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MICHIGAN ACCIDENT DATA IN PLANNING APPLICATIONS

M J Bowman, Charles P. Compton, J A Green

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Abstract

This report documents an evaluation of the Michigan Accident Data File in terms of the quality of the data contained in it, the procedures involved in the data collection, the scope of potential users of the data, and the utility of the data to such potential users. The quality of the data is discussed first in terms of missing data rates on specific variables and the consistency of the data across variables. The procedures used to collect and digitize the data are then reviewed. Recommendations to improve the utility of the data and to facilitate the data collection procedures are presented.

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What this paper is about

This report documents an evaluation of the Michigan Accident Data File in terms of the quality of the data contained in it, the procedures involved in the data collection, the scope of potential users of the data, and the utility of the data to such potential users. The quality of the data is discussed first in terms of missing data rates on specific variables and the consistency of the data across variables. The procedures used to collect and digitize the data are then reviewed. Recommendations to improve the utility of the data and to facilitate the data collection procedures are presented.

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Available abstract

This report documents an evaluation of the Michigan Accident Data File in terms of the quality of the data contained in it, the procedures involved in the data collection, the scope of potential users of the data, and the utility of the data to such potential users. The quality of the data is discussed first in terms of missing data rates on specific variables and the consistency of the data across variables. The procedures used to collect and digitize the data are then reviewed. Recommendations to improve the utility of the data and to facilitate the data collection procedures are presented.

Key concepts: Data quality, Data collection, Computer science, Missing data, Data consistency, Consistency (knowledge bases), Scope (computer science), Data mining

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