Challenges and Opportunities of Emerging Data Sources to Estimate Network-Wide Bike Counts

Author(s):  
Md. Mintu Miah ◽  
Kate Kyung Hyun ◽  
Stephen P. Mattingly ◽  
Joseph Broach ◽  
Nathan McNeil ◽  
...  
2017 ◽  
Vol 98 (9) ◽  
pp. 1879-1896 ◽  
Author(s):  
Zengchao Hao ◽  
Xing Yuan ◽  
Youlong Xia ◽  
Fanghua Hao ◽  
Vijay P. Singh

Abstract In past decades, severe drought events have struck different regions around the world, leading to huge losses to a wide array of environmental and societal sectors. Because of wide impacts of drought, it is of critical importance to monitor drought in near–real time and provide early warning. This article provides an overview of the development of drought monitoring and prediction systems (DMAPS) at regional and global scales. After introducing drought indicators, drought monitoring (based on different data sources and tools) is summarized, along with an introduction of statistical and dynamical drought prediction approaches. The current progress of the development and implementation of DMAPS with various indicators at different temporal and/or spatial resolutions, based on the land surface modeling, remote sensing, and seasonal climate forecast, at the regional and global scales is then reviewed. Advances in drought monitoring with multiple data sources and tools and prediction from multimodel ensembles are highlighted. Also highlighted are challenges and opportunities, including near-real-time and long-term data products, indicator linkage to impacts, prediction skill improvement, and information dissemination/communication. The review of different components of these systems will provide useful guidelines and insights for the future development of effective DMAPS to aid drought modeling and management.


Author(s):  
Billy Kaombe ◽  

This research paper addresses the challenges and opportunities in building resilient Micro, Small and Medium Enterprises (MSMEs) in Zambia. The paper also examines the different forms of resilience and there implications on organisational resilience. The findings indicates that challenges encountered by MSMEs in building resilient business organisations can be addressed in different ways including through the development and implementation of monitoring and response capabilities, learning abilities and anticipation. The research study relied on secondary data sources and was able to conclude that building resilient MSMEs was vital for the survival of these business organisations.


2013 ◽  
Vol 10 (12) ◽  
pp. 15525-15624 ◽  
Author(s):  
J. Hall ◽  
B. Arheimer ◽  
M. Borga ◽  
R. Brázdil ◽  
P. Claps ◽  
...  

Abstract. There is growing concern that flooding is becoming more frequent and severe in Europe. A better understanding of flood regime changes and their drivers is therefore needed. The paper reviews the current knowledge on flood regime changes in European rivers that has been obtained through two approaches. The first approach is the detection of change based on observed flood events. Current methods are reviewed together with their challenges and opportunities. For example, observation biases, the merging of different data sources and accounting for non-linear drivers and responses. The second approach consists of modelled scenarios of future floods. Challenges and opportunities are discussed again such as fully accounting for uncertainties in the modelling cascade and feedbacks. To make progress in flood change research, we suggest that a synthesis of these two approaches is needed. This can be achieved by focusing on flood-rich and flood-poor periods rather than on flood trends only, by formally attributing causes of observed flood changes, by validating scenarios against observed flood regime dynamics, and by developing low-dimensional models of flood changes and feedbacks. The paper finishes with a call for a joint European flood change research network.


2017 ◽  
Vol 21 (2) ◽  
pp. 171-182
Author(s):  
Gill Matthewson

That architects leave the profession is something that seems ‘known’. In addition, there has been continuous concern that women in particular leave. However, the extent of departure is unclear. Much of the information around these observations come from surveys, is anecdotal or study women in isolation from men. This paper provides some firmer data on the movement of men and women into and out of the profession using Australia as a case study. It collates and analyses historical and contemporary data to delineate the complex patterns of participation in and leaving of architecture.While the sources of data are often limited and approximate, this analysis nonetheless highlights a number of factors affecting the tenure of architects in their profession. The economy is an obvious factor and the data mirrors the economic fate of the country. The paper firmly demonstrates that gender is a factor with a strong impact on leaving the profession – a movement that clearly adversely affects the diversity of the profession. A further factor in leaving is age, which interacts with gender: women begin to leave when young and men when older. Diversity is increasingly proving to be an important factor in the ability of an organisation or a profession to survive, let alone meet, the challenges and opportunities of the globalised twenty-first century.The paper concludes with a plea for better data sources to better clarify how, and to what extent, biases nudge many architects out of the profession. Understanding the extent and nature of these biases helps the formulation of tactics to foster greater diversity to engender a more resilient profession.


2021 ◽  
Author(s):  
Heinrich Peters ◽  
Zachariah Marrero ◽  
Samuel D. Gosling

As human interactions have shifted to virtual spaces and as sensing systems have become more affordable, an increasing share of peoples’ everyday lives can be captured in real time. The availability of such fine-grained behavioral data from billions of people has the potential to enable great leaps in our understanding of human behavior. However, such data also pose challenges to engineers and behavioral scientists alike, requiring a specialized set of tools and methodologies to generate psychologically relevant insights.In particular, researchers may need to utilize machine learning techniques to extract information from unstructured or semi-structured data, reduce high-dimensional data to a smaller number of variables, and efficiently deal with extremely large sample sizes. Such procedures can be computationally expensive, requiring researchers to balance computation time with processing power and memory capacity. Whereas modelling procedures on small datasets will usually take mere moments to execute, applying modeling procedures to big data can take much longer with typical execution times spanning hours, days, or even weeks depending on the complexity of the problem and the resources available. Seemingly subtle decisions regarding preprocessing and analytic strategy can end up having a huge impact on the viability of executing analyses within a reasonable timeframe. Consequently, researchers must anticipate potential pitfalls regarding the interplay of their analytic strategy with memory and computational constraints.Many researchers who are interested in using “big data” report having problems learning about new analytic methods or software, finding collaborators with the right skills and knowledge, and getting access to commercial or proprietary data for their research (Metzler et al. 2016). This chapter aims to serve as a practical introduction for psychologists who want to use large datasets and datasets from non-traditional data sources in their research (i.e., data not generated in the lab or through conventional surveys). First, we discuss the concept of big data and review some of the theoretical challenges and opportunities that arise with the availability of ever larger amounts of data. Second, we discuss practical implications and best practices with respect to data collection, data storage, data processing, and data modelling for psychological research in the age of big data.


2020 ◽  
Vol 2 (1) ◽  
pp. 55
Author(s):  
Ferry Khusnul Mubarok ◽  
Muhammad Khoirul Imam

<p>The global halal industry has shown significant development, and Indonesia has become one of the countries with great potential. This study aims to identify opportunities and challenges in developing the halal industry in Indonesia. This research uses a qualitative approach. Data sources used in the form of secondary data, which comes from library sources—technical analysis of data using the SWOT analysis approach. The results showed that the development of the halal industry in Indonesia included several sectors, namely the food and beverage sector, tourism, fashion, media and recreation, pharmaceuticals and cosmetics, and renewable energy. Based on SWOT analysis, it was found that there are strengths, weaknesses, opportunities, and challenges in developing the halal industry in Indonesia. Thus, in the future, to improve the halal industry in Indonesia, it is necessary to optimize the synergy of various elements ranging from the community, industry players, government, financial institutions, associations, academics, and educational institutions, as well as other related parties.</p>


2020 ◽  
Vol 1 (2) ◽  
pp. 107-120
Author(s):  
Ida Ayu Made Gayatri ◽  
I Nengah Suriata

The purpose of this study was to analyze the challenges and opportunities of Indonesian blind masseurs in improving competency through the implementation of the business standards of massage parlor. The research method is a descriptive-qualitative, theoretical approach on competency in human resource management. Primary data sources selected by purposive sampling. The informants involved were representatives of blind massage entrepreneurs. Secondary data sources in this study is PERMENPAR No.20 of 2015 concerning Business Standards of Massage Parlor. Data analysis techniques by means of data triangulation. The results of the study indicate the challenges faced by the blind masseurs namely: 1) requirements of massage business licenses have not been fulfilled; 2) aspects of the organization, management, and human resources have not been maximized. Increasing the competency of the masseur as a traditional health practitioner is an innovation, and it can extend their opportunities to work in the center of public health and hospitals.   Keywords: competency, standards, blind, masseur


2015 ◽  
Vol 8 (4) ◽  
pp. 509-515 ◽  
Author(s):  
Thomas J. Whelan ◽  
Amy M. DuVernet

As discussed in Guzzo, Fink, King, Tonidandel, and Landis's (2015) focal article, big data is more than a passing trend in business analytics. The plethora of information available presents a host of interesting challenges and opportunities for industrial and organizational (I-O) psychology. When utilizing big data sources to make organizational decisions, our field has a considerable amount to offer in the form of advice on how big data metrics are derived and used and on the potential threats to validity that their use presents. We’ve all heard the axiom, “garbage in, garbage out,” and that applies regardless of whether the scale is a small wastebasket or a dump truck.


Author(s):  
Serkan Polat ◽  
M. Fevzi Esen

A variety of data sources are available from smart devices which are connected to each other via different communication protocols. These devices are designed to be used in human-centric environments through a distributed physical-virtual interaction. Internet of things (IoT) is a concept of gathering the variety of devices through wired or wireless connections anytime and anyplace. This helps to create new services by integrating the physical world into virtual systems within various domains of tourism. In this chapter, the authors discuss the importance of IoT data for travel services. In the study, the major challenges and opportunities of IoT that allow tour operators and travel agencies to improve the customer experience and provide personalized services are examined. It is concluded that although there are studies on the use of IoT applications within tourism industries, there have been very limited studies conducted on integrated applications of IoT, especially for tour operators and travel agencies.


2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
J Pita Costa ◽  
F Fuart ◽  
B Cleland ◽  
J Wallace ◽  
A Staines ◽  
...  

Abstract Background The growing challenges and opportunities of Big Data for Public Health have revealed the potential to improve the efficiency and cost-effectiveness of public policy, for example through better targeting of resources with regard to General Practice (GP) prescribing. Open data has an important role due to its easy access and potential to complement proprietary data sources from, e.g., regional hospitals, and also itself be complemented with social data acquired by specialized approaches. Methods MIDAS pipeline of open source tools aiming integrating, analysing and visualising Open Data enabling health professionals and decision-makers to: (i) improve the usability of open data in combination with proprietary data through combining multiple visualisation tools in an integrated dashboard (ii) to explore the meaning of data in a global/local context based on new information using tone analysis and natural language techniques; and (iii) to have better informed decision-making based on evidence from trusted knowledge-bases. Specific data sources used have included information extracted from the biomedical database MEDLINE, worldwide news and government open data. Social media sources have also been used to gather information from the general public. Results Results include a strong correlation between antidepressant prescribing and economic deprivation, and a wide variation in how individual GP practices respond to demographic conditions. Automated anomaly detection based on the Local Outlier Probability has also been shown to be an easily understood and controllable approach to identifying prescribing outliers. Conclusions MIDAS demonstrates the significant value of open data from heterogeneous sources as basis decision-making in public health and healthcare, particularly when it is combined with proprietary or closed datasets. A key challenge in this regard is the ability to integrate and utilize data from diverse sources in a variety of formats and standards. Key messages MIDAS is exemplar on tackling the need for improved standards of open data, and new software architectures, tools and platforms addressing a complex ecosystem of heterogenous data sources and formats. MIDAS demonstrates the significant value of open data from heterogeneous sources as basis decision-making in public health and healthcare, particularly when combined with proprietary datasets.


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