Big Data, Open Data and the Climate Risk Market

2017 ◽  
pp. 83-93 ◽  
Author(s):  
Jo Bates
Keyword(s):  
Big Data ◽  
Open Data ◽  
Climate Risk

scholarly journals Understanding policing demand and deployment through the lens of the city and with the application of big data

Urban Studies ◽  
2021 ◽  
pp. 004209802098100
Author(s):  
Mark Ellison ◽  
Jon Bannister ◽  
Won Do Lee ◽  
Muhammad Salman Haleem
Keyword(s):  
Big Data ◽  
Urban Environment ◽  
Urban Areas ◽  
Multilevel Model ◽  
Open Data ◽  
Front Line ◽  
Criminological Research ◽  
The Social ◽  
Generalised Method Of Moments ◽  
The City

The effective, efficient and equitable policing of urban areas rests on an appreciation of the qualities and scale of, as well as the factors shaping, demand. It also requires an appreciation of the factors shaping the resources deployed in their address. To this end, this article probes the extent to which policing demand (crime, anti-social behaviour, public safety and welfare) and deployment (front-line resource) are similarly conditioned by the social and physical urban environment, and by incident complexity. The prospect of exploring policing demand, deployment and their interplay is opened through the utilisation of big data and artificial intelligence and their integration with administrative and open data sources in a generalised method of moments (GMM) multilevel model. The research finds that policing demand and deployment hold varying and time-sensitive association with features of the urban environment. Moreover, we find that the complexities embedded in policing demands serve to shape both the cumulative and marginal resources expended in their address. Beyond their substantive policy relevance, these findings serve to open new avenues for urban criminological research centred on the consideration of the interplay between policing demand and deployment.

scholarly journals Big Data Affluence in Statistics Application: A Comparison of Real Life and Simulated Open Data

2020 ◽  
Author(s):  
Nureni Adeboye ◽  
◽  
Oyedunsi Olayiwola ◽  
Keyword(s):  
Big Data ◽  
Real Life ◽  
Open Data ◽  
Large Data ◽  
Learning Ability ◽  
Sufficient Data ◽  
Data Repositories ◽  
Classroom Activities ◽  
Life Data ◽  
Real Life Data

Large data repositories or database management still remain a mirage and tough challenge to accomplish by most developing countries and establishments around the globe. This necessitates the need to improvise on the gathering of suitable data with a good spread to serve as a complement, in the absence of sufficient real-life data. Statisticians are increasingly posed with thought-provoking and even paradoxical questions, challenging our qualifications for entering the statistical paradises created by Big Data. Through classroom activities that involved both sourced real-life and simulated big data in R-environment, models were built and estimates obtained from the adopted techniques revealed the robustness of simulated datasets in a unified observation with improved significant values as reflected in the results. Students appreciated the use of such big data as it enhances their machine learning ability and the availability of sufficient data without delay.

scholarly journals Landsat Images Classification Algorithm (LICA) to Automatically Extract Land Cover Information in Google Earth Engine Environment

2020 ◽  
Vol 12 (7) ◽  
pp. 1201 ◽  
Author(s):  
Alessandra Capolupo ◽  
Cristina Monterisi ◽  
Eufemia Tarantino
Keyword(s):  
Big Data ◽  
Land Cover ◽  
Open Data ◽  
Google Earth ◽  
Classification Algorithm ◽  
Specific Class ◽  
Landsat Images ◽  
Sparse Vegetation ◽  
Main Technique ◽  
Google Earth Engine

Remote sensing has been recognized as the main technique to extract land cover/land use (LC/LU) data, required to address many environmental issues. Therefore, over the years, many approaches have been introduced and explored to optimize the resultant classification maps. Particularly, index-based methods have highlighted its efficiency and effectiveness in detecting LC/LU in a multitemporal and multisensors analysis perspective. Nevertheless, the developed indices are suitable to extract a specific class but not to completely classify the whole area. In this study, a new Landsat Images Classification Algorithm (LICA) is proposed to automatically detect land cover (LC) information using satellite open data provided by different Landsat missions in order to perform a multitemporal and multisensors analysis. All the steps of the proposed method were implemented within Google Earth Engine (GEE) to automatize the procedure, manage geospatial big data, and quickly extract land cover information. The algorithm was tested on the experimental site of Siponto, a historic municipality located in Apulia Region (Southern Italy) using 12 radiometrically and atmospherically corrected satellite images collected from Landsat archive (four images, one for each season, were selected from Landsat 5, 7, and 8, respectively). Those images were initially used to assess the performance of 82 traditional spectral indices. Since their classification accuracy and the number of identified LC categories were not satisfying, an analysis of the different spectral signatures existing in the study area was also performed, generating a new algorithm based on the sequential application of two new indices (SwirTirRed (STRed) index and SwiRed index). The former was based on the integration of shortwave infrared (SWIR), thermal infrared (TIR), and red bands, whereas the latter featured a combination of SWIR and red bands. The performance of LICA was preferable to those of conventional indices both in terms of accuracy and extracted classes number (water, dense and sparse vegetation, mining areas, built-up areas versus water, and dense and sparse vegetation). GEE platform allowed us to go beyond desktop system limitations, reducing acquisition and processing times for geospatial big data.

scholarly journals Round table: AI and Big Data: Ethical Challenges and Health Opportunities

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
Keyword(s):  
Big Data ◽  
Digital Health ◽  
Rapid Development ◽  
Open Data ◽  
Wearable Devices ◽  
Good Evidence ◽  
Electronic Patient Records ◽  
Ethical Challenges ◽  
Data Movement ◽  
Recent Emergence

Abstract The recent emergence of Big Data in healthcare (including large linked data from electronic patient records (EPR) as well as streams of real-time geolocated health data collected by personal wearable devices, etc.) and the open data movement enabling sharing datasets are creating new challenges around ownership of personal data whilst at the same time opening new research opportunities and drives for commercial exploitation. A balance must be struck between an individual’s desire for privacy and their desire for good evidence to drive healthcare, which may sometimes be in conflict. With the increasing use of mobile and wearable devices, new opportunities have been created for personalized health (tailored care to the needs of an individual), crowdsourcing, participatory surveillance, and movement of individuals pledging to become “data donors” and the “quantified self” initiative (where citizens share data through mobile device-connected technologies). These initiatives created large volumes of data with considerable potential for research through open data initiatives. In this workshop we will hear from a panel of international speakers working across the digital health, Big Data ethics, computer science, public health divide on how they have addressed the challenges presented by increased use of Big Data and AI systems in healthcare with insights drawn from their own experience to illustrate the new opportunities that development of these movements has opened up. Key messages The potential of open access to healthcare data, sharing Big Data sets and rapid development of AI technology, is enormous - so as are the challenges and barriers to achieve this goal. Policymakers, scientific and business communities should work together to find novel approaches for underlying challenges of a political and legal nature associated with use of big data for health.

“Big data” and “open data”: What kind of access should researchers enjoy?

Therapies ◽  
2016 ◽  
Vol 71 (1) ◽  
pp. 107-114 ◽  
Author(s):  
Gilles Chatellier ◽  
Vincent Varlet ◽  
Corinne Blachier-Poisson ◽  
Nathalie Beslay ◽  
Jehan-Michel Behier ◽  
...  
Keyword(s):  
Big Data ◽  
Open Data
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