 ◽  
Vol 12 (20) ◽  
pp. 8369
Author(s):  
Mohammad Rahimi
Keyword(s):  
Climate Change ◽  
Social Media ◽  
Large Scale ◽  
Public Awareness ◽  
Connected Network ◽  
Multi Scale ◽  
Valuable Addition ◽  
Scientific Approach ◽  
Design Solutions ◽  
Mitigate Climate Change

In this Opinion, the importance of public awareness to design solutions to mitigate climate change issues is highlighted. A large-scale acknowledgment of the climate change consequences has great potential to build social momentum. Momentum, in turn, builds motivation and demand, which can be leveraged to develop a multi-scale strategy to tackle the issue. The pursuit of public awareness is a valuable addition to the scientific approach to addressing climate change issues. The Opinion is concluded by providing strategies on how to effectively raise public awareness on climate change-related topics through an integrated, well-connected network of mavens (e.g., scientists) and connectors (e.g., social media influencers).

scholarly journals Social Media in and Around a Temporary Large-Scale Refugee Shelter in the Netherlands

2021 ◽  
Vol 7 (2) ◽  
pp. 205630512110249
Author(s):  
Peer Smets ◽  
Younes Younes ◽  
Marinka Dohmen ◽  
Kees Boersma ◽  
Lenie Brouwer
Keyword(s):  
Social Media ◽  
The Netherlands ◽  
Crisis Communication ◽  
Asylum Seekers ◽  
Large Scale ◽  
State Policies ◽  
Refugee Crisis ◽  
Top Down ◽  
And Migration

During the 2015 refugee crisis in Europe, temporary refugee shelters arose in the Netherlands to shelter the large influx of asylum seekers. The largest shelter was located in the eastern part of the country. This shelter, where tents housed nearly 3,000 asylum seekers, was managed with a firm top-down approach. However, many residents of the shelter—mainly Syrians and Eritreans—developed horizontal relations with the local receiving society, using social media to establish contact and exchange services and goods. This case study shows how various types of crisis communication played a role and how the different worlds came together. Connectivity is discussed in relation to inclusion, based on resilient (non-)humanitarian approaches that link society with social media. Moreover, we argue that the refugee crisis can be better understood by looking through the lens of connectivity, practices, and migration infrastructure instead of focusing only on state policies.

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