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The purpose of this paper is to better understand the variability in burglary geocoding positional accuracy between United States Census Topologically Integrated…
The purpose of this paper is to better understand the variability in burglary geocoding positional accuracy between United States Census Topologically Integrated Geographic Encoding and Referencing (TIGER)-based street geocoding and results produced using reference data made publicly available by Google.
This research compares the Euclidian distance between ground-truthed burglaries and results produced using two different geocoding reference data sets: TIGER-based street geocoding and publicly available data within Google Earth. T-tests and z-tests are used to discern whether positional errors are statistically significant.
Both within suburban and urban jurisdictions, Google outperformed street geocoding in terms of positional accuracy. Positional errors on average were 1/4th as large for Google in a suburban setting and 1/5th as large in an urban setting compared to street geocoding.
Police departments that are relying on street geocoding techniques may achieve improved spatial precision by using Google’s reference data if they contain parcel-level information. Moreover, relying on less precise spatial referencing methods may place burglaries in locations where the events do not actually occur or cluster.
This is the first analysis of law enforcement data to examine the positional accuracy of geocoded offense data using Google Earth compared to the commonly used street geocoding method of interpolation.
The purpose of this paper is to examine the role of big data text analytics as an enabler of knowledge management (KM). The paper argues that big data text analytics…
The purpose of this paper is to examine the role of big data text analytics as an enabler of knowledge management (KM). The paper argues that big data text analytics represents an important means to visualise and analyse data, especially unstructured data, which have the potential to improve KM within organisations.
The study uses text analytics to review 196 articles published in two of the leading KM journals – Journal of Knowledge Management and Journal of Knowledge Management Research & Practice – in 2013 and 2014. The text analytics approach is used to process, extract and analyse the 196 papers to identify trends in terms of keywords, topics and keyword/topic clusters to show the utility of big data text analytics.
The findings show how big data text analytics can have a key enabler role in KM. Drawing on the 196 articles analysed, the paper shows the power of big data-oriented text analytics tools in supporting KM through the visualisation of data. In this way, the authors highlight the nature and quality of the knowledge generated through this method for efficient KM in developing a competitive advantage.
The research has important implications concerning the role of big data text analytics in KM, and specifically the nature and quality of knowledge produced using text analytics. The authors use text analytics to exemplify the value of big data in the context of KM and highlight how future studies could develop and extend these findings in different contexts.
Results contribute to understanding the role of big data text analytics as a means to enhance the effectiveness of KM. The paper provides important insights that can be applied to different business functions, from supply chain management to marketing management to support KM, through the use of big data text analytics.
The study demonstrates the practical application of the big data tools for data visualisation, and, with it, improving KM.