scholarly journals Technology and Big Data Are Changing Economics: Mining Text to Track Methods

2020 ◽  
Vol 110 ◽  
pp. 42-48
Author(s):  
Janet Currie ◽  
Henrik Kleven ◽  
Esmée Zwiers
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Data Collection ◽  
Applied Economics ◽  
Data Generation ◽  
Administrative Records ◽  
New Methods

The last 40 years have seen huge innovations in computing and in the availability of data. Data derived from millions of administrative records or by using (as we do) new methods of data generation such as text mining are now common. New data often requires new methods, which in turn can inspire new data collection. If history is any guide, some methods will stick and others will prove to be a flash in the pan. However, the larger trends toward demanding greater credibility and transparency from researchers in applied economics and a 'collage' approach to assembling evidence will likely continue.

Geospatial Data Analysis: A Review of Theory and Methods

2017 ◽  
Author(s):  
Prof. Rajagopalan S ◽  
Yogalakshmi Jayabal
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Collection ◽  
Geospatial Data ◽  
Data Generation ◽  
Vast Amount ◽  
Temporal Aspect ◽  
Geospatial Data Collection

A vast amount of data is generated and collected every moment and often, data has a spatial and/or temporal aspect. This increasing data generation and collection is resulting in increasing volume and varying formats of data being collected and the geospatial data collection is no exception. This posses challenges in storing, processing, analyzing and visualizing the geospatial data. This paper discusses the big data paradigm of the geospatial data and presents a taxonomy for analysis of the geospatial data. The existing literature is studied and discussed based on the proposed taxonomy for analysis of geospatial data.

scholarly journals Historical Databases, Big and Small

2021 ◽  
Vol 10 ◽  
pp. 24-29
Author(s):  
Peter Doorn
Keyword(s):  
Big Data ◽  
Historical Geography ◽  
Small Data ◽  
Graph Analysis ◽  
Data Generation ◽  
Relative Term ◽  
The Past ◽  
New Methods ◽  
Historical Databases ◽  
The Look

Big Data is a relative term, and Small Data can be equally important. Not only the volume of data defines if data is 'Big', but three more Vs characterise the term: velocity (speed of data generation and processing), veracity (referring to data quality) and variety. Perhaps the most defining is methodological: data becomes really big when new methods are needed to process and analyse it. In contrast, this paper demonstrates how even a tiny dataset can contribute to our understanding of the past, in this case of the historical geography of two provinces in Ottoman Greece in the 17th century. Graph analysis is used on a dataset of just 16 data pairs, illustrating the point that a close-up view of data complements the look from farther away at bigger data volumes.

scholarly journals Thinking about police data: Analysts’ perceptions of data quality in Canadian policing

2021 ◽  
pp. 0032258X2110214
Author(s):  
Christopher D O’Connor ◽  
John Ng ◽  
Dallas Hill ◽  
Tyler Frederick
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Data Quality ◽  
Research Culture ◽  
Police Services ◽  
Police Data ◽  
Data Collection And Analysis ◽  
Quality Issues

Policing is increasingly being shaped by data collection and analysis. However, we still know little about the quality of the data police services acquire and utilize. Drawing on a survey of analysts from across Canada, this article examines several data collection, analysis, and quality issues. We argue that as we move towards an era of big data policing it is imperative that police services pay more attention to the quality of the data they collect. We conclude by discussing the implications of ignoring data quality issues and the need to develop a more robust research culture in policing.

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