National Academy of Sciences | 150 Year Anniversary

Questions? Call 800-624-6242

| Items in cart [0]

The National Academies Press

PAPERBACK
price:$63.75
add to cart

Rights & Permissions

topleft topright

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

Citation Manager

. "Some Ideas About the Exploratory Spatial Analysis of Large Data Sets." Massive Data Sets: Proceedings of a Workshop. Washington, DC: The National Academies Press, 1997.

Please select a format:

BibTeX EndNote RefMan


Page
150
bottomleft bottomright

The following HTML text is provided to enhance online readability. Many aspects of typography translate only awkwardly to HTML. Please use the page image as the authoritative form to ensure accuracy.


geographic map related data to non-traditional sources such as 2 and 3 dimensional images of virtually any kind. Indeed, it is worth observing that several existing GIS's already contain sufficient numeric resolution to work down to nanometre scales! But lets stick with the more traditional geographic data. The technology needed to create, store, and manipulate these land and people related databases exists, it well developed, and is fairly mature. What is almost entirely missing are the geographical analysis and modelling technologies able to deal with the many new potential opportunities that these data rich environments now make possible.

It is not unusual for governments and commercial organisations to spend vast sums of money on building databases relevant to their activities. Many have huge amounts of capital tied up in their databases and concepts such as data warehouses are providing the necessary IT infrastructure. They know that their future depends on building and using these information resources. Most businesses and governmental agencies are becoming information industries but currently there seems to be an almost universal neglect of investment in the analysis tools needed to make the most of the databases.

2 A global data swamp

Maybe the problem is that the data holders and potential users are becoming ''data swamped'' and can no longer appreciate the opportunities that exist. Often their ideas for analysis on these massive databases seems to mainly relate to an earlier period in history when data was scarce, the numbers of observation were small and the capabilities of the computer hardware limited. As a result there are seemingly increasingly large numbers of important, sometimes life critical and sometimes highly commercial, databases that are not being fully analysed; if indeed they are being spatially analysed at all. Openshaw (1994a) refers to this neglect as a type of spatial analysis crime. For some data there is already an over-whelming public imperative for analysis once the data exist in a suitable form for analysis. Is it not a crime against society if critical databases of considerable contemporary importance to the public good are not being adequately and thoroughly analysed. This applies especially when there might well be a public expectation that such analysis already occurs or when there is a strong moral imperative on the professions involved to use the information for the betterment of people's lives. Examples in the UK would include the non-analysis of much spatially referenced information: examples include most types of cancer data, mortality data, real-time crime event data, pollution data, data needed for real-time local weather forecasting, climatic change information, and personal information about people who are to be targeted for special assistance because they exist in various states of deprivation. There are many examples too involving the spatial non-analysis of major central and local government databases: for example, tax, social security payments, education performance and housing benefits. In the commercial sector also, it not unusual for large financial sector organisations to create massive customer databases, often containing longitudinal profiles of behaviour, increasingly being updated in real-time; and then do virtually nothing clever when it comes to analysis. Yet every single business in the IT Age knows that their long term future viability depends on themselves making good use of their information resources. Some of the opportunities involve spatial analysis; for example, customer targeting strategies, spatial planning and re-

Page
150
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)