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Massive Data Sets: Proceedings of a Workshop (1997)
Commission on Physical Sciences, Mathematics, and Applications (CPSMA)

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. "Management Issues in the Analysis of Large-Scale Crime Data Sets." Massive Data Sets: Proceedings of a Workshop. Washington, DC: The National Academies Press, 1997.

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or is it those households that are replaced at every interview period? Moving the appropriate incident data from the lower levels of the nested file to the upper level of the household can complicate obtaining a "true" count of the number of households experiencing a victimization event, since many statistical software packages duplicate information at the upper level of the file structure down to the lower level.

5 Future Issues

Local law enforcement agencies will be participating on a voluntary basis. NIBRS data collection and aggregation at the agency-level will be far more labor and resource-intensive than the current UCR system. What are the implications for coverage and data accuracy?

Criminal justice data have a short shelf life, because detection of current trends is important for planning and interdiction effectiveness. Can new methods be found to process massive data files and produce information in a time frame that is useful to the criminal justice community? Numerous offenses such as sports tampering are not of great national interest. A subset of the full NIBRS file based on scientific sampling procedures could facilitate many types of analyses.

How easy is it to integrate change into such a data system, as evaluations of NIBRS identify new information needs that it will be required to address? Does the sheer volume of data and reporting agencies make this need any more difficult than for smaller on-going data collections? As data storage technology continues to evolve, it is important to weigh both cost and future compatibility needs, particularly in regards to distributing the data to law enforcement agencies and the public. BJS will continue to monitor these technological changes so that we will be able to utilize such advances in order to enhance our analytic capabilities with these large scale datasets.

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FRONT MATTER (R1-R10)
Opening Remarks (1-2)
PART I Participant's Expectations for the Workshop (3-12)
PART II Applications Papers (13-14)
Earth Observation Systems: What Shall We Do with the Data we Are Expecting in 1998? (15-22)
Information Retrieval: Finding Needles in Massive Haystacks (23-32)
Statistics and Massive Data Sets: one View from the Social Sciences (33-38)
The Challenge of Functional Magnetic Resonance Imaging (39-46)
Marketing (47-50)
Massive Data Sets: Guidelines and Practical Experience from Health Care (51-68)
Massive Data Sets in Semiconductor Manufacturing (69-76)
Management Issues in the Analysis of Large-Scale Crime Data Sets (77-80)
Analyzing Telephone Network Data (81-92)
Massive Data Assimilation/Fusion in Atmospheric Models and Analysis: Statistical, Physical, and Computational Challenges (93-103)
PART III Additional Invited Papers (103-104)
Massive Data Sets and Artificial Intelligence Planning (105-114)
Massive Data Sets: Problems and Possiblities, with Application to Environmental Monitoring (115-120)
Visualizing Large Datasets (121-128)
From Massive Data Sets to Science Catalogs: Applications and Challenges (129-142)
Information Retrieval and the Statistics of Large Data Sets (143-148)
Some Ideas About the Exploratory Spatial Analysis of Large Data Sets (149-156)
Massive Data Sets in Navy Problems (157-168)
Massive Data Sets Workshop: The Morning After (169-184)
PART IV Fundamental Issues and Grand Challenges (185-186)
Panel Discussion (187-202)
Items for Ongoing Consideration (203-204)
Closing Remarks (205-206)
Appendix: Workshop Participants (207-208)