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From: beard <[e-mail]>
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Subject: RESEARCH: Data Quality Visualization, NCGIA, US EPA, Soil Conservation
Date: Tue, 31 Dec 1992 15:53:45 LCL
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Crossposted from comp.infosystems.gis


CALL FOR PARTICIPATION

VISUALIZATION OF SPATIAL DATA QUALITY CHALLENGE

sponsored by

National Center for Geographic Information and Analysis
U. S. Environmental Protection Agency, Center for Environmental Statistics
USDA Soil Conservation Service
Statistical Graphics Section of the American Statistical Association


This announces an open invitation to participate in a challenge to
develop techniques for visualizing spatial data quality.  The
challenge is sponsored by the National Center for Geographic
Information and Analysis (NCGIA) along with the U. S. Environmental
Protection Agency, Center for Environmental Statistics; the USDA Soil
Conservation Service (SCS); and the Statistical Graphics Section of
the American Statistical Association (ASA). The intent of the
Challenge is to provide a catalyst for experimental research on
effective ways of managing and communicating the quality of spatial
data to users of geographic information systems. As geographic
information systems (GIS) are now widely used to analyze data and make
policy decisions, techniques to help understand and communicate data
quality have become important.  This challenge will provide an
opportunity for exchange among researchers from the disciplines of
geography and cartography, statistics, computer science, engineering,
and the scientific visualization community.

The challenge will run for approximately one year. Tentatively the
annual GIS conference GIS/LIS '93, will serve as the concluding event.
Challenge participants are invited to submit papers and posters (other
display media such as interactive demos or videos are also possible)
of their visualization techniques at the concluding conference. Best
Data Quality Visualization awards, in the form of certificates of
recognition will be presented at the conference.  In addition a set of
these visualization techniques will be selected for publication in a
special journal issue.

The challenge is open to any interested parties and there are several ways to
participate in the challenge. These include:

1. Develop a prototype visualization technique which communicates one or more
aspects of geographic data quality (see References). This should indicate what
data quality components were modeled and how they were linked to a visual
model.
2. Implement a visualization technique which communicates one or more aspects
of geographic data quality (see References).  The implementation may be stand
alone software, software which links to an existing GIS, or to another existing
software package.

Challenge awards will presented for:
#165# Best Overall Data Quality Visualization
#165# Best Student Entry


Challenge  Rules and Specifications

Two data sets consisting of original observations and metadata (information on
collection techniques, instrumentation, and processing or compilation
procedures) will be made available.

Environmental Protection Agency data set
The US EPA Chesapeake Bay Program Office will supply this data set. The data
set includes the concentrations of dissolved inorganic nitrogen (DIN) measured
at 49 stations in the main stem of the Chesapeake Bay.  Since nitrogen is an
essential phytoplankton nutrient, dissolved inorganic nitrogen concentration is
often used to evaluate the condition of nitrogen limitation on algal growth.

The sample collection period is October, 1985 through September, 1991. Data are
collected 20 times each year (1991 to present data are collected 18 times a
year), biweekly in March through October, and monthly thereafter. Two or four
water samples are collected at fixed depths at each station for DIN analysis.
The complete data record includes the latitude and longitude of the field
station, date, depth of sampling, and the concentration of dissolved inorganic
nitrogen.

If each observation is a vector V=(x,y,z,t,DIN) where x,y are the horizontal
coordinates in latitude and longitude, z is depth in the water column, t is
date and time, and DIN is the response variable, one may choose to display the
uncertainty in DIN in:
(1) Time
(2) Space
(3) An unknown space, time and/or depth, using model specification to
interpolate, forecast or predict DIN; or any combination thereof.
Alternatively, the uncertainty in the thematic component (i.e. DIN) calculated
using univariate descriptive statistics could be an approach taken.

Ancillary data include digital boundary files of the Chesapeake Bay,
descriptions of the data formats and associated metadata. The complete data
package is available from the US EPA as a four diskette set in ASCII DOS format
or as a set of files available on Internet via anonymous ftp from
ipc1.was.epa.gov (134.67.240.16) in directory pub/chesapeake_bay. To request
the data on diskette or to ask questions regarding the data please contact Judy
Calem at the US EPA Center for Environmental Statistics at
[e-mail] or at [phone].


Soil Conservation Service data set
This data set includes one 7.5 minute quad sheet (State College) of soil data
from Centre County (central) Pennsylvania. The soils data take the form of
irregularly shaped polygons which correspond to different soil types in the
field. The field mapping for this data was carried out between 1965 and 1974
and soil names and descriptions were approved in 1975. The soil survey was
published in 1981 at a scale of 1:20,000 on 1977 orthophotography.  The soil
maps were converted to digital form (SSURGO- Soil Survey Geographic Database)
in the mid-1980s. The digital files were prepared by manually digitizing
1:20,000 scale scribe coats. (Scribe coats are traditional negative preparation
products used to separate the soil boundary delineations from the half-tone
image of the orthophotography). Uncertainty issues associated with this data
set include the locational uncertainty of soil map unit boundaries, the
differential uncertainty of boundaries (slope breaks vs. profile differences),
and the uncertainty of the soil descriptions associated with each soil map
unit. Additional uncertainty can be generated by the digital conversion process
and subsequent GIS processing and analysis. Ancillary data: USGS digital
elevation data (DEM) are available from USGS for this same quadrangle. These
data can be requested from SCS in DLG, GRASS or ARC/Info export format.
Distribution will be by DOS HD/2S diskettes unless otherwise arranged with SCS.
For further information regarding these data sets, participants should contact
Sharon Waltman at the National Soil Survey Center, USDA Soil Conservation
Service, 100 Centennial Mall North Room 152, Lincoln NE, Internet:
[e-mail].

Participants are required to use these data sets in the development and
presentation of their visualization concepts or implementations. The primary
emphasis will be on displaying the quality of the data or the
quality/reliability of products generated from the data. The data sets will be
available as of November 1, 1992.

The agencies supplying the data sets (EPA and SCS) request that all
visualization concepts and executable code be made available for agency use.
All software rights, however, remain with the developers unless they choose to
make them public.

Instructions for challenge participants
To enter the challenge participants should:
#165# Obtain and preview a challenge data set(s).
#165# Register (be put on the Challenge mailing list) and submit a one-page
proposal of the visualization project. The proposal should include a general
description, objective, and scope of the proposed implementation, such as
anticipated software development environment, hardware platform, and
integration with any supporting software. Proposals should be submitted by
February 5, 1993. A list of participants will be distributed by February 15,
1993.
#165# Submit final project reports by July 19, 1993. These should not exceed 12
pages, should describe the data quality visualization implementation and
include appropriate graphics.
#165# Challenge winners will be notified by mid August 1993.
#165# Final challenge projects will be displayed and challenge awards presented
 at
GIS/LIS, November 1993.

Implementation proposals and final papers should be sent to :
Kate Beard
National Center for Geographic Information and Analysis
University of Maine
Orono, ME 04469
Phone: [phone]
Fax: [phone]
email: [e-mail]

A panel of judges from the Soil Conservation Service, the Environmental
Protection Agency, and from the areas of Geography, Cartography, Statistics,
and Computer Science will review and judge the entries.

Suggested problem areas

The following paragraphs describe a number of possible contexts for data
quality visualization in addition to the more specific quality issues described
above for each data set. These are offered purely as suggestions and
participants need not feel constrained by these suggestions.

#165##202#Visualization of lineage information.  This could cover ways of
 accessing and
visualizing the narrative information describing a data set, such as how the
data were collected, over what time period, using what instrumentation, by
whom, and how they were compiled and updated.

#165# Visualization of data processing errors:  GIS processes can introduce
 errors.
For example fuzzy polygon overlay can introduce changes in the positional
accuracy of the data, generalization or aggregation can introduce attribute
errors or changes in resolution. Visualization methods could be developed to
track and document errors generated by specific GIS processes.

#165##202#Visualization of product quality. This would include methods for
 documenting
the quality of final products generated by a GIS. This could include
visualization techniques for documenting quality on hardcopy products.

For additional information on the topic, participants may request a copy of the
NCGIA Technical Report 91-26 Report on the Specialist Meeting for I7-
Visualization of Data Quality. This report is available via anonymous ftp from
ncgia.ucsb.edu in directory pub/tech-reports/postscript.

Additional references

Beard M. K, and  B. Buttenfield. 1992. Spatial, Statistical, and Graphical
Dimensions of Data Quality. Proceedings Interface '92.

Chrisman, N. R. 1983.  The Role of Quality Information in the Long-term
Functioning of a Geographic Information System.  Cartographica  21 (2/3):
79-87.
Clapham, S. and M. K. Beard, 1991. The Development of an Initial Framework for
the Visualization of Spatial Data Quality. Proceedings of ACSM
2: 73-82.

Defanti, T. A., M. D. Brown and B. H. McCormick. 1989. Visualization: Expanding
the Scientific and Engineering Research Opportunities. Computer  22:6 27-38.

Goodchild, M. and S. Gopal. 1989. Accuracy of Spatial Databases.  New York:
Taylor and Francis.

Lanter, D. P. 1991. Design of a Lineage-Based Meta-Data Base for GIS
Cartography and Geographic Information Systems. 18:4 255-261.

Lanter, D. P. and Veregin, H. 1992. A Research Paradigm for Propagating Error
in Layer Based GIS. Photogrammetric Engineering and Remote Sensing. 58:6
825-833.

Moellering, H. 1988.  The Proposed Standard for Digital Cartographic Data:
Report of the Digital Cartographic Data Standards Task Force.  The American
Cartographer, 15:1 (entire issue).

Veregin, H. 1989.  A Taxonomy of Error in Spatial Databases 89-12 National
Center for Geographic Information and Analysis.