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4CHAPTER 1 INTRODUCTION AND RESEARCH APPROACH 1.1 PROBLEM STATEMENT Transportation agencies use spatial data to locate or describe events on a transportation system. The spatial representation of a network can be expressed in one, two, or three dimen- sions. Each representation level has sources of uncertainty. Transportation data are usually referenced to highway net- works by using a one-dimensional (1D) linear referencing model. With this model, objects along a network are located using a set of known points on the network and distances and directions from the known points to the objects. Linear refer- encing is currently the most common practice used to locate or describe transportation features. Many methods have been used to measure the positions of objects or events relative to the highway network. Emerg- ing technologies, such as Global Positioning System (GPS) receivers, are providing highly efficient means for establishing two-dimensional (2D) and three-dimensional (3D) positions that can be used to locate point features, including crashes, signs, and intersections, rapidly and conveniently. GPS also can be used for locating moving vehicles in real time. Recently, data collection vehicles with GPS positioning capability have been acquired by some transportation agencies to support highway inventories and photologging. Difficulties arise with the use of GPS technology because, traditionally, the location component of a data item is cap- tured in coordinates (2D) that must be transformed to some linear reference such as a log-mile point (1D). Furthermore, analytical operations on spatial data, in support of trans- portation applications, are complicated because coordinate geometry cannot be applied to positions referenced in linear space (e.g., the distance from A to B is measured along a path, not along a straight line between coordinates). Spatial data quality is associated with the idea of fitness for use, which refers to the fact that different transportation applications require spatial data at different scales and that no one scale can support all transportation applications. Data quality assesses the degree of uncertainty associated with data. A better understanding and means to assess the quality of positional data (i.e., 1D spatial data) offers various bene- fits. The implications of spatial data quality in the 1D model are not well understood, placing significant limitations on the value of analyses using these data and the efficacy of subse- quent decision making. National spatial data quality standards have been established for 2D and 3D data. These standards allow users to understand the robustness of the data and to make judgments concerning the level of risk in decision making. There is also a need for methods that will allow the transformation between location referencing systems in the field and in the office as well as measures of the confidence limits of these transformations. 1.2 OBJECTIVES AND SCOPE This research is intended to compile and develop informa- tion needed to address issues related to positional data qual- ity. This includes the formulation of methodologies to ana- lyze the effects when considering trade-offs or transforming the location data obtained from different measurement sys- tems. The specific objectives of the project are as follows: ⢠Identify the positional data quality needs for common transportation applications, ⢠Document the effectiveness of various techniques for establishing spatial positions, ⢠Develop methodologies for assessing the effects of positional data accuracy in transformations between measurement techniques and spatial referencing sys- tems, and ⢠Package the findings into materials that can be readily implemented by DOT personnel. A primary focus of this project is on linearly referenced data that are predominant in transportation agencies. Also, positional-data quality is intended to include, at least, data accuracy, precision, and resolution. 1.3 REPORT ORGANIZATION This report is organized into four main chapters, the first of which provides an overview of the problem statement, objectives, and research approach. The second chapter, which describes the research findings, is divided into several sec- tions as follows: ⢠The first section describes spatial data characteristics, which include linear referencing methods and systems;
5⢠The second section describes spatial data quality, spa- tial data errors, and data quality standards; ⢠The third section describes measuring systems and accu- racy capabilities; and ⢠The fourth section identifies the transportation applica- tions of positional data and the applicationsâ sensitivity to positional data quality These sections also include summaries of data collected from the states in order to reflect practices performed by state departments of transportation (DOTs). The fifth section of Chapter 2 describes the data error model. This includes a description of the data error modeling concept, sources of error, transformation methodology, pre- sentation and visualization of positional data error, and a pro- totype error model. Chapter 3 discusses the results of evaluation of the data error model (i.e., case study analysis), guidelines for incor- porating data error indices in GIS applications, and recom- mendations for positional data quality standards. Chapter 4 presents the conclusions and recommendations for suggested research. Appendixes A, B, and C contain a prototype Data Error Model User Guide, results of case studies, and the question- naire used to interview state DOTs, respectively.
