Most transportation data in North America are linearly referenced in a one-dimensional (1-D) model. The implications of spatial data quality in the 1-D model are not well understood, placing significant limitations on the value of analyses using these data and the efficacy of subsequent decision making. National spatial data quality standards have been established for 2-D and 3-D data. These standards allow users to understand the robustness of the data and to make judgments concerning the level of risk in decision making.
Many methods have been used to measure the positions of objects or events relative to the highway network, and emerging technologies, such as Global Positioning Systems (GPS), are providing highly efficient means for accurately establishing 2-D and 3-D positions that can be used to rapidly and conveniently locate point features such as accidents, signs, and intersections. GPS can also 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 captured in coordinates (2-D) that must be transformed to some linear reference such as log-mile point (1-D). Moreover, analytical operations on spatial data, in support of transportation 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).
There are a variety of needs that provide impetus for developing a better understanding and means to assess positional-data quality (i.e., 1-D spatial data). Decision makers need to understand the implications of positional-data quality on device and system capabilities, because increased quality implies higher costs. For example, the designers and managers of GIS need guidance on the appropriate scales and number of calibration points in formulating DOT base maps. Persons involved in using GIS-generated data summaries need to know the bounds on "true" location that can be derived from the integration of diverse data sources (e.g., data collected using distance measuring instruments and GPS). There is also the need for methods that will allow the transformation between location referencing systems in the field and in the office and measures of the confidence limits of these transformations. This research is intended to compile and develop information needed to address issues related to positional-data quality and to formulate methodologies to analyze the impacts or effects when considering trade-offs or transforming the location data obtained from different measurement systems.
The objectives of this research are to (1) identify the positional-data quality needs for common transportation applications, (2) document the effectiveness of various techniques for establishing spatial positions, (3) develop methodologies for assessing the impacts of positional-data accuracy in transformations between measurement techniques and spatial referencing systems, and (4) package the findings into materials that can be readily implemented by DOT personnel.
For this project, all types of spatial data should be included with a primary focus on linearly referenced data that is predominant in transportation agencies. Also, positional-data quality is intended to include, at least, data accuracy, precision, and resolution.
It is envisioned that the following tasks need to be undertaken: (1) Review literature and other sources of information to (a) characterize spatial data currently used by transportation agencies; (b) identify uses of spatial data; and (c) identify methods for measuring, describing, and reporting the quality of spatial data. (2) Assess the current and potential applications of spatial data and their sensitivity to positional-data quality in transportation. Identify emerging and future applications and their positional-data quality requirements. (3) Compile information on the accuracy capabilities of various spatial-measurement techniques. These should include map scaling, milepost referencing, survey methods, distance measuring instruments, aerial photography, GPS, and others. Use information from recent studies on accident-location referencing, sign inventories, environmental analysis, ITS technology, and other subjects, as appropriate. (4) Describe the sources and measures of errors in spatial data. Develop a conceptual model for data error and a methodology for transformatting data across the different dimensions with appropriate measures of data error. It is expected that both field and office procedures will be included in the methodology. Provide examples of the implications of data quality and describe methods for documenting, labeling, testing, and displaying indices of positional-data quality. (5) Identify potential case studies that could be used to indicate positional-data quality impacts. Describe study methods that would be used to test the efficacy of the model developed in Task 4. (6) Submit an interim report describing the findings of Tasks 1 through 5 for review by the project panel. The interim report should (a) describe the conceptual data-error model, (b) outline the methodology for data transformation, (c) summarize the information compiled on positional-data quality requirements, and (d) synthesize the capabilities of various spatial measurement techniques. Outline the efforts planned to assess the impacts of altering positional-data quality requirements on transportation applications. The interim report should describe a means for disseminating information derived from this project and procedures for its maintenance after the completion of the project. (7) Develop and document methodologies for making the transformations identified in Task 4, incorporating positional-data quality measures. The methodologies should include algorithms, flow charts, pseudo-code, and other materials, as appropriate. (8) Develop a prototype data-error model and test it using the case studies described in Task 5. Document the case study results. Recommend revisions to the model based on the results and make those changes to the model approved by the panel. (9) Prepare materials to facilitate the use of the data-error model and methodologies developed in this project. (10) Develop guidelines for incorporating indices of positional-data quality into GIS displays (e.g., define on-screen messages, identify potential data integration problems, use fuzzy lines to represent the confidence limits on spatial locations). (11) Prepare recommendations for additions, modifications, and/or deletions to positional-data quality standards. (12) Prepare a final report that documents the efforts conducted under this research project. The final report should include an executive summary that can be used as stand-alone document. It should also include explicit recommendations for future research on spatial data error.
The final report has been published as NCHRP Report 506